Natural Hazards and Disaster Risks Reduction

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Dear Colleagues,

The physical forces governing the Earth system can give rise to abrupt and severe natural events as a violent expression of ordinary environmental processes. Their impact is unevenly distributed on the land surface because of complex continental, regional, and local natural processes that overlap with anthropogenic forcing. The derived climate variations can directly or indirectly exacerbate most of the occurrences at different spatial and temporal scales. When such phenomena interact directly with inhabited areas and society, different risk scenarios can develop, characterised by a continuous and persistent dynamic or by a rapid mutability. From this perspective, natural hazards create a potential disaster that could impact anthropic activities, either through loss of life or injury, or through economic loss. The degree of safety in a community is the result of differential exposures to these events and of the level of preparation for them based on awareness and perception. The social development and spatial growth of human activities by consuming soil and natural resources has further contributed to creating vulnerability, increasing the challenges of conscious societies to cope with severe natural processes and their effects. The protection of territory is a key element in the UN 2030 Agenda’s the action strategy for sustainable development. The risk reduction is one of the guiding criteria of the 2015–2030 Sendai Framework’s sustainability policy.

This Topic collects original papers and inherent studies of different types of natural hazards (extreme climate and weather-related events and geological occurrences such as floods, landslides, subsidence, volcanic eruptions, earthquakes, etc.), vulnerability domains, exposure to disaster risk, but also manuscripts whose contents can help to mitigate risks. Among them, technical interventions and operational methodologies oriented to risk reduction strategies such as plans, protocols, working procedures, early warning systems, and any other innovations in the sector or elements that combine modern concepts with consolidated realities of the past should be included. State-of-the-art techniques are encouraged in the following three operating areas: spaceborne, aerial, and terrestrial activities. Numerical and experimental investigations for basic or application research and representative case studies are welcome too. Interdisciplinary and multidisciplinary approaches are considered added values to contribute to progress in the field of responsible and sustainable risk mitigation.

Dr. Stefano Morelli
Dr. Veronica Pazzi
Dr. Mirko Francioni
Topic Editors

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Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
GeoHazards geohazards	-	2.6	2020	20.4 Days	CHF 1000
Land land	3.2	4.9	2012	17.8 Days	CHF 2600
Remote Sensing remotesensing	4.2	8.3	2009	24.7 Days	CHF 2700
Sustainability sustainability	3.3	6.8	2009	20 Days	CHF 2400
Water water	3.0	5.8	2009	16.5 Days	CHF 2600

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16 pages, 7349 KiB  

Open AccessArticle

Assessment of a Machine Learning Algorithm Using Web Images for Flood Detection and Water Level Estimates

by Marco Tedesco and Jacek Radzikowski

GeoHazards 2023, 4(4), 437-452; https://doi.org/10.3390/geohazards4040025 - 6 Nov 2023

Cited by 1 | Viewed by 2185

Abstract

Improving our skills to monitor flooding events is crucial for protecting populations and infrastructures and for planning mitigation and adaptation strategies. Despite recent advancements, hydrological models and remote sensing tools are not always useful for mapping flooding at the required spatial and temporal [...] Read more.

Improving our skills to monitor flooding events is crucial for protecting populations and infrastructures and for planning mitigation and adaptation strategies. Despite recent advancements, hydrological models and remote sensing tools are not always useful for mapping flooding at the required spatial and temporal resolutions because of intrinsic model limitations and remote sensing data. In this regard, images collected by web cameras can be used to provide estimates of water levels during flooding or the presence/absence of water within a scene. Here, we report the results of an assessment of an algorithm which uses web camera images to estimate water levels and detect the presence of water during flooding events. The core of the algorithm is based on a combination of deep convolutional neural networks (D-CNNs) and image segmentation. We assessed the outputs of the algorithm in two ways: first, we compared estimates of time series of water levels obtained from the algorithm with those measured by collocated tide gauges and second, we performed a qualitative assessment of the algorithm to detect the presence of flooding from images obtained from the web under different illumination and weather conditions and with low spatial or spectral resolutions. The comparison between measured and camera-estimated water levels pointed to a coefficient of determination R² of 0.84–0.87, a maximum absolute bias of 2.44–3.04 cm and a slope ranging between 1.089 and 1.103 in the two cases here considered. Our analysis of the histogram of the differences between gauge-measured and camera-estimated water levels indicated mean differences of −1.18 cm and 5.35 cm for the two gauges, respectively, with standard deviations ranging between 4.94 and 12.03 cm. Our analysis of the performances of the algorithm to detect water from images obtained from the web and containing scenes of areas before and after a flooding event shows that the accuracy of the algorithm exceeded ~90%, with the Intersection over Union (IoU) and the boundary F1 score (both used to assess the output of segmentation analysis) exceeding ~80% (IoU) and 70% (BF1). Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>Architecture of the algorithm adopted in this study.</p> Full article ">Figure 2
<p>Examples of outputs of the web camera images for gauge #0204295505 collected on (<b>a</b>) 29 April 2023, 6:30 AM, (<b>b</b>) 30 April 2023, 3:30 PM, (<b>c</b>) 1 May 2023, 21:30 and (<b>d</b>) 2 May 2023, 00:25. Blue shaded regions indicate where the algorithm identified the presence of water. The digital gauge used by the algorithm to estimate the water level is also reported together with the value estimated by the algorithm. Original image resolution: 300 dpi. Original image size: 700 × 700.</p> Full article ">Figure 3
<p>(<b>a</b>) Time series of water levels (in cm) estimated from tide gauge measurements (blue line) and the algorithm using webcam images (orange squares) for the USGS gauge #0204295505 between 29 April 2023 and 5 May 2023. (<b>b</b>) Scatterplot of the water level (in cm) obtained from gauge (<span class="html-italic">x</span>-axis) and webcam images (<span class="html-italic">y</span>-axis) for the same period as (<b>a</b>). The 1:1 line is also reported as a continuous black line. The shaded line represents the linear fitting with its equation reported in the inset of (<b>b</b>) together with the coefficient of determination (R²). (<b>c</b>) Histogram of the difference between the gauge-measured and the camera-estimated water levels for all available images between 29 April 2023 and 5 May 2023. The mean and standard deviation of the normal distribution fitting the data are also reported within the plot.</p> Full article ">Figure 3 Cont.
<p>(<b>a</b>) Time series of water levels (in cm) estimated from tide gauge measurements (blue line) and the algorithm using webcam images (orange squares) for the USGS gauge #0204295505 between 29 April 2023 and 5 May 2023. (<b>b</b>) Scatterplot of the water level (in cm) obtained from gauge (<span class="html-italic">x</span>-axis) and webcam images (<span class="html-italic">y</span>-axis) for the same period as (<b>a</b>). The 1:1 line is also reported as a continuous black line. The shaded line represents the linear fitting with its equation reported in the inset of (<b>b</b>) together with the coefficient of determination (R²). (<b>c</b>) Histogram of the difference between the gauge-measured and the camera-estimated water levels for all available images between 29 April 2023 and 5 May 2023. The mean and standard deviation of the normal distribution fitting the data are also reported within the plot.</p> Full article ">Figure 4
<p>Examples of outputs of the web camera images for gauge #04188100. Blue shaded regions indicate where the algorithm identified the presence of water. The digital gauge used by the algorithm to estimate the water level is also reported together with the value estimated by the algorithm. (<b>a</b>) the image was collected under cloudy skies conditions and when rain was falling; (<b>b</b>), the image was acquired under sunny conditions. Original image resolution: 300 dpi. Original image size: 1200 × 700.</p> Full article ">Figure 5
<p>(<b>a</b>) Time series of water levels (in cm) estimated from tide gauge measurements (blue line) and the algorithm using webcam images (orange squares) for the USGS gauge #04188100 between 29 April 2023 and 5 May 2023. (<b>b</b>) Scatterplot of the water level (in cm) obtained from gauge (<span class="html-italic">x</span>-axis) and webcam images (<span class="html-italic">y</span>-axis) for the same period as (<b>a</b>). The 1:1 line is also reported as a continuous black line. The shaded line represents the linear fitting with its equation reported in the inset of (<b>b</b>) together with the coefficient of determination (R²). (<b>c</b>) Histogram of the difference between the gauge-measured and the camera-estimated water levels for all available images between 29 April 2023 and 5 May 2023. The mean and standard deviation of the normal distribution fitting the data are also reported within the plot.</p> Full article ">Figure 5 Cont.
<p>(<b>a</b>) Time series of water levels (in cm) estimated from tide gauge measurements (blue line) and the algorithm using webcam images (orange squares) for the USGS gauge #04188100 between 29 April 2023 and 5 May 2023. (<b>b</b>) Scatterplot of the water level (in cm) obtained from gauge (<span class="html-italic">x</span>-axis) and webcam images (<span class="html-italic">y</span>-axis) for the same period as (<b>a</b>). The 1:1 line is also reported as a continuous black line. The shaded line represents the linear fitting with its equation reported in the inset of (<b>b</b>) together with the coefficient of determination (R²). (<b>c</b>) Histogram of the difference between the gauge-measured and the camera-estimated water levels for all available images between 29 April 2023 and 5 May 2023. The mean and standard deviation of the normal distribution fitting the data are also reported within the plot.</p> Full article ">Figure 6
<p>Comparison between the original images (<b>a</b>,<b>b</b>) and those obtained as the output to the flood detection algorithm (<b>c</b>,<b>d</b>). Water is marked with the blue layer overlaying the original images. Image source adapted from <a href="https://www.theguardian.com/us-news/2017/aug/29/before-and-after-images-show-how-hurricane-harvey-swamped-houston" target="_blank">https://www.theguardian.com/us-news/2017/aug/29/before-and-after-images-show-how-hurricane-harvey-swamped-houston</a>, accessed on 29 October 2023. Original image resolution: 72 dpi. Original image size: 1000 × 1200. (<b>d</b>) Accuracy: 93.5%; IoU = 89.3%; BF = 73.2%.</p> Full article ">Figure 7
<p>Comparison between the original images (<b>a</b>,<b>b</b>) and those obtained as the output to the flood detection algorithm (<b>c</b>,<b>d</b>). Water is marked with the blue layer overlaying the original images. Original images obtained ftom <a href="https://www.nbc4i.com/news/before-and-after-photos-illustrate-massive-houston-flooding/" target="_blank">https://www.nbc4i.com/news/before-and-after-photos-illustrate-massive-houston-flooding/</a>, accessed on 29 October 2023. Original image resolution: 72 dpi. Original image size: 864 × 486. (<b>c</b>) Accuracy: 94.1%; IoU = 86.1%; BF = 74.8%.; (<b>d</b>) Accuracy: 90.1%; IoU = 84.3%; BF = 69.2%.</p> Full article ">Figure 8
<p>Comparison between the original images (<b>a,b</b>) and those obtained as the output to the flood detection algorithm (<b>c,d</b>). Water is marked with the blue layer overlaying the original images. Original images obtained from <a href="https://www.huffingtonpost.co.uk/entry/before-and-after-pictures-february-uk-floods_uk_5e539ebbc5b6b82aa655ab2b" target="_blank">https://www.huffingtonpost.co.uk/entry/before-and-after-pictures-february-uk-floods_uk_5e539ebbc5b6b82aa655ab2b</a>, accessed on 29 October 2023. Original image resolution: 72 dpi. Original image size: 410 × 312. (<b>c</b>) Accuracy: 98.2%; IoU = 90%; BF = 78.2%; (<b>d</b>) Accuracy: 96.1%; IoU = 81.6%; BF = 70.9%.</p> Full article ">Figure 9
<p>Comparison between the original images (<b>a</b>,<b>b</b>) and those obtained as the output to the flood detection algorithm (<b>c</b>,<b>d</b>). Water is marked with the blue layer overlaying the original images. for West End, Hebden Bridge, West Yorkshire. Images adapted from <a href="https://www.huffingtonpost.co.uk/entry/before-and-after-pictures-february-uk-floods_uk_5e539ebbc5b6b82aa655ab2" target="_blank">https://www.huffingtonpost.co.uk/entry/before-and-after-pictures-february-uk-floods_uk_5e539ebbc5b6b82aa655ab2</a>, accessed on 29 October 2023. Original image resolution: 72 dpi. Original image size: 410 × 312. (<b>d</b>) Accuracy: 98.2%; IoU = 86.7%; BF = 77.4%.</p> Full article ">

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34 pages, 29359 KiB  

Open AccessArticle

Geo-Environment Vulnerability Assessment of Multiple Geohazards Using VWT-AHP: A Case Study of the Pearl River Delta, China

by Peng Huang, Xiaoyu Wu, Chuanming Ma and Aiguo Zhou

Remote Sens. 2023, 15(20), 5007; https://doi.org/10.3390/rs15205007 - 18 Oct 2023

Viewed by 1474

Abstract

Geohazards pose significant risks to communities and infrastructure, emphasizing the need for accurate susceptibility assessments to guide land-use planning and hazard management. This study presents a comprehensive method that combines Variable Weight Theory (VWT) with Analytic Hierarchy Process (AHP) to assess geo-environment vulnerability [...] Read more.

Geohazards pose significant risks to communities and infrastructure, emphasizing the need for accurate susceptibility assessments to guide land-use planning and hazard management. This study presents a comprehensive method that combines Variable Weight Theory (VWT) with Analytic Hierarchy Process (AHP) to assess geo-environment vulnerability based on susceptibility to various geohazards. The method was applied to the Pearl River Delta in China, resulting in the classification of areas into high vulnerability (5961.85 km²), medium vulnerability (19,227.93 km²), low vulnerability (14,892.02 km²), and stable areas (1616.19 km²). The findings demonstrate improved accuracy and reliability compared to using AHP alone. ROC curve analysis confirms the enhanced performance of the integrated method, highlighting its effectiveness in discerning susceptibility levels and making informed decisions in hazard preparedness and risk reduction. Additionally, this study assessed the risks posed by geohazards to critical infrastructures, roads, and artificial surfaces, while discussing prevention strategies. However, this study acknowledges certain limitations, including the subjective determination of its judgment matrix and data constraints. Future research could explore the integration of alternative methods to enhance the objectivity of factor weighting. In practical applications, this study contributes to the understanding of geo-environment vulnerability assessments, providing insight into the intricate interplay among geological processes, human activities, and disaster resilience. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The altitude, precipitation, and topography of the study area. (<b>a</b>) Topography, (<b>b</b>) Altitude, (<b>c</b>) Precipitation.</p> Full article ">Figure 2
<p>The lithology of the study area.</p> Full article ">Figure 3
<p>Flowchart of this study.</p> Full article ">Figure 4
<p>The distribution map of geohazards in the study area.</p> Full article ">Figure 5
<p>Distribution maps of assessment indicators for landslide and collapse susceptibility. (<b>a</b>) Elevation, (<b>b</b>) Slope, (<b>c</b>) Lithology, (<b>d</b>) Topography, (<b>e</b>) Distance to fault, (<b>f</b>) Distance to river, (<b>g</b>) Precipitation.</p> Full article ">Figure 6
<p>Distribution maps of assessment indicators for debris flow susceptibility. (<b>a</b>) Elevation, (<b>b</b>) Slope, (<b>c</b>) Lithology, (<b>d</b>) Topography, (<b>e</b>) Distance to fault, (<b>f</b>) Distance to river, (<b>g</b>) Distance to landslide and collapse, (<b>h</b>) Precipitation.</p> Full article ">Figure 7
<p>Distribution maps of assessment indicators for karst collapse susceptibility. (<b>a</b>) Lithology, (<b>b</b>) Degree of karst development, (<b>c</b>) Thickness of overlying layer, (<b>d</b>) Water yield property, (<b>e</b>) Distance to fault.</p> Full article ">Figure 8
<p>Distribution maps of assessment indicators for ground subsidence susceptibility. (<b>a</b>) Thickness of soft soil layer, (<b>b</b>) Age of soft soil layer, (<b>c</b>) Water yield property, (<b>d</b>) Distance to fault.</p> Full article ">Figure 9
<p>Distribution maps of assessment indicators for soil erosion susceptibility. (<b>a</b>) Slope, (<b>b</b>) Topography, (<b>c</b>) Type of vegetation, (<b>d</b>) Type of soil, (<b>e</b>) Distance to river, (<b>f</b>) Precipitation.</p> Full article ">Figure 10
<p>Distribution maps of assessment indicators for sea water intrusion susceptibility. (<b>a</b>) Topography, (<b>b</b>) Type of Quaternary rock, (<b>c</b>) Groundwater level, (<b>d</b>) Precipitation.</p> Full article ">Figure 11
<p>Distribution map of LULC, road construction and critical infrastructure.</p> Full article ">Figure 12
<p>Distribution map of landslide and collapse susceptibility.</p> Full article ">Figure 13
<p>Distribution map of debris flow susceptibility.</p> Full article ">Figure 14
<p>Distribution map of karst collapse susceptibility.</p> Full article ">Figure 15
<p>Distribution map of ground subsidence susceptibility.</p> Full article ">Figure 16
<p>Distribution map of soil erosion susceptibility.</p> Full article ">Figure 17
<p>Distribution map of sea water intrusion susceptibility.</p> Full article ">Figure 18
<p>Distribution map of geo-environment vulnerability.</p> Full article ">Figure 19
<p>ROC curves of susceptibility assessment results for various geohazards. (<b>a</b>) Landslide and collapse, (<b>b</b>) Debris flow, (<b>c</b>) Karst collapse, (<b>d</b>) Ground subsidence, (<b>e</b>) Soil erosion, (<b>f</b>) Sea water intrusion.</p> Full article ">Figure 20
<p>Distribution maps of critical infrastructures, roads, and artificial surfaces in different vulnerability areas. (<b>a</b>) Guangzhou and Foshan, (<b>b</b>) Dongguan, (<b>c</b>) the entire study area, (<b>d</b>) Shenzhen, (<b>e</b>) Jiangmen and Zhongshan.</p> Full article ">

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Supplementary material:
Supplementary File 1 (ZIP, 1869 KiB)

13 pages, 1890 KiB  

Open AccessArticle

Is Sea Level Rise a Known Threat? A Discussion Based on an Online Survey

by Stefano Solarino, Elena Eva, Marco Anzidei, Gemma Musacchio and Maddalena De Lucia

GeoHazards 2023, 4(4), 367-379; https://doi.org/10.3390/geohazards4040021 - 3 Oct 2023

Cited by 2 | Viewed by 2115

Abstract

Since the last century, global warming has been triggering sea level rise at an unprecedented rate. In the worst case climate scenario, sea level could rise by up to 1.1 m above the current level, causing coastal inundation and cascading effects, thus affecting [...] Read more.

Since the last century, global warming has been triggering sea level rise at an unprecedented rate. In the worst case climate scenario, sea level could rise by up to 1.1 m above the current level, causing coastal inundation and cascading effects, thus affecting about one billion people around the world. Though widespread and threatening, the phenomenon is not well known to citizens as it is often overshadowed by other effects of global warming. Here, we show the results of an online survey carried out in 2020–2021 to understand the level of citizens’ knowledge on sea level rise including causes, effects, exacerbation in response to land subsidence and best practice towards mitigation and adaptation. The most important result of the survey is that citizens believe that it is up to governments to take action to cope with the effects of rising sea levels or mitigate the rise itself. This occurs despite the survey showing that they actually know what individuals can do and that a failure to act poses a threat to society. Gaps and preconceptions need to be eradicated by strengthening the collaboration between scientists and schools to improve knowledge, empowering our society. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>Global mean sea level rise from 2006 to 2100 relative to 1986–2005 for lowest (RCP2.6 in blue) and highest (RCP8.5 in red) projected emissions with related uncertainties (shaded colors). Modified from Climate Change 2014 Synthesis Report Fifth Assessment Report (AR5) Intergovernmental Panel on Climate Change at “<a href="https://ar5-syr.ipcc.ch/topic_summary.php" target="_blank">https://ar5-syr.ipcc.ch/topic_summary.php</a> (accessed on 5 September 2023)”.</p> Full article ">Figure 2
<p>How do respondents obtain information about SLR. Upper panel: respondents who input only one answer. Lower panel: more than one choice.</p> Full article ">Figure 3
<p>Answers to the question “who should primarily work to reduce the damage caused by rising sea level”.</p> Full article ">Figure 4
<p>Answers to the question “who should primarily work to reduce the damage caused by rising sea level” divided for sub-groups according to employment.</p> Full article ">Figure 5
<p>Answers to the question “what we need to do for our cities to adapt to the rising sea level effects”.</p> Full article ">

49 pages, 52986 KiB  

Open AccessArticle

Investigation of Landslide Susceptibility Decision Mechanisms in Different Ensemble-Based Machine Learning Models with Various Types of Factor Data

by Jiakai Lu, Chao Ren, Weiting Yue, Ying Zhou, Xiaoqin Xue, Yuanyuan Liu and Cong Ding

Sustainability 2023, 15(18), 13563; https://doi.org/10.3390/su151813563 - 11 Sep 2023

Cited by 3 | Viewed by 1550

Abstract

Machine learning (ML)-based methods of landslide susceptibility assessment primarily focus on two dimensions: accuracy and complexity. The complexity is not only influenced by specific model frameworks but also by the type and complexity of the modeling data. Therefore, considering the impact of factor [...] Read more.

Machine learning (ML)-based methods of landslide susceptibility assessment primarily focus on two dimensions: accuracy and complexity. The complexity is not only influenced by specific model frameworks but also by the type and complexity of the modeling data. Therefore, considering the impact of factor data types on the model’s decision-making mechanism holds significant importance in assessing regional landslide characteristics and conducting landslide risk warnings given the achievement of good predictive performance for landslide susceptibility using excellent ML methods. The decision-making mechanism of landslide susceptibility models coupled with different types of factor data in machine learning methods was explained in this study by utilizing the Shapley Additive exPlanations (SHAP) method. Furthermore, a comparative analysis was carried out to examine the differential effects of diverse data types for identical factors on model predictions. The study area selected was Cenxi, Guangxi, where a geographic spatial database was constructed by combining 23 landslide conditioning factors with 214 landslide samples from the region. Initially, the factors were standardized using five conditional probability models, frequency ratio (FR), information value (IV), certainty factor (CF), evidential belief function (EBF), and weights of evidence (WOE), based on the spatial arrangement of landslides. This led to the formation of six types of factor databases using the initial data. Subsequently, two ensemble-based ML methods, random forest (RF) and XGBoost, were utilized to build models for predicting landslide susceptibility. Various evaluation metrics were employed to compare the predictive capabilities of different models and determined the optimal model. Simultaneously, the analysis was conducted using the interpretable SHAP method for intrinsic decision-making mechanisms of different ensemble-based ML models, with a specific focus on explaining and comparing the differential impacts of different types of factor data on prediction results. The results of the study illustrated that the XGBoost-CF model constructed with CF values of factors not only exhibited the best predictive accuracy and stability but also yielded more reasonable results for landslide susceptibility zoning, and was thus identified as the optimal model. The global interpretation results revealed that slope was the most crucial factor influencing landslides, and its interaction with other factors in the study area collectively contributed to landslide occurrences. The differences in the internal decision-making mechanisms of models based on different data types for the same factors primarily manifested in the extent of influence on prediction results and the dependency of factors, providing an explanation for the performance of standardized data in ML models and the reasons behind the higher predictive performance of coupled models based on conditional probability models and ML methods. Through comprehensive analysis of the local interpretation results from different models analyzing the same sample with different sample characteristics, the reasons for model prediction errors can be summarized, thereby providing a reference framework for constructing more accurate and rational landslide susceptibility models and facilitating landslide warning and management. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>Location of the study area and landslide distribution. (<b>a</b>) The location of the research area in Guangxi; (<b>b</b>) the location of the study area and the distribution of landslides and non-landslides.</p> Full article ">Figure 2
<p>Landslide conditioning factors (I). (<b>a</b>) Elevation; (<b>b</b>) slope; (<b>c</b>) slope variation; (<b>d</b>) profile curvature; (<b>e</b>) plane curvature; (<b>f</b>) TWI; (<b>g</b>) SPI; (<b>h</b>) MNDWI; (<b>i</b>) NDVI.</p> Full article ">Figure 3
<p>Landslide conditioning factors (II). (<b>a</b>) Fracture zone density; (<b>b</b>) mineral point density; (<b>c</b>) road density; (<b>d</b>) river density; (<b>e</b>) population density; (<b>f</b>) number of days with heavy rainfall; (<b>g</b>) soil erodibility; (<b>h</b>) soil moisture; (<b>i</b>) total rainfall.</p> Full article ">Figure 4
<p>Landslide conditioning factors (III). (<b>a</b>) Slope direction; (<b>b</b>) soil type; (<b>c</b>) type of landform; (<b>d</b>) thickness of weathering layer; (<b>e</b>) hydrogeology.</p> Full article ">Figure 5
<p>Flowchart of the study.</p> Full article ">Figure 6
<p>Correlation analyses between landslide conditioning factors. 1: MNDWI; 2: NDVI; 3: SPI; 4: TWI; 5: type of landform; 6: fracture zone density; 7: thickness of weathering layer; 8: elevation; 9: river density; 10: mineral point density; 11: road density; 12: plane curvature; 13: slope; 14: slope variation; 15: slope direction; 16: profile curvature; 17: number of days with heavy rainfall; 18: population density; 19: hydrogeology; 20: soil erodibility; 21: soil type; 22: soil moisture; 23: total rainfall.</p> Full article ">Figure 7
<p>Landslide susceptibility maps based on different types of data using the RF model. (<b>a</b>) RF-Initial; (<b>b</b>) RF-FR; (<b>c</b>) RF-IV; (<b>d</b>) RF-CF; (<b>e</b>) RF-EBF; (<b>f</b>) RF-WOE.</p> Full article ">Figure 8
<p>Landslide susceptibility maps based on different types of data using the XGBoost model. (<b>a</b>) XGBoost-Initial; (<b>b</b>) XGBoost-FR; (<b>c</b>) XGBoost-IV; (<b>d</b>) XGBoost-CF; (<b>e</b>) XGBoost-EBF; (<b>f</b>) XGBoost-WOE.</p> Full article ">Figure 9
<p>The statistical results of risk area. (<b>a</b>) RF models; (<b>b</b>) XGBoost models.</p> Full article ">Figure 10
<p>ROC curves of the seven models for the testing set. (<b>a</b>) RF models; (<b>b</b>) XGBoost models.</p> Full article ">Figure 11
<p>ROC curves of the seven models for the full sample set. (<b>a</b>) RF models; (<b>b</b>) XGBoost models.</p> Full article ">Figure 12
<p>RMSE for RF models based on different data types. (<b>a</b>) RF-Initial; (<b>b</b>) RF-FR; (<b>c</b>) RF-IV; (<b>d</b>) RF-CF; (<b>e</b>) RF-EBF; (<b>f</b>) RF-WOE.</p> Full article ">Figure 13
<p>RMSE for XGBoost models based on different data types. (<b>a</b>) XGBoost-Initial; (<b>b</b>) XGBoost-FR; (<b>c</b>) XGBoost-IV; (<b>d</b>) XGBoost-CF; (<b>e</b>) XGBoost-EBF; (<b>f</b>) XGBoost-WOE.</p> Full article ">Figure 14
<p>Summary plots of SHAP values derived from RF models (top 20). (<b>a</b>) RF-Initial; (<b>b</b>) RF-CF.</p> Full article ">Figure 15
<p>Summary plots of SHAP values derived from XGBoost models (top 20). (<b>a</b>) XGBoost-Initial; (<b>b</b>) XGBoost-CF.</p> Full article ">Figure 16
<p>Factor importance plot derived from RF models. (<b>a</b>) RF-Initial; (<b>b</b>) RF-CF.</p> Full article ">Figure 17
<p>Factor importance plot derived from XGBoost models. (<b>a</b>) XGBoost-Initial; (<b>b</b>) XGBoost-CF.</p> Full article ">Figure 18
<p>Heatmap plots derived from RF models. (<b>a</b>) RF-Initial; (<b>b</b>) RF-CF.</p> Full article ">Figure 19
<p>Heatmap plots derived from XGBoost models. (<b>a</b>) XGBoost-Initial; (<b>b</b>) XGBoost-CF.</p> Full article ">Figure 20
<p>Single-factor dependence plots of the main factors based on the XGBoost-Initial model. (<b>a</b>) Slope; (<b>b</b>) SPI; (<b>c</b>) TWI; (<b>d</b>) mineral point density; (<b>e</b>) elevation; (<b>f</b>) plane curvature; (<b>g</b>) MNDWI; (<b>h</b>) NDVI; (<b>i</b>) total rainfall.</p> Full article ">Figure 21
<p>Single-factor dependence plots of main factors based on the XGBoost-CF model. (<b>a</b>) Slope; (<b>b</b>) SPI; (<b>c</b>) elevation; (<b>d</b>) TWI; (<b>e</b>) mineral point density; (<b>f</b>) MNDWI; (<b>g</b>) NDVI; (<b>h</b>) soil moisture; (<b>i</b>) total rainfall.</p> Full article ">Figure 22
<p>Plots of SHAP interaction effects based on the XGBoost-Initial model. (<b>a</b>) Slope and SPI; (<b>b</b>) slope and TWI; (<b>c</b>) slope and mineral point density; (<b>d</b>) slope and elevation; (<b>e</b>) slope and plane curvature; (<b>f</b>) slope and NDVI; (<b>g</b>) slope and MNDWI; (<b>h</b>) slope and total rainfall.</p> Full article ">Figure 23
<p>Two-factor dependence plots of main factors based on the XGBoost-CF model. (<b>a</b>) Slope and SPI; (<b>b</b>) slope and elevation; (<b>c</b>) slope and TWI; (<b>d</b>) slope and mineral point density; (<b>e</b>) slope and MNDWI; (<b>f</b>) slope and NDVI; (<b>g</b>) slope and soil moisture; (<b>h</b>) slope and total rainfall.</p> Full article ">Figure 24
<p>Global feature importance calculation in XGBoost. (<b>a</b>) XGBoost-Initial model (im portance_type = “weight”); (<b>b</b>) XGBoost-CF model (importance_type = “weight”); (<b>c</b>) XGBoost-Initial model (importance_type = “cover”); (<b>d</b>) XGBoost- CF model (importance_type = “cover”); (<b>e</b>) XGBoost-Initial model (importance_type = “gain”); (<b>f</b>) XGBoost- CF model (importance_type = “gain”).</p> Full article ">Figure 25
<p>SHAP-based global feature importance calculation (top 20). (<b>a</b>) XGBoost-Initial model; (<b>b</b>) XGBoost-CF model.</p> Full article ">Figure 26
<p>Local interpretation of the susceptibility of landslide Case 1. (<b>a</b>) Time sequence image of landslide area (from Google Earth); (<b>b</b>) RF-Initial; (<b>c</b>) XGBoost-Initial; (<b>d</b>) RF-CF; (<b>e</b>) XGBoost-CF.</p> Full article ">Figure 27
<p>Local interpretation of the susceptibility of landslide Case 2. (<b>a</b>) Time sequence image of landslide area (from Google Earth); (<b>b</b>) RF-Initial; (<b>c</b>) XGBoost-Initial; (<b>d</b>) RF-CF; (<b>e</b>) XGBoost-CF.</p> Full article ">Figure 28
<p>Local interpretation of the susceptibility of non-landslide Case 1. (<b>a</b>) Time sequence image of non-landslide area (from Google Earth); (<b>b</b>) RF-Initial; (<b>c</b>) XGBoost-Initial; (<b>d</b>) RF-CF; (<b>e</b>) XGBoost-CF.</p> Full article ">Figure 29
<p>Local interpretation of the susceptibility of non-landslide Case 2. (<b>a</b>) Time sequence image of non-landslide area (from Google Earth); (<b>b</b>) RF-Initial; (<b>c</b>) XGBoost-Initial; (<b>d</b>) RF-CF; (<b>e</b>) XGBoost-CF.</p> Full article ">Figure 30
<p>Local interpretation of the susceptibility of landslide sample with wrong prediction. (<b>a</b>) Time sequence image of landslide area (from Google Earth); (<b>b</b>) RF-Initial; (<b>c</b>) XGBoost-Initial; (<b>d</b>) RF-CF; (<b>e</b>) XGBoost-CF.</p> Full article ">Figure 31
<p>Local interpretation of the susceptibility of non-landslide sample with wrong prediction. (<b>a</b>) Time sequence image of non-landslide area (from Google Earth); (<b>b</b>) RF-Initial; (<b>c</b>) XGBoost-Initial; (<b>d</b>) RF-CF; (<b>e</b>) XGBoost-CF.</p> Full article ">

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23 pages, 10410 KiB  

Open AccessArticle

Subgrid Model of Fluid Force Acting on Buildings for Three-Dimensional Flood Inundation Simulations

by Riku Kubota, Jin Kashiwada and Yasuo Nihei

Water 2023, 15(17), 3166; https://doi.org/10.3390/w15173166 - 4 Sep 2023

Cited by 1 | Viewed by 1567

Abstract

In recent years, large-scale heavy rainfall disasters have occurred frequently in several parts of the world. Therefore, a quantitative approach to understanding how buildings are damaged during floods is necessary to develop appropriate flood-resistant technologies. In flood inundation simulations for the quantitative evaluation [...] Read more.

In recent years, large-scale heavy rainfall disasters have occurred frequently in several parts of the world. Therefore, a quantitative approach to understanding how buildings are damaged during floods is necessary to develop appropriate flood-resistant technologies. In flood inundation simulations for the quantitative evaluation of a building’s resistance to flooding, a subgrid model is necessary to appropriately evaluate the resistance of buildings smaller than the grid size at a medium grid resolution. In this study, a new subgrid (SG) 3D inundation model is constructed to evaluate the fluid force acting on buildings and assess the damage to individual buildings during flood inundation. The proposed method does not increase the computational load. The model is incorporated into a 2D and 3D hybrid model with high computational efficiency to construct a 3D river and inundation flow model. Its validity and effectiveness are evaluated through comparisons with field observations and the conventional equivalent roughness model. Considering horizontal and vertical velocity distributions, the proposed model showed statistically significant improvements in performance in terms of building loss indices such as velocity and fluid force. These results suggest that the SG model can effectively evaluate the fluid force acting on buildings, including the vertical distribution of flow velocities. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>Schematic of fundamental concept of subgrid model for building fluid force. (<b>a</b>) When medium grid resolutions are adopted, buildings of various heights are located in several computational grids. (<b>b</b>) In Step 1, each building is divided horizontally and vertically for each computational grid. (<b>c</b>) In Step 2, the flow velocity at each grid is interpolated at the center of each building. (<b>d</b>) In Step 3, the fluid force obtained for each building is distributed to each grid.</p> Full article ">Figure 2
<p>Interpolation method of calculated velocities at the center of each building using IDW for evaluation of building fluid force. Velocities in <span class="html-italic">s</span> and <span class="html-italic">n</span> directions, <span class="html-italic">u_s</span> and <span class="html-italic">u_n</span>, respectively, are defined in staggered grids.</p> Full article ">Figure 3
<p>Time interval concept in 2D and 3D calculations in Hy2-3D model. (<b>a</b>) Case when Δt_2D = Δt_3D1 and (<b>b</b>) case when Δt_2D &gt; Δt_3D1.</p> Full article ">Figure 4
<p>(<b>a</b>) Location and elevation map of the Kuma River Basin; (<b>b</b>) computational domain from 51.8 kp to 68.6 kp along the Kuma River.</p> Full article ">Figure 5
<p>(<b>a</b>) Temporal variations in basin-averaged precipitation and water level at Ohashi (61.5 kp) in the Kuma River. Precipitation and water level data were obtained from <a href="http://www.jmbsc.or.jp/en/index-e.html" target="_blank">http://www.jmbsc.or.jp/en/index-e.html</a> (accessed on 22 November 2022) and <a href="https://www.river.go.jp/index" target="_blank">https://www.river.go.jp/index</a> (accessed on 22 November 2022), respectively; (<b>b</b>) boundary conditions of inflow discharge at upstream points and tributaries, and water level at the downstream point. River discharges in the Kuma River and 11 major tributaries were obtained from the runoff calculation results [<a href="#B49-water-15-03166" class="html-bibr">49</a>]. Water level at the downstream end was obtained from the computational results using 1D unsteady flow analysis performed by the authors.</p> Full article ">Figure 6
<p>(<b>a</b>) Longitudinal distribution of calculated and observed water levels at various time points in the Kuma River; (<b>b</b>) calculated and observed peak water levels; and (<b>c</b>) difference in peak water levels between Case 1 and other cases. The calculated results for Case 1 are used in parts (<b>a</b>,<b>b</b>).</p> Full article ">Figure 7
<p>Temporal variation in calculated and observed water levels in the Kuma River. The calculated results for Case 1 are shown. The results at water-level observatories Ichibu (68.6 kp), Hitoyoshi (62.2 kp), Ohashi (61.5 kp), Nishizebashi (59.4 kp), Gogan (57.4 kp), and Watari (52.7 kp) are depicted.</p> Full article ">Figure 8
<p>Scatter plots of (<b>a</b>) calculated and observed peak water levels and (<b>b</b>) water depth in inundated area. The calculated results for Case 1 are used in the figure. Observed results are based on those reported by Ogata et al. [<a href="#B55-water-15-03166" class="html-bibr">55</a>].</p> Full article ">Figure 9
<p>(<b>a</b>) Contour maps of calculated depth horizontal velocities at 10:00 a.m. on 4 July 2020, near the Hitoyoshi city area and (<b>b</b>) cross-sectional distributions of calculated horizontal velocities and water levels with locations of buildings along section A-A′. Magnitude of depth-averaged horizontal velocities in all cases is depicted.</p> Full article ">Figure 10
<p>Vertical distribution of streamwise velocity at 11:30 a.m. on 4 July 2020 in Chaya District. (<b>a</b>) Locations of four stations. (<b>b</b>) Equivalent roughness <span class="html-italic">n</span> in this area. Calculated velocities for (<b>c</b>) Case 1 and (<b>d</b>) Case 2 are shown.</p> Full article ">Figure 11
<p>Correlation diagram of building loss indices for Cases 1 and 2 in lost buildings (160 buildings). <span class="html-italic">p</span>-value showing a statistically significant difference between Cases 1 and 2 is also illustrated (* <span class="html-italic">p</span> &lt; 0.10).</p> Full article ">Figure 12
<p>Boxplot showing flood index by flood depth level in lost buildings for Cases 1 and 2. <span class="html-italic">p</span>-value indicating a statistically significant difference between Cases 1 and 2 is also shown (* <span class="html-italic">p</span> &lt; 0.05).</p> Full article ">

17 pages, 2560 KiB  

Open AccessArticle

Research on a Scheduling Model for Social Emergency Resource Sharing Based on Emergency Contribution Index

by Wenqi Cui, Xinwu Chen, Boyu Liu, Qian Hu, Miaomiao Ma, Xing Xu, Zhanyun Feng, Jiale Chen and Wei Cui

Sustainability 2023, 15(17), 13029; https://doi.org/10.3390/su151713029 - 29 Aug 2023

Viewed by 796

Abstract

A large number of massive repair machines are urgently necessary for a post-disaster rescue. These machines also need to be operated by professionals, and the demands require the participation of different industries in the whole society since they cannot be met via the [...] Read more.

A large number of massive repair machines are urgently necessary for a post-disaster rescue. These machines also need to be operated by professionals, and the demands require the participation of different industries in the whole society since they cannot be met via the national emergency resource storage system. Therefore, the support of extensive emergency resources from different industries across the entire society is needed in the rescue process, that is, social emergency resource sharing. To achieve this sharing, an emergency resource scheduling model should have the ability to allocate resources from the whole society. However, traditional emergency scheduling models have not considered the suppliers’ willingness to take part in the scheduling activities and their abilities to supply the resources. To solve the above issues, this paper designs a scheduling model for social emergency resource sharing based on an emergency contribution index (SSERS). The emergency contribution index (ECI) can be used to find the enterprises that not only have the ability to provide efficient emergency resources on time but also have the willingness to participate in emergency rescue. The results show that our model effectively optimizes the basic models to some extent and achieves social emergency resource sharing. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1
<p>The research area of this study (Wenchuan).</p> Full article ">Figure 2
<p>The process of our experiment.</p> Full article ">Figure 3
<p>The spatial distribution of the six selected enterprises.</p> Full article ">

15 pages, 2535 KiB  

Open AccessArticle

Protecting Built Heritage against Flood: Mapping Value Density on Flood Hazard Maps

by Agnes W. Brokerhof, Renate van Leijen and Berry Gersonius

Water 2023, 15(16), 2950; https://doi.org/10.3390/w15162950 - 16 Aug 2023

Cited by 1 | Viewed by 1445

Abstract

This paper describes the development and trial of a method (Quick Flood Risk Scan method) to determine the vulnerable value of monuments for flood risk assessment. It was developed in the context of the European Flood Directive for the Dutch Flood Risk Management [...] Read more.

This paper describes the development and trial of a method (Quick Flood Risk Scan method) to determine the vulnerable value of monuments for flood risk assessment. It was developed in the context of the European Flood Directive for the Dutch Flood Risk Management Plan. The assessment method enables differentiation of cultural heritage by cultural value and vulnerability to water from rainfall or flooding. With this method, hazard or exposure maps can be turned into risk maps showing the potential loss of cultural value in case of flooding with a particular probability. The Quick Flood Risk Scan method has been tested and validated in the City of Dordrecht, the Netherlands. This application was facilitated by an Open Lab of the SHELTER project. The trial in Dordrecht showed the potential of a simple method to prioritize monuments without calculations. The Quick Flood Risk Scan method enables even the non-expert assessor to make a preliminary qualitative assessment that can be followed by further analysis of a relevant selection of assets. It is useful as a low tier that feeds into higher tiers of a multi-level framework. The non-expert assessor may be a policy maker, an owner of a heritage asset, or an inhabitant. Nonetheless, the trial also raised several questions, ranging from where in a building valuable heritage is located and what the role of the building owner is to how policy makers implement the method and its outcomes. These questions provide relevant input for fine-tuning the method. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>Principles of the (original) Quick Risk Scan method as developed for heritage collections: an asset is at risk when it has cultural value, is susceptible to a particular hazard, and is exposed to that hazard (<b>left</b>) and the equivalent in overlaying maps (<b>right</b>).</p> Full article ">Figure 2
<p>Matrix to assess potential loss or impact (here: vulnerable value) for monuments in the Netherlands.</p> Full article ">Figure 3
<p><b>Top</b>: map of the historic port area of Dordrecht with buildings listed at national (red) and local (green) level [<a href="#B35-water-15-02950" class="html-bibr">35</a>]. <b>Bottom</b>: exposure map of the same area with the 19 buildings of the self-selected sample (dots) coloured according to their vulnerable value (given in <a href="#water-15-02950-f002" class="html-fig">Figure 2</a>).</p> Full article ">Figure 3 Cont.
<p><b>Top</b>: map of the historic port area of Dordrecht with buildings listed at national (red) and local (green) level [<a href="#B35-water-15-02950" class="html-bibr">35</a>]. <b>Bottom</b>: exposure map of the same area with the 19 buildings of the self-selected sample (dots) coloured according to their vulnerable value (given in <a href="#water-15-02950-f002" class="html-fig">Figure 2</a>).</p> Full article ">

18 pages, 3655 KiB  

Open AccessArticle

The Hydraulic and Boundary Characteristics of a Dike Breach Based on Cluster Analysis

by Mingxiao Liu, Yaru Luo, Chi Qiao, Zezhong Wang, Hongfu Ma and Dongpo Sun

Water 2023, 15(16), 2908; https://doi.org/10.3390/w15162908 - 11 Aug 2023

Cited by 1 | Viewed by 1258

Abstract

It is important to determine the hydraulic boundary eigenvalues of typical embankment breaches before carrying out research on their occurrence mechanisms and assessing their repair technology. However, it is difficult to obtain the hydraulic boundary conditions of the typical levee breaches accurately with [...] Read more.

It is important to determine the hydraulic boundary eigenvalues of typical embankment breaches before carrying out research on their occurrence mechanisms and assessing their repair technology. However, it is difficult to obtain the hydraulic boundary conditions of the typical levee breaches accurately with minor or incomplete measured data due to the complexity and instability of the levee breach. Based on more than 100 groups of domestic and foreign test data of embankment/earth dam failures, the correlation between the hydraulic boundary eigenvalues of a breach was established based on the cluster analysis approach. Additionally, the missing values were imputed after correlating and fitting. Meanwhile, the hydraulic boundary parameters and the related equations of a generalized typical breach were obtained through the statistical analysis of the probability density of the dimensionless eigenvalues of the breach. The analysis showed that the width of the breach mainly ranges in 20~100 m, while the water head of the breach is 4~12 m, and the velocity of the breach is 2~8 m/s. The distribution probabilities of all them are about 64~71%. The probability density of the width-to-depth ratio and the Froude number of the breach are both subject to normal distribution characteristics. The distribution frequency of the width-to-depth ratio of 3~8 is approximately 55%, and the Froude number of 0.4~0.8 is approximately 60%. These methods and findings might provide valuable support for the statistical research of the boundary and hydraulic characteristics of the breach, and the closure technology of breach. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>Schematic diagram of occurrence characteristics of embankment breach. Bi, Hi and vi denotes the width, water level and velocity of the breach at moment I, respectively, while <span class="html-italic">B</span>, <span class="html-italic">H</span>, and <span class="html-italic">v</span> denotes the maximum value of the width, water level and velocity of the breach, respectively.</p> Full article ">Figure 2
<p>The transverse and longitudinal section of the generalizes breach. The left figure (<b>a</b>) shows that the cross section of the breach is generalized as a trapezoid shape with side slope <span class="html-italic">m</span> = 1.0 and height of the dyke as h. The right figure (<b>b</b>) shows that there is a water head drop Δz between two sides of the breach along the flood direction. The meaning of other symbols in the figure are same as above of the respective characteristic value, and the abscissa is time breach developing.</p> Full article ">Figure 3
<p>Research logic chart by means of cluster analysis method. During the process, the feature values are considered dimensionless, and the probability density distribution characteristics of each dimensionless parameter are analysed. On this basis, the hydraulic-boundary feature value of the generalized breach are determined.</p> Full article ">Figure 4
<p>Hydraulic boundary eigenvalues of embankment breach. Considering the similarities and differences in hydraulic boundary conditions, these three kinds of data were classified into 6 types.</p> Full article ">Figure 5
<p>Cluster analysis tree.</p> Full article ">Figure 6
<p>Schematic diagram of calculation process of fitted and imputed value. The missing value estimation of each eigenvalue can be sequentially performed.</p> Full article ">Figure 7
<p>Fitting regression curve of <span class="html-italic">Q</span>~<span class="html-italic">B</span>. According to the <span class="html-italic">t</span> test method, the standard error of the fitting line is 3.733, and the <span class="html-italic">t</span> value is 10.098.</p> Full article ">Figure 8
<p>Fitting regression curve of <span class="html-italic">H</span>-<span class="html-italic">Q</span>. The intercept <span class="html-italic">C</span> of the fitting line is 5.486, and the standard error is 0.974.</p> Full article ">Figure 9
<p>Comparison of breach water head before and after fitting. The fitted data of water depth is more uniform, and is centred in about 6 m.</p> Full article ">Figure 10
<p>Comparison of gate flow rate before and after fitting. The fitted data of velocity ranges mainly in 2 m/s and 7 m/s.</p> Full article ">Figure 11
<p>Fitting error distribution of breach rate and water head. The relative deviation e of the data is decreased largely after fitting.</p> Full article ">Figure 12
<p>Discrete distribution of wide-to-depth ratio (<span class="html-italic">B</span>/<span class="html-italic">H</span>).</p> Full article ">Figure 13
<p>Probability density of width-to-depth ratio (<span class="html-italic">B</span>/<span class="html-italic">H</span>) and its percentile distribution. The probability density distribution of the width–depth ratio basically conforms to the lognormal distribution.</p> Full article ">Figure 14
<p>Discrete distribution of Froude number (<span class="html-italic">Fr</span>). Froude number mainly ranges in 0.1 and 0.8.</p> Full article ">Figure 15
<p>Probability density and percentile distribution of Froude number (<span class="html-italic">Fr</span>). The probability density distribution of the <span class="html-italic">Fr</span> approximates the general normal distribution, i.e., <span class="html-italic">Fr</span>~<span class="html-italic">N</span>(<span class="html-italic">μ</span>, <span class="html-italic">σ</span>₂).</p> Full article ">

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26 pages, 12658 KiB  

Open AccessArticle

Machine Learning Approaches for Streamflow Modeling in the Godavari Basin with CMIP6 Dataset

by Subbarayan Saravanan, Nagireddy Masthan Reddy, Quoc Bao Pham, Abdullah Alodah, Hazem Ghassan Abdo, Hussein Almohamad and Ahmed Abdullah Al Dughairi

Sustainability 2023, 15(16), 12295; https://doi.org/10.3390/su151612295 - 11 Aug 2023

Cited by 4 | Viewed by 1720

Abstract

Accurate streamflow modeling is crucial for effective water resource management. This study used five machine learning models (support vector regressor (SVR), random forest (RF), M5-pruned model (M5P), multilayer perceptron (MLP), and linear regression (LR)) to simulate one-day-ahead streamflow in the Pranhita subbasin (Godavari [...] Read more.

