Natural Hazards and Disaster Risks Reduction, 2nd Volume

Topic Information

Dear Colleagues,

Given the success of the first edition of the topic “Natural Hazards and Disaster Risks Reduction”, which also led to the publication of three re-print books (https://www.mdpi.com/topics/Natural_Hazards_Disaster_Risks_Reduction), we are pleased to announce its second edition. The physical forces governing Earth’s systems can give rise to abrupt and severe natural events, which come in the form of violent expressions of ordinary environmental processes. Their impact is unevenly distributed on land because of complex continental, regional, and local natural processes that overlap with anthropogenic forcing. The resultant climate variations can directly or indirectly exacerbate these occurrences at different spatial and temporal scales. When such phenomena interact directly with both inhabited areas and societies, different risk scenarios can develop, characterized by a continuous and persistent dynamic or by rapid mutability. From this perspective, natural hazards create potential disasters that can impact anthropic activities, either through the loss of life or injury or through economic loss. The degree of safety in a community equates to the impact of and exposure to these events, and of the level of preparation for them is based on awareness and perception. The social development and spatial growth of human activities by our use of soil and natural resources has further contributed to creating vulnerability, increasing the challenges to conscious societies trying to cope with severe natural processes and their effects. The protection of territory is a key element in the UN 2030 Agenda’s action strategy for sustainable development, and risk reduction is one of the guiding criteria of the 2015–2030 Sendai Framework’s sustainability policy. This topic will collect original 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.), vulnerable domains, and exposure to disaster risk. also It will also feature manuscripts whose contents can help to mitigate risks. Among them, technical interventions and operational methodologies for implementing risk-reduction strategies, such as plans, protocols, working procedures, early warning systems, and other innovations in the sector; elements that combine modern concepts with consolidated realities of the past are also to be included. Papers on state-of-the-art techniques are welcome, especially those in the following three operating areas: spaceborne, aerial, and terrestrial activities. Numerical and experimental investigations for basic or applied research and representative case studies are also welcome, as are interdisciplinary and multidisciplinary approaches, which we think add additional value in progressing the field of responsible and sustainable risk mitigation.

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

Keywords

Participating Journals

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

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Published Papers (3 papers)

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All GeoHazards Land Remote Sensing Sustainability Water

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24 pages, 10977 KiB  

Open AccessArticle

Examining the Controls on the Spatial Distribution of Landslides Triggered by the 2008 Wenchuan Ms 8.0 Earthquake, China, Using Methods of Spatial Point Pattern Analysis

by Guangshun Bai, Xuemei Yang, Guangxin Bai, Zhigang Kong, Jieyong Zhu and Shitao Zhang

Sustainability 2024, 16(16), 6974; https://doi.org/10.3390/su16166974 - 14 Aug 2024

Abstract

Landslide risk management contributes to the sustainable development of the region. Understanding the spatial controls on the distribution of landslides triggered by earthquakes (EqTLs) is difficult in terms of the prediction and risk assessment of EqTLs. In this study, landslides are regarded as [...] Read more.

Landslide risk management contributes to the sustainable development of the region. Understanding the spatial controls on the distribution of landslides triggered by earthquakes (EqTLs) is difficult in terms of the prediction and risk assessment of EqTLs. In this study, landslides are regarded as a spatial point pattern to test the controls on the spatial distribution of landslides and model the landslide density prediction. Taking more than 190,000 landslides triggered by the 2008 Wenchuan Ms 8.0 earthquake (WcEqTLs) as the research object, the relative density estimation, Kolmogorov–Smirnov testing based on cumulative distribution, receiver operating characteristic curve (ROC) analysis, and Poisson density modeling are comprehensively applied to quantitatively determine and discuss the different control effects of seven factors representing earthquakes, geology, and topography. The distance to the surface ruptures (dSR) and the distance to the epicenter (dEp) show significant and strong control effects, which are far stronger than the other five factors. Using only the dSR, dEp, engineering geological rock group (Eg), and the range, a particularly effective Poisson model of landslide density is constructed, whose area under the ROC (AUC) reaches 0.9244 and whose very high-density (VHD) zones can contain 50% of landslides and only comprise 3.9% of the study areas. This research not only deepens our understanding of the spatial distribution of WcEqTLs but also provides new technical methods for such investigation and analysis. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction, 2nd Volume)

