Articles | Volume 17, issue 6
https://doi.org/10.5194/nhess-17-971-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/nhess-17-971-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
30 Jun 2017
Research article |
| 30 Jun 2017
Sensitivity analysis and calibration of a dynamic physically based slope stability model
Thomas Zieher
CORRESPONDING AUTHOR
Institute of Geography, University of Innsbruck, Innrain 52f, 6020 Innsbruck, Austria
Institute for Interdisciplinary Mountain Research, Austrian Academy of Sciences, Technikerstraße 21a, 6020 Innsbruck, Austria
Martin Rutzinger
Institute for Interdisciplinary Mountain Research, Austrian Academy of Sciences, Technikerstraße 21a, 6020 Innsbruck, Austria
Barbara Schneider-Muntau
Unit of Geotechnical and Tunnel Engineering, Institute of Infrastructure, University of Innsbruck, Technikerstraße 13, 6020 Innsbruck, Austria
Frank Perzl
Austrian Research and Training Centre for Forests, Natural Hazards and Landscape, Rennweg 1, 6020 Innsbruck, Austria
David Leidinger
Institute of Meteorology, University of Natural Resources and Life Sciences Vienna, Peter Jordan Straße 82, 1190 Vienna, Austria
Herbert Formayer
Institute of Meteorology, University of Natural Resources and Life Sciences Vienna, Peter Jordan Straße 82, 1190 Vienna, Austria
Clemens Geitner
Institute of Geography, University of Innsbruck, Innrain 52f, 6020 Innsbruck, Austria
Related authors
No articles found.
Jiapan Wang, Rasoul Eskandari, ChengHan Lin, János Mészáros, Laura Obrecht, Evangeline Rowe, Jules Salzinger, Martin Rutzinger, and Andreas Mayr
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-G-2025, 937–944, https://doi.org/10.5194/isprs-annals-X-G-2025-937-2025, https://doi.org/10.5194/isprs-annals-X-G-2025-937-2025, 2025
Lotte de Vugt, Thomas Zieher, Barbara Schneider-Muntau, Frank Perzl, Marc Adams, and Martin Rutzinger
EGUsphere, https://doi.org/10.5194/egusphere-2025-2647, https://doi.org/10.5194/egusphere-2025-2647, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
We performed an analysis on semi-automatically mapped shallow landslide scarps and forest cover in the Sellrain valley, Tyrol (Austria), to investigate how the morphology and topographic profiles of landslides are affected by the forest. The results show that landslides located in dense forest cover occurred on steeper slopes and were deeper than others. The results also show that the use of forest stand density parameters, such as tree spacing, enhanced the found differences in the study area.
Philipp Maier, Fabian Lehner, Tatiana Klisho, and Herbert Formayer
EGUsphere, https://doi.org/10.5194/egusphere-2024-670, https://doi.org/10.5194/egusphere-2024-670, 2024
Preprint withdrawn
Short summary
Short summary
In this article, we analyse the ability of climate models to produce south foehn in the valleys of western Austria using a machine learning technique and assess, how well they capture different aspects of annual foehn occurrence. The performance of these models is largely influenced by the underlying global climate model. After weighting the models based on their performance, we found that foehn occurrence slightly decreases with global warming, but events tend to become more widespread.