Accurate streamflow modeling is crucial for effective water resource management. This study used five machine learning models (support vector regressor (SVR), random forest (RF), M5-pruned model (M5P), multilayer perceptron (MLP), and linear regression (LR)) to simulate one-day-ahead streamflow in the Pranhita subbasin (Godavari basin), India, from 1993 to 2014. Input parameters were selected using correlation and pairwise correlation attribution evaluation methods, incorporating a two-day lag of streamflow, maximum and minimum temperatures, and various precipitation datasets (including Indian Meteorological Department (IMD), EC-Earth3, EC-Earth3-Veg, MIROC6, MRI-ESM2-0, and GFDL-ESM4). Bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets were utilized in the modeling process. Model performance was evaluated using Pearson correlation (R), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and coefficient of determination (R²). IMD outperformed all CMIP6 datasets in streamflow modeling, while RF demonstrated the best performance among the developed models for both CMIP6 and IMD datasets. During the training phase, RF exhibited NSE, R, R², and RMSE values of 0.95, 0.979, 0.937, and 30.805 m³/s, respectively, using IMD gridded precipitation as input. In the testing phase, the corresponding values were 0.681, 0.91, 0.828, and 41.237 m³/s. The results highlight the significance of advanced machine learning models in streamflow modeling applications, providing valuable insights for water resource management and decision making. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>Location map of the study area.</p> Full article ">Figure 2
<p>Flowchart of the methodology adopted in this study.</p> Full article ">Figure 3
<p>Line plot for observed vs. simulated streamflow for (<b>a</b>) SVR, (<b>b</b>) RF, (<b>c</b>) MLP, (<b>d</b>) M5P, and (<b>e</b>) LR during training.</p> Full article ">Figure 4
<p>Line plot for observed vs. simulated streamflow for (<b>a</b>) SVR, (<b>b</b>) RF, (<b>c</b>) MLP, (<b>d</b>) M5P, and (<b>e</b>) LR during testing.</p> Full article ">Figure 5
<p>Scatter plot for observed vs. simulated streamflow for (<b>a</b>) SVR, (<b>b</b>) RF, (<b>c</b>) MLP, (<b>d</b>) M5P, and (<b>e</b>) LR during training.</p> Full article ">Figure 6
<p>Scatter plot for observed vs. simulated streamflow for (<b>a</b>) SVR, (<b>b</b>) RF, (<b>c</b>) MLP, (<b>d</b>) M5P, and (<b>e</b>) LR during testing.</p> Full article ">Figure 7
<p>Radar plot during training (<b>a</b>) NSE and R, (<b>b</b>) RMSE.</p> Full article ">Figure 8
<p>Radar plot during testing (<b>a</b>) NSE and R, (<b>b</b>) RMSE.</p> Full article ">Figure 9
<p>Violin plots during (<b>a</b>) training and (<b>b</b>) testing periods.</p> Full article ">Figure 10
<p>Taylor diagrams during (<b>a</b>) training and (<b>b</b>) testing using EC-Earth3 dataset.</p> Full article ">Figure 11
<p>Taylor diagrams during (<b>a</b>) training and (<b>b</b>) testing using EC-Earth3-Veg dataset.</p> Full article ">Figure 12
<p>Taylor diagrams during (<b>a</b>) training and (<b>b</b>) testing using GFDL-ESM4 dataset.</p> Full article ">Figure 13
<p>Taylor diagrams during (<b>a</b>) training and (<b>b</b>) testing using IMD dataset.</p> Full article ">Figure 14
<p>Taylor diagrams during (<b>a</b>) training and (<b>b</b>) testing using MIROC6 dataset.</p> Full article ">Figure 15
<p>Taylor diagrams during (<b>a</b>) training and (<b>b</b>) testing using MRI-ESM2-0 dataset.</p> Full article ">

17 pages, 6912 KiB  

Open AccessArticle

Experimental Research on Backward Erosion Piping Progression

by Jaromir Riha and Lubomir Petrula

Water 2023, 15(15), 2749; https://doi.org/10.3390/w15152749 - 29 Jul 2023

Cited by 5 | Viewed by 1463

Abstract

Internal erosion is caused by seepage body forces acting on the soil particles. One of the most dangerous modes of internal erosion at hydraulic structures is backward erosion piping, which usually initiates at the downstream end of a seepage path, e.g., at the [...] Read more.

Internal erosion is caused by seepage body forces acting on the soil particles. One of the most dangerous modes of internal erosion at hydraulic structures is backward erosion piping, which usually initiates at the downstream end of a seepage path, e.g., at the downstream toe of the dam. The progress of backward erosion and the development of erosion pipes were tested in a newly developed laboratory device for three types of sand with grain sizes of 0/2, 0.25/2, and 0.25/1. The piezometric head along the gradually developing seepage “pipe” was observed by seventeen piezometers and seven pressure sensors. The seepage amount was measured by the volumetric method. The critical hydraulic gradient was determined and related to the soil porosity. The progression of the seepage path and relevant characteristics such as the piezometric and pressure heads and the amount of trapped sediment were observed by two synchronous cameras. Based on the analysis of the results of 42 tests, a new empirical formula for the backward erosion rate was proposed. The characteristics of lateral erosion were evaluated and compared with the available literature, which provided reasonably good agreement. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1
<p>The experimental device.</p> Full article ">Figure 2
<p>Local critical hydraulic gradients at the pipe tip in relation to sample porosity for 0/2 mm sand.</p> Full article ">Figure 3
<p>Mean critical hydraulic gradients at the pipe tip in relation to sample porosity for 0/2 mm sand.</p> Full article ">Figure 4
<p>Relationship between local and mean critical gradients.</p> Full article ">Figure 5
<p>Mean critical hydraulic gradients in relation to sample porosity.</p> Full article ">Figure 6
<p>Measured mean critical hydraulic gradients compared with the prediction by Sellmeijer et al. [<a href="#B13-water-15-02749" class="html-bibr">13</a>].</p> Full article ">Figure 7
<p>Changes in the pipe shape during BEP—two instants related to <a href="#water-15-02749-f006" class="html-fig">Figure 6</a> are marked by the red color.</p> Full article ">Figure 8
<p>Separation of backward and lateral erosion: <span class="html-italic">t_j</span> = 20,309 s; <span class="html-italic">t_j+1</span> = 20,312 s; and Δ<span class="html-italic">t</span> = 3 s.</p> Full article ">Figure 9
<p>The dependence between BEP rate and mean hydraulic gradient.</p> Full article ">Figure 10
<p>The dependence between BEP rate and soil porosity.</p> Full article ">Figure 11
<p>The dependence between BEP rate and <span class="html-italic">d</span>₅₀.</p> Full article ">Figure 12
<p>Comparison of experimentally obtained and calculated backward erosion rates.</p> Full article ">Figure 13
<p>Comparison of experimentally obtained and calculated backward erosion rates—detail.</p> Full article ">Figure 14
<p>Critical shear stress related to sample porosity.</p> Full article ">Figure 15
<p>Coefficient of soil erosion related to sample porosity.</p> Full article ">

16 pages, 9980 KiB  

Open AccessArticle

Experimental Investigation of Levee Erosion during Overflow and Infiltration with Varied Hydraulic Conductivities of Levee and Foundation Properties in Saturated Conditions

by Liaqat Ali and Norio Tanaka

GeoHazards 2023, 4(3), 286-301; https://doi.org/10.3390/geohazards4030016 - 25 Jul 2023

Cited by 2 | Viewed by 1799

Abstract

This study investigated erosion during infiltration and overflow events and considered different grain sizes and hydraulic conductivity properties; four experimental cases were conducted under saturated conditions. The importance of understanding flow regimes during overflow experiments including their distinct flow characteristics, shear stresses, and [...] Read more.

This study investigated erosion during infiltration and overflow events and considered different grain sizes and hydraulic conductivity properties; four experimental cases were conducted under saturated conditions. The importance of understanding flow regimes during overflow experiments including their distinct flow characteristics, shear stresses, and erosion mechanisms in assessing the potential for levee failure are discussed. The failure mechanism of levee slopes during infiltration experiments involves progressive collapse due to piping followed by increased liquefaction and loss of shear stress, with the failure progression dependent on the permeability of the foundation material and shear strength. The infiltration experiments illustrate that the rate of failure varied based on the permeability of the foundation material. In the case of IO-E7-F5, where the levee had No. 7 sand in the embankment and No. 5 sand in the foundation (lower permeability), the failure was slower and limited. It took around 90 min for 65% of the downstream slope to fail, allowing more time for response measures. On the other hand, in the case of IO-E8-F4, with No. 8 sand in the embankment and No. 4 sand in the foundation (higher hydraulic conductivity), the failure was rapid and extensive. The whole downstream slope failed within just 18 min, and the collapse extended to 75% of the levee crest. These findings emphasize the need for proactive measures to strengthen vulnerable sections of levees and reduce the risk of extensive failure. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>Schematic detail of experimental setup inside the channel. (<b>a</b>) A side view of experimental flume with embankment model; (<b>b</b>) cross section of embankment model in O-E7-F5 overflow condition; (<b>c</b>) cross section of embankment model in IO-E7-F5 infiltration and overflow condition; (<b>d</b>) cross section of embankment model in O-E8-F4 overflow condition; (<b>e</b>) cross section of embankment model in IO-E8-F4 infiltration and overflow condition.</p> Full article ">Figure 2
<p>The Levee model preparation: (<b>a</b>) sieve size distribution of embankment material, (<b>b</b>) degree of compaction and optimum water content check during embankment levee model preparation. The levee model in the flume: (<b>c</b>) side view, (<b>d</b>) top view.</p> Full article ">Figure 3
<p>Overflow erosion process in O-E7-F5 and O-E8-F4. (<b>a</b>) Stage I, arc surface headcut formation. (<b>b</b>) Stage II, smooth erosion of downstream slope and submerged hydraulic jump formation. (<b>c</b>) Stage III, nape flow formation and S shape failure. (<b>d</b>) Stage IV, upstream slope failure and high erosion rate.</p> Full article ">Figure 4
<p>Levee component erosion and surface profiles at various elapsed times. (<b>a</b>) Erosion profiles for overflow O-E7-F5; (<b>b</b>) for overflow O-E8-F4. (<b>c</b>) Levee components (downstream slope, crest, and upstream slope) erosion percentage vs. elapsed time in O-E7-F5 and O-E8-F4.</p> Full article ">Figure 5
<p>Erosion process in IO-E7-F5; top and side views at different elapsed times (m for minutes, s for seconds).</p> Full article ">Figure 6
<p>Levee surface profiles at various elapsed times: (<b>a</b>) for infiltration case IO-E7-F5; (<b>b</b>) for overflow after 110 min.</p> Full article ">Figure 7
<p>Erosion process in IO-E8-F4; top and sides views at different elapsed times (m for minutes, s for seconds).</p> Full article ">Figure 8
<p>Levee surface profiles at various elapsed times: (<b>a</b>) for infiltration case IO-E8-F4 (<b>b</b>), for overflow after 110 min.</p> Full article ">Figure 9
<p>Levee components erosion percentage vs. elapsed time in (<b>a</b>) IO-E7-F5 and (<b>b</b>) IO-E8-F4.</p> Full article ">Figure 10
<p>Flow characteristics during levee overflow: (<b>a</b>) O-E7-F5 and (<b>b</b>) O-E8-F4.</p> Full article ">Figure 11
<p>Erosion or collapse mechanism and permeability chart. (<b>a</b>) Stage I and Stage II collapse pattern during infiltration experiments IO-E7-F5 and IO-E8-F4. (<b>b</b>) Hydraulic conductivities vs. different grain sizes.</p> Full article ">

25 pages, 14144 KiB  

Open AccessArticle

Geographic-Information-System-Based Risk Assessment of Flooding in Changchun Urban Rail Transit System

by Gexu Liu, Yichen Zhang, Jiquan Zhang, Qiuling Lang, Yanan Chen, Ziyang Wan and Huanan Liu

Remote Sens. 2023, 15(14), 3533; https://doi.org/10.3390/rs15143533 - 13 Jul 2023

Cited by 6 | Viewed by 1873

Abstract

The frequent occurrence of urban flooding in recent years has resulted in significant damage to ground-level infrastructure and poses a substantial threat to the metro system. As the central city’s core transportation network for public transit, this threat can have unpredictable consequences on [...] Read more.

The frequent occurrence of urban flooding in recent years has resulted in significant damage to ground-level infrastructure and poses a substantial threat to the metro system. As the central city’s core transportation network for public transit, this threat can have unpredictable consequences on travel convenience and public safety. Therefore, assessing the risk of urban flooding in the metro system is of utmost importance. This study is the first of its kind to employ comprehensive natural disaster risk assessment theory, establishing an assessment database with 22 indicators. We propose a GIS-based method combined with the analytical hierarchy process (AHP) and an improved entropy weight method to comprehensively evaluate the urban flood risk in Changchun City’s metro systems in China. This study includes a total of nine metro lines, including those that are currently operational as well as those that are in the planning and construction phases, situated in six urban areas of Changchun City. In this study, we utilize the regional risk level within the 500 m buffer zone of the metro lines to represent the flood risk of the metro system. The proposed method assesses the flood risk of Changchun’s rail transit system. The results reveal that over 30% of Changchun’s metro lines are located in high-risk flood areas, mainly concentrated in the densely populated and economically prosperous western part of the central city. To validate the risk assessment, we vectorized the inundation points and overlaid them with the regional flood risk assessment results, achieving a model accuracy of over 90%. As no large-scale flood events have occurred in the Changchun rail transit system, we employed receiver operating characteristic (ROC) curves to verify the accuracy of the flood risk assessment model, resulting in an accuracy rate of 91%. These findings indicate that the present study is highly reliable and can provide decision makers with a scientific basis for mitigating future flood disasters. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1
<p>Flowchart of the flood risk assessment for the Changchun rail transit system.</p> Full article ">Figure 2
<p>The geographical location of the study area and the Changchun rail transit system.</p> Full article ">Figure 3
<p>Hazard index: (<b>a</b>) maximum daily rainfall; (<b>b</b>) NDVI; (<b>c</b>) average annual rainfall; (<b>d</b>) rainfall days (DR &gt; 50 mm).</p> Full article ">Figure 4
<p>Exposure index: (<b>a</b>) population density; (<b>b</b>) elevation; (<b>c</b>) slope; (<b>d</b>) LULC; (<b>e</b>) main road density; (<b>f</b>) river network density; (<b>g</b>) exit number.</p> Full article ">Figure 4 Cont.
<p>Exposure index: (<b>a</b>) population density; (<b>b</b>) elevation; (<b>c</b>) slope; (<b>d</b>) LULC; (<b>e</b>) main road density; (<b>f</b>) river network density; (<b>g</b>) exit number.</p> Full article ">Figure 5
<p>Vulnerability index: (<b>a</b>) type of exit; (<b>b</b>) river network proximity; (<b>c</b>) metro station density; (<b>d</b>) passenger flow; (<b>e</b>) percentage of vulnerable population; (<b>f</b>) metro line proximity; (<b>g</b>) metro line density.</p> Full article ">Figure 5 Cont.
<p>Vulnerability index: (<b>a</b>) type of exit; (<b>b</b>) river network proximity; (<b>c</b>) metro station density; (<b>d</b>) passenger flow; (<b>e</b>) percentage of vulnerable population; (<b>f</b>) metro line proximity; (<b>g</b>) metro line density.</p> Full article ">Figure 6
<p>Emergency response and recovery capability index: (<b>a</b>) GDP; (<b>b</b>) distance to main road; (<b>c</b>) education status; (<b>d</b>) density of drainage network.</p> Full article ">Figure 7
<p>Level maps of hazard (<b>a</b>), exposure (<b>b</b>), vulnerability (<b>c</b>), and emergency response and recovery capability (<b>d</b>).</p> Full article ">Figure 8
<p>(<b>a</b>) Regional flood risk level map; (<b>b</b>) regional flood risk level verification map.</p> Full article ">Figure 9
<p>Rail transit flood risk level map.</p> Full article ">Figure 10
<p>Receiver operating characteristic (ROC) curve.</p> Full article ">

34 pages, 10093 KiB  

Open AccessArticle

Enhanced Absence Sampling Technique for Data-Driven Landslide Susceptibility Mapping: A Case Study in Songyang County, China

by Zijin Fu, Fawu Wang, Jie Dou, Kounghoon Nam and Hao Ma

Remote Sens. 2023, 15(13), 3345; https://doi.org/10.3390/rs15133345 - 30 Jun 2023

Cited by 12 | Viewed by 1717

Abstract

Accurate prediction of landslide susceptibility relies on effectively handling absence samples in data-driven models. This study investigates the influence of different absence sampling methods, including buffer control sampling (BCS), controlled target space exteriorization sampling (CTSES), information value (IV), and mini-batch k-medoids (MBKM), on [...] Read more.

Accurate prediction of landslide susceptibility relies on effectively handling absence samples in data-driven models. This study investigates the influence of different absence sampling methods, including buffer control sampling (BCS), controlled target space exteriorization sampling (CTSES), information value (IV), and mini-batch k-medoids (MBKM), on landslide susceptibility mapping in Songyang County, China, using support vector machines and random forest algorithms. Various evaluation metrics are employed to compare the efficacy of these sampling methods for susceptibility zoning. The results demonstrate that CTSES, IV, and MBKM methods exhibit an expansion of the high susceptibility region (maximum susceptibility mean value reaching 0.87) and divergence in the susceptibility index when extreme absence samples are present, with MBKM showing a comparative advantage (lower susceptibility mean value) compared to the IV model. Building on the strengths of different sampling methods, a novel integrative sampling approach that incorporates multiple existing methods is proposed. The integrative sampling can mitigate negative effects caused by extreme absence samples (susceptibility mean value is approximately 0.5 in the same extreme samples and presence-absence ratio) and obtain significantly better prediction results (AUC = 0.92, KC = 0.73, POA = 2.46 in the best model). Additionally, the mean level of susceptibility is heavily influenced by the proportion of absent samples. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Geographical location of Songyang County.</p> Full article ">Figure 2
<p>Typical landslides in Songyang County. (<b>a</b>) Chengtian landslide; (<b>b</b>) Xiangxi town landslide; (<b>c</b>) potential landslide in Fanshantui, Shaqiu Village. The red lines indicate the geometric boundary of the landslides and the arrow indicates the direction of the main slide.</p> Full article ">Figure 3
<p>Conditioning factors of LSM. (<b>a</b>) Altitude; (<b>b</b>) slope; (<b>c</b>) slope aspect; (<b>d</b>) plan curvature; (<b>e</b>) profile curvature; (<b>f</b>) <span class="html-italic">TRI</span>; (<b>g</b>) <span class="html-italic">TWI</span>; (<b>h</b>) <span class="html-italic">STI</span>; (<b>i</b>) lithology; (<b>j</b>) distance to faults; (<b>k</b>) soil type; (<b>l</b>) annual rainfall; (<b>m</b>) distance to stream; (<b>n</b>) distance to the road; (<b>o</b>) land use; (<b>p</b>) <span class="html-italic">NDVI</span>.</p> Full article ">Figure 3 Cont.
<p>Conditioning factors of LSM. (<b>a</b>) Altitude; (<b>b</b>) slope; (<b>c</b>) slope aspect; (<b>d</b>) plan curvature; (<b>e</b>) profile curvature; (<b>f</b>) <span class="html-italic">TRI</span>; (<b>g</b>) <span class="html-italic">TWI</span>; (<b>h</b>) <span class="html-italic">STI</span>; (<b>i</b>) lithology; (<b>j</b>) distance to faults; (<b>k</b>) soil type; (<b>l</b>) annual rainfall; (<b>m</b>) distance to stream; (<b>n</b>) distance to the road; (<b>o</b>) land use; (<b>p</b>) <span class="html-italic">NDVI</span>.</p> Full article ">Figure 4
<p>Flowchart of comparison and evaluation process of absence sample sampling methods.</p> Full article ">Figure 5
<p>Schematic of the two-dimensional feature space of the CTSES method.</p> Full article ">Figure 6
<p>Schematic of the three-dimensional feature space of the CTSES method. (<b>a</b>) Three-dimensional feature space of the study area; (<b>b</b>) deconstruction map of the three-dimensional feature space in the CTSES method.</p> Full article ">Figure 6 Cont.
<p>Schematic of the three-dimensional feature space of the CTSES method. (<b>a</b>) Three-dimensional feature space of the study area; (<b>b</b>) deconstruction map of the three-dimensional feature space in the CTSES method.</p> Full article ">Figure 7
<p>Schematic of the procession of integrated sampling. The different colors represent the best quality sample sets in each absence sampling method.</p> Full article ">Figure 8
<p>Pearson correlation coefficient heat map of 16 conditioning factors.</p> Full article ">Figure 9
<p>Absence sample location schematic of the BCS method.</p> Full article ">Figure 10
<p>Absence sampling location of CTSES results.</p> Full article ">Figure 11
<p>Results of the prior model. (<b>a</b>) IV model; (<b>b</b>) MBKM model. Absence samples were created by random sampling within 10 sampling intervals delineated by landslide susceptibility.</p> Full article ">Figure 12
<p>SVM-based LSM results of four absence sampling methods.</p> Full article ">Figure 13
<p>RF-based LSM results of four absence sampling methods.</p> Full article ">Figure 14
<p>LSM of integrative sampling with different ratios. (<b>a</b>) SVM_IS_1:1; (<b>b</b>) RF_IS_1:1; (<b>c</b>) SVM_IS_3:7; (<b>d</b>) RF_IS_3:7.</p> Full article ">Figure 15
<p>Accuracy results of SVM-based absence sampling. (<b>a</b>) BCS; (<b>b</b>) CTSES; (<b>c</b>) IV; (<b>d</b>) MBKM. The “+” represents a small number of abnormal values that are outside the normal range.</p> Full article ">Figure 16
<p>Accuracy results of RF-based absence sampling. (<b>a</b>) BCS; (<b>b</b>) CTSES; (<b>c</b>) IV; (<b>d</b>) MBKM. The “+” represents a small number of abnormal values that are outside the normal range.</p> Full article ">Figure 17
<p>Prediction performance results of SVM-based absence sampling. (<b>a</b>) BCS; (<b>b</b>) CTSES; (<b>c</b>) IV; (<b>d</b>) MBKM.</p> Full article ">Figure 18
<p>Prediction performance results of RF-based absence sampling. (<b>a</b>) BCS; (<b>b</b>) CTSES; (<b>c</b>) IV; (<b>d</b>) MBKM.</p> Full article ">Figure 19
<p>Means and standard deviations of four absence sampling methods with different intervals. (<b>a</b>) BCS; (<b>b</b>) CTSES; (<b>c</b>) IV; (<b>d</b>) MBKM.</p> Full article ">

19 pages, 19655 KiB  

Open AccessFeature PaperEditor’s ChoiceArticle

Agricultural Drought Risk Assessment Based on a Comprehensive Model Using Geospatial Techniques in Songnen Plain, China

by Fengjie Gao, Si Zhang, Rui Yu, Yafang Zhao, Yuxin Chen and Ying Zhang

Land 2023, 12(6), 1184; https://doi.org/10.3390/land12061184 - 5 Jun 2023

Cited by 1 | Viewed by 2255

Abstract

Drought is a damaging and costly natural disaster that will become more serious in the context of global climate change in the future. Constructing a reliable drought risk assessment model and presenting its spatial pattern could be significant for agricultural production. However, agricultural [...] Read more.

Drought is a damaging and costly natural disaster that will become more serious in the context of global climate change in the future. Constructing a reliable drought risk assessment model and presenting its spatial pattern could be significant for agricultural production. However, agricultural drought risk mapping scientifically still needs more effort. Considering the whole process of drought occurrence, this study developed a comprehensive agricultural drought risk assessment model that involved all risk components (exposure, hazard, vulnerability and mitigation capacity) and their associated criteria using geospatial techniques and fuzzy logic. The comprehensive model was applied in Songnen Plain to justify its applicability. ROC and AUC techniques were applied to evaluate its efficiency, and the prediction rate was 88.6%. The similar spatial distribution of water resources further verified the model’s reliability. The southwestern Songnen Plain is a very-high-risk (14.44%) region, determined by a high vulnerability, very high hazardousness and very low mitigation capacity, and is the region that should be paid the most attention to; the central part is a cross-risk region of high risk (24.68%) and moderate risk (27.28%) with a serious disturbance of human agricultural activities; the northeastern part is a dry grain production base with a relatively optimal agricultural production condition of very low risk (22.12%) and low risk (11.48%). Different drought mitigation strategies should be adopted in different regions due to different drought causes. The findings suggest that the proposed model is highly effective in mapping comprehensive drought risk for formulating strong drought mitigation strategies and could be used in other drought-prone areas. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1
<p>Location of the study area.</p> Full article ">Figure 2
<p>The research framework.</p> Full article ">Figure 3
<p>Land use map.</p> Full article ">Figure 4
<p>Spatial pattern of standardized drought factor.</p> Full article ">Figure 5
<p>Spatial pattern of (<b>a</b>) exposure, (<b>b</b>) hazard, (<b>c</b>) vulnerability and (<b>d</b>) mitigation capacity.</p> Full article ">Figure 6
<p>Agricultural drought risk map of the Songnen Plain.</p> Full article ">Figure 7
<p>Schemes follow the same formatting.</p> Full article ">Figure 8
<p>Total water resources and precipitation in Songnen Plain.</p> Full article ">Figure 9
<p>Main water uses and the total volume of water consumption in Songnen Plain.</p> Full article ">Figure 10
<p>Spatial overlay of drought risk and water resources utilization.</p> Full article ">

17 pages, 3781 KiB  

Open AccessArticle

Spatial and Temporal Change in Meteorological Drought in Gansu Province from 1969 to 2018 Based on REOF

by Yuxuan Wang, Fan Deng, Yongxiang Cai and Yi Zhao

Sustainability 2023, 15(11), 9014; https://doi.org/10.3390/su15119014 - 2 Jun 2023

Cited by 3 | Viewed by 1189

Abstract

Meteorological drought is one of the most serious natural disasters, and its impact in arid and semi-arid areas is significant. In order to explore the temporal and spatial distribution of meteorological disasters in Gansu Province, we first calculated the standardized precipitation evapotranspiration index [...] Read more.

Meteorological drought is one of the most serious natural disasters, and its impact in arid and semi-arid areas is significant. In order to explore the temporal and spatial distribution of meteorological disasters in Gansu Province, we first calculated the standardized precipitation evapotranspiration index (SPEI) based on the monthly meteorological data from 1969 to 2018 and extracted the drought events through the theory of runs. Then, REOF rotation orthogonal decomposition was performed to divide the study area into five climatic subregions. With each subregion as the basic unit, the variation characteristics and evolution trends of drought events at different time scales were compared based on the B-G segmentation algorithm (BG-algorithm). Finally, a correlation analysis was conducted to explore the driving factors of drought events in each subregion. The main conclusions are as follows: (1) The cumulative duration of drought in the study area showed a slight increase trend (0.475 day/decade) and a 19-year main cycle. The drought intensity showed a trend of first easing and then intensifying, especially after 2000; the drought intensified significantly and showed a spatial trend of decreasing drought in the northwest and worsening drought in the southeast. (2) The cumulative contribution rate of the first five modes of REOF decomposition was 64.46%, and the study was divided into five arid subregions: the Hexi region, middle Hedong region, eastern Hedong region, Wushaoling region and western Hedong region. (3) The meteorological drought in the Hexi region has eased significantly since 1988. In the eastern, central and western parts of the Yellow River, drought intensification was observed to have occurred in different degrees (0.12/decade, 0.129/decade, and 0.072/decade). The meteorological drought in the Wuelyaling region has alleviated significantly with a watershed region formed between drought alleviation and drought intensification. (4) Seasonally, the eastern Hedong region showed a significant trend of drought in spring, but the opposite in autumn. The trend of climate drying was obvious in the spring and summer, rather than in autumn and winter. The spring drought trend is the most obvious in the middle of the Hedong region. (5) The meteorological drought in the study area was affected by local climatic factors and circulation factors, but there were significant differences in the responses of different arid subregions to these factors. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>Overview of the study area.</p> Full article ">Figure 2
<p>Drought recognition based on the theory of runs.</p> Full article ">Figure 3
<p>Trends and cumulative anomaly curve of drought duration (<b>a</b>) and drought intensity (<b>b</b>).</p> Full article ">Figure 4
<p>Wavelet variance (<b>a</b>), in semi-humid region (<b>b</b>) of drought duration.</p> Full article ">Figure 5
<p>Spatial distribution of drought duration and drought intensity (<b>a</b>,<b>c</b>); interannual change rate of drought duration and drought intensity (<b>b</b>,<b>d</b>).</p> Full article ">Figure 6
<p>REOF Mode 1 (<b>a</b>), REOF Mode 2 (<b>b</b>), REOF Mode 3 (<b>c</b>), REOF Mode 4 (<b>d</b>), REOF Mode 5 (<b>e</b>), and REOF subregion (<b>f</b>).</p> Full article ">Figure 7
<p>Interannual variation and segmentation stage of drought intensity in Hexi region (<b>a</b>), central Hedong central region (<b>b</b>), eastern Hedong region (<b>c</b>), Wushaoling region (<b>d</b>) and western Hedong region (<b>e</b>).</p> Full article ">

22 pages, 15235 KiB  

Open AccessArticle

Landslide Susceptibility Mapping Based on Deep Learning Algorithms Using Information Value Analysis Optimization

by Junjie Ji, Yongzhang Zhou, Qiuming Cheng, Shoujun Jiang and Shiting Liu

Land 2023, 12(6), 1125; https://doi.org/10.3390/land12061125 - 25 May 2023

Cited by 11 | Viewed by 1862

Abstract

Selecting samples with non-landslide attributes significantly impacts the deep-learning modeling of landslide susceptibility mapping. This study presents a method of information value analysis in order to optimize the selection of negative samples used for machine learning. Recurrent neural network (RNN) has a memory [...] Read more.

Selecting samples with non-landslide attributes significantly impacts the deep-learning modeling of landslide susceptibility mapping. This study presents a method of information value analysis in order to optimize the selection of negative samples used for machine learning. Recurrent neural network (RNN) has a memory function, so when using an RNN for landslide susceptibility mapping purposes, the input order of the landslide-influencing factors affects the resulting quality of the model. The information value analysis calculates the landslide-influencing factors, determines the input order of data based on the importance of any specific factor in determining the landslide susceptibility, and improves the prediction potential of recurrent neural networks. The simple recurrent unit (SRU), a newly proposed variant of the recurrent neural network, is characterized by possessing a faster processing speed and currently has less application history in landslide susceptibility mapping. This study used recurrent neural networks optimized by information value analysis for landslide susceptibility mapping in Xinhui District, Jiangmen City, Guangdong Province, China. Four models were constructed: the RNN model with optimized negative sample selection, the SRU model with optimized negative sample selection, the RNN model, and the SRU model. The results show that the RNN model with optimized negative sample selection has the best performance in terms of AUC value (0.9280), followed by the SRU model with optimized negative sample selection (0.9057), the RNN model (0.7277), and the SRU model (0.6355). In addition, several objective measures of accuracy (0.8598), recall (0.8302), F1 score (0.8544), Matthews correlation coefficient (0.7206), and the receiver operating characteristic also show that the RNN model performs the best. Therefore, the information value analysis can be used to optimize negative sample selection in landslide sensitivity mapping in order to improve the model’s performance; second, SRU is a weaker method than RNN in terms of model performance. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>(<b>a</b>) Location of the study area; (<b>b</b>) and (<b>c</b>) are field photos.</p> Full article ">Figure 2
<p>Spatial distribution of landslide influencing factors: (<b>a</b>) elevation, (<b>b</b>) slope, (<b>c</b>) aspect, (<b>d</b>) plan curvature, (<b>e</b>) profile curvature, (<b>f</b>) degree of relief, (<b>g</b>) land use, (<b>h</b>) rock type, (<b>i</b>) NDVI, (<b>j</b>) distance to faults, (<b>k</b>) distance to river, (<b>l</b>) distance to roads, (<b>m</b>) TWI, (<b>n</b>) TRI, and (<b>o</b>) TPI.</p> Full article ">Figure 2 Cont.
<p>Spatial distribution of landslide influencing factors: (<b>a</b>) elevation, (<b>b</b>) slope, (<b>c</b>) aspect, (<b>d</b>) plan curvature, (<b>e</b>) profile curvature, (<b>f</b>) degree of relief, (<b>g</b>) land use, (<b>h</b>) rock type, (<b>i</b>) NDVI, (<b>j</b>) distance to faults, (<b>k</b>) distance to river, (<b>l</b>) distance to roads, (<b>m</b>) TWI, (<b>n</b>) TRI, and (<b>o</b>) TPI.</p> Full article ">Figure 3
<p>Methodology of the study.</p> Full article ">Figure 4
<p>(<b>a</b>) RNN architecture and (<b>b</b>) SRU architecture.</p> Full article ">Figure 5
<p>Data representation of models.</p> Full article ">Figure 6
<p>WOE and the selection of negative samples.</p> Full article ">Figure 7
<p>ROC curves of the four models.</p> Full article ">Figure 8
<p>Accuracy and loss curves of the four models.</p> Full article ">Figure 9
<p>Landslide susceptibility maps by (<b>a</b>) RNN, (<b>b</b>) SRU, (<b>c</b>) RNN_random, and (<b>d</b>) SRU_random.</p> Full article ">

25 pages, 6644 KiB  

Open AccessArticle

The 100-Year Series of Weather-Related Fatalities in the Czech Republic: Interactions of Climate, Environment, and Society

by Rudolf Brázdil, Kateřina Chromá, Lukáš Dolák, Pavel Zahradníček, Jan Řehoř, Petr Dobrovolný and Ladislava Řezníčková

Water 2023, 15(10), 1965; https://doi.org/10.3390/w15101965 - 22 May 2023

Cited by 2 | Viewed by 2061

Abstract

The paper investigates weather-related fatalities over the territory of the Czech Republic in the 100-year period from 1921 to 2020. The unique database, created from documentary evidence (particularly newspapers), includes, for each deadly event, information about the weather event, the fatality itself, and [...] Read more.

The paper investigates weather-related fatalities over the territory of the Czech Republic in the 100-year period from 1921 to 2020. The unique database, created from documentary evidence (particularly newspapers), includes, for each deadly event, information about the weather event, the fatality itself, and related circumstances. A total of 2729 fatalities were detected during the 100-year period and were associated with various weather categories including frost (38%), convective storms (19%), floods (17%), fog (11%), snow and glaze ice (8%), windstorms (5%), and other inclement weather (2%). A detailed analysis was performed for each individual category. Fatalities occurred throughout the country, with a main maximum in winter (January) and a secondary maximum in summer (July), corresponding to the occurrence of extreme weather. Deaths were mainly interpreted as direct, caused by freezing to death/hypothermia or drowning, and occurred in the afternoon and at night in open countryside or on rivers and water bodies. Males outnumbered females, and adults outnumbered children and the elderly. Hazardous behavior was more frequent than non-hazardous behavior among victims. The information on fatalities and the structure of their characteristics strongly reflects historical milestones of the country, political and socioeconomic changes, as well as changes in lifestyle. Although important weather effects were observed on the deadliest events, the character of the data did not allow for clear evidence of the effects of long-term climate variability. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>Basic information about the Czech Republic: (<b>a</b>) location in Central Europe and historical parts; (<b>b</b>) physical-geographic map; (<b>c</b>) fluctuations and linear trends in mean areal annual temperatures and precipitation totals in 1921–2020 smoothed by 10-year Gaussian filter (data in [<a href="#B38-water-15-01965" class="html-bibr">38</a>], extended); (<b>d</b>) age pyramids for 1 July 1921, 31 December 1971, and 31 December 2020 (data from [<a href="#B39-water-15-01965" class="html-bibr">39</a>]).</p> Full article ">Figure 2
<p>Temporal coverage of basic newspapers used and major historical and socio-political events in the Czech Republic: 1918—establishment of Czechoslovakia, 1939–1945—the Second World War, 1948—communist coup, 1968—“Prague spring”, 1989—“velvet revolution”, 1993—the Czech Republic establishment, 2004—EU membership.</p> Full article ">Figure 3
<p>Characteristics of flood-related fatalities in the Czech Republic during the 1921–2020 period (1—flood, 2—flash flood): (<b>a</b>) long-term fluctuation; (<b>b</b>) annual variation (J—January, F—February, …, D—December); (<b>c</b>) spatial distribution (15 fatalities lack exact locations); (<b>d</b>) cause of death; (<b>e</b>) type of fatality; (<b>f</b>) place of death; (<b>g</b>) part of the day; (<b>h</b>) age (years); (<b>i</b>) gender; (<b>j</b>) behavior. Symbols and abbreviations: A—drowning, B—tree/branch fall, C—traffic (vehicle/plane/train) accident, D—underlying health reason, E—freezing to death/hypothermia, F—lightning strike, G—other reason; Di—direct death, ND—non-direct death; a—river/lake/reservoir/bank, b—within a building, c—road, d—open space in built-up area, e—open countryside, f—other place; mo—morning, fn—forenoon, an—afternoon, ev—evening, ni—night; M—males, F—females; Ha—hazardous behavior, NH—non-hazardous behavior; X—unknown.</p> Full article ">Figure 4
<p>Characteristics of windstorm-related fatalities in the Czech Republic during the 1921–2020 period: (<b>a</b>) long-term fluctuation; (<b>b</b>) annual variation; (<b>c</b>) spatial distribution (four fatalities lack exact locations); (<b>d</b>) cause of death; (<b>e</b>) type of fatality; (<b>f</b>) place of death; (<b>g</b>) part of the day; (<b>h</b>) age; (<b>i</b>) gender; (<b>j</b>) behavior. For symbols and abbreviations see <a href="#water-15-01965-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 5
<p>Characteristics of fatalities related to convective storms (1—lightning strike, 2—strong wind, 3—during a thunderstorm) in the Czech Republic during the 1921–2020 period: (<b>a</b>) long-term fluctuation; (<b>b</b>) annual variation; (<b>c</b>) spatial distribution (42 fatalities lack exact locations); (<b>d</b>) cause of death; (<b>e</b>) type of fatality; (<b>f</b>) place of death; (<b>g</b>) part of the day; (<b>h</b>) age; (<b>i</b>) gender; (<b>j</b>) behavior. For symbols and abbreviations see <a href="#water-15-01965-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 6
<p>Characteristics of fatalities related to snow and glaze ice (1—snow, 2—avalanche, 3—glaze of ice) in the Czech Republic during the 1921–2020 period: (<b>a</b>) long-term fluctuation; (<b>b</b>) annual variation; (<b>c</b>) spatial distribution (four fatalities lack exact locations); (<b>d</b>) cause of death; (<b>e</b>) type of fatality; (<b>f</b>) place of death; (<b>g</b>) part of the day; (<b>h</b>) age; (<b>i</b>) gender; (<b>j</b>) behavior. For symbols and abbreviations see <a href="#water-15-01965-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 7
<p>Characteristics of frost-related fatalities (1—cold spell, 2—ice) in the Czech Republic during the 1921–2020 period: (<b>a</b>) long-term fluctuation; (<b>b</b>) annual variation; (<b>c</b>) spatial distribution (19 fatalities lack exact locations); (<b>d</b>) cause of death; (<b>e</b>) type of fatality; (<b>f</b>) place of death; (<b>g</b>) part of the day; (<b>h</b>) age; (<b>i</b>) gender; (<b>j</b>) behavior. For symbols and abbreviations see <a href="#water-15-01965-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 8
<p>Characteristics of fog-related fatalities in the Czech Republic during the 1921–2020 period: (<b>a</b>) long-term fluctuation; (<b>b</b>) annual variation; (<b>c</b>) spatial distribution; (<b>d</b>) cause of death; (<b>e</b>) type of fatality; (<b>f</b>) place of death; (<b>g</b>) part of the day; (<b>h</b>) age; (<b>i</b>) gender; (<b>j</b>) behavior. For symbols and abbreviations see <a href="#water-15-01965-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 9
<p>Characteristics of weather-related fatalities (1—flood, 2—windstorm, 3—convective storm, 4—snow and glaze ice, 5—frost, 6—fog, 7—other inclement weather) in the Czech Republic during the 1921–2020 period: (<b>a</b>) long-term fluctuation; (<b>b</b>) annual variation; (<b>c</b>) spatial distribution (88 fatalities lack exact locations); (<b>d</b>) cause of death; (<b>e</b>) type of fatality; (<b>f</b>) place of death; (<b>g</b>) part of the day; (<b>h</b>) age; (<b>i</b>) gender; (<b>j</b>) behavior. For symbols and abbreviations see <a href="#water-15-01965-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 10
<p>Distribution of weather-related fatalities for the individual districts of the Czech Republic during the period of 1921–2020, expressed in colored intervals and numbers of fatalities.</p> Full article ">Figure A1
<p>Location of places reported in this study (1—Český Krumlov; 2—Jeseník; 3—Karlovice; 4—Liberec; 5—Loučky; 6—Nedvězí; 7—Nový Jičín; 8—Olomouc; 9—Ostrava; 10—Plzeň; 11—Polička; 12—Prague-Ruzyně; 13—Prague-Suchdol; 14—Přerov; 15—Radotín; 16—Radvanice; 17—Sivice; 18—Šardice; 19—Šumperk; 20—Šumperk-Temenice; 21—Trutnov; 22—Třebíč; 23—Třemošná-Záluží; 24—Turnov; 25—Větřní).</p> Full article ">

24 pages, 6325 KiB  

Open AccessArticle

Dynamic Response Law and Failure Mechanism of Slope with Weak Interlayer under Combined Action of Reservoir Water and Seismic Force

by Wenpeng Ning and Hua Tang

Water 2023, 15(10), 1956; https://doi.org/10.3390/w15101956 - 21 May 2023

Cited by 1 | Viewed by 2025

Abstract

The southwestern region of China is close to the Eurasian earthquake zone. Many engineering areas in southwestern China are affected by earthquakes and are close to the epicenter of earthquakes that occur in this region. During earthquakes, slopes with weak interlayers are more [...] Read more.

The southwestern region of China is close to the Eurasian earthquake zone. Many engineering areas in southwestern China are affected by earthquakes and are close to the epicenter of earthquakes that occur in this region. During earthquakes, slopes with weak interlayers are more likely to cause large-scale landslides. In response to the low stability of slopes with weak interlayers in reservoir dam areas, the dynamic response law and failure mechanism of weak interlayered slopes under the combined action of reservoir water and seismic forces were studied through shaking table model tests and finite element numerical simulation software. The height of the water level and the size of the seismic waves were changed during these tests. The research results indicate that seismic waves are influenced by weak interlayers and are repeatedly superimposed between the weak interlayers and the slope surface, resulting in an acceleration amplification effect that increases by approximately 1.8 times compared to homogeneous slopes. Vertical earthquakes have a significant impact on the dynamic response of slopes, and their peak acceleration amplification coefficient can reach 0.83 times the horizontal peak acceleration. The stability of weak interlayers during earthquakes is the worst within the range of the direct action of reservoir water. The failure mode of a slope is as follows: earthquake action causes cracking in the upper part of the slope, and as the earthquake increases in intensity, and the infiltration of reservoir water intensifies, the cracks expand. The soft and muddy interlayer in the front section of the slope forms a sliding surface, and ultimately, the sliding failure forms an accumulation body at the foot of the slope. In reservoir dam areas, the stability of a slope is closely related to the engineering safety of the reservoir dam. Therefore, when a strong earthquake and the water level in a reservoir jointly affect a weak-interlayer slope, the slope is in the stage of plastic deformation and instability. The stability of the slope may be overestimated, and the slope is likely vulnerable to sliding instability, which needs to be monitored and treated. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>Topography of engineering area.</p> Full article ">Figure 2
<p>Geological section of the left bank of the reservoir area.</p> Full article ">Figure 3
<p>Laboratory and in situ tests. (<b>a</b>) Model MTS815−03 Triaxle Tester. (<b>b</b>) Original rock shear test.</p> Full article ">Figure 4
<p>Layout position diagram of monitoring point A1–A15 (unit: mm).</p> Full article ">Figure 5
<p>Input horizontal seismic wave curve.</p> Full article ">Figure 6
<p>Initial model.</p> Full article ">Figure 7
<p>Variation curve of PGA amplification coefficient under different earthquakes. (<b>a</b>) Vertical seismic waves. (<b>b</b>) Horizontal seismic waves.</p> Full article ">Figure 8
<p>Evolution curve of surface displacement. (<b>a</b>) Vertical square load. (<b>b</b>) Horizontal load.</p> Full article ">Figure 9
<p>Model grouping and mesh division.</p> Full article ">Figure 10
<p>Finite element model of the slope (unit: m).</p> Full article ">Figure 11
<p>Layout of monitoring points.</p> Full article ">Figure 12
<p>Acceleration cloud map in X and Y directions under different earthquakes. (<b>a</b>) X−direction acceleration cloud image at 0.1 g. (<b>b</b>) Y−direction acceleration cloud image at 0.1 g. (<b>c</b>) X−direction acceleration cloud image at 0.2 g. (<b>d</b>) Y−direction acceleration cloud image at 0.2 g. (<b>e</b>) X−direction acceleration cloud image at 0.3 g. (<b>f</b>) Y−direction acceleration cloud image at 0.3 g.</p> Full article ">Figure 12 Cont.
<p>Acceleration cloud map in X and Y directions under different earthquakes. (<b>a</b>) X−direction acceleration cloud image at 0.1 g. (<b>b</b>) Y−direction acceleration cloud image at 0.1 g. (<b>c</b>) X−direction acceleration cloud image at 0.2 g. (<b>d</b>) Y−direction acceleration cloud image at 0.2 g. (<b>e</b>) X−direction acceleration cloud image at 0.3 g. (<b>f</b>) Y−direction acceleration cloud image at 0.3 g.</p> Full article ">Figure 13
<p>Variation curve of PGA amplification coefficient during different earthquakes. (<b>a</b>) Variation regularity of PGA during different vertical earthquakes in X direction. (<b>b</b>) Variation regularity of PGA during different vertical earthquakes in Y direction.</p> Full article ">Figure 14
<p>Permanent displacement of slope due to different seismic accelerations.</p> Full article ">Figure 15
<p>Failure of model slope during earthquake. (<b>a</b>) Description of 0–0.2 g crack. (<b>b</b>) A 0–0.2 g normal water level crack. (<b>c</b>) Description of 0.3 g crack. (<b>d</b>) Photograph of 0.3 g normal water level crack. (<b>e</b>) Depiction of 0.4 g cracks and damage. (<b>f</b>) Pictures depicting 0.4 cracks and damage.</p> Full article ">Figure 16
<p>Photos of model layer removal. (<b>a</b>) Photo after removal of the first layer. (<b>b</b>) Photo after removal of the second layer. (<b>c</b>) Photo after removal of the third layer.</p> Full article ">

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22 pages, 3837 KiB  

Open AccessArticle

Toward a Real-Time Analysis of Column Height by Visible Cameras: An Example from Mt. Etna, in Italy

by Alvaro Aravena, Giuseppe Carparelli, Raffaello Cioni, Michele Prestifilippo and Simona Scollo

Remote Sens. 2023, 15(10), 2595; https://doi.org/10.3390/rs15102595 - 16 May 2023

Viewed by 1693

Abstract

Volcanic plume height is one the most important features of explosive activity; thus, it is a parameter of interest for volcanic monitoring that can be retrieved using different remote sensing techniques. Among them, calibrated visible cameras have demonstrated to be a promising alternative [...] Read more.

Volcanic plume height is one the most important features of explosive activity; thus, it is a parameter of interest for volcanic monitoring that can be retrieved using different remote sensing techniques. Among them, calibrated visible cameras have demonstrated to be a promising alternative during daylight hours, mainly due to their low cost and low uncertainty in the results. However, currently these measurements are generally not fully automatic. In this paper, we present a new, interactive, open-source MATLAB tool, named ‘Plume Height Analyzer’ (PHA), which is able to analyze images and videos of explosive eruptions derived from visible cameras, with the objective of automatically identifying the temporal evolution of eruption columns. PHA is a self-customizing tool, i.e., before operational use, the user must perform an iterative calibration procedure based on the analysis of images of previous eruptions of the volcanic system of interest, under different eruptive, atmospheric and illumination conditions. The images used for the calibration step allow the computation of ad hoc expressions to set the model parameters used to recognize the volcanic plume in new images, which are controlled by their individual characteristics. Thereby, the number of frames used in the calibration procedure will control the goodness of the model to analyze new videos/images and the range of eruption, atmospheric, and illumination conditions for which the program will return reliable results. This also allows improvement of the performance of the program as new data become available for the calibration, for which PHA includes ad hoc routines. PHA has been tested on a wide set of videos from recent explosive activity at Mt. Etna, in Italy, and may represent a first approximation toward a real-time analysis of column height using visible cameras on erupting volcanoes. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1
<p>Illustrative example of the application of the procedures used to identify the plume in the program PHA. (<b>a</b>) Original image. (<b>b</b>) Application of the fixed mask. (<b>c</b>) Application of the Lab mask. (<b>d</b>–<b>f</b>) Application of the different algorithms aimed at discarding the clouds from the image.</p> Full article ">Figure 2
<p>Examples of the pixel-height conversion matrix used in this work. They are based on the dominant wind field observed during four events of Mount Etna (see titles and <a href="#remotesensing-15-02595-t001" class="html-table">Table 1</a>) [<a href="#B4-remotesensing-15-02595" class="html-bibr">4</a>]. Winds blowing to the E (to the right in the images) translate into height isocurves dipping to the W, while when winds blow to the W (to the left in the images), the resulting height isocurves dip to the E. Data are presented in m a.s.l.</p> Full article ">Figure 3
<p>Illustrative example of the iterative procedure used to define the threshold value of the Lab mask, necessary to recognize the pixels belonging to the volcanic plume as a function of the image properties. In these images, the lightened areas correspond to pixels potentially considered as part of the ash plume. In this case, the recommended choice is E or F.</p> Full article ">Figure 4
<p>Threshold values for the Lab mask as a function of frame number of the video V18, computed using three different calibrations based on five frames extracted from V18 (see legends). In the different panels, we present the application of different fit strategies to define the threshold for the Lab mask. Results derived from the application of clustering and polynomial fit are presented in panel a, results in panel b are associated with a criterion of nearest value, and panel c presents, for each frame, the more conservative choice between panels a and b (i.e., the minimum value). Note that PHA (see <a href="#remotesensing-15-02595-t002" class="html-table">Table 2</a>) can generate this figure automatically.</p> Full article ">Figure 5
<p>Plume height as a function of time for some reference videos (see titles) of Mt. Etna (see <a href="#remotesensing-15-02595-t001" class="html-table">Table 1</a>) using different files of internal calibration (see legends and <a href="#sec3dot1-remotesensing-15-02595" class="html-sec">Section 3.1</a>). The measurement limit in panel b is 9326 m a.s.l.</p> Full article ">Figure 6
<p>Plume height as a function of time for some reference videos (see titles) of Mt. Etna (see <a href="#remotesensing-15-02595-t001" class="html-table">Table 1</a>) using different files of internal calibration (see legends and <a href="#sec3dot1-remotesensing-15-02595" class="html-sec">Section 3.1</a>). The measurement limit in panels a and b is 9774 m a.s.l. and that of panel c is 9517 m a.s.l.</p> Full article ">Figure 7
<p>Plume height as a function of time for some reference videos (see titles) of Mt. Etna (see <a href="#remotesensing-15-02595-t001" class="html-table">Table 1</a>), using a common calibration file (see <a href="#sec3dot2-remotesensing-15-02595" class="html-sec">Section 3.2</a>). These videos are characterized by favorable atmospheric and illumination conditions, and the plumes present a well-defined outline.</p> Full article ">Figure 8
<p>Plume height as a function of time for some reference videos (see titles) of Mt. Etna (see <a href="#remotesensing-15-02595-t001" class="html-table">Table 1</a>), using a common calibration file (see <a href="#sec3dot2-remotesensing-15-02595" class="html-sec">Section 3.2</a>). These videos are characterized by unfavorable atmospheric conditions (e.g., presence of clouds interfering with the visual field), and the plumes present a well-defined outline.</p> Full article ">Figure 9
<p>Plume height as a function of time for some reference videos (see titles) of Mt. Etna (see <a href="#remotesensing-15-02595-t001" class="html-table">Table 1</a>), using a common calibration file (see <a href="#sec3dot2-remotesensing-15-02595" class="html-sec">Section 3.2</a>). These videos are characterized by plumes with diffuse outlines.</p> Full article ">

16 pages, 24299 KiB  

Open AccessArticle

PSI Spatially Constrained Clustering: The Sibari and Metaponto Coastal Plains

by Nicola Amoroso, Roberto Cilli, Davide Oscar Nitti, Raffaele Nutricato, Muzaffer Can Iban, Tommaso Maggipinto, Sabina Tangaro, Alfonso Monaco and Roberto Bellotti

Remote Sens. 2023, 15(10), 2560; https://doi.org/10.3390/rs15102560 - 14 May 2023

Cited by 3 | Viewed by 1798

Abstract

PSI data are extremely useful for monitoring on-ground displacements. In many cases, clustering algorithms are adopted to highlight the presence of homogeneous patterns; however, clustering algorithms can fail to consider spatial constraints and be poorly specific in revealing patterns at lower scales or [...] Read more.

