More than 9000 sinkholes have been documented by the Geological Survey of Thuringia in different ... more More than 9000 sinkholes have been documented by the Geological Survey of Thuringia in different lithological units of Thuringia of which many posed a serious threat on life, personal property and infrastructure. While it is clear that they are caused by hollows which formed due to solution processes within the local bedrock material, little is known about the surface processes and dynamics of erosion of the sinkhole visible above ground. The objective of this study was to analyze sinkhole surface dynamics over time with 3D models derived from terrestrial photos by structure from motion and multi-view 3D reconstruction. The sinkhole was surveyed by terrestrial photos on two days with a two months break. During each photo session 84 and 237 photos have been taken from all around the sinkhole. The photos were processed to 3D point clouds using Agisoft PhotoScan and compared using the software CloudCompare and the M3C2 plugin. The resulting point clouds show an area with significant change that covers about 26% of the sinkhole. Toppling and a few erosion processes have successfully been detected with an observed change of up to 10 cm. Nevertheless, for future studies the study design has to be improved regarding the point cloud registration process, a longer observation duration and a quantitative evaluation of the quality of the individual point clouds is pending.
Die Berucksichtigung gravitativer Massenbewegungen, insbesondere Rutschungen, stellt eine besonde... more Die Berucksichtigung gravitativer Massenbewegungen, insbesondere Rutschungen, stellt eine besondere Herausforderung in der Raumordnung dar. In diesem Kontext wurden Gefahrenhinweiskarten fur gravitative Massenbewegungen als hilfreiche Grundlage identifiziert, welche den Planungsprozess in der ortlichen Raumplanung unterstutzen kann. Diese Gefahrenhinweiskarten weisen flachendeckend drei unterschiedliche Klassen aus. Jede Klasse entspricht einer Empfehlung zu Masnahmen, welche im Fall einer Widmung, bzw. vor einem Bauvorhaben seitens der Gemeinde getatigt werden konnen. Diese Masnahmen sollen dazu beitragen unerwunschte Entwicklungsarten (Widmungsarten) zu vermeiden, bzw. moglichen Schaden durch gravitative Massenbewegungen vorzubeugen. Vor allem in Fallen wo Gefahrenhinweiskarten in der ortlichen Raumplanung zur Anwendung kommen, ist eine Aussage uber ihre Qualitat und deren zulassigen Interpretation unabkommlich. Die Qualitat einer Gefahrenhinweiskarte wird masgeblich durch die Eingangsdaten, allen voran dem Inventar zu gravitativen Massenbewegungen, beeinflusst. In der vorliegenden Dissertation wurden verschiedene Aspekte der Qualitat (Modellgute, thematische Ubereinstimmung verschiedener Modelllaufe, Ubertragbarkeit des Modells auf andere Gebiete) einer Gefahrenhinweiskarte vor allem mit quantitativen Methoden, wie der wiederholten mehrfachen Kreuzvalidierung, untersucht. Speziell bei der Bearbeitung von sehr grosen und heterogenen Gebieten entstehen Herausforderungen bezuglich Datenverfugbarkeit, beschrankter Ressourcen zur Kartierung und der Vergleichbarkeit der Gefahrdung in allen Bereichen des Untersuchungsgebietes. Diesen Herausforderungen wurde mit Uberlegungen zur grostmoglichen Effektivitat bei der Kartierung von Rutschungen und einem neuen Forschungsdesign zur statistischen Modellierung der Rutschungsanfalligkeit eines Gebietes begegnet. Die Kartierung erfolgte auf Basis von Schummerungen eines hochauflosenden digitalen Gelandemodells. Die Modellierung der Gefahrdung wurde innerhalb geotechnisch und topographisch homogener Teilgebiete durchgefuhrt. Die Vollstandigkeit des Inventars und der menschliche Einfluss auf die Auslosung von Rutschungen wurden mittels einer Persistenzanalyse der Morphologie der Rutschung und der Abschatzung des Einflusses von alten Weganalagen auf das Einzugsgebiet einer Rutschung beurteilt. Zusatzlich wurden Unsicherheiten in der „Vorhersage“ der Rutschungsgefahrdung durch die Analyse ihrer Konfidenzintervalle bestimmt. Die Uberlappung von Gefahrdungsklassen der Gefahrenhinweiskarten der vorhergesagten Auftretens-Wahrscheinlichkeit von Rutschungen und der Konfidenzintervalle der Vorhersage wurde ermittelt. Diese Uberlappungen entsprechen den raumlichen Unsicherheiten, welche auf einer Karte visualisiert wurden. Diese Visualisierung soll die Kommunikation uber Unsicherheiten in der Gefahrdungsmodellierung mit den Anwendern der Gefahrenhinweiskarte erleichtern bzw. eine Diskussion zum zulassigen Ausmas von Unsicherheiten ankurbeln. Im Rahmen dieser Forschungsarbeit konnte eine effektive Methode zur Kartierung von Rutschungen auf Basis des hochaufgelosten digitalen Gelandemodells erarbeitet werden. Das resultierende Inventar ist besonders auf die Anforderungen der statistischen Gefahrdungsmodellierung von heterogenen Gebieten abgestimmt. Die Ergebnisse bestatigen die Entscheidung fur das neue Forschungsdesign, da in jedem homogenen Teilgebiet eine unterschiedliche Variablenauswahl zur besten Charakterisierung der Rutschungsanfalligkeit gefuhrt hat. Des Weiteren konnten Unterschiede bezuglich der Modellgute, je nach Grose der verwendeten Stichprobe zum Modellieren festgestellt werden. Teilgebiete mit sehr grosen Stichproben zeigten eine geringere Spannweite der Validierungsmase auf als Teilgebiete mit kleinen Stichprobengrosen. Die Darstellung der Uberlappung von verschiedenen Gefahrdungsklassen, welche durch Berechnung der Konfidenzintervalle der Vorhersage ermittelt wurden, mit der ursprunglichen Gefahrenhinweiskarte zeigen deutlich Bereiche mit sehr grosen Unsicherheiten aber auch mit sehr kleinen Unsicherheiten auf. Die Bearbeitung der Fortpflanzung von Unsicherheiten der Eingangsdaten zu Unsicherheiten der Modellierung ist eine der Perspektiven dieser Forschungsarbeit.
