Articles | Volume 14, issue 1
https://doi.org/10.5194/nhess-14-95-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/nhess-14-95-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Assessing the quality of landslide susceptibility maps – case study Lower Austria
H. Petschko
Department of Geography and Regional Research, University of Vienna, Austria
A. Brenning
Department of Geography and Environmental Management, University of Waterloo, Ontario N2L 3G1, Canada
R. Bell
Department of Geography and Regional Research, University of Vienna, Austria
J. Goetz
Department of Geography and Regional Research, University of Vienna, Austria
Department of Geography and Environmental Management, University of Waterloo, Ontario N2L 3G1, Canada
Department of Geography and Regional Research, University of Vienna, Austria
Related authors
No articles found.
Charlotte Heinzlef, Bruno Barocca, Mattia Leone, Thomas Glade, and Damien Serre
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2020-217, https://doi.org/10.5194/nhess-2020-217, 2020
Preprint withdrawn
Heidi Kreibich, Thomas Thaler, Thomas Glade, and Daniela Molinari
Nat. Hazards Earth Syst. Sci., 19, 551–554, https://doi.org/10.5194/nhess-19-551-2019, https://doi.org/10.5194/nhess-19-551-2019, 2019
Ekrem Canli, Martin Mergili, Benni Thiebes, and Thomas Glade
Nat. Hazards Earth Syst. Sci., 18, 2183–2202, https://doi.org/10.5194/nhess-18-2183-2018, https://doi.org/10.5194/nhess-18-2183-2018, 2018
Short summary
Short summary
Regional-scale landslide forecasting traditionally strongly relies on empirical approaches and landslide-triggering rainfall thresholds. Today, probabilistic methods utilizing ensemble predictions are frequently used for flood forecasting. In our study, we specify how such an approach could also be applied for landslide forecasts and for operational landslide forecasting and early warning systems. To this end, we implemented a physically based landslide model in a probabilistic framework.
Sven Fuchs, Margreth Keiler, and Thomas Glade
Nat. Hazards Earth Syst. Sci., 17, 1203–1206, https://doi.org/10.5194/nhess-17-1203-2017, https://doi.org/10.5194/nhess-17-1203-2017, 2017
Stefan Steger, Alexander Brenning, Rainer Bell, and Thomas Glade
Nat. Hazards Earth Syst. Sci., 16, 2729–2745, https://doi.org/10.5194/nhess-16-2729-2016, https://doi.org/10.5194/nhess-16-2729-2016, 2016
Short summary
Short summary
This study investigates the propagation of landslide inventory-based positional errors into statistical landslide susceptibility models by artificially introducing such spatial inaccuracies. The findings highlight that (i) an increasing positional error is related to increasing distortions of modelling and validation results, (ii) interrelations between inventory-based errors and modelling results are complex, and (iii) inventory-based errors can be counteracted by adapting the study design.
J. N. Goetz, R. H. Guthrie, and A. Brenning
Nat. Hazards Earth Syst. Sci., 15, 1311–1330, https://doi.org/10.5194/nhess-15-1311-2015, https://doi.org/10.5194/nhess-15-1311-2015, 2015
B. Schwendtner, M. Papathoma-Köhle, and T. Glade
Nat. Hazards Earth Syst. Sci., 13, 2195–2207, https://doi.org/10.5194/nhess-13-2195-2013, https://doi.org/10.5194/nhess-13-2195-2013, 2013
Related subject area
Landslides and Debris Flows Hazards
Temporal clustering of precipitation for detection of potential landslides
Shallow-landslide stability evaluation in loess areas according to the Revised Infinite Slope Model: a case study of the 7.25 Tianshui sliding-flow landslide events of 2013 in the southwest of the Loess Plateau, China
Probabilistic assessment of postfire debris-flow inundation in response to forecast rainfall
Evaluating post-wildfire debris-flow rainfall thresholds and volume models at the 2020 Grizzly Creek Fire in Glenwood Canyon, Colorado, USA
Addressing class imbalance in soil movement predictions
Assessing the impact of climate change on landslides near Vejle, Denmark, using public data
Analysis of three-dimensional slope stability combined with rainfall and earthquake
Assessing landslide damming susceptibility in Central Asia
Assessing locations susceptible to shallow landslide initiation during prolonged intense rainfall in the Lares, Utuado, and Naranjito municipalities of Puerto Rico
Evaluation of debris-flow building damage forecasts
Characteristics of debris-flow-prone watersheds and debris-flow-triggering rainstorms following the Tadpole Fire, New Mexico, USA
Morphological characteristics and conditions of drainage basins contributing to the formation of debris flow fans: an examination of regions with different rock strength using decision tree analysis
Comparison of debris flow observations, including fine-sediment grain size and composition and runout model results, at Illgraben, Swiss Alps
Simulation analysis of 3D stability of a landslide with a locking segment: a case study of the Tizicao landslide in Maoxian County, southwest China
Space–time landslide hazard modeling via Ensemble Neural Networks
Optimization strategy for flexible barrier structures: investigation and back analysis of a rockfall disaster case in southwestern China
InSAR-Informed In-Situ Monitoring for Deep-Seated Landslides: Insights from El Forn (Andorra)
Numerical-model-derived intensity–duration thresholds for early warning of rainfall-induced debris flows in a Himalayan catchment
Slope Unit Maker (SUMak): an efficient and parameter-free algorithm for delineating slope units to improve landslide modeling
Probabilistic Hydrological Estimation of LandSlides (PHELS): global ensemble landslide hazard modelling
A new analytical method for stability analysis of rock blocks with basal erosion in sub-horizontal strata by considering the eccentricity effect
A coupled hydrological and hydrodynamic modelling approach for estimating rainfall thresholds of debris-flow occurrence
Rockfall monitoring with a Doppler radar on an active rockslide complex in Brienz/Brinzauls (Switzerland)
More than one landslide per road kilometer – surveying and modeling mass movements along the Rishikesh-Joshimath (NH-7) highway, Uttarakhand, India
Landslide initiation thresholds in data-sparse regions: application to landslide early warning criteria in Sitka, Alaska, USA
Lessons learnt from a rockfall time series analysis: data collection, statistical analysis, and applications
The concept of event-size-dependent exhaustion and its application to paraglacial rockslides
Coastal earthquake-induced landslide susceptibility during the 2016 Mw 7.8 Kaikōura earthquake, New Zealand
Characteristics of debris flows recorded in the Shenmu area of central Taiwan between 2004 and 2021
Semi-automatic mapping of shallow landslides using free Sentinel-2 images and Google Earth Engine
The role of thermokarst evolution in debris flow initiation (Hüttekar Rock Glacier, Austrian Alps)
Accounting for the effect of forest and fragmentation in probabilistic rockfall hazard
Comprehensive landslide susceptibility map of Central Asia
The influence of large woody debris on post-wildfire debris flow sediment storage
Statistical modeling of sediment supply in torrent catchments of the northern French Alps
A data-driven evaluation of post-fire landslide susceptibility
Deciphering seasonal effects of triggering and preparatory precipitation for improved shallow landslide prediction using generalized additive mixed models
Brief communication: The northwest Himalaya towns slipping towards potential disaster
Dynamic response and breakage of trees subject to a landslide-induced air blast
Debris-flow surges of a very active alpine torrent: a field database
Rainfall thresholds estimation for shallow landslides in Peru from gridded daily data
Instantaneous limit equilibrium back analyses of major rockslides triggered during the 2016–2017 central Italy seismic sequence
Deadly disasters in southeastern South America: flash floods and landslides of February 2022 in Petrópolis, Rio de Janeiro
Multi-event assessment of typhoon-triggered landslide susceptibility in the Philippines
Antecedent rainfall as a critical factor for the triggering of debris flows in arid regions
Sensitivity analysis of a built environment exposed to the synthetic monophasic viscous debris flow impacts with 3-D numerical simulations
Characteristics and causes of natural and human-induced landslides in a tropical mountainous region: the rift flank west of Lake Kivu (Democratic Republic of the Congo)
Spatio-temporal analysis of slope-type debris flow activity in Horlachtal, Austria, based on orthophotos and lidar data since 1947
Assessing the relationship between weather conditions and rockfall using terrestrial laser scanning to improve risk management
Using principal component analysis to incorporate multi-layer soil moisture information in hydrometeorological thresholds for landslide prediction: an investigation based on ERA5-Land reanalysis data
Fabiola Banfi, Emanuele Bevacqua, Pauline Rivoire, Sérgio C. Oliveira, Joaquim G. Pinto, Alexandre M. Ramos, and Carlo De Michele
Nat. Hazards Earth Syst. Sci., 24, 2689–2704, https://doi.org/10.5194/nhess-24-2689-2024, https://doi.org/10.5194/nhess-24-2689-2024, 2024
Short summary
Short summary
Landslides are complex phenomena causing important impacts in vulnerable areas, and they are often triggered by rainfall. Here, we develop a new approach that uses information on the temporal clustering of rainfall, i.e. multiple events close in time, to detect landslide events and compare it with the use of classical empirical rainfall thresholds, considering as a case study the region of Lisbon, Portugal. The results could help to improve the prediction of rainfall-triggered landslides.