PSI data are extremely useful for monitoring on-ground displacements. In many cases, clustering algorithms are adopted to highlight the presence of homogeneous patterns; however, clustering algorithms can fail to consider spatial constraints and be poorly specific in revealing patterns at lower scales or possible anomalies. Hence, we proposed a novel framework which combines a spatially-constrained clustering algorithm (SKATER) with a hypothesis testing procedure which evaluates and establishes the presence of significant local spatial correlations, namely the LISA method. The designed workflow ensures the retrieval of homogeneous clusters and a reliable anomaly detection; to validate this workflow, we collected Sentinel-1 time series from the Sibari and Metaponto coastal plains in Italy, ranging from 2015 to 2021. This particular study area is interesting due to the presence of important industrial and agricultural settlements. The proposed workflow effectively outlines the presence of both subsidence and uplifting that deserve to be focused and continuous monitoring, both for environmental and infrastructural purposes. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>Map of the lithological units, active faults, and subduction contours of the areas of concerns.</p> Full article ">Figure 2
<p>PSI analyses are carried out to reconstruct time series of on-ground displacements (<b>a</b>); time series undergo then the SKATER spatially constrained cluster analysis (<b>b</b>); finally, the LISA method is considered to highlight within each clusters coherent local patterns or possible anomalies as depicted in red (<b>c</b>).</p> Full article ">Figure 3
<p>Plots comparing the quality of the partition against the number of clusters in terms of the BSS/TSS ratio.</p> Full article ">Figure 4
<p>On the top: SKATER optimal clustering for Sibari (<b>a</b>), Trebisacce-Villapiana (<b>b</b>) and Policoro area (<b>c</b>); the violin plots on the bottom show the velocity distributions of each optimal cluster. The color code links each spatial cluster to its velocity distribution (<b>d</b>–<b>f</b>).</p> Full article ">Figure 5
<p>SPINUA measured velocities for Sibari (<b>a</b>), Trebisacce-Villapiana (<b>b</b>) and Policoro area (<b>c</b>).</p> Full article ">Figure 6
<p>LISA analyses of Sibari: coldspots (red) and hotspots (green) are shown (<b>a</b>). SKATER optimal clustering is shown in panel (<b>b</b>); the spatial distribution of the LOS velocities retrieved by the SPINUA algorithm is shown in panel (<b>c</b>).</p> Full article ">Figure 7
<p>The industrial area of Corigliano Calabro (<b>a</b>) and the Sibari lakes (<b>b</b>) are shown. These areas are two examples of coldspots in the Sibari region.</p> Full article ">Figure 8
<p>LISA analyses of Trebisacce-Villapiana: coldspots (red) and hotspots (green) are shown (<b>a</b>); interestingly, near the Saraceno river, debris movements are detected. SKATER optimal clustering is shown in panel (<b>b</b>); the spatial distribution of the LOS velocities retrieved by the SPINUA algorithm is shown in panel (<b>c</b>).</p> Full article ">Figure 9
<p>Two areas of interest in the Trebisacce-Villapiana region: the mouth of river Saraceno near Trebisacce (<b>a</b>) and the Villapiana shore subsidence (<b>b</b>).</p> Full article ">Figure 10
<p>LISA analyses of Policoro dataset: coldspots (red) and hotspots (green) are shown (<b>a</b>); the analysis reveals three major areas of concerns, namely, two portions of the SS 106 Jonica highway and a subsidence coldspot in Policoro Lido. SKATER optimal clustering is shown in panel (<b>b</b>); the spatial distribution of the LOS velocities retrieved by the SPINUA algorithm is shown in panel (<b>c</b>).</p> Full article ">Figure 11
<p>Subsidence phenomena in the Policoro area: the SS 106 Jonica highway (<b>a</b>) and the Policoro Lido settlement (<b>b</b>). For what concerns the highway, traits with extremely varying velocities, ranging from <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>15</mn> </mrow> </semantics></math> mm to 15 per year are highlighted (dotted circles).</p> Full article ">

20 pages, 54462 KiB  

Open AccessTechnical Note

Study on the Source of Debris Flow in the Northern Scenic Spot of Changbai Mountain Based on Multi-Source Data

by Jiahao Yan, Yichen Zhang, Jiquan Zhang, Yanan Chen and Zhen Zhang

Remote Sens. 2023, 15(9), 2473; https://doi.org/10.3390/rs15092473 - 8 May 2023

Cited by 1 | Viewed by 1653

Abstract

The northern scenic area of Changbai Mountain is a high-incidence area of debris flow disasters, which seriously threaten the safety of tourist’s lives and property. Monitoring debris flow and providing early warning is critical for timely avoidance. Monitoring the change of debris flow [...] Read more.

The northern scenic area of Changbai Mountain is a high-incidence area of debris flow disasters, which seriously threaten the safety of tourist’s lives and property. Monitoring debris flow and providing early warning is critical for timely avoidance. Monitoring the change of debris flow source is an effective way to predict debris flow, and the change of source can be reflected in the settlement deformation of the study area. The offset tracking technique (OT) is insensitive to the coherence of SAR images and can resist the decoherence of D-InSAR and SBSA-InSAR to a certain extent. It is a technical means for monitoring large gradient deformation. It has been widely used in the field of seismic activity, glaciers and landslides in recent years, but few scholars have applied this technique in the field of debris flow. In this paper, we use OT techniques in combination with field surveys, Google imagery and Sentinel-1 data to monitor surface deformation in the northern scenic area of Changbai Mountain in 2017 and use D-InSAR techniques to compare and complement the OT monitoring results. The results of this study show that for monitoring surface deformation in the study area after a mudslide, it is better to use both methods to determine the surface deformation in the study area rather than one, and that both methods have their own advantages and disadvantages and yet can complement each other. Finally, we have predicted the development trend of mudflows in the study area by combining the calculated single mudflow solids washout, which will help to improve the long-term monitoring and warning capability of mudflows in the study area. The study also enriches the application of offset-tracking technology and D-InSAR in the field of geohazard monitoring and provides new ideas and methods for the study of mudflow material source changes. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1
<p>Distribution of disaster sites in study area. (<b>a</b>) Location of the study area in China; (<b>b</b>) location of the study area in Baishan City; (<b>c</b>) extent of debris flow and location of the collapse in the study area.</p> Full article ">Figure 2
<p>No. 1 debris flow ditch, three sections.</p> Full article ">Figure 3
<p>No. 1 debris flow ditch sectional drawing.</p> Full article ">Figure 4
<p>Flowchart of offset tracking data processing.</p> Full article ">Figure 5
<p>Relationship between source reserves and solid discharge of debris flow in the study area.</p> Full article ">Figure 6
<p>Results of surface deformation offset tracking in the study area (2017/01/01–2017/12/27). (<b>a</b>) Range direction; (<b>b</b>) azimuth direction.</p> Full article ">Figure 7
<p>OT processing results of surface deformation in the study area in 2017. (<b>a</b>) Spring deformation (2017/03/07–2017/05/30); (<b>b</b>) summer deformation (2017/05/30–2017/09/03); (<b>c</b>) autumn deformation (2017/09/03–2017/11/26); (<b>d</b>) winter deformation (2017/11/26–2018/03/03).</p> Full article ">Figure 7 Cont.
<p>OT processing results of surface deformation in the study area in 2017. (<b>a</b>) Spring deformation (2017/03/07–2017/05/30); (<b>b</b>) summer deformation (2017/05/30–2017/09/03); (<b>c</b>) autumn deformation (2017/09/03–2017/11/26); (<b>d</b>) winter deformation (2017/11/26–2018/03/03).</p> Full article ">Figure 8
<p>The slope debris near the debris flow channel and the vegetation that begins to grow in summer.</p> Full article ">Figure 9
<p>D-InSAR processing results of surface deformation in the study area in 2017. (<b>a</b>) Spring deformation (2017/03/07–2017/05/30); (<b>b</b>) summer deformation (2017/05/30–2017/09/03); (<b>c</b>) autumn deformation (2017/09/03–2017/11/26); (<b>d</b>) winter deformation (2017/11/26–2018/03/03).</p> Full article ">Figure 10
<p>(<b>a</b>) North scenic dangerous area; (<b>b</b>) Longmen peak dangerous rock belt top; (<b>c</b>) Longmen peak dangerous rock belt side.</p> Full article ">Figure 11
<p>Correlation analysis results.</p> Full article ">Figure 12
<p>Significance analysis results. * expressed <span class="html-italic">p</span> &lt; 0.05, ** expressed <span class="html-italic">p</span> &lt; 0.01, *** expressed <span class="html-italic">p</span> &lt; 0.001, **** expressed <span class="html-italic">p</span> &lt; 0.0001.</p> Full article ">

21 pages, 22272 KiB  

Open AccessArticle

2D Numerical Simulation of Floods in Ebro River and Analysis of Boundary Conditions to Model the Mequinenza Reservoir Dam

by Pablo Vallés, Isabel Echeverribar, Juan Mairal, Sergio Martínez-Aranda, Javier Fernández-Pato and Pilar García-Navarro

GeoHazards 2023, 4(2), 136-156; https://doi.org/10.3390/geohazards4020009 - 27 Apr 2023

Cited by 3 | Viewed by 2917

Abstract

The computational simulation of rivers is a useful tool that can be applied in a wide range of situations from providing real time alerts to the design of future mitigation plans. However, for all the applications, there are two important requirements when modeling [...] Read more.

The computational simulation of rivers is a useful tool that can be applied in a wide range of situations from providing real time alerts to the design of future mitigation plans. However, for all the applications, there are two important requirements when modeling river behavior: accuracy and reasonable computational times. This target has led to recent developments in numerical models based on the full two-dimensional (2D) shallow water equations (SWE). This work presents a GPU accelerated 2D SW model for the simulation of flood events in real time. It is based on a well-balanced explicit first-order finite volume scheme able to run over dry beds without the numerical instabilities that are likely to occur when used in complex topography. The model is applied to reproduce a real event in the reach of the Ebro River (Spain) with a downstream reservoir, in which a study of the most appropriate boundary condition (BC) for modeling of the dam is assessed (time-dependent level condition and weir condition). The whole creation of the model is detailed in terms of mesh optimization and validation. The simulation results are compared with field data over the flood duration (up to 20 days), allowing an analysis of the performance and time saved by different GPU devices and with the different BCs. The high values of fit between observed and simulated results, as well as the computational times achieved, are encouraging to propose the use of the model as a forecasting system. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>Damage produced by a flood event in crops around Zaragoza (Spain): flooding in Pina de Ebro (2015) (<bold>a</bold>) (Source: EFE) and flooding in Novillas (2018) (<bold>b</bold>) (Source: Guardia Civil).</p> Full article ">Figure 2
<p>Location of Spain in Europe (<bold>a</bold>); location of the Ebro River basin in Spain (<bold>b</bold>) and location of the computational domain of the study in the basin (<bold>c</bold>).</p> Full article ">Figure 3
<p>Diagram of the cells in a two-dimensional case with triangular cells.</p> Full article ">Figure 4
<p>Representation of the 2D simulation domain of the Ebro River with the most important cities and gauging stations of CHE. The labels correspond to the official names of the gauging stations.</p> Full article ">Figure 5
<p>Satellite view of the final stretch of the Mequinenza reservoir. [Source: Mapquest].</p> Full article ">Figure 6
<p>Front view of the Mequinenza dam. [Source: CHE].</p> Full article ">Figure 7
<p>Raster representation with an elevation scale in meters of Galacho de la Alfranca and its surroundings with a resolution of <inline-formula><mml:math id="mm71"><mml:semantics><mml:mrow><mml:mn>5</mml:mn><mml:mo>×</mml:mo><mml:mn>5</mml:mn></mml:mrow></mml:semantics></mml:math></inline-formula> m.</p> Full article ">Figure 8
<p>Examples of the sections used to interpolate the reservoir bed level.</p> Full article ">Figure 9
<p>Comparison between two maps showing a part of the Mequinenza reservoir. (<bold>a</bold>) shows the historical topographic map (Source: IGN) and (<bold>b</bold>) shows the same area photographed today (Source: Google Maps).</p> Full article ">Figure 10
<p>Comparative images of the DTM raster at the height of the Mequinenza dam. (<bold>a</bold>) shows the IGN DTM with the reservoir at constant elevation 117.9 m. (<bold>b</bold>) shows the result of interpolating the sections obtained from the old topographic maps.</p> Full article ">Figure 11
<p>Example of the result of interpolating the sections shown in <xref ref-type="fig" rid="geohazards-04-00009-f008">Figure 8</xref>.</p> Full article ">Figure 12
<p>Manning coefficient distribution in the domain of the Ebro River.</p> Full article ">Figure 13
<p>Real satellite view (<bold>a</bold>) (Source: Google Maps) and the meshing (<bold>b</bold>) of an area of Ebro River domain.</p> Full article ">Figure 14
<p>Frontal (<bold>a</bold>) and side (<bold>b</bold>) view of the weir boundary condition.</p> Full article ">Figure 15
<p>Comparison between the first computational mesh, M1 (left), and the refined mesh, M2 (right), in a certain area of the domain.</p> Full article ">Figure 16
<p>Comparison between the first computational mesh, M1 (left), and the refined mesh, M2 (right), at a certain area of the domain.</p> Full article ">Figure 17
<p>Discharge temporal evolution comparison between preliminary mesh, M1, refined mesh, M2, and optimized mesh, M3, in Gelsa (A263).</p> Full article ">Figure 18
<p>Water level temporal evolution comparison between preliminary mesh, M1, refined mesh, M2, and optimized mesh, M3, in Gelsa (A263).</p> Full article ">Figure 19
<p>Cell size histogram for preliminary mesh, M1; refined mesh, M2; and optimized mesh, M3.</p> Full article ">Figure 20
<p>Inlet hydrograph for the Ebro River flooding event in 2018 in Zaragoza (A011).</p> Full article ">Figure 21
<p>Discharge temporal evolution comparison between the models and observations at Gelsa (A263) for the 2018 flooding event.</p> Full article ">Figure 22
<p>Water level temporal evolution comparison between the models and observations at Gelsa (A263) for the 2018 flooding event.</p> Full article ">Figure 23
<p>Discharge temporal evolution comparison between the models and observations at Mequinenza (E003) for the 2018 flooding event.</p> Full article ">Figure 24
<p>Water level temporal evolution comparison between the models and observations at Mequinenza (E003) for the 2018 flooding event.</p> Full article ">

28 pages, 10105 KiB  

Open AccessArticle

Permafrost Stability Mapping on the Tibetan Plateau by Integrating Time-Series InSAR and the Random Forest Method

by Fumeng Zhao, Wenping Gong, Tianhe Ren, Jun Chen, Huiming Tang and Tianzheng Li

Remote Sens. 2023, 15(9), 2294; https://doi.org/10.3390/rs15092294 - 27 Apr 2023

Viewed by 1975

Abstract

The ground deformation rate is an important index for evaluating the stability and degradation of permafrost. Due to limited accessibility, in-situ measurement of the ground deformation of permafrost areas on the Tibetan Plateau is a challenge. Thus, the technique of time-series interferometric synthetic [...] Read more.

The ground deformation rate is an important index for evaluating the stability and degradation of permafrost. Due to limited accessibility, in-situ measurement of the ground deformation of permafrost areas on the Tibetan Plateau is a challenge. Thus, the technique of time-series interferometric synthetic aperture radar (InSAR) is often adopted for measuring the ground deformation rate of the permafrost area, the effectiveness of which is, however, degraded in areas with geometric distortions in synthetic aperture radar (SAR) images. In this study, a method that integrates InSAR and the random forest method is proposed for an improved permafrost stability mapping on the Tibetan Plateau; to demonstrate the application of the proposed method, the permafrost stability mapping in a small area located in the central region of the Tibetan Plateau is studied. First, the ground deformation rate in the concerned area is studied with InSAR, in which 67 Sentinel-1 scenes taken in the period from 2014 to 2020 are collected and analyzed. Second, the relationship between the environmental factors (i.e., topography, land cover, land surface temperature, and distance to road) and the permafrost stability is mapped with the random forest method based on the high-quality data extracted from the initial InSAR analysis. Third, the permafrost stability in the whole study area is mapped with the trained random forest model, and the issue of data scarcity in areas where the terrain visibility of SAR images is poor or InSAR results are not available in permafrost stability mapping can be overcome. Comparative analyses demonstrate that the integration of the InSAR and the random forest method yields a more effective permafrost stability mapping compared with the sole application of InSAR analysis. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>General information of the study area: (<b>a</b>) Permafrost types on the Tibetan plateau; (<b>b</b>) Ground elevation map of the study area.</p> Full article ">Figure 2
<p>Principle and implementation procedures of the integrated method for permafrost stability mapping.</p> Full article ">Figure 3
<p>Environmental factors extracted in the study area: (<b>a</b>) Ground elevation; (<b>b</b>) Aspect; (<b>c</b>) Slope; (<b>d</b>) Curvature; (<b>e</b>) Land cover; (<b>f</b>) NDVI; (<b>g</b>) Land surface temperature; (<b>h</b>) Distance to the Qinghai–Tibet Highway.</p> Full article ">Figure 4
<p>InSAR analysis results of the ground deformation in the study area: (<b>a</b>) Vertical ground deformation rate from October 2014 to August 2020 [<a href="#B39-remotesensing-15-02294" class="html-bibr">39</a>]; (<b>b</b>) Ground deformation rate in east-facing slopes; (<b>c</b>) Ground deformation rate in west-facing slopes; (<b>d</b>,<b>e</b>) Google Earth images for east-facing slopes (Image from © Google Earth 2020).</p> Full article ">Figure 5
<p>Fitting analysis of the ground deformation in the period from 2014 to 2020: (<b>a</b>) Fitting analysis between the ground deformation and the time; (<b>b</b>) Correlation analysis between the seasonal deformation and the air temperature (note: the air temperature is from the 2.0 m air temperature dataset from the European Centre for Medium-Range Weather Forecasting—Fifth-Generation Reanalysis (ECMWF ERA5)).</p> Full article ">Figure 5 Cont.
<p>Fitting analysis of the ground deformation in the period from 2014 to 2020: (<b>a</b>) Fitting analysis between the ground deformation and the time; (<b>b</b>) Correlation analysis between the seasonal deformation and the air temperature (note: the air temperature is from the 2.0 m air temperature dataset from the European Centre for Medium-Range Weather Forecasting—Fifth-Generation Reanalysis (ECMWF ERA5)).</p> Full article ">Figure 6
<p>The maximum ground deformations of the study area occurred during the thawing periods in 2015, 2017, 2018, and 2019.</p> Full article ">Figure 7
<p>The average seasonal thaw subsidence of the whole study area and the three regions in 2015, 2017, 2018, and 2019.</p> Full article ">Figure 8
<p>The influence of the topography on the seasonal thaw subsidence of the permafrost: (<b>a</b>) Average seasonal thaw subsidence of the study area took place in the period from 2015 to 2019; (<b>b</b>) Relationship between the seasonal thaw subsidence and the ground elevation along profile AB; (<b>c</b>) Variations in the seasonal thaw subsidence and the ground elevation with the distance measured from A to B along profile AB; (<b>d</b>) A detailed survey of the topography (Zone I is located in a river valley; Zone II is located on a north-facing slope).</p> Full article ">Figure 9
<p>The influence of the vegetation coverage on the seasonal thaw subsidence of the permafrost: (<b>a</b>) Vegetation coverage in the study area; (<b>b</b>) Relationship between the seasonal thaw subsidence and the NDVI along profile AB; (<b>c</b>) Variations in the seasonal thaw subsidence and the NDVI with the distance measured from A to B along profile AB.</p> Full article ">Figure 10
<p>Screening analysis of the InSAR analysis results of ground deformation in the study area: (<b>a</b>) Geometric distortion analysis results; (<b>b</b>) A detailed survey of the geometric distortions on an east-facing slope; (<b>c</b>) Zonation of the high-quality and low-quality areas.</p> Full article ">Figure 11
<p>Permafrost stability mapping in the study area: (<b>a</b>) Results of the permafrost stability mapping with the trained random forest model; (<b>b</b>) Ground deformation rate obtained by the Kriging interpolation of initial InSAR analysis (Note: Profiles AB and CD are delineated to compare the two permafrost stability results).</p> Full article ">Figure 12
<p>Validation of the trained random forest model using the ROC curve.</p> Full article ">Figure 13
<p>Permafrost stability mapping using different classification schemes: (<b>a</b>) Equal intervals; (<b>b</b>) Standard deviations.</p> Full article ">Figure 14
<p>The relative importance of the environmental factors to the permafrost stability.</p> Full article ">Figure 15
<p>Verifications of the time-series InSAR analysis results with the leveling data and InSAR analysis results obtained by Wu et al. [<a href="#B39-remotesensing-15-02294" class="html-bibr">39</a>] (Reprinted with permission from ref. [<a href="#B39-remotesensing-15-02294" class="html-bibr">39</a>]. Copyright 2018 Copyright Sciences in Cold and Arid Regions).</p> Full article ">Figure 16
<p>Relationships between the ground deformation and the air temperature: (<b>a</b>) Relationships between the ground deformation and the air temperature at points P1, P2, and P3; (<b>b</b>–<b>d</b>) Correlations between the ground deformation and the air temperature at points P1, P2, and P3.</p> Full article ">Figure 17
<p>InSAR analysis results in the study area obtained from the ascending Sentinel-1 SAR images: (<b>a</b>) Vertical ground deformation rate from October 2014 to August 2020; (<b>b</b>) Geometric distortion analysis results.</p> Full article ">Figure 18
<p>Comparison of the ground deformation rate obtained from ascending and descending SAR images: (<b>a</b>) Correlation analysis of the vertical ground deformation rates obtained from ascending and descending SAR images; (<b>b</b>) Distribution of the ground deformation rate differences between ascending and descending SAR images.</p> Full article ">Figure 19
<p>Comparisons between the permafrost stability mapping results in Test Area 1 obtained with the proposed method and the ground deformations obtained from the ascending SAR images: (<b>a</b>) Permafrost stability mapping results obtained with the proposed method; (<b>b</b>) Vertical ground deformation rates obtained from the ascending SAR images; (<b>c</b>) Vertical ground deformation rates obtained from the descending SAR images.</p> Full article ">Figure 20
<p>Comparisons between the proposed method and the sole application of InSAR analysis: (<b>a</b>) Permafrost stability zonation obtained by the proposed method versus the ground deformation rate obtained by the Kriging interpolation of initial InSAR analysis along profile AB; (<b>b</b>) Permafrost stability zonation obtained by the integrated method versus the ground deformation rate obtained by the Kriging interpolation of initial InSAR analysis along profile CD; (<b>c</b>) A detailed survey of the permafrost stability in Zones III, IV, V, and VI with the Google Earth images (note: Zones III and VI are located in areas with medium and high permafrost stability and Zones IV and V are located in areas with low permafrost stability. Image from © Google Earth 2019).</p> Full article ">

18 pages, 4952 KiB  

Open AccessArticle

Urban Flood Resilience Evaluation Based on GIS and Multi-Source Data: A Case Study of Changchun City

by Zhen Zhang, Jiquan Zhang, Yichen Zhang, Yanan Chen and Jiahao Yan

Remote Sens. 2023, 15(7), 1872; https://doi.org/10.3390/rs15071872 - 31 Mar 2023

Cited by 11 | Viewed by 2935

Abstract

With extreme rainfall events and rapid urbanization, urban flood disaster events are increasing dramatically. As a key flood control city in China, Changchun City suffers casualties and economic losses every year due to floods. The improvement of flood resilience has become an important [...] Read more.

With extreme rainfall events and rapid urbanization, urban flood disaster events are increasing dramatically. As a key flood control city in China, Changchun City suffers casualties and economic losses every year due to floods. The improvement of flood resilience has become an important means for cities to resist flood risks. Therefore, this paper constructs an assessment model of urban flood resilience from four aspects: infrastructure, environment, society and economy. Then, it quantifies infrastructure and environmental vulnerability based on GIS, and uses TOPSIS to quantify social and economic recoverability. Finally, based on k-means clustering of infrastructure and environmental vulnerability and social and economic recoverability, the flood resilience of Changchun City was evaluated. The results show that different factors have different effects on flood resilience, and cities with low infrastructure and environmental vulnerability and high socioeconomic recoverability are more resilient in the face of floods. In addition, cities in the same cluster have the same flood resilience characteristics. The proposed framework can be extended to other regions of China or different countries by simply modifying the indicator system according to different regions, providing experience for regional flood mitigation and improving flood resilience. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Location map of the study area.</p> Full article ">Figure 2
<p>Flow chart of flood resilience evaluation.</p> Full article ">Figure 3
<p>Spatial distribution map of infrastructure and environmental indicators: road density (<b>a</b>); altitude (<b>b</b>); drainage density (<b>c</b>); LULC (<b>d</b>); NDVI (<b>e</b>); building density (<b>f</b>); rainfall (<b>g</b>); distance to water bodies (<b>h</b>); slope (<b>i</b>).</p> Full article ">Figure 3 Cont.
<p>Spatial distribution map of infrastructure and environmental indicators: road density (<b>a</b>); altitude (<b>b</b>); drainage density (<b>c</b>); LULC (<b>d</b>); NDVI (<b>e</b>); building density (<b>f</b>); rainfall (<b>g</b>); distance to water bodies (<b>h</b>); slope (<b>i</b>).</p> Full article ">Figure 4
<p>Infrastructure and environmental vulnerability map: vulnerability value (<b>a</b>); spatial distribution of vulnerability (<b>b</b>).</p> Full article ">Figure 5
<p>Socioeconomic recoverability map: recoverable value (<b>a</b>); spatial distribution of recoverability (<b>b</b>).</p> Full article ">Figure 5 Cont.
<p>Socioeconomic recoverability map: recoverable value (<b>a</b>); spatial distribution of recoverability (<b>b</b>).</p> Full article ">Figure 6
<p>Changchun flood resilience cluster: cluster classification (<b>a</b>); spatial distribution of clusters (<b>b</b>).</p> Full article ">Figure 7
<p>Waterlogging point verification map.</p> Full article ">

22 pages, 6460 KiB  

Open AccessReview

Understanding the Mechanisms of Earth Fissuring for Hazard Mitigation in Najran, Saudi Arabia

by Mabkhoot Alsaiari, Basil Onyekayahweh Nwafor, Maman Hermana, Al Marzouki Hassan H. M. and Mohammed Irfan

Sustainability 2023, 15(7), 6006; https://doi.org/10.3390/su15076006 - 30 Mar 2023

Cited by 3 | Viewed by 2267

Abstract

Being a fast-growing city with a high rate of urbanization and agricultural development, the city of Najran, situated in the southwest of the Kingdom of Saudi Arabia, has witnessed a series of earth fissuring events and some other geo-environmental hazards in recent times. [...] Read more.

Being a fast-growing city with a high rate of urbanization and agricultural development, the city of Najran, situated in the southwest of the Kingdom of Saudi Arabia, has witnessed a series of earth fissuring events and some other geo-environmental hazards in recent times. These fissures have posed a significant threat to inhabitants and infrastructure in the area. A few studies suggest that excessive groundwater withdrawal is responsible for fissuring activities. Because of the intensity of this geo-hazard, this article presupposes that groundwater extraction alone cannot be responsible for the magnitude of fissuring activity in the area and discusses other severe factors that could be responsible for the earth fissures. The study proposes that the cause of the problem is multifaceted and synergistic, and outlines threatening factors that can inherently trigger more fissures in the region, based on the geologic history of the area and a critical review of investigative studies conducted in the area and beyond. Predicated on the region’s structural history, some undiscovered elements that can potentially cause fissuring in the region were identified and discussed. Some of these include the pre-existence of a fault system, a crack from the bedrock ridge, the existence of paleochannels, the collapsibility of loess, the tectonic (earthquake) history of the area, and differential compaction due to heterogeneity. The use of a metaheuristic and a combined application integrating other optimization algorithms can be utilized to determine optimum hyperparameters and present their statistical importance, thereby improving accuracy and dependability in fissure prediction in Najran. Reliable models would primarily be used to monitor active fissures and identify key factors utilizing spatial information, subsidence, groundwater-related data sets, etc. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>(<b>a</b>) Location map of the city of Najran, the Kingdom of Saudi Arabia; (<b>b</b>) digitized geologic map of Najran from [<a href="#B15-sustainability-15-06006" class="html-bibr">15</a>].</p> Full article ">Figure 2
<p>Shows the stratigraphic sequence of the Wajid Group sandstones together with their litho characteristics in the right column (after [<a href="#B49-sustainability-15-06006" class="html-bibr">49</a>,<a href="#B50-sustainability-15-06006" class="html-bibr">50</a>]).</p> Full article ">Figure 3
<p>Illustrates the interdependence of earth fissure formation processes.</p> Full article ">Figure 4
<p>Illustrating the deformation mechanism of bedrock-induced fissures, where S is the vertical displacement due to differential stress, Ƿ is horizontal displacement which is a function of S, and H and L are vertical and horizontal thickness, respectively.</p> Full article ">Figure 5
<p>The aquifer-driven dynamics of ground fissuring. τ = shear stress; σ = tensile stress; θ = inclination of earth fissure; ΔH and ΔF = the settling rate of the ground surface.</p> Full article ">Figure 6
<p>Heterogeneous layer of a highly compressible clay causes differential compaction, leading to vertical shear, rotation, and horizontal extension [<a href="#B56-sustainability-15-06006" class="html-bibr">56</a>].</p> Full article ">Figure 7
<p>Images of ground fissures found in different parts of Saudi Arabia [<a href="#B34-sustainability-15-06006" class="html-bibr">34</a>].</p> Full article ">Figure 8
<p>Fissures linked to subsidence because of groundwater over-withdrawal in Najran are shown in subfigures (<b>a</b>,<b>b</b>) in non-residence locations [<a href="#B2-sustainability-15-06006" class="html-bibr">2</a>].</p> Full article ">

14 pages, 9923 KiB  

Open AccessArticle

Spatial and Temporal Characteristics of Dust Storms and Aeolian Processes in the Southern Balkash Deserts in Kazakhstan, Central Asia

by Gulnura Issanova, Azamat Kaldybayev, Yongxiao Ge, Jilili Abuduwaili and Long Ma

Land 2023, 12(3), 668; https://doi.org/10.3390/land12030668 - 12 Mar 2023

Cited by 6 | Viewed by 2516

Abstract

Sand and dust storms are hazardous to the environment and have a significant role in desertification. Under the influence of climate change and human activities, dust storms and aeolian processes have been common phenomena in the Southern Balkash deserts in Kazakhstan, Central Asia. [...] Read more.

Sand and dust storms are hazardous to the environment and have a significant role in desertification. Under the influence of climate change and human activities, dust storms and aeolian processes have been common phenomena in the Southern Balkash deserts in Kazakhstan, Central Asia. However, knowledge gaps on spatial and temporal characteristics of dust storms and aeolian process in the Southern Balkash deserts still exist. Therefore, in present study, meteorological observations and numerous cartographic materials were used to identify the powerful sources with the highest frequency of dust storms and aeolian processes in the Southern Balkash deserts. The result showed that the Southern Balkash deserts were covered mainly by transverse parabolic sands (48%), dome dunes (24%), and transverse dome dunes (23%), where the aeolian processes occurred to a significant degree. Significant and strong degrees of aeolian processes occurred in most of the Southern Balkash deserts. The eastern part of the Taukum and the northern part of the Zhamankum and Karakum deserts were prone to aeolian processes to a substantial degree. The Moiynkum, Bestas, Saryesikatyrau, and Taukum deserts had the most frequent storms, occuring, on average, 17 to 43 days/per year. The occurrence of dust storms has been of a stable decreasing trend since the 1990s, except for 2008–2009. Aeolian dust in the Southern Balkash deserts flowed mainly from the western and southwestern to the eastern and northeastern. The results of the present study shed light on the temporal and spatial characteristics of dust storms and aeolian processes in the Southern Balkash deserts. This is of great importance in helping to monitor and predict dust storms and motion patterns of aeolian dust in this region. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1
<p>Desert sands and their geomorphological types in the Southern Balkash deserts.</p> Full article ">Figure 2
<p>Spatial distribution of dust storms in the Southern Balkash deserts.</p> Full article ">Figure 3
<p>Long-term dynamics of sand and dust storms in the Southern Balkash deserts.</p> Full article ">Figure 4
<p>Seasonal frequency of storms for the period 1966–2003 in the Southern Balkash deserts.</p> Full article ">Figure 5
<p>Spatial wind speed and its directions in the Southern Balkash deserts.</p> Full article ">Figure 6
<p>The relationship between the origin of strong and very strong dust storms and soil texture.</p> Full article ">Figure 7
<p>Regional division (zoning) of the aeolian processes in the Southern Balkash deserts.</p> Full article ">

18 pages, 3762 KiB  

Open AccessArticle

Ground Surface Deformation Analysis Integrating InSAR and GPS Data in the Karstic Terrain of Cheria Basin, Algeria

by Loubna Hamdi, Nabil Defaflia, Abdelaziz Merghadi, Chamssedine Fehdi, Ali P. Yunus, Jie Dou, Quoc Bao Pham, Hazem Ghassan Abdo, Hussein Almohamad and Motrih Al-Mutiry

Remote Sens. 2023, 15(6), 1486; https://doi.org/10.3390/rs15061486 - 7 Mar 2023

Cited by 3 | Viewed by 2720

Abstract

Karstic terrains are usually dominated by aquifer systems and/or underground cavities. Overexploitation of groundwater in such areas often induces land subsidence and sometimes causes sinkholes. The Cheria basin in Algeria suffers from severe land subsidence issues, and this phenomenon has been increasing in [...] Read more.

Karstic terrains are usually dominated by aquifer systems and/or underground cavities. Overexploitation of groundwater in such areas often induces land subsidence and sometimes causes sinkholes. The Cheria basin in Algeria suffers from severe land subsidence issues, and this phenomenon has been increasing in recent years due to population expansion and uncontrolled groundwater exploitation. This work uses GPS data and persistent scatterer interferometry synthetic aperture radar (PS-InSAR) techniques to monitor the land subsidence rate by employing Sentinel-1 satellite data for the period from 2016 to 2022. Our results demonstrate that the Cheria basin experiences both uplift and subsidence in places, with an overall substantial change in the land surface. The total cumulative subsidence over 6 years reached a maximum of 500 mm. Comparison of land deformation between PSI and GPS showed root mean square error (RMSE) values of about 2.83 mm/year, indicating that our analyzed results are satisfactorily reproducing the actual changes. Nonetheless, these results can be used to extract the susceptible zones for vertical ground displacement and evaluate the surface deformation inventory map of the region for reducing damages (e.g., human losses, economic impact, and environmental degradation) that may occur in the future (e.g., sinkholes) and can be further utilized in perspective for a sinkhole early warning system. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>Location map of the study area (background image: Landsat-8 OLI dated 12 September 2022). Orange stars are past sinkhole sites; B1, B2, and B3 are borehole wells where the water level was monitored; pink rectangular boxes are the location of ground control points; and dashed white lines are profiles selected for the analysis of PSI and GPS results.</p> Full article ">Figure 2
<p>Regional geological map of the study area showing the dominant Quaternary formation and the Maastrichtian and Eocene formations.</p> Full article ">Figure 3
<p>Sinkhole example images: (<b>a</b>) Draa-Douamis sinkholes 1 and 2 collapsed with a diameter of 66.47 and 24.88 m, respectively (location: see <a href="#remotesensing-15-01486-f001" class="html-fig">Figure 1</a>—sinkhole sites; date: during 2004); (<b>b</b>) example of sinkhole diameter enlargement in surface; (<b>c</b>) Harkat Bouziane sinkhole collapsed in the city with a diameter of ~50 m and height of 2 m (location: see <a href="#remotesensing-15-01486-f001" class="html-fig">Figure 1</a>—sinkhole sites; date: February 2009), destroying infrastructure; and (<b>d</b>) damage created by a sinkhole event (i.e., sewer network and roads broken).</p> Full article ">Figure 4
<p>Flowchart depicting the overall methodology adopted in this research.</p> Full article ">Figure 5
<p>Vertical and horizontal mean velocity map of the study area showing (<b>a</b>) vertical mean velocity (up and down directions) and (<b>b</b>) horizontal mean velocity (east and west directions). (To enhance the visibility of positive and negative values, values close to 0 were rendered transparent.)</p> Full article ">Figure 6
<p>Graphs of the cumulative deformation of the selected points (P1, P2, P3, P4, and P5) from PSI and GPS results used to detect subsidence; the <span class="html-italic">x</span>-axis is time, 2016–2022, and the <span class="html-italic">y</span>-axis represents the movement of the ground.</p> Full article ">Figure 7
<p>Cumulative vertical ground movement profiles: (<b>a</b>) AB and (<b>b</b>) CD profiles. The <span class="html-italic">x</span>-axis in the figure represents the profile direction along NE–SW and NWW–SEE directions, while the <span class="html-italic">y</span>-axis represents the cumulative vertical ground movement (mm). The black circle referring to selected points A, B, C, and D through the AB profile, and 1, 2, 3 and 4 through the CD profile, are also presented in <a href="#remotesensing-15-01486-f008" class="html-fig">Figure 8</a>.</p> Full article ">Figure 8
<p>(<b>a</b>) Positions and overview of the eight selected sample sites (A, B, C and D through the AB profile, and 1, 2, 3 and 4 through the CD profile) from <a href="#remotesensing-15-01486-f007" class="html-fig">Figure 7</a>; (<b>b</b>–<b>f</b>) represent a close-up overview of selected sample sites (i.e., highlighted with white circles).</p> Full article ">Figure 9
<p>Water changes of three boreholes, B1, B2, and B3, monitored between 2011 and 2012, located in the northern part of the study area (modified after [<a href="#B16-remotesensing-15-01486" class="html-bibr">16</a>]).</p> Full article ">

20 pages, 13155 KiB  

Open AccessArticle

A Scenario-Based Case Study: Using AI to Analyze Casualties from Landslides in Chittagong Metropolitan Area, Bangladesh

by Edris Alam, Fahim Sufi and Abu Reza Md. Towfiqul Islam

Sustainability 2023, 15(5), 4647; https://doi.org/10.3390/su15054647 - 6 Mar 2023

Cited by 1 | Viewed by 2331

Abstract

Understanding the complex dynamics of landslides is crucial for disaster planners to make timely and effective decisions that save lives and reduce the economic impact on society. Using the landslide inventory of the Chittagong Metropolitan Area (CMA), we have created a new artificial [...] Read more.

Understanding the complex dynamics of landslides is crucial for disaster planners to make timely and effective decisions that save lives and reduce the economic impact on society. Using the landslide inventory of the Chittagong Metropolitan Area (CMA), we have created a new artificial intelligence (AI)-based insight system for the town planners and senior disaster recovery strategists of Chittagong, Bangladesh. Our system generates dynamic AI-based insights for a range of complex scenarios created from 7 different landslide feature attributes. The users of our system can select a particular kind of scenario out of the exhaustive list of 1.054 × 10⁴¹ possible scenario sets, and our AI-based system will immediately predict how many casualties are likely to occur based on the selected kind of scenario. Moreover, an AI-based system shows how landslide attributes (e.g., rainfall, area of mass, elevation, etc.) correlate with landslide casualty by drawing detailed trend lines by performing both linear and logistic regressions. According to the literature and the best of our knowledge, our CMA scenario-based AI insight system is the first of its kind, providing the most comprehensive understanding of landslide scenarios and associated deaths and damages in the CMA. The system was deployed on a wide range of platforms including Android, iOS, and Windows systems so that it could be easily adapted for strategic disaster planners. The deployed solutions were handed down to 12 landslide strategists and disaster planners for evaluations, whereby 91.67% of users found the solution easy to use, effective, and self-explanatory while using it via mobile. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>High level methodology of AI insight system for analyzing landslides in the CMA of SE Bangladesh, particularly the landslide susceptible areas in the Chittagong, Rangamati, and Cox’s Bazar districts.</p> Full article ">Figure 2
<p>Landslide vulnerability in different areas of Chittagong (map and photo) (Source: Field visit, October 2018).</p> Full article ">Figure 3
<p>Location of SE Bangladesh, particularly the landslide susceptible areas in the Chittagong, Rangamati, and Cox’s Bazar districts.</p> Full article ">Figure 4
<p>Data modelling of the CMA landslide database.</p> Full article ">Figure 5
<p>AI-based insights and landslide analysis system.</p> Full article ">Figure 6
<p>The process of obtaining AI insights from CMA landslide data using machine learning algorithms.</p> Full article ">Figure 7
<p>Decomposition tree visualization allows the user to perform interactive analysis by area of mass, elevation, rainfall, state, and types.</p> Full article ">Figure 8
<p>Filter area for the selection of landslide attributes.</p> Full article ">Figure 9
<p>Decomposition analysis showing what causes the most casualties.</p> Full article ">Figure 10
<p>Decompression analysis showing what caused the highest casualties when types= “Topple”.</p> Full article ">Figure 11
<p>The proposed system running on mobile devices and providing AI-based insights on CMA landslides.</p> Full article ">

21 pages, 9011 KiB  

Open AccessArticle

Numerical Investigation of the Dynamic Response of a Sand Cushion with Multiple Rockfall Impacts

by Yu Zhang, Jierui Feng, Longhuan Du, Peng Zhao, Jiao Peng, Chen Yang, Hua Fan and Liangpu Li

Sustainability 2023, 15(4), 3554; https://doi.org/10.3390/su15043554 - 15 Feb 2023

Cited by 1 | Viewed by 1585

Abstract

A shed cave structure with a sand cushion is often used as a protective structure for rockfall disasters. Because of the randomness and unpredictability of rockfall disasters, the cushions of shed caves often suffer multiple impacts from rockfalls. Aiming at the problem of [...] Read more.

A shed cave structure with a sand cushion is often used as a protective structure for rockfall disasters. Because of the randomness and unpredictability of rockfall disasters, the cushions of shed caves often suffer multiple impacts from rockfalls. Aiming at the problem of multiple impacts of rockfall, this paper uses the three-dimensional discrete element method to study the dynamic response of multiple rockfall impacts on sand cushions from different heights. Before conducting large-scale simulation studies, the input parameters in the numerical model are verified with data from laboratory experiments. Analyzing the simulation results shows that when the same point is impacted multiple times, the maximum impact force and the maximum penetration depth will increase with the number of impacts. According to the numerical results, a calculation formula of the maximum impact force that considers the number of impacts is fitted. At the same time, considering the impact response when the rockfall impacts different positions multiple times, the distance range that the subsequent impact is not affected by the previous impact is given. The significance of studying the multiple impacts of rockfalls is shown by a numerical study of rockfalls impacting a sand cushion multiple times from different heights, and it provides a reference for the design of rockfall disaster-protection structures in practical engineering. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>Laboratory test device and schematic diagram: (<b>a</b>) test device; (<b>b</b>) schematic diagram.</p> Full article ">Figure 2
<p>Small-scale numerical model diagram.</p> Full article ">Figure 3
<p>Comparison of the impact force time–history curve between the laboratory tests and numerical model under multiple impacts: (<b>a</b>) first impact; (<b>b</b>) second impact; (<b>c</b>) third impact; and (<b>d</b>) fourth impact.</p> Full article ">Figure 4
<p>Large-scale numerical model diagram.</p> Full article ">Figure 5
<p>Nephogram of rockfall and cushion position changes (t = 0.05 s): (<b>a</b>) first impact; (<b>b</b>) second impact; (<b>c</b>) third impact; and (<b>d</b>) fourth impact.</p> Full article ">Figure 6
<p>Time–history curve of the impact force of a spherical rockfall impacting a sand cushion multiple times from different heights. (<b>a</b>) H = 10 m; (<b>b</b>) H = 20 m; (<b>c</b>) H = 30 m; (<b>d</b>) H = 40 m; and (<b>e</b>) H = 50 m.</p> Full article ">Figure 7
<p>Maximum impact force of a spherical rock falling on a sand cushion multiple times from five falling heights.</p> Full article ">Figure 8
<p>Comparison of the formula and simulated impact force results [<a href="#B27-sustainability-15-03554" class="html-bibr">27</a>].</p> Full article ">Figure 9
<p>Comparison of impact force results between numerical and fitted formulas.</p> Full article ">Figure 10
<p>Impact force results from improved algorithms and numerical simulations.</p> Full article ">Figure 11
<p>Time–history curve of the penetration depth of a spherical rockfall impacting a sand cushion multiple times from different heights. (<b>a</b>) H = 10 m; (<b>b</b>) H = 20 m; (<b>c</b>) H = 30 m; (<b>d</b>) H = 40 m; and (<b>e</b>) H = 50 m.</p> Full article ">Figure 12
<p>Maximum penetration depth of a spherical rock falling on a sand cushion multiple times at five falling heights.</p> Full article ">Figure 13
<p>Schematic diagram of different impact positions.</p> Full article ">Figure 14
<p>Time–history curve of the impact force of the falling rock at different positions on the top surface of the sand cushion when falling from 50 m. (<b>a</b>) Impact only once; (<b>b</b>) second impact.</p> Full article ">Figure 15
<p>Impact force results of rockfall impacting different positions on the top surface of the sand cushion from different heights.</p> Full article ">Figure 16
<p>Time–history curve of the penetration depth of the falling rock at different positions on the top surface of the sand cushion when falling from 50 m. (<b>a</b>) Impact only once; (<b>b</b>) second impact.</p> Full article ">Figure 17
<p>The vertical displacement nephogram of particles at different positions when falling from 50 m (t = 0.05 s). (<b>a</b>) Impact only once; (<b>b</b>) second impact.</p> Full article ">Figure 17 Cont.
<p>The vertical displacement nephogram of particles at different positions when falling from 50 m (t = 0.05 s). (<b>a</b>) Impact only once; (<b>b</b>) second impact.</p> Full article ">Figure 18
<p>Penetration depth results of rockfall impacting different positions on the top surface of the sand cushion from different heights.</p> Full article ">

26 pages, 8178 KiB  

Open AccessArticle

A Spatial Model of Landslides with A Micro-Topography and Vegetation Approach for Sustainable Land Management in the Volcanic Area

by Heni Masruroh, Soemarno Soemarno, Syahrul Kurniawan and Amin Setyo Leksono

Sustainability 2023, 15(4), 3043; https://doi.org/10.3390/su15043043 - 7 Feb 2023

Cited by 2 | Viewed by 2302

Abstract

This study aims to produce a spatial model for sustainable land management in landslide-prone areas, based on exploring non-stationary relationships between landslide events, geomorphological and anthropogenic variables on tropical hillsides, especially in Taji Village, Jabung District, East Java Province, Indonesia. A series of [...] Read more.