<p>Since 2014, a landslide susceptibility model is used by the Geological Survey an... more <p>Since 2014, a landslide susceptibility model is used by the Geological Survey and Spatial Planning Unit from the Regional Council of Lower Austria to guide decision-making and strategic development in the approx. 19,200 km² province. This existing map (1:25000) has been compiled by using a multi-temporal inventory composed of 12889 slides. In order to obtain the landslide susceptibility model, a generalized additive model (GAM) has been applied, using a large range of predictors. Predictions were performed on the basis of sixteen lithological units. To spatially communicate the landslide propensity, predictions are divided into three categories: low, medium, and high, based on quantiles. By design, the low landslide susceptibility covers 78% of the territory while containing 5% of the landslides. The medium susceptibility class covers 16% of the territory, including 25% of the landslides. The high susceptibility class covers 6% of the territory while containing 70% of the landslides. </p> <p> </p> <p>Although apparently able to correctly predict landslide occurrences over these nearly ten years, this map was never quantitatively evaluated. Since late 2021, a following up review project aims to evaluate how well the existing landslide susceptibility model from 2014 was able to correctly predict the landslides occurring after its implementation. This evaluation is based on landslides that occurred after 2014. Subsequently, the landslide susceptibility will be recalculated, and potential differences between the landslide susceptibility models investigated. To assure fair comparison, an identical methodological design is applied. Changes in the spatial prediction are quantified and explored.</p> <p>Preliminary analysis suggests that the adequacy of the 2014 map to predict future landslides is good but highly determined by the inventories characteristics (i.e., quality and mapping method). For instance, 61% of the landslides coming from a high-quality inventory occur over highly susceptible zones. For a low-quality inventory, this percentage is observed to be rather lower (36%). However, it is also determined that, even for the landslides not occurring in the highly susceptible zone, their locations are rather close to predicted highly unstable zones. For instance, more than 80% of any landslide observations are at least 40m away from a predicted highly unstable zone. The preliminary remodeling of the landslide susceptibility (by including these new landslides) suggests for the regional scale that 88% of the territory remains with the same predicted landslide susceptibility class. However, the arrangement for the individual lithological units might substantially differ. Strategies on how to perform a comparison and updating of landslide susceptibility models are discussed. </p>
<p>Many examples of regional scale statistical landslide susceptibility assessments... more <p>Many examples of regional scale statistical landslide susceptibility assessments can be found in scientific literature. A real-life application of these maps for spatial planning decisions is less common. As result of the MoNOE research project (Method development for landslide susceptibility modelling in Lower Austria), a landslide susceptibility map has been created. Since 2014, this map is constantly used by provincial spatial planners and geologists to guide strategic settlement development in Lower Austria (approx. 19200 km²). Resulting from a multi-temporal inventory of 12,889 slides, a generalized additive model (GAM) was applied to predict the landslide susceptibility using a variety of meaningful morphological and geo-environmental predictors. These easily-applicable, local-scale (1:25,000) landslide susceptibility maps consist of three susceptibility classes. The three classes correspond to low landslide susceptibility (covering 78% of all pixels within the study area), moderate (16% of all pixels) and high (6% of all pixels). Although well accepted by the stakeholders, a few important questions recently arise: a) Is this map able to correctly predict new landslide events that occurred after the implementation of this map? b) With the inclusion of these new samples, is the terrain susceptibility still the same? c) If the terrain susceptibility has changed with the inclusion of the unused (partly recently mapped) samples, why and to what extent?</p><p>By aiming to answer these questions, a review project named MoNEW is currently in place, which has the overall objective to quantify the accuracy of the MoNOE spatial predictions. The new landslides were obtained from two main different sources: 1) recently occurred damage related landslides from a cadaster of landslide events (in German: “Baugrundkataster"), and 2) landslides mapped from hillshades of a high-resolution LiDAR DTM. Based on these new landslides, the final quality of MoNOE will be explored and the landslide susceptibility recalculated to identify potential differences. Therefore, the identical MoNOE methodological design will be applied to ensure comparability and quality control. Changes in the spatial prediction will be quantified and deeply explored.</p><p>First exploratory analysis has demonstrated that most of the new landslides occurred within the highest landslide susceptibility class, indicating an apparent good ability of the past MoNOE susceptibility model to predict these landslides. Depending on the inventory source, 34 to 64% of the landslides occurred within the higher susceptibility class (this percentage was 70% by design in the original <em>MoNOE </em>project). This variation might be explained by the positional accuracy and mapping methodologies of the new landslides. Additionally, it was observed that most of the new landslides occurring in other less susceptible classes (i.e., “low” and “moderate”) were actually located in close proximity to the highest susceptibility class. Given the applicability scale of the MoNOE landslide susceptibility map (1:25,000), these (mostly very low) quantified distances between the landslide locations and the high susceptibility pixels might be inside of the new landslide mapping accuracy. However, how much the landslide susceptibility of the terrain might change with the addition of these new samples is currently under analysis.</p>
<p>With changing environmental conditions, the risk of landslides will also change.... more <p>With changing environmental conditions, the risk of landslides will also change. For the Styrian basin, Austria, we investigate how storylines of climate and land use/land cover change may affect future landslide susceptibility (2071-2100). Our analysis is based on two extreme rainfall events in Styria in 2009 and 2014, which triggered more than three thousand landslides causing a major threat to the local population and significant damage to settlements and infrastructure.</p><p>Furthermore, while the number of studies analysing the impact of climate and land use change on landslide dynamics is rising, the assessment of their uncertainties is still often neglected. However, the quantification of uncertainties is not only essential for the development of business strategies and policy interventions, but also for increasing transparency and confidence in scientific analysis. Therefore, we additionally analyse the joint contribution of climate change uncertainty and landslide model uncertainty for the developed storylines of landslide susceptibility.</p><p>We found for the worst-case storyline (4 K warming scenario) a substantial increase in highly susceptible areas due to much heavier rain. However, the estimated prediction uncertainties were generally high in all storylines. We discovered that the parametric landslide model uncertainty was of the same order as the climate scenario uncertainty, while uncertainties due to internal climate model variability were negligible. With an improved availability of event-based landslide inventories and high-resolution ground data, uncertainties in storylines of landslide susceptibility may be reduced.</p>