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Figure 1

Figure 1
<p>Landslide point locations [<a href="#B33-sustainability-16-06974" class="html-bibr">33</a>], WcEq epicenter location, and surface ruptures [<a href="#B43-sustainability-16-06974" class="html-bibr">43</a>,<a href="#B44-sustainability-16-06974" class="html-bibr">44</a>]. The gray grid lines in the study area display a custom coordinate system, with the epicenter as (0,0), along the surface rupture zone as the X-axis, the main propagation direction of the earthquake as the positive X-axis, and the vertical surface rupture as the Y-axis, with an interval of 10 km. The inset map shows major tectonic features in Longmenshan vicinity [<a href="#B43-sustainability-16-06974" class="html-bibr">43</a>]: The red box in the map indicates the location of the study area. LTB—Longmenshan thrust belt (southwestern China, eastern edge of the Qinghai–Tibet Plateau); ATF—Altyn Tagh fault; HF—Haiyuan fault; JLF—Jiali fault; NCB—North China block; RRF—Red River fault; SCB—South China block; XF—Xianshuihe fault; XJF—Xiaojiang fault; I—Qaidam–Qilian block; II—Bayan Har block; III—Sichuan–Yunnan block. The white arrow indicates the block motion direction.</p> Full article ">Figure 2
<p>Maps of seven covariates.</p> Full article ">Figure 3
<p>The relative distribution estimation of WcEqTLs density on the dSR. A is the curve of the estimation. B is the map of the estimation. The red horizontal dashed line in the (<b>A</b>) and the red circular dashed line in the (<b>B</b>) indicate that the average landslide density in the whole study area is 2.6 landslides/km². The solid black line is the estimation result of this method. The blue dots are results of the discrete method.</p> Full article ">Figure 4
<p>The relative distribution estimation of WcEqTLs density on the dEp. A is the curve of the estimation. B is the map of the estimation. The red horizontal dashed line in (<b>A</b>) and the red circular dashed line in (<b>B</b>) indicate that the average landslide density in the whole study area is 2.6 landslides/km². The blue dots are results of the discrete method.</p> Full article ">Figure 5
<p>The relative distribution estimation of the WcEqTLs density on the Elv, the range, the Slp, and the Asp. The red horizontal dashed lines in (<b>A</b>–<b>D</b>) indicate the average landslide density. The red dots are results of the discrete method.</p> Full article ">Figure 6
<p>The relative distribution estimation of the WcEqTLs density on the Eg. (<b>A</b>) is the density histogram of classified landslides. The blue horizontal dashed line indicates that the average landslide density in the whole study area is 2.6 landslides/km². (<b>B</b>) is the spatial distribution map of landslide density in different engineering geological rock groups.</p> Full article ">Figure 7
<p>The statistical curve of the landslide cumulative probability relative to the dSR (<b>A</b>) and the dEp (<b>B</b>). The solid black line is the observation statistical curve, and the red dashed line is the CSR hypothetical statistical curve.</p> Full article ">Figure 8
<p>The landslide density depends on the topographical and geomorphological factors significance test result chart. (<b>A</b>,<b>B</b>,<b>C</b>,<b>D</b>) are the range, the Slp, the Elv, and the Asp, respectively.</p> Full article ">Figure 9
<p>The ROC chart of WcEqTLs dependents on covariates.</p> Full article ">Figure 10
<p>ROC charts of models. (<b>B</b>) is a partial enlargement of (<b>A</b>), whose range of the X axis is 0.10~0.25 and the range of the Y axis is 0.80~0.95. The red dashed line in the figure (<b>A</b>) is for the CSR.</p> Full article ">Figure 11
<p>A landslide density prediction map and classification map of each model.</p> Full article ">

32 pages, 31386 KiB  

Open AccessEssay

Street Community-Level Urban Flood Risk Assessment Based on Numerical Simulation

by Cailin Li, Yue Wang, Baoyun Guo, Yihui Lu and Na Sun

Sustainability 2024, 16(16), 6716; https://doi.org/10.3390/su16166716 - 6 Aug 2024

Viewed by 643

Abstract

Urban waterlogging is a serious urban disaster, which brings huge losses to the social economy and environment of the city. As an important means of urban rainfall inundation analysis, numerical simulation plays an important role in promoting the risk assessment of urban waterlogging. [...] Read more.