Jianqi Zhuang, Jianbing Peng, Chenhui Du, Yi Zhu, and Jiaxu Kong
Nat. Hazards Earth Syst. Sci., 24, 2615–2631, https://doi.org/10.5194/nhess-24-2615-2024, https://doi.org/10.5194/nhess-24-2615-2024, 2024
Short summary
Short summary
The Revised Infinite Slope Model (RISM) is proposed using the equal differential unit method and correcting the deficiency of the safety factor increasing with the slope increasing when the slope is larger than 40°, as calculated using the Taylor slope infinite model. The intensity–duration (I–D) prediction curve of the rainfall-induced shallow loess landslides with different slopes was constructed and can be used in forecasting regional shallow loess landslides.
Alexander B. Prescott, Luke A. McGuire, Kwang-Sung Jun, Katherine R. Barnhart, and Nina S. Oakley
Nat. Hazards Earth Syst. Sci., 24, 2359–2374, https://doi.org/10.5194/nhess-24-2359-2024, https://doi.org/10.5194/nhess-24-2359-2024, 2024
Short summary
Short summary
Fire can dramatically increase the risk of debris flows to downstream communities with little warning, but hazard assessments have not traditionally included estimates of inundation. We unify models developed by the scientific community to create probabilistic estimates of inundation area in response to rainfall at forecast lead times (≥ 24 h) needed for decision-making. This work takes an initial step toward a near-real-time postfire debris-flow inundation hazard assessment product.
Francis K. Rengers, Samuel Bower, Andrew Knapp, Jason W. Kean, Danielle W. vonLembke, Matthew A. Thomas, Jaime Kostelnik, Katherine R. Barnhart, Matthew Bethel, Joseph E. Gartner, Madeline Hille, Dennis M. Staley, Justin K. Anderson, Elizabeth K. Roberts, Stephen B. DeLong, Belize Lane, Paxton Ridgway, and Brendan P. Murphy
Nat. Hazards Earth Syst. Sci., 24, 2093–2114, https://doi.org/10.5194/nhess-24-2093-2024, https://doi.org/10.5194/nhess-24-2093-2024, 2024
Short summary
Short summary
Every year the U.S. Geological Survey produces 50–100 postfire debris-flow hazard assessments using models for debris-flow likelihood and volume. To refine these models they must be tested with datasets that clearly document rainfall, debris-flow response, and debris-flow volume. These datasets are difficult to obtain, but this study developed and analyzed a postfire dataset with more than 100 postfire storm responses over a 2-year period. We also proposed ways to improve these models.
Praveen Kumar, Priyanka Priyanka, Kala Venkata Uday, and Varun Dutt
Nat. Hazards Earth Syst. Sci., 24, 1913–1928, https://doi.org/10.5194/nhess-24-1913-2024, https://doi.org/10.5194/nhess-24-1913-2024, 2024
Short summary
Short summary
Our study focuses on predicting soil movement to mitigate landslide risks. We develop machine learning models with oversampling techniques to address the class imbalance in monitoring data. The dynamic ensemble model with K-means SMOTE (synthetic minority oversampling technique) achieves high precision, high recall, and a high F1 score. Our findings highlight the potential of these models with oversampling techniques to improve soil movement predictions in landslide-prone areas.
Kristian Svennevig, Julian Koch, Marie Keiding, and Gregor Luetzenburg
Nat. Hazards Earth Syst. Sci., 24, 1897–1911, https://doi.org/10.5194/nhess-24-1897-2024, https://doi.org/10.5194/nhess-24-1897-2024, 2024
Short summary
Short summary
In our study, we analysed publicly available data in order to investigate the impact of climate change on landslides in Denmark. Our research indicates that the rising groundwater table due to climate change will result in an increase in landslide activity. Previous incidents of extremely wet winters have caused damage to infrastructure and buildings due to landslides. This study is the first of its kind to exclusively rely on public data and examine landslides in Denmark.
Jiao Wang, Zhangxing Wang, Guanhua Sun, and Hongming Luo
Nat. Hazards Earth Syst. Sci., 24, 1741–1756, https://doi.org/10.5194/nhess-24-1741-2024, https://doi.org/10.5194/nhess-24-1741-2024, 2024
Short summary
Short summary
With a simplified formula linking rainfall and groundwater level, the rise of the phreatic surface within the slope can be obtained. Then, a global analysis method that considers both seepage and seismic forces is proposed to determine the safety factor of slopes subjected to the combined effect of rainfall and earthquakes. By taking a slope in the Three Gorges Reservoir area as an example, the safety evolution of the slope combined with both rainfall and earthquake is also examined.
Carlo Tacconi Stefanelli, William Frodella, Francesco Caleca, Zhanar Raimbekova, Ruslan Umaraliev, and Veronica Tofani
Nat. Hazards Earth Syst. Sci., 24, 1697–1720, https://doi.org/10.5194/nhess-24-1697-2024, https://doi.org/10.5194/nhess-24-1697-2024, 2024
Short summary
Short summary
Central Asia regions are marked by active tectonics, high mountains with glaciers, and strong rainfall. These predisposing factors make large landslides a serious threat in the area and a source of possible damming scenarios, which endanger the population. To prevent this, a semi-automated geographic information system (GIS-)based mapping method, centered on a bivariate correlation of morphometric parameters, was applied to give preliminary information on damming susceptibility in Central Asia.
Rex L. Baum, Dianne L. Brien, Mark E. Reid, William H. Schulz, and Matthew J. Tello
Nat. Hazards Earth Syst. Sci., 24, 1579–1605, https://doi.org/10.5194/nhess-24-1579-2024, https://doi.org/10.5194/nhess-24-1579-2024, 2024
Short summary
Short summary
We mapped potential for heavy rainfall to cause landslides in part of the central mountains of Puerto Rico using new tools for estimating soil depth and quasi-3D slope stability. Potential ground-failure locations correlate well with the spatial density of landslides from Hurricane Maria. The smooth boundaries of the very high and high ground-failure susceptibility zones enclose 75 % and 90 %, respectively, of observed landslides. The maps can help mitigate ground-failure hazards.
Katherine R. Barnhart, Christopher R. Miller, Francis K. Rengers, and Jason W. Kean
Nat. Hazards Earth Syst. Sci., 24, 1459–1483, https://doi.org/10.5194/nhess-24-1459-2024, https://doi.org/10.5194/nhess-24-1459-2024, 2024
Short summary
Short summary
Debris flows are a type of fast-moving landslide that start from shallow landslides or during intense rain. Infrastructure located downstream of watersheds susceptible to debris flows may be damaged should a debris flow reach them. We present and evaluate an approach to forecast building damage caused by debris flows. We test three alternative models for simulating the motion of debris flows and find that only one can forecast the correct number and spatial pattern of damaged buildings.
Luke A. McGuire, Francis K. Rengers, Ann M. Youberg, Alexander N. Gorr, Olivia J. Hoch, Rebecca Beers, and Ryan Porter
Nat. Hazards Earth Syst. Sci., 24, 1357–1379, https://doi.org/10.5194/nhess-24-1357-2024, https://doi.org/10.5194/nhess-24-1357-2024, 2024
Short summary
Short summary
Runoff and erosion increase after fire, leading to a greater likelihood of floods and debris flows. We monitored debris flow activity following a fire in western New Mexico, USA, and observed 16 debris flows over a <2-year monitoring period. Rainstorms with recurrence intervals of approximately 1 year were sufficient to initiate debris flows. All debris flows initiated during the first several months following the fire, indicating a rapid decrease in debris flow susceptibility over time.
Ken'ichi Koshimizu, Satoshi Ishimaru, Fumitoshi Imaizumi, and Gentaro Kawakami
Nat. Hazards Earth Syst. Sci., 24, 1287–1301, https://doi.org/10.5194/nhess-24-1287-2024, https://doi.org/10.5194/nhess-24-1287-2024, 2024
Short summary
Short summary
Morphological conditions of drainage basins that classify the presence or absence of debris flow fans were analyzed in areas with different rock strength using decision tree analysis. The relief ratio is the most important morphological factor regardless of the geology. However, the thresholds of morphological parameters needed for forming debris flow fans differ depending on the geology. Decision tree analysis is an effective tool for evaluating the debris flow risk for each geology.
Daniel Bolliger, Fritz Schlunegger, and Brian W. McArdell
Nat. Hazards Earth Syst. Sci., 24, 1035–1049, https://doi.org/10.5194/nhess-24-1035-2024, https://doi.org/10.5194/nhess-24-1035-2024, 2024
Short summary
Short summary
We analysed data from the Illgraben debris flow monitoring station, Switzerland, and we modelled these flows with a debris flow runout model. We found that no correlation exists between the grain size distribution, the mineralogical composition of the matrix, and the debris flow properties. The flow properties rather appear to be determined by the flow volume, from which most other parameters can be derived.
Yuntao Zhou, Xiaoyan Zhao, Guangze Zhang, Bernd Wünnemann, Jiajia Zhang, and Minghui Meng
Nat. Hazards Earth Syst. Sci., 24, 891–906, https://doi.org/10.5194/nhess-24-891-2024, https://doi.org/10.5194/nhess-24-891-2024, 2024
Short summary
Short summary
We developed three rock bridge models to analyze 3D stability and deformation behaviors of the Tizicao landslide and found that the contact surface model with high strength parameters combines advantages of the intact rock mass model in simulating the deformation of slopes with rock bridges and the modeling advantage of the Jennings model. The results help in choosing a rock bridge model to simulate landslide stability and reveal the influence laws of rock bridges on the stability of landslides.
