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Slow-moving landslides are complex processes that represent a significant challenge for landslide dynamic analysis and disaster risk reduction. In some cases, they have been considered as early signals of potential destructive events as... more
Slow-moving landslides are complex processes that represent a significant challenge for landslide dynamic analysis and disaster risk reduction. In some cases, they have been considered as early signals of potential destructive events as they can accelerate under specific climatic conditions, causing significant damage. However, slow-moving landslides have been constantly neglected as the require significant time, human resources, and specific numerical models to assess their non-uniformity. Considering the existing gaps and the lack of data of slow-moving landslides in Austria, a long-term monitoring project has been carried out by the ENGAGE group of the University of Vienna. Several investigation techniques for hydro-geo monitoring have been installed in Lower Austria for multi-temporal landslide investigation in several landslides, using them as living laboratories. Therefore, the present study aims to integrate the valuable hydro-mechanical data to bring light on potential acceleration conditions of slow-moving landslides, frequency and intensity relationships and cascading hazards initiated from within the slow-moving landslide mass. The geographical and geological conditions of the province of Lower Austria place it as a very susceptible region to the occurrence of landslides. The predominant geology correspond to the units of the Flysch Zone and the Klippen Zone, which are mechanically weak units composed by intercalation of limestones and deeply weathered materials. These conditions, along with the hydrological conditions, land use changes and other anthropogenic impacts contribute to the instability of the region. Consequently, in order to understand landslide processes and mechanisms, we attempt to integrate the hydro-mechanical data compiled from the monitoring sites to model a complex event triggered in 2013, in the Hofermühle catchment, district of Waidhofen an der Ybbs, in order to improve our understanding of landslide conditioning factors and triggering mechanisms of potential cascading hazards in the region.
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The Covid-19 pandemic as well as the climate crisis, to name two examples only, have taught us the importance of the systemic impact of compounding disasters. Stakeholders in disaster risk management are faced with the challenge to adapt... more
The Covid-19 pandemic as well as the climate crisis, to name two examples only, have taught us the importance of the systemic impact of compounding disasters. Stakeholders in disaster risk management are faced with the challenge to adapt their risk reduction policies and emergency plans but lack the tools to account for the cross-sectoral impacts and dynamic nature of the risks involved. The EU Horizon project PARATUS (Promoting disaster preparedness and resilience by co-developing stakeholder support tools for managing the systemic risk of compounding disasters – CL3-2021-DRS-01) aims to develop an open and online, user-centred platform for systemic risk assessment with the possibility for analysing and evaluating multi-hazard impact chains, risk reduction measures and disaster response scenarios incorporating systemic vulnerabilities and uncertainties. This platform is co-developed with stakeholders and addressing the dynamic physical, socio-economic, and environmental aspects. The development of this platform will be achieved by learning from past events to understand their dynamic and interactive behaviour of hazards and related risks. Disaster histories will be collected through the analysis of representative past events in so-called learning case studies. From the gained knowledge a generic methodology will be developed for a systemic multi-sectoral and multi-hazard risk assessment which will be applied within the PARATUS project in four application case study areas. The application case study in the European Alps will be introduced in this contribution and refers to the stretch between Innsbruck (Austria) and Bozen (Italy). Here, we focus on the impact of the interruption of cross-border transportation of the Brenner highway caused by extreme events in a mountainous environment, such as extreme wind, floods and flash floods, landslides including rockfall, debris- and mudflow, snow avalanches, and heat. Besides the experiences of the responsible stakeholder ASFiNAG, another focus will be on local communities. For instance, the future regional economic impact will be projected for various climate and hazard scenarios related to the interruption of cross-border transportation due to compounding events. Additionally, the involvement of Austrian and Italian local and regional stakeholders in the above-mentioned activities will foster the co-development of the project platform with their experiences. The final platform will allow the access to additional information in order to support and foster the local and regional developments to achieve a safer environment.