This study aims to produce a spatial model for sustainable land management in landslide-prone areas, based on exploring non-stationary relationships between landslide events, geomorphological and anthropogenic variables on tropical hillsides, especially in Taji Village, Jabung District, East Java Province, Indonesia. A series of approaches combine in this research, and methods are used to construct independent and dependent variables so that GWR can analyze them to obtain the best model. Transformation of categorical data on microtopography, landform, and land cover variables was carried out. When modelled, landscape metrics can explain landslide events in the study area better than distance metrics with adj. R² = 0.75 and AICc = 2526.38. Generally, local coefficient maps for each variable are mapped individually to reveal their relationship with landslide events, but in this study they are integrated to make it more intuitive and less confusing. From this map, it was found that most of the variables that showed the most positive relationship to the occurrence of landslides in the study area were the divergent footslopes. At the same time, the negative one was plantation land. It was concluded that the methodological approach offered and implemented in this study provides significant output results for the spatial analysis of the interaction of landslide events with geomorphological and anthropogenic variables locally, which cannot be explained in a global regression. This study produces a detailed scale landslide-prone conservation model in tropical hill areas and can be reproduced under the same geo-environmental conditions. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1
<p>Location of the study area in a small part of the Taji watershed.</p> Full article ">Figure 2
<p>Flowchart of research on landslide local spatial relationships using the GWR model.</p> Full article ">Figure 3
<p>Schematic diagram of microtopographic zoning using (<b>A</b>) vertex point line morphology; and (<b>B</b>) Aggregation polygon based on the ID of each morphological unit.</p> Full article ">Figure 4
<p>Morphological mapping with a modified geomorphological symbolization system.</p> Full article ">Figure 5
<p>Microtopographic Zoning Map from Morphology in the Study Area.</p> Full article ">Figure 6
<p>The independent variables include (<b>A</b>) curvature-based landform; (<b>B</b>) land cover; and (<b>C</b>) vegetation density.</p> Full article ">Figure 7
<p>Map of the local coefficient of determination (R²) between the observed values and the fit of the GWR model (<b>left</b>) and the standardized residual (<b>right</b>).</p> Full article ">Figure 8
<p>Map of integration of multivariate local coefficients that have the largest (positive) and smallest (negative) significant impacts on landslides.</p> Full article ">Figure 9
<p>Inset map of the integration map of multivariate local coefficients focused on landslide areas (<b>A</b>–<b>C</b>); and non-slip areas (<b>D</b>,<b>E</b>).</p> Full article ">

16 pages, 11713 KiB  

Open AccessArticle

Proposal of a Disrupted Road Detection Method in a Tsunami Event Using Deep Learning and Spatial Data

by Jun Sakamoto

Sustainability 2023, 15(4), 2936; https://doi.org/10.3390/su15042936 - 6 Feb 2023

Cited by 1 | Viewed by 1544

Abstract

Tsunamis generated by undersea earthquakes can cause severe damage. It is essential to quickly assess tsunami-damaged areas to take emergency measures. In this study, I employ deep learning and develop a model using aerial photographs and road segment data. I obtained data from [...] Read more.

Tsunamis generated by undersea earthquakes can cause severe damage. It is essential to quickly assess tsunami-damaged areas to take emergency measures. In this study, I employ deep learning and develop a model using aerial photographs and road segment data. I obtained data from the aerial photographs taken after the Great East Japan Earthquake; the deep learning model used was YOLOv5. The proposed method based on YOLOv5 can determine damaged roads from aerial pictures taken after a disaster. The feature of the proposed method is to use training data from images separated by a specific range and to distinguish the presence or absence of damage related to the tsunami. The results show that the proposed method is more accurate than a comparable traditional method, which is constructed by labeling and learning the damaged areas. The highest F1 score of the traditional method was 60~78%, while the highest F1 score of the proposed method was 72~83%. The traditional method could not detect locations where it is difficult to determine the damage status from aerial photographs, such as where houses are not completely damaged. However, the proposed method was able to detect them. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>Method flowchart.</p> Full article ">Figure 2
<p>Labeling for training images in the traditional model.</p> Full article ">Figure 3
<p>Labeling for training images in the proposed method.</p> Full article ">Figure 4
<p>YOLOv5 architecture.</p> Full article ">Figure 5
<p>Locations of training and test images.</p> Full article ">Figure 6
<p>Classification of the tsunami inundation area.</p> Full article ">Figure 7
<p>Training images with inundation in the proposed method (<span class="html-italic">n</span> = 1419).</p> Full article ">Figure 8
<p>Training images with non-inundation in the proposed method (<span class="html-italic">n</span> = 1689).</p> Full article ">Figure 9
<p>Example of distinguishment.</p> Full article ">Figure 10
<p>Training epoch versus loss function and versus mAP.</p> Full article ">Figure 11
<p>Visualization of the results of the traditional method (YOLOv5s).</p> Full article ">Figure 12
<p>Visualization of the results of the proposed method (YOLOv5s).</p> Full article ">Figure 13
<p>Example of the detection results of the traditional method (YOLOv5s).</p> Full article ">

25 pages, 15121 KiB  

Open AccessArticle

Integrated Risk Assessment of Agricultural Drought Disasters in the Major Grain-Producing Areas of Jilin Province, China

by Jiawang Zhang, Jianguo Wang, Shengbo Chen, Mingchang Wang, Siqi Tang and Wutao Zhao

Land 2023, 12(1), 160; https://doi.org/10.3390/land12010160 - 3 Jan 2023

Cited by 5 | Viewed by 2083

Abstract

The impact of global climate change has intensified, and the frequent occurrence of meteorological disasters has posed a serious challenge to crop production. This article conducts an integrated risk assessment of agricultural drought disasters in the main grain-producing areas of Jilin Province using [...] Read more.

The impact of global climate change has intensified, and the frequent occurrence of meteorological disasters has posed a serious challenge to crop production. This article conducts an integrated risk assessment of agricultural drought disasters in the main grain-producing areas of Jilin Province using the temperature and precipitation data of the study area from 1955 to 2020, the sown area of crops, historical disaster data, regional remote sensing images, and statistical yearbook data. The agricultural drought integrated risk assessment model was built around four factors: drought hazards, vulnerability of hazard-bearing bodies, sensitivity of disaster-pregnant environments, and stability of disaster mitigation capacity. The results show that the study area has shown a trend of changing from wet to dry and then wet over the past 66 years, with the occasional occurrence of severe drought, and a decreasing trend at a rate of −0.089. (10a)⁻¹ overall. The integrated risk of drought in the study area exhibits regional clustering, and the overall risk level has some relationship spatially with the regional geological tectonic units, with the high-risk level concentrated in the central area of Song Liao Basin and close to the geological structure of Yishu Graben and the low risk level concentrated in the marginal area of Song Liao Basin. Based on the results of the risk factor analysis, integrated risk prevention suggestions for drought in the main grain-producing areas of Jilin Province were put forward from four aspects. Fine identification and evaluation of high-risk areas of agricultural drought can provide a quantitative basis for effective drought resistance activities in relevant areas. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1
<p>Research area map.</p> Full article ">Figure 2
<p>Research flowchart.</p> Full article ">Figure 3
<p>Characteristics of interannual variation of SPEI−12 in study area from 1955 to 2020.</p> Full article ">Figure 4
<p>Seasonal trends of SPEI−3 index in study area from 1955–2020: (<b>a</b>) spring; (<b>b</b>) summer.</p> Full article ">Figure 5
<p>Drought hazard level of the study area.</p> Full article ">Figure 6
<p>Drought probability density under different disaster index.</p> Full article ">Figure 7
<p>Drought exceedance probability density under different disaster index.</p> Full article ">Figure 8
<p>Distribution of different disaster-bearing bodies factor: (<b>a</b>) distribution of agricultural drought disaster recurrence levels; (<b>b</b>) distribution of exposure levels of agriculture.</p> Full article ">Figure 9
<p>Distribution of vulnerability levels of agricultural disaster-bearing bodies.</p> Full article ">Figure 10
<p>Distribution of different disaster-pregnant environmental factors: (<b>a</b>) distribution of topographic position index (TPI) levels; (<b>b</b>) distribution of vegetation cover levels; (<b>c</b>) distribution of soil types levels; (<b>d</b>) distribution of river density levels.</p> Full article ">Figure 10 Cont.
<p>Distribution of different disaster-pregnant environmental factors: (<b>a</b>) distribution of topographic position index (TPI) levels; (<b>b</b>) distribution of vegetation cover levels; (<b>c</b>) distribution of soil types levels; (<b>d</b>) distribution of river density levels.</p> Full article ">Figure 11
<p>Distribution of agricultural disaster-pregnant environment integrated index.</p> Full article ">Figure 12
<p>Various disaster mitigation capabilities in study area: (<b>a</b>) distribution of emergency management capacity levels; (<b>b</b>) distribution of resource security capacity levels; (<b>c</b>) distribution of agricultural modernization levels; (<b>d</b>) distribution of integrated disaster reduction capacity levels.</p> Full article ">Figure 13
<p>Integrated risk level of agricultural drought.</p> Full article ">Figure 14
<p>Integrated risk prevention model for agricultural drought.</p> Full article ">

21 pages, 10569 KiB  

Open AccessArticle

Study on the Evolution of a Flooded Tailings Pond and Its Post-Failure Effects

by Mengchao Chang, Weimin Qin, Hao Wang, Haibin Wang, Chengtang Wang and Xiuli Zhang

Water 2023, 15(1), 173; https://doi.org/10.3390/w15010173 - 31 Dec 2022

Cited by 1 | Viewed by 2260

Abstract

In order to avoid the risk of tailing pond failures and to minimize the post-failure losses, it is necessary to analyze the current operation status of tailings ponds, to explore the evolution law of their failure process, to grasp their post-failure impact range, [...] Read more.

In order to avoid the risk of tailing pond failures and to minimize the post-failure losses, it is necessary to analyze the current operation status of tailings ponds, to explore the evolution law of their failure process, to grasp their post-failure impact range, and to propose corresponding effective prevention and control measures. Based on a tailings pond in China, this paper establishes a 1:200 scale indoor model to explore the evolution law of post-failure tailings discharge in a tailings pond under flooded roof conditions; secondly, the finite element difference method and smooth particle fluid dynamics are combined to compare and analyze the post-failure impact area and to delineate the risk prevention and control area. The results of the study show that, during the dam break, the lower tailing sand in the breach is the first to slip, and after forming a steep can, the upper tailing sand in the steep can is pulled to slip, so that the erosion trench mainly develops vertically first, and then laterally. The velocity of the discharged tailing sand will quickly reach its peak value in a short period of time and then decrease to the creeping stage; the front edge of the sand flow is the first to stop moving, and the trailing edge of the tailing sand accumulation depth continues to increase until the end of the dam failure, at which point the initial bottom dam area of the discharge tailing sand flow velocity is the largest. The further the tailings are released from the initial dam, the smaller the accumulation depth and the larger the particle size, and the larger the elevation of the foundation in the same section, the smaller the accumulation depth and the larger the particle size; further, the presence of blocking materials will increase the local tailings accumulation depth. Based on the maximum flow velocity of the discharged tailings and the accumulation depth, the risk area downstream of the tailings pond is divided, so that relocation measures can be formulated. The test results can provide an important reference for the operation and management of similar tailings ponds. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1
<p>Topographical map of the prototype tailings pond.</p> Full article ">Figure 2
<p>Indoor test modeling. (<b>a</b>) Cut off panel. (<b>b</b>) Standing plate. (<b>c</b>) Fill sandy soil. (<b>d</b>) Conservation. (<b>e</b>) Stacking of sub-dams. (<b>f</b>) Final model.</p> Full article ">Figure 2 Cont.
<p>Indoor test modeling. (<b>a</b>) Cut off panel. (<b>b</b>) Standing plate. (<b>c</b>) Fill sandy soil. (<b>d</b>) Conservation. (<b>e</b>) Stacking of sub-dams. (<b>f</b>) Final model.</p> Full article ">Figure 3
<p>Schematic diagram of test platform.</p> Full article ">Figure 4
<p>Tailing sand particle size gradation curve.</p> Full article ">Figure 5
<p>Dam breach process. (<b>a</b>) Water injection in the tailings pond. (<b>b</b>) Ulcer formation. (<b>c</b>) Erosion trench formation. (<b>d</b>) Steep can formation. (<b>e</b>) Erosion trench horizontal development. (<b>f</b>) End of dam failure.</p> Full article ">Figure 6
<p>Variation in the depth of the erosion trench on the dam body with time. (<b>a</b>) Variation in erosion trench depth with time. (<b>b</b>) Variation in erosion trench width with time.</p> Full article ">Figure 7
<p>Flood topping dam breach full-field flow velocity change curve with time. (<b>a</b>) Instantaneous flow rate. (<b>b</b>) Mean flow rate.</p> Full article ">Figure 8
<p>Impact of downstream tailing after dam failure. (<b>a</b>) Depth of tailing sand accumulation in each section. (<b>b</b>) Range of tailing sand accumulation.</p> Full article ">Figure 9
<p>Calculation model. (<b>a</b>) Massflow calculation model; (<b>b</b>) SPH calculation model.</p> Full article ">Figure 10
<p>The process of tailing sand discharge from the breached dam. (<b>a</b>) Height distribution of tailing sand accumulation at 20 s. (<b>b</b>) Tail sand flow rate distribution at 20 s. (<b>c</b>) Height distribution of tailing sand accumulation at 40 s. (<b>d</b>) Tail sand flow rate distribution at 40 s. (<b>e</b>) Height distribution of tailing sand accumulation at 60 s. (<b>f</b>) Tail sand flow rate distribution at 60 s. (<b>g</b>) Height distribution of tailing sand accumulation at 80 s. (<b>h</b>) Tail sand flow rate distribution at 80 s. (<b>i</b>) Height distribution of tailing sand accumulation at 300 s. (<b>j</b>) Tail sand flow velocity distribution at 300 s.</p> Full article ">Figure 10 Cont.
<p>The process of tailing sand discharge from the breached dam. (<b>a</b>) Height distribution of tailing sand accumulation at 20 s. (<b>b</b>) Tail sand flow rate distribution at 20 s. (<b>c</b>) Height distribution of tailing sand accumulation at 40 s. (<b>d</b>) Tail sand flow rate distribution at 40 s. (<b>e</b>) Height distribution of tailing sand accumulation at 60 s. (<b>f</b>) Tail sand flow rate distribution at 60 s. (<b>g</b>) Height distribution of tailing sand accumulation at 80 s. (<b>h</b>) Tail sand flow rate distribution at 80 s. (<b>i</b>) Height distribution of tailing sand accumulation at 300 s. (<b>j</b>) Tail sand flow velocity distribution at 300 s.</p> Full article ">Figure 11
<p>Monitoring section and monitoring point layout map.</p> Full article ">Figure 12
<p>Tailing sand accumulation pattern at each monitoring section. (<b>a</b>) MS1 monitoring cross-section; (<b>b</b>) MS2 monitoring cross-section; (<b>c</b>) MS3 monitoring cross-section; (<b>d</b>) MS4 monitoring cross-section.</p> Full article ">Figure 13
<p>Variation curves of tailing sand accumulation height and flow rate with time at each monitoring point. (<b>a</b>) Height–time curve of tailing sand accumulation at each monitoring point; (<b>b</b>) velocity–time curve of sand flow at each monitoring point.</p> Full article ">Figure 14
<p>Process of tailing sand discharge from the breached dam. (<b>a</b>) Cloud map of tailing sand displacement distribution at 20 s. (<b>b</b>) Cloud plot of tailing sand velocity distribution at 20 s. (<b>c</b>) Cloud map of tail sand displacement distribution at 40 s. (<b>d</b>) Cloud map of tailing sand velocity distribution at 40 s. (<b>e</b>) Cloud map of tail sand displacement distribution at 60 s. (<b>f</b>) Cloud map of tailing sand velocity distribution at 60 s. (<b>g</b>) Cloud map of tail sand displacement distribution at 80 s. (<b>h</b>) Cloud plot of tailing sand velocity distribution at 80 s. (<b>i</b>) Cloud map of tail sand displacement distribution at 300 s. (<b>j</b>) Cloud plot of tailing sand velocity distribution at 300 s.</p> Full article ">Figure 14 Cont.
<p>Process of tailing sand discharge from the breached dam. (<b>a</b>) Cloud map of tailing sand displacement distribution at 20 s. (<b>b</b>) Cloud plot of tailing sand velocity distribution at 20 s. (<b>c</b>) Cloud map of tail sand displacement distribution at 40 s. (<b>d</b>) Cloud map of tailing sand velocity distribution at 40 s. (<b>e</b>) Cloud map of tail sand displacement distribution at 60 s. (<b>f</b>) Cloud map of tailing sand velocity distribution at 60 s. (<b>g</b>) Cloud map of tail sand displacement distribution at 80 s. (<b>h</b>) Cloud plot of tailing sand velocity distribution at 80 s. (<b>i</b>) Cloud map of tail sand displacement distribution at 300 s. (<b>j</b>) Cloud plot of tailing sand velocity distribution at 300 s.</p> Full article ">Figure 15
<p>Post-collapse tailing sand accumulation pattern.</p> Full article ">Figure 16
<p>Location of measurement points.</p> Full article ">Figure 17
<p>Velocity–time curves of tail sand movement at different measurement points.</p> Full article ">Figure 18
<p>Range of tailing sand accumulation given by different methods.</p> Full article ">Figure 19
<p>Dam failure risk prevention and control area.</p> Full article ">

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22 pages, 5818 KiB  

Open AccessArticle

Modelling Erosion and Floods in Volcanic Environment: The Case Study of the Island of Vulcano (Aeolian Archipelago, Italy)

by Rosanna Bonasia, Agnese Turchi, Paolo Madonia, Alessandro Fornaciai, Massimiliano Favalli, Andrea Gioia and Federico Di Traglia

Sustainability 2022, 14(24), 16549; https://doi.org/10.3390/su142416549 - 9 Dec 2022

Cited by 1 | Viewed by 2090

Abstract

The re-mobilization of volcaniclastic material poses a hazard factor which, although it decreases with time since the last eruption, remains present in the hydrographic basins of volcanic areas. Herein, we present the results of the numerical modelling of erosive phenomena of volcanic deposits, [...] Read more.

The re-mobilization of volcaniclastic material poses a hazard factor which, although it decreases with time since the last eruption, remains present in the hydrographic basins of volcanic areas. Herein, we present the results of the numerical modelling of erosive phenomena of volcanic deposits, as well as of flooding in the volcanic area. The proposed approach includes runoff estimation, land use analysis, and the application of hydraulic and erosion modelling. It exploits the Iber software, a widely used and validated model for rainfall-runoff, river flooding, and erosion and sediment transport modelling. The methodology was applied to the Island of Vulcano (Italy), known for the erosion phenomena that affect the slopes of one of its volcanic cones (La Fossa cone). The rainfall excess was calculated using a 19-year dataset of hourly precipitations, and the curve number expressed by the information on soil cover in the area, derived from the land cover and land use analysis. The erosion and flow models were performed considering different rainfall scenarios. Results show a particularly strong erosion, with thicknesses greater than 0.4 m. This is consistent with field observations, in particular with some detailed data collected both after intense events and by long-term observation. Results of the hydraulic simulations show that moderate and torrential rainfall scenarios can lead to flood levels between 0.2 and 0.6 m, which mostly affect the harbours located in the island’s inhabited area. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1
<p>Orthophotograph superimposed on the shadow model of the Island of Vulcano (data derived from PLÉIADES-1 constellation). The main geographic and geological features of the island are reported. In the inset, the location of the Island of Vulcano is reported, as well as the main geographic feature cited in the text.</p> Full article ">Figure 2
<p>Litho-technical map of the northern side of the Island of Vulcano. Lithology description and boundaries are derived from the geological map of De Astis et al. [<a href="#B31-sustainability-14-16549" class="html-bibr">31</a>], Di Traglia et al. [<a href="#B17-sustainability-14-16549" class="html-bibr">17</a>], and Fusillo et al. [<a href="#B29-sustainability-14-16549" class="html-bibr">29</a>], while litho-technical characterization is derived from Madonia et al. [<a href="#B16-sustainability-14-16549" class="html-bibr">16</a>] and Tommasi et al. [<a href="#B22-sustainability-14-16549" class="html-bibr">22</a>].</p> Full article ">Figure 3
<p>Flow chart of the adopted method.</p> Full article ">Figure 4
<p>Maximum annual values of rainfall intensity.</p> Full article ">Figure 5
<p>Rainfall intensity distribution on 14 September 2008.</p> Full article ">Figure 6
<p>Land use in 2021 of the northern part of the Island of Vulcano: (<b>a</b>) a view of artificial areas in Vulcano Porto from the summit of La Fossa cone; (<b>b</b>) a detail of recent buildings and roads in Vulcano Porto; (<b>c</b>) a view of wooded, semi-natural vegetated and semi-natural not vegetate areas of La Fossa cone from the Vulcano Porto-Il Piano road; (<b>d</b>) a detail of areas with herbaceous and shrubby vegetation in Palizzi Valley.</p> Full article ">Figure 7
<p>2021 land use map of the northern part of the Island of Vulcano.</p> Full article ">Figure 8
<p>Evidence of erosion in the NW sector of the La Fossa cone during the 14 September 2008 event. The location is indicated by a black box in <a href="#sustainability-14-16549-f010" class="html-fig">Figure 10</a>. (<b>a</b>–<b>d</b>) are in increasing elevation. In (<b>c</b>) it is possible to see the erosion under the rockfall net positioned inside the channel.</p> Full article ">Figure 9
<p>Effects of the 14 September 2008 event: (<b>a</b>–<b>c</b>) are located at the base of the cone, whereas (<b>d</b>) is located on the Vulcano Porto wharf.</p> Full article ">Figure 10
<p>Map of the simulated erosion, considering an event characterised by the same rainfall which occurred on the 14 September 2008 on the Island of Vulcano. Stronger erosion characterised the main channel in the area. The rectangle shows the area of <a href="#sustainability-14-16549-f008" class="html-fig">Figure 8</a>, where the greatest erosion was observed during the field survey carried out a few hours after the event. The subdivision into classes derives from the standard deviation of the data (σ = 0.05 m).</p> Full article ">Figure 11
<p>Erosion maps for the (<b>a</b>) moderate and (<b>b</b>) heavy-torrential rainfall scenarios.</p> Full article ">Figure 12
<p>Inundation maps for the (<b>a</b>) moderate and (<b>b</b>) heavy-torrential rainfall scenarios.</p> Full article ">Figure 13
<p>Erosional features in the mixed natural/anthropic environment of La Fossa cone (see main text for the explanation of the panels). (<b>a</b>) sector of the La Fossa cone where most of the accelerated erosion phenomena of the slope occur; (<b>b</b>) rill erosion on the ascent path to the crater, to the detriment of the more erodible deposits (deposits referable to the Great Crater Eruptive Cluster [<a href="#B17-sustainability-14-16549" class="html-bibr">17</a>]); (<b>c</b>) rill erosion on less erodible deposits (Varicoloured Ash); (<b>d</b>) gully erosion on less erodible deposits (Varicoloured Ash); (<b>e</b>–<b>g</b>) damage to the water management systems on the side of the La Fossa cone.</p> Full article ">

26 pages, 10127 KiB  

Open AccessArticle

Post Evaluation of Slope Cutting on Loess Slopes under Long-Term Rainfall Based on a Model Test

by Guodong Liu, Zhijun Zhou, Shiqiang Xu and Yuanmeng Cheng

Sustainability 2022, 14(23), 15838; https://doi.org/10.3390/su142315838 - 28 Nov 2022

Cited by 1 | Viewed by 1689

Abstract

The failure of treated slopes around the world, especially in China, is occurring at a noteworthy rate, resulting in an urgent requirement for post evaluation of the treated slopes; however, there is no mature technique established for post evaluation. By using a real [...] Read more.

The failure of treated slopes around the world, especially in China, is occurring at a noteworthy rate, resulting in an urgent requirement for post evaluation of the treated slopes; however, there is no mature technique established for post evaluation. By using a real loess slope treated by slope cutting in Shaanxi Province as the prototype, indoor geotechnical tests and model tests were performed to reveal the rainwater infiltration characteristics and pressure-varying characteristics inside the slope, the results of which were used to conduct a post evaluation of the slope in situ. The results mainly showed that the effect of rainwater scouring on the slope surface weakened gradually into a steady state at the end of the first year. The rainwater upon the slope surface preferentially infiltrated the platforms with gradually reducing rates; however, the observed wetting front cannot be regarded as the border between the unsaturated and saturated loesses. The soil pressures inside the slope did not increase, but decreased during the early period of rainfall. The displacements of key points mainly occurred during the first two years and then steady periods were entered. The above results were utilized to conduct a post evaluation of the slope prototype, by which a post evaluation framework was constructed. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1
<p>Slope prototype location in China (The red star denotes the capital of China while the black star denotes the provincial capital Xi’an).</p> Full article ">Figure 2
<p>Slope prototype section with collapse.</p> Full article ">Figure 3
<p>Model test box and rainfall simulator: (<b>a</b>) Model test box, (<b>b</b>) Rainfall simulator.</p> Full article ">Figure 4
<p>Slope model.</p> Full article ">Figure 5
<p>Slope model construction process.</p> Full article ">Figure 6
<p>Photos of indoor geotechnical tests: (<b>a</b>) Density test, (<b>b</b>) Direct shear test, (<b>c</b>) Compression test, (<b>d</b>) Water content test.</p> Full article ">Figure 7
<p>Full dimensions of the model slope and layout of the measurement points.</p> Full article ">Figure 8
<p>Scour failure process during three rounds of rainfall: (<b>a</b>) First round of the first rainfall event lasted for 5 min, (<b>b</b>) First round of the first rainfall event lasted for 70 min, (<b>c</b>) First round of the second rainfall event lasted for 120 min, (<b>d</b>) Third round of the third rainfall event lasted for 120 min.</p> Full article ">Figure 9
<p>Development of the dimensions of the main gully on the left of the first slope grade: (<b>a</b>) During the first round, (<b>b</b>) During the second round, (<b>c</b>) During the third round.</p> Full article ">Figure 10
<p>Rainwater infiltration process within the slope model: (<b>a</b>) After 30 min of the first round of the first rainfall event, (<b>b</b>) After 120 min of the first round of the first rainfall event, (<b>c</b>) 5.2 h after the first round of the second rainfall event, (<b>d</b>) After 120 min of the second round of the third rainfall event, (<b>e</b>) After 120 min of the third round of the first rainfall event.</p> Full article ">Figure 11
<p>Rainwater infiltration distance with time: (<b>a</b>) Within the first round, (<b>b</b>) Within the second round, (<b>c</b>) Within the third round.</p> Full article ">Figure 12
<p>Pore water pressure variations within the test: (<b>a</b>) First round, (<b>b</b>) Second round, (<b>c</b>) Third round, (<b>d</b>) Fourth round, (<b>e</b>) Fifth round.</p> Full article ">Figure 12 Cont.
<p>Pore water pressure variations within the test: (<b>a</b>) First round, (<b>b</b>) Second round, (<b>c</b>) Third round, (<b>d</b>) Fourth round, (<b>e</b>) Fifth round.</p> Full article ">Figure 13
<p>Variations in soil pressure inside the model slope: (<b>a</b>) First round, (<b>b</b>) Second round, (<b>c</b>) Third round, (<b>d</b>) Fourth round, (<b>e</b>) Fifth round.</p> Full article ">Figure 14
<p>Variations in the horizontal displacements of the six key points over time: (<b>a</b>) First round, (<b>b</b>) Second round, (<b>c</b>) Third round, (<b>d</b>) Fourth round, (<b>e</b>) Fifth round.</p> Full article ">Figure 14 Cont.
<p>Variations in the horizontal displacements of the six key points over time: (<b>a</b>) First round, (<b>b</b>) Second round, (<b>c</b>) Third round, (<b>d</b>) Fourth round, (<b>e</b>) Fifth round.</p> Full article ">Figure 15
<p>Critical sliding surface from the Morgenstern–Price method (S1, S2, S3 and S4 are displacement markers identical to those in <a href="#sustainability-14-15838-f007" class="html-fig">Figure 7</a>).</p> Full article ">Figure 16
<p>Slice division profile of the sliding mass.</p> Full article ">Figure 17
<p>Post evaluation framework for slopes treated by slope cutting.</p> Full article ">

20 pages, 5469 KiB  

Open AccessArticle

A Monitoring Method Based on Vegetation Abnormal Information Applied to the Case of Jizong Shed-Tunnel Landslide

by Qing Guo, Lianzi Tong and Hua Wang

Remote Sens. 2022, 14(22), 5640; https://doi.org/10.3390/rs14225640 - 8 Nov 2022

Cited by 2 | Viewed by 1941

Abstract

Landslides are one of the most dangerous natural disasters, which have affected national economic development and social stability. This paper proposes a method to indirectly monitor the deformation characteristics of landslides by extracting the abnormal vegetation information, especially for the inaccessible high-mountain landslides [...] Read more.

Landslides are one of the most dangerous natural disasters, which have affected national economic development and social stability. This paper proposes a method to indirectly monitor the deformation characteristics of landslides by extracting the abnormal vegetation information, especially for the inaccessible high-mountain landslides in southwestern China. This paper extracts the vegetation anomaly information in the Jizong Shed-Tunnel landslide which is located on the main traffic road to Tibet by the optical remote sensing Gaofen-1 (GF-1) data, and analyzes the temporal and spatial characteristics of the vegetation anomaly information through a time series. Then, we use the small baseline subsets interferometry synthetic aperture radar (SBAS-InSAR) technology to process Sentinel-1 data to obtain the time-series surface deformation information. Finally, we analyze and verify the results of the two methods. The results show that there is obvious vegetation coverage (VC) decline, with a maximum increasing percentage of 8.77% for the low and medium VC, and obvious surface deformation around the landslide, with the highest settlement rate of between 0 mm/year and 30 mm/year. Through the time-series analysis, we find that the change trends of the two methods are basically the same. This paper shows that the method of using abnormal vegetation information to monitor the Jizong Shed-Tunnel landslide has a certain degree of reliability and practicability. It can provide a new idea and effective supplement for landslide monitoring. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The location and Google Earth image of Jizong Shed-Tunnel landslide. (<b>a</b>) Optical GF-1 fused true color image; (<b>b</b>) Site investigation image.</p> Full article ">Figure 2
<p>The VC calculation flow chart.</p> Full article ">Figure 3
<p>The vegetation spectrum curves of different fusion images. (<b>a</b>) MS image; (<b>b</b>) GS fusion image; (<b>c</b>) PCA fusion image; (<b>d</b>) NND fusion image; (<b>e</b>) Pansharpening fusion image; (<b>f</b>) HPF fusion image.</p> Full article ">Figure 4
<p>The bare ground spectrum curves of different fusion images. (<b>a</b>) MS image; (<b>b</b>) GS fusion image; (<b>c</b>) PCA fusion image; (<b>d</b>) NND fusion image; (<b>e</b>) Pansharpening fusion image; (<b>f</b>) HPF fusion image.</p> Full article ">Figure 5
<p>Pseudo-color VC maps in the upper of the Jizong Shed-Tunnel landslide. (<b>a</b>) Optical GF-1 fused true color image; (<b>b</b>) VC Map in 2013; (<b>c</b>) VC Map in 2014; (<b>d</b>) VC Map in 2015; (<b>e</b>) VC Map in 2016; (<b>f</b>) VC Map in 2017; (<b>g</b>) VC Map in 2018; (<b>h</b>) VC Map in 2019; (<b>i</b>) VC Map in 2020.</p> Full article ">Figure 6
<p>Time series change curve of the pixel number percentage with medium and low VC values.</p> Full article ">Figure 7
<p>The surface deformation extraction result map of the study area (LOS direction). (<b>a</b>) The surface subsidence rate map; (<b>b</b>) The surface cumulative deformation map.</p> Full article ">Figure 8
<p>Location maps of the abnormal vegetation area. (<b>a</b>) The location on optical GF-1 fused true color image; (<b>b</b>) The location on the surface subsidence rate map.</p> Full article ">Figure 9
<p>Time series diagrams of the abnormal vegetation information and the surface deformation. (<b>a</b>) Time series surface deformation of A area; (<b>b</b>) Time series of VC changes in A area; (<b>c</b>) Time series surface deformation of B area; (<b>d</b>) Time series of VC changes in B area.</p> Full article ">Figure 9 Cont.
<p>Time series diagrams of the abnormal vegetation information and the surface deformation. (<b>a</b>) Time series surface deformation of A area; (<b>b</b>) Time series of VC changes in A area; (<b>c</b>) Time series surface deformation of B area; (<b>d</b>) Time series of VC changes in B area.</p> Full article ">Figure 10
<p>The correlation and regression analysis of the abnormal vegetation information and the surface deformation.</p> Full article ">

18 pages, 7016 KiB  

Open AccessArticle

High-Resolution Hazard Assessment for Tropical Cyclone-Induced Wind and Precipitation: An Analytical Framework and Application

by Jiting Tang, Fuyu Hu, Yimeng Liu, Weiping Wang and Saini Yang

Sustainability 2022, 14(21), 13969; https://doi.org/10.3390/su142113969 - 27 Oct 2022

Cited by 6 | Viewed by 1944

Abstract

Intensified tropical cyclones (TCs) threaten the socioeconomic development of coastal cities. The coupling of strong wind and precipitation with the TC process usually amplifies the destructive effects of storms. Currently, an integrated analytical framework for TC hazard assessment at the city level that [...] Read more.

Intensified tropical cyclones (TCs) threaten the socioeconomic development of coastal cities. The coupling of strong wind and precipitation with the TC process usually amplifies the destructive effects of storms. Currently, an integrated analytical framework for TC hazard assessment at the city level that combines the joint statistical characteristics of multiple TC-induced hazards and local environmental features does not exist. In this study, we developed a novel hazard assessment framework with a high spatiotemporal resolution that includes a fine-tuned K-means algorithm for clustering TC tracks and a Copula model to depict the wind–precipitation joint probability distribution of different TC categories. High-resolution wind and precipitation data were used to conduct an empirical study in Shenzhen, a coastal megacity in Guangdong Province, China. The results show that the probabilities of TC-induced wind speed and precipitation exhibit significant spatial heterogeneity in Shenzhen, which can be explained by the characteristics of TC tracks and terrain environment factors. In general, the hazard intensity of TCs landing from the west side is higher than that from the east side, and the greatest TC intensity appears on the southeast coast of Shenzhen, implying that more disaster prevention efforts are needed. The proposed TC hazard assessment method provides a solid base for highly precise risk assessment at the city level. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>The analytical framework.</p> Full article ">Figure 2
<p>Case study area: Shenzhen. (<b>a</b>) The geographical location; (<b>b</b>) The distribution of multisource environmental data.</p> Full article ">Figure 3
<p>Clustering results of TCs affecting Shenzhen.</p> Full article ">Figure 4
<p>The fitting Cumulative Distribution Function of the hourly wind speed (<b>a</b>,<b>b</b>) and precipitation (<b>c</b>,<b>d</b>) in grid point <math display="inline"><semantics> <mi mathvariant="script">A</mi> </semantics></math> for TC Class 1 (<b>a</b>,<b>c</b>) and Class 2 (<b>b</b>,<b>d</b>).</p> Full article ">Figure 5
<p>Bivariate distribution of wind speed and precipitation in grid point <math display="inline"><semantics> <mi mathvariant="script">A</mi> </semantics></math> for TC Class 1 (<b>a</b>) and Class 2 (<b>b</b>). The blue line is the linear regression fit, and the shading along the lines is the confidence interval (95%).</p> Full article ">Figure 6
<p>The fitting results of the coupled hazards in <math display="inline"><semantics> <mrow> <mi mathvariant="script">A</mi> <mo>.</mo> </mrow> </semantics></math> (<b>a</b>) Joe Copula PDF for TC Class 1; (<b>b</b>) Joe Copula CDF for TC Class 1; (<b>c</b>) Clayton Copula PDF for TC Class 2; (<b>d</b>) Clayton Copula CDF for TC Class 2.</p> Full article ">Figure 7
<p>Spatial distribution of the probability of a single hazard for road traffic. (<b>a</b>) P(west_wind); (<b>b</b>) P(west_rain); (<b>c</b>) P(east_wind); (<b>d</b>) P(east_rain).</p> Full article ">Figure 8
<p>Spatial distribution of the probability of coupled hazards for road traffic. (<b>a</b>) P(west_and); (<b>b</b>) P(west_or); (<b>c</b>) P(east_and); (<b>d</b>) P(east_or).</p> Full article ">Figure 9
<p>The box plot of wind–rain correlation at different time scales.</p> Full article ">

23 pages, 7128 KiB  

Open AccessFeature PaperArticle

Long-Term Flooding Maps Forecasting System Using Series Machine Learning and Numerical Weather Prediction System

by Ming-Jui Chang, I-Hang Huang, Chih-Tsung Hsu, Shiang-Jen Wu, Jihn-Sung Lai and Gwo-Fong Lin

Water 2022, 14(20), 3346; https://doi.org/10.3390/w14203346 - 21 Oct 2022

Cited by 3 | Viewed by 2684

Abstract

Accurate real-time forecasts of inundation depth and area during typhoon flooding is crucial to disaster emergency response. The development of an inundation forecasting model has been recognized as essential to manage disaster risk. In the past, most researchers used multiple single-point forecasts to [...] Read more.

Accurate real-time forecasts of inundation depth and area during typhoon flooding is crucial to disaster emergency response. The development of an inundation forecasting model has been recognized as essential to manage disaster risk. In the past, most researchers used multiple single-point forecasts to obtain surface flooding depth forecasts with spatial interpolation. In this study, a forecasting model (QPF-RIF) integrating a hydrodynamic model (SOBEK), support vector machine–multi-step forecast (SVM-MSF), and a self-organizing map (SOM) were proposed. The task of this model was divided into four parts: hydrodynamic simulation, point forecasting, inundation database clustering, and spatial expansion. First, the SOBEK model was used in simulating inundation hydrodynamics to construct the flooding maps database. Second, the SVM-MSF yields water level (inundation volume) forecasted with a 1 to 72 h lead time. Third, the SOM clustered the previous flooding maps database into several groups representing different flooding characteristics. Finally, a spatial expansion module produced inundation maps based on forecasting information from forecasting flood volume and flood causative factors. To demonstrate the effectiveness of the proposed forecasting model, we presented an application to the Yilan River basin in Taiwan. Our forecasting results indicated that the proposed model yields accurate flood inundation maps (less than 1 cm error) for a 1 h lead time. For long-term forecasting (46 h to 72 h ahead), the model controlled the error of the forecast results within 7 cm. In the testing events, the model forecasted an average of 83% of the flooding area in the long term. This flood inundation forecasting model is expected to be useful in providing early flood warning information for disaster emergency response. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>Study area.</p> Full article ">Figure 2
<p>Flowchart of research.</p> Full article ">Figure 3
<p>The structures of (<b>a</b>) SVM and (<b>b</b>) SOM.</p> Full article ">Figure 4
<p>The order in which flooding occurred.</p> Full article ">Figure 5
<p>Nine sub-regions in Yilan County divided by flooding characteristics.</p> Full article ">Figure 6
<p>The mesh of ensemble rainfall and virtual rainfall stations settle in this study.</p> Full article ">Figure 7
<p>Comparison of the total flooding volume for 1 h lead-time forecasts of the SOBEK and SVM-MSF for S3 and S5 in the training and testing phases.</p> Full article ">Figure 8
<p>Comparison of the total flooding volume from 1 to 72 h lead-time forecasts of the SOBEK and SVM-MSF for S3 and S5 in the training and testing phases.</p> Full article ">Figure 9
<p>The results of SOM classification.</p> Full article ">Figure 10
<p>Comparing the flooding maps simulated by SOBEK and forecasted by QPF-RIF in the training and testing phases.</p> Full article ">Figure 11
<p>Comparing the flooding maps forecasted by QPF-RIF and flooding area reported by EMIC in 4 historical events.</p> Full article ">

13 pages, 4644 KiB  

Open AccessArticle

Buffer Capacity of Steel Shed with Two Layer Absorbing System against the Impact of Rockfall Based on Coupled SPH-FEM Method

by Chun Liu and Hongjun Liao

Sustainability 2022, 14(20), 13680; https://doi.org/10.3390/su142013680 - 21 Oct 2022

Cited by 2 | Viewed by 1981

Abstract

This study aimed to find the optimal thickness combination of the two-layered absorbing system combinated with an expanded polystyrene (EPS) cushion and a soil layer in a steel shed under dynamic loadings. The coupled Smooth Particle Hydrodynamic method (SPH) and Finite Element Method [...] Read more.

This study aimed to find the optimal thickness combination of the two-layered absorbing system combinated with an expanded polystyrene (EPS) cushion and a soil layer in a steel shed under dynamic loadings. The coupled Smooth Particle Hydrodynamic method (SPH) and Finite Element Method (FEM) were introduced to simulate the impact of the rockfall against the steel shed with a two-layer absorbing system. By comparing the numerical results with test data, the coupled numerical model was well validated. Through the verified numerical model, a series of numerical experiments were carried out to find the optimal combination for the two-layered absorbing system. The values of the EPS layer thickness as a percentage of the total thickness were set as 0% (P1), 20% (P2), 40% (P3), 60% (P4), 80% (P5), and 100% (P6). The results show that the coupled FEM–SPH method was an effective method to simulate rockfall impacting the steel rock shed; P4 (0.6 m thickness EPS cushion and 0.9 m thickness soil layer) was the most efficient combination, which can significantly reduce the structural displacement response by 43%. A two-layered absorbing system can effectively absorb about 90% of the total energy. The obtained results yield scientifically sound guidelines for further research on the design of steel sheds against rockfall. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>Flowchart of the coupled SPH–FEM method [<a href="#B27-sustainability-14-13680" class="html-bibr">27</a>].</p> Full article ">Figure 2
<p>The finite elements in contact with the SPH particles.</p> Full article ">Figure 3
<p>Test model of steel shed structure.</p> Full article ">Figure 4
<p>Bottom view of the steel shed and arrangement of the strain measuring points.</p> Full article ">Figure 5
<p>Numerical model of steel shed structure.</p> Full article ">Figure 6
<p>Constitutive model curve of material: (<b>a</b>) sand; (<b>b</b>) EPS; (<b>c</b>) steel.</p> Full article ">Figure 7
<p>Comparison of the strain time history: (<b>a</b>) strain gauge 1; (<b>b</b>) strain gauge 2.</p> Full article ">Figure 8
<p>The final penetration of the impactor into the sand cushion.</p> Full article ">Figure 9
<p>Dynamic impact process of a 0.75 t impactor (EPS thickness = 40% of the total thickness of the buffer layer) (stress unit: MPa): (<b>a</b>) t = 0.0 s; (<b>b</b>) t = 0.013 s; (<b>c</b>) t = 0.057 s; (<b>d</b>) t = 0.2 s.</p> Full article ">Figure 10
<p>Center displacement of main girder of steel shed: (<b>a</b>) center displacement time history; (<b>b</b>) maximum center displacement under each case.</p> Full article ">Figure 11
<p>Evolution of the energy of steel shed: (<b>a</b>) evolution of the energy of P3 case (EPS thickness = 40% of the total thickness of the buffer layer); (<b>b</b>) energy consumption of the buffer layer.</p> Full article ">

16 pages, 736 KiB  

Open AccessArticle

A Case Study of the “7-20” Extreme Rainfall and Flooding Event in Zhengzhou, Henan Province, China from the Perspective of Fragmentation

by Zhouying Chen, Feng Kong and Meng Zhang

Water 2022, 14(19), 2970; https://doi.org/10.3390/w14192970 - 22 Sep 2022

Cited by 14 | Viewed by 3694

Abstract

Disaster crisis management is the last defensive line in the face of extreme rainstorm disasters. However, fragmentation undermines the effectiveness of disaster crisis management, and the “7-20” extreme rainfall flooding disaster in Zhengzhou, Henan province, China in 2021 revealed a series of fragmentation [...] Read more.

Disaster crisis management is the last defensive line in the face of extreme rainstorm disasters. However, fragmentation undermines the effectiveness of disaster crisis management, and the “7-20” extreme rainfall flooding disaster in Zhengzhou, Henan province, China in 2021 revealed a series of fragmentation problems. The effectiveness of China’s emergency storm flooding management must be seriously considered. We used the “7-20” extreme rainfall event in Zhengzhou, Henan province in China as a case study to perform an inductive, qualitative investigation to understand what fragmentation is and how fragmentation reduces efficacy. Most of the data used for this research were gathered from Chinese official records and online news articles. This study first highlights pertinent studies that have been performed and then presents a comprehensive theoretical framework of fragmentation in catastrophe crisis management, which consists of five aspects: fragmented emergency legislation, emergency organization, information, perception, and services. Second, we have deduced which human responses in the “7-20” event represent the fragmentation issues, and we have examined the detrimental effects of fragmentation in flood crisis management. Finally, suggestions are made for China to increase the effectiveness of disaster crisis management, including encouraging regulatory convergence, matching emergency responsibility and authority, establishing an information-sharing platform, bolstering emergency education and raising risk perception, and changing the dualistic system in disaster crisis management. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>The logical framework of this paper.</p> Full article ">

24 pages, 55551 KiB  

Open AccessArticle

Numerical Analysis of an Explicit Smoothed Particle Finite Element Method on Shallow Vegetated Slope Stability with Different Root Architectures

by Xichun Jia, Wei Zhang, Xinghan Wang, Yuhao Jin and Peitong Cong

Sustainability 2022, 14(18), 11272; https://doi.org/10.3390/su141811272 - 8 Sep 2022

Cited by 8 | Viewed by 2311

Abstract

Planting vegetation is an environmentally friendly method for reducing landslides. Current vegetated slope analysis fails to consider the influence of different root architectures, and the accuracy and effectiveness of the numerical simulations need to be improved. In this study, an explicit smoothed particle [...] Read more.

Planting vegetation is an environmentally friendly method for reducing landslides. Current vegetated slope analysis fails to consider the influence of different root architectures, and the accuracy and effectiveness of the numerical simulations need to be improved. In this study, an explicit smoothed particle finite element method (eSPFEM) was used to evaluate slope stability under the influence of vegetation roots. The Mohr–Coulomb constitutive model was extended by incorporating apparent root cohesion into the shear strength of the soil. The slope factors of safety (FOS) of four root architectures (uniform, triangular, parabolic, and exponential) for various planting distances, root depths, slope angles, and planting locations were calculated using the shear strength reduction technique with a kinetic energy-based criterion. The results indicated that the higher the planting density, the stronger the reinforcement effect of the roots on the slope. With increasing root depth, the FOS value first decreased and then increased. The FOS value decreased with an increase in slope angle. Planting on the entire ground surface had the best improvement effect on the slope stability, followed by planting vegetation with a uniform root architecture in the upper slope region or planting vegetation with triangular or exponential root architecture on the slope’s toe. Our findings are expected to deepen our understanding of the contributions of different root architectures to vegetated slope protection and guide the selection of vegetation species and planting locations. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>Construction of smoothing cell associated with particle <span class="html-italic">k</span>.</p> Full article ">Figure 2
<p>Different real root architectures: (<b>a</b>) Uniform distribution [<a href="#B68-sustainability-14-11272" class="html-bibr">68</a>]; (<b>b</b>) Triangular distribution [<a href="#B68-sustainability-14-11272" class="html-bibr">68</a>]; (<b>c</b>) Parabolic distribution [<a href="#B69-sustainability-14-11272" class="html-bibr">69</a>]; (<b>d</b>) Exponential distribution [<a href="#B10-sustainability-14-11272" class="html-bibr">10</a>].</p> Full article ">Figure 3
<p>Normalised function curves of the four different root architectures.</p> Full article ">Figure 4
<p>Boundaries of the four different root zones: (<b>a</b>) Uniform distribution; (<b>b</b>) Triangular distribution; (<b>c</b>) Parabolic distribution; (<b>d</b>) Exponential distribution.</p> Full article ">Figure 5
<p>Geometric parameters of the vegetated slope.</p> Full article ">Figure 6
<p>Geometry and boundary conditions of the slope-stability problem.</p> Full article ">Figure 7
<p>Root zones and overlapping root zones of different root architectures in vegetated slopes: (<b>a</b>) Uniform distribution; (<b>b</b>) Triangular distribution; (<b>c</b>) Parabolic distribution; (<b>d</b>) Exponential distribution.</p> Full article ">Figure 8
<p>Evolution of kinetic energy with time for various SRFs: (<b>a</b>) Bare slope; (<b>b</b>) Vegetated slope.</p> Full article ">Figure 9
<p>Variation of maximum horizontal displacement at the slope’s toe with time for various SRFs: (<b>a</b>) Bare slope; (<b>b</b>) Vegetated slope.</p> Full article ">Figure 10
<p>Equivalent plastic strain and final configurations for various SRFs: (<b>a</b>) Bare slope; (<b>b</b>) Vegetated slope.</p> Full article ">Figure 11
<p>With the increase in SRF, the maximum horizontal displacement at the slope’s toe for different root architectures after slope failure: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>a</mi> <mo> </mo> <mo>=</mo> <mo> </mo> <mn>5</mn> <mo> </mo> <mo>m</mo> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>a</mi> <mo> </mo> <mo>=</mo> <mo> </mo> <mn>2.5</mn> <mo> </mo> <mo>m</mo> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>a</mi> <mo> </mo> <mo>=</mo> <mo> </mo> <mn>1.25</mn> <mo> </mo> <mo>m</mo> </mrow> </semantics></math>.</p> Full article ">Figure 12
<p>With the increase in SRF, the maximum horizontal displacement at the slope’s toe for various planting distances after slope failure: (<b>a</b>) Uniform distribution; (<b>b</b>) Triangular distribution; (<b>c</b>) Parabolic distribution; (<b>d</b>) Exponential distribution.</p> Full article ">Figure 13
<p>With the increase in SRF, the maximum horizontal displacement at the slope’s toe for different root architectures after slope failure: (<b>a</b>) <math display="inline"><semantics> <mrow> <msup> <mi>z</mi> <mi>r</mi> </msup> <mo> </mo> <mo>=</mo> <mo> </mo> <mn>0.5</mn> <mo> </mo> <mo>m</mo> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msup> <mi>z</mi> <mi>r</mi> </msup> <mo> </mo> <mo>=</mo> <mo> </mo> <mn>0.75</mn> <mo> </mo> <mo>m</mo> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <msup> <mi>z</mi> <mi>r</mi> </msup> <mo> </mo> <mo>=</mo> <mo> </mo> <mn>1</mn> <mo> </mo> <mo>m</mo> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <msup> <mi>z</mi> <mi>r</mi> </msup> <mo> </mo> <mo>=</mo> <mo> </mo> <mn>1.25</mn> <mo> </mo> <mo>m</mo> </mrow> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <mrow> <msup> <mi>z</mi> <mi>r</mi> </msup> <mo> </mo> <mo>=</mo> <mo> </mo> <mn>1.5</mn> <mo> </mo> <mo>m</mo> </mrow> </semantics></math>.</p> Full article ">Figure 14
<p>With the increase in SRF, the maximum horizontal displacement at the slope’s toe for various root depths after slope failure: (<b>a</b>) Uniform distribution; (<b>b</b>) Triangular distribution; (<b>c</b>) Parabolic distribution; (<b>d</b>) Exponential distribution.</p> Full article ">Figure 15
<p>With the increase in SRF, the maximum horizontal displacement at the slope’s toe for different root architectures after slope failure: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>β</mi> <mo> </mo> <mo>=</mo> <mo> </mo> <mn>40</mn> <mo>°</mo> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>β</mi> <mo> </mo> <mo>=</mo> <mo> </mo> <mn>45</mn> <mo>°</mo> </mrow> </semantics></math> (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>β</mi> <mo> </mo> <mo>=</mo> <mo> </mo> <mn>50</mn> <mo>°</mo> </mrow> </semantics></math>.</p> Full article ">Figure 16
<p>With the increase in SRF, the maximum horizontal displacement at the slope’s toe for various slope angles after slope failure: (<b>a</b>) Uniform distribution; (<b>b</b>) Triangular distribution; (<b>c</b>) Parabolic distribution; (<b>d</b>) Exponential distribution.</p> Full article ">Figure 17
<p>Vegetated slopes for different planting locations and the final equivalent plastic strain for FOS = 1.6: (<b>a</b>) slope’s surface; (<b>b</b>) slope’s toe; (<b>c</b>) slope’s surface and toe; (<b>d</b>) upper slope region; (<b>e</b>) lower slope region; (<b>f</b>) upper and lower slope regions; (<b>g</b>) entire ground surface.</p> Full article ">Figure 18
<p>With the increase in SRF, the maximum horizontal displacement at the slope’s toe for different root architectures after slope failure: (<b>a</b>) slope’s surface; (<b>b</b>) slope’s toe; (<b>c</b>) slope’s surface and toe; (<b>d</b>) upper slope region; (<b>e</b>) lower slope region; (<b>f</b>) upper and lower slope regions; (<b>g</b>) entire ground surface.</p> Full article ">Figure 19
<p>With the increase in SRF, the maximum horizontal displacement at the slope’s toe of the slopes with different planting locations after slope failure: (<b>a</b>) Uniform distribution; (<b>b</b>) Triangular distribution; (<b>c</b>) Parabolic distribution; (<b>d</b>) Exponential distribution.</p> Full article ">

24 pages, 5318 KiB  

Open AccessFeature PaperArticle

Comparing Root Cohesion Estimates from Three Models at a Shallow Landslide in the Oregon Coast Range

by Collin Cronkite-Ratcliff, Kevin M. Schmidt and Charlotte Wirion

GeoHazards 2022, 3(3), 428-451; https://doi.org/10.3390/geohazards3030022 - 1 Sep 2022

Cited by 2 | Viewed by 2486

Abstract

Although accurate root cohesion model estimates are essential to quantify the effect of vegetation roots on shallow slope stability, few means exist to independently validate such model outputs. One validation approach for cohesion estimates is back-calculation of apparent root cohesion at a landslide [...] Read more.