M. Potůčková, J. Albrechtová, K. Anders, L. Červená, J. Dvořák, K. Gryguc, B. Höfle, L. Hunt, Z. Lhotáková, A. Marcinkowska-Ochtyra, A. Mayr, E. Neuwirthová, A. Ochtyra, M. Rutzinger, A. Šedová, A. Šrollerů, and L. Kupková
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 989–996, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-989-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-989-2023, 2023
Katharina Ramskogler, Bettina Knoflach, Bernhard Elsner, Brigitta Erschbamer, Florian Haas, Tobias Heckmann, Florentin Hofmeister, Livia Piermattei, Camillo Ressl, Svenja Trautmann, Michael H. Wimmer, Clemens Geitner, Johann Stötter, and Erich Tasser
Biogeosciences, 20, 2919–2939, https://doi.org/10.5194/bg-20-2919-2023, https://doi.org/10.5194/bg-20-2919-2023, 2023
Short summary
Short summary
Primary succession in proglacial areas depends on complex driving forces. To concretise the complex effects and interaction processes, 39 known explanatory variables assigned to seven spheres were analysed via principal component analysis and generalised additive models. Key results show that in addition to time- and elevation-dependent factors, also disturbances alter vegetation development. The results are useful for debates on vegetation development in a warming climate.
Fabian Lehner, Imran Nadeem, and Herbert Formayer
Adv. Stat. Clim. Meteorol. Oceanogr., 9, 29–44, https://doi.org/10.5194/ascmo-9-29-2023, https://doi.org/10.5194/ascmo-9-29-2023, 2023
Short summary
Short summary
Climate model output has systematic errors which can be reduced with statistical methods. We review existing bias-adjustment methods for climate data and discuss their skills and issues. We define three demands for the method and then evaluate them using real and artificially created daily temperature and precipitation data for Austria to show how biases can also be introduced with bias-adjustment methods themselves.
Jan Pfeiffer, Thomas Zieher, Jan Schmieder, Thom Bogaard, Martin Rutzinger, and Christoph Spötl
Nat. Hazards Earth Syst. Sci., 22, 2219–2237, https://doi.org/10.5194/nhess-22-2219-2022, https://doi.org/10.5194/nhess-22-2219-2022, 2022
Short summary
Short summary
The activity of slow-moving deep-seated landslides is commonly governed by pore pressure variations within the shear zone. Groundwater recharge as a consequence of precipitation therefore is a process regulating the activity of landslides. In this context, we present a highly automated geo-statistical approach to spatially assess groundwater recharge controlling the velocity of a deep-seated landslide in Tyrol, Austria.
A. B. Voordendag, B. Goger, C. Klug, R. Prinz, M. Rutzinger, and G. Kaser
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 1093–1099, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1093-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1093-2022, 2022
V. Zahs, L. Winiwarter, K. Anders, M. Bremer, M. Rutzinger, M. Potůčková, and B. Höfle
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 1109–1116, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1109-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1109-2022, 2022
B. Hiebl, A. Mayr, A. Kollert, M. Rutzinger, M. Bremer, N. Helm, and K. Chytry
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 367–374, https://doi.org/10.5194/isprs-annals-V-2-2022-367-2022, https://doi.org/10.5194/isprs-annals-V-2-2022-367-2022, 2022
L. Müller, M. Rutzinger, A. Mayr, and A. Kollert
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 391–398, https://doi.org/10.5194/isprs-annals-V-2-2022-391-2022, https://doi.org/10.5194/isprs-annals-V-2-2022-391-2022, 2022
Christopher J. L. D'Amboise, Michael Neuhauser, Michaela Teich, Andreas Huber, Andreas Kofler, Frank Perzl, Reinhard Fromm, Karl Kleemayr, and Jan-Thomas Fischer
Geosci. Model Dev., 15, 2423–2439, https://doi.org/10.5194/gmd-15-2423-2022, https://doi.org/10.5194/gmd-15-2423-2022, 2022
Short summary
Short summary
The term gravitational mass flow (GMF) covers various natural hazard processes such as snow avalanches, rockfall, landslides, and debris flows. Here we present the open-source GMF simulation tool Flow-Py. The model equations are based on simple geometrical relations in three-dimensional terrain. We show that Flow-Py is an educational, innovative GMF simulation tool with three computational experiments: 1. validation of implementation, 2. performance, and 3. expandability.