. The assessment of uncertainties in landslide susceptibility modelling in a changing environment... more . The assessment of uncertainties in landslide susceptibility modelling in a changing environment is an important, yet often neglected task. In an Austrian case study, we investigated the uncertainty cascade in storylines of landslide susceptibility emerging from climate change and parametric landslide model uncertainty. In June 2009, extreme events of heavy thunderstorms occurred in the Styrian basin, triggering thousands of landslides. Using a storyline approach, we discovered a generally lower landslide susceptibility for pre-industrial climate, while for future climate (2071–2100) a potential increase of 35 % in highly susceptible areas (storyline of much heavier rain) may be compensated by much drier soils (-45 % areas highly susceptible to landsliding). However, the estimated uncertainties in predictions were generally high. While uncertainties related to within-event internal climate model variability were substantially lower than parametric uncertainties of the landslide susceptibility model (ratio of around 0.25), parametric uncertainties were of the same order as the climate scenario uncertainty for the higher warming levels (+3 K and +4 K). We suggest that in future uncertainty assessments, an improved availability of event-based landslide inventories and high-resolution soil and precipitation data will help to reduce parametric uncertainties of landslide susceptibility models used to assess the impacts of climate change on landslide hazard and risk.
<p>Between 22-26 June 2009, Austria witnessed a rampant rainfall spell that... more <p>Between 22-26 June 2009, Austria witnessed a rampant rainfall spell that spread across populated areas of the country. High-intensity rainfall caused 3000+ landslides in southeast Styria, and property damages worth €10 Million in Styria itself. Elsewhere in Austria, flooding amounted to reparations worth €40 Million. Numerous synoptic-scale studies indicated the presence of a cut-off low over central Europe and excessive moisture convergence behind the extreme event. In a warmer climate change scenario, such an extreme precipitation event may manifest into a more intense event due to the higher water holding capacity of air with increased temperatures, but this reasoning may not be so straightforward considering the complex physics of precipitation, more so in a topographically heterogeneous region such as the GAR (Greater Alpine Region).</p><p>The flooding and landslides caused in the region raise an alarm and thus motivate this study whereby we investigate if the rainfall event did become stronger with time due to climate change compared to how it would have been in a counterfactual (climate change free) past. Here we have deployed the CCLM high-resolution regional model coupled with a statistical landslide model to simulate this event (rainfall and landslides) in a pseudo (surrogate) warming scenario. A marked decrease in rainfall intensity is observed in the simulations for 1° cooler climate (pre-industrial past) and the consequent landslide risk is reduced varying across GCMs that were used to derive the boundary conditions from.</p><p>We discuss the results from the lens of attribution perspective - how conditional attribution is much more useful compared to the conventional risk-based approach of attributing extreme events. The novelty of our approach lies in using a high-resolution convection-permitting regional model for a landslide attribution study.</p>
<p>For many years, statistical based landslide susceptibility maps have bee... more <p>For many years, statistical based landslide susceptibility maps have been used to spatially display the relative landslide probability of large areas. Consequently, such maps serve as guidance for strategic territorial planning. In Lower Austria (approx. 19200 km²) a complete set of landslide susceptibility maps for all municipalities has been implemented in 2014. These maps resulted from using 12889 slides as observations and fitting a generalized additive model (GAM) with a variety of geomorphically meaningful explanatory variables. Aiming at easy interpretable maps, the three susceptibility classes minor (78% of all pixels within Lower Austria), moderate (16%) and major (6%) were defined.  In these classes, 5%, 25% and 70% of the landslides were in the categories 1, 2 and 3, respectively. Since the completion of these susceptibility maps, nearly eight years have passed, and many new landslides have been mapped. This study investigates, if and to which degree the existing landslide susceptibility maps can correctly predict these new events.</p><p>This research aims to quantify the accuracy of the spatial predictions. Recently mapped landslides were obtained from two different sources: damage reports related to the “Baugrundkataster", and landslides mapped from hillshades of a high-resolution LiDAR DTM. Additionally, information on the quality of the original landslide inventory and the new ones is used to analyze the effects of only using high quality inventories in this explorative comparison.</p><p>First results give a similar occurrence percentage of recently mapped landslides in the same classes, in comparison with the original classification design. Depending on the inventory the occurrence percentage varies especially in the 3rd class. Preliminary analysis indicates that, depending on the inventory, 34 to 63% of the new landslides are situated in the 3<sup>rd </sup>category (designed to contain 70%). However, it is also observed even for the lower quality inventories, that more than 90% of the landslides are not more than 30 meters away from merged 2<sup>nd </sup>and 3<sup>rd </sup>category susceptibility class. Depending on the new inventory, this percentage can reach 97%, while up to 94% of the points are at 0m distance of the 2<sup>nd</sup> and 3<sup>rd</sup> classes. This is of major importance for the application of these maps, e.g. within spatial planning. Additionally other preliminary analyses already indicate a better proportional correspondence of landslides coinciding with the most landslide-prone 3<sup>rd</sup> category, when excluding lower quality samples.</p><p>The landslide susceptibility map will be recalculated based on the newly recorded events. The potential change of the spatial prediction will be quantified, and the causes of these potential changes will be analyzed. The identical methodological design is applied to ensure comparability and quality control.</p>
More than 9000 sinkholes have been documented by the Geological Survey of Thuringia in different ... more More than 9000 sinkholes have been documented by the Geological Survey of Thuringia in different lithological units of Thuringia of which many posed a serious threat on life, personal property and infrastructure. While it is clear that they are caused by hollows which formed due to solution processes within the local bedrock material, little is known about the surface processes and dynamics of erosion of the sinkhole visible above ground. The objective of this study was to analyze sinkhole surface dynamics over time with 3D models derived from terrestrial photos by structure from motion and multi-view 3D reconstruction. The sinkhole was surveyed by terrestrial photos on two days with a two months break. During each photo session 84 and 237 photos have been taken from all around the sinkhole. The photos were processed to 3D point clouds using Agisoft PhotoScan and compared using the software CloudCompare and the M3C2 plugin. The resulting point clouds show an area with significant change that covers about 26% of the sinkhole. Toppling and a few erosion processes have successfully been detected with an observed change of up to 10 cm. Nevertheless, for future studies the study design has to be improved regarding the point cloud registration process, a longer observation duration and a quantitative evaluation of the quality of the individual point clouds is pending.