Ashok Dahal, Hakan Tanyas, Cees van Westen, Mark van der Meijde, Paul Martin Mai, Raphaël Huser, and Luigi Lombardo
Nat. Hazards Earth Syst. Sci., 24, 823–845, https://doi.org/10.5194/nhess-24-823-2024, https://doi.org/10.5194/nhess-24-823-2024, 2024
Short summary
Short summary
We propose a modeling approach capable of recognizing slopes that may generate landslides, as well as how large these mass movements may be. This protocol is implemented, tested, and validated with data that change in both space and time via an Ensemble Neural Network architecture.
Li-Ru Luo, Zhi-Xiang Yu, Li-Jun Zhang, Qi Wang, Lin-Xu Liao, and Li Peng
Nat. Hazards Earth Syst. Sci., 24, 631–649, https://doi.org/10.5194/nhess-24-631-2024, https://doi.org/10.5194/nhess-24-631-2024, 2024
Short summary
Short summary
We performed field investigations on a rockfall near Jiguanshan National Forest Park, Chengdu. Vital information was obtained from an unmanned aerial vehicle survey. A finite element model was created to reproduce the damage evolution. We found that the impact kinetic energy was below the design protection energy. Improper member connections prevent the barrier from producing significant deformation to absorb energy. Damage is avoided by improving the ability of the nets and ropes to slide.
Rachael Lau, Carolina Seguí, Tyler Waterman, Nathaniel Chaney, and Manolis Veveakis
EGUsphere, https://doi.org/10.48550/arXiv.2311.01564, https://doi.org/10.48550/arXiv.2311.01564, 2024
Short summary
Short summary
This work examines the use of Interferometric Synthetic Aperture Radar (InSAR) alongside in-situ borehole measurements to assess the stability of deep-seated landslides for the case study of El Forn (Andorra). InSAR data compared with borehole data suggests a key tradeoff between accuracy and precision for various InSAR resolutions. Spatial interpolation with InSAR informed how many remote observations are necessary to lower error on remote-sensing recreation of ground motion over the landslide.
Sudhanshu Dixit, Srikrishnan Siva Subramanian, Piyush Srivastava, Ali P. Yunus, Tapas Ranjan Martha, and Sumit Sen
Nat. Hazards Earth Syst. Sci., 24, 465–480, https://doi.org/10.5194/nhess-24-465-2024, https://doi.org/10.5194/nhess-24-465-2024, 2024
Short summary
Short summary
Rainfall intensity–duration (ID) thresholds can aid in the prediction of natural hazards. Large-scale sediment disasters like landslides, debris flows, and flash floods happen frequently in the Himalayas because of their propensity for intense precipitation events. We provide a new framework that combines the Weather Research and Forecasting (WRF) model with a regionally distributed numerical model for debris flows to analyse and predict intense rainfall-induced landslides in the Himalayas.
Jacob B. Woodard, Benjamin B. Mirus, Nathan J. Wood, Kate E. Allstadt, Benjamin A. Leshchinsky, and Matthew M. Crawford
Nat. Hazards Earth Syst. Sci., 24, 1–12, https://doi.org/10.5194/nhess-24-1-2024, https://doi.org/10.5194/nhess-24-1-2024, 2024
Short summary
Short summary
Dividing landscapes into hillslopes greatly improves predictions of landslide potential across landscapes, but their scaling is often arbitrarily set and can require significant computing power to delineate. Here, we present a new computer program that can efficiently divide landscapes into meaningful slope units scaled to best capture landslide processes. The results of this work will allow an improved understanding of landslide potential and can help reduce the impacts of landslides worldwide.
Anne Felsberg, Zdenko Heyvaert, Jean Poesen, Thomas Stanley, and Gabriëlle J. M. De Lannoy
Nat. Hazards Earth Syst. Sci., 23, 3805–3821, https://doi.org/10.5194/nhess-23-3805-2023, https://doi.org/10.5194/nhess-23-3805-2023, 2023
Short summary
Short summary
The Probabilistic Hydrological Estimation of LandSlides (PHELS) model combines ensembles of landslide susceptibility and of hydrological predictor variables to provide daily, global ensembles of hazard for hydrologically triggered landslides. Testing different hydrological predictors showed that the combination of rainfall and soil moisture performed best, with the lowest number of missed and false alarms. The ensemble approach allowed the estimation of the associated prediction uncertainty.
Xushan Shi, Bo Chai, Juan Du, Wei Wang, and Bo Liu
Nat. Hazards Earth Syst. Sci., 23, 3425–3443, https://doi.org/10.5194/nhess-23-3425-2023, https://doi.org/10.5194/nhess-23-3425-2023, 2023
Short summary
Short summary
A 3D stability analysis method is proposed for biased rockfall with external erosion. Four failure modes are considered according to rockfall evolution processes, including partial damage of underlying soft rock and overall failure of hard rock blocks. This method is validated with the biased rockfalls in the Sichuan Basin, China. The critical retreat ratio from low to moderate rockfall susceptibility is 0.33. This method could facilitate rockfall early identification and risk mitigation.
Zhen Lei Wei, Yue Quan Shang, Qiu Hua Liang, and Xi Lin Xia
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-180, https://doi.org/10.5194/nhess-2023-180, 2023
Revised manuscript accepted for NHESS
Short summary
Short summary
The initiation of debris flows is influenced significantly by rainfall-induced hydrological processes. We propose a novel framework, which is based on an integrated hydrological and hydrodynamic model, aimed at estimating Intensity-Duration (I-D) rainfall thresholds responsible for triggering debris flows. In comparison to traditional statistical approaches, this physically-based framework particularly suitable for application in ungauged catchments where historical debris flow data is scarce.
Marius Schneider, Nicolas Oestreicher, Thomas Ehrat, and Simon Loew
Nat. Hazards Earth Syst. Sci., 23, 3337–3354, https://doi.org/10.5194/nhess-23-3337-2023, https://doi.org/10.5194/nhess-23-3337-2023, 2023
Short summary
Short summary
Rockfalls and their hazards are typically treated as statistical events based on rockfall catalogs, but only a few complete rockfall inventories are available today. Here, we present new results from a Doppler radar rockfall alarm system, which has operated since 2018 at a high frequency under all illumination and weather conditions at a site where frequent rockfall events threaten a village and road. The new data set is used to investigate rockfall triggers in an active rockslide complex.
Jürgen Mey, Ravi Kumar Guntu, Alexander Plakias, Igo Silva de Almeida, and Wolfgang Schwanghart
EGUsphere, https://doi.org/10.5194/egusphere-2023-1975, https://doi.org/10.5194/egusphere-2023-1975, 2023
Short summary
Short summary
The Himalayan road network links remote areas, but fragile terrain and poor construction lead to frequent landslides. This study on NH-7 in India's Uttarakhand region analyzed 300 landslides after heavy 2022 rainfall. Factors like slope, rainfall, rock type, and road work influence landslides. The study's model predicts landslide locations for better road maintenance planning, highlighting the risk from climate change and increased road use.
Annette I. Patton, Lisa V. Luna, Joshua J. Roering, Aaron Jacobs, Oliver Korup, and Benjamin B. Mirus
Nat. Hazards Earth Syst. Sci., 23, 3261–3284, https://doi.org/10.5194/nhess-23-3261-2023, https://doi.org/10.5194/nhess-23-3261-2023, 2023
Short summary
Short summary
Landslide warning systems often use statistical models to predict landslides based on rainfall. They are typically trained on large datasets with many landslide occurrences, but in rural areas large datasets may not exist. In this study, we evaluate which statistical model types are best suited to predicting landslides and demonstrate that even a small landslide inventory (five storms) can be used to train useful models for landslide early warning when non-landslide events are also included.
Sandra Melzner, Marco Conedera, Johannes Hübl, and Mauro Rossi
Nat. Hazards Earth Syst. Sci., 23, 3079–3093, https://doi.org/10.5194/nhess-23-3079-2023, https://doi.org/10.5194/nhess-23-3079-2023, 2023
Short summary
Short summary
The estimation of the temporal frequency of the involved rockfall processes is an important part in hazard and risk assessments. Different methods can be used to collect and analyse rockfall data. From a statistical point of view, rockfall datasets are nearly always incomplete. Accurate data collection approaches and the application of statistical methods on existing rockfall data series as reported in this study should be better considered in rockfall hazard and risk assessments in the future.
Stefan Hergarten
Nat. Hazards Earth Syst. Sci., 23, 3051–3063, https://doi.org/10.5194/nhess-23-3051-2023, https://doi.org/10.5194/nhess-23-3051-2023, 2023
Short summary
Short summary
Rockslides are a major hazard in mountainous regions. In formerly glaciated regions, the disposition mainly arises from oversteepened topography and decreases through time. However, little is known about this decrease and thus about the present-day hazard of huge, potentially catastrophic rockslides. This paper presents a new theoretical framework that explains the decrease in maximum rockslide size through time and predicts the present-day frequency of large rockslides for the European Alps.