Marcel Lerch, Tobias Bromm, Clemens Geitner, Jean Nicolas Haas, Dieter Schäfer, Bruno Glaser, and Michael Zech
Biogeosciences, 19, 1135–1150, https://doi.org/10.5194/bg-19-1135-2022, https://doi.org/10.5194/bg-19-1135-2022, 2022
Short summary
Short summary
Faecal biomarker analyses present a useful tool in geoarcheological research. For a better understanding of the lives of our ancestors in alpine regions, we investigated modern livestock faeces and Holocene soils at the prehistorical encampment site of Ullafelsen in the Fotsch Valley, Stubai Alps, Austria. Initial results show a high input of livestock faeces and a negligible input of human faeces for this archeological site. Future studies will focus on mire archives in the Fotsch Valley.
Fabian Lehner, Imran Nadeem, and Herbert Formayer
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-498, https://doi.org/10.5194/hess-2021-498, 2021
Manuscript not accepted for further review
Short summary
Short summary
Climate model output usually has systematic errors which can be reduced with statistical methods. We review existing bias adjustment methods for climate data and discuss their shortcomings. We define three demands for the method and combine two existing methods for our own approach. We then test it in comparison to two other methods using real and artificially created daily temperature and precipitation data for Austria. The results show that our method is able to meet all three demands.
Michael Zech, Marcel Lerch, Marcel Bliedtner, Tobias Bromm, Fabian Seemann, Sönke Szidat, Gary Salazar, Roland Zech, Bruno Glaser, Jean Nicolas Haas, Dieter Schäfer, and Clemens Geitner
E&G Quaternary Sci. J., 70, 171–186, https://doi.org/10.5194/egqsj-70-171-2021, https://doi.org/10.5194/egqsj-70-171-2021, 2021
A. B. Voordendag, B. Goger, C. Klug, R. Prinz, M. Rutzinger, and G. Kaser
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2021, 153–160, https://doi.org/10.5194/isprs-annals-V-2-2021-153-2021, https://doi.org/10.5194/isprs-annals-V-2-2021-153-2021, 2021
A. Kollert, M. Rutzinger, M. Bremer, K. Kaufmann, and T. Bork-Hüffer
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2021, 201–208, https://doi.org/10.5194/isprs-annals-V-4-2021-201-2021, https://doi.org/10.5194/isprs-annals-V-4-2021-201-2021, 2021
Fabian Lehner, Imran Nadeem, and Herbert Formayer
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-515, https://doi.org/10.5194/hess-2020-515, 2020
Manuscript not accepted for further review
Short summary
Short summary
We present an improved statistical bias correction method for climate models and put a focus on precipitation. Our method corrects model data so that it matches the observations in the historical period in a climatological sense better than other state-of-the-art methods while at the same time keeping long term trends in the future period unmodified. The corrected data can serve as a more accurate input for climate impact studies e.g. in hydrology or forestry.
M. Rutzinger, K. Anders, M. Bremer, B. Höfle, R. Lindenbergh, S. Oude Elberink, F. Pirotti, M. Scaioni, and T. Zieher
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B5-2020, 243–250, https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-243-2020, https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-243-2020, 2020
Andrea Franco, Jasper Moernaut, Barbara Schneider-Muntau, Michael Strasser, and Bernhard Gems
Nat. Hazards Earth Syst. Sci., 20, 2255–2279, https://doi.org/10.5194/nhess-20-2255-2020, https://doi.org/10.5194/nhess-20-2255-2020, 2020
Short summary
Short summary
This study highlights the use of the software Flow-3D in reproducing landslide-generated impulse waves. Due to the available data and the possibility of comparing the results with other previous works, a numerical modelling investigation on the 1958 Lituya Bay tsunami event is proposed. It is noted that the rockslide impact into the waterbody has a key role in the wave initiation and thus its propagation. The concept used in this work can be applied to prevent such phenomena in future.