Die Berucksichtigung gravitativer Massenbewegungen, insbesondere Rutschungen, stellt eine besonde... more Die Berucksichtigung gravitativer Massenbewegungen, insbesondere Rutschungen, stellt eine besondere Herausforderung in der Raumordnung dar. In diesem Kontext wurden Gefahrenhinweiskarten fur gravitative Massenbewegungen als hilfreiche Grundlage identifiziert, welche den Planungsprozess in der ortlichen Raumplanung unterstutzen kann. Diese Gefahrenhinweiskarten weisen flachendeckend drei unterschiedliche Klassen aus. Jede Klasse entspricht einer Empfehlung zu Masnahmen, welche im Fall einer Widmung, bzw. vor einem Bauvorhaben seitens der Gemeinde getatigt werden konnen. Diese Masnahmen sollen dazu beitragen unerwunschte Entwicklungsarten (Widmungsarten) zu vermeiden, bzw. moglichen Schaden durch gravitative Massenbewegungen vorzubeugen. Vor allem in Fallen wo Gefahrenhinweiskarten in der ortlichen Raumplanung zur Anwendung kommen, ist eine Aussage uber ihre Qualitat und deren zulassigen Interpretation unabkommlich. Die Qualitat einer Gefahrenhinweiskarte wird masgeblich durch die Eingangsdaten, allen voran dem Inventar zu gravitativen Massenbewegungen, beeinflusst. In der vorliegenden Dissertation wurden verschiedene Aspekte der Qualitat (Modellgute, thematische Ubereinstimmung verschiedener Modelllaufe, Ubertragbarkeit des Modells auf andere Gebiete) einer Gefahrenhinweiskarte vor allem mit quantitativen Methoden, wie der wiederholten mehrfachen Kreuzvalidierung, untersucht. Speziell bei der Bearbeitung von sehr grosen und heterogenen Gebieten entstehen Herausforderungen bezuglich Datenverfugbarkeit, beschrankter Ressourcen zur Kartierung und der Vergleichbarkeit der Gefahrdung in allen Bereichen des Untersuchungsgebietes. Diesen Herausforderungen wurde mit Uberlegungen zur grostmoglichen Effektivitat bei der Kartierung von Rutschungen und einem neuen Forschungsdesign zur statistischen Modellierung der Rutschungsanfalligkeit eines Gebietes begegnet. Die Kartierung erfolgte auf Basis von Schummerungen eines hochauflosenden digitalen Gelandemodells. Die Modellierung der Gefahrdung wurde innerhalb geotechnisch und topographisch homogener Teilgebiete durchgefuhrt. Die Vollstandigkeit des Inventars und der menschliche Einfluss auf die Auslosung von Rutschungen wurden mittels einer Persistenzanalyse der Morphologie der Rutschung und der Abschatzung des Einflusses von alten Weganalagen auf das Einzugsgebiet einer Rutschung beurteilt. Zusatzlich wurden Unsicherheiten in der „Vorhersage“ der Rutschungsgefahrdung durch die Analyse ihrer Konfidenzintervalle bestimmt. Die Uberlappung von Gefahrdungsklassen der Gefahrenhinweiskarten der vorhergesagten Auftretens-Wahrscheinlichkeit von Rutschungen und der Konfidenzintervalle der Vorhersage wurde ermittelt. Diese Uberlappungen entsprechen den raumlichen Unsicherheiten, welche auf einer Karte visualisiert wurden. Diese Visualisierung soll die Kommunikation uber Unsicherheiten in der Gefahrdungsmodellierung mit den Anwendern der Gefahrenhinweiskarte erleichtern bzw. eine Diskussion zum zulassigen Ausmas von Unsicherheiten ankurbeln. Im Rahmen dieser Forschungsarbeit konnte eine effektive Methode zur Kartierung von Rutschungen auf Basis des hochaufgelosten digitalen Gelandemodells erarbeitet werden. Das resultierende Inventar ist besonders auf die Anforderungen der statistischen Gefahrdungsmodellierung von heterogenen Gebieten abgestimmt. Die Ergebnisse bestatigen die Entscheidung fur das neue Forschungsdesign, da in jedem homogenen Teilgebiet eine unterschiedliche Variablenauswahl zur besten Charakterisierung der Rutschungsanfalligkeit gefuhrt hat. Des Weiteren konnten Unterschiede bezuglich der Modellgute, je nach Grose der verwendeten Stichprobe zum Modellieren festgestellt werden. Teilgebiete mit sehr grosen Stichproben zeigten eine geringere Spannweite der Validierungsmase auf als Teilgebiete mit kleinen Stichprobengrosen. Die Darstellung der Uberlappung von verschiedenen Gefahrdungsklassen, welche durch Berechnung der Konfidenzintervalle der Vorhersage ermittelt wurden, mit der ursprunglichen Gefahrenhinweiskarte zeigen deutlich Bereiche mit sehr grosen Unsicherheiten aber auch mit sehr kleinen Unsicherheiten auf. Die Bearbeitung der Fortpflanzung von Unsicherheiten der Eingangsdaten zu Unsicherheiten der Modellierung ist eine der Perspektiven dieser Forschungsarbeit.