Although accurate root cohesion model estimates are essential to quantify the effect of vegetation roots on shallow slope stability, few means exist to independently validate such model outputs. One validation approach for cohesion estimates is back-calculation of apparent root cohesion at a landslide site with well-documented failure conditions. The catchment named CB1, near Coos Bay, Oregon, USA, which experienced a shallow landslide in 1996, is a prime locality for cohesion model validation, as an abundance of data and observations from the site generated broad insights related to hillslope hydrology and slope stability. However, previously published root cohesion values at CB1 used the Wu and Waldron model (WWM), which assumes simultaneous root failure and therefore likely overestimates root cohesion. Reassessing published cohesion estimates from this site is warranted, as more recently developed models include the fiber bundle model (FBM), which simulates progressive failure with load redistribution, and the root bundle model-Weibull (RBMw), which accounts for differential strain loading. We applied the WWM, FBM, and RBMw at CB1 using post-failure root data from five vegetation species. At CB1, the FBM and RBMw predict values that are less than 30% of the WWM-estimated values. All three models show that root cohesion has substantial spatial heterogeneity. Most parts of the landslide scarp have little root cohesion, with areas of high cohesion concentrated near plant roots. These findings underscore the importance of using physically realistic models and considering lateral and vertical spatial heterogeneity of root cohesion in shallow landslide initiation and provide a necessary step towards independently assessing root cohesion model validity. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>Post-landslide oblique aerial photo, taken roughly towards the NNW direction, reveals both upslope shallow landslide extent and downslope debris flow scour. Vehicles on the road near the bottom of the image indicate the relative size of the landslide area. Photograph by K.M. Schmidt, U.S. Geological Survey.</p> Full article ">Figure 2
<p>View of the CB1 catchment (<bold>a</bold>) before and (<bold>b</bold>) after the landslide. Both images are taken from the same location at the top of the scarp. The view is to the north, looking directly down the central axis of the hollow. Panel (<bold>a</bold>) shows some of the instrumentation which was installed at the site before the landslide. The instrumentation included tipping bucket rain gages, tensiometers, piezometers, catwalks to minimize ground surface disturbance, and a downslope weir. Photograph by K.M. Schmidt, U.S. Geological Survey.</p> Full article ">Figure 3
<p>Field-measured relations of species type with tensile load at failure (solid circles) for a given thread diameter with best fit second-order polynomial model from Equation (A1) (see <xref ref-type="app" rid="app1-geohazards-03-00022">Appendix A</xref>) (except for Douglas fir, where the equation of Burroughs and Thomas [<xref ref-type="bibr" rid="B37-geohazards-03-00022">37</xref>] was used). Equations are shown for foxglove (<bold>a</bold>), Douglas fir (<bold>b</bold>), elderberry (<bold>c</bold>), blackberry (<bold>d</bold>), and thimbleberry (<bold>e</bold>). Panel (<bold>f</bold>) shows the estimated curves for all species color-coded by the individual species. In all plots, the diameter range of the curves represents the range over which the tensile load was estimated. All data are available in Schmidt and Cronkite-Ratcliff [<xref ref-type="bibr" rid="B35-geohazards-03-00022">35</xref>].</p> Full article ">Figure 4
<p>Panel (<bold>a</bold>) shows the plan view of the landslide scarp perimeter constructed with a tape and compass survey, showing the locations of numbered segments. Only segments where roots were measured are numbered. Segments where no roots were measured (because of obstructions including collapsed soil masses and broken site infrastructure) are shown as dashed lines. Segments of scarp perimeter are constrained to the boundary of the initial landslide and do not include the downslope debris-flow. Data on the location of the scarp segments are available in Schmidt and Cronkite-Ratcliff [<xref ref-type="bibr" rid="B35-geohazards-03-00022">35</xref>]. Panel (<bold>b</bold>) shows the location and diameter of live roots in the scarp, showing the depth below ground surface and the position along the landslide scarp perimeter for each root. The vertical dashed lines demarcate the lateral boundaries of each segment along the scarp perimeter, with shaded areas showing the approximate areal extent of each scarp segment. A small amount of “jitter” (Gaussian noise with variances of 0.1 m and 0.01 m in the horizontal and vertical dimensions, respectively) has been applied to the location and depth to visually differentiate roots located at the same perimeter and depth location. Data on the location and diameter of roots are available in Schmidt and Cronkite-Ratcliff [<xref ref-type="bibr" rid="B35-geohazards-03-00022">35</xref>].</p> Full article ">Figure 5
<p>Histograms of broken live root diameter for each of the vegetation species identified along the landslide scarp. Histograms of broken live root diameter are shown for foxglove (<bold>a</bold>), Douglas fir (<bold>b</bold>), elderberry (<bold>c</bold>), blackberry (<bold>d</bold>), and thimbleberry (<bold>e</bold>). Panel (<bold>f</bold>) shows the histogram of broken live root diameter for roots of all species together.</p> Full article ">Figure 6
<p>Histograms of depth below ground surface for the roots of each of the vegetation species identified along the landslide scarp. Histograms of depth below ground surface are shown for foxglove (<bold>a</bold>), Douglas fir (<bold>b</bold>), elderberry (<bold>c</bold>), blackberry (<bold>d</bold>), and thimbleberry (<bold>e</bold>). Panel (<bold>f</bold>) shows the histogram of depth below ground surface for roots of all species together.</p> Full article ">Figure 7
<p>Estimated normalized displacement data (circles) and curve from the fitted Weibull survival function (see <xref ref-type="app" rid="app2-geohazards-03-00022">Appendix B</xref>). Estimated normalized displacement and fitted Weibull survival curves are shown for foxglove (<bold>a</bold>), Douglas fir (<bold>b</bold>), elderberry (<bold>c</bold>), blackberry (<bold>d</bold>), and thimbleberry (<bold>e</bold>). Panel (<bold>f</bold>) shows the estimated normalized displacement and the fitted Weibull survival curves for roots of all species plotted together.</p> Full article ">Figure 8
<p>Results from the FBM, showing the number of surviving roots after application of different loads. Vertical dashed line indicates the maximum activated force.</p> Full article ">Figure 9
<p>Force-displacement curve resulting from the RBMw model. Horizontal dashed line shows the maximum activated force.</p> Full article ">Figure 10
<p>Root strength variation by depth calculated over the perimeter of the landslide scarp. Panel (<bold>a</bold>) shows maximum activated force, and (<bold>b</bold>) shows cohesion, superimposed over the scarp-averaged cohesion values for comparison depicted as vertical dashed lines. Panel (<bold>c</bold>) shows the cumulative strength contribution with increasing depth for each of the three models. All quantities are calculated over all roots within 10-cm depth increments along the scarp plane; negligible roots intersecting the basal surface are not included.</p> Full article ">Figure 11
<p>Maximum activated force (<bold>a</bold>) and root cohesion (<bold>b</bold>) calculated independently for each scarp segment, showing lateral variation along the perimeter of the landslide scarp. Panel (<bold>a</bold>) shows maximum activated force, and (<bold>b</bold>) shows root cohesion, with the scarp-averaged cohesion superimposed as horizontal dashed lines for comparison. Both quantities are calculated within each segment along the length of the scarp perimeter; negligible roots intersecting the basal surface are not included. S1 through S16 denote the scarp segments depicted in <xref ref-type="fig" rid="geohazards-03-00022-f004">Figure 4</xref>a.</p> Full article ">Figure 12
<p>Root cohesion (<bold>a</bold>–<bold>c</bold>) and percent strength contribution (<bold>d</bold>–<bold>f</bold>) calculated by segment and 10-cm depth bin along the landslide scarp for each of the three models. The distribution of cohesion across different segment and depth bins (<bold>g</bold>–<bold>i</bold>) shows that cohesion has a high degree of spatial heterogeneity across the scarp.</p> Full article ">Figure 13
<p>Contribution of root cohesion by species calculated for each of the three models. Because of the nonlinearity of the FBM and RBMw models, the contributions for FBM and RBMw will not necessarily sum to 100%.</p> Full article ">

23 pages, 1214 KiB  

Open AccessArticle

Breaking the Negative Feedback Loop of Disaster, Conflict, and Fragility: Analyzing Development Aid by Japan and South Korea

by Suyeon Lee and Huck-ju Kwon

Sustainability 2022, 14(16), 10003; https://doi.org/10.3390/su141610003 - 12 Aug 2022

Cited by 3 | Viewed by 2143

Abstract

Disaster risk reduction (DRR) has become an important element of donor policy, because numerous governments have expressed their commitment to helping countries vulnerable to natural hazards by mainstreaming DRR into their development programs. Meanwhile, countries that are considered fragile, as well as conflict-affected [...] Read more.

Disaster risk reduction (DRR) has become an important element of donor policy, because numerous governments have expressed their commitment to helping countries vulnerable to natural hazards by mainstreaming DRR into their development programs. Meanwhile, countries that are considered fragile, as well as conflict-affected states, have faced a high risk of disasters brought on by natural hazards. However, there has been little research that addresses the complex relationship between disasters, conflict, and fragility in the context of development cooperation. Against this backdrop, this study analyzed the determinants of DRR aid allocation from Japan and South Korea—two East Asian countries that have shown a strong commitment to disaster resilience and peacebuilding—to investigate whether they are responsive to countries experiencing the combined risks of disasters and conflicts and/or fragility. Despite the vulnerable countries being in the most need, the study found that both Japan and Korea’s aid allocation has not been influenced much by the concurrence of disasters and conflict. Rather, it has been more driven by the level of a country’s climate vulnerability than the level of a country’s fragility. This suggests that developing countries facing multiple risks and challenges are at a major disadvantage in terms of the responsiveness of donors toward their needs and vulnerability. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>Frequency of disasters in low-income and lower-middle income countries in 1990–2018 (fragile states marked with “F” at the bottom). Note: The author used disaster occurrence data from the emergency events database (EM-DAT) and the World Banks’ 2020 harmonized list to specify fragile states.</p> Full article ">Figure 2
<p>Top 10 DRR recipient countries, 2006–2019 (unit: in USD millions). Note: the author used data from OECD Creditor Reporting System (CRS). Here, [F] indicates fragile states.</p> Full article ">Figure 3
<p>Changes in Japan and Korea’s DRR spending as % of total ODA in 2006–2019. Note: author used data from OECD Creditor Reporting System (CRS).</p> Full article ">

18 pages, 5687 KiB  

Open AccessArticle

The Effect of Land Use and Land Cover Changes on Flood Occurrence in Teunom Watershed, Aceh Jaya

by Sugianto Sugianto, Anwar Deli, Edy Miswar, Muhammad Rusdi and Muhammad Irham

Land 2022, 11(8), 1271; https://doi.org/10.3390/land11081271 - 8 Aug 2022

Cited by 53 | Viewed by 9783

Abstract

The change in land use and land cover in upstream watersheds will change the features of drainage systems such that they will impact surface overflow and affect the infiltration capacity of a land surface, which is one of the factors that contributes to [...] Read more.

The change in land use and land cover in upstream watersheds will change the features of drainage systems such that they will impact surface overflow and affect the infiltration capacity of a land surface, which is one of the factors that contributes to flooding. The key objective of this study is to identify vulnerable areas of flooding and to assess the causes of flooding using ground-based measurement, remote sensing data, and GIS-based flood risk mapping approaches for the flood hazard mapping of the Teunom watershed. The purposes of this investigation were to: (1) examine the level and characteristics of land use and land cover changes that occurred in the area between 2009 and 2019; (2) determine the impact of land use and land cover changes on the water overflow and infiltration capacity; and (3) produce flood risk maps for the Teunom sub-district. Landsat imagery of 2009, 2013, and 2019; slope maps; and field measurement soil characteristics data were utilized for this study. The results show a significant increase in the use of residential land, open land, rice fields, and wetlands (water bodies) and different infiltration rates that contribute to the variation of flood zone hazards. The Teunom watershed has a high and very high risk of ~11.98% of the total area, a moderate risk of 56.24%, and a low and very low risk of ~31.79%. The Teunom watershed generally has a high flood risk, with a total of ~68% of the area (moderate to very high risk). There was a substantial reduction in forest land, agricultural land, and shrubs from 2009 to 2019. Therefore, the segmentation of flood-risk zones is essential for preparation in the region. The study offers basic information about flood hazard areas for central governments, local governments, NGOs, and communities to intervene in preparedness, responses, and flood mitigation and recovery processes, respectively. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1
<p>Map of the study area in the Krueng Teunom watershed.</p> Full article ">Figure 2
<p>Flow model and research design.</p> Full article ">Figure 3
<p>DEM of the study area and surroundings of Teunom watershed.</p> Full article ">Figure 4
<p>Geomorphological map of the Teunom watershed (combination of field survey results and geospatial data).</p> Full article ">Figure 5
<p>Spatial map of Krueng Teunom watershed land use from 2009 to 2019.</p> Full article ">Figure 6
<p>Results of land use analysis for the Krueng Teunom watershed in 2009, 2013, and 2019.</p> Full article ">Figure 7
<p>Land cover characteristics in relation to flood risk occurrence: (<b>A</b>) land use–land cover condition and (<b>B</b>) flood risk map.</p> Full article ">Figure 8
<p>Topographic map (elevation and slope) of Krueng Teunom and its relation to flood disasters: (<b>A</b>) elevation and slope map and (<b>B</b>) flood risk map.</p> Full article ">Figure 9
<p>Soil type map (<b>A</b>) and soil flood risk map (<b>B</b>) of Krueng Teunom watershed.</p> Full article ">Figure 10
<p>Overlaid flood risk map results from the overall analysis.</p> Full article ">

17 pages, 3822 KiB  

Open AccessArticle

Study on the Deformation of Filling Bodies in a Loess Mountainous Area Based on InSAR and Monitoring Equipment

by Yuming Wu and Hengxing Lan

Land 2022, 11(8), 1263; https://doi.org/10.3390/land11081263 - 7 Aug 2022

Cited by 5 | Viewed by 1748

Abstract

Several land-creation projects, such as the Lanzhou New Area (LNA), have been undertaken in China as part of the Belt and Road Initiative to bring more living space to the local people in loess areas. However, undisturbed loess and remolded loess have different [...] Read more.

Several land-creation projects, such as the Lanzhou New Area (LNA), have been undertaken in China as part of the Belt and Road Initiative to bring more living space to the local people in loess areas. However, undisturbed loess and remolded loess have different mechanical characteristics, which may influence the stability of the filling process. Therefore, we monitored the deformation through InSAR and field monitoring to investigate the deformation characteristics and their causes. We obtained the horizontal and vertical displacements, internal deformation, water content, and pressure, according to the air–space–ground integrated monitoring technique. The results show that stress and deformation increase rapidly during construction. Deformation in different places is different during the winter: (1) for vertical displacement, uplift is present in the cut area, settlement is present in the fill area, and heterogeneity is evident in other areas; (2) for horizontal displacement, the expansion state is present in the filling area and the compression state is present at the boundary. Laboratory tests show that the difference in soil compression properties is one of the reasons for these deformation characteristics. Additionally, the difference in volumetric water content and permeability coefficient may trigger different mechanical properties on both sides of the boundary. All the evidence indicates that the boundary region is critical for filling projects. It is also necessary to install monitoring equipment to observe deformation. When abnormal deformations appear, we should take measures to control them. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>Study area and photos of different stages ((<b>A</b>) is the location of the research area, (<b>B</b>) is the distribution of the filling areas in Lanzhou, (<b>C</b>) is a photo of our research area taken on 12 September 2018, (<b>D</b>) is a photo of our research area taken on 16 October 2018, and (<b>E</b>) is a photo of our research area taken on 6 June 2019. LZ: Lanzhou city, GL: Gaolan county, BY: Baiyin city).</p> Full article ">Figure 2
<p>DEM differences before and after filling in the study area (positive value represent filling height and negative values represent excavation depth).</p> Full article ">Figure 3
<p>Workflow chart of InSAR.</p> Full article ">Figure 4
<p>The selection of the PS correction point in the reference PS points.</p> Full article ">Figure 5
<p>Schematic diagram of buried equipment layout.</p> Full article ">Figure 6
<p>The vertical deformation based on InSAR data ((<b>A</b>) is the spatial distribution of vertical deformation, (<b>B</b>) is the deformation in region A, and (<b>C</b>) is the deformation in region B).</p> Full article ">Figure 7
<p>East–west displacement based on InSAR data ((<b>A</b>) is the spatial distribution of displacement in the east–west direction, (<b>B</b>) is the deformation in region A, and (<b>C</b>) is the deformation in region B).</p> Full article ">Figure 8
<p>Vertical stress at 21 m and displacement from 16 m to 21 m in the filling areas in the first stage ((<b>A</b>) is the pressure over time, and (<b>B</b>) is the displacement over time. The solid line is the measured data, and the dashed line is the estimation of missing data due to a lack of data).</p> Full article ">Figure 9
<p>Vertical stress at 21 m and displacement from 16 m to 21 m in the filling areas during winter break ((<b>A</b>) is the pressure over time during winter, and (<b>B</b>) is the displacement over time during winter. The solid line is the measured data, and the dashed line is the estimation of missing data due to a lack of data).</p> Full article ">Figure 10
<p>Horizontal displacement ((<b>A</b>) is a schematic diagram of the deformation of the landfill area, (<b>B</b>) is a schematic diagram of the deformation at the boundary, and (<b>C</b>) is the deformation over time).</p> Full article ">Figure 11
<p>Horizontal stresses (the blue line is at 19.4 m, and the orange line is at 16 m).</p> Full article ">Figure 12
<p>Compression curve shown by <span class="html-italic">e</span>-log<span class="html-italic">p</span> plots (the orange line is undisturbed loess, and the blue line is remolded loess).</p> Full article ">Figure 13
<p>VWC on both sides of the boundary (the blue line is VWC in the original mountain, and the red line is VWC in the filling area).</p> Full article ">Figure 14
<p>Changes in the research area ((<b>A</b>) is on 12 October 2018; (<b>B</b>) is on 4 July 2019; (<b>C</b>) is on 4 May 2022).</p> Full article ">

22 pages, 5349 KiB  

Open AccessArticle

An Efficient User-Friendly Integration Tool for Landslide Susceptibility Mapping Based on Support Vector Machines: SVM-LSM Toolbox

by Wubiao Huang, Mingtao Ding, Zhenhong Li, Jianqi Zhuang, Jing Yang, Xinlong Li, Ling’en Meng, Hongyu Zhang and Yue Dong

Remote Sens. 2022, 14(14), 3408; https://doi.org/10.3390/rs14143408 - 15 Jul 2022

Cited by 25 | Viewed by 3563

Abstract

Landslide susceptibility mapping (LSM) is an important element of landslide risk assessment, but the process often needs to span multiple platforms and the operation process is complex. This paper develops an efficient user-friendly toolbox including the whole process of LSM, known as the [...] Read more.

Landslide susceptibility mapping (LSM) is an important element of landslide risk assessment, but the process often needs to span multiple platforms and the operation process is complex. This paper develops an efficient user-friendly toolbox including the whole process of LSM, known as the SVM-LSM toolbox. The toolbox realizes landslide susceptibility mapping based on a support vector machine (SVM), which can be integrated into the ArcGIS or ArcGIS Pro platform. The toolbox includes three sub-toolboxes, namely: (1) influence factor production, (2) factor selection and dataset production, and (3) model training and prediction. Influence factor production provides automatic calculation of DEM-related topographic factors, converts line vector data to continuous raster factors, and performs rainfall data processing. Factor selection uses the Pearson correlation coefficient (PCC) to calculate the correlations between factors, and the information gain ratio (IGR) to calculate the contributions of different factors to landslide occurrence. Dataset sample production includes the automatic generation of non-landslide data, data sample production and dataset split. The accuracy, precision, recall, F1 value, receiver operating characteristic (ROC) and area under curve (AUC) are used to evaluate the prediction ability of the model. In addition, two methods—single processing and multiprocessing—are used to generate LSM. The prediction efficiency of multiprocessing is much higher than that of the single process. In order to verify the performance and accuracy of the toolbox, Wuqi County, Yan’an City, Shaanxi Province was selected as the test area to generate LSM. The results show that the AUC value of the model is 0.8107. At the same time, the multiprocessing prediction tool improves the efficiency of the susceptibility prediction process by about 60%. The experimental results confirm the accuracy and practicability of the proposed toolbox in LSM. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>Flowchart of SVM-LSM toolbox.</p> Full article ">Figure 2
<p>Overall module of SVM-LSM toolbox.</p> Full article ">Figure 3
<p>Influencing factor production toolbox interface: (<b>a</b>) topographic factors calculation; (<b>b</b>) convert line vector data to continuous raster factor; (<b>c</b>) rainfall data processing; and (<b>d</b>) batch clipping of each factor layer.</p> Full article ">Figure 4
<p>Dataset production and factor selected toolbox interface: (<b>a</b>) non-landslide data generation; (<b>b</b>) data sample production; (<b>c</b>) dataset split; and (<b>d</b>) PCC and IGR calculation.</p> Full article ">Figure 5
<p>Model training and prediction toolbox interface: (<b>a</b>) image generation to be predicted; (<b>b</b>) model training and performance evaluation of SVM; (<b>c</b>) landslide susceptibility map prediction (single process); and (<b>d</b>) landslide susceptibility map prediction (multiprocessing).</p> Full article ">Figure 6
<p>(<b>a</b>) Location of Shaanxi Province, (<b>b</b>) location of Wuqi County, Yan’an City, (<b>c</b>) landslide inventory mapping in Wuqi County.</p> Full article ">Figure 7
<p>Landslide influencing factors in Wuqi County. (<b>a</b>) Altitude, (<b>b</b>) slope, (<b>c</b>) aspect, (<b>d</b>) curvature, (<b>e</b>) plane curvature, (<b>f</b>) profile curvature, (<b>g</b>) relief amplitude, (<b>h</b>) surface roughness, (<b>i</b>) topographic wetness index (TWI), (<b>j</b>) normalized difference vegetation index (NDVI), (<b>k</b>) rainfall, (<b>l</b>) lithology, (<b>m</b>) distance to roads, (<b>n</b>) distance to rivers.</p> Full article ">Figure 8
<p>Pearson correlation coefficient matrix for the Wuqi County case study. Note that “slp” represents slope, “asp” represents aspect, “cur” represents curvature, “plancur” represents plane curvature, “profilecur” represents profile curvature, “rivers” represents distance to rivers, “roads” represents distance to roads, “lithology” represents lithology, “SroughnessC” represents surface roughness, “relief” represents relief amplitude, and “rainfall” represents rainfall.</p> Full article ">Figure 9
<p>Information gain ratio for the Wuqi County case study. Note that “slp” represents slope, “asp” represents aspect, “cur” represents curvature, “plancur” represents plane curvature, “profilecur” represents profile curvature, “rivers” represents distance to rivers, “roads” represents distance to roads, “lithology” represents lithology, “SroughnessC” represents surface roughness, “relief” represents relief amplitude, and “rainfall” represents rainfall.</p> Full article ">Figure 10
<p>AUC values and accuracy differences under different parameter values.</p> Full article ">Figure 11
<p>The ROC curve of the optimal model.</p> Full article ">Figure 12
<p>Classification map of landslide susceptibility in Wuqi County.</p> Full article ">

24 pages, 6921 KiB  

Open AccessArticle

Multi-Hazard Emergency Response for Geological Hazards Amid the Evolving COVID-19 Pandemic: Good Practices and Lessons Learned from Earthquake Disaster Management in Greece

by Spyridon Mavroulis, Maria Mavrouli, Asimina Kourou, Thekla Thoma and Efthymis Lekkas

Sustainability 2022, 14(14), 8486; https://doi.org/10.3390/su14148486 - 11 Jul 2022

Cited by 9 | Viewed by 2973

Abstract

Since the beginning of 2020, the COVID-19 pandemic has caused unprecedented global disruption with considerable impact on human activities. However, natural hazards and related disasters do not wait for SARS-CoV-2 to vanish, resulting in the emergence of many conflicting issues between earthquake emergency [...] Read more.

Since the beginning of 2020, the COVID-19 pandemic has caused unprecedented global disruption with considerable impact on human activities. However, natural hazards and related disasters do not wait for SARS-CoV-2 to vanish, resulting in the emergence of many conflicting issues between earthquake emergency response actions and pandemic mitigation measures. In this study, these conflicting issues are highlighted through the cases of four earthquakes that struck Greece at different phases of the pandemic. The earthquake effects on the local population and on the natural environment and building stock form ideal conditions for local COVID-19 outbreaks in earthquake-affected communities. However, the implementation of response actions and mitigation measures in light of a multi-hazard approach to disaster risk reduction and disaster risk management has led not only to the maintenance of pre-existing low viral load in the earthquake-affected areas, but in some cases even to their reduction. This fact suggests that the applied measures are good practice and an important lesson for improving disaster management in the future. Taking into account the aforementioned, a series of actions are proposed for the effective management of the impact of a geological hazard in the midst of an evolving biological hazard with epidemiological characteristics similar to the COVID-19 pandemic. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>The epicenters of the studied earthquakes in Epirus, Samos, Thessaly, and Crete. They were all generated in different waves of the COVID-19 pandemic in Greece.</p> Full article ">Figure 2
<p>The laboratory-confirmed daily-reported COVID-19 cases, intubated patients and fatalities in Greece from the pandemic onset in February 2020 until late April 2022 based on the daily reports of the National Public Health Organization (NPHO) of Greece. The studied earthquakes of Epirus, Samos, Thessaly, and Crete are also presented.</p> Full article ">Figure 3
<p>Flow chart showing the approach followed in the present study.</p> Full article ">Figure 4
<p>Several types of emergency shelters for the immediate housing of people in need after an earthquake which were used at the Damassi (Thessaly) camp (<b>a</b>) after the 3 March 2021 Thessaly earthquake. They comprised (<b>b</b>) camper vans, (<b>c</b>) tents, and (<b>d</b>) temporary container-type structures in the same area. Amid the pandemic, the use of many different types of shelters contributed to the avoidance of overcrowding in camps and the maintenance of physical distance in order to limit the spread of the novel virus in the earthquake-affected community.</p> Full article ">Figure 5
<p>Typical views of the distribution of relief supplies: (<b>a</b>) in the operational center in the Town Hall Square of the Eastern Samos municipality; (<b>b</b>,<b>c</b>) in the earthquake camp in Damassi (Thessaly); and (<b>d</b>) in the earthquake camp at the exhibition center of the earthquake-affected town of Arkalochori on Crete Island. The distribution of relief supplies was adapted to the new conditions formed by the pandemic. Civil Protection personnel, members of the armed forces and voluntary groups used personal protective equipment at every stage of the preparation and distribution of supplies (<b>c</b>), and the meals were served packed (<b>c</b>,<b>d</b>).</p> Full article ">Figure 6
<p>Information activities for building inspections on the affected island took place in indoor sports facilities after the 2020 Samos earthquake.</p> Full article ">Figure 7
<p>(<b>a</b>,<b>b</b>) The coordination operation center after the 2020 Samos earthquake was set up outdoors with spatial arrangement adapted to the pandemic mitigation measures. (<b>c</b>,<b>d</b>) The same approach was applied in the case of the Arkalochori (Crete) earthquake. The coordination operation center was also set up outdoors in the courtyard of a school, providing space for maintaining physical distance and avoiding overcrowding.</p> Full article ">Figure 8
<p>Awareness-raising and educational activities were conducted by the staff of the Earthquake Planning and Protection Organization of Greece in the earthquake-affected Samos. Amid the pandemic, the activities were held outdoors with participants using personal protective equipment (mask, gloves and antiseptics). (<b>a</b>–<b>d</b>) Views from workshops for the directors of primary and secondary schools in Vathy town located at the northeastern part of Samos Island.</p> Full article ">Figure 9
<p>The earthquake-affected regional units in Greece during the COVID-19 pandemic.</p> Full article ">Figure 10
<p>The evolution of COVID-19 cases in the earthquake-affected regional units of the Epirus region before and after the occurrence of the 21 March 2020 Epirus earthquake. The laboratory-confirmed, daily-recorded COVID-19 cases are from the NPHO’s COVID-19 epidemiological surveillance daily reports [<a href="#B21-sustainability-14-08486" class="html-bibr">21</a>] covering the period from 14 March to 12 April 2020.</p> Full article ">Figure 11
<p>The evolution of COVID-19 cases in the earthquake-affected regional units of the North Aegean region before and after the occurrence of the 30 October 2020 Samos earthquake. The laboratory-confirmed, daily-recorded COVID-19 cases are from the NPHO’s COVID-19 epidemiological surveillance daily reports [<a href="#B21-sustainability-14-08486" class="html-bibr">21</a>] covering the period from 23 October to 21 November 2020.</p> Full article ">Figure 12
<p>The evolution of COVID-19 cases in the earthquake-affected regional units of the Thessaly region before and after the occurrence of the 3 March 2021 earthquake. The laboratory-confirmed, daily-recorded COVID-19 cases are from the NPHO’s COVID-19 epidemiological surveillance daily reports [<a href="#B27-sustainability-14-08486" class="html-bibr">27</a>] covering the period from 24 February to 24 March 2021.</p> Full article ">Figure 13
<p>The evolution of COVID-19 cases in the earthquake-affected regional units of the Crete region before and after the occurrence of the 27 September 2021 Arkalochori earthquake. The laboratory-confirmed, daily-recorded COVID-19 cases are from the NPHO’s COVID-19 epidemiological surveillance daily reports [<a href="#B27-sustainability-14-08486" class="html-bibr">27</a>] covering the period from 20 September to 18 October 2021.</p> Full article ">

22 pages, 6972 KiB  

Open AccessArticle

An Assessment of Social Resilience against Natural Hazards through Multi-Criteria Decision Making in Geographical Setting: A Case Study of Sarpol-e Zahab, Iran

by Davoud Shahpari Sani, Mohammad Taghi Heidari, Hossein Tahmasebi Mogaddam, Saman Nadizadeh Shorabeh, Saman Yousefvand, Anahita Karmpour and Jamal Jokar Arsanjani

Sustainability 2022, 14(14), 8304; https://doi.org/10.3390/su14148304 - 7 Jul 2022

Cited by 9 | Viewed by 3067

Abstract

The aim of this study was to propose an approach for assessing the social resilience of citizens, using a locative multi-criteria decision-making (MCDM) model for an exemplary case study of Sarpol-e Zahab city, Iran. To do so, a set of 10 variables and [...] Read more.

The aim of this study was to propose an approach for assessing the social resilience of citizens, using a locative multi-criteria decision-making (MCDM) model for an exemplary case study of Sarpol-e Zahab city, Iran. To do so, a set of 10 variables and 28 criteria affecting social resilience were used and their weights were measured using the Analytical Hierarchy Process, which was then inserted into the Weighted Linear Combination (WLC) model for mapping social resilience across our case study. Finally, the accuracy of the generated social resilience map, the correlation coefficient between the results of the WLC model and the accuracy level of the social resilience map were assessed, based on in-situ data collection after conducting a survey. The outcomes revealed that more than 60% of the study area falls into the low social resilience category, categorized as the most vulnerable areas. The correlation coefficient between the WLC model and the social resilience level was 79%, which proves the acceptability of our approach for mapping social resilience of citizens across cities vulnerable to diverse risks. The proposed methodological approach, which focuses on chosen data and presented discussions, borne from this study can be beneficial to a wide range of stakeholders and decision makers in prioritizing resources and efforts to benefit more vulnerable areas and inhabitants. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>Study area.</p> Full article ">Figure 2
<p>The flowchart of the main steps of the study.</p> Full article ">Figure 3
<p>The standardized maps of different criteria related to the demographic index.</p> Full article ">Figure 4
<p>The standardized maps of different criteria related to social harms index.</p> Full article ">Figure 5
<p>The standardized maps of different criteria related to social capital index.</p> Full article ">Figure 6
<p>The standardized maps of the criteria related to religious beliefs and values index.</p> Full article ">Figure 7
<p>The standardized maps of the criteria related to general capability of local community index.</p> Full article ">Figure 8
<p>The standardized map of resources and skills index.</p> Full article ">Figure 9
<p>The standardized map of social inequality index.</p> Full article ">Figure 10
<p>The standardized maps of the criteria related to social security index.</p> Full article ">Figure 11
<p>The standardized maps of the criteria related to human assets index.</p> Full article ">Figure 12
<p>The standardized map of awareness and education index.</p> Full article ">Figure 13
<p>The standardized maps of the variables affecting social resilience.</p> Full article ">Figure 14
<p>Final social resilience map prepared based on wlc model.</p> Full article ">Figure 15
<p>Correlation coefficient between the results of WLC model and real-world social resilience data.</p> Full article ">

21 pages, 2877 KiB  

Open AccessEditor’s ChoiceArticle

Data-Driven Community Flood Resilience Prediction

by Moustafa Naiem Abdel-Mooty, Wael El-Dakhakhni and Paulin Coulibaly

Water 2022, 14(13), 2120; https://doi.org/10.3390/w14132120 - 2 Jul 2022

Cited by 9 | Viewed by 4149

Abstract

Climate change and the development of urban centers within flood-prone areas have significantly increased flood-related disasters worldwide. However, most flood risk categorization and prediction efforts have been focused on the hydrologic features of flood hazards, often not considering subsequent long-term losses and recovery [...] Read more.

Climate change and the development of urban centers within flood-prone areas have significantly increased flood-related disasters worldwide. However, most flood risk categorization and prediction efforts have been focused on the hydrologic features of flood hazards, often not considering subsequent long-term losses and recovery trajectories (i.e., community’s flood resilience). In this study, a two-stage Machine Learning (ML)-based framework is developed to accurately categorize and predict communities’ flood resilience and their response to future flood hazards. This framework is a step towards developing comprehensive, proactive flood disaster management planning to further ensure functioning urban centers and mitigate the risk of future catastrophic flood events. In this framework, resilience indices are synthesized considering resilience goals (i.e., robustness and rapidity) using unsupervised ML, coupled with climate information, to develop a supervised ML prediction algorithm. To showcase the utility of the framework, it was applied on historical flood disaster records collected by the US National Weather Services. These disaster records were subsequently used to develop the resilience indices, which were then coupled with the associated historical climate data, resulting in high-accuracy predictions and, thus, utility in flood resilience management studies. To further demonstrate the utilization of the framework, a spatial analysis was developed to quantify communities’ flood resilience and vulnerability across the selected spatial domain. The framework presented in this study is employable in climate studies and patio-temporal vulnerability identification. Such a framework can also empower decision makers to develop effective data-driven climate resilience strategies. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>Multi-stage framework layout for resilience-based flood categorization and prediction.</p> Full article ">Figure 2
<p>Descriptive spatio-temporal analysis of the employed dataset where (<b>a</b>) the annual number of floods between 1996 and 2019 indicated by season and (<b>b</b>) a multilayer spatial analysis of the dataset with the total number of records and the total damage in USD per state indicated by color.</p> Full article ">Figure 3
<p>Spatial distribution of the number of records and the average FRI over different counties in the state of Texas.</p> Full article ">Figure 4
<p>Exploratory and sensitivity data analysis of the climate information, and the FRI variables used in the prediction framework.</p> Full article ">Figure 5
<p>Prediction performance indices for the four utilized models where: (<b>a</b>) is the training subset performance, and (<b>b</b>) is the testing subset performance.</p> Full article ">Figure 6
<p>Mean decrease Gini and mean decrease accuracy in (<b>a</b>) Random Forest model with 300 trees and (<b>b</b>) Random Forest model with 1000 trees.</p> Full article ">

20 pages, 9924 KiB  

Open AccessArticle

Combined ERT and GPR Data for Subsurface Characterization of Weathered Hilly Slope: A Case Study in Zhejiang Province, Southeast China

by Yajing Yan, Yongshuai Yan, Guizhang Zhao, Yanfang Zhou and Zhoufeng Wang

Sustainability 2022, 14(13), 7616; https://doi.org/10.3390/su14137616 - 22 Jun 2022

Cited by 7 | Viewed by 2181

Abstract

Rain-triggered landslides frequently threaten public safety, infrastructure, and the economy during typhoon seasons in Zhejiang Province. Landslides are complex structural systems, and the subsurface features play a significant role in their stability. Their early identification and the evaluation of potential danger in terms [...] Read more.

Rain-triggered landslides frequently threaten public safety, infrastructure, and the economy during typhoon seasons in Zhejiang Province. Landslides are complex structural systems, and the subsurface features play a significant role in their stability. Their early identification and the evaluation of potential danger in terms of the rupture surface and unstable body are essential for geohazard prevention and protection. However, the information about the subsurface acquired by conventional exploration approaches is generally limited to sparse data. This paper describes a joint application of ground-penetrating radar (GPR) with a 100 MHz antenna and the electrical resistivity tomography (ERT) method with the Wenner configuration to identify the stratum structure and delineate the potentially unstable body of a clay-rich slope, the results of which were further verified using borehole data and field observation. The acquired results from the GPR and ERT surveys, consistent with each other, indicate two stratigraphic layers comprising silty clay and silty mudstone. Moreover, the potential rupture zone very likely exists in the highly weathered mudstone in the depth range of 3–7 m, and the average depth is 5 m. In addition, the thickness of the unstable mass is greater on the east and crest parts of the slope. Conclusively, the optimum combination of ERT and GPR is reliable for conducting rapid and effective delineation of subsurface characteristics of clayey slopes for risk assessment and mitigation during the typhoon season. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>Location of the study site (red circle) in Zhejiang Province. (<b>a</b>) Map of China and its capital city Beijing, and of (<b>b</b>) Zhejiang Province and its capital city Hangzhou.</p> Full article ">Figure 2
<p>(<b>a</b>) Photo of the study site from the air. The red circle marks the study site, and the red arrow represents the direction of the camera that obtained the photo shown in (<b>c</b>). (<b>b</b>) Photo of the acquisition work showing the GPR system, step-shape landform, and vegetation coverage. (<b>c</b>) Field observation of the stratum.</p> Full article ">Figure 3
<p>(<b>a</b>) Locations of geophysical survey lines (A, B, C) and boreholes (ZK1, ZK2, ZK3). (<b>b</b>) Drilling core logs of the three boreholes (ZK1, ZK2, ZK3).</p> Full article ">Figure 4
<p>Drilling core samples from three boreholes (ZK1, ZK2, ZK3).</p> Full article ">Figure 4 Cont.
<p>Drilling core samples from three boreholes (ZK1, ZK2, ZK3).</p> Full article ">Figure 5
<p>ERT profile A and its geological interpretations. (<b>a</b>) ERT image of profile A, (<b>b</b>) geological interpretations from ERT profile A.</p> Full article ">Figure 6
<p>ERT profile B and its geological interpretations. (<b>a</b>) ERT image of profile B, (<b>b</b>) geological interpretations from ERT profile B.</p> Full article ">Figure 7
<p>ERT profile C and its geological interpretations. (<b>a</b>) ERT image of profile C, (<b>b</b>) geological interpretations from ERT profile C.</p> Full article ">Figure 8
<p>Waveforms of a single-channel GPR wave at three sites near the three boreholes, showing variations in amplitude and energy with depth. The black solid lines represent the amplitude, while the red solid lines denote the energy. The green dashed line with two arrows corresponds to the peak-to-peak phenomenon. (<b>a</b>) Waveform of ZK1, of (<b>b</b>) ZK2, and of (<b>c</b>) ZK3.</p> Full article ">Figure 8 Cont.
<p>Waveforms of a single-channel GPR wave at three sites near the three boreholes, showing variations in amplitude and energy with depth. The black solid lines represent the amplitude, while the red solid lines denote the energy. The green dashed line with two arrows corresponds to the peak-to-peak phenomenon. (<b>a</b>) Waveform of ZK1, of (<b>b</b>) ZK2, and of (<b>c</b>) ZK3.</p> Full article ">Figure 9
<p>GPR image profiles. (<b>a</b>) Profile A superimposed elevation values (<span class="html-italic">Y</span> axis), (<b>b</b>) profile B superimposed elevation values (<span class="html-italic">Y</span> axis), and (<b>c</b>) profile C at the elevation of approximately 384 m. The white dashed line indicates the boundary between different geological layers, while the white solid and dashed line with two-way arrows in (<b>a</b>,<b>b</b>) represents the maximum and minimum thicknesses of the overlying layer.</p> Full article ">Figure 9 Cont.
<p>GPR image profiles. (<b>a</b>) Profile A superimposed elevation values (<span class="html-italic">Y</span> axis), (<b>b</b>) profile B superimposed elevation values (<span class="html-italic">Y</span> axis), and (<b>c</b>) profile C at the elevation of approximately 384 m. The white dashed line indicates the boundary between different geological layers, while the white solid and dashed line with two-way arrows in (<b>a</b>,<b>b</b>) represents the maximum and minimum thicknesses of the overlying layer.</p> Full article ">Figure 10
<p>Comparison between ERT and GPR images of profile B. (<b>a</b>) ERT image of profile B, (<b>b</b>) GPR image of profile B.</p> Full article ">Figure 11
<p>Three-dimensionally (3D) interpreted model of a potentially unstable body based on ERT and GPR images. The light blue dashed lines represent the boundary of the potential failure surface.</p> Full article ">

25 pages, 9635 KiB  

Open AccessArticle

Multi-Hazard Meteorological Disaster Risk Assessment for Agriculture Based on Historical Disaster Data in Jilin Province, China

by Jiawang Zhang, Jianguo Wang, Shengbo Chen, Siqi Tang and Wutao Zhao

Sustainability 2022, 14(12), 7482; https://doi.org/10.3390/su14127482 - 19 Jun 2022

Cited by 17 | Viewed by 3869

Abstract

The impact of global climate change is gradually intensifying, and the frequent occurrence of meteorological disasters poses a serious challenge to crop production. Analyzing and evaluating agricultural multi-hazard meteorological disaster risks based on historical disaster data and a summary of disaster occurrences and [...] Read more.

The impact of global climate change is gradually intensifying, and the frequent occurrence of meteorological disasters poses a serious challenge to crop production. Analyzing and evaluating agricultural multi-hazard meteorological disaster risks based on historical disaster data and a summary of disaster occurrences and development patterns are important bases for the effective reduction of natural disaster risks and the regulation of agricultural production. This paper explores the technical system of agricultural multi-hazard meteorological disaster risk assessment and establishes a disaster risk assessment model based on the historical disaster data at the regional level from 1978–2020 in the first national comprehensive natural disaster risk census, carrying out multi-hazard meteorological disaster risk assessments in 18 major grain-producing regions in Jilin province. The empirical evidence shows: (1) drought and flood disasters are the key disasters for agricultural meteorological disaster prevention in Jilin province. Hotspots of drought and flood disasters are widely distributed in the study area, while hail and typhoons are mainly concentrated in the eastern region with a certain regionality. (2) The risk values of the four major meteorological disasters all decreased with the increase of the disaster index. Under the same disaster index, the disaster risk of various disasters in the main grain-producing areas is as follows: drought > flood > typhoon > hail. Under different disaster indices, Jiutai, Nongan, Yitong, Tongyu, and other places all presented high and medium–high risk levels. (3) From the spatial evolution trend, along with the rising disaster index, the risk of multi-hazard meteorological hazards is spatially oriented in a southeastern direction, and the risk level of multi-hazard meteorological hazards in the central part of the study area decreases gradually along with the increasing damage index. In addition, regional agricultural multi-hazard meteorological disaster risk reduction recommendations are made in three aspects: institutional construction, management model, and reduction capacity. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>The main grain-producing area of Jilin province, China: (<b>a</b>) distribution of grain yield classes in Jilin province (<b>left</b>); (<b>b</b>) distribution of land use types in the study area (<b>right</b>).</p> Full article ">Figure 2
<p>Average affected (demolished) area and frequency of each disaster species in the study area.</p> Full article ">Figure 3
<p>Multi-hazard risk assessment technical route.</p> Full article ">Figure 4
<p>Grain production and average area affected by each disaster in major grain-producing areas ((<b>a</b>). Drought; (<b>b</b>). Flood; (<b>c</b>). Hail; (<b>d</b>). Typhoon).</p> Full article ">Figure 5
<p>Distribution of each disaster hotspot area in the main grain-producing areas of Jilin province ((<b>a</b>). Drought; (<b>b</b>). Flood; (<b>c</b>). Hail; (<b>d</b>). Typhoon).</p> Full article ">Figure 6
<p>Probability density of multi-hazard meteorological hazards in major grain-producing areas.</p> Full article ">Figure 7
<p>Risk values of multi-hazard meteorological hazards in major grain-producing areas.</p> Full article ">Figure 8
<p>Risk evaluation value of multi-hazard meteorological disasters under the disaster index of 5% ≤ x &lt; 10%.</p> Full article ">Figure 9
<p>Risk evaluation value of multi-hazard meteorological disasters under the disaster index of 10% ≤ x &lt; 15%.</p> Full article ">Figure 10
<p>Risk evaluation value of multi-hazard meteorological disasters under the disaster index of 15% ≤ x &lt; 20%.</p> Full article ">Figure 11
<p>Risk evaluation value of multi-hazard meteorological disasters under the disaster index of x ≥ 20%.</p> Full article ">Figure 12
<p>Risk assessment map of multi-hazard meteorological disasters under different disaster indices ((<b>a</b>). 5% ≤ x &lt; 10%; (<b>b</b>). 10% ≤ x &lt; 15%; (<b>c</b>). 15% ≤ x &lt; 20%; (<b>d</b>). x ≥ 20%).</p> Full article ">Figure 13
<p>Risk trend of multi-hazard meteorological disasters under different disaster indices ((<b>a</b>). 5% ≤ x &lt; 10%; (<b>b</b>). 10% ≤ x &lt; 15%; (<b>c</b>). 15% ≤ x &lt; 20%; (<b>d</b>). x ≥ 20%).</p> Full article ">

19 pages, 5913 KiB  

Open AccessArticle

The Outburst of a Lake and Its Impacts on Redistribution of Surface Water Bodies in High-Altitude Permafrost Region

by Zekun Ding, Fujun Niu, Guoyu Li, Yanhu Mu, Mingtang Chai and Pengfei He

Remote Sens. 2022, 14(12), 2918; https://doi.org/10.3390/rs14122918 - 18 Jun 2022

Cited by 8 | Viewed by 2201

Abstract

The lakes distributed in permafrost areas on the Tibetan Plateau (TP) have been experiencing significant changes during the past few decades as a result of the climate warming and regional wetting. In September 2011, an outburst occurred on an endorheic lake (Zonag Lake) [...] Read more.