A. Mayr, M. Bremer, and M. Rutzinger
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 765–772, https://doi.org/10.5194/isprs-annals-V-2-2020-765-2020, https://doi.org/10.5194/isprs-annals-V-2-2020-765-2020, 2020
J. Boehm, M. Rutzinger, B. Yang, M. Weinmann, B. Riveiro, and W. Yao
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 915–917, https://doi.org/10.5194/isprs-archives-XLII-2-W13-915-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-915-2019, 2019
M. Bremer, V. Wichmann, M. Rutzinger, T. Zieher, and J. Pfeiffer
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 943–950, https://doi.org/10.5194/isprs-archives-XLII-2-W13-943-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-943-2019, 2019
J. Boehm, M. Rutzinger, B. Yang, M. Weinmann, B. Riveiro, and W. Yao
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 313–315, https://doi.org/10.5194/isprs-annals-IV-2-W5-313-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-313-2019, 2019
A. Mayr, M. Bremer, M. Rutzinger, and C. Geitner
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 405–412, https://doi.org/10.5194/isprs-annals-IV-2-W5-405-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-405-2019, 2019
J. Pfeiffer, T. Zieher, M. Rutzinger, M. Bremer, and V. Wichmann
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 421–428, https://doi.org/10.5194/isprs-annals-IV-2-W5-421-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-421-2019, 2019
T. Zieher, M. Bremer, M. Rutzinger, J. Pfeiffer, P. Fritzmann, and V. Wichmann
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 461–467, https://doi.org/10.5194/isprs-annals-IV-2-W5-461-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-461-2019, 2019
A. Mayr, M. Rutzinger, and C. Geitner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 691–697, https://doi.org/10.5194/isprs-archives-XLII-2-691-2018, https://doi.org/10.5194/isprs-archives-XLII-2-691-2018, 2018
T. Zieher, I. Toschi, F. Remondino, M. Rutzinger, Ch. Kofler, A. Mejia-Aguilar, and R. Schlögel
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1243–1250, https://doi.org/10.5194/isprs-archives-XLII-2-1243-2018, https://doi.org/10.5194/isprs-archives-XLII-2-1243-2018, 2018
Heidelinde Trimmel, Philipp Weihs, David Leidinger, Herbert Formayer, Gerda Kalny, and Andreas Melcher
Hydrol. Earth Syst. Sci., 22, 437–461, https://doi.org/10.5194/hess-22-437-2018, https://doi.org/10.5194/hess-22-437-2018, 2018
Short summary
Short summary
In eastern Austria, where air temperature rise is double that recorded globally, stream temperatures of a human-impacted river were simulated during heat waves, as calculated by regional climate models until 2100. An increase of up to 3 °C was predicted – thus exceeding thresholds of resident cold-adapted species. Vegetation management scenarios showed that adding vegetation can reduce both absolute temperatures and its rate of increase but is not able to fully mitigate the expected rise.
M. Rutzinger, B. Höfle, R. Lindenbergh, S. Oude Elberink, F. Pirotti, R. Sailer, M. Scaioni, J. Stötter, and D. Wujanz
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-6, 15–22, https://doi.org/10.5194/isprs-annals-III-6-15-2016, https://doi.org/10.5194/isprs-annals-III-6-15-2016, 2016
M. Mergili, I. Marchesini, M. Alvioli, M. Metz, B. Schneider-Muntau, M. Rossi, and F. Guzzetti
Geosci. Model Dev., 7, 2969–2982, https://doi.org/10.5194/gmd-7-2969-2014, https://doi.org/10.5194/gmd-7-2969-2014, 2014
Short summary
Short summary
The article deals with strategies to (i) reduce computation time and to (ii) appropriately account for uncertain input parameters when applying an open source GIS sliding surface model to estimate landslide susceptibility for a 90km² study area in central Italy. For (i), the area is split into a large number of tiles, enabling the exploitation of multi-processor computing environments. For (ii), the model is run with various parameter combinations to compute the slope failure probability.