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Landslides are one of the most important and frequent geological hazards worldwide. Among the many different types and processes, slow and very slow mass movements are often underestimated, even if they can impact local infrastructures... more
Landslides are one of the most important and frequent geological hazards worldwide. Among the many different types and processes, slow and very slow mass movements are often underestimated, even if they can impact local infrastructures and permanently affect agricultural practices and land use planning. Slow-moving landslides are common in clay-rich layers and areas that are typically characterized by mechanically weak materials. In the field of slow-moving landslide monitoring, understanding the factors driving the slope instability is the key to assessing the landslide hazard and to supporting local authorities in hazard management.In this study, the first results of an ongoing monitoring setup for a complex, slow-moving earth-slide system in Lower Austria are presented. The Brandstatt landslide is located in a complex geological transition zone between the Flysch and Klippen zones, which is known to be prone to shallow and deep landslides because its susceptibility to sliding processes has been investigated in recent years. Considering the predisposing conditions (geological and climatic settings), the unstable slope can be considered as a representative site of complex landslide processes in this region.Landslide movements monitoring includes a combination of surface and subsurface methods to investigate the spatio-temporal evolution of factors that prepare, trigger, and control landslide dynamics. Geological characterization of the subsurface was obtained through a dynamic penetration test (DPH) campaign and percussion drilling. In addition, the subsurface displacements and potential shear planes were evaluated using repeated inclinometric measurements. A meteorological station is also installed on-site, as well as piezometers and time-domain reflectometry (TDR) sensors in selected locations on the slope. These instruments provide high temporal resolution data, which are automatically transmitted to a server for the real-time monitoring of hydrometeorological conditions. However, the monitoring strategy to detect surficial changes is currently limited to the application of Terrestrial Laser Scanning (TLS) because an Unmanned Aerial Vehicle (UAV)-based Structure from Motion (SfM) is not possible for vegetation cover issues.The current results suggest the following: i) the connection between soil properties, soil moisture, and changes in groundwater level in the evolution of the slope instability, ii) potential shear surfaces within the shallow layers of the unstable slope, and iii) the importance of combining hydrological and geotechnical monitoring to set up an integrated network for landslide interpretation. Accordingly, obtaining information from a multi-parameter monitoring system is fundamental to identifying the relationship between the triggering and kinematic mechanisms of a complex, slow-moving landslide. However, the nonlinear behavior of slow movements restricts the temporal capability to properly understand the processes of complex mass movements. Consequently, landslide dynamics need to be further understood to establish a long-term monitoring system.
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Landslides are the most frequent and numerous geological hazards that pose a serious threat to human safety and property. Landslide susceptibility mapping (LSM) has been focused on over the years as an essential step of landslide risk... more
Landslides are the most frequent and numerous geological hazards that pose a serious threat to human safety and property. Landslide susceptibility mapping (LSM) has been focused on over the years as an essential step of landslide risk assessment. Numerous statistical or machine learning models have been proposed for LSM, but few consider mapping units' spatial correlation. This study proposed a deep learning model based on graph convolutional network (GCN) and K-Nearest Neighbor (KNN), named KNN-GCN, for slope-units-based LSM and experimentally applied to the Lueyang region. It’s constructed and validated with the following steps: First, 15 landslide causal factors and landslide inventory were collected, and a slope units map (SUM) was obtained based on slope unit division. Then, the training and test sets were divided with the ratio 7:3 after the multicollinearity analysis for landslide causal factors. Next, a four-layer GCN model was constructed based on the slope units graph (SUG), in which the SUG was generated from the SUM by the KNN algorithm. After that, the proposed KNN-GCN model was trained and validated on training and test sets separately, then applied for LSM. Finally, the performance of the KNN-GCN model was compared with the three other models, including KNN, Support Vector Machine (SVC), and AutoML. The results show that the proposed model achieved the best performance (AUC=0.8473) than other models, and a more readable susceptibility map was generated with it, which has clear boundaries between different susceptibility levels. Notably, although the proposed KNN-GCN model shows excellent performance for slope-units-based LSM, it requires high computer hardware and is not recommended for small datasets.