Urban waterlogging is a serious urban disaster, which brings huge losses to the social economy and environment of the city. As an important means of urban rainfall inundation analysis, numerical simulation plays an important role in promoting the risk assessment of urban waterlogging. Scientific and accurate assessment of waterlogging disaster losses is of scientific significance for the formulation of disaster prevention and mitigation measures and the guidance of post-disaster recovery and reconstruction. In this study, the SCS-CN hydrological model and GIS coupling numerical simulation method were used to simulate the inundation of urban waterlogging under four different rainfall return periods and to realize the visualization of the inundation range and waterlogging depth in Zhengzhou. At the same time, based on the numerical simulation results, the building is used as the basic assessment unit to construct a refined assessment framework for urban waterlogging risk at the street community level based on hazard, exposure, and vulnerability analysis. The refined risk assessment results have an important reference value for optimizing the working ideas of waterlogging control and providing a reference for local management departments to effectively deal with waterlogging disasters. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction, 2nd Volume)

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Figure 1

Figure 1
<p>Study area map.</p> Full article ">Figure 2
<p>Geographical location and remote sensing image of street research area.</p> Full article ">Figure 3
<p>Technology roadmap.</p> Full article ">Figure 4
<p>Flowchart for inundation simulation.</p> Full article ">Figure 5
<p>Distribution map of 93 sub-catchments.</p> Full article ">Figure 6
<p>Depth disaster loss curve of different land use types.</p> Full article ">Figure 7
<p>The schematic diagram of land use type division in the study area.</p> Full article ">Figure 8
<p>Rainfall process under different return periods: (<b>a</b>) 10-year rainfall process; (<b>b</b>) 20-year rainfall process; (<b>c</b>) 50-year rainfall process; and (<b>d</b>) 100-year rainfall process.</p> Full article ">Figure 9
<p>Soil type distribution and hydrological soil distribution. (<b>a</b>) Soil type distribution; (<b>b</b>) Hydrological soil type distribution.</p> Full article ">Figure 10
<p>Land use type distribution map.</p> Full article ">Figure 11
<p>CN value distribution map.</p> Full article ">Figure 12
<p>Submerged range distribution map: (<b>a</b>) 10-year inundation range map; (<b>b</b>) 20-year inundation range map; (<b>c</b>) 50-year inundation range map; and (<b>d</b>) one-hundred-year flooding range map.</p> Full article ">Figure 13
<p>Waterlogging depth distribution map: (<b>a</b>) 10-year waterlogging depth map; (<b>b</b>) 20-year waterlogging depth map; (<b>c</b>) 50-year waterlogging depth map; and (<b>d</b>) 100-year waterlogging depth map.</p> Full article ">Figure 14
<p>Schematic diagram of building interpretation.</p> Full article ">Figure 15
<p>Building type distribution map.</p> Full article ">Figure 16
<p>The risk distribution map of buildings in a 20-year return period.</p> Full article ">Figure 17
<p>Risk distribution map of buildings in a 100-year return period.</p> Full article ">Figure 18
<p>Building off-ground height map.</p> Full article ">Figure 19
<p>Distribution of building exposure: (<b>a</b>) the exposure distribution map of buildings once in 20 years; (<b>b</b>) the exposure distribution map of buildings once in a hundred years.</p> Full article ">Figure 20
<p>Direct economic loss degree map: (<b>a</b>) a diagram of the degree of direct economic loss in a 20-year return period; (<b>b</b>) a map of the degree of direct economic loss once in a hundred years.</p> Full article ">

17 pages, 25206 KiB  

Open AccessArticle

The Use of an Unmanned Aerial Vehicle (UAV) for First-Failure Landslide Detection

by Michele Mercuri, Deborah Biondino, Mariantonietta Ciurleo, Gino Cofone, Massimo Conforti, Giovanni Gullà, Maria Carmela Stellato and Luigi Borrelli

GeoHazards 2024, 5(3), 683-699; https://doi.org/10.3390/geohazards5030035 - 12 Jul 2024

Viewed by 510

Abstract

The use of unmanned aerial vehicles (UAVs) can significantly assist landslide detection and characterization in different geological contexts at a detailed scale. This study investigated the role of UAVs in detecting a first-failure landslide occurring in Calabria, South Italy, and involving weathered granitoid [...] Read more.