Related subject area
Landslides and Debris Flows Hazards
Is higher resolution always better? A comparison of open-access DEMs for optimized slope unit delineation and regional landslide prediction
Brief communication: AI-driven rapid landslide mapping following the 2024 Hualien earthquake in Taiwan
Landslide activation during deglaciation in a fjord-dominated landscape: observations from southern Alaska (1984–2022)
Brief communication: Weak correlation between building damage and loss of life from landslides
Comparative analysis of μ(I) and Voellmy-type grain flow rheologies in geophysical mass flows: insights from theoretical and real case studies
Exploring implications of input parameter uncertainties in glacial lake outburst flood (GLOF) modelling results using the modelling code r.avaflow
Hillslope-Torrential Hazard Cascades in Tropical Mountains
From rockfall source area identification to susceptibility zonation: a proposed workflow tested on El Hierro (Canary Islands, Spain)
Brief communication: Visualizing uncertainties in landslide susceptibility modelling using bivariate mapping
Constraining landslide frequency across the United States to inform county-level risk reduction
Topographic controls on landslide mobility: modeling hurricane-induced landslide runout and debris-flow inundation in Puerto Rico
Characterizing the scale of regional landslide triggering from storm hydrometeorology
Debris Flow Susceptibility in the Jinsha River Basin, China: A Bayesian Assessment Framework Based on Geomorphodynamic Parameters
A participatory approach to determine the use of road cut slope design guidelines in Nepal to lessen landslides
An integrated method for assessing vulnerability of buildings caused by debris flows in mountainous areas
Identifying unrecognised risks to life from debris flows
Predicting the thickness of shallow landslides in Switzerland using machine learning
Unraveling landslide failure mechanisms with seismic signal analysis for enhanced pre-survey understanding
Review Article: Analysis of sediment disaster risk assessment surveys in Brazil: A critical review and recommendations
The Parraguirre ice-rock avalanche 1987, semi-arid Andes, Chile – A holistic revision
Comparison of conditioning factor classification criteria in large-scale statistically based landslide susceptibility models
Invited perspectives: Integrating hydrologic information into the next generation of landslide early warning systems
Predicting deep-seated landslide displacement on Taiwan's Lushan through the integration of convolutional neural networks and the Age of Exploration-Inspired Optimizer
Limit analysis of earthquake-induced landslides considering two strength envelopes
The vulnerability of buildings to a large-scale debris flow and outburst flood hazard cascade that occurred on 30 August 2020 in Ganluo, southwest China
Optimizing rainfall-triggered landslide thresholds for daily landslide hazard warning in the Three Gorges Reservoir area
Brief communication: Monitoring impending slope failure with very high-resolution spaceborne synthetic aperture radar
Size scaling of large landslides from incomplete inventories
InSAR-informed in situ monitoring for deep-seated landslides: insights from El Forn (Andorra)
A coupled hydrological and hydrodynamic modeling approach for estimating rainfall thresholds of debris-flow occurrence
More than one landslide per road kilometer – surveying and modeling mass movements along the Rishikesh–Joshimath (NH-7) highway, Uttarakhand, India
The dynamics of peak head responses at Dutch canal dikes and the impact of climate change
Transformations in Exposure to Debris Flows in Post-Earthquake Sichuan, China
Large-scale assessment of rainfall-induced landslide hazard based on hydrometeorological information: application to Partenio Massif (Italy)
Shaping shallow landslide susceptibility as a function of rainfall events
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
Mahnoor Ahmed, Giacomo Titti, Sebastiano Trevisani, Lisa Borgatti, and Mirko Francioni
Nat. Hazards Earth Syst. Sci., 25, 2519–2539, https://doi.org/10.5194/nhess-25-2519-2025, https://doi.org/10.5194/nhess-25-2519-2025, 2025
Short summary
Short summary
Elevation models are compared with a true dataset for terrain characteristics, which selects a better-ranking model to compare with different parameters to partition the terrain. The partitioning of the terrain is measured by how well a partitioned unit can support the mapped landslide area and number of landslides. The effect of this relationship is reflected with different metrics in the susceptibility maps.