Colin K. Bloom, Corinne Singeisen, Timothy Stahl, Andrew Howell, Chris Massey, and Dougal Mason
Nat. Hazards Earth Syst. Sci., 23, 2987–3013, https://doi.org/10.5194/nhess-23-2987-2023, https://doi.org/10.5194/nhess-23-2987-2023, 2023
Short summary
Short summary
Landslides are often observed on coastlines following large earthquakes, but few studies have explored this occurrence. Here, statistical modelling of landslides triggered by the 2016 Kaikōura earthquake in New Zealand is used to investigate factors driving coastal earthquake-induced landslides. Geology, steep slopes, and shaking intensity are good predictors of landslides from the Kaikōura event. Steeper slopes close to the coast provide the best explanation for a high landslide density.
Yi-Min Huang
Nat. Hazards Earth Syst. Sci., 23, 2649–2662, https://doi.org/10.5194/nhess-23-2649-2023, https://doi.org/10.5194/nhess-23-2649-2023, 2023
Short summary
Short summary
Debris flows are common hazards in Taiwan, and debris-flow early warning is important for disaster responses. The rainfall thresholds of debris flows are analyzed and determined in terms of rainfall intensity, accumulated rainfall, and rainfall duration, based on case histories in Taiwan. These thresholds are useful for disaster management, and the cases in Taiwan are useful for global debris-flow databases.
Davide Notti, Martina Cignetti, Danilo Godone, and Daniele Giordan
Nat. Hazards Earth Syst. Sci., 23, 2625–2648, https://doi.org/10.5194/nhess-23-2625-2023, https://doi.org/10.5194/nhess-23-2625-2023, 2023
Short summary
Short summary
We developed a cost-effective and user-friendly approach to map shallow landslides using free satellite data. Our methodology involves analysing the pre- and post-event NDVI variation to semi-automatically detect areas potentially affected by shallow landslides (PLs). Additionally, we have created Google Earth Engine scripts to rapidly compute NDVI differences and time series of affected areas. Datasets and codes are stored in an open data repository for improvement by the scientific community.
Simon Seelig, Thomas Wagner, Karl Krainer, Michael Avian, Marc Olefs, Klaus Haslinger, and Gerfried Winkler
Nat. Hazards Earth Syst. Sci., 23, 2547–2568, https://doi.org/10.5194/nhess-23-2547-2023, https://doi.org/10.5194/nhess-23-2547-2023, 2023
Short summary
Short summary
A rapid sequence of cascading events involving thermokarst lake outburst, rock glacier front failure, debris flow development, and river blockage hit an alpine valley in Austria during summer 2019. We analyze the environmental conditions initiating the process chain and identify the rapid evolution of a thermokarst channel network as the main driver. Our results highlight the need to account for permafrost degradation in debris flow hazard assessment studies.
Camilla Lanfranconi, Paolo Frattini, Gianluca Sala, Giuseppe Dattola, Davide Bertolo, Juanjuan Sun, and Giovanni Battista Crosta
Nat. Hazards Earth Syst. Sci., 23, 2349–2363, https://doi.org/10.5194/nhess-23-2349-2023, https://doi.org/10.5194/nhess-23-2349-2023, 2023
Short summary
Short summary
This paper presents a study on rockfall dynamics and hazard, examining the impact of the presence of trees along slope and block fragmentation. We compared rockfall simulations that explicitly model the presence of trees and fragmentation with a classical approach that accounts for these phenomena in model parameters (both the hazard and the kinetic energy change). We also used a non-parametric probabilistic rockfall hazard analysis method for hazard mapping.
Ascanio Rosi, William Frodella, Nicola Nocentini, Francesco Caleca, Hans Balder Havenith, Alexander Strom, Mirzo Saidov, Gany Amirgalievich Bimurzaev, and Veronica Tofani
Nat. Hazards Earth Syst. Sci., 23, 2229–2250, https://doi.org/10.5194/nhess-23-2229-2023, https://doi.org/10.5194/nhess-23-2229-2023, 2023
Short summary
Short summary
This work was carried out within the Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia (SFRARR) project and is focused on the first landslide susceptibility analysis at a regional scale for Central Asia. The most detailed available landslide inventories were implemented in a random forest model. The final aim was to provide a useful tool for reduction strategies to landslide scientists, practitioners, and administrators.
Francis K. Rengers, Luke A. McGuire, Katherine R. Barnhart, Ann M. Youberg, Daniel Cadol, Alexander N. Gorr, Olivia J. Hoch, Rebecca Beers, and Jason W. Kean
Nat. Hazards Earth Syst. Sci., 23, 2075–2088, https://doi.org/10.5194/nhess-23-2075-2023, https://doi.org/10.5194/nhess-23-2075-2023, 2023
Short summary
Short summary
Debris flows often occur after wildfires. These debris flows move water, sediment, and wood. The wood can get stuck in channels, creating a dam that holds boulders, cobbles, sand, and muddy material. We investigated how the channel width and wood length influenced how much sediment is stored. We also used a series of equations to back calculate the debris flow speed using the breaking threshold of wood. These data will help improve models and provide insight into future field investigations.
Maxime Morel, Guillaume Piton, Damien Kuss, Guillaume Evin, and Caroline Le Bouteiller
Nat. Hazards Earth Syst. Sci., 23, 1769–1787, https://doi.org/10.5194/nhess-23-1769-2023, https://doi.org/10.5194/nhess-23-1769-2023, 2023
Short summary
Short summary
In mountain catchments, damage during floods is generally primarily driven by the supply of a massive amount of sediment. Predicting how much sediment can be delivered by frequent and infrequent events is thus important in hazard studies. This paper uses data gathered during the maintenance operation of about 100 debris retention basins to build simple equations aiming at predicting sediment supply from simple parameters describing the upstream catchment.
Elsa S. Culler, Ben Livneh, Balaji Rajagopalan, and Kristy F. Tiampo
Nat. Hazards Earth Syst. Sci., 23, 1631–1652, https://doi.org/10.5194/nhess-23-1631-2023, https://doi.org/10.5194/nhess-23-1631-2023, 2023
Short summary
Short summary
Landslides have often been observed in the aftermath of wildfires. This study explores regional patterns in the rainfall that caused landslides both after fires and in unburned locations. In general, landslides that occur after fires are triggered by less rainfall, confirming that fire helps to set the stage for landslides. However, there are regional differences in the ways in which fire impacts landslides, such as the size and direction of shifts in the seasonality of landslides after fires.
Stefan Steger, Mateo Moreno, Alice Crespi, Peter James Zellner, Stefano Luigi Gariano, Maria Teresa Brunetti, Massimo Melillo, Silvia Peruccacci, Francesco Marra, Robin Kohrs, Jason Goetz, Volkmar Mair, and Massimiliano Pittore
Nat. Hazards Earth Syst. Sci., 23, 1483–1506, https://doi.org/10.5194/nhess-23-1483-2023, https://doi.org/10.5194/nhess-23-1483-2023, 2023
Short summary
Short summary
We present a novel data-driven modelling approach to determine season-specific critical precipitation conditions for landslide occurrence. It is shown that the amount of precipitation required to trigger a landslide in South Tyrol varies from season to season. In summer, a higher amount of preparatory precipitation is required to trigger a landslide, probably due to denser vegetation and higher temperatures. We derive dynamic thresholds that directly relate to hit rates and false-alarm rates.
Yaspal Sundriyal, Vipin Kumar, Neha Chauhan, Sameeksha Kaushik, Rahul Ranjan, and Mohit Kumar Punia
Nat. Hazards Earth Syst. Sci., 23, 1425–1431, https://doi.org/10.5194/nhess-23-1425-2023, https://doi.org/10.5194/nhess-23-1425-2023, 2023
Short summary
Short summary
The NW Himalaya has been one of the most affected terrains of the Himalaya, subject to disastrous landslides. This article focuses on two towns (Joshimath and Bhatwari) of the NW Himalaya, which have been witnessing subsidence for decades. We used a slope stability simulation to determine the response of the hillslopes accommodating these towns under various loading conditions. We found that the maximum displacement in these hillslopes might reach up to 20–25 m.
Yu Zhuang, Aiguo Xing, Perry Bartelt, Muhammad Bilal, and Zhaowei Ding
Nat. Hazards Earth Syst. Sci., 23, 1257–1266, https://doi.org/10.5194/nhess-23-1257-2023, https://doi.org/10.5194/nhess-23-1257-2023, 2023
Short summary
Short summary
Tree destruction is often used to back calculate the air blast impact region and to estimate the air blast power. Here we established a novel model to assess air blast power using tree destruction information. We find that the dynamic magnification effect makes the trees easier to damage by a landslide-induced air blast, but the large tree deformation would weaken the effect. Bending and overturning are two likely failure modes, which depend heavily on the properties of trees.
Suzanne Lapillonne, Firmin Fontaine, Frédéric Liebault, Vincent Richefeu, and Guillaume Piton
Nat. Hazards Earth Syst. Sci., 23, 1241–1256, https://doi.org/10.5194/nhess-23-1241-2023, https://doi.org/10.5194/nhess-23-1241-2023, 2023
Short summary
Short summary
Debris flows are fast flows most often found in torrential watersheds. They are composed of two phases: a liquid phase which can be mud-like and a granular phase, including large boulders, transported along with the flow. Due to their destructive nature, accessing features of the flow, such as velocity and flow height, is difficult. We present a protocol to analyse debris flow data and results of the Réal torrent in France. These results will help experts in designing models.