<p>Since 2014, a landslide susceptibility model is used by the Geological Survey an... more <p>Since 2014, a landslide susceptibility model is used by the Geological Survey and Spatial Planning Unit from the Regional Council of Lower Austria to guide decision-making and strategic development in the approx. 19,200 km² province. This existing map (1:25000) has been compiled by using a multi-temporal inventory composed of 12889 slides. In order to obtain the landslide susceptibility model, a generalized additive model (GAM) has been applied, using a large range of predictors. Predictions were performed on the basis of sixteen lithological units. To spatially communicate the landslide propensity, predictions are divided into three categories: low, medium, and high, based on quantiles. By design, the low landslide susceptibility covers 78% of the territory while containing 5% of the landslides. The medium susceptibility class covers 16% of the territory, including 25% of the landslides. The high susceptibility class covers 6% of the territory while containing 70% of the landslides. </p> <p> </p> <p>Although apparently able to correctly predict landslide occurrences over these nearly ten years, this map was never quantitatively evaluated. Since late 2021, a following up review project aims to evaluate how well the existing landslide susceptibility model from 2014 was able to correctly predict the landslides occurring after its implementation. This evaluation is based on landslides that occurred after 2014. Subsequently, the landslide susceptibility will be recalculated, and potential differences between the landslide susceptibility models investigated. To assure fair comparison, an identical methodological design is applied. Changes in the spatial prediction are quantified and explored.</p> <p>Preliminary analysis suggests that the adequacy of the 2014 map to predict future landslides is good but highly determined by the inventories characteristics (i.e., quality and mapping method). For instance, 61% of the landslides coming from a high-quality inventory occur over highly susceptible zones. For a low-quality inventory, this percentage is observed to be rather lower (36%). However, it is also determined that, even for the landslides not occurring in the highly susceptible zone, their locations are rather close to predicted highly unstable zones. For instance, more than 80% of any landslide observations are at least 40m away from a predicted highly unstable zone. The preliminary remodeling of the landslide susceptibility (by including these new landslides) suggests for the regional scale that 88% of the territory remains with the same predicted landslide susceptibility class. However, the arrangement for the individual lithological units might substantially differ. Strategies on how to perform a comparison and updating of landslide susceptibility models are discussed. </p>
<p>Many examples of regional scale statistical landslide susceptibility assessments... more <p>Many examples of regional scale statistical landslide susceptibility assessments can be found in scientific literature. A real-life application of these maps for spatial planning decisions is less common. As result of the MoNOE research project (Method development for landslide susceptibility modelling in Lower Austria), a landslide susceptibility map has been created. Since 2014, this map is constantly used by provincial spatial planners and geologists to guide strategic settlement development in Lower Austria (approx. 19200 km²). Resulting from a multi-temporal inventory of 12,889 slides, a generalized additive model (GAM) was applied to predict the landslide susceptibility using a variety of meaningful morphological and geo-environmental predictors. These easily-applicable, local-scale (1:25,000) landslide susceptibility maps consist of three susceptibility classes. The three classes correspond to low landslide susceptibility (covering 78% of all pixels within the study area), moderate (16% of all pixels) and high (6% of all pixels). Although well accepted by the stakeholders, a few important questions recently arise: a) Is this map able to correctly predict new landslide events that occurred after the implementation of this map? b) With the inclusion of these new samples, is the terrain susceptibility still the same? c) If the terrain susceptibility has changed with the inclusion of the unused (partly recently mapped) samples, why and to what extent?</p><p>By aiming to answer these questions, a review project named MoNEW is currently in place, which has the overall objective to quantify the accuracy of the MoNOE spatial predictions. The new landslides were obtained from two main different sources: 1) recently occurred damage related landslides from a cadaster of landslide events (in German: “Baugrundkataster"), and 2) landslides mapped from hillshades of a high-resolution LiDAR DTM. Based on these new landslides, the final quality of MoNOE will be explored and the landslide susceptibility recalculated to identify potential differences. Therefore, the identical MoNOE methodological design will be applied to ensure comparability and quality control. Changes in the spatial prediction will be quantified and deeply explored.</p><p>First exploratory analysis has demonstrated that most of the new landslides occurred within the highest landslide susceptibility class, indicating an apparent good ability of the past MoNOE susceptibility model to predict these landslides. Depending on the inventory source, 34 to 64% of the landslides occurred within the higher susceptibility class (this percentage was 70% by design in the original <em>MoNOE </em>project). This variation might be explained by the positional accuracy and mapping methodologies of the new landslides. Additionally, it was observed that most of the new landslides occurring in other less susceptible classes (i.e., “low” and “moderate”) were actually located in close proximity to the highest susceptibility class. Given the applicability scale of the MoNOE landslide susceptibility map (1:25,000), these (mostly very low) quantified distances between the landslide locations and the high susceptibility pixels might be inside of the new landslide mapping accuracy. However, how much the landslide susceptibility of the terrain might change with the addition of these new samples is currently under analysis.</p>