Lorenzo Nava, Alessandro Novellino, Chengyong Fang, Kushanav Bhuyan, Kathryn Leeming, Itahisa Gonzalez Alvarez, Claire Dashwood, Sophie Doward, Rahul Chahel, Emma McAllister, Sansar Raj Meena, and Filippo Catani
Nat. Hazards Earth Syst. Sci., 25, 2371–2377, https://doi.org/10.5194/nhess-25-2371-2025, https://doi.org/10.5194/nhess-25-2371-2025, 2025
Short summary
Short summary
On 2 April 2024, a Mw 7.4 earthquake hit Taiwan's eastern coast, causing extensive landslides and damage. We used automated methods combining Earth observation (EO) data with AI to quickly inventory the landslides. This approach identified 7090 landslides over 75 km2 within 3 h of acquiring the EO imagery. The study highlights AI's role in improving landslide detection efforts in disaster response.
Jane Walden, Mylène Jacquemart, Bretwood Higman, Romain Hugonnet, Andrea Manconi, and Daniel Farinotti
Nat. Hazards Earth Syst. Sci., 25, 2045–2073, https://doi.org/10.5194/nhess-25-2045-2025, https://doi.org/10.5194/nhess-25-2045-2025, 2025
Short summary
Short summary
We studied eight glacier-adjacent landslides in Alaska and found that slope movement increased at four sites as the glacier retreated past the landslide area. Movement at other sites may be due to heavy precipitation or increased glacier thinning, and two sites showed little to no motion. We suggest that landslides near waterbodies may be especially vulnerable to acceleration, which we guess is due to faster retreat rates of water-terminating glaciers and changing water flow in the slope.
Maximillian Van Wyk de Vries, Alexandre Dunant, Amy L. Johnson, Erin L. Harvey, Sihan Li, Katherine Arrell, Jeevan Baniya, Dipak Basnet, Gopi K. Basyal, Nyima Dorjee Bhotia, Simon J. Dadson, Alexander L. Densmore, Tek Bahadur Dong, Mark E. Kincey, Katie Oven, Anuradha Puri, and Nick J. Rosser
Nat. Hazards Earth Syst. Sci., 25, 1937–1942, https://doi.org/10.5194/nhess-25-1937-2025, https://doi.org/10.5194/nhess-25-1937-2025, 2025
Short summary
Short summary
Mapping exposure to landslides is necessary to mitigate risk and reduce vulnerability. In this study, we show that there is a poor correlation between building damage and deaths from landslides, such that the deadliest landslides do not always destroy the most buildings and vice versa. This has important implications for our management of landslide risk.
Yu Zhuang, Brian W. McArdell, and Perry Bartelt
Nat. Hazards Earth Syst. Sci., 25, 1901–1912, https://doi.org/10.5194/nhess-25-1901-2025, https://doi.org/10.5194/nhess-25-1901-2025, 2025
Short summary
Short summary
The experimentally based μ(I) rheology, widely used for gravitational mass flows, is reinterpreted as a Voellmy-type relationship to highlight its link to grain flow theory. Through block modeling and case studies, we establish its equivalence to μ(R) rheology. μ(I) models shear thinning but fails to capture acceleration and deceleration processes and deposit structure. Incorporating fluctuation energy in μ(R) improves accuracy, refining mass flow modeling and revealing practical challenges.