Carlos Millán-Arancibia and Waldo Lavado-Casimiro
Nat. Hazards Earth Syst. Sci., 23, 1191–1206, https://doi.org/10.5194/nhess-23-1191-2023, https://doi.org/10.5194/nhess-23-1191-2023, 2023
Short summary
Short summary
This study is the first approximation of regional rainfall thresholds for shallow landslide occurrence in Peru. This research was generated from a gridded precipitation data and landslide inventory. The analysis showed that the threshold based on the combination of mean daily intensity–duration variables gives the best results for separating rainfall events that generate landslides. Through this work the potential of thresholds for landslide monitoring at the regional scale is demonstrated.
Luca Verrucci, Giovanni Forte, Melania De Falco, Paolo Tommasi, Giuseppe Lanzo, Kevin W. Franke, and Antonio Santo
Nat. Hazards Earth Syst. Sci., 23, 1177–1190, https://doi.org/10.5194/nhess-23-1177-2023, https://doi.org/10.5194/nhess-23-1177-2023, 2023
Short summary
Short summary
Stability analyses in static and seismic conditions were performed on four rockslides that occurred during the main shocks of the 2016–2017 central Italy seismic sequence. These results also indicate that specific structural features of the slope must carefully be accounted for in evaluating potential hazards on transportation infrastructures in mountainous regions.
Enner Alcântara, José A. Marengo, José Mantovani, Luciana R. Londe, Rachel Lau Yu San, Edward Park, Yunung Nina Lin, Jingyu Wang, Tatiana Mendes, Ana Paula Cunha, Luana Pampuch, Marcelo Seluchi, Silvio Simões, Luz Adriana Cuartas, Demerval Goncalves, Klécia Massi, Regina Alvalá, Osvaldo Moraes, Carlos Souza Filho, Rodolfo Mendes, and Carlos Nobre
Nat. Hazards Earth Syst. Sci., 23, 1157–1175, https://doi.org/10.5194/nhess-23-1157-2023, https://doi.org/10.5194/nhess-23-1157-2023, 2023
Short summary
Short summary
The municipality of Petrópolis (approximately 305 687 inhabitants) is nestled in the mountains 68 km outside the city of Rio de Janeiro. On 15 February 2022, the city of Petrópolis in Rio de Janeiro, Brazil, received an unusually high volume of rain within 3 h (258 mm). This resulted in flash floods and subsequent landslides that caused 231 fatalities, the deadliest landslide disaster recorded in Petrópolis. This work shows how the disaster was triggered.
Joshua N. Jones, Georgina L. Bennett, Claudia Abancó, Mark A. M. Matera, and Fibor J. Tan
Nat. Hazards Earth Syst. Sci., 23, 1095–1115, https://doi.org/10.5194/nhess-23-1095-2023, https://doi.org/10.5194/nhess-23-1095-2023, 2023
Short summary
Short summary
We modelled where landslides occur in the Philippines using landslide data from three typhoon events in 2009, 2018, and 2019. These models show where landslides occurred within the landscape. By comparing the different models, we found that the 2019 landslides were occurring all across the landscape, whereas the 2009 and 2018 landslides were mostly occurring at specific slope angles and aspects. This shows that landslide susceptibility must be considered variable through space and time.
Shalev Siman-Tov and Francesco Marra
Nat. Hazards Earth Syst. Sci., 23, 1079–1093, https://doi.org/10.5194/nhess-23-1079-2023, https://doi.org/10.5194/nhess-23-1079-2023, 2023
Short summary
Short summary
Debris flows represent a threat to infrastructure and the population. In arid areas, they are observed when heavy rainfall hits steep slopes with sediments. Here, we use digital surface models and radar rainfall data to detect and characterize the triggering and non-triggering rainfall conditions. We find that rainfall intensity alone is insufficient to explain the triggering. We suggest that antecedent rainfall could represent a critical factor for debris flow triggering in arid regions.
Xun Huang, Zhijian Zhang, and Guoping Xiang
Nat. Hazards Earth Syst. Sci., 23, 871–889, https://doi.org/10.5194/nhess-23-871-2023, https://doi.org/10.5194/nhess-23-871-2023, 2023
Short summary
Short summary
A sensitivity analysis on the building impact force resulting from the representative built environment parameters is executed through the FLOW-3D model. The surrounding buildings' properties, especially the azimuthal angle, have been confirmed to play significant roles in determining the peak impact forces. The single and combined effects of built environments are analyzed in detail. This will improve understanding of vulnerability assessment and migration design against debris flow hazards.
Jean-Claude Maki Mateso, Charles L. Bielders, Elise Monsieurs, Arthur Depicker, Benoît Smets, Théophile Tambala, Luc Bagalwa Mateso, and Olivier Dewitte
Nat. Hazards Earth Syst. Sci., 23, 643–666, https://doi.org/10.5194/nhess-23-643-2023, https://doi.org/10.5194/nhess-23-643-2023, 2023
Short summary
Short summary
This research highlights the importance of human activities on the occurrence of landslides and the need to consider this context when studying hillslope instability patterns in regions under anthropogenic pressure. Also, this study highlights the importance of considering the timing of landslides and hence the added value of using historical information for compiling an inventory.
Jakob Rom, Florian Haas, Tobias Heckmann, Moritz Altmann, Fabian Fleischer, Camillo Ressl, Sarah Betz-Nutz, and Michael Becht
Nat. Hazards Earth Syst. Sci., 23, 601–622, https://doi.org/10.5194/nhess-23-601-2023, https://doi.org/10.5194/nhess-23-601-2023, 2023
Short summary
Short summary
In this study, an area-wide slope-type debris flow record has been established for Horlachtal, Austria, since 1947 based on historical and recent remote sensing data. Spatial and temporal analyses show variations in debris flow activity in space and time in a high-alpine region. The results can contribute to a better understanding of past slope-type debris flow dynamics in the context of extreme precipitation events and their possible future development.
Tom Birien and Francis Gauthier
Nat. Hazards Earth Syst. Sci., 23, 343–360, https://doi.org/10.5194/nhess-23-343-2023, https://doi.org/10.5194/nhess-23-343-2023, 2023
Short summary
Short summary
On highly fractured rockwalls such as those found in northern Gaspésie, most rockfalls are triggered by weather conditions. This study highlights that in winter, rockfall frequency is 12 times higher during a superficial thaw than during a cold period in which temperature remains below 0 °C. In summer, rockfall frequency is 22 times higher during a heavy rainfall event than during a mainly dry period. This knowledge could be used to implement a risk management strategy.
Nunziarita Palazzolo, David J. Peres, Enrico Creaco, and Antonino Cancelliere
Nat. Hazards Earth Syst. Sci., 23, 279–291, https://doi.org/10.5194/nhess-23-279-2023, https://doi.org/10.5194/nhess-23-279-2023, 2023
Short summary
Short summary
We propose an approach exploiting PCA to derive hydrometeorological landslide-triggering thresholds using multi-layered soil moisture data from ERA5-Land reanalysis. Comparison of thresholds based on single- and multi-layered soil moisture information provides a means to identify the significance of multi-layered data for landslide triggering in a region. In Sicily, the proposed approach yields thresholds with a higher performance than traditional precipitation-based ones (TSS = 0.71 vs. 0.50).
Cited articles
Abteilung Feuerwehr und Zivilschutz, Amt der NÖ Landesregierung: Zusammenfassung Ereignisse 2009, available at: http://www.noe.gv.at/Land-Zukunft/Katastrophenschutz/Archiv/zusammenfassung_ ereignisse_2009.wai.html, (last access: 9 December 2010), 2010 (in German).
Akaike, H.: A new look at the statistical model identification, IEEE T. Automat. Contr., 19, 716–723, 1974.
Ardizzone, F., Cardinali, M., Carrara, A., Guzzetti, F., and Reichenbach, P.: Impact of mapping errors on the reliability of landslide hazard maps, Nat. Hazards Earth Syst. Sci., 2, 3–14, https://doi.org/10.5194/nhess-2-3-2002, 2002.
Atkinson, P., Jiskoot, H., Massari, R. and Murray, T.: Generalized linear modelling in geomorphology, Earth Surf. Proc. Land., 23, 1185–1195, 1998.
Bathurst, J. C., Bovolo, C. I., and Cisneros, F.: Modelling the effect of forest cover on shallow landslides at the river basin scale, Ecol. Eng., 36, 317–327, https://doi.org/10.1016/j.ecoleng.2009.05.001, 2010.
Beguería, S.: Validation and Evaluation of Predictive Models in Hazard Assessment and Risk Management, Nat. Hazards, 37, 315–329, 2006a.
Beguería, S.: Changes in land cover and shallow landslide activity: A case study in the Spanish Pyrenees, Geomorphology, 74, 196–206, 2006b.
Bell, R.: Lokale und regionale Gefahren- und Risikoanalyse gravitativer Massenbewegungen an der Schwäbischen Alb, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn. available at: http://hss.ulb.uni-bonn.de/2007/1107/1107.pdf, (last access: 20 March 2012), 2007.
Bell, R., Petschko, H., Röhrs, M., and Dix, A.: Assessment of landslide age, landslide persistence and human impact using airborne laser scanning digital terrain models, Geogr. Ann. A, 94, 135–156, 2012.