The lakes distributed in permafrost areas on the Tibetan Plateau (TP) have been experiencing significant changes during the past few decades as a result of the climate warming and regional wetting. In September 2011, an outburst occurred on an endorheic lake (Zonag Lake) in the interior of the TP, which caused the spatial expansion of three downstream lakes (Kusai Lake, Haidingnor Lake and Salt Lake) and modified the four independent lake catchments to one basin. In this study, we investigate the changes in surficial areas and water volumes of the outburst lake and related downstream water bodies 10 years after the outburst. Based on the meteorological and satellite data, the reasons for the expansion of downstream lakes were analyzed. Additionally, the importance of the permafrost layer in determining hydrological process on the TP and the influence of from lake expansion on engineering infrastructures were discussed. The results in this study showed the downstream lakes increased both in area and volume after the outburst of the headwater. Meanwhile, we hope to provide a reference about surface water changes and permafrost degradation for the management of lake overflow and flood on the TP in the background of climate warming and wetting. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Overview of the study area: (<b>a</b>) the permafrost on the TP; (<b>b</b>) the topography and lake distribution of the study area.</p> Full article ">Figure 2
<p>Comparison of several water body indices around Zonag Lake: (<b>a</b>) Selection of different features and (<b>b</b>) comparison of different water index.</p> Full article ">Figure 3
<p>Flowchart of the acquisition of water body changes and impacts of lake outburst on adjacent lake on ZSLB.</p> Full article ">Figure 4
<p>Total surface water area in the ZSLB from 2000 to 2020.</p> Full article ">Figure 5
<p>Areas of four lakes in ZSLB between 2000 and 2020.</p> Full article ">Figure 6
<p>Changes in shorelines in 2000, 2010, 2012 and 2019 of the (<b>a</b>) Zoang Lake; (<b>b</b>) Kusai Lake; (<b>c</b>) Haidingnor Lake and (<b>d</b>) Salt Lake.</p> Full article ">Figure 7
<p>Mass changes of (<b>a</b>) Zonag Lake; (<b>b</b>) Kusai Lake; (<b>c</b>) Haidingnor Lake; and (<b>d</b>) Salt Lake between 1976 and 2019.</p> Full article ">Figure 8
<p>The EWI results of hydraulic connection in ZSLB (2013).</p> Full article ">Figure 9
<p>The EWI results of hydraulic connection between 2000 and 2020: (<b>a</b>) region between Kusai Lake and Haidingnor Lake; (<b>b</b>) region between Haidingnor and Salt Lake.</p> Full article ">Figure 10
<p>Number of small lakes and ponds in ZSLB during the period from 2000 to 2020.</p> Full article ">Figure 11
<p>The mean annual air temperature and annual precipitation at Wudaoliang meteorological station.</p> Full article ">Figure 12
<p>Ground temperature around (<b>a</b>) Zonag Lake (from Liu. et al. [<a href="#B35-remotesensing-14-02918" class="html-bibr">35</a>]) and (<b>b</b>) Salt Lake.</p> Full article ">Figure 13
<p>Changes of lakes in (<b>a</b>) continuous permafrost region and (<b>b</b>) discontinuous permafrost.</p> Full article ">Figure 14
<p>Countermeasures used by the (<b>a</b>) highway and (<b>b</b>) railway downstream to cope with flowing water drained from Salt Lake.</p> Full article ">

26 pages, 12995 KiB  

Open AccessArticle

Landslide Deformation Extraction from Terrestrial Laser Scanning Data with Weighted Least Squares Regularization Iteration Solution

by Lidu Zhao, Xiaping Ma, Zhongfu Xiang, Shuangcheng Zhang, Chuan Hu, Yin Zhou and Guicheng Chen

Remote Sens. 2022, 14(12), 2897; https://doi.org/10.3390/rs14122897 - 17 Jun 2022

Cited by 8 | Viewed by 2676

Abstract

The extraction of landslide deformation using terrestrial laser scanning (TLS) has many important applications. The landslide deformation can be extracted based on a digital terrain model (DTM). However, such methods usually suffer from the ill-posed problem of a multiplicative error model as illustrated [...] Read more.

The extraction of landslide deformation using terrestrial laser scanning (TLS) has many important applications. The landslide deformation can be extracted based on a digital terrain model (DTM). However, such methods usually suffer from the ill-posed problem of a multiplicative error model as illustrated in previous studies. Moreover, the edge drift of commonly used spherical targets for point cloud registration (PCR) is ignored in the existing method, which will result in the unstable precision of the PCR. In response to these problems, we propose a method for extracting landslide deformations from TLS data. To archive the PCR of different period point clouds, a new triangular pyramid target is designed to eliminate the edge drift. If a fixed target is inconvenient, we also propose a PCR method based on total station orientation. Then, the use of the Tikhonov regularization method to derive the weighted least squares regularization solution is presented. Finally, the landslide deformation is extracted by DTM deference. The experiments are conducted on two datasets with more than 1.5 billion points. The first dataset takes Lashagou NO. 3 landslide in Gansu Province, China, as the research object; the point cloud data were collected on 26 February 2021 and 3 May 2021. The registration accuracy was 0.003 m based on the permanent triangular pyramid target and 0.005 m based on the total station orientation. The landslide deforms within 3 cm due to the ablation of the frozen soil. The second dataset is TLS data from the Lihua landslide in Chongqing, China, collected on 20 April 2021 and 1 May 2021. The overall deformation of the Lihua landslide is small, with a maximum value of 0.011 m. The result shows that the proposed method achieves a better performance than previous sphere-based registration and that the weighted least square regularization iterative solution can effectively reduce the ill-condition of the model. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Spherical target.</p> Full article ">Figure 2
<p>Point cloud of spherical target.</p> Full article ">Figure 3
<p>GNSS elevation point before and after being disturbed by multiplicative error.</p> Full article ">Figure 4
<p>DTM.</p> Full article ">Figure 5
<p>DTM with multiplicative error.</p> Full article ">Figure 6
<p>(<b>a</b>) Optical remote sensing imagery of Lashagou NO. 3 landslide acquired from Google Earth; (<b>b</b>) UAV image of Lashagou NO. 3 landslide.</p> Full article ">Figure 7
<p>(<b>a</b>) Study area and photos showing Lashagou NO. 3 landslide disease. (<b>b</b>) ground fracture (<b>c</b>) staggered platform (<b>d</b>) tensile crack caused by the landslide.</p> Full article ">Figure 8
<p>Triangular pyramid target.</p> Full article ">Figure 9
<p>The point cloud of triangular pyramid.</p> Full article ">Figure 10
<p>Prism ball target.</p> Full article ">Figure 11
<p>Prism ball target.</p> Full article ">Figure 12
<p>Point cloud of phase 1.</p> Full article ">Figure 13
<p>Lihua landslide point cloud of phase 1.</p> Full article ">Figure 14
<p>UAV image of Lihua landslide.</p> Full article ">Figure 15
<p>GNSS elevation obtained by the RWLS method and LS.</p> Full article ">Figure 16
<p>The regularization parameters with the number of iterations.</p> Full article ">Figure 17
<p>DTM by the RWLS method.</p> Full article ">Figure 18
<p>The regularization parameters with the number of iterations.</p> Full article ">Figure 19
<p>Initial point cloud of landslide in two phases.</p> Full article ">Figure 20
<p>Landslide point cloud after the PCR of two period data.</p> Full article ">Figure 21
<p>Photos of Lashagou NO.3 landslide.</p> Full article ">Figure 22
<p>Point cloud after accurate registration.</p> Full article ">Figure 23
<p>PCR based on triangular target (1 m wide detail in <a href="#remotesensing-14-02897-f020" class="html-fig">Figure 20</a>).</p> Full article ">Figure 24
<p>Lashagou NO.3 landslide DTM.</p> Full article ">Figure 25
<p>Deformation of Lashagou NO.3 landslide.</p> Full article ">Figure 26
<p>Point cloud of Lihua landslide.</p> Full article ">Figure 27
<p>DTM disturbed by multiplicative error.</p> Full article ">Figure 28
<p>Deformation of Lihua landslide.</p> Full article ">

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Supplementary material:
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25 pages, 6965 KiB  

Open AccessArticle

Flash Flood Risk Assessment and Mitigation in Digital-Era Governance Using Unmanned Aerial Vehicle and GIS Spatial Analyses Case Study: Small River Basins

by Ștefan Bilașco, Gheorghe-Gavrilă Hognogi, Sanda Roșca, Ana-Maria Pop, Vescan Iuliu, Ioan Fodorean, Alexandra-Camelia Marian-Potra and Paul Sestras

Remote Sens. 2022, 14(10), 2481; https://doi.org/10.3390/rs14102481 - 22 May 2022

Cited by 21 | Viewed by 4572

Abstract

Watercourses act like a magnet for human communities and were always a deciding factor when choosing settlements. The reverse of these services is a potential hazard in the form of flash flooding, for which human society has various management strategies. These strategies prove [...] Read more.

Watercourses act like a magnet for human communities and were always a deciding factor when choosing settlements. The reverse of these services is a potential hazard in the form of flash flooding, for which human society has various management strategies. These strategies prove to be increasingly necessary in the context of increased anthropic pressure on the floodable areas. One of these strategies, Strategic Flood Management (SFM), a continuous cycle of planning, acting, monitoring, reviewing and adapting, seems to have better chances to succeed than other previous strategies, in the context of the Digital-Era Governance (DEG). These derive, among others, from the technological and methodological advantages of DEG. Geographic Information Systems (GIS) and Unmanned Aerial Vehicles (UAV) stand out among the most revolutionary tools for data acquisition and processing of data in the last decade, both in qualitative and quantitative terms. In this context, this study presents a hybrid risk assessment methodology for buildings in case of floods. The methodology is based on detailed information on the terrestrial surface—digital surface model (DSM) and measurements of the last historical flash flood level (occurred on 20 June 2012)—that enabled post-flood peak discharge estimation. Based on this methodology, two other parameters were calculated together with water height (depth): shear stress and velocity. These calculations enabled the modelling of the hazard and risk map, taking into account the objective value of buildings. The two components were integrated in a portal available for the authorities and inhabitants. Both the methodology and the portal are perfectible, but the value of this material consists of the detailing and replicability potential of the data that can be made available to administration and local community. Conceptually, the following are relevant (a) the framing of the SFM concept in the DEG framework and (b) the possibility to highlight the involvement and contribution of the citizens in mapping the risks and their adaptation to climate changes. The subsequent version of the portal is thus improved by further contributions and the participatory approach of the citizens. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>The geographic location of the study area.</p> Full article ">Figure 2
<p>Methodological flowchart.</p> Full article ">Figure 3
<p>The geographical position of GCP and CP.</p> Full article ">Figure 4
<p>Cross-sectional profile used for the calculation of the maximum flow in the section.</p> Full article ">Figure 5
<p>The water height corresponding to the floodable stripe.</p> Full article ">Figure 6
<p>Shear Stress Map.</p> Full article ">Figure 7
<p>Velocity Map.</p> Full article ">Figure 8
<p>The cumulated effects of shear stress and velocity on the road infrastructure and buildings.</p> Full article ">Figure 9
<p>Hazard and risk map for study area.</p> Full article ">

21 pages, 5985 KiB  

Open AccessArticle

Calculating Economic Flood Damage through Microscale Risk Maps and Data Generalization: A Pilot Study in Southern Italy

by Gianna Ida Festa, Luigi Guerriero, Mariano Focareta, Giuseppe Meoli, Silvana Revellino, Francesco Maria Guadagno and Paola Revellino

Sustainability 2022, 14(10), 6286; https://doi.org/10.3390/su14106286 - 21 May 2022

Cited by 5 | Viewed by 2556

Abstract

In recent decades, floods have caused significant loss of human life as well as interruptions in economic and social activities in affected areas. In order to identify effective flood mitigation measures and to suggest actions to be taken before and during flooding, microscale [...] Read more.

In recent decades, floods have caused significant loss of human life as well as interruptions in economic and social activities in affected areas. In order to identify effective flood mitigation measures and to suggest actions to be taken before and during flooding, microscale risk estimation methods are increasingly applied. In this context, an implemented methodology for microscale flood risk evaluation is presented, which considers direct and tangible damage as a function of hydrometric height and allows for quick estimates of the damage level caused by alluvial events. The method has been applied and tested on businesses and residential buildings of the town of Benevento (southern Italy), which has been hit by destructive floods several times in the past; the most recent flooding occurred in October 2015. The simplified methodology tries to overcome the limitation of the original method—the huge amounts of input data—by applying a simplified procedure in defining the data of the physical features of buildings (e.g., the number of floors, typology, and presence of a basement). Data collection for each building feature was initially carried out through careful field surveys (FAM, field analysis method) and subsequently obtained through generalization of data (DGM, data generalization method). The basic method (FAM) allows for estimating in great detail the potential losses for representative building categories in an urban context and involves a higher degree of resolution, but it is time-consuming; the simplified method (DGM) produces a damage value in a shorter time. By comparison, the two criteria show very similar results and minimal differences, making generalized data acquisition most efficient. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>Administrative boundaries of the municipality of Benevento. Black rectangles demarcate the study areas: (1) Industrial area, (2) “Rione Ferrovia” area, and (3) “Rione Libertà” area. The buildings investigated are in red. Overflow of October 2015 of the Calore River is shown in light blue.</p> Full article ">Figure 2
<p>“Rione Ferrovia” area (part of sector 2, <a href="#sustainability-14-06286-f001" class="html-fig">Figure 1</a>) flooded in October 1949 (available at <a href="https://napoli.repubblica.it/cronaca/2015/10/15/foto/benevento_l_alluvione_del_1949-125118463/1/" target="_blank">https://napoli.repubblica.it/cronaca/2015/10/15/foto/benevento_l_alluvione_del_1949-125118463/1/</a> (accessed on 12 May 2022) (<b>a</b>), overflow of October 2015 of the Calore River (photo: P. Revellino) (<b>b</b>).</p> Full article ">Figure 3
<p>Flowchart of the methodological procedure for microscale flood risk assessments using FAM and DGM building datasets.</p> Full article ">Figure 4
<p>Flood hazard map (<b>a</b>) and flood hazard zonation map (<b>b</b>) of the three selected areas of Benevento (data from [<a href="#B33-sustainability-14-06286" class="html-bibr">33</a>]).</p> Full article ">Figure 5
<p>Example of different numbers of floors obtained by surveying (FAM) and computing (DGM) for part of the area #2 “Rione Ferrovia.”</p> Full article ">Figure 6
<p>Map of Benevento’s buildings (black polygons) selected for the analysis and zonation of the OMI (Real Estate Market Observatory) areas. (B1) central area/historic center area; (B2) central urban area; (C1) semicentral urban area; (C2) semicentral/“Rione Libertà” area; (D1) suburban/agricultural area.</p> Full article ">Figure 7
<p>The generic flood hazard curve (black line) for the Benevento area compared with the height of a generic building (red line) located at a given position with respect to the watercourse and the hydrometric zero.</p> Full article ">Figure 8
<p>(<b>a</b>) Vertical distribution of the economic value for building with and without a cellar; (<b>b</b>) examples of stage–damage curves for buildings with different numbers of floors, with or without a cellar.</p> Full article ">Figure 9
<p>Example of damage degree (%) for buildings, considering their features extracted with FAM (<b>a</b>) and DGM (<b>b</b>) for a flood event with a return time (Tr) of 100 years.</p> Full article ">Figure 10
<p>Flood risk maps (EUR/m²y) from FAM for Industrial area (1), Rione Ferrovia area (2), and Rione Libertà area (3).</p> Full article ">Figure 11
<p>Flood risk maps (EUR/m²y) from DGM for Industrial area (1), Rione Ferrovia area (2), and Rione Libertà area (3).</p> Full article ">Figure 12
<p>Conceptual model of the flood risk evaluation. See <a href="#sustainability-14-06286-t001" class="html-table">Table 1</a> and <a href="#sustainability-14-06286-f008" class="html-fig">Figure 8</a>, <a href="#sustainability-14-06286-f010" class="html-fig">Figure 10</a> and <a href="#sustainability-14-06286-f011" class="html-fig">Figure 11</a> for acronyms and legends.</p> Full article ">

25 pages, 13377 KiB  

Open AccessArticle

Impact of Urbanization on Seismic Risk: A Study Based on Remote Sensing Data

by Liqiang An and Jingfa Zhang

Sustainability 2022, 14(10), 6132; https://doi.org/10.3390/su14106132 - 18 May 2022

Cited by 2 | Viewed by 2743

Abstract

The management of seismic risk is an important aspect of social development. However, urbanization has led to an increase in disaster-bearing bodies, making it more difficult to reduce seismic risk. To understand the changes in seismic risk associated with urbanization and then adjust [...] Read more.

The management of seismic risk is an important aspect of social development. However, urbanization has led to an increase in disaster-bearing bodies, making it more difficult to reduce seismic risk. To understand the changes in seismic risk associated with urbanization and then adjust the risk management strategy, remote-sensing technology is necessary. By identifying the types of earthquake-bearing bodies, it is possible to estimate the seismic risk and then determine the changes. For this purpose, this study proposes a set of algorithms that combine deep-learning models with object-oriented image classification and extract building information using multisource remote sensing data. Following this, the area of the building is estimated, the vulnerability is determined, and, lastly, the economic and social impacts of an earthquake are determined based on the corresponding ground motion level and fragility function. Our study contributes to the understanding of changes in seismic risk caused by urbanization processes and offers a practical reference for updating seismic risk management, as well as a methodological framework to evaluate the effectiveness of seismic policies. Experimental results indicate that the proposed model is capable of effectively capturing buildings’ information. Through verification, the overall accuracy of the classification of vulnerability types reaches 86.77%. Furthermore, this study calculates social and economic losses of the core area of Tianjin Baodi District in 2011, 2012, 2014, 2016, 2018, 2020, and 2021, obtaining changes in seismic risk in the study area. The result shows that for rare earthquakes at night, although the death rate decreased from 2.29% to 0.66%, the possible death toll seems unchanged, due to the increase in population. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1
<p>(<b>a</b>) Location of the study area in China. (<b>b</b>) Baodi District of Tianjin. (<b>c</b>) Location of the study area in Baodi. (<b>d</b>) Satellite map of the study area.</p> Full article ">Figure 2
<p>3D visualization of single building datasets.</p> Full article ">Figure 3
<p>Example of the instance segmentation dataset.</p> Full article ">Figure 4
<p>Field survey samples and examples of structure types: (<b>a</b>) shear wall structure (dwelling), (<b>b</b>) RC structure (hospital), (<b>c</b>) brick wood (dwelling), (<b>d</b>) RC structure (dwelling), (<b>e</b>) confined masonry (dwelling), (<b>f</b>) field survey samples.</p> Full article ">Figure 5
<p>The overall workflow consists of four major sections: part 0 to pre-train the segmentation and classification models, part 1 to extract image features, part 2 to classify structural vulnerability, and part 3, a seismic risk assessment.</p> Full article ">Figure 6
<p>Structure and main components of Mask R-CNN framework [<a href="#B46-sustainability-14-06132" class="html-bibr">46</a>].</p> Full article ">Figure 7
<p>The overall architecture of boundary-preserving Mask R-CNN (BMask R-CNN). The dotted arrow denotes 3 × 3 convolutions, and the solid arrow denotes identity connection unless there is a specified annotation in the boundary-preserving mask head. “×4/×2” denotes a stack of four/two consecutive convs [<a href="#B68-sustainability-14-06132" class="html-bibr">68</a>].</p> Full article ">Figure 8
<p>(<b>a</b>) Blue boxes indicate a single household, while red boxes indicate buildings to be counted; (<b>b</b>) red outline indicates rural building groups.</p> Full article ">Figure 9
<p>Building shadow geometry; the red and blue lines indicate the line-of-sight directions of the sun and satellites, respectively: (<b>a</b>) side view of satellite and sun on the same side; (<b>b</b>) top view of satellite and sun on the same side; (<b>c</b>) side view of satellite and sun on different sides; (<b>d</b>) top view of satellite and sun on different sides.</p> Full article ">Figure 9 Cont.
<p>Building shadow geometry; the red and blue lines indicate the line-of-sight directions of the sun and satellites, respectively: (<b>a</b>) side view of satellite and sun on the same side; (<b>b</b>) top view of satellite and sun on the same side; (<b>c</b>) side view of satellite and sun on different sides; (<b>d</b>) top view of satellite and sun on different sides.</p> Full article ">Figure 10
<p>Example of single building extraction results.</p> Full article ">Figure 11
<p>Example of the rural building group.</p> Full article ">Figure 12
<p>Accuracy of height and floor estimation: (<b>a</b>) height and floor estimation; (<b>b</b>) layers estimation.</p> Full article ">Figure 13
<p>Distribution of building footprints over different years in the study area.</p> Full article ">Figure 14
<p>Construction area of buildings in different years.</p> Full article ">Figure 15
<p>Construction area over different years in the study area, in a 200 m grid.</p> Full article ">Figure 16
<p>Variation trend of loss under different ground-motion intensity levels. The blue, orange and green lines represent rare earthquakes, moderate earthquakes, and frequent earthquakes, respectively: (<b>a</b>) estimated death toll when an earthquake occurs during daytime, (<b>b</b>) estimated death toll when the earthquake occurs at night, (<b>c</b>) estimated death toll per 10k people when an earthquake occurs at night, (<b>d</b>) estimated direct economic loss.</p> Full article ">Figure 17
<p>Spatial distribution of population deaths caused by rare earthquakes occurring at night, in a 200 m grid.</p> Full article ">

21 pages, 10752 KiB  

Open AccessArticle

Improved Shallow Landslide Susceptibility Prediction Based on Statistics and Ensemble Learning

by Zhu Liang, Wei Liu, Weiping Peng, Lingwei Chen and Changming Wang

Sustainability 2022, 14(10), 6110; https://doi.org/10.3390/su14106110 - 18 May 2022

Cited by 4 | Viewed by 2295

Abstract

Rainfall-induced landslides bring great damage to human life in mountain areas. Landslide susceptibility assessment (LSA) as an essential step toward landslide prevention has attacked a considerate focus for years. However, defining a reliable or accurate susceptibility model remains a challenge although various methods [...] Read more.

Rainfall-induced landslides bring great damage to human life in mountain areas. Landslide susceptibility assessment (LSA) as an essential step toward landslide prevention has attacked a considerate focus for years. However, defining a reliable or accurate susceptibility model remains a challenge although various methods have been applied. The main purpose of this paper is to explore a comprehensive model with high reliability, accuracy, and intelligibility in LSA by combing statistical methods and ensemble learning techniques. Miyun country in Beijing is selected as the study area. Firstly, the dataset containing 370 landslide locations inventories and 13 conditioning factors were collected and non-landslide samples were prepared by clustering analysis. Secondly, random forest (RF), gradient boosting decision tree (GBDT), and adaptive boosting decision tree (Ada-DT) were selected as base learners for the Stacking ensemble method, and these methods were evaluated using measures like area under the curve (AUC). Finally, the Gini index and frequent ratio (FR) were combined to analyze the major conditioning factors. The results indicated that the performance of the Stacking method was enhanced with an AUC value of 0.944 while the basic classifiers also performed well with 0.906, 0.910, and 0.917 for RF, GBDT, and Ada-DT, respectively. Regions with a distance to a stream less than 2000 m, a distance to a road less than 3000 m, and elevation less than 600 m were susceptible to the landslide hazard. The conclusion demonstrates that the performance of LSA desires enhancement and the reliability and intelligibility of a model can be improved by combining binary and multivariate statistical methods. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>Location map of the study area showing landslide inventory.</p> Full article ">Figure 2
<p>Field investigation photos. (<b>a</b>) shallow landslide in Lama Gate South gully; (<b>b</b>) falls in Lama Gate South gully.</p> Full article ">Figure 3
<p>Field investigation photos. (<b>a</b>) early debris-flow deposits in Dawa gully; (<b>b</b>) Partial enlargement.</p> Full article ">Figure 4
<p>Stereo remote sensing map of landslides in Duitaizi county (Chen et al., 2016).</p> Full article ">Figure 5
<p>Study area thematic maps: (<b>a</b>) Elevation; (<b>b</b>) Plan curvature; (<b>c</b>) Profile curvature; (<b>d</b>) TWI; (<b>e</b>) MED; (<b>f</b>) Slope; (<b>g</b>) Aspect; (<b>h</b>) DTR; (<b>i</b>) DTF; (<b>j</b>) DTS; (<b>k</b>) Lithology; (<b>l</b>) Maximum 24 h Rainfall; (<b>m</b>) Maximum seven days Rainfall.</p> Full article ">Figure 5 Cont.
<p>Study area thematic maps: (<b>a</b>) Elevation; (<b>b</b>) Plan curvature; (<b>c</b>) Profile curvature; (<b>d</b>) TWI; (<b>e</b>) MED; (<b>f</b>) Slope; (<b>g</b>) Aspect; (<b>h</b>) DTR; (<b>i</b>) DTF; (<b>j</b>) DTS; (<b>k</b>) Lithology; (<b>l</b>) Maximum 24 h Rainfall; (<b>m</b>) Maximum seven days Rainfall.</p> Full article ">Figure 5 Cont.
<p>Study area thematic maps: (<b>a</b>) Elevation; (<b>b</b>) Plan curvature; (<b>c</b>) Profile curvature; (<b>d</b>) TWI; (<b>e</b>) MED; (<b>f</b>) Slope; (<b>g</b>) Aspect; (<b>h</b>) DTR; (<b>i</b>) DTF; (<b>j</b>) DTS; (<b>k</b>) Lithology; (<b>l</b>) Maximum 24 h Rainfall; (<b>m</b>) Maximum seven days Rainfall.</p> Full article ">Figure 6
<p>The structure of Stacking.</p> Full article ">Figure 7
<p>Flowchart of the methodology followed in this study.</p> Full article ">Figure 8
<p>Clustering validity function Vcs.</p> Full article ">Figure 9
<p>Analysis of ROC curve for the landslide susceptibility map: (<b>a</b>) Success rate curve of landslide using the training dataset; (<b>b</b>) Prediction rate curve of landslide using the validation dataset.</p> Full article ">Figure 10
<p>Landslide susceptibility map using the Stacking model.</p> Full article ">Figure 11
<p>The distribution of susceptible classes on landslide susceptibility maps.</p> Full article ">Figure 12
<p>Parametric importance graphics obtained from Ada-DT.</p> Full article ">

18 pages, 6415 KiB  

Open AccessArticle

Pre-Seismic Temporal Integrated Anomalies from Multiparametric Remote Sensing Data

by Zhonghu Jiao and Xinjian Shan

Remote Sens. 2022, 14(10), 2343; https://doi.org/10.3390/rs14102343 - 12 May 2022

Cited by 15 | Viewed by 2298

Abstract

Pre-seismic anomalies have the potential to indicate imminent strong earthquakes in the short to medium terms. However, an improved understanding of the statistical significance between anomalies and earthquakes is required to develop operational forecasting systems. We developed a temporal integrated anomaly (TIA) method [...] Read more.

Pre-seismic anomalies have the potential to indicate imminent strong earthquakes in the short to medium terms. However, an improved understanding of the statistical significance between anomalies and earthquakes is required to develop operational forecasting systems. We developed a temporal integrated anomaly (TIA) method to obtain the temporal trends of multiparametric anomalies derived from the Atmospheric Infrared Sounder (AIRS) product before earthquakes. A total of 169 global earthquakes that occurred from 2006 to 2020 and had magnitudes of ≥7.0 and focal depths of ≤70 km were used to test this new method in a retrospective manner. In addition, 169 synthetic earthquakes were randomly generated to demonstrate the suppression capacity of the TIA method for false alarms. We identified four different TIA trends according to the temporal characteristics of positive and negative TIAs. Long-term correlation analyses show that the recognition ability was 12.4–28.4% higher for true earthquakes than for synthetic earthquakes (i.e., higher than that of a random guess). Incorporating 2–5 kinds of TIAs offered the best chance of recognizing imminent shocks, highlighting the importance of multiparameter anomalies. Although the TIA trend characteristics before the earthquakes were not unique, we identified certain unexplained pre-seismic phenomena within the remote sensing data. The results provide new insight into the relationships between pre-seismic anomalies and earthquakes; moreover, the recognition ability of the proposed approach exceeds that of random guessing. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Characteristics of 169 global earthquakes (EQ) and 169 synthetic (SYN) earthquakes with magnitudes ≥7 and focal depths ≤70 km from 2006 to 2020. (<b>a</b>) Spatial distribution of both true and SYN EQs. The gray circles denote global 15,646 earthquakes with magnitudes ≥5.5 and focal depths ≤70 km from 1980 to 2020, representing global active seismic regions. (<b>b</b>) Temporal characteristics of EQ occurrences. (<b>c</b>) Statistics of interoccurrence time of both true and SYN EQs.</p> Full article ">Figure 2
<p>Constant (CST), Gaussian (GAU), and Laplace (LAP) weight integration functions used in the temporal integrated anomaly (TIA) calculation within a 60-day interval. In the LAP and GAU functions, <span class="html-italic">μ</span> is set to 0 and <span class="html-italic">σ</span> is 15. The red, blue, and green dots denote GAU, LAP, and CST weight integration functions, respectively.</p> Full article ">Figure 3
<p>Trend characteristics for multiparametric temporal integrated anomalies (TIAs) before the 2011 M9.1 Great Tohoku Earthquake, Japan, demonstrating enhanced negative TIAs close to earthquake occurrence. (<b>a</b>) Skin temperature (ST) TIA; (<b>b</b>) air temperature (AT) TIA; (<b>c</b>) total integrated column water vapor burden (CWV) TIA; (<b>d</b>) outgoing longwave radiation (OLR) TIA; (<b>e</b>) clear-sky OLR (COLR) TIA. The two subfigures for each anomaly show the positive and negative TIAs, respectively. Negative days at the abscissa denote the days before the earthquake. The gray dashed frame indicates the TIA trend characteristics. The red, blue, and green dots denote Gaussian (GAU), Laplace (LAP), and Constant (CST) weight integration functions, respectively.</p> Full article ">Figure 4
<p>Trend characteristics for multiparametric temporal integrated anomalies (TIAs) before the 2009 M7.7 Papua earthquake, Indonesia, demonstrating enhanced positive TIAs close to earthquake occurrence. (<b>a</b>) Skin temperature (ST) TIA; (<b>b</b>) air temperature (AT) TIA; (<b>c</b>) total integrated column water vapor burden (CWV) TIA; (<b>d</b>) outgoing longwave radiation (OLR) TIA; (<b>e</b>) clear-sky OLR (COLR) TIA. The two subfigures for each anomaly show the positive and negative TIAs, respectively. Negative days at the abscissa denote the days before the earthquake. The gray dashed frame indicates the TIA trend characteristics. The red, blue, and green dots denote Gaussian (GAU), Laplace (LAP), and Constant (CST) weight integration functions, respectively.</p> Full article ">Figure 5
<p>Trend characteristics for multiparametric temporal integrated anomalies (TIAs) before the 2013 M7.7 Awaran earthquake, Pakistan, demonstrating simultaneously enhanced positive &amp; negative TIAs close to earthquake occurrence. (<b>a</b>) Skin temperature (ST) TIA; (<b>b</b>) air temperature (AT) TIA; (<b>c</b>) total integrated column water vapor burden (CWV) TIA; (<b>d</b>) outgoing longwave radiation (OLR) TIA; (<b>e</b>) clear-sky OLR (COLR) TIA. The two subfigures for each anomaly show the positive and negative TIAs, respectively. Negative days at the abscissa denote the days before the earthquake. The gray dashed frame indicates the TIA trend characteristics. The red, blue, and green dots denote Gaussian (GAU), Laplace (LAP), and Constant (CST) weight integration functions, respectively.</p> Full article ">Figure 6
<p>Trend characteristics for multiparametric temporal integrated anomalies (TIAs) before the 2015 M7.2 Murghob earthquake, Tajikistan, demonstrating enhanced positive/negative TIAs close to earthquake occurrence. (<b>a</b>) Skin temperature (ST) TIA; (<b>b</b>) air temperature (AT) TIA; (<b>c</b>) total integrated column water vapor burden (CWV) TIA; (<b>d</b>) outgoing longwave radiation (OLR) TIA; (<b>e</b>) clear-sky OLR (COLR) TIA. The two subfigures for each anomaly show the positive and negative TIAs, respectively. Negative days at the abscissa denote the days before the earthquake. The gray dashed frame indicates the TIA trend characteristics. The red, blue, and green dots denote Gaussian (GAU), Laplace (LAP), and Constant (CST) weight integration functions, respectively.</p> Full article ">Figure 7
<p>Statistical results of multiparametric temporal integrated anomaly (TIA) trends for true and synthetic (SYN) earthquakes (EQ). (<b>a</b>) Alarmed earthquake ratios as a function of TIA type counts for TIAs based on 5 parameters multiplied by 2 types of positive or negative anomalies; anomaly counts of different TIA types for (<b>b</b>) true and (<b>c</b>) synthetic earthquakes; “P”/“N” before the underscore denotes positive or negative TIA (e.g., ST_P is positive skin temperature TIA); earthquake counts of different TIA types for (<b>d</b>) true and (<b>e</b>) synthetic earthquakes.</p> Full article ">Figure 8
<p>Statistical results of temporal integrated anomalies (TIAs) for each geophysical parameter. (<b>a</b>) Skin temperature (ST) TIA; (<b>b</b>) air temperature (AT) TIA; (<b>c</b>) total integrated column water vapor burden (CWV) TIA; (<b>d</b>) outgoing longwave radiation (OLR) TIA; (<b>e</b>) clear-sky OLR (COLR) TIA. ‘None’ denotes neither positive nor negative TIA; P denotes only positive TIA; N denotes only negative TIA; and P &amp; N denotes synchronous positive and negative TIAs.</p> Full article ">Figure 9
<p>Statistical results of multiparameter temporal integrated anomalies (TIAs) for (<b>a</b>) inland, (<b>b</b>) oceanic, and (<b>c</b>) coastal earthquakes (EQ). ST, skin temperature; AT, air temperature; CWV, total integrated column water vapor burden; OLR, outgoing longwave radiation; COLR, clear-sky OLR.</p> Full article ">Figure 10
<p>Map of alarmed (temporal integrated anomaly [TIA] counts <math display="inline"><semantics> <mrow> <mo>≥</mo> <mn>2</mn> </mrow> </semantics></math>) and not alarmed earthquakes for both (<b>a</b>) true and (<b>b</b>) synthetic (SYN) events.</p> Full article ">Figure 11
<p>Statistical results of multiparameter temporal integrated anomalies (TIAs) for (<b>a</b>) normal, (<b>b</b>) thrust, and (<b>c</b>) strike-slip earthquakes (EQ). ST, skin temperature; AT, air temperature; CWV, total integrated column water vapor burden; OLR, outgoing longwave radiation; COLR, clear-sky OLR.</p> Full article ">

21 pages, 5575 KiB  

Open AccessArticle

A Multimodal Data Analysis Approach to Social Media during Natural Disasters

by Mengna Zhang, Qisong Huang and Hua Liu

Sustainability 2022, 14(9), 5536; https://doi.org/10.3390/su14095536 - 5 May 2022

Cited by 7 | Viewed by 3160

Abstract

During natural disasters, social media can provide real time or rapid disaster, perception information to help government managers carry out disaster response efforts efficiently. Therefore, it is of great significance to mine social media information accurately. In contrast to previous studies, this study [...] Read more.

During natural disasters, social media can provide real time or rapid disaster, perception information to help government managers carry out disaster response efforts efficiently. Therefore, it is of great significance to mine social media information accurately. In contrast to previous studies, this study proposes a multimodal data classification model for mining social media information. Using the model, the study employs Late Dirichlet Allocation (LDA) to identify subject information from multimodal data, then, the multimodal data is analyzed by bidirectional encoder representation from transformers (Bert) and visual geometry group 16 (Vgg-16). Text and image data are classified separately, resulting in real mining of topic information during disasters. This study uses Weibo data during the 2021 Henan heavy storm as the research object. Comparing the data with previous experiment results, this study proposes a model that can classify natural disaster topics more accurately. The accuracy of this study is 0.93. Compared with a topic-based event classification model KGE-MMSLDA, the accuracy of this study is improved by 12%. This study results in a real-time understanding of different themed natural disasters to help make informed decisions. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>A structure diagram of multimodal data classification.</p> Full article ">Figure 2
<p>Probabilistic graphical model of LDA model.</p> Full article ">Figure 3
<p>The framework of topic classification.</p> Full article ">Figure 4
<p>The model structure diagram.</p> Full article ">Figure 5
<p>The image feature extraction model structure diagram.</p> Full article ">Figure 6
<p>The number of social media.</p> Full article ">Figure 7
<p>The part of the theme distribution.</p> Full article ">Figure 8
<p>The histogram of natural disaster classification.</p> Full article ">Figure 9
<p>The number of Weibo posts on every topic.</p> Full article ">Figure 10
<p>The space distribution map by region.</p> Full article ">Figure 11
<p>The number of loss reports by region.</p> Full article ">Figure 12
<p>The loss reported rate by region.</p> Full article ">Figure 13
<p>The area distribution map of rescue information.</p> Full article ">Figure 14
<p><b>The</b> number distribution map of rescue information.</p> Full article ">Figure 15
<p>The loss reported rate by region.</p> Full article ">Figure 16
<p>The area distribution map of casualties and losses.</p> Full article ">Figure 17
<p>The number distribution map of casualties and losses.</p> Full article ">Figure 18
<p>The casualties and losses reported rate by region.</p> Full article ">

13 pages, 2570 KiB  

Open AccessArticle

Large Scale Laboratory Experiment: The Impact of the Hydraulic Characteristics of Flood Waves Caused by Gradual Levee Failure on Inundation Areas

by Kwang Seok Yoon, Khawar Rehman, Hyung Ju Yoo, Seung Oh Lee and Seung Ho Hong

Water 2022, 14(9), 1446; https://doi.org/10.3390/w14091446 - 30 Apr 2022

Cited by 3 | Viewed by 2189

Abstract

As a levee failure and the consequent flooding cause significant financial losses and sometimes human casualties, they have led to considerable concern among city officials. Therefore, researchers have devoted considerable effort to investigating the hydraulic characteristics of sudden transient flow in the form [...] Read more.

As a levee failure and the consequent flooding cause significant financial losses and sometimes human casualties, they have led to considerable concern among city officials. Therefore, researchers have devoted considerable effort to investigating the hydraulic characteristics of sudden transient flow in the form of propagated waves to inundation areas during a levee and/or dam failure. A large number of studies, however, have mostly focused on simple one-dimensional cases investigated numerically and/or experimentally, and thus, important hydraulic characteristics, particularly near the failure zone, have not been adequately captured because of three-dimensional complexities. Taking these complexities into consideration, this study conducts a large-scale experiment to examine the characteristics of wave propagation in an open area caused by a gradual levee failure. From the experimental observations, this study provides the propagation speed of a wave front and suggests a formula for the maximum flood depth corresponding to the peak flood wave in the inundation area. We expect the findings to provide hydraulic engineers and scientists with fundamental insights into transient flow during a gradual levee failure. By contributing to our theoretical understanding, the measurements can also be used as validation tools for future numerical simulation and are likely to contribute to the establishment of emergency action plans that can help city officials cope with flood inundation. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>Experimental basin (<b>a</b>) and shape (<b>b</b>) of the opening (failure) area.</p> Full article ">Figure 2
<p>A schematic diagram of levee failure experiment.</p> Full article ">Figure 3
<p>Propagation of the wave front over time (<b>a</b>) and the wave front speed (<b>b</b>) with respect to different values of the initial head over opening (<math display="inline"><semantics> <mrow> <msub> <mi>h</mi> <mn>0</mn> </msub> </mrow> </semantics></math>) for <math display="inline"><semantics> <mi>B</mi> </semantics></math> = 1.0 m case.</p> Full article ">Figure 4
<p>Chronological changes in the flood waveform with respect to various locations, <math display="inline"><semantics> <mi>y</mi> </semantics></math>, meaured along <span class="html-italic">θ</span> = 90° (cases with <math display="inline"><semantics> <mrow> <msub> <mi>h</mi> <mn>0</mn> </msub> </mrow> </semantics></math> = 0.55 m and <span class="html-italic">B</span> = 1.0 m).</p> Full article ">Figure 5
<p>Morphological characteristics of a flood wave with respect to different failure widths, <span class="html-italic">B</span>, at two locations: (<b>a</b>) <math display="inline"><semantics> <mi>y</mi> </semantics></math> = 0.03 m and (<b>b</b>) <math display="inline"><semantics> <mi>y</mi> </semantics></math> = 1.0 m along <span class="html-italic">θ</span> = 90°.</p> Full article ">Figure 6
<p>Morphological characteristics of the flood wave with respect to the initial head <span class="html-italic">h</span>₀ over the opening at two different locations: (<b>a</b>) <math display="inline"><semantics> <mi>y</mi> </semantics></math> = 0.03 m and (<b>b</b>) <math display="inline"><semantics> <mi>y</mi> </semantics></math> = 1.0 m with <span class="html-italic">B</span> = 1.0 m measured along <span class="html-italic">θ</span> = 90°.</p> Full article ">Figure 7
<p>Evolution of the dimensionless wave front speed <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mi>f</mi> </msub> </mrow> </semantics></math> over dimensionless time <span class="html-italic">T</span>.</p> Full article ">Figure 8
<p>Comparison between the experimental results in this paper and those in Lauber and Hager [<a href="#B23-water-14-01446" class="html-bibr">23</a>] with respect to the different failure widths (<b>a</b>) and the initial head over the failure (<b>b</b>).</p> Full article ">Figure 9
<p>Relationship between relative propagation distance (<math display="inline"><semantics> <mrow> <mi>y</mi> <mo>/</mo> <mi>B</mi> </mrow> </semantics></math>) and maximum wave height (<math display="inline"><semantics> <mrow> <msub> <mi>h</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>/</mo> <msub> <mi>h</mi> <mn>0</mn> </msub> </mrow> </semantics></math>).</p> Full article ">

30 pages, 12163 KiB  

Open AccessReview

Catastrophic Floods in Large River Basins: Surface Water and Groundwater Interaction under Dynamic Complex Natural Processes–Forecasting and Presentation of Flood Consequences

by Tatiana Trifonova, Mileta Arakelian, Dmitriy Bukharov, Sergei Abrakhin, Svetlana Abrakhina and Sergei Arakelian

Water 2022, 14(9), 1405; https://doi.org/10.3390/w14091405 - 27 Apr 2022

Cited by 1 | Viewed by 2026

Abstract

A unique approach has been developed for explaining and forecasting the processes of flood and/or mudflow (debris) formation and their spread along riverbeds in mountainous areas, caused by flash increases in the water masses involved (considerably increasing in their expected level because of [...] Read more.

A unique approach has been developed for explaining and forecasting the processes of flood and/or mudflow (debris) formation and their spread along riverbeds in mountainous areas, caused by flash increases in the water masses involved (considerably increasing in their expected level because of precipitation intensity) due to groundwater contributions. Three-dimensional crack-nets within the confines of unified rivershed basins in mountain massifs are a natural transportation system (as determined by some dynamic external stress factors) for groundwater, owing to hydrostatic/hydrodynamic pressure distribution, varied due to different reasons (e.g., earthquakes). This process reveals a wave nature characterized by signs of obvious self-organization, and can be described via the soliton model in nonlinear hydrodynamics on the surface propagation after a local exit of groundwater as the trigger type. This approach (and related concepts) might result in a more reliable forecasting and early warning system in case of natural water hazards/disasters, taking into account a groundwater-dominant role in some cases. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>Water balance estimation for the example of the 2015 Louisiana flood. Blue bars refer to the whole volume of daily precipitation in the whole basin (summarized through regions) in units 10⁹ m³; red bars–the whole volume of daily evaporation + permeation in the whole basin (summarized throughout the region), 10⁹ m³; black line–the whole volume of accumulated water mass in the whole basin (summarized through regions), 10⁹ m³; red line–the maximum of observed water mass, 10⁹ m³. On the vertical axis–the water level (10⁹ m³). On the horizontal axis–measurement days (date). Positive values–excess water mass compared to normal conditions, negative values–decrease compared to normal conditions.</p> Full article ">Figure 2
<p>Water balance estimation for the example of the 2015 Assam flood. Blue bars refer to the whole volume of daily precipitation in the whole basin (summarized through regions), in units 10⁹ m³; red bars–the whole volume of daily evaporation + permeation in the whole basin (summarized throughout the region), 10⁹ m³; black line–the whole volume of accumulated water mass in the whole basin (summarized through regions), 10⁹ m³; red line–the maximum of observed water mass, 10⁹ m³. On the vertical axis–the water level (10⁹ m³). On the horizontal axis–measurement days (date).</p> Full article ">Figure 3
<p>Collection of space images (NASA). Flood inundation areas versus the hydrological situation during calm times: (<b>a</b>) before the flood; (<b>b</b>) during the flood; (<b>c</b>) in the Komsomolsk-on-Amur city area (Russia). The pictures were simultaneously obtained for some surface water states, but show irregular distribution for opening the transport waterways for groundwater over a large area along the “activated” parts of the 3D river-drainage system.</p> Full article ">Figure 4
<p>General schematic model of water accumulation and water-balance estimation.</p> Full article ">Figure 5
<p>The key parts of a river basin system’s functions: (<b>a</b>) traditional view; (<b>b</b>) our proposed model.</p> Full article ">Figure 6
<p>River functioning in a “normal” state.</p> Full article ">Figure 7
<p>Reconstruction of the mountain’s massive fracturing and the river’s underground water supply.</p> Full article ">Figure 8
<p>Highest probability for catastrophic water events (by analysis from different sources): magnitudes, intensity (in points/earth scores), and focal depth (hypocenters) that cause catastrophic floods when an earthquake occurs. The data shown as a scheme is the max-risk for the event (all the above 3 factors come together).</p> Full article ">Figure 9
<p>SIR model for seismic wave propagation from sources: S(t) + I(t) + R(t) = ???????????????????? = ???? (from the number of objects N). Solutions (in arbitrary units) of the trait propagation model by cellular automaton method for β = 0.029, γ = 0.01, T = 100: (<b>a</b>) N = 100, S(0) = 10; (<b>b</b>) N = 10000, S(0) = 10. Different colors indicate the cell states—from 0 to 2; for β = 0.029, γ = 0.01, N = 10000: (<b>c</b>) T = 100, lower-right corner; (<b>d</b>) T = 500, lower-right corner; (<b>e</b>) T = 100, upper-left corner; (<b>f</b>) T = 500, upper-left corner; for β = 0.029, γ = 0.01, N = 10,000: (<b>g</b>) T = 100, the upper limit of the computation domain; (<b>h</b>) T = 500, the lower limit; (<b>i</b>) T = 100, the left border; (<b>j</b>) T = 100, the right border. Here, T stands for the relative number of steps in time and specifies the distribution in the uniform grid with step h.</p> Full article ">Figure 10
<p>Seismic process propagation from a single isolated source (radius r). Initial conditions and solution–image for the propagation region of the studied state (in arbitrary units): (<b>a</b>) initial conditions r = 2, T = 4; (<b>b</b>) corresponding solution, but already for T = 100; (<b>c</b>) initial conditions r = 10, T = 4; (<b>d</b>) decision, but for T = 100; (<b>e</b>) initial conditions r = 20; T = 4; (<b>f</b>) decision, but for T = 100, where T is also the relative number of steps in time and specifies the partition on the uniform grid with step h.</p> Full article ">Figure 11
<p>Computer simulation program for modeling groundwater pressure maps in fractured rocks: (<b>a</b>) dilution procedure; cracks that do not go out are excluded; (<b>b</b>) all cracks come to the surface.</p> Full article ">Figure 12
<p>The Mississippi River, near New Orleans, Louisiana. (<b>a</b>) Monthly statistics graph based on water flow rate data in the Mississippi River. Water discharge behavior in the Mississippi River during the period from 1 January 2008 to 31 December 2016. (<b>b</b>) Monthly statistics graph based on the water table data of the Mississippi River. The groundwater level behavior in the Mississippi River from 1 January 2008 to 31 December 2016 (positive correlations). (<b>c</b>) Monthly statistics graph based on Mississippi basin rainfall data. Precipitation amount behavior in the Mississippi basin from 1 January 2008 to 31 December 2016. We received positive pair correlations for (<b>a</b>,<b>c</b>) and negative correlations for (<b>a</b>,<b>b</b>); the facts probably demonstrate a tendency to flooding. On the horizontal axis–the breakdown of data by year/month. On vertical axes–(<b>a</b>) water discharge (in feet/sec); (<b>b</b>) groundwater level (in feet); (<b>c</b>) precipitation level (in cubic feet).</p> Full article ">Figure 12 Cont.
<p>The Mississippi River, near New Orleans, Louisiana. (<b>a</b>) Monthly statistics graph based on water flow rate data in the Mississippi River. Water discharge behavior in the Mississippi River during the period from 1 January 2008 to 31 December 2016. (<b>b</b>) Monthly statistics graph based on the water table data of the Mississippi River. The groundwater level behavior in the Mississippi River from 1 January 2008 to 31 December 2016 (positive correlations). (<b>c</b>) Monthly statistics graph based on Mississippi basin rainfall data. Precipitation amount behavior in the Mississippi basin from 1 January 2008 to 31 December 2016. We received positive pair correlations for (<b>a</b>,<b>c</b>) and negative correlations for (<b>a</b>,<b>b</b>); the facts probably demonstrate a tendency to flooding. On the horizontal axis–the breakdown of data by year/month. On vertical axes–(<b>a</b>) water discharge (in feet/sec); (<b>b</b>) groundwater level (in feet); (<b>c</b>) precipitation level (in cubic feet).</p> Full article ">Figure 13
<p>Statistics for the Santee River, South Carolina (“up/down” correlation for water consumption and groundwater showing the local reduction of underground reserves due to flooding). On the horizontal axis–the data by year/month. On vertical axes–volumes of water for precipitation (in cubic feet), water consumption (in cubic feet per second) and groundwater level (in feet). Red diamonds mean a noticeable anticorrelation of groundwater level with water masses in the form of precipitation and water consumption.</p> Full article ">Figure 14
<p>The results of mathematical modeling for flood forecasting based on statistical data for the entire research period (i–number of months): (<b>a</b>) for the Mississippi river; (<b>b</b>) for the Santee river; (<b>c</b>) for the Missouri river; (<b>d</b>) more detailed scale fragment for the Santee river (see text above the figures). Here, Q–real water flow (feet/s) (red) and predicted dependence (blue).</p> Full article ">Figure 15
<p>Correlations (vertical axis) between river discharge and groundwater level vs. selected days’ shift (horizontal axis).</p> Full article ">Figure 16
<p>Relative positions of groups of earthquake epicenters regarding the flooding area: (<b>a</b>,<b>b</b>)—one-directional arrangement; (<b>c</b>,<b>d</b>)—two-directional arrangement; (<b>e</b>,<b>f</b>)—multi-directional arrangement. White hexagons—the earthquakes epicenters; black ovals–the flooding areas.</p> Full article ">Figure 17
<p>The Boulder County (Colorado, USA, purple circle on the map) event, located exactly where three wave circles cross, with centers in the earthquakes’ epicenters (yellow circles on the map); i.e., this region has experienced a great conflict of seismic waves.</p> Full article ">Figure 18
<p>Unexpected consequences of disastrous floods in the USA (2017). (<b>a</b>)—white hexagons–the earthquakes epicenters; black oval–the flooding area; (<b>b</b>)—the wildfires seats.</p> Full article ">Figure 19
<p>The river basins’ interconnection. (<b>a</b>)—the Amur and the Lena Rivers, (<b>b</b>)—the Ob and the Yenisei Rivers. White hexagons—the earthquakes epicenters; black ovals (1)—the flooding areas; black ovals (2)—the areas of wildfires propagation.</p> Full article ">Figure 20
<p>A groundwater map of both the Amur River channel and the upper reaches of the Yenisei River (marked by closed areas), which lie close to the earth’s surface and are characterized by instability in the hydrological regime: (a) at the junction of the Baikal and Caledonian folding; (b) at the junction of Baikal, Herzian and Mesozoic folding (according to the World-Wide Hydrogeological Mapping and Assessment Program).</p> Full article ">Figure 21
<p>Computer simulation of trigger water discharge/mudflow process—soliton nonlinear hydrodynamic model. The multiple solitonic variation regime propagation is shown and developed from a single soliton from the very beginning due to pressure variation over the induced stable channels in the void cavity system (the discharge/mudflow exit on the surface is indicated by the red star, 1). BB–collecting funnel; CB–mudflow soliton wave; Δh–hydrostatic thrust/pressure head; 1.–mudflow gate; 2.–surface water with drainage process contribution; 3.–multisoluton movement; 4.–final surface flows.</p> Full article ">Figure 22
<p>Flooding in Europe–05-06, 2013 (both without and with designation): yellow circles–earthquake epicenters; purple circles–fixed location of the flood areas; transparent red circles–schematic representation of seismic wave propagation; gray curves–lithospheric plate boundaries; areas with cranial border–potentially dangerous zones (marked by red color areas) for catastrophic floods based on seismic factor analysis in association with the river basin landscape.</p> Full article ">Figure 23
<p>Analysis results for solar–terrestrial relations with regard to earthquakes occurring. Seasonal changes in earthquake frequencies of more than magnitude 7 for 1900–2004, in comparison to the average value percentage (%) over 2659 events for each 20-yr period. Months 6–7 and 10–11 of the year are usually the most dangerous for the occurrence of catastrophic floods.</p> Full article ">Figure 24
<p>Natural time-scale dependence for the warming and cooling periods over the last 2000 years.</p> Full article ">Figure A1
<p>Due to heavy rains since 12 July 2021, the tributaries of the Rhine Ar and Moselle, as well as several smaller rivers, have overflowed their banks in the west and southwest of Germany. The main impact of these elements fell on the lands of North Rhine-Westphalia and Rhineland-Palatinate.</p> Full article ">

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Supplementary material:
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15 pages, 3237 KiB  

Open AccessArticle

Correlation Dimension in Sumatra Island Based on Active Fault, Earthquake Data, and Estimated Horizontal Crustal Strain to Evaluate Seismic Hazard Functions (SHF)

by Wahyu Triyoso, David P. Sahara, Dina A. Sarsito, Danny H. Natawidjaja and Sigit Sukmono

GeoHazards 2022, 3(2), 227-241; https://doi.org/10.3390/geohazards3020012 - 22 Apr 2022

Cited by 9 | Viewed by 2963

Abstract

This study intends to evaluate the possible correlation between the correlation dimension (D_C) and the seismic moment rate for different late Quaternary active fault data, shallow crustal earthquakes, and GPS on the island of Sumatra Probabilistic Seismic Hazard Analysis (PSHA). The [...] Read more.