The use of unmanned aerial vehicles (UAVs) can significantly assist landslide detection and characterization in different geological contexts at a detailed scale. This study investigated the role of UAVs in detecting a first-failure landslide occurring in Calabria, South Italy, and involving weathered granitoid rocks. After the landslide event, which caused the interruption of State Road 107, a UAV flight was carried out to identify landslide boundaries and morphological features in areas where there are problems of safe access. The landslide was classified as flow-type, with a total length of 240 m, a maximum width of 70 m, and a maximum depth of about 6.5 m. The comparison of the DTMs generated from UAV data with previously available LIDAR data indicated significant topographic changes across the landslide area. A minimum negative value of −6.3 m suggested material removal at the landslide source area. An approximate value of −2 m in the transportation area signified bed erosion and displacement of material as the landslide moved downslope. A maximum positive value of 4.2 m was found in the deposition area. The landslide volume was estimated to be about 6000 m³. These findings demonstrated the effectiveness of UAVs for landslide detection, showing their potentiality as valuable tools in planning further studies for a detailed landslide characterization and for defining the most appropriate risk mitigation measures. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction, 2nd Volume)

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Figure 1

Figure 1
<p>The study area. (<b>a</b>) Geographical location of the study area; (<b>b</b>) geological map of the study area; (<b>c</b>) daily precipitation (Cerenzia rain gauge, 663 m a.s.l.).</p> Full article ">Figure 2
<p>Photos of the “Serra” tunnel invaded by landslide debris (from Web). (<b>a</b>) Photo taken during landslide event; (<b>b</b>,<b>c</b>) photos taken after landslide event.</p> Full article ">Figure 3
<p>(<b>a</b>) Flight plan scheme of the UAV for the acquisition of aerial photographs with ground control points (GCPs), check points (CHKs), and takeoff point; (<b>b</b>) 3D model with photo shot point positions.</p> Full article ">Figure 4
<p>UAV flight data. (<b>a</b>) Orthophoto generated through the UAV flight; (<b>b</b>) DSM hillshade; (<b>c</b>) DTM hillshade.</p> Full article ">Figure 5
<p>Landslide view from the UAV flight and photos taken during an in situ survey. (<b>a</b>) Landslide boundary; (<b>b</b>) landslide crown; (<b>c</b>) a view of transportation area; the photo was taken at the point where the green arrows open; (<b>d</b>) the primary landslide accumulation area N-E oriented; photos taken by Serra tunnel; (<b>e</b>,<b>f</b>) landslide mass accumulated in the Serra tunnel (<b>g</b>) a detail of the accumulated landslide mass. Legend: (1) landslide crown; (2) landslide boundary; (3) landslide boundary not visible by image because it is covered by the Serra tunnel and vegetation; (4) boundary of landslide material removed to restore State Road 107 viability.</p> Full article ">Figure 6
<p>Landslide photos taken by the UAV. (<b>a</b>,<b>b</b>) Landslide source area; (<b>c</b>) central part of landslide source area; (<b>d</b>,<b>e</b>) details of transportation area; (<b>f</b>) aerial view of accumulation area.</p> Full article ">Figure 7
<p>Video frames of the landslide debris invading the “Serra” tunnel. (<b>a</b>) The first available video frame; (<b>b</b>) the last video frame (<a href="https://www.facebook.com/100063821340885/videos/galleria-castelsilanosan-giovanni-in-fiore/264334655621611/" target="_blank">https://www.facebook.com/100063821340885/videos/galleria-castelsilanosan-giovanni-in-fiore/264334655621611/</a>, accessed on 25 October 2021).</p> Full article ">Figure 8
<p>(<b>a</b>) Elevation difference map and (<b>b</b>) longitudinal and transversal cross section profiles.</p> Full article ">Figure 9
<p>Slope gradient maps. Legend: (<b>a</b>) slope gradient map pre-landslide; (<b>b</b>) slope gradient map post-landslide; (<b>c</b>) frequency distribution of slope gradient before and after the landslide event.</p> Full article ">Figure 10
<p>Orthophoto and drainage network of the study area pre-landslide event (in red the landslide boundary). (<b>a</b>) Orthophoto of the study area; (<b>b</b>) Drainage network (blue line) obtained by LIDAR-DTM with a pixel resolution of 1 m.</p> Full article ">

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