Bell, R., Petschko, H., Proske, H., Leopold, P., Heiss, G., Bauer, C., Goetz, J. N., Granica, K., and Glade, T.: Methodenentwicklung zur Gefährdungsmodellierung von Massenbewegungen in Niederösterreich MoNOE – Vorläufiger Endbericht, Projektbericht, Universität Wien, Wien., 2013.
Beven, K. J. and Kirkby, M. J.: A physically based, variable contributing area model of basin hydrology, Hydrolog. Sci. Bullet., 24, 43–69, 1979.
Blahůt, J., van Westen, C. J., and Sterlacchini, S.: Analysis of landslide inventories for accurate prediction of debris-flow source areas, Geomorphology, 119, 36–51, https://doi.org/10.1016/j.geomorph.2010.02.017, 2010.
BMLFUW: Nachhaltig geschützt – Naturgefahrenmanagement im Unwetterjahr 2009, Jahresbericht, Bundesministerium für Land- und Forstwirtschaft, Umwelt und Wasserwirtschaft (Lebensministerium), Wien, available at: http://www.forstnet.at/article/articleview/82437/1/4932, (last access: 3 December 2010), 2010.
Boehner, J., Koethe, R., Conrad, O., Gross, J., Ringeler, A., and Selige, T.: Soil Regionalisation by Means of Terrain Analysis and Process Parameterisation, Research Report, European Soil Bureau, Luxembourg, 2002.
Brabb, E. E.: Innovative approaches to landslide hazard mapping, 4th International Symposium on Landslides, 16–21 September, Toronto, Canada, 307–324, 1984.
Brenning, A.: Spatial prediction models for landslide hazards: review, comparison and evaluation, Nat. Hazards Earth Syst. Sci., 5, 853–862, https://doi.org/10.5194/nhess-5-853-2005, 2005.
Brenning, A.: Statistical Geocomputing combining R and SAGA: The Example of Landslide susceptibility Analysis with generalized additive Models, SAGA – Seconds Out, 19, 23–32, 2008.
Brenning, A.: Benchmarking classifiers to optimally integrate terrain analysis and multispectral remote sensing in automatic rock glacier detection, Remote Sens. Environ., 113, 239–247, 2009.
Brenning, A.: RSAGA: SAGA Geoprocessing and Terrain Analysis in R, available at: http://CRAN.R-project.org/package=RSAGA, (last access: 22 January 2013), 2011.
Brenning, A.: Improved spatial analysis and prediction of landslide susceptibility: Practical recommendations, in Landslides and Engineered Slopes, Protecting Society through Improved Understanding, edited by: Eberhardt, E., Froese, C., Turner, A. K., and Leroueil, S., Taylor & Francis, Banff, Alberta, Canada., 789–795, 2012a.
Brenning, A.: Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: the R package "sperrorest", IEEE Int. Geosci. Remote Se., 23, 5372–5375, 2012b.
Brierley, G.: Communicating Geomorphology, J. of Geography in Higher Educ., 33, 3–17, 2009.
Brugnach, M., Tagg, A., Keil, F., and Lange, W. J.: Uncertainty Matters: Computer Models at the Science-Policy Interface, Water Resources Management, 21, 1075–1090, https://doi.org/10.1007/s11269-006-9099-y, 2006.
Carrara, A.: Uncertainty in evaluating landslide hazard and risk, in Prediction and Perception of Natural Hazards, 101–109, Kluwer Academic Publishers, the Netherlands, 1993.
Carrara, A., Cardinali, M., Guzzetti, F., and Reichenbach, P.: GIS technology in mapping landslide hazard, in Geographical Information Systems in Assessing Natural Hazards, edited by: Carrara, A. and Guzzetti, F., Kluwer Academic Publishers, the Netherlands, 135–175, 1995.
Carrara, A., Guzzetti, F., Cardinali, M., and Reichenbach, P.: Use of GIS technology in the prediction and monitoring of landslide hazard, Nat. Hazards, 20, 117–135, 1999.
Chung, C. J. F. and Fabbri, A. G.: Probabilistic prediction models for landslide hazard mapping, Photogramm. Eng. Rem. S., 65, 1389–1399, 1999.
Chung, C. J. F. and Fabbri, A. G.: Validation of spatial prediction models for landslide hazard mapping, Nat. Hazards, 30, 451–472, 2003.
Chung, C. J. and Fabbri, A. G.: Predicting landslides for risk analysis-Spatial models tested by a cross-validation technique, Geomorphology, 94, 438–452, 2008.
Conrad, O.: SAGA – Entwurf, Funktionsumfang und Anwendung eines Systems für Automatisierte Geowissenschaftliche Analysen, PhD thesis, University of Göttingen, Germany, available at: http://hdl.handle.net/11858/00-1735-0000-0006-B26C-6, (last access: 27 February 2013), 2007 (in German).
Crozier, M. J.: Landslides: causes, consequences and environment, Croom Helm, London, Sydney, Dover, New Hampshire, 1986.
Crozier, M. J.: Deciphering the effect of climate change on landslide activity: A review, Geomorphology, 124, 260–267, 2010.
Cruden, D. M. and Varnes, D. J.: Landslide types and processes, Landslides, Investigation and Mitigation, edited by: Turner, A. K. and Schuster, R. L., Transportation Research Board Special Report 247, Washington DC, USA, 36–75, 1996.
Dai, F. C., Lee, C. F., and Ngai, Y. Y.: Landslide risk assessment and management: an overview, Eng. Geol., 64, 65–87, 2002.
Damm, A., Eberhard, K., and Patt, A.: Risikowahrnehmung von Erdrutschen: Ergebnisse einer empirischen Untersuchung in der Südoststeiermark, Wegener Zentrum für Klima und Globalen Wandel, International Institute for Applied Systems Analysis (IIASA), Graz, Wien, 2010.
Demoulin, A. and Chung, C.-J. F.: Mapping landslide susceptibility from small datasets: A case study in the Pays de Herve (E Belgium), Geomorphology, 89, 391–404, 2007.
Dikau, R., Brunsden, D., Schrott, L., and Ibsen, M. L.: Landslide recognition: identification, movement, and causes, Wiley, Chichester, 1996.
Draper, D.: Assessment and Propagation of Model Uncertainty, J. R. Stat. Soc. B, 57, 45–97, 1995.
Eder, A., Sotier, B., Klebiner, K., Sturmlechner, R., Dorner, J., Markat, G., Schmid, G. and Strauss, P.: Hydrologische Bodenkenndaten der Böden Niederösterreichs (HydroBodNÖ) (Data on hydrological soil characteristics of soils in Lower Austria), unpublished final Report, Bundesamt für Wasserwirtschaft, Institut für Kulturtechnik und Bodenwasserhaushalt; Bundesforschungszentrum für Wald, Institut für Naturgefahren, Petzenkirchen, Innsbruck, 2011 (in German).
Egner, H. and Pott, A.: Risiko und Raum: Das Angebot einer Beobachtungstheorie, in Geographische Risikoforschung – Zur Konstruktion verräumlichter Risiken und Sicherheiten, edited by: Egner, H. and Pott, A., Franz Steiner Verlag, Stuttgart, 9–35, 2010 (in German).
Elith, J., Burgman, M. A., and Regan, H. M.: Mapping epistemic uncertainties and vague concepts in predictions of species distribution, Ecol. Model., 157, 313–329, 2002.
Fabbri, A. G., Chung, C. J. F., Cendrero, A., and Remondo, J.: Is prediction of future landslides possible with a GIS?, Nat. Hazards, 30, 487–503, 2003.
Fell, R., Corominas, J., Bonnard, C., Cascini, L., Leroi, E., and Savage, W. Z.: Guidelines for landslide susceptibility, hazard and risk zoning for land use planning, Eng. Geol., 102, 85–98, https://doi.org/10.1016/j.enggeo.2008.03.022, 2008.
Frattini, P., Crosta, G., and Carrara, A.: Techniques for evaluating the performance of landslide susceptibility models, Eng. Geol., 111, 62–72, 2010.
Freeman, G. T.: Calculating catchment area with divergent flow based on a regular grid, Comput. Geosci., 17, 413–422, 1991.
Frost, C. and Thompson, S.: Correcting for regression dilution bias: comparison of methods for a single predictor variable, J. R. Stat. Soc. Ser. A, 163, 173–190, 2000.
Glade, T.: Landslide occurrence as a response to land use change: a review of evidence from New Zealand, Catena, 51, 297–314, 2003.
Glade, T. and Crozier, M. J.: A review of Scale Dependency in Landslide Hazard and Risk Analysis, in Landslide Hazard and Risk, edited by: Glade, T., Anderson, M., and Crozier, M. J., John Wiley and Sons, Ltd, Chichester, England, 75–138, 2005.
Glade, T., Anderson, M. G. and Crozier, M. J.: Landslide Hazard and Risk, John Wiley and Sons, Ltd, Chichester, England, 2005.
Glade, T., Petschko, H., Bell, R., Bauer, C., Granica, K., Heiss, G., Leopold, P., Pomaroli, G., Proske, H., and Schweigl, J.: Landslide susceptibility maps for Lower Austria – Methods and Challenges, vol. 1, edited by: Koboltschnig, G., Hübl, J., and Braun, J., International Research Society INTERPRAEVENT, Grenoble, France, 497–508, 2012.
Goetz, J. N., Guthrie, R. H., and Brenning, A.: Integrating physical and empirical landslide susceptibility models using generalized additive models, Geomorphology, 129, 376–386, 2011.