<p>With changing environmental conditions, the risk of landslides will also change.... more <p>With changing environmental conditions, the risk of landslides will also change. For the Styrian basin, Austria, we investigate how storylines of climate and land use/land cover change may affect future landslide susceptibility (2071-2100). Our analysis is based on two extreme rainfall events in Styria in 2009 and 2014, which triggered more than three thousand landslides causing a major threat to the local population and significant damage to settlements and infrastructure.</p><p>Furthermore, while the number of studies analysing the impact of climate and land use change on landslide dynamics is rising, the assessment of their uncertainties is still often neglected. However, the quantification of uncertainties is not only essential for the development of business strategies and policy interventions, but also for increasing transparency and confidence in scientific analysis. Therefore, we additionally analyse the joint contribution of climate change uncertainty and landslide model uncertainty for the developed storylines of landslide susceptibility.</p><p>We found for the worst-case storyline (4 K warming scenario) a substantial increase in highly susceptible areas due to much heavier rain. However, the estimated prediction uncertainties were generally high in all storylines. We discovered that the parametric landslide model uncertainty was of the same order as the climate scenario uncertainty, while uncertainties due to internal climate model variability were negligible. With an improved availability of event-based landslide inventories and high-resolution ground data, uncertainties in storylines of landslide susceptibility may be reduced.</p>
. The assessment of uncertainties in landslide susceptibility modelling in a changing environment... more . The assessment of uncertainties in landslide susceptibility modelling in a changing environment is an important, yet often neglected task. In an Austrian case study, we investigated the uncertainty cascade in storylines of landslide susceptibility emerging from climate change and parametric landslide model uncertainty. In June 2009, extreme events of heavy thunderstorms occurred in the Styrian basin, triggering thousands of landslides. Using a storyline approach, we discovered a generally lower landslide susceptibility for pre-industrial climate, while for future climate (2071–2100) a potential increase of 35 % in highly susceptible areas (storyline of much heavier rain) may be compensated by much drier soils (-45 % areas highly susceptible to landsliding). However, the estimated uncertainties in predictions were generally high. While uncertainties related to within-event internal climate model variability were substantially lower than parametric uncertainties of the landslide susceptibility model (ratio of around 0.25), parametric uncertainties were of the same order as the climate scenario uncertainty for the higher warming levels (+3 K and +4 K). We suggest that in future uncertainty assessments, an improved availability of event-based landslide inventories and high-resolution soil and precipitation data will help to reduce parametric uncertainties of landslide susceptibility models used to assess the impacts of climate change on landslide hazard and risk.
<p>Between 22-26 June 2009, Austria witnessed a rampant rainfall spell that... more <p>Between 22-26 June 2009, Austria witnessed a rampant rainfall spell that spread across populated areas of the country. High-intensity rainfall caused 3000+ landslides in southeast Styria, and property damages worth €10 Million in Styria itself. Elsewhere in Austria, flooding amounted to reparations worth €40 Million. Numerous synoptic-scale studies indicated the presence of a cut-off low over central Europe and excessive moisture convergence behind the extreme event. In a warmer climate change scenario, such an extreme precipitation event may manifest into a more intense event due to the higher water holding capacity of air with increased temperatures, but this reasoning may not be so straightforward considering the complex physics of precipitation, more so in a topographically heterogeneous region such as the GAR (Greater Alpine Region).</p><p>The flooding and landslides caused in the region raise an alarm and thus motivate this study whereby we investigate if the rainfall event did become stronger with time due to climate change compared to how it would have been in a counterfactual (climate change free) past. Here we have deployed the CCLM high-resolution regional model coupled with a statistical landslide model to simulate this event (rainfall and landslides) in a pseudo (surrogate) warming scenario. A marked decrease in rainfall intensity is observed in the simulations for 1° cooler climate (pre-industrial past) and the consequent landslide risk is reduced varying across GCMs that were used to derive the boundary conditions from.</p><p>We discuss the results from the lens of attribution perspective - how conditional attribution is much more useful compared to the conventional risk-based approach of attributing extreme events. The novelty of our approach lies in using a high-resolution convection-permitting regional model for a landslide attribution study.</p>
<p>For many years, statistical based landslide susceptibility maps have bee... more <p>For many years, statistical based landslide susceptibility maps have been used to spatially display the relative landslide probability of large areas. Consequently, such maps serve as guidance for strategic territorial planning. In Lower Austria (approx. 19200 km²) a complete set of landslide susceptibility maps for all municipalities has been implemented in 2014. These maps resulted from using 12889 slides as observations and fitting a generalized additive model (GAM) with a variety of geomorphically meaningful explanatory variables. Aiming at easy interpretable maps, the three susceptibility classes minor (78% of all pixels within Lower Austria), moderate (16%) and major (6%) were defined.  In these classes, 5%, 25% and 70% of the landslides were in the categories 1, 2 and 3, respectively. Since the completion of these susceptibility maps, nearly eight years have passed, and many new landslides have been mapped. This study investigates, if and to which degree the existing landslide susceptibility maps can correctly predict these new events.</p><p>This research aims to quantify the accuracy of the spatial predictions. Recently mapped landslides were obtained from two different sources: damage reports related to the “Baugrundkataster", and landslides mapped from hillshades of a high-resolution LiDAR DTM. Additionally, information on the quality of the original landslide inventory and the new ones is used to analyze the effects of only using high quality inventories in this explorative comparison.</p><p>First results give a similar occurrence percentage of recently mapped landslides in the same classes, in comparison with the original classification design. Depending on the inventory the occurrence percentage varies especially in the 3rd class. Preliminary analysis indicates that, depending on the inventory, 34 to 63% of the new landslides are situated in the 3<sup>rd </sup>category (designed to contain 70%). However, it is also observed even for the lower quality inventories, that more than 90% of the landslides are not more than 30 meters away from merged 2<sup>nd </sup>and 3<sup>rd </sup>category susceptibility class. Depending on the new inventory, this percentage can reach 97%, while up to 94% of the points are at 0m distance of the 2<sup>nd</sup> and 3<sup>rd</sup> classes. This is of major importance for the application of these maps, e.g. within spatial planning. Additionally other preliminary analyses already indicate a better proportional correspondence of landslides coinciding with the most landslide-prone 3<sup>rd</sup> category, when excluding lower quality samples.</p><p>The landslide susceptibility map will be recalculated based on the newly recorded events. The potential change of the spatial prediction will be quantified, and the causes of these potential changes will be analyzed. The identical methodological design is applied to ensure comparability and quality control.</p>