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<p>Rainfall data with high spatiotemporal resolutions are of great value in many research fields, such as meteorology, hydrology, global warming, and urban disaster monitoring. Current rainfall observation systems... more
<p>Rainfall data with high spatiotemporal resolutions are of great value in many research fields, such as meteorology, hydrology, global warming, and urban disaster monitoring. Current rainfall observation systems include ground-based rain gauges, remote sensing-based radar and satellites. However, there is an increasing demand of the spatiotemporal rainfall data with high resolution. Thanks to the advocacy from many research institutions and international organizations, several innovative crowdsourcing ideas including opportunistic sensing and citizen science initiatives have been followed in recent years. Commercial cellular communication networks, windshield wipers or optical sensors in moving vehicles, smart phones, social medias, and surveillance cameras/videos have been identified as alternative rain gauges. In particular environmental audio recordings are a rich and underexploited source to identified and even characterize rainfall events.<br>Widespread surveillance cameras can continuously record rainfall information, which even provides a basis for the possibility of rainfall monitoring. Comparing the aforementioned methods, surveillance audio-based rainfall estimation has been discussed in existing studies with advantages of high-spatiotemporal-resolution, low cost and all-weather. Therefore, this study focuses on mining the rainfall information from urban surveillance audio for quantitative inversion on precipitation. Rain sound is generated by the collision of rain particles with other underlying objects in the process of falling. In real applications, the complex subsurface structure and random background noises from human activity in urban areas make surveillance rainfall sound vulnerable and surveillance audio-based rainfall estimation more challenging. In our study, the rainfall acoustic indicators were selected for rainfall sound representation. Deep learning-based rainfall observation systems were built based on urban surveillance audio data. Experimental results show the efficiency of our system in rainfall estimation. Our research is a new attempt to develop crowdsourcing-based rainfall observations, which can also provide a beneficent supplement to the current rainfall observation networks.</p>
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<p>Landslides have caused significant losses in terms of lives and economic damage across the globe. Understanding their triggering factors, dynamics and potential impact is fundamental to implement more comprehensive... more
<p>Landslides have caused significant losses in terms of lives and economic damage across the globe. Understanding their triggering factors, dynamics and potential impact is fundamental to implement more comprehensive disaster risk reduction measures to strengthen communities´ resilience. However, the lack of baseline data and impact information remains challenging. In order to improve our knowledge and to fill existing gaps between practitioners, disaster risk reduction institutions and other stakeholders, risk assessment and risk management projects can be appropriate starting points. Here, we present the results of a risk assessment analysis of three selected locations in Lower Austria.</p><p>The region of Lower Austria is particularly affected by landslides due to its complex geology and anthropogenic impact. The present research focuses on three earth mass movements with rotational characteristics in the regions of Erla, Behamberg, and Kreisbach. We developed different physical-based models to visualize different scenarios of potential runout areas and fluxes. Then, we performed an analysis of the cascading risk to estimate the potential economic damage in these regions to be able to propose adequate disaster risk reduction measures that could contribute to the region´s resilience.</p>
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Research Interests: Geology and Meteorology
<p>Slow-moving landslides play an important role in both theoretical slope evolution and practical landslide hazard and risk research. Their process rates impede the quantitative analysis of related dynamics over short time... more
<p>Slow-moving landslides play an important role in both theoretical slope evolution and practical landslide hazard and risk research. Their process rates impede the quantitative analysis of related dynamics over short time periods, given that the actual changes are often lying within the error margins of the respective methodological approaches. In this study, current results are presented for a long-term monitoring setup of a slow-moving earth slide – earth flow system in the Flysch and Klippen Zone of Lower Austria. The aim is to further assess surface and subsurface characteristics, their interrelations, and implications on spatio-temporal landslide dynamics.</p><p>The research strategy comprises the utilization and analysis of both surface and subsurface monitoring data. The methodology includes the application of Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle (UAV) based Structure from Motion (SfM). Geotechnical methods, such as penetration tests, percussion drilling and inclinometer measurements are used to gain information about subsurface characterization. A meteorological station and piezometer measurements provide information on hydro-meteorological conditions. Surface monitoring data is available since 2015, subsurface monitoring started in 2018.