Gottschling, P.: Massenbewegungen, in Geologie der Bundesländer – Niederösterreich, Geologische Bundesanstalt, Wien, 335–340, 2006 (in German).
Guisan, A., Edwards, T. C., and Hastie, T.: Generalized linear and generalized additive models in studies of species distributions: setting the scene, Ecol. Model., 157, 89–100, 2002.
Guns, M. and Vanacker, V.: Logistic regression applied to natural hazards: rare event logistic regression with replications, Nat. Hazards Earth Syst. Sci., 12, 1937–1947, https://doi.org/10.5194/nhess-12-1937-2012, 2012.
Guzzetti, F.: Landslide hazard and risk assessment, Dissertation, Rheinischen Friedrich-Wilhelms-Universität Bonn, Bonn, November, 2005.
Guzzetti, F., Carrara, A., Cardinali, M., and Reichenbach, P.: Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy, Geomorphology, 31, 181–216, 1999.
Guzzetti, F., Cardinali, M., Reichenbach, P., and Carrara, A.: Comparing Landslide Maps: A Case Study in the Upper Tiber River Basin, Central Italy, Environ. Manage., 25, 247–263, 2000.
Guzzetti, F., Reichenbach, P., Ardizzone, F., Cardinali, M., and Galli, M.: Estimating the quality of landslide susceptibility models, Geomorphology, 81, 166–184, 2006.
Hand, D.: Construction and Assessment of Classification Rules, Wiley, West Sussex, 1997.
Hanley, J. A. and McNeil, B. J.: The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology, 143, 29–36, 1982.
Harp, E. L., Castañeda, M. R., and Held, M. D.: Landslides triggered by Hurricane Mitch in Tegucigalpa, Honduras., US Geological Survey Open-File Report, USGS, 2002.
Hastie, T.: gam: Generalized Additive Models, available at: http://CRAN.R-project.org/package=gam, (last access: 27 February 2013), 2011.
Hastie, T. and Tibshirani, R.: Generalized additive models, Chapman and Hall/CRC, London, 1990.
Heckmann, T., Gegg, K., Gegg, A., and Becht, M.: Sample size matters: investigating the effect of sample size on a logistic regression debris flow susceptibility model, Nat. Hazards Earth Syst. Sci. Discuss., 1, 2731–2779, https://doi.org/10.5194/nhessd-1-2731-2013, 2013.
Helton, J. C., Johnson, J. D., Oberkampf, W. L., and Sallaberry, C. J.: Representation of analysis results involving aleatory and epistemic uncertainty, Int. J. Gen. Syst., 39, 605–646, 2010.
Hervás, J.: Lessons Learnt from Landslide Disasters in Europe, European Commission Joint Research Centre, Institute for Environment and Sustainability, Ispra (VA), Italy, 2003.
Hill, L. J., Sparks, R. S. J. and Rougier, J. C.: Risk assessment and uncertainty in natural hazards, in Risk and uncertainty assessment for natural hazards, edited by: Rougier, J. C., Sparks, R. S. J., and Hill, L. J., 1–18, Cambridge University Press, Cambridge, 2013.
Hjort, J. and Marmion, M.: Effects of sample size on the accuracy of geomorphological models, Geomorphology, 102, 341–350, 2008.
Hoffman, F. O. and Hammonds, J. S.: Propagation of uncertainty in risk assessments: the need to distinguish between uncertainty due to lack of knowledge and uncertainty due to variability, Risk Analysis, 14, 707–712, 1994.
Hora, S. C.: Aleatory and epistemic uncertainty in probability elicitation with an example from hazardous waste management, Reliability Engineering and System Safety, 54, 217–223, 1996.
Hornich, R. and Adelwöhrer, R.: Landslides in Styria in 2009/Hangrutschungsereignisse 2009 in der Steiermark, Geomechanics and Tunnelling, 3, 455–461, 2010 (in German).
Hosmer, D. W. and Lemeshow, S.: Applied logistic regression, Wiley, New York, NY, 2000.
Huggel, C., Clague, J. J., and Korup, O.: Is climate change responsible for changing landslide activity in high mountains?, Earth Surf. Process. Landforms, 37, 77–91, 2012.
Hydrographischer Dienst des Landes Niederösterreich: Wasserstandsnachrichten und Hochwasserprognosen Niederösterreich, available at: http://www.noel.gv.at/Externeseiten/wasserstand/wiskiwebpublic/maps\textunderscore N\textunderscore 0.htm?entryparakey=N, (last access: 2 March 2011), 2011 (in German).
Jia, G., Tian, Y., Liu, Y., and Zhang, Y.: A static and dynamic factors-coupled forecasting model of regional rainfall-induced landslides: A case study of Shenzhen, Sci. China Ser. E, 51, 164–175, 2008.
Karam, K. S.: Landslide hazards assessment and uncertainties, PhD Thesis, Massachusetts Institute of Technology, 2005.
Klimeš, J. and Blahůt, J.: Landslide risk analysis and its application in regional planning: an example from the highlands of the Outer Western Carpathians, Czech Republic, Nat. Hazards, 64, 1779–1803, 2012.
Knuepfer, P. L. and Petersen, J. F.: Geomorphology in the public eye: policy issues, education, and the public, Geomorphology, 47, 95–105, 2002.
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection, 14, 1137–1145, 1995.
Kunz, M., Grêt-Regamey, A., and Hurni, L.: Visualization of uncertainty in natural hazards assessments using an interactive cartographic information system, Nat. Hazards, 59, 1735–1751, 2011.
Kurz, W.: Erstellung einer digitalen, strukturgeologischen, tektonischen Karte von Niederösterreich (Endbericht), unpublished final report, Karl-Franzens-University Graz, Graz, 2012 (in German).
Lee, C.-T., Huang, C.-C., Lee, J.-F., Pan, K.-L., Lin, M.-L., and Dong, J.-J.: Statistical approach to earthquake-induced landslide susceptibility, Eng. Geol., 100, 43–58, 2008.
Lettner, C. and Wrbka, T.: Historical Development of the Cultural Landscape at the Northern Border of the Eastern Alps: General Trends and Regional Peculiarities, edited by: Balázs, P. and Konkoly-Gyuró, E., 109–121, University of West Hungary Press, Sopron, Hungary, 2011.
Luoto, M., Marmion, M., and Hjort, J.: Assessing spatial uncertainty in predictive geomorphological mapping: A multi-modelling approach, Comput. & Geosci., 36, 355–361, 2010.
Malamud, B. D., Turcotte, D. L., Guzzetti, F., and Reichenbach, P.: Landslide inventories and their statistical properties, Earth Surf. Proc. Land., 29, 687–711, 2004.
Marjanović, M., Kovačević, M., Bajat, B. and Voženílek, V.: Landslide susceptibility assessment using SVM machine learning algorithm, Eng. Geol., 123, 225–234, 2011.
McCalpin, J.: Preliminary age classification of landslides for inventory mapping, In: Proceedings 21st annual Enginnering Geology and Soils Engineering Symposium, 5–6 April, University of Idaho, Moscow, 99–111, 1984.
Mosleh, A.: Hidden sources of uncertainty: judgment in the collection and analysis of data, Nucl. Eng. Des., 93, 187–198, 1986.
Muenchow, J., Brenning, A., and Richter M.: Geomorphic process rates of landslides along a humidity gradient in the tropical Andes, Geomorphology, 139, 271–284, 2012.
Nefeslioglu, H. A., Gokceoglu, C., and Sonmez, H.: An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps, Eng. Geol., 97, 171–191, 2008.
Neuhäuser, B. and Terhorst, B.: Landslide susceptibility assessment using "weights-of-evidence" applied to a study area at the Jurassic escarpment (SW-Germany), Geomorphology, 86, 12–24, 2007.
Oberkampf, W. L., Helton, J. C., Joslyn, C. A., Wojtkiewicz, S. F., and Ferson, S.: Challenge problems: uncertainty in system response given uncertain parameters, Reliability Engineering & System Safety, 85, 11–19, https://doi.org/10.1016/j.ress.2004.03.002, 2004.
Orme, A. R.: Shifting paradigms in geomorphology: the fate of research ideas in an educational context, Geomorphology, 47, 325–342, 2002.
Park, N.-W. and Chi, K.-H.: Quantitative assessment of landslide susceptibility using high-resolution remote sensing data and a generalized additive model, Int. J. Remote Sens., 29, 247–264, 2008.
Petschko, H., Glade, T., Bell, R., Schweigl, J. and Pomaroli, G.: Landslide inventories for regional early warning systems, in: Proceedings of the International Conference Mountain Risks: Bringing Science to Society', Firenze, 24–26 November 2010, edited by: Malet, J. P., Glade, T., and Casagli, N., CERG Editions, Strasbourg, 277–282, 2010.
Petschko, H., Bell, R., Brenning, A., and Glade, T.: Landslide susceptibility modeling with generalized additive models – facing the heterogeneity of large regions, in: Landslides and Engineered Slopes, Protecting Society through Improved Understanding, Vol. 1, edited by: Eberhardt, E., Froese, C., Turner, A. K., and Leroueil, S., Taylor & Francis, Banff, Alberta, Canada, 769–777, 2012.