This study intends to evaluate the possible correlation between the correlation dimension (D_C) and the seismic moment rate for different late Quaternary active fault data, shallow crustal earthquakes, and GPS on the island of Sumatra Probabilistic Seismic Hazard Analysis (PSHA). The seismicity smoothing was applied to estimate the D_C of active faults (D_F) and earthquake data (D_E) and then to correlate that with the b-value, which will be used to identify seismic hazard functions (SHF) along with the Sumatra Fault Zone (SFZ). The seismicity based on GPS data was calculated by the seismic moment rate that is estimated based on pre-seismic horizontal surface displacement data. The correlation between D_F, D_E, and the b-value was analyzed, and a reasonable correlation between the two seismotectonic parameters, D_F-b, and D_E-b, respectively, could be found. The relatively high D_C coincides with the high seismic moment rate model derived from the pre-seismic GPS data. Furthermore, the SHF curve of total probability of exceedance versus the mean of each observation point’s peak ground acceleration (PGA) shows that the relatively high correlation dimension coincides with the high SHF. The results of this study might be very beneficial for seismic mitigation in the future. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>The Sumatran seismotectonic map depicting the Sumatran Subduction Zone and Sumatran Fault Zone (SFZ) overlays with the historical shallow large earthquake data of 1925–2014 with a magnitude (M_w) larger than or equal to 6.0. Historical earthquake data are based on Ref. [<a href="#B9-geohazards-03-00012" class="html-bibr">9</a>].</p> Full article ">Figure 2
<p>The shallow earthquake catalog data from 1963 to 2020 with the magnitude of Mw ≥ 4.7 and a maximum depth of 50 km [<a href="#B9-geohazards-03-00012" class="html-bibr">9</a>] of 1963–2016 and the GCMT catalog of 2017–2020, the active fault, and pre-seismic GPS data (<b>A</b>). The b-value map overlays with the 15 zones area (<b>B</b>). The b-value is estimated based on the maximum likelihood (2) using a constant number of 50 events on each grid.</p> Full article ">Figure 3
<p>The cross-plot D_F, D_E, and the mean b-values are estimated based on the 15 zones (<b>A</b>). The focal mechanism plot is based on the GCMT catalog of shallow earthquake data for earthquakes at depths less than or equal to 50 km with a magnitude larger than or equal to 4.7 in the earthquake period between January 1976 to December 2020 [<a href="#B65-geohazards-03-00012" class="html-bibr">65</a>,<a href="#B66-geohazards-03-00012" class="html-bibr">66</a>] (<b>B</b>).</p> Full article ">Figure 4
<p>The map of D_F overlay with the historical earthquake catalog with the M_w ≥ 6.0 around the SFZ (<b>A</b>). D_F is calculated using equation D_F = 2.851 – 1.272b with the input of the b-value map of <a href="#geohazards-03-00012-f002" class="html-fig">Figure 2</a>B. The map of (D_F—mean of D_F) over the entire clustered zone boundary of Burton and Hall [<a href="#B21-geohazards-03-00012" class="html-bibr">21</a>] and selected about ten sites to evaluate the SHF starting from the North-West to South-East (<b>B</b>). The relatively high D_C coincides with the historical shallow large earthquakes data of Ref. [<a href="#B9-geohazards-03-00012" class="html-bibr">9</a>] from 1925 to 2014.</p> Full article ">Figure 5
<p>The estimated seismic moment rate is based on the horizontal crustal strain model around Sumatra Island (<b>A</b>). The annual seismic rate model for the SHF calculation was constructed based on the seismic smoothing of the earthquake catalog weighted by the normalized fault seismic model and the normalized seismic moment rate model based on the GPS data (<b>B</b>).</p> Full article ">Figure 6
<p>The graphs show the SHF curve of each observation point (<b>A</b>,<b>B</b>). The SHF curve of total probability of exceedance versus the mean of the peak ground acceleration of each observation point (sites #1 to #10) was constructed using the maximum radius distance of about 100 km with a magnitude range of 6.0–8.0. The source depth was set at half of the seismogenic thickness, which was about 20 km, and the starting locking depth of 5 km was used; thus, 15 km of the source depth was used. The period of the SHF evaluation was set at about 50 years.</p> Full article ">

18 pages, 5827 KiB  

Open AccessArticle

Fast Displacement Estimation of Multiple Close Targets with MIMO Radar and MUSICAPES Method

by Jian Wang, Yuming Wang, Yueli Li and Xiaotao Huang

Remote Sens. 2022, 14(9), 2005; https://doi.org/10.3390/rs14092005 - 21 Apr 2022

Cited by 7 | Viewed by 1907

Abstract

Interferometric radar is a hot research topic in manmade target displacement measuring applications, as it features high precision, a large operation range, and a remote multiple point measuring ability. Most one-dimensional interferometric radars use single-input single-output (SISO) radar architecture to achieve a high [...] Read more.

Interferometric radar is a hot research topic in manmade target displacement measuring applications, as it features high precision, a large operation range, and a remote multiple point measuring ability. Most one-dimensional interferometric radars use single-input single-output (SISO) radar architecture to achieve a high repetition measuring rate of more than 200 Hz; however, it cannot resolve multiple targets with the same radial range but different azimuth angles. This paper presents a multiple-input multiple-output (MIMO) radar that adopts a limited number of antennas (usually tens) to simultaneously improve azimuth resolution and achieve a high repetition measuring rate. A MUSICAPES algorithm is proposed, which is cascades the multiple signal classification (MUSIC) algorithm and the amplitude and phase estimation (APES) filter. The MUSIC algorithm is used to further improve the angular resolution of the small array. The APES is used to precisely recover the phases of the multiple close targets by suppressing their mutual interferences. Simulations and experiments with a millimeter-wave radar validate the performance of the proposed method. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Multiple target deformation measurement with a MIMO interferometric radar.</p> Full article ">Figure 2
<p>Antenna array layout of the MIMO radar.</p> Full article ">Figure 3
<p>Direction of arrival estimation with an array.</p> Full article ">Figure 4
<p>The simulated displacement curve and the estimated ones by the two algorithms. (<b>a</b>) DOA curves in a one-target situation; (<b>b</b>) real value of simulated displacement curve; (<b>c</b>) errors between the estimated displacements and the simulated one.</p> Full article ">Figure 5
<p>Multiple targets resolving ability. (<b>a</b>) DOA curves of two targets separated by one angular resolution; (<b>b</b>) DOA curves of two targets separated by half the angular resolution.</p> Full article ">Figure 6
<p>Displacement curves of the real value and the estimated ones. (<b>a</b>) The real value of displacement; (<b>b</b>) the displacement curves estimated by MUSICAPES and CZT.</p> Full article ">Figure 7
<p>Mean error of displacement difference vs. angle interval. (<b>a</b>) The mean error of ‘Target1’; (<b>b</b>) the mean error of ‘Target2’.</p> Full article ">Figure 8
<p>STD of displacement difference vs. angle interval. (<b>a</b>) STD of ‘Target1’; (<b>b</b>) STD of ‘Target2’.</p> Full article ">Figure 9
<p>Mean error of displacement difference vs. array length. (<b>a</b>) The mean error of ‘Target1’; (<b>b</b>) the mean error of ‘Target2’.</p> Full article ">Figure 10
<p>STD of displacement difference vs. array length. (<b>a</b>) STD of ‘Target1’; (<b>b</b>) STD of ‘Target2’.</p> Full article ">Figure 11
<p>Mean error of displacement difference vs. SNR. (<b>a</b>) The mean error of ‘Target1’; (<b>b</b>) the mean error of ‘Target2’.</p> Full article ">Figure 12
<p>STD of displacement difference vs. SNR. (<b>a</b>) STD of ‘Target1’; (<b>b</b>) STD of ‘Target2’.</p> Full article ">Figure 13
<p>Multiple close targets displacement measuring experiment with an MMW MIMO radar. The radar is mounted on a tripod and connected to a laptop computer via a USB3.0 hub. (<b>a</b>) Two trihedral reflectors are placed at the end of a table. The left one is still and the right one can be moved on a sliding platform. The azimuth distance between them is 1.2 m; (<b>b</b>) the TI AWR2243BOOST is mounted on the TI DAC1000 EVM. (<b>c</b>) The right reflector is on the sliding platform which can be measured by a micrometer. The platform is stuck onto the desktop to suppress additional displacements caused by manual operations.</p> Full article ">Figure 14
<p>One-dimensional image and DOA results of the first experiment. (<b>a</b>) High resolution one-dimensional radar image. The first peak is the responses of the two trihedral reflectors separated 1.2 m in azimuth; (<b>b</b>) MUSIC, CZT and root-MUSIC can discriminate between the two reflectors.</p> Full article ">Figure 15
<p>Displacement curves of the two trihedral reflectors estimated by the MUSICAPES and the CZT method. Target 1 is the still reflector and Target 2 is the moving one mounted on the sliding platform.</p> Full article ">Figure 16
<p>One-dimensional image and DOA results of the second experiment. (<b>a</b>) High resolution one-dimensional radar image. The first peak is the responses of two trihedral reflectors separated by 0.7 m in azimuth; (<b>b</b>) the angle between the two trihedral reflectors is smaller than the angular resolution, root-MUSIC can discriminate between them, but MUSIC and CZT fail.</p> Full article ">Figure 17
<p>Displacement curves of the two trihedral reflectors estimated by the MUSICAPES and the CZT method. Target 1 is the still reflector and Target 2 is the moving one mounted on the sliding platform.</p> Full article ">

20 pages, 3037 KiB  

Open AccessArticle

Spatiotemporal Characteristics of Drought and Wet Events and Their Impacts on Agriculture in the Yellow River Basin

by Qingqing Li, Yanping Cao, Shuling Miao and Xinhe Huang

Land 2022, 11(4), 556; https://doi.org/10.3390/land11040556 - 9 Apr 2022

Cited by 11 | Viewed by 2533

Abstract

Droughts and floods have proven to be threats to food security worldwide. This research used the standardized precipitation index (SPI) to examine the spatiotemporal characteristics of drought and wet events from 1961 to 2020 in the Yellow River basin (YRB). Grain yield data [...] Read more.

Droughts and floods have proven to be threats to food security worldwide. This research used the standardized precipitation index (SPI) to examine the spatiotemporal characteristics of drought and wet events from 1961 to 2020 in the Yellow River basin (YRB). Grain yield data were combined to assess how drought and wet frequency have affected the agricultural system. The occurrence frequency of drought was greater than that of wetness in time, drought frequency decreased, and wetness increased. Spatially, the frequency of drought in all provinces except Shanxi was higher than that of wetness. The grain yield per unit area of the YRB was generally highest in Shandong province and lowest in Gansu province. The grain yield per unit area have shown a significant growth trend in the nine provinces of the YRB since 1961. Drought had a negative effect on the grain yield per unit area in each province, while wetness had a positive effect on the grain yield per unit area in all provinces except Shandong. In general, the influence of drought on grain yield per unit area decreased, while the influence of wetness on grain yield per unit area increased. The results indicate that human activities are effective against preventing and controlling drought and wet disasters and can provide a reference for other parts of the world. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>Meteorological stations and land use distribution map in the nine provinces of the Yellow River basin.</p> Full article ">Figure 2
<p>Frequency of different degrees of drought and wet events in spring (<b>a</b>), summer (<b>b</b>), autumn (<b>c</b>), and winter (<b>d</b>) in nine provinces of the Yellow River basin from 1961 to 2020.</p> Full article ">Figure 3
<p>Frequency of different degrees of drought and wetness in the nine provinces and regions of the Yellow River basin from 1961 to 2020.</p> Full article ">Figure 4
<p>Characteristics of drought and wetness in spring (<b>a</b>), summer (<b>b</b>), autumn (<b>c</b>), and winter (<b>d</b>) in the Yellow River basin.</p> Full article ">Figure 5
<p>Spatial distribution of drought and wet degrees and grain output per unit area in the Yellow River basin from 1961 to 2020.</p> Full article ">Figure 6
<p>Spatial distribution of drought and wetness degree trends and grain yield per unit area trends in various provinces and regions of the Yellow River basin.</p> Full article ">

23 pages, 6813 KiB  

Open AccessArticle

Some Considerations for Using Numerical Methods to Simulate Possible Debris Flows: The Case of the 2013 and 2020 Wayao Debris Flows (Sichuan, China)

by Xianzheng Zhang, Chenxiao Tang, Yajie Yu, Chuan Tang, Ning Li, Jiang Xiong and Ming Chen

Water 2022, 14(7), 1050; https://doi.org/10.3390/w14071050 - 27 Mar 2022

Cited by 2 | Viewed by 2745

Abstract

Using a numerical simulation method based on physical equations to obtain the debris flow risk range is important for local-scale debris flow risk assessment. While many debris flow models have been used to reproduce processes after debris flow occurrence, their predictability in potentially [...] Read more.

Using a numerical simulation method based on physical equations to obtain the debris flow risk range is important for local-scale debris flow risk assessment. While many debris flow models have been used to reproduce processes after debris flow occurrence, their predictability in potentially catastrophic debris flow scenarios has mostly not been evaluated in detail. Two single-phase flow models and two two-phase models were used to reproduce the Wayao debris flow event in 2013. Then the Wayao debris flow event in 2020 was predicted by the four models with the same parameters in 2013. The depth distributions of the debris source and deposition fan were mapped by visual interpretation, electric resistivity surveys, field measurements, and unmanned aerial vehicle (UAV) surveys. The digital elevation model (DEM), rainfall data, and other simulation parameters were collected. These models can reproduce the geometry and thickness distribution of the debris flow fan in 2013. However, the predictions of the runout range and the deposition depth are quite different from the actuality in 2020. The performance and usability of these models are compared and discussed. This could provide a reference for selecting physical models to assess debris-flow risk. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1

Figure 1
<p>Overview of the study area. The landslides and deposits along the channel were identified on a satellite image from 15 April 2015.</p> Full article ">Figure 2
<p>(<b>A</b>) Panoramic view of Wayao debris flow taken on 7 August 2013. The dashed red line indicates the catchment boundary, and the solid red line indicates the extent of the debris fan. (<b>B</b>) The debris deposition along the channel was eroded by the debris flow in 2013. The blue line indicates the debris flow direction, and the red lines indicate the trace of the debris flow. (<b>C</b>) The debris fan of the Wayao debris flow in 2013.</p> Full article ">Figure 3
<p>(<b>A</b>) The landslides are identified on a satellite image from 27 August 2020. It shows the locations of (<b>B</b>,<b>C</b>). (<b>B</b>) Destroyed drainage channel and debris flow runout on UAV image from 25 October 2020. (<b>C</b>) A drone photo shows that the check dam was filled with debris-flow deposits after the debris flow, and it was taken on 25 October 2020.</p> Full article ">Figure 4
<p>(<b>A</b>,<b>B</b>) The depth distributions of eroded debris and deposited debris in the 2013 debris flow event and the 2020 debris flow event, respectively. (<b>C</b>,<b>D</b>) The longitudinal profiles along the channel, and their positions are shown in A and B, respectively. The location of profiles a-a’ is shown in (<b>A</b>) and the location of profile b-b’ is shown in (<b>B</b>).</p> Full article ">Figure 5
<p>Resistivity results and interpretations. (<b>A</b>) Resistivity profile along L1. (<b>B</b>) Resistivity profile along with L2. The white dotted line is the dividing line between the debris fan and the underlying rock layer. L1, L2, and bp1 are shown in <a href="#water-14-01050-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 6
<p>Wayao debris flow fan reproduced by four models. (<b>A</b>) Massflow simulations-sensitivity to the Coulomb-type friction <span class="html-italic">μ</span> = 0.4, <span class="html-italic">μ</span> = 0.439, and <span class="html-italic">μ</span> = 0.45. (<b>B</b>) Flow-3D simulations-sensitivity to multiplier in internal friction angle θ = 20, θ = 32, and θ = 35. (<b>C</b>) OpenLISEM_A simulations-sensitivity to internal friction angle θ = 20, θ = 24, and θ = 27. (<b>D</b>) OpenLISEM_B simulations-sensitivity to internal friction angle coefficient θ = 17, θ = 20, and θ = 24.</p> Full article ">Figure 7
<p>Debris flow fan in 2013: Actual runout (<b>A</b>) and best simulations by Massflow (<b>B</b>), Flow-3D (<b>C</b>), OpenLISEM_A (<b>D</b>), OpenLISEM_B (<b>E</b>). The full sets of model parameters are given in <a href="#water-14-01050-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 8
<p>Schematic diagram of verification results of debris flow events. A predicted area was measured, and the observed area was from the simulation result. X is the positive accuracy area, Y represents the missing accuracy area, Z is negative.</p> Full article ">Figure 9
<p>Comparison of debris fan thickness in 2013 (along two representative cross-sections). Including the actual debris fan and the debris fan of best-fit simulations by Massflow, Flow-3D, OpenLISEM_A, and OpenLISEM_B. (<b>A</b>) shows the debris fan thickness along the cross-section a-a’, and (<b>C</b>) shows the debris fan thickness along the cross-section b-b’. The locations of a-a’ and b-b’ are shown in (<b>B</b>).</p> Full article ">Figure 10
<p>Snapshots of the debris flow height of Wayao debris flow in 2013 simulated by Massflow (<b>A</b>), Flow-3D (<b>B</b>), OpenLISEM_A (<b>C</b>), and OpenLISEM_B (<b>D</b>). Abbreviations: s means seconds, and m means minutes.</p> Full article ">Figure 11
<p>Debris flow runout in 2020: actual runout (<b>A</b>) and simulations by Massflow (<b>B</b>), Flow-3D (<b>C</b>), OpenLISEM_A (<b>D</b>), OpenLISEM_B (<b>E</b>).</p> Full article ">Figure 12
<p>Snapshots of the debris flow height of Wayao debris flow in 2020 simulated by Massflow (<b>A</b>), Flow-3D (<b>B</b>), OpenLISEM_A (<b>C</b>), and OpenLISEM_B (<b>D</b>). Abbreviations: s means seconds, and m means minutes.</p> Full article ">Figure 13
<p>The prediction results without mitigation structures. Simulations for Massflow (<b>A</b>), Flow-3D (<b>B</b>), OpenLISEM_A (<b>C</b>), and OpenLISEM_B (<b>D</b>).</p> Full article ">

19 pages, 13928 KiB  

Open AccessFeature PaperArticle

Sequence Analysis of Ancient River Blocking Events in SE Tibetan Plateau Using Multidisciplinary Approaches

by Yiwei Zhang, Jianping Chen, Qing Wang, Yongchao Li, Shengyuan Song, Feifan Gu and Chen Cao

Water 2022, 14(6), 968; https://doi.org/10.3390/w14060968 - 18 Mar 2022

Cited by 1 | Viewed by 2413

Abstract

The temporary or permanent river blocking event caused by mass movement usually occurs on steep terrain. With the increase of mountain population and land use pressure and the construction of water conservancy and hydropower projects, river blocking events have gradually attracted people’s attention [...] Read more.

The temporary or permanent river blocking event caused by mass movement usually occurs on steep terrain. With the increase of mountain population and land use pressure and the construction of water conservancy and hydropower projects, river blocking events have gradually attracted people’s attention and understanding. The area in this study is affected by strong tectonic activity in the Jinsha River suture zone and the rapid uplift of the Tibetan Plateau. In the past 6000 years, there have been at least five obvious river blocking events in the reach. The number and density are very rare. Combining field investigation, indoor interpretation, laboratory tests, optically stimulated luminescence (OSL) dating, SBAS-InSAR and previous studies, multidisciplinary approaches are used to systematically summarize the analysis methods and further the understanding of one river blocking event and multiple river blocking events from different perspectives. Especially in multiple river blocking events, we can get the wrong results if interaction is not considered. Through this study, the general method of analyzing the river blocking event and the problems that should be paid attention to in sampling are given, and relatively reliable historical results of river blocking events are obtained. This method has applicability to the identification and analysis of river blocking events and age determination of dams with multiple river blockages. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1
<p>Map showing the locations and regional geologic settings (<b>a</b>) located on the southeastern edge of the Tibetan Plateau, (<b>b</b>) tectonic outline map (according to 1:1,000,000 geological map), (<b>c</b>) regional geological map (according to 1:1,000,000 geological map).</p> Full article ">Figure 2
<p>Map showing the specific geological settings of the dam site. Note: Due to the limitations in data collection, WDL, RCR and SWL were generated according to 1:50,000 geological map; SDX and GD were generated according to 1:200,000 geological map. (Note: these figures were created by ArcGIS 10.2).</p> Full article ">Figure 3
<p>Remote sensing map of river blocking event. (Note: these images are from Google Earth).</p> Full article ">Figure 4
<p>Geometry of GD landslide dam.</p> Full article ">Figure 5
<p>Lake sediments formed in dammed lakes. (<b>a</b>) In WDL-RCR reach; (<b>b</b>) in WDL-RCR reach; (<b>c</b>) in RCR-SWL reach.</p> Full article ">Figure 6
<p>Speculative profile of each dam.</p> Full article ">Figure 7
<p>Diagrammatic drawing of river geomorphology.</p> Full article ">Figure 8
<p>Diagram of the relationship between lacustrine sediments. (<b>a</b>) Situation 1; (<b>b</b>) Situation 2; (<b>c</b>) Situation 3 and Situation 4.</p> Full article ">Figure 9
<p>Unprocessed OSL dating data.</p> Full article ">Figure 10
<p>Frequency histogram of OSL dating data.</p> Full article ">Figure 11
<p>Grouping results of post-processing OSL dating data.</p> Full article ">Figure 12
<p>Stratification phenomenon in SWL-SDX reach.</p> Full article ">Figure 13
<p>Typical geological phenomena. (<b>a</b>) Inclined lacustrine sedimentary layer on SDX dam body upstream. (<b>b</b>) Horizontal sedimentary layer covers inclined sedimentary layer phenomenon on GD dam body. (<b>c</b>) Inclined lacustrine sedimentary layer on SDX dam body downstream. (<b>d</b>) Horizontally stratified lacustrine sediments in WDLII-RCR section. (<b>e</b>) Horizontally bedding lacustrine deposits layer on RCR dam body upstream.</p> Full article ">Figure 14
<p>Fluvial response to river blocking dam. (<b>a</b>) is generated from ALOS 12.5 m DEM. (<b>b</b>) is generated from ALOS 12.5 m DEM in order to determine the scope of (<b>a</b>,<b>c</b>) which is generated from GDEMV2 30M as the control group of (<b>b</b>).</p> Full article ">Figure 15
<p>The surface deformation of the residual dam body. (<b>a</b>) The average deformation rate of the whole dam body; (<b>b</b>) the average deformation rate of part of the whole area along the river; (<b>c</b>) the whole dam body response to the flood from Baige barrier lake; (<b>d</b>) the response to the flood from Baige barrier lake of part of the whole area along the river.</p> Full article ">Figure 15 Cont.
<p>The surface deformation of the residual dam body. (<b>a</b>) The average deformation rate of the whole dam body; (<b>b</b>) the average deformation rate of part of the whole area along the river; (<b>c</b>) the whole dam body response to the flood from Baige barrier lake; (<b>d</b>) the response to the flood from Baige barrier lake of part of the whole area along the river.</p> Full article ">Figure 16
<p>The general flow chart of studying the river blocking event.</p> Full article ">

14 pages, 3997 KiB  

Open AccessArticle

Numerical Simulation of Fluid Pore Pressure Diffusion and Its Mechanical Effects during Wenchuan Aftershocks

by Tao Chen, Yaowei Liu and Guomeng Zhang

Water 2022, 14(6), 952; https://doi.org/10.3390/w14060952 - 18 Mar 2022

Viewed by 2112

Abstract

The Ms 8.0 Wenchuan earthquake occurred on 12 May 2008, in the Sichuan Province of China, and it was accompanied by a series of strong aftershocks. The mechanisms contributing to the triggering of the Wenchuan aftershocks have attracted international attention. In this paper, [...] Read more.

The Ms 8.0 Wenchuan earthquake occurred on 12 May 2008, in the Sichuan Province of China, and it was accompanied by a series of strong aftershocks. The mechanisms contributing to the triggering of the Wenchuan aftershocks have attracted international attention. In this paper, based on previous analysis of spatiotemporal distribution of aftershocks regarding pore pressure diffusion of deep fluid, we established a three-dimensional hydraulic–mechanical coupling model and investigated the influence of fluid migration and its mechanical effects in the Longmenshan fault zone by using FLAC3D software. We obtained the characteristics of the pore pressure diffusion and fault reactivation within 70 days in an area NA. The results show that the pore pressure significantly increases up to 80 MPa during fluid intrusion into the fault plane. The pore pressure increase along the fault dip is greater than that along the fault strike, with a maximum difference of 3.18 MPa. The increase in pore pressure along the fault reduces the effective stress and leads to fault reactivation. The evolution of the fault reactivation area calculated in the model is compared with the spatiotemporal characteristics of the aftershocks. This study is meaningful for furthering the understanding of the role of deep fluids in fault dynamics and aftershocks triggering. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1
<p>Spatiotemporal distributions of aftershocks [<a href="#B1-water-14-00952" class="html-bibr">1</a>]. Reproduced with permission from Liu et al., Tectonophysics; published by Elsevier, 2014, with Number 5270230803519.</p> Full article ">Figure 2
<p>(<b>a</b>) The area NA. The star denotes the location of the Wenchuan mainshock, the solid black lines denote faults, F1 denotes the back fault, F2 denotes the central fault, and F3 denotes the piedmont fault (Deng et al., 2003). (<b>b</b>) Spatiotemporal distribution of aftershocks in the area NA. The grey color represents the topography of the area. (<b>c</b>) The <span class="html-italic">r–t</span> plot for the area NA. The red lines are the envelope lines for different hydraulic diffusivities D and the blue circles denote aftershocks. (<b>d</b>) The M–<span class="html-italic">t</span> plot for the area NA.</p> Full article ">Figure 3
<p>The model geometry and meshes used in the study. (<b>a</b>) A 3D view of the grid. (<b>b</b>) Plane view of the fault zone in the Y–Z plane. The triangle denotes the fluid intrusion point.</p> Full article ">Figure 4
<p>The distribution of increasing pore pressure at different times during the fluid intrusion.</p> Full article ">Figure 5
<p>The increases of pore pressure along the dip (solid line) and strike (dashed line) from the intrusion point in the fault plane.</p> Full article ">Figure 6
<p>The distribution of effective stress (<b>a</b>) in the X-direction <math display="inline"><semantics> <mrow> <msub> <msup> <mi>σ</mi> <mo>′</mo> </msup> <mrow> <mi>H</mi> <mi>max</mi> </mrow> </msub> </mrow> </semantics></math>; (<b>b</b>) in the Z-direction <math display="inline"><semantics> <mrow> <msubsup> <mi>σ</mi> <mi>v</mi> <mo>′</mo> </msubsup> </mrow> </semantics></math> during the fluid intrusion.</p> Full article ">Figure 7
<p>The distribution of the stress ratio <math display="inline"><semantics> <mrow> <msub> <msup> <mi>σ</mi> <mo>′</mo> </msup> <mrow> <mi>H</mi> <mi>max</mi> </mrow> </msub> </mrow> </semantics></math>/<math display="inline"><semantics> <mrow> <msubsup> <mi>σ</mi> <mi>v</mi> <mo>′</mo> </msubsup> </mrow> </semantics></math> during the fluid intrusion.</p> Full article ">Figure 8
<p>A comparison of the spatiotemporal distribution of aftershocks and the numerical simulation of the fault reactivation area. (<b>a</b>) The aftershock distribution at different times. (<b>b</b>) The calculated fault reactivation area at different times.</p> Full article ">Figure 9
<p>Pore pressure along the dip from the intrusion point in the fault plane for varied model and grid dimensions.</p> Full article ">

24 pages, 12252 KiB  

Open AccessArticle

Susceptibility Assessment of Debris Flows Coupled with Ecohydrological Activation in the Eastern Qinghai-Tibet Plateau

by Hu Jiang, Qiang Zou, Bin Zhou, Zhenru Hu, Cong Li, Shunyu Yao and Hongkun Yao

Remote Sens. 2022, 14(6), 1444; https://doi.org/10.3390/rs14061444 - 17 Mar 2022

Cited by 16 | Viewed by 3120

Abstract

The eastern margin of the Qinghai-Tibet Plateau is an extreme topography transition zone, and characterized by significant vegetation zonation, in addition to geographic features (such as enormous topographic relief and active tectonics) that control the occurrence of debris flows, which are rapid, surging [...] Read more.

The eastern margin of the Qinghai-Tibet Plateau is an extreme topography transition zone, and characterized by significant vegetation zonation, in addition to geographic features (such as enormous topographic relief and active tectonics) that control the occurrence of debris flows, which are rapid, surging flows of water-charged clastic sediments moving along a steep channel and are one of the most dangerous mountain hazards in this region. There is thus an urgent need in this region to conduct a regional-scale debris flow susceptibility assessment to determine the spatial likelihood of a debris flow occurrence and guarantee the safety of people and property, in addition to the smooth operation of the Sichuan-Tibet transport corridor. It is, however, a challenging task to estimate the region’s debris flow susceptibility while taking into consideration the comprehensive impacts of vegetation on the occurrence of debris flows, such as the positive effect of root anchoring and the negative effect of vegetation weight loads. In this study, a novel regional-scale susceptibility assessment method was constructed by integrating state-of-the-art machine learning algorithms (such as support vector classification (SVC), random forest (RF), and eXtreme Gradient Boosting (XGB)) with the removing outliers (RO) algorithm and particle swarm optimization (PSO), allowing the impacts of vegetation on debris flow initiation to be integrated with the topographical conditions, hydrological conditions, and geotechnical conditions. This method is finally applied to assess the regional-scale susceptibility of debris flows in the Dadu River basin on the eastern margin of the Qinghai-Tibet Plateau. The study results show that (i) all hybrid machine learning techniques can effectively predict the occurrence of debris flows in the extreme topography transition zone; (ii) the hybrid machine learning technique RO-PSO-SVC has the best performance, and its accuracy (ACC) is 0.946 and the area under the ROC curve (AUC) is 0.981; (iii) the RO-PSO algorithm improves SVC, RF, and XGB performance (according to the ACC value) by 3.84%, 2.59%, and 5.94%, respectively; and (iv) the contribution rate of ecology-related variables is almost only one-tenth that of topography- and hydrology-related factors, according to the factor important analysis for RO-PSO-SVC. Furthermore, debris flow susceptibility maps for the Dadu River basin were created, which can be used to assess and mitigate debris flow hazards. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>Location of the study area (Dadu River basin) and the distribution of faults.</p> Full article ">Figure 2
<p>Methodological flow chart.</p> Full article ">Figure 3
<p>Catchment units in the Dadu River basin.</p> Full article ">Figure 4
<p>Susceptibility prediction index system sketch coupled with eco-hydrological activation.</p> Full article ">Figure 5
<p>Correlation matrix among the predictor variables.</p> Full article ">Figure 6
<p>The data used for the susceptibility prediction: (<b>a</b>) the soil mass strength, (<b>b</b>) soil mass thickness, (<b>c</b>) vegetation types, (<b>d</b>) channel connectivity, (<b>e</b>) flow depth, and (<b>f</b>) runoff velocity.</p> Full article ">Figure 6 Cont.
<p>The data used for the susceptibility prediction: (<b>a</b>) the soil mass strength, (<b>b</b>) soil mass thickness, (<b>c</b>) vegetation types, (<b>d</b>) channel connectivity, (<b>e</b>) flow depth, and (<b>f</b>) runoff velocity.</p> Full article ">Figure 7
<p>The composition structure of the cross-validation dataset.</p> Full article ">Figure 8
<p>Receiver operating characteristic (ROC) curve and the AUC of different machine learning models.</p> Full article ">Figure 9
<p>Spatial consistency matrix among the debris flow susceptibility maps.</p> Full article ">Figure 10
<p>The debris flow susceptibility maps of the Dadu River based on the (<b>a</b>) SVC, (<b>b</b>) RF, (<b>c</b>) XGB, (<b>d</b>) PSO-SVC, (<b>e</b>) PSO-RF, (<b>f</b>) PSO-XGB, (<b>g</b>) RO-PSO-SVC, (<b>h</b>) RO-PSO-RF, and (<b>i</b>) RO-PSO-XGB models.</p> Full article ">Figure 11
<p>Proportions of the different debris susceptibility levels among the hybrid or non-hybrid models.</p> Full article ">Figure 12
<p>Proportions of the historical debris flow occurring at the catchments with different susceptibility levels among the different models.</p> Full article ">Figure 13
<p>The relative importance of disaster-causing factors in the RO-PSO-SVC model. The relative importance is normalized so that they sum to 1.</p> Full article ">Figure 14
<p>Proportions of the catchments with different debris flow susceptibility obtained by RO-PSO-SVC for each level of different triggering factors.</p> Full article ">

14 pages, 15589 KiB  

Open AccessArticle

BLP3-SP: A Bayesian Log-Pearson Type III Model with Spatial Priors for Reducing Uncertainty in Flood Frequency Analyses

by Dan Tian and Lei Wang

Water 2022, 14(6), 909; https://doi.org/10.3390/w14060909 - 14 Mar 2022

Cited by 3 | Viewed by 3009

Abstract

Gauge stations have uneven lengths of discharge records owing to the historical hydrologic data collection efforts. For watersheds with limited water data length, the flood frequency model, such as the Log-Pearson Type III, will have large uncertainties. To improve the flood frequency prediction [...] Read more.

Gauge stations have uneven lengths of discharge records owing to the historical hydrologic data collection efforts. For watersheds with limited water data length, the flood frequency model, such as the Log-Pearson Type III, will have large uncertainties. To improve the flood frequency prediction for these watersheds, we propose a Bayesian Log-Pearson Type III model with spatial priors (BLP3-SP), which uses a spatial regression model to estimate the prior distribution of the parameters from nearby stations with longer data records and environmental factors. A Markov chain Monte Carlo (MCMC) algorithm is used to estimate the posterior distribution and associated flood quantiles. The method is validated using a case study watershed with 15 streamflow gauge stations located in the San Jacinto River Basin in Texas, US. The result shows that the BLP3-SP outperforms other choices of the priors for the Bayesian Log-Pearson Type III model by significantly reducing the uncertainty in the flood frequency estimation for the station with short data length. The results have confirmed that the spatial prior knowledge can improve the Bayesian inference of the Log-Pearson Type III flood frequency model for watersheds with short gauge period. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>Flowchart of the BLP3-SP processing.</p> Full article ">Figure 2
<p>Study area, locations of the 15 gauge stations, and associated watersheds.</p> Full article ">Figure 3
<p>Data availability of the entire set of streamflow gauges used in this study (the gray row is the site for testing and validation and the red dots are the 10-year time series used in the model).</p> Full article ">Figure 4
<p>The means and 95% confidence limits for the three scenarios: non-informative prior and 54-year data, non-informative prior and the last 10-year data, and spatial regression prior and the last 10-year data.</p> Full article ">Figure 5
<p>The 95% confidence interval for four scenarios: non-informative prior, mean prior, areal interpolation priors, and spatial regression prior.</p> Full article ">Figure 6
<p>Applying spatial regression prior to (<b>a</b>) 20-year and (<b>b</b>) 30-year data series.</p> Full article ">

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Supplementary material:
Supplementary File 1 (ZIP, 656 KiB)

14 pages, 5292 KiB  

Open AccessTechnical Note

Distinct Susceptibility Patterns of Active and Relict Landslides Reveal Distinct Triggers: A Case in Northwestern Turkey

by Marco Loche, Luigi Lombardo, Tolga Gorum, Hakan Tanyas and Gianvito Scaringi

Remote Sens. 2022, 14(6), 1321; https://doi.org/10.3390/rs14061321 - 9 Mar 2022

Cited by 8 | Viewed by 3580

Abstract

To understand the factors that make certain areas especially prone to landslides, statistical approaches are typically used. The interpretation of statistical results in areas characterised by complex geological and geomorphological patterns can be challenging, and this makes the understanding of the causes of [...] Read more.

To understand the factors that make certain areas especially prone to landslides, statistical approaches are typically used. The interpretation of statistical results in areas characterised by complex geological and geomorphological patterns can be challenging, and this makes the understanding of the causes of landslides more difficult. In some cases, landslide inventories report information on the state of activity of landslides, adding a temporal dimension that can be beneficial in the analysis. Here, we used an inventory covering a portion of Northwestern Turkey to demonstrate that active and relict landslides (that is, landslides that occurred in the past and are now stabilised) could be related to different triggers. To do so, we built two landslide susceptibility models and observed that the spatial patterns of susceptibility were completely distinct. We found that these patterns were correlated with specific controlling factors, suggesting that active landslides are regulated by current rainfalls while relict landslides may represent a signature of past earthquakes on the landscape. The importance of this result resides in that we obtained it with a purely data-driven approach, and this was possible because the active/relict landslide classification in the inventory was accurate. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>Identification of the study area in Turkey and the North Anatolian Fault (<b>a</b>); density maps of inactive (<b>b</b>) and active (<b>c</b>) landslides (see <a href="#sec2dot2-remotesensing-14-01321" class="html-sec">Section 2.2</a> for definitions); Peak Ground Acceleration map (<b>d</b>) from [<a href="#B40-remotesensing-14-01321" class="html-bibr">40</a>]; mean annual precipitation map (<b>e</b>) from [<a href="#B41-remotesensing-14-01321" class="html-bibr">41</a>].</p> Full article ">Figure 2
<p>Slope Unit partition of the study area: SUs containing inactive (<b>left</b>) and active (<b>right</b>) landslides are shown. The sub-panels show a detail for a small region, in which it is possible to observe the flat areas excluded by the SU calculation (see <a href="#sec4dot2-remotesensing-14-01321" class="html-sec">Section 4.2</a> for explanation). The legend is valid for the whole area and zoomed panels.</p> Full article ">Figure 3
<p>Fixed effects of geomorphological variables expressed as marginal distributions for inactive and active landslides.</p> Full article ">Figure 4
<p>Nonlinear effects of slope (<b>left</b> column) and precipitation (<b>right</b> column) for inactive (<b>top</b> row) and active (<b>bottom</b> row) landslides. The effect is modelled as a random effect estimated over 20 classes with adjacent dependency. Thick coloured lines represent the posterior means whereas the coloured dashed lines indicate the posterior 95% credible interval. Dashed grey lines indicate the zero line along which coefficients play no role in the modelling outcome.</p> Full article ">Figure 5
<p>(<b>a</b>) ROC curves and their AUCs for ten cross-validations for the inactive (<b>left</b>) and active (<b>right</b>) landslide models. (<b>b</b>) Confusion plot (<b>left</b>) constructed via the percentage of Observed TP and fitted TP against the percentage of Observed TN and fitted TN (for each landslide type), and error rates (<b>right</b>), both have been obtained from a tenfold CV.</p> Full article ">Figure 6
<p>Susceptibility maps for inactive (<b>a</b>) and active (<b>b</b>) landslides. The maps are obtained by merging ten cross-validated subsets and thus entirely come from predicted estimates. The resulting probability values have been binned into seven susceptibility classes using a quantile criterion. The difference in susceptibility between (<b>a</b>) and (<b>b</b>) is shown in (<b>c</b>), while the graph in (<b>d</b>) displays their Pearson correlation.</p> Full article ">

18 pages, 7541 KiB  

Open AccessFeature PaperArticle

In Situ Experimental Study of Natural Diatomaceous Earth Slopes under Alternating Dry and Wet Conditions

by Zhixing Deng, Wubin Wang, Tengfei Yan, Kang Xie, Yandong Li, Yangyang Liu and Qian Su

Water 2022, 14(5), 831; https://doi.org/10.3390/w14050831 - 7 Mar 2022

Cited by 5 | Viewed by 3609

Abstract

Very few studies have focused on diatomaceous earth slopes along high-speed railways, and the special properties of diatomaceous earth under alternating dry and wet conditions are unknown. This paper studies diatomaceous earth in the Shengzhou area, through which the newly built Hangzhou–Taizhou high-speed [...] Read more.

Very few studies have focused on diatomaceous earth slopes along high-speed railways, and the special properties of diatomaceous earth under alternating dry and wet conditions are unknown. This paper studies diatomaceous earth in the Shengzhou area, through which the newly built Hangzhou–Taizhou high-speed railway passes, and the basic physical and hydraulic properties of diatomaceous earth are analyzed by indoor test methods. A convenient, efficient, and controllable high-speed railway slope artificial rainfall simulation system is designed, and in situ comprehensive monitoring and fissure observation are performed on site to analyze the changes in various diatomaceous soil slope parameters under rainfall infiltration, and to explore the cracking mechanisms of diatomaceous earth under alternating dry and wet conditions. The results indicate extremely poor hydrophysical properties of diatomaceous earth in the Shengzhou area; the disintegration resistance index values of natural diatomaceous earth samples subjected to dry and wet cycles are 1.8–5.6%, and the disintegration is strong. Comprehensive indoor tests and water content monitoring show that natural diatomaceous earth has no obvious influence when it contacts water, but it disintegrates and cracks under alternating dry and wet conditions. The horizontal displacement of both slope types mainly occurs within 0.75–2.75 m of the surface layer, indicating shallow surface sliding; after testing, natural slope crack widths of diatomaceous earth reach 10–25 mm, and their depths reach 40–60 cm. To guarantee safety during high-speed railway engineering construction, implementing proper protection for diatomaceous earth slopes is recommended. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>Test site and white diatomaceous earth: (<b>a</b>) Shengzhou diatomaceous earth slope test section; (<b>b</b>) white diatomaceous earth; (<b>c</b>) white diatomaceous earth drilling and construction.</p> Full article ">Figure 1 Cont.
<p>Test site and white diatomaceous earth: (<b>a</b>) Shengzhou diatomaceous earth slope test section; (<b>b</b>) white diatomaceous earth; (<b>c</b>) white diatomaceous earth drilling and construction.</p> Full article ">Figure 2
<p>Slope failure problem: (<b>a</b>) natural slope with a slope ratio of 1:1.5; (<b>b</b>) penetrating cracks that appear under the alternating action of dry and wet conditions.</p> Full article ">Figure 3
<p>Natural diatomaceous earth and natural diatomaceous earth after shade-drying to observe the phenomenon of water immersion.</p> Full article ">Figure 4
<p>Disintegration test results of the two sets of samples: (<b>a</b>) comparison chart showing the disintegration resistance index values of the first group of samples; (<b>b</b>) comparison chart showing the disintegration resistance index values of the second group of samples; (<b>c</b>) comparison chart showing the disintegration rates of the first group of samples (average mass of 151.22 g).</p> Full article ">Figure 5
<p>Schematic diagram of a convenient, efficient, and controllable high-speed railway slope artificial rainfall simulation system.</p> Full article ">Figure 6
<p>Site installation of the rainfall system.</p> Full article ">Figure 7
<p>Dry and wet cycle simulation test: (<b>a</b>) rain at night; (<b>b</b>) dry during the day; (<b>c</b>) rainproof measures.</p> Full article ">Figure 8
<p>Sensor layout (side view).</p> Full article ">Figure 9
<p>Water content analysis: (<b>a</b>) 1:2 natural slope water content variation trend; (<b>b</b>) 1:1.5 natural slope water content variation trend; (<b>c</b>) excavation of the diatomaceous earth slope.</p> Full article ">Figure 10
<p>Horizontal displacement analysis: (<b>a</b>) 1:2 natural slope horizontal displacement variation trend; (<b>b</b>) 1:1.5 natural slope horizontal displacement variation trend.</p> Full article ">Figure 11
<p>Lateral earth pressure analysis: (<b>a</b>) 1:2 natural slope lateral earth pressure variation trend; (<b>b</b>) 1:1.5 natural slope lateral earth pressure variation trend; (<b>c</b>) stacking on the top of the slope.</p> Full article ">Figure 12
<p>Groundwater level change.</p> Full article ">Figure 13
<p>Slope fissure diagram: (<b>a</b>) test section on the 1:2 side slope; (<b>b</b>) test section on the 1:1.5 side slope.</p> Full article ">Figure 14
<p>Natural side slope observation map.</p> Full article ">

19 pages, 5926 KiB  

Open AccessArticle

Investigation of Tsunami Waves in a Wave Flume: Experiment, Theory, Numerical Modeling

by Boris Vladimirovich Boshenyatov

GeoHazards 2022, 3(1), 125-143; https://doi.org/10.3390/geohazards3010007 - 3 Mar 2022

Viewed by 3515

Abstract

To protect the coastal areas of the seas and oceans from the destructive force of tsunami waves, coastal and surface barriers are usually built. However, for high waves, these barriers turn into underwater barriers through which tsunami waves pass practically without losing their [...] Read more.