</p><p>Results suggest that a) very high-resolution surface data is necessary to capture real surface changes and that TLS is more suited for processes such as these than UAV based SfM, b) the interpretation of morphological features based on multi-temporal mapping can increase the DoD based level of surface change detection, c) only prolonged observation periods can reveal interrelations on surface and sub-surface dynamics and d) that in-depth knowledge on the study area is important to interpret results and that the impact of natural, but especially artificial disturbances of the hillslope system more or less temporarily close to recent process activities remains difficult to evaluate.</p><p>Current monitoring results reveal the complexity and non-linearity of slow-moving, complex landslide behaviour. Both high spatial and temporal resolution of on-going monitoring data enables an assessment of low rates and changes. However, the slower the process, the longer the observation needs to be. Otherwise the actual process dynamics might be misinterpreted, e.g. the data might be superimposed by technical restrictions. </p>
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Open image in new window Landslide susceptibility maps are frequently produced by fitting multiple variable statistical models that generate a relationship between a binary response variable (presence and absence of past landslides) and a... more
Open image in new window Landslide susceptibility maps are frequently produced by fitting multiple variable statistical models that generate a relationship between a binary response variable (presence and absence of past landslides) and a set of predisposing environmental factors. Within this study, we investigated the hypothesis that an inclusion of a high portion of “trivial areas” (e.g. flat areas) affects modelled relationships, quantitative validation results and the appearance of the final maps. This assumption was tested by systematically comparing logistic regression models that were based on data sets which ignored respectively included a high portion of “trivial areas”. Modelled relationships were evaluated by estimating odds ratios for all predictors. The Area under the Receiver Operating Characteristic Curve (AUROC) provided information on the prediction skill of each model. This performance measure was assessed by applying non-spatial and spatial partitioning techniques. Each analysis was additionally performed with artificial samples to confirm our observations. The results showed that the delineation of the study area affected modelled relationships and consequently the spatial pattern of landslide susceptibility maps as well. AUROC values confirmed that the apparent prediction skill of a model may increase whenever a high portion of easily classifiable areas (e.g. flat area) is included. Therefore we concluded that an interpretation of modelled relationships and prediction skills should always consider the spatial extent to which the respective statistical landslide susceptibility analysis was carried out. The apparent prediction performance of a geomorphic meaningless model can be enhanced by including a high portion of easily classifiable areas.
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Abstract Slow-moving landslides play an important role in both theoretical slope evolution and practical landslide hazard and risk research. Related dynamics therefore are of major interest, including the investigation of surficial... more
Abstract Slow-moving landslides play an important role in both theoretical slope evolution and practical landslide hazard and risk research. Related dynamics therefore are of major interest, including the investigation of surficial changes. Yet, very slow process rates imped their quantitative analysis over short time periods, the actual change not uncommonly lying within the error margins of the respective methodological approaches. This study aims in investigating the surface dynamics of a small, retrogressive, slow-moving (cm-dm/a) earth slide-earth flow system in the Flysch Zone of Lower Austria via multi-temporal, high-resolution terrestrial laser scanning (TLS) data. 7 epochs covering a 10 years’ observation period were utilized to apply both detailed morphological mapping and the computation of digital elevation models (DEMs) of difference (DoDs). Data was analysed to 1) determine and delineate (recently active) process areas, 2) to describe their characteristics, rates and tendencies comparatively via mapping and DoDs - but also 3) to assess the applicability of TLS regarding vegetation cover and to 4) evaluate the added value of this comparative approach when it comes to interpreting landslide dynamics on such detailed scale. Two small Subsystems of the respective landslide, I (~3 300 m2) and II (2 100 m2), exhibit the highest activity within the observation period. Results show 1) areas of changes in surface height correspond with changes in the distribution and characterisation of morphological landslide features, indicating landslide activity. 2) Both Subsystems exhibit different results regarding the magnitude of changes in surface height (DoDs) and feature assembly (mapping), but show similarity regarding the frequency of both changes in surface height and feature evolution, identifying them as rotational and translational process types interrelated with the main landslide system. 3) Findings suggest TLS based DoD computations to be able to detect real surface change on detailed scale (0.05 m raster, ±0.05 error range, 0.05 m steps) in areas of optimum conditions regarding vegetation cover, but also that 4) real surface change could be assessed in areas of less optimum conditions (±020 m error range, 0.20 m steps) where real surface change was overshadowed by changes in vegetation cover via comparatively analysing both DoD and mapping results.