European Geosciences Union General Assembly EGU2017-14431, 2017
Sinkholes are a serious threat on life, personal property and infrastructure in large parts of Th... more Sinkholes are a serious threat on life, personal property and infrastructure in large parts of Thuringia. Over 9000 sinkholes have been documented by the Geological Survey of Thuringia, which are caused by collapsing hollows which formed due to solution processes within the local bedrock material. However, little is known about surface processes and their dynamics at the flanks of the sinkhole once the sinkhole has shaped. These processes are of high interest as they might lead to dangerous situations at or within the vicinity of the sinkhole. Our objective was the analysis of these deformations over time in 3D by applying terrestrial photogrammetry with a simple DSLR camera. Within this study, we performed an analysis of deformations within a sinkhole close to Bad Frankenhausen (Thuringia) using terrestrial photogrammetry and multi-view stereo 3D reconstruction to obtain a 3D point cloud describing the morphology of the sinkhole. This was performed for multiple data collection campaigns over a 6-month period. The photos of the sinkhole were taken with a Nikon D3000 SLR Camera. For the comparison of the point clouds the Multiscale Model to Model Comparison (M3C2) plugin of the software CloudCompare was used. It allows to apply advanced methods of point cloud difference calculation which considers the co-registration error between two point clouds for assessing the significance of the calculated difference (given in meters). Three Styrofoam cuboids of known dimensions (16 cm wide/29 cm high/11.5 cm deep) were placed within the sinkhole to test the accuracy of the point cloud difference calculation. The multi-view stereo 3D reconstruction was performed with Agisoft Photoscan. Preliminary analysis indicates that about 26% of the sinkhole showed changes exceeding the co-registration error of the point clouds. The areas of change can mainly be detected on the flanks of the sinkhole and on an earth pillar that formed in the center of the sinkhole. These changes describe toppling (positive change of a few centimeters at the earth pillar) and a few erosion processes along the flanks (negative change of a few centimeters) compared to the first date of data acquisition. Additionally, the Styrofoam cuboids have successfully been detected with an observed depth change of 10 cm. However, the limitations of this approach related to the co-registration of the point clouds and data acquisition (windy conditions) have to be analyzed in more detail.
Land cover and precipitation are dynamic variables amongst preparatory and triggering factors for... more Land cover and precipitation are dynamic variables amongst preparatory and triggering factors for landslides. Therefore, the future spatial distribution of landslide occurrence is particularly determined by these two factors. However, with changes in land cover and precipitation also the future distribution of elements at risk is altered, which in combination with a changed landslide susceptibility results in a change of the landslide risk. The emerging research question for this study is the analysis of the past and future landslide susceptibility in the periods 1962 - 2100, considering past and future changes in land cover and precipitation. The study area Waidhofen/Ybbs is located in Lower Austria and covers an area of approximately 112km². The geological setting is combined of Flysch and the Northern Calcareous Alps. The land cover is mainly composed of forest and grassland as well as building area in the valley floors. In this study logistic regression is applied to derive land...
Landslides frequently cause damage to agricultural land and infrastructure in Lower Austria - a p... more Landslides frequently cause damage to agricultural land and infrastructure in Lower Austria - a province of Austria. Also settlements and people are threatened by landslides. To reduce landslide risks and to prevent the establishment of new settlements in highly landslide prone areas, the project "MoNOE" (Method development for landslide susceptibility modeling in Lower Austria) was set up by the provincial government. The main aim of the project is the development of methods to model rock fall and slide susceptibility for an area of approx. 15,900 km2 and to implement the resulting susceptibility maps into the spatial planning strategies of the state. Right from the beginning of the project a close cooperation between the involved scientists and the stakeholders from the Geological Survey of Lower Austria and the Department of Spatial Planning and Regional Policy of Lower Austria was established to ensure that method development and final susceptibility maps meet exactly ...
With so many techniques now available for landslide susceptibility modelling, it can be challengi... more With so many techniques now available for landslide susceptibility modelling, it can be challenging to decide on which technique to apply. Generally speaking, the criteria for model selection should be tied closely to end users' purpose, which could be spatial prediction, spatial analysis or both. In our research, we focus on comparing the spatial predictive abilities of landslide susceptibility models. We illustrate how spatial cross-validation, a statistical approach for assessing spatial prediction performance, can be applied with the area under the receiver operating characteristic curve (AUROC) as a prediction measure for model comparison. Several machine learning and statistical techniques are evaluated for prediction in Lower Austria: support vector machine, random forest, bundling with penalized linear discriminant analysis, logistic regression, weights of evidence, and the generalized additive model. In addition to predictive performance, the importance of predictor variables in each model was estimated using spatial cross-validation by calculating the change in AUROC performance when variables are randomly permuted. The susceptibility modelling techniques were tested in three areas of interest in Lower Austria, which have unique geologic conditions associated with landslide occurrence.
Overall, we found for the majority of comparisons that there were little practical or even statistically significant differences in AUROCs. That is the models' prediction performances were very similar. Therefore, in addition to prediction, the ability to interpret models for spatial analysis and the qualitative qualities of the prediction surface (map) are considered and discussed. The measure of variable importance provided some insight into the model behaviour for prediction, in particular for “black-box” models. However, there were no clear patterns in all areas of interest to why certain variables were given more importance over others.