To protect the coastal areas of the seas and oceans from the destructive force of tsunami waves, coastal and surface barriers are usually built. However, for high waves, these barriers turn into underwater barriers through which tsunami waves pass practically without losing their energy. In this paper, we study a new principle of suppression of the energy of tsunami waves by underwater barriers. The problems of experimental and numerical modeling of the processes of generation, propagation, and interaction of gravity wave of the tsunami type with underwater barriers are considered. It is shown that, under certain conditions near the underwater barriers, large-scale vortex structures occur that accumulate a significant part of the energy of the incident wave. Here, if the barriers parameter h/(H + A) = 0.84 ÷ 0.85 (h—height of the barriers, A—amplitude of incident wave on a barrier, H—depth of the reservoir), then the vortex structures accumulate up to 50% of the wave energy incident on the barrier. A theoretical model explaining the effect of anomalous vortex suppression of tsunami wave energy by underwater barriers has been developed. Theoretical calculations and results of numerical modeling based on the Navier–Stokes Equations are consistent with experimental studies in a hydrodynamic wave flume. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>The hydrodynamic channel (wave flume) of the IPRIM RAS.</p> Full article ">Figure 2
<p>Calibration stand and equipment.</p> Full article ">Figure 3
<p>Тypical calibration curve. (a) Schematic diagram of the resistive sensor.</p> Full article ">Figure 4
<p>Schematic drawing of the caisson-type generator.</p> Full article ">Figure 5
<p>Modeling the process of generating a tsunami-type wave in a wave flume by an ideal generator at <span class="html-italic">H</span> = 0.103 m, <span class="html-italic">A/H</span> = 0.1.</p> Full article ">Figure 6
<p>Comparison of experimental dependence wave height—time (green line) ξ(<span class="html-italic">t</span>) = <span class="html-italic">H</span>_ξ(<span class="html-italic">t</span>) − <span class="html-italic">H</span> with the numerical calculation for ideal generator (black line) at a distance of 1.5 m from the front wall of wave generator. The red dashed line is the model wave profile.</p> Full article ">Figure 7
<p>The diagram (<span class="html-italic">x-t</span>) of gravity tsunami-like waves, which propagate in the hydrodynamic channel at the following initial parameters: <span class="html-italic">η</span>₀ = 15 mm, <span class="html-italic">H</span> = 102 mm, <span class="html-italic">A/H</span> = 0.074. Makers—experiments. Lines—linear theory of shallow water. The blue line is the trajectory of the incident wave; the red lines are the reflected waves. <span class="html-italic">G</span>—wave generator; <span class="html-italic">W</span>—reflecting wall; 1–4—locations of wave level sensors.</p> Full article ">Figure 8
<p>Dependence of long gravity waves velocity in the hydrodynamic channel on the nonlinearity parameter <span class="html-italic">A/H</span>: 1—linear theory of shallow water, 2—Navier–Stokes Equations, 3—nonlinear theory of shallow water (4А).</p> Full article ">Figure 9
<p>Breaking of wave interacting with shallow water. The comparison of experimental results with numerical simulation at <span class="html-italic">H</span> = 0.135 m, <span class="html-italic">A/H</span> = 0.61, <span class="html-italic">H</span>_sh = 0.054 m, <span class="html-italic">A</span>/<span class="html-italic">H</span>_sh = 1.52: (<b>a</b>,<b>c</b>)—<span class="html-italic">t</span> = 8.3 s; (<b>b</b>,<b>d</b>)—<span class="html-italic">t</span> = 8.4 s.</p> Full article ">Figure 10
<p>Schematic diagram of the interaction of a tsunami-type wave with impenetrable barriers No. 1 and No. 2.</p> Full article ">Figure 11
<p>Reflection coefficient as a function of dimensionless parameter <span class="html-italic">h</span>/(<span class="html-italic">H + A</span>): (1)—No. 1, <span class="html-italic">A/H</span> = 0.286 [<a href="#B4-geohazards-03-00007" class="html-bibr">4</a>]; (2)—No. 1, <span class="html-italic">A/H</span> = 0.04–0.05; (3)—No. 2, <span class="html-italic">A/H</span> = 0.04–0.10; (4)—Navier–Stokes Equations, <span class="html-italic">A/H</span> = 0.07 and (5)—linear theory of long waves.</p> Full article ">Figure 12
<p>The sum of the relative energies of the waves reflected and transmitted through the barrier as a function of the generalized parameter of the barrier height: 1—experiments; 2—numerical experiment based on f Equations; 3—calculations by author’s theory at <span class="html-italic">Н</span> = 0.103 m, <span class="html-italic">А</span> = 0.007 m, and <span class="html-italic">k</span> = 0.68.</p> Full article ">Figure 13
<p>Visualization for various instants of time of velocity fields near thin barrier No. 2 (black color) in the case of transmission through it of a tsunami type wave: <span class="html-italic">A/H</span> = 0.07, parameter <span class="html-italic">h</span>/(<span class="html-italic">H</span> + <span class="html-italic">A</span>) = 0.9. The dashed line corresponds to the unperturbed water flow level.</p> Full article ">Figure 14
<p>Schematic diagram of the interaction of a tsunami-type wave with a thin impenetrable barrier. Shallow water theory is not applicable near the barrier: −<span class="html-italic">L/2</span> &lt; <span class="html-italic">x</span> &lt; +<span class="html-italic">L/2</span>.</p> Full article ">Figure A1
<p>A typical oscillogram of tsunami-type wave registration in a hydrodynamic channel.</p> Full article ">Figure A2
<p>The process of generating a model wave by a caisson-type generator.</p> Full article ">Figure A3
<p>Schematic drawing of the motion of a gravitational wave in various frames of reference: (<span class="html-italic">a</span>) in a fixed (laboratory) frame of reference; (<span class="html-italic">b</span>) in a moving (with the speed of a wave) frame of reference.</p> Full article ">

12 pages, 4000 KiB  

Open AccessTechnical Note

Tropical Cyclone Impact and Forest Resilience in the Southwestern Pacific

by Baptiste Delaporte, Thomas Ibanez, Marc Despinoy, Morgan Mangeas and Christophe Menkes

Remote Sens. 2022, 14(5), 1245; https://doi.org/10.3390/rs14051245 - 3 Mar 2022

Cited by 6 | Viewed by 3146

Abstract

Tropical cyclones (TCs) can have profound effects on the dynamics of forest vegetation that need to be better understood. Here, we analysed changes in forest vegetation induced by TCs using the normalized difference vegetation index (NDVI). We used an accurate historical database of [...] Read more.

Tropical cyclones (TCs) can have profound effects on the dynamics of forest vegetation that need to be better understood. Here, we analysed changes in forest vegetation induced by TCs using the normalized difference vegetation index (NDVI). We used an accurate historical database of TC tracks and intensities, together with the Willoughby cyclone model to reconstruct the 2D surface wind speed structure of TCs and analyse how TCs affect forest vegetation. We used segmented linear models to identify significant breakpoints in the relationship between the reconstructed maximum sustained wind speed (Wmax) and the observed changes in NDVI. We tested the hypothesis that the rate of change in damage caused by TCs to forest and recovery time would increase according to Wmax thresholds as defined in the widely used Saffir–Simpson hurricane wind scale (SSHWS). We showed that the most significant breakpoint was located at 50 m/s. This breakpoint corresponds to the transition between categories 2 and 3 TCs in the SSHWS. Below this breakpoint, damages caused to forest vegetation and the time needed to recover from these damages were negligable. We found a second breakpoint, with a sharp increase in damages for winds >75 m/s. This suggested that extremely intense tropical cyclones, which might be more frequent in the future, can cause extreme damages to forest vegetation. Nevertheless, we found high variation in the observed damages and time needed to recover for a given Wmax. Further studies are needed to integrate other factors that might affect the exposure and resistance to TCs as well as forests’ capacity to recover from these disturbances. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1
<p>Trajectories of all tropical cyclones reaching category 1 or higher (i.e., Wmax ≥ 33 m/s) at some point on their tracks between 2000 and 2020 in the South West Pacific Basin.</p> Full article ">Figure 2
<p>Example of 2D surface wind speed structure reconstructed using the Willoughby model (Equation (2) (f and g)) for category 1 tropical cyclone Mick (3–5 December 2009), category 3 tropical cyclone Ivy (21–28 February 2004), and category 5 tropical cyclone Winston (7 February–3 March 2016). Vertical dotted lines represent RMW, the distance r from the center of the tropical cyclone where winds reach their maximum speed (Wmax.)</p> Full article ">Figure 3
<p>Mean and standard deviation of relative change in NDVI (<math display="inline"><semantics> <mo>Δ</mo> </semantics></math><math display="inline"><semantics> <mrow> <mi>N</mi> <mi>D</mi> <mi>V</mi> <msub> <mi>I</mi> <mrow> <mi>t</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>) (<b>a</b>,<b>b</b>) and recovery time (TR) (<b>c</b>,<b>d</b>) as a function of maximum sustained wind speed (<math display="inline"><semantics> <msub> <mi>W</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </semantics></math>). Values in bold represent the average for each TC category (SSHWS). Blue lines correspond to piecewise linear regressions with breakpoints based on the SSHWS TC classification and yellow lines correspond to piecewise linear regressions with optimized breakpoints minimizing the Bayesian Information Criterion.</p> Full article ">Figure 4
<p>Example of relative change in NDVI observed after tropical cyclone Winston on Viti Levu in Fiji (<b>a</b>), reconstructed maximum sustained wind speed (<b>b</b>), predicted changes in NDVI (<b>c</b>) using the optimized models, and predicted recovery time (<b>d</b>). Winston severely impacted Viti Levu with wind gusts up to 78 m/s between 19 and 20 February 2016.</p> Full article ">

17 pages, 3704 KiB  

Open AccessArticle

Extracting Disaster-Related Location Information through Social Media to Assist Remote Sensing for Disaster Analysis: The Case of the Flood Disaster in the Yangtze River Basin in China in 2020

by Tengfei Yang, Jibo Xie, Guoqing Li, Lianchong Zhang, Naixia Mou, Huan Wang, Xiaohan Zhang and Xiaodong Wang

Remote Sens. 2022, 14(5), 1199; https://doi.org/10.3390/rs14051199 - 28 Feb 2022

Cited by 20 | Viewed by 3242

Abstract

Social media texts spontaneously produced and uploaded by the public contain a wealth of disaster information. As a supplementary data source for remote sensing, they have played an important role in disaster reduction and emergency response in recent years. However, social media also [...] Read more.

Social media texts spontaneously produced and uploaded by the public contain a wealth of disaster information. As a supplementary data source for remote sensing, they have played an important role in disaster reduction and emergency response in recent years. However, social media also has certain flaws, such as insufficient location information, etc. This affects the efficiency of combining these data with remote sensing data. For flood disasters in particular, extensively flooded areas limit the distribution of social media data, which makes it difficult for these data to function as they should. In this paper, we propose a disaster reduction framework to solve these problems. We first used an approach that was based on search engine and lexical rules to automatically extract disaster-related location information from social media texts. Then, we combined the extracted information with the upload location of social media itself to construct location-pointing relationships. These relationships were used to build a new social network, which can be used in combination with remote sensing images for disaster analysis. The analysis integrated the advantages of social media and remote sensing. It can not only provide macro disaster information in the study area but can also assist in evaluating the disaster situation in different flooded areas from the perspective of public observation. In addition, the timeliness of social media data also improved the continuity and situational awareness of flood monitoring. A case study of the flood disaster in the Yangtze River Basin in China in 2020 was used to verify the effectiveness of the method described in this paper. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>The study area shown in this paper. Among them, (<b>a</b>) depicts the cities involved in the study area; (<b>b</b>) shows the SAR remote sensing image covering the study area.</p> Full article ">Figure 2
<p>The structure of the proposed framework in this paper.</p> Full article ">Figure 3
<p>The process of extracting locational words in social media text.</p> Full article ">Figure 4
<p>The structure of the reconstructed social network.</p> Full article ">Figure 5
<p>Process of flooded area extraction in this work using remotely sensed data.</p> Full article ">Figure 6
<p>The spatial distribution relationship between social media data and flooded areas. Among them, (<b>a</b>) shows the flooded area based on remote sensing images; (<b>b</b>) overlays social media data, which have location tags, on a remote sensing image.</p> Full article ">Figure 7
<p>Superposition analysis of multi-source disaster information.</p> Full article ">Figure 8
<p>Monitoring the disaster in “Tongda Town” based on social media data. Among them, (<b>a</b>) depicts how the themes of social media data related to “Tongda Town” changed over time; (<b>b</b>) depicts how the amount of social media data related to “Tongda Town” changed over time.</p> Full article ">

19 pages, 9966 KiB  

Open AccessArticle

Tsunami Hazard Zone and Multiple Scenarios of Tsunami Evacuation Route at Jetis Beach, Cilacap Regency, Indonesia

by Fx Anjar Tri Laksono, Asmoro Widagdo, Maulana Rizki Aditama, Muhammad Rifky Fauzan and János Kovács

Sustainability 2022, 14(5), 2726; https://doi.org/10.3390/su14052726 - 25 Feb 2022

Cited by 9 | Viewed by 3754

Abstract

The 2006 tsunami, throughout the Pangandaran to Cilacap Coast, resulted in 802 deaths and 1623 houses being destroyed. At Jetis beach, Cilacap Regency, 12 people died, and hundreds of houses were damaged. This area is a tourism destination, visited by hundreds of people [...] Read more.

The 2006 tsunami, throughout the Pangandaran to Cilacap Coast, resulted in 802 deaths and 1623 houses being destroyed. At Jetis beach, Cilacap Regency, 12 people died, and hundreds of houses were damaged. This area is a tourism destination, visited by hundreds of people per week. Therefore, this study aims to determine a tsunami hazard zone and the most effective evacuation route based on multiple factors and scenarios. The method of this study includes scoring, weighting, and overlaying the distance of the Jetis beach from the shoreline and the river, including the elevation and topography. The study results depict five levels of tsunami hazard zone at the Jetis beach: an area of high potential impact, moderately high, moderate, moderately low, and low. The southern Jetis beach is the most vulnerable area with regard to tsunamis, characterized by low elevation, proximity to the beach and rivers, and gentle slopes. The simulation results show the four fastest evacuation routes with the distance from the high-risk zone to the safe zone of around 683–1683 m. This study infers that the southern part of the Jetis beach, in the moderate to high impact zone, needs greater attention as it would suffer worst impact from a tsunami. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>Jetis beach lies in Nusawungu District, Cilacap Regency, Central Java Province. This beach is a tourism destination in Cilacap. The study area in the image is shown in pink.</p> Full article ">Figure 2
<p>Flowchart of tsunami hazard zone mapping at Jetis beach, Cilacap. The final result of this process is determining tsunami hazard map multiple factors.</p> Full article ">Figure 3
<p>Flowchart of establishing tsunami evacuation routes at Jetis Beach using Dijkstra algorithm.</p> Full article ">Figure 4
<p>The tsunami hazard zone map of Jetis beach based on the distance from the coastline, divided into four classes: less than 1400 m, 1401–2404 m, 2405–3528 m, and more than 3528 m.</p> Full article ">Figure 5
<p>Based on elevation, the tsunami hazard zone at Jetis beach consists of four classes: 0–5 m, 5–10 m, 10–20 m, and more than 20 m.</p> Full article ">Figure 6
<p>Based on the slope, the tsunami hazard zone at Jetis beach is divided into four classes: flat–gentle, gentle–tilt, tilt–steep, and steep–very steep.</p> Full article ">Figure 7
<p>Based on the distance of Jetis beach to the river, the tsunami hazard zone is categorized into four levels: less than 900 m, 900–1800 m, 1800–2250 m, and more than 2250 m.</p> Full article ">Figure 8
<p>Five groups of the tsunami hazard zone based on overlaying Jetis beach’s distance from the river and shoreline as well as its elevation and slope: high risk, medium to high risk, medium, medium to low risk, and low risk.</p> Full article ">Figure 9
<p>The first evacuation route from point 1, a vulnerable zone, to point 8, a safe zone, is 1683 m.</p> Full article ">Figure 10
<p>The second evacuation route from point 1, a vulnerable zone, to point 19, a safe zone, is 998 m.</p> Full article ">Figure 11
<p>The third evacuation route from point 13, a vulnerable zone, to point 19, a safe zone, is 683 m.</p> Full article ">Figure 12
<p>The fourth evacuation route from point 16, a vulnerable zone, to point 19, a safe zone, is 1125 m.</p> Full article ">Figure A1
<p>The m-file formula of the Dijkstra algorithm was used in the evacuation route map.</p> Full article ">Figure A2
<p>The first evacuation route from points 1 to 8.</p> Full article ">Figure A3
<p>The second evacuation route from points 13 to 19.</p> Full article ">Figure A4
<p>The third evacuation route from points 16 to 19.</p> Full article ">Figure A5
<p>The fourth evacuation route from points 16 to 19.</p> Full article ">

17 pages, 4454 KiB  

Open AccessArticle

Regional Landslide Hazard Assessment Using Extreme Value Analysis and a Probabilistic Physically Based Approach

by Hyuck-Jin Park, Kang-Min Kim, In-Tak Hwang and Jung-Hyun Lee

Sustainability 2022, 14(5), 2628; https://doi.org/10.3390/su14052628 - 24 Feb 2022

Cited by 6 | Viewed by 2398

Abstract

The accurate assessment of landslide hazards is important in order to reduce the casualties and damage caused by landslides. Landslide hazard assessment combines the evaluation of spatial and temporal probabilities. Although various statistical approaches have been used to estimate spatial probability, these methods [...] Read more.

The accurate assessment of landslide hazards is important in order to reduce the casualties and damage caused by landslides. Landslide hazard assessment combines the evaluation of spatial and temporal probabilities. Although various statistical approaches have been used to estimate spatial probability, these methods only evaluate the statistical relationships between factors that have triggered landslides in the past rather than the slope failure process. Therefore, a physically based approach with probabilistic analysis was adopted here to estimate the spatial distribution of landslide probability. Meanwhile, few studies have addressed temporal probability because historical records of landslides are not available for most areas of the world. Therefore, an indirect approach based on rainfall frequency and using extreme value analysis and the Gumbel distribution is proposed and used in this study. In addition, to incorporate the nonstationary characteristics of rainfall data, an expanding window approach was used to evaluate changes in the mean annual maximum rainfall and the location and scale parameters of the Gumbel distribution. Using this approach, the temporal probabilities of future landslides were estimated and integrated with spatial probabilities to assess and map landslide hazards. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>The study area and location of the landslides.</p> Full article ">Figure 2
<p>Geology of the study area.</p> Full article ">Figure 3
<p>The infinite slope model.</p> Full article ">Figure 4
<p>Distribution of slope angle.</p> Full article ">Figure 5
<p>Distribution of soil thickness.</p> Full article ">Figure 6
<p>Hourly rainfall, 14–16 July 2006.</p> Full article ">Figure 7
<p>Relationship between the mean annual maximum (AM) rainfall and time using an expanding window.</p> Full article ">Figure 8
<p>Relationship between the mean annual maximum (AM) rainfall and location parameters.</p> Full article ">Figure 9
<p>Relationship between the mean annual maximum (AM) rainfall and scale parameters.</p> Full article ">Figure 10
<p>Spatial probability of landslide occurrence.</p> Full article ">Figure 11
<p>Landslide hazard maps. (<b>a</b>) 10 years; (<b>b</b>) 50 years; (<b>c</b>) 100 years; (<b>d</b>) 150 years.</p> Full article ">Figure 11 Cont.
<p>Landslide hazard maps. (<b>a</b>) 10 years; (<b>b</b>) 50 years; (<b>c</b>) 100 years; (<b>d</b>) 150 years.</p> Full article ">

19 pages, 5831 KiB  

Open AccessArticle

Interseismic Fault Coupling and Slip Rate Deficit on the Central and Southern Segments of the Tanlu Fault Zone Based on Anhui CORS Measurements

by Tingye Tao, Hao Chen, Shuiping Li, Xiaochuan Qu and Yongchao Zhu

Remote Sens. 2022, 14(5), 1093; https://doi.org/10.3390/rs14051093 - 23 Feb 2022

Cited by 4 | Viewed by 2198

Abstract

The Tanlu fault zone, extending over 2400 km from South China to Russia, is one of the most conspicuous tectonic elements in eastern Asia. In this study, we processed the Global Positioning System (GPS) measurements of Anhui Continuously Operating Reference System (AHCORS) between [...] Read more.

The Tanlu fault zone, extending over 2400 km from South China to Russia, is one of the most conspicuous tectonic elements in eastern Asia. In this study, we processed the Global Positioning System (GPS) measurements of Anhui Continuously Operating Reference System (AHCORS) between January 2013 and June 2018 to derive a high-precision velocity field in the central and southern segments of the Tanlu fault zone. We integrated the AHCORS data with those publicly available for geodetic imaging of the interseismic coupling and slip rate deficit distribution in the central and southern segments of the Tanlu fault zone. This work aims at a better understanding of strain accumulation and future seismic hazard in the Tanlu fault zone. The result indicates lateral variation of coupling distribution along the strike of the Tanlu fault zone. The northern segment of the Tanlu fault zone has a larger slip rate deficit and a deeper locking depth than the southern segment. Then, we analyzed three velocity profiles across the fault. The result suggests that the central and southern segments of the Tanlu fault zone are characterized by right-lateral strike-slip (0.29–0.44 mm/y) with compression components (0.35–0.76 mm/y). Finally, we estimated strain rates using the least-squares collocation method. The result shows that the dilatation rates concentrate in the region where the principal strain rates are very large. The interface of extension and compression is always accompanied by sudden change of direction of principal strain rates. Especially, in the north of Anhui, the dilatation rate is largest, reaching $3.780\times {10}^{-8}/a$. Our study suggests that the seismic risk in the northern segment of the Tanlu fault zone remains very high for its strong strain accumulation and the lack of historical large earthquakes. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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Figure 1
<p>Distribution map of Anhui CORS stations. The red triangles are where the Anhui CORS stations are located. White circles show earthquakes with <math display="inline"><semantics> <mrow> <mn>3.0</mn> <mo>≤</mo> <msub> <mi>M</mi> <mi>s</mi> </msub> <mo>≤</mo> <mn>8.5</mn> </mrow> </semantics></math> from 1 January 1900 to 31 December 2020 (<a href="https://earthquake.usgs.gov" target="_blank">https://earthquake.usgs.gov</a>, accessed on 14 December 2021). The largest white circle is an <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mi>s</mi> </msub> </mrow> </semantics></math> 8.5 earthquake that struck Tancheng on 25 July 1668. The blue broken line represents the central and southern segments of the Tanlu fault zone, and the Yishu fault zone is a part of the Tanlu fault zone in Shandong Province. The three red lines labeled by (<b>a</b>–<b>c</b>) represent the locations of three velocity profiles across the Tanlu fault zone.</p> Full article ">Figure 2
<p>GPS velocity field with respect to Eurasian plate. Blue and red arrows represent the velocity field of CMONOC and AHCORS, respectively. The areas surrounded by dashed line are North China block, Ludong block, and South China block. Error ellipses represent 70% confidence.</p> Full article ">Figure 3
<p>Comparison between observed and predicted GPS velocity field (<b>a</b>). Black and red arrows are observed velocity and predicted velocity, respectively. GPS velocity residuals distribution for the optimal model (<b>b</b>). The images in the upper left corner are the statistical histogram of residuals of the east components and north components, respectively.</p> Full article ">Figure 3 Cont.
<p>Comparison between observed and predicted GPS velocity field (<b>a</b>). Black and red arrows are observed velocity and predicted velocity, respectively. GPS velocity residuals distribution for the optimal model (<b>b</b>). The images in the upper left corner are the statistical histogram of residuals of the east components and north components, respectively.</p> Full article ">Figure 4
<p>The three-dimensional (3D) spatial distribution of coupling ratios of the optimal model inverted by AHCORS and CMONOC velocity field. Purple to red indicates the fault coupling coefficient. The red places indicate that the fault is fully locked, and the purple places mean that the fault is freely creeping; other colors suggest that the fault is partly locked.</p> Full article ">Figure 5
<p>The 3D spatial distribution of slip rate deficit of the optimal model inverted by AHCORS and CMONOC velocity field (mm/a). Purple to red indicates the slip rate deficit. The red places indicate the slip rate deficit is large, and the purple places mean that there is no slip rate deficit.</p> Full article ">Figure 6
<p>GPS velocity profiles from north to south (<b>a</b>–<b>c</b>). The length of the profiles is less than 350 km, and the width is 160 km. The red points and lines represent the observed and calculated GPS velocity parallel to the profile lines, respectively. The blue points and lines represent the observed and calculated GPS velocity perpendicular to the profile lines. The gray dashed line represents the Tanlu fault zone, and the median value of the yellow rectangle is the average of the velocities.</p> Full article ">Figure 7
<p>Resolution tests for coupling ratios inverted by different distances between adjacent fault nodes. Figures on the left (<b>a</b>,<b>c</b>,<b>e</b>) are 3D distribution of coupling ratios for forward modeling, and on the right (<b>b</b>,<b>d</b>,<b>f</b>) are recovered 3D distribution of coupling ratios using the same inversion strategy.</p> Full article ">Figure 8
<p>The 3D spatial distribution of coupling ratios inverted only by CMONOC velocity field (<b>a</b>). The 3D spatial distribution of slip rate deficit inverted only by CMONOC velocity field (<b>b</b>). The values in the northern segment are mostly larger than those in the southern segment.</p> Full article ">Figure 9
<p>Strain rates on the central and southern segments of the Tanlu fault zone. (<b>a</b>) Principal strain rates and dilatation rates. Principle strain rates are shown as vector pairs and dilatation rates are shown in background color. Positive dilatation rates show extension, while negative show compression. (<b>b</b>) Maximum shear strain rates. White circles show earthquakes with <math display="inline"><semantics> <mrow> <mn>3.0</mn> <mo>≤</mo> <msub> <mi>M</mi> <mi>s</mi> </msub> <mo>≤</mo> <mn>8.5</mn> </mrow> </semantics></math> from 1 January 1900 to 31 December 2020.</p> Full article ">

23 pages, 1559 KiB  

Open AccessArticle

Reported Occurrence of Multiscale Flooding in an Alpine Conurbation over the Long Run (1850–2019)

by Jean-Dominique Creutin, Juliette Blanchet, Alix Reverdy, Antoine Brochet, Céline Lutoff and Yannick Robert

Water 2022, 14(4), 548; https://doi.org/10.3390/w14040548 - 12 Feb 2022

Cited by 3 | Viewed by 3021

Abstract

This paper deals with the identification of extreme multiscale flooding events in the Alpine conurbation of Grenoble, France. During such events, typically over one to several days, the organization in space and time of the generating hydrometeorological situation triggers the concurrent reaction of [...] Read more.

This paper deals with the identification of extreme multiscale flooding events in the Alpine conurbation of Grenoble, France. During such events, typically over one to several days, the organization in space and time of the generating hydrometeorological situation triggers the concurrent reaction of varied sets of torrents and main rivers and creates diverse socioeconomic damages and disruptions. Given the limits of instrumental data over the long run, in particular at the torrent scale, we explore the potential of a database of reported extreme flood events to study multiscale flooding over a Metropolitan domain. The definition of Metropolitan events is mainly based on the database built by the RTM (Restauration des Terrains de Montagne, a technical service of the French Forest Administration). Relying on expert reports, the RTM database covers the long lifetime of this French national service for the management of mountainous areas (1850–2019). It provides quantitative information about the time and place of inundation events as well as qualitative information about the generating phenomena and the consequent damages. The selection process to define Metropolitan events simply chronologically explores the RTM database and complements it with historical research data. It looks for concurrence between site events at the same date under a chosen set of criteria. All scales together, we selected 104 Metropolitan events between 1850 and 2019. Exploring the list of dates, we examine the homogeneity of the Metropolitan events over 1850–2019 and their space–time characteristics. We evidence the existence of multiscale flooding at the Metropolitan scale, and we discuss some implications for flood risk management. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>Map (<b>left</b>) of the RTM torrential units of the Grenoble conurbation colored according to the number of events observed over the 1850–2019 period. Map (<b>right</b>) showing the Metropolitan area nested in the Isère and Drac watersheds.</p> Full article ">Figure 2
<p>Logarithmic (base 10) window showing the instrumental time and space resolution and their period of availability in the study area of Grenoble agglomeration, France (dotted grey rectangles—the upper time-limit of the rectangles is not meaningful). The time and space characteristics of four atmospheric processes controlling rainfall formation at different scales are shaded in light blue after [<a href="#B33-water-14-00548" class="html-bibr">33</a>]. The relationship established by [<a href="#B34-water-14-00548" class="html-bibr">34</a>] from extreme flash floods in Europe between the response time of a basin and its size is represented in bold dotted green. From the cited literature, we also show the response times of three rivers (blue crosses—after hydrographs shown in [<a href="#B31-water-14-00548" class="html-bibr">31</a>]) and one torrent (red cross—after [<a href="#B30-water-14-00548" class="html-bibr">30</a>]) of the agglomeration. The size of the Metropolitan torrential units of the RTM database are represented in orange (the bold part of the line represents the inter-quantiles 10% to 90% and the thin part the min–max interval—the response time is taken from the relationship of [<a href="#B34-water-14-00548" class="html-bibr">34</a>]). The two continuous grey rectangles summarize the datasets used in this study.</p> Full article ">Figure 3
<p>Cumulative distribution function (CDF) of the size of the 139 Metropolitan RTM units in a semi-logarithmic graph. The red curve shows the most likely log-normal CDF.</p> Full article ">Figure 4
<p>Cumulative count of torrential flood events reported in the RTM database over the period 1850–2019. In total, 282 torrential events (light grey curve) have been reported over the period. The represented slopes (dark grey lines) are computed after a Poisson hypothesis (ratio between the total counts and the duration of the considered periods—1850–1970 and 1980–2019). The cumulative counts for three classes of flood intensity are displayed in green (1-very-weak), yellow (2-weak) and red (3-medium). The cumulative counts for the Summer season and the other three seasons pulled together are displayed in dotted blue and dotted red, respectively.</p> Full article ">Figure 5
<p>Distribution of the torrential flooding causes for 133 reported RTM events for which a narrative of causes is proposed. We distinguish five types of causes: rainfall as the single cause of the flood (blue), snow melt (green), soil moisture and river morphology (yellow), logjams blocking the torrent (orange), and counter-efficient structure protection (red). The proportions are given for the three RTM classes of intensity that are attributed to the considered basins over the study period (the single 4-high intensity event that occurred in 1867 has no narrative about causes).</p> Full article ">Figure 6
<p>Cumulative counts of riverine flood events reported in the RTM database over the period 1850–2019. In total, over the period, 41 events have been reported for the 5 RTM riverine sites (continuous thin grey curve). Taking into account the multiple-site events—i.e., events concerning several sites of either the Isère or the Drac Rivers—these 41 events reduce to 28 events (continuous bold grey curve), among which 18 events concern the Isère River (yellow bold curve) and 10 events concern the Drac River (orange bold curve). These last two curves are compared with the series of 51 events of the “Historisque” research dataset for the Isère (dashed yellow curve) and Drac Rivers (dashed orange curve). The represented slopes (dotted grey lines) are the ratios between the total counts and the duration of the considered period (Poisson hypothesis). The time evolution of the storage capacity of the reservoirs built on the Isère an Drac Rivers (dotted black curve graduated in percent of the final capacity reached in the 1990’s—right hand y-axis) as well as the temporality of the main post-World-War-II protection programs (three dotted grey bars representing successively the so-called Schneider Project, the update of Grenoble dikes, and the rising of the Isère Left Bank dike) are also sketched on the graph (arbitrary y-coordinate).</p> Full article ">Figure 7
<p>Cumulative count of Metropolitan flood events over the period 1850–2019. In total 104, Metropolitan events (light grey curve) have been selected over the period. The cumulative counts for events that occurred on rivers only (blue curve); on torrents only (red curve); and the multiscale events, i.e., co-occurrence of torrential and riverine flooding (dotted orange curve), are also plotted in the same coordinate systems, with the years (<span class="html-italic">x</span>-Axis) and the event counts (<span class="html-italic">y</span>-Axis).</p> Full article ">Figure 8
<p>Distribution of the duration of Metropolitan events in days. The number of events (<span class="html-italic">y</span>-Axis) is given as a function of the duration (<span class="html-italic">x</span>-Axis) for events involving only torrents (green curve, 53 events in total) and only rivers (orange curve, 34 events), for multiscale events (yellow dotted curve, 17 events), and for all the events (blue dotted curve, 104 events).</p> Full article ">Figure 9
<p>Distribution of the number of RTM watershed units touched by Metropolitan events. The number of events (<span class="html-italic">y</span>-Axis) is given as a function of the number of units (<span class="html-italic">x</span>-Axis) for events involving only torrents (green curve, 53 events in total) and only rivers (orange curve, 34 events), for multiscale events (yellow dotted curve, 17 events), and for all the events (blue dotted curve, 104 events).</p> Full article ">

14 pages, 22842 KiB  

Open AccessArticle

Correlation between Building Damages and Losses with the Microzonation Map of Mataram—Case Study: Lombok Earthquake 2018, Indonesia

by Bambang Setyogroho, Dicky Muslim, Muhammad Suwongso Sadewo, Ghazi Oktavidi Muslim, Safri Burhanuddin and Hendarmawan Hendarmawan

Sustainability 2022, 14(4), 2028; https://doi.org/10.3390/su14042028 - 10 Feb 2022

Cited by 3 | Viewed by 2981

Abstract

The high intensity of the earthquake on Lombok Island on 5 August 2018, with a magnitude of 7.0 Mb, caused material losses experienced by the affected residential areas. The Indonesian Geological Agency in 2015 published a microzonation map that mapped zones prone to [...] Read more.

The high intensity of the earthquake on Lombok Island on 5 August 2018, with a magnitude of 7.0 Mb, caused material losses experienced by the affected residential areas. The Indonesian Geological Agency in 2015 published a microzonation map that mapped zones prone to earthquake shocks to mitigate disasters. This study aimed to compare the level of damage and loss in residential areas due to earthquakes in Mataram City with earthquake-prone zones using a microzonation map. The correlation between damage and loss value of residentials with microzonation maps was evaluated using the overlay method. The results showed that the level of damage and the value of the loss of houses in the high disaster-prone zone (red zone) showed the highest loss value. In comparison, the level of losses in the moderate disaster-prone zone (yellow zone) and light disaster-prone zone (blue zone) on the microzonation map shows a low and lower loss value. This study concludes that the microzonation map helps determine the damage zone and the level of disaster vulnerability caused by the earthquake hazard. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>(<b>A</b>) Map view of Lombok Island (Google Maps). (<b>B</b>) Cross-section of Lombok Island [<a href="#B10-sustainability-14-02028" class="html-bibr">10</a>].</p> Full article ">Figure 2
<p>Distribution and intensity map of the Lombok Island Earthquakes, January–October 2018 (Epicenter coordinate based on USGS).</p> Full article ">Figure 3
<p>Buildings Damaged Map by the 2018 Lombok Earthquake in Mataram City.</p> Full article ">Figure 4
<p>Microzonation Map of Mataram City (Modified from Geological Agency).</p> Full article ">Figure 5
<p>Illustration of overlay method in this research.</p> Full article ">Figure 6
<p>Overlay of two polygon maps, producing a new set of polygons common to both maps. The polygons in C are not linked to the polygons of maps A and B in a polygon attribute <a href="#sustainability-14-02028-t002" class="html-table">Table 2</a>.</p> Full article ">

17 pages, 1023 KiB  

Open AccessArticle

Using ARC-D Toolkit for Measuring Community Resilience to Disasters

by Muhammad Awfa Islam, Musabber Ali Chisty, Abdullah Fuad, Md. Mostafizur Rahman, Maliha Muhtasim, Syeda Erena Alam Dola, Fariha Jahin Biva and Nesar Ahmed Khan

Sustainability 2022, 14(3), 1758; https://doi.org/10.3390/su14031758 - 3 Feb 2022

Cited by 4 | Viewed by 4239

Abstract

Increased levels of resilience will reduce the negative consequences of any disaster and develop the capacities of communities to mitigate future disasters. The main objective of this study was to measure the level of resilience of two different communities in two different study [...] Read more.

Increased levels of resilience will reduce the negative consequences of any disaster and develop the capacities of communities to mitigate future disasters. The main objective of this study was to measure the level of resilience of two different communities in two different study areas and compare the resilience levels in terms of a flood. The study used the Analysis of Resilience of Communities to Disasters (ARC-D) toolkit. The study was conducted in two different areas to compare the level of community resilience. Both quantitative and qualitative methods were used in the study. A structured questionnaire was developed by using the toolkit. Results of the study indicated that communities in study area 1 were more resilient than communities in study area 2. Communities from study area 1 were more aware of their risk(s) and problem(s) and ensured proper strategies and actions to solve problems. On the other hand, communities in study area 2 were less aware of their risk(s). The strategies and actions implemented by the communities of study area 1 focused on the short-term problem(s), which reduced their level of resilience. Measuring resilience is very important in terms of developing disaster risk reduction (DRR) plans and incorporating DRR in the development process in lower-income countries and developing countries. As data scarcity is one of the major issues in developing countries, introducing a community resilience assessment mechanism can be a great help to reduce gaps in the planning and implementation process. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>Study area map (source: developed by the study).</p> Full article ">Figure 2
<p>Community Resilience Level (Based on SFDRR Priorities).</p> Full article ">

20 pages, 12170 KiB  

Open AccessFeature PaperArticle

Sea Level Rise Impact on Compound Coastal River Flood Risk in Klaipėda City (Baltic Coast, Lithuania)

by Erika Čepienė, Lina Dailidytė, Edvinas Stonevičius and Inga Dailidienė

Water 2022, 14(3), 414; https://doi.org/10.3390/w14030414 - 29 Jan 2022

Cited by 10 | Viewed by 4148

Abstract

Due to climate change, extreme floods are projected to increase in the 21st century in Europe. As a result, flood risk and flood-related losses might increase. It is therefore essential to simulate potential floods not only relying on historical but also future projecting [...] Read more.

Due to climate change, extreme floods are projected to increase in the 21st century in Europe. As a result, flood risk and flood-related losses might increase. It is therefore essential to simulate potential floods not only relying on historical but also future projecting data. Such simulations can provide necessary information for the development of flood protection measures and spatial planning. This paper analyzes the risk of compound flooding in the Danė River under different river discharge and Klaipėda Strait water level probabilities. Additionally, we examine how a water level rise of 1 m in the Klaipėda Strait could impact Danė River floods in Klaipėda city. Flood extent was estimated with the Hydrologic Engineering Center’s River Analysis System (HEC-RAS) and visualized with ArcGIS Pro. Research results show that a rise in the water level in the Klaipėda Strait has a greater impact on the central part of Klaipėda city, while that of the maximum discharge rates of the river affected the northern upstream part of the analyzed river section. A sea level rise of 1 m could lead to an increase in areas affected by Danė floods by up to three times. Floods can cause significant damage to the infrastructure of Klaipėda port city, urbanized territories in the city center, and residential areas in the northern part of the city. Our results confirm that, in the long run, sea level rise will significantly impact the urban areas of the Klaipėda city situated near the Baltic Sea coast. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>The study area: southeastern part (SE) of the Baltic Sea (<b>a</b>); the Klaipėda Strait, which connects the Curonian Lagoon with the SE Baltic Sea and the Akmena–Danė River (<b>b</b>); Klaipėda city (<b>c</b>).</p> Full article ">Figure 2
<p>Boundary of the Last Glacial (<b>a</b>); Quaternary-type sediment of the Danė River shore area (<b>b</b>) [<a href="#B49-water-14-00414" class="html-bibr">49</a>].</p> Full article ">Figure 3
<p>Danė River riverbank types in the central and northern parts of Klaipėda city.</p> Full article ">Figure 4
<p>Components of the compound Akmena–Danė River flood scenarios in Klaipėda city.</p> Full article ">Figure 5
<p>Mean and maximal sea level change (cm, in the BS—Baltic Sea height altitude system) in the Klaipėda Strait, 1902–2018 (maximum water level rise trend, <span class="html-italic">R</span>² = 0.13).</p> Full article ">Figure 6
<p>Inundated areas according to three river discharge probabilities (i.e., mean annual maximum, 10-year flood, and 100-year flood) at each water level of the Klaipėda Strait, where the mean water level is 0 m, the 10-year water level is 1.4 m, and the 100-year water level is 2 m.</p> Full article ">Figure 7
<p>Inundated areas with climate change impact according to three river discharge probabilities (i.e., mean annual maximum, 10-year flood, and 100-year flood) at each water level of the Klaipėda Strait, where the mean water level is 1 m, 10-year water level is 2.4 m, and the 100-year water level is 3 m.</p> Full article ">Figure 8
<p>Inundated areas with and without climate change impact according to Klaipėda Strait water level scenarios when the Danė River discharge is at the mean annual maximum (59 m³/s).</p> Full article ">

16 pages, 2527 KiB  

Open AccessArticle

A Multiscale Normalization Method of a Mixed-Effects Model for Monitoring Forest Fires Using Multi-Sensor Data

by Lanbo Feng, Huashun Xiao, Zhigao Yang and Gui Zhang

Sustainability 2022, 14(3), 1139; https://doi.org/10.3390/su14031139 - 20 Jan 2022

Cited by 7 | Viewed by 2297

Abstract

This paper points out the shortcomings of existing normalization methods, and proposes a brightness temperature inversion normalization method for multi-source remote sensing monitoring of forest fires. This method can satisfy both radiation normalization and observation angle normalization, and reduce the discrepancies in forest [...] Read more.

This paper points out the shortcomings of existing normalization methods, and proposes a brightness temperature inversion normalization method for multi-source remote sensing monitoring of forest fires. This method can satisfy both radiation normalization and observation angle normalization, and reduce the discrepancies in forest fire monitoring between multi-source sensors. The study was based on Himawari-8 data; the longitude, latitude, solar zenith angle, solar azimuth angle, emissivity, slope, aspect, elevation, and brightness temperature values were collected as modeling parameters. The mixed-effects brightness temperature inversion normalization (MEMN) model based on FY-4A and Himawari-8 satellite sensors is fitted by multiple stepwise regression and mixed-effects modeling methods. The results show that, when the model is tested by Himawari-8 data, the coefficient of determination ($R^2$) reaches 0.8418, and when it is tested by FY-4A data, $R^2$ reaches 0.8045. At the same time, through comparison and analysis, the accuracy of the MEMN method is higher than that of the random forest normalization method (RF) ($R^2=0.7318$), the pseudo-invariant feature method (PIF) ($R^2=0.7264$), and the automatic control scatter regression method (ASCR) ($R^2=0.6841$). The MEMN model can not only reduce the discrepancies in forest fire monitoring owing to different satellite sensors between FY-4A and Himawari-8, but also improve the accuracy and timeliness of forest fire monitoring. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>The map of the study area.</p> Full article ">Figure 2
<p>The values in the graph indicate the degree of autocorrelation among the factors; the higher absolute value indicates higher correlation, where * represents the significance of the significant factors, each * is a 5% significance level, and more * means more significance. The diagonal line of the grid in the figure indicates the trend of correlation; the diagonal line to the left indicates negative correlation and the diagonal line to the right indicates positive correlation.</p> Full article ">Figure 3
<p>The linear relationship between the statistical values of the brightness temperature and the calculated values of the surface specific emissivity, which is the main correlation of the underlying model, can be seen by analyzing the statistical values for different grid size conditions. The two possess the most relevant linear relationship when the grid of the statistics is set to <a href="#sustainability-14-01139-f002" class="html-fig">Figure 2</a>c.</p> Full article ">Figure 4
<p>The orange diamond indicates the dispersion of the mixed-effects model predictions, and the black circle indicates the dispersion of the basic model predictions.</p> Full article ">Figure 5
<p>Original image and normalization results of different methods (mid-wave infrared channel).</p> Full article ">Figure 6
<p>Comparison of the accuracy of the mixed-effects model normalization method with the random forest normalization method.</p> Full article ">Figure 7
<p>Comparison of the fire point monitoring results of different images.</p> Full article ">

19 pages, 7344 KiB  

Open AccessArticle

Determining the Risk Level of Heavy Rain Damage by Region in South Korea

by Jongsung Kim, Donghyun Kim, Myungjin Lee, Heechan Han and Hung Soo Kim

Water 2022, 14(2), 219; https://doi.org/10.3390/w14020219 - 12 Jan 2022

Cited by 8 | Viewed by 3316

Abstract

For risk assessment, two methods, quantitative risk assessment and qualitative risk assessment, are used. In this study, we identified the regional risk level for a disaster-prevention plan for an overall area at the national level using qualitative risk assessment. To overcome the limitations [...] Read more.

For risk assessment, two methods, quantitative risk assessment and qualitative risk assessment, are used. In this study, we identified the regional risk level for a disaster-prevention plan for an overall area at the national level using qualitative risk assessment. To overcome the limitations of previous studies, a heavy rain damage risk index (HDRI) was proposed by clarifying the framework and using the indicator selection principle. Using historical damage data, we also carried out hierarchical cluster analysis to identify the major damage types that were not considered in previous risk-assessment studies. The result of the risk-level analysis revealed that risk levels are relatively high in some cities in South Korea where heavy rain damage occurs frequently or is severe. Five causes of damage were derived from this study—A: landslides, B: river inundation, C: poor drainage in arable areas, D: rapid water velocity, and E: inundation in urban lowlands. Finally, a prevention project was proposed considering regional risk level and damage type in this study. Our results can be used when macroscopically planning mid- to long-term disaster prevention projects. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>Locations of nine provinces and eight cities in South Korea.</p> Full article ">Figure 2
<p>Elevation and river map in study area.</p> Full article ">Figure 3
<p>Procedure of heavy rain damage risk assessment.</p> Full article ">Figure 4
<p>Conceptual diagram of dendrogram [<a href="#B31-water-14-00219" class="html-bibr">31</a>].</p> Full article ">Figure 5
<p>Normalized spatial distribution of each sub–index. (<b>a</b>) Hazard index, (<b>b</b>) Exposure index, (<b>c</b>) Vulnerability index, and (<b>d</b>) Capacity index.</p> Full article ">Figure 6
<p>Classification of risk level based on probability distribution. (<b>a</b>) PDF of HDRI and (<b>b</b>) classification of risk level using CDF.</p> Full article ">Figure 7
<p>Result of risk assessment.</p> Full article ">Figure 8
<p>Distribution of facilities affected by heavy rain damage from 2003–2019 in South Korea.</p> Full article ">Figure 9
<p>Hierarchical cluster analysis result based on dendrogram.</p> Full article ">Figure 10
<p>Regional classification of damage type from the analysis.</p> Full article ">Figure 11
<p>Regional risk level and heavy rain damage types from the analysis.</p> Full article ">

19 pages, 8563 KiB  

Open AccessArticle

Spatially Varying Relationships between Land Subsidence and Urbanization: A Case Study in Wuhan, China

by Zhengyu Wang, Yaolin Liu, Yang Zhang, Yanfang Liu, Baoshun Wang and Guangxia Zhang

Remote Sens. 2022, 14(2), 291; https://doi.org/10.3390/rs14020291 - 9 Jan 2022

Cited by 15 | Viewed by 3046

Abstract

Land subsidence has become an increasing global concern over the past few decades due to natural and anthropogenic factors. However, although several studies have examined factors affecting land subsidence in recent years, few have focused on the spatial heterogeneity of relationships between land [...] Read more.

Land subsidence has become an increasing global concern over the past few decades due to natural and anthropogenic factors. However, although several studies have examined factors affecting land subsidence in recent years, few have focused on the spatial heterogeneity of relationships between land subsidence and urbanization. In this paper, we adopted the small baseline subset-synthetic aperture radar interferometry (SBAS-InSAR) method using Sentinel-1 radar satellite images to map land subsidence from 2015 to 2018 and characterized its spatial pattern in Wuhan. The bivariate Moran’s I index was used to test and visualize the spatial correlations between land subsidence and urbanization. A geographically weighted regression (GWR) model was employed to explore the strengths and directions of impacts of urbanization on land subsidence. Our findings showed that land subsidence was obvious and unevenly distributed in the study area, the annual deformation rate varied from −42.85 mm/year to +29.98 mm/year, and its average value was −1.0 mm/year. A clear spatial pattern for land subsidence in Wuhan was mapped, and several apparent subsidence funnels were primarily located in central urban areas. All urbanization indicators were found to be significantly spatially correlated with land subsidence at different scales. In addition, the GWR model results showed that all urbanization indicators were significantly associated with land subsidence across the whole study area in Wuhan. The results of bivariate Moran’s I and GWR results confirmed that the relationships between land subsidence and urbanization spatially varied in Wuhan at multiple spatial scales. Although scale dependence existed in both the bivariate Moran’s I and GWR models for land subsidence and urbanization indicators, a “best” spatial scale could not be confirmed because the disturbance of factors varied over different sampling scales. The results can advance the understanding of the relationships between land subsidence and urbanization, and they will provide guidance for subsidence control and sustainable urban planning. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>Location of study area and Wuhan city. The study area is represented by the red square. The simplified geological setting of the study area is shown in the right panel.</p> Full article ">Figure 2
<p>The average land subsidence velocity in the vertical direction from 2015 to 2018 across the study area in Wuhan city using Sentinel-1A SAR images. The Landsat-8 OLI optical image acquired on 27 June 2018 is used as the background.</p> Full article ">Figure 3
<p>Local indicator of spatial association for land subsidence at block scales of 0.5 km × 0.5 km (<b>a</b>), 1 km × 1 km (<b>b</b>), 1.5 km × 1.5 km (<b>c</b>), and 2 km × 2 km (<b>d</b>) across the study area in Wuhan city.</p> Full article ">Figure 4
<p>Spatial distribution of urbanization indicators of Wuhan city in 2018. Abbreviations: impervious surface area (ISA); night-time lights (NTL); building kernel density (BKD); road line density (RLD).</p> Full article ">Figure 5
<p>Bivariate local indicators of spatial associations between land subsidence and four kinds of urbanization indicators in Wuhan city at four block scales.</p> Full article ">Figure 6
<p>Spatial patterns of local adjusted <math display="inline"><semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> </mrow> </semantics></math> obtained from the GWR model for urbanization indicators at four block scales.</p> Full article ">Figure 7
<p>Spatial distribution of regression coefficients between urbanization indicators and land subsidence.</p> Full article ">

12 pages, 457 KiB  

Open AccessArticle

Income Diversification and Income Inequality: Household Responses to the 2013 Floods in Pakistan

by Shaikh M. S. U. Eskander and Sam Fankhauser

Sustainability 2022, 14(1), 453; https://doi.org/10.3390/su14010453 - 1 Jan 2022

Cited by 4 | Viewed by 2942

Abstract

In this paper we investigate the economic response of rural households to the 2013 floods in Pakistan. The case study illustrates the important roles of labor supply adjustments and income diversification in coping with climate-related risks. Using detailed household panel data that were [...] Read more.

In this paper we investigate the economic response of rural households to the 2013 floods in Pakistan. The case study illustrates the important roles of labor supply adjustments and income diversification in coping with climate-related risks. Using detailed household panel data that were collected before and after the 2013 floods, we find that the exposure to flood results in lower participation in farm activities. The overall effects are decreased diversification in the sources of income and ambiguous reduction in inequality which is associated with overall declines in incomes. These changes could be locked in if affected households do not have sufficient assets to resume farming. The results suggest intervention points for public policy, related to labor mobility and access to capital. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction)

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<p>Timeline of 2010–2014 events.</p> Full article ">

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