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Landslide inventories form an essential basis for landslide susceptibility, hazard and risk analysis. In contrast to this, only a few complete/consistent landslide inventories are available covering larger areas. Within this study, the... more
Landslide inventories form an essential basis for landslide susceptibility, hazard and risk analysis. In contrast to this, only a few complete/consistent landslide inventories are available covering larger areas. Within this study, the main objective is to identify past and present slope instabilities in Lower Austria and to compile a landslide inventory which is sufficient complete to enable the modelling of reliable landslide susceptibility maps. Therefore, different data sources are used and a strategy is developed how such an inventory for a study area of 10.200 km² can be set up under the restriction of limited time and manpower. The study area is characterised by five major geological units (from North to South): the Bohemian Masif, the Molasse Zone and related basin sediments, the Flysch- and the Klippen Zone, the Northern Calcareous Alps and different types of eastalpine crystalline. Each of them showing a different susceptibility to landsliding, with the Flysch- and Klippen Zone showing the highest susceptibility. Different institutions are acquiring spatial data on landslides: e.g. the Geological Survey of the Federal state government of Lower Austria, the Geological Survey of Austria and the Torrent and Avalanche Control of Austria. According to the background of the institution and the purpose of the archive, landslide data is collected and archived in different ways. Either point, line or polygon information is available. The accuracy of landslide location varies significantly between the different data sets. Since an accurate location of the landslides is absolutely essential for carrying out reliable landslide susceptibility modelling, the spatial accuracy of the data sets must be carefully checked. With the state wide availability of a Lidar digital terrain model (DTM) with a resolution of 1m x 1m, the control of spatial accuracy can be carried out at the required accuracy level. However, even using the Lidar DTM there are some limitations due to the fact that past landslides might have already been disappeared from the earth surface by natural erosion or human impact. Recent landslides might also be not visible just because they are younger than the Lidar DTM. Above all time and manpower are restricted, so that not all landslides visible in the Lidar DTM can be mapped to provide an absolutely complete landslide inventory. Thus, a strategy is developed and presented how a sufficient complete landslide inventory can be created mainly based on mapping from the Lidar DTM but also taking the other data sources into account. The main mapping criteria are that all geological units must be sufficiently covered, the minimum landslide size is 100m² and that only easy to detect landslides are mapped. The strategy is tested in the most landslide prone district of Waidhofen/Ybbs. Furthermore, the potential and limitations of all data sets are discussed regarding their value for landslide susceptibility modelling. The final strategy will be applied to almost the whole state of Lower Austria. The resulting landslide inventory will then serve as the basis for preparing landslide susceptibility maps which will be implemented in the spatial planning strategies of Lower Austria. This study is carried out within the project MoNOE (Method development for landslide susceptibility modelling in Lower Austria), which is funded by the federal state government of Lower Austria.
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In August 2005 more than 500 landslides caused huge damages as well as two fatalities in eastern Styria (Austria). These landslides were triggered by exceptional rainfall (up to 210mm/36h) during the night between 21st and 22nd of... more
In August 2005 more than 500
landslides caused huge damages
as well as two fatalities in eastern
Styria (Austria).
These landslides were triggered by exceptional rainfall (up
to 210mm/36h) during the night between 21st and 22nd
of August. Most of landslides occurred in the region
Gasen/Haslau (eastern Styria).
The study area consists of two communities - Gasen and
Haslau – which cover an area of about 60 km² and count
about 1131 residents. The landslides have been mapped
by the Department of Engineering Geology of the
Geological Survey of Austria.
In this study the Spatial Prediction Modelling System
(SPM) and particularly the likelihood ratio model proposed
by Chung and Fabbri (2003, 2005) is applied to generate
and validate landslide susceptibility maps.
landslides caused huge damages
as well as two fatalities in eastern
Styria (Austria).
These landslides were triggered by exceptional rainfall (up
to 210mm/36h) during the night between 21st and 22nd
of August. Most of landslides occurred in the region
Gasen/Haslau (eastern Styria).
The study area consists of two communities - Gasen and
Haslau – which cover an area of about 60 km² and count
about 1131 residents. The landslides have been mapped
by the Department of Engineering Geology of the
Geological Survey of Austria.
In this study the Spatial Prediction Modelling System
(SPM) and particularly the likelihood ratio model proposed
by Chung and Fabbri (2003, 2005) is applied to generate
and validate landslide susceptibility maps.
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This section discusses the analysis of multi-hazards in a mountainous environment at a medium scale (1:25,000) using Geographic Information Systems. Although the term 'multi-hazards' has been used extensively in literature there are still... more
This section discusses the analysis of multi-hazards in a mountainous environment at a medium scale (1:25,000) using Geographic Information Systems. Although the term 'multi-hazards' has been used extensively in literature there are still very limited approaches to analyze the effects of more than one hazard in the same area, especially related to their interaction. The section starts with an overview of the problem of multi-hazard risk assessment, and indicates the various types of multi-hazard interactions, such as coupled events, concatenated events, and events changing the predisposing factors for other ones. An illustration is given of multi-hazards in a mountainous environment, and their interrelationships,