Landslides pose threats not only for specific localities, they are also influencing
larger areas... more Landslides pose threats not only for specific localities, they are also influencing
larger areas and consequently require spatial analysis methods for assessing the
susceptibility to landslides. The availability of landslide susceptibility maps and
their consideration in spatial planning practises is a major step towards avoidance
of damage due to landslides.
In this contribution we present a study design developed with the objective to
determine the best suited landslide susceptibility maps for debris and earth slides
and for rock falls for spatial planning in Lower Austria. Therefore, two different
methods were tested for slide and rock fall susceptibility modelling respectively.
The increasing availability of airborne (ALS) and terrestrial (TLS) laser scanning data in geomor... more The increasing availability of airborne (ALS) and terrestrial (TLS) laser scanning data in geomorphological studies has started to lead to a "revolution in geomorphology". Laser scanning data offers new details not only on vegetation surface but also on earth surface and subsequently provides new insights into geomorphological forms and respective past and present processes which created these forms. Within this study, mainly the potential of ALS in regional landslide hazard assessments is addressed. The starting point for each regional landslide hazard assessment should be an excellent landslide inventory, which often is not available at the beginning of such an assessment. Usually, landslide inventories were set up by e.g. field mapping, digitizing of landslide information from geological or geomorphological maps or interpretation of aerial photos. High resolution digital terrain models (DTM) derived from ALS provide new and excellent data sources for more efficiently mapping landslides. Very accurate landslide inventories can be mapped regarding especially the location and completeness of landslides by analysing ALS DTM's. However, there are quite some limitations involved, since past landslides might be invisible in the DTM due to natural erosion or human impact. Other landslides might be too young to be captured in the DTM. New challenges arise if the study area gets too large. Quite often, resources on time and manpower are limited so that not all landslides can be mapped. Thus, new strategies for efficient landslide mapping and preparation of sufficient complete inventories must be developed beside activities to automate landslide mapping from DTM's. Since landslide structures are modified over time, relative age might be estimated from the freshness of the structures itself. However, these are strongly dependent on the type of land use. Whereas they are preserved under forest, they change more rapidly e.g. in agricultural areas. The challenge is, if the age of the landslides can be roughly estimated based on freshness of the structures and the land use. If possible, general ideas of landslide activity in respective regions can be checked and revised if necessary. Multi-temporal DTM's might be very helpful in this respect, but are very rarely available at present. Furthermore, such return periods might be calculated for a larger region if complete landslide inventories are mapped for a sub-region and a maximum age of the landslides is assumed. Regarding landslide susceptibility modelling quite often important information is not spatially available, e.g. the location of important natural or artificial structures (terraces, road cuts, etc.). The challenge is in which form such information can be extracted from ALS DTM's to improve subsequently the landslide susceptibility models. Potentials and limitations of these aspects are discussed and examples are given.
Landslide susceptibility modelling and implementation of the resulting maps is still a challenge ... more Landslide susceptibility modelling and implementation of the resulting maps is still a challenge for geoscientists, spatial and infrastructure planners. Particularly on a regional scale landslide processes and their dynamics are poorly understood. Furthermore, the availability of appropriate spatial data in high resolution is often a limiting factor for modelling high quality landslide susceptibility maps for large study areas. However, these maps form an important basis for preventive spatial planning measures. Thus, new methods have to be developed, especially focussing on the implementation of final maps into spatial planning processes. The main objective of the project "MoNOE" (Method development for landslide susceptibility modelling in Lower Austria) is to design a method for landslide susceptibility modelling for a large study area (about 10.200 km²) and to produce landslide susceptibility maps which are finally implemented in the spatial planning strategies of the Federal state of Lower Austria. The project focuses primarily on the landslide types fall and slide. To enable susceptibility modelling, landslide inventories for the respective landslide types must be compiled and relevant data has to be gathered, prepared and homogenized. Based on this data new methods must be developed to tackle the needs of the spatial planning strategies. Considerable efforts will also be spent on the validation of the resulting maps for each landslide type. A great challenge will be the combination of the susceptibility maps for slides and falls in just one single susceptibility map (which is requested by the government) and the definition of the final visualisation. Since numerous landslides have been favoured or even triggered by human impact, the human influence on landslides will also have to be investigated. Furthermore possibilities to integrate respective findings in regional susceptibility modelling will be explored. According to these objectives the project is structured in four work packages namely data preparation and homogenization (WP1), susceptibility modelling and validation (WP2), integrative susceptibility assessment (WP3) and human impact (WP4). The expected results are a landslide inventory map covering all endangered parts of the Federal state of Lower Austria, a land cover map of Lower Austria with high spatial resolution, processed spatial input data and an optimized integrative susceptibility map visualized at a scale of 1:25.000. The structure of the research project, research strategies as well as first results will be presented at the conference. The project is funded by the Federal state government of Lower Austria.
Landslide inventories form an essential basis for landslide susceptibility, hazard and risk analy... 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.
In August 2005 more than 500
landslides caused huge damages
as well as two fatalities in easter... 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.
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Overall, we found for the majority of comparisons that there were little practical or even statistically significant differences in AUROCs. That is the models' prediction performances were very similar. Therefore, in addition to prediction, the ability to interpret models for spatial analysis and the qualitative qualities of the prediction surface (map) are considered and discussed. The measure of variable importance provided some insight into the model behaviour for prediction, in particular for “black-box” models. However, there were no clear patterns in all areas of interest to why certain variables were given more importance over others.
larger areas and consequently require spatial analysis methods for assessing the
susceptibility to landslides. The availability of landslide susceptibility maps and
their consideration in spatial planning practises is a major step towards avoidance
of damage due to landslides.
In this contribution we present a study design developed with the objective to
determine the best suited landslide susceptibility maps for debris and earth slides
and for rock falls for spatial planning in Lower Austria. Therefore, two different
methods were tested for slide and rock fall susceptibility modelling respectively.
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.