Petschko, H., Bell, R., Glade, T., Leopold, P., and Heiss, G.: Landslide inventories for large regions – mapping effectiveness of visually analyzing LiDAR derivatives, in prep., 2013a.
Petschko, H., Bell, R., and Glade, T.: Relative age estimation at landslide mapping on LiDAR derivatives – revealing the applicability of land cover data in statistical susceptibility modelling, Proceedings of the World Landslide Forum 3, 2–6 June 2014, Beijing, submitted, 2013b.
Petschko, H., Bell, R., Leopold, P., Heiss, G., and Glade, T.: Landslide inventories for reliable susceptibility maps in Lower Austria, in: Landslide Science and Practice. Volume 1: Landslide Inventory and Susceptibility and Hazard Zoning, edited by: Margottini C, Canuti P, Sassa K, Springer, 281–286, 2013c.
Pradhan, B.: A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS, Computers & Geosciences, 51, 350–365, 2013.
Pradhan, B. and Lee, S.: Landslide risk analysis using artificial neural network model focusing on different training sites, Int. J. Phy. Sci., 4, 1–15, 2009.
Quinn, P. F., Beven, K. J., Chevallier, P., and Planchon, O.: The prediction of hillslope flow paths for distributed hydrological modelling using digital terrain models, Hydrol. Process., 5, 59–79, 1991.
R Development Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria. available at: http://www.R-project.org, (last access: 27 February 2013), 2011.
Razak, K. A., Straatsma, M. W., Van Westen, C. J., Malet, J.-P., and De Jong, S. M.: Airborne laser scanning of forested landslides characterization: Terrain model quality and visualization, Geomorphology, 126, 186–200, 2011.
Remondo, J., González, A., De Terán, J. R. D., Cendrero, A., Fabbri, A., and Chung, C. J. F.: Validation of landslide susceptibility maps; examples and applications from a case study in Northern Spain, Nat. Hazards, 30, 437–449, 2003.
Rogers, K. H.: The real river management challenge: integrating scientists, stakeholders and service agencies, River Res. Appl., 22, 269–280, 2006.
Rossi, M., Guzzetti, F., Reichenbach, P., Mondini, A. C., and Peruccacci, S.: Optimal landslide susceptibility zonation based on multiple forecasts, Geomorphology, 114, 129–142, 2010.
Rougier, J. C.: Quantifying hazard losses, in Risk and uncertainty assessment for natural hazards, edited by: Rougier, J. C.,Sparks, R. S. J. , and Hill, L. J., 19–39, Cambridge University Press, Cambridge., 2013.
Rougier, J. C. and Beven, K. J.: Model and data limitations: the sources and implications of epistemic uncertainty, in Risk and uncertainty assessment for natural hazards, edited by: Rougier, J. C., Sparks, R. S. J., and Hill, L. J., 19–39, Cambridge University Press, Cambridge., 2013.
Roy, C. J. and Oberkampf, W. L.: A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing, Comput. Methods Appl. M., 200, 2131–2144, 2011.
Ruß, G. and Brenning, A.: Data mining in precision agriculture: management of spatial information, Lect. Notes in Comput. Sci., 6178, 350–359, 2010.
Schicker, R. and Moon, V.: Comparison of bivariate and multivariate statistical approaches in landslide susceptibility mapping at a regional scale, Geomorphology, 161–162, 40–57, 2012.
Schnabel, W.: Die geologischen Ursachen der Schäden an der II. Wiener Hochquellenleitung bei Scheibbs, in: Festschrift der MA 31: 75 Jahre II. Wiener Hochquellenleitung, edited by: Magistrat der Stadt Wien, MA 31-Wasserwerke, Wien, 1985 (in German).
Schnabel, W.: Geologische Karte von Niederösterreich 1 : 200 000, Geologische Bundesanstalt, Wien, Austria, 2002 (in German).
Schwab, J. C., Gori, P. L., and Jeer, S.: Landslide Hazards and Planning, Planning Advisory Service Report, American Planning Association, Chicago, 2005.
Schwarz, L. and Tilch, N.: Möglichkeiten und Limitierungen der Regionalisierung mittels Neuronaler Netze am Beispiel einer Rutschungsanfälligkeitskarte für die Region Gasen-Haslau, Beiträge zum 20. AGIT-Symposium, Angewandte Geoinformatik, 2–4 July 2008, Salzburg, Austria, 643–648, 2008 (in German).
Schweigl, J. and Hervás, J.: Landslide Mapping in Austria, JRC Scientific and Technical Reports, European Commission Joint Research Centre, Institute for Environment and Sustainability, Italy, available at: http://eusoils.jrc.ec.europa.eu/ESDB_ Archive/eusoils_ docs/other/EUR23785EN.pdf, (last access: 1 March 2011), 2009.
Schwenk, H.: Massenbewegungen in Niederösterreich 1953–1990, in: Jahrbuch der Geologischen Bundesanstalt, Geologische Bundesanstalt, Wien, 135, 597–660, 1992 (in German).
Scott, A. J. and Wild, C. J.: Fitting logistic models under case-control or choice based sampling, J. Roy. Stat. Soc. B, 48, 170–182, 1986.
Seibert, J., Stendahl, J., and Sørensen, R.: Topographical influences on soil properties in boreal forests, Geoderma, 141, 139–148, 2007.
Soeters, R. and Van Westen, C. J.: Slope instability recognition, analysis and zonation., in Landslides, Investigation and Mitigation, edited by: Turner, A. K. and Schuster, R. L., 129–177, National Academy Press, Washington, USA, 1996.
Spiegelhalter, D. J. and Riesch, H.: Don't know, can't know: embracing deeper uncertainties when analysing risks, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 369, 4730–4750, 2011.
Sterlacchini, S., Ballabio, C., Blahůt, J., Masetti, M., and Sorichetta, A.: Spatial agreement of predicted patterns in landslide susceptibility maps, Geomorphology, 125, 51–61, 2011.
Stockwell, D. R. and Peterson, A. T.: Effects of sample size on accuracy of species distribution models, Ecol. Model., 148, 1–13, 2002.
Tarolli, P., Borga, M., Chang, K.-T., and Chiang, S.-H.: Modeling shallow landsliding susceptibility by incorporating heavy rainfall statistical properties, Geomorphology, 133, 199–211, 2011.
Townsend Peterson, A., Papeş, M., and Eaton, M.: Transferability and model evaluation in ecological niche modeling: a comparison of GARP and Maxent, Ecography, 30, 550–560, 2007.
Trigila, A., Frattini, P., Casagli, N., Catani, F., Crosta, G., Esposito, C., Iadanza, C., Lagomarsino, D., Mugnozza, G., Segoni, S., Spizzichino, D., Tofani, V., and Lari, S.: Landslide Susceptibility Mapping at National Scale: The Italian Case Study, in Landslide Science and Practice, edited by: Margottini, C., Canuti, P., and Sassa, K., 287–295, Springer Berlin Heidelberg, 2013.
Van den Eeckhaut, M., Vanwalleghem, T., Poesen, J., Govers, G., Verstraeten, G., and Vandekerckhove, L.: Prediction of landslide susceptibility using rare events logistic regression: a case-study in the Flemish Ardennes (Belgium), Geomorphology, 76, 392–410, 2006.
Van den Eeckhaut, M., Poesen, J., Verstraeten, G., Vanacker, V., Nyssen, J., Moeyersons, J., Van Beek, L. P. H., and Vandekerckhove, L.: Use of LIDAR-derived images for mapping old landslides under forest, Earth Surf. Proc. Land., 32, 754–769, 2007.
Van den Eeckhaut, M., Moeyersons, J., Nyssen, J., Abraha, A., Poesen, J., Haile, M., and Deckers, J.: Spatial patterns of old, deep-seated landslides: A case-study in the northern Ethiopian highlands, Geomorphology, 105, 239–252, 2009.
Van Westen, C. J., Rengers, N., Terlien, M. T. J., and Soeters, R.: Prediction of the occurrence of slope instability phenomena through GIS-based hazard zonation, Geologische Rundschau, 86, 404–414, 1997.
Van Westen, C. J., Asch, T. W. J., and Soeters, R.: Landslide hazard and risk zonation–-why is it still so difficult?, B. Eng. Geol. Environ., 65, 167–184, 2005.
Van Westen, C. J., Castellanos, E., and Kuriakose, S. L.: Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview, Eng. Geol., 102, 112–131, 2008.
Varnes, D. J.: Landslilde hazard zonation: a review of principles and practice, United Nations Educational, Scientific and Cultural Organization, Paris, France, 1984.
Von Ruette, J., Papritz, A., Lehmann, P., Rickli, C., and Or, D.: Spatial statistical modeling of shallow landslides – Validating predictions for different landslide inventories and rainfall events, Geomorphology, 133, 11–22, 2011.
Vorpahl, P., Elsenbeer, H., Märker, M., and Schröder, B.: How can statistical models help to determine driving factors of landslides?, Ecol. Model., 239, 27–39, 2012.
Wessely, G.: Geologie der österreichischen Bundesländer – Niederösterreich, Geologische Bundesanstalt, Wien, 2006 (in German).
Zevenbergen, L. W. and Thornes, J. B.: Quantitative analysis of land surface topography, Earth Surf. Proc. Land., 12, 47–56, 1987.
Special issue
Altmetrics
Final-revised paper
Preprint