Sonam Rinzin, Stuart Dunning, Rachel Joanne Carr, Ashim Sattar, and Martin Mergili
Nat. Hazards Earth Syst. Sci., 25, 1841–1864, https://doi.org/10.5194/nhess-25-1841-2025, https://doi.org/10.5194/nhess-25-1841-2025, 2025
Short summary
Short summary
We modelled multiple glacial lake outburst flood (GLOF) scenarios (84 simulations) and tested the effect of nine key input parameters on the modelling results using r.avaflow. Our results highlight that GLOF modelling results are subject to uncertainty from the multiple input parameters. The variation in the volume of mass movement entering the lake causes the highest uncertainty in the modelled GLOF, followed by the DEM dataset and the origin of mass movement.
Maria Isabel Arango-Carmona, Paul Voit, Marcel Hürlimann, Edier Aristizábal, and Oliver Korup
EGUsphere, https://doi.org/10.5194/egusphere-2025-1698, https://doi.org/10.5194/egusphere-2025-1698, 2025
Short summary
Short summary
We studied 22 cascading landslide and torrential events in tropical mountains to understand how rainfall, slopes, and soil types interact to trigger them. We found that extreme rainfall alone is not always the cause, but long wet periods and sediment type also play a role. Our findings can help improve warning systems and reduce disaster risks in vulnerable regions.
Roberto Sarro, Mauro Rossi, Paola Reichenbach, and Rosa María Mateos
Nat. Hazards Earth Syst. Sci., 25, 1459–1479, https://doi.org/10.5194/nhess-25-1459-2025, https://doi.org/10.5194/nhess-25-1459-2025, 2025
Short summary
Short summary
This study proposes a novel systematic workflow that integrates source area identification, deterministic runout modelling, the classification of runout outputs to derive susceptibility zonation, and robust procedures for validation and comparison. The proposed approach enables the integration and comparison of different modelling, introducing a robust and consistent workflow/methodology that allows us to derive and verify rockfall susceptibility zonation, considering different steps.
Matthias Schlögl, Anita Graser, Raphael Spiekermann, Jasmin Lampert, and Stefan Steger
Nat. Hazards Earth Syst. Sci., 25, 1425–1437, https://doi.org/10.5194/nhess-25-1425-2025, https://doi.org/10.5194/nhess-25-1425-2025, 2025
Short summary
Short summary
Communicating uncertainties is a crucial yet challenging aspect of spatial modelling – especially in applied research, where results inform decisions. In disaster risk reduction, susceptibility maps for natural hazards guide planning and risk assessment, yet their uncertainties are often overlooked. We present a new type of landslide susceptibility map that visualizes both susceptibility and associated uncertainty alongside guidelines for creating such maps using free and open-source software.
Lisa V. Luna, Jacob B. Woodard, Janice L. Bytheway, Gina M. Belair, and Benjamin B. Mirus
EGUsphere, https://doi.org/10.5194/egusphere-2025-947, https://doi.org/10.5194/egusphere-2025-947, 2025
Short summary
Short summary
Landslide frequency (how often landslides occur) is needed to assess landslide hazard and risk but has rarely been quantified at near continental scales. Here, we used statistical models to estimate landslide frequency across the United States while addressing gaps in landslide reporting. Our results showed strong variations in landslide frequency that followed topography, earthquake probability, and ecological region and highlighted areas with potential for widespread landsliding.
Dianne L. Brien, Mark E. Reid, Collin Cronkite-Ratcliff, and Jonathan P. Perkins
Nat. Hazards Earth Syst. Sci., 25, 1229–1253, https://doi.org/10.5194/nhess-25-1229-2025, https://doi.org/10.5194/nhess-25-1229-2025, 2025
Short summary
Short summary
Landslide runout zones are the areas downslope or downstream of landslide initiation. People often live and work in these areas, leading to property damage and deaths. Landslide runout may occur on hillslopes or in channels, requiring different modeling approaches. We develop methods to identify potential runout zones and apply these methods to identify susceptible areas for three municipalities in Puerto Rico.
Jonathan Perkins, Nina S. Oakley, Brian D. Collins, Skye C. Corbett, and W. Paul Burgess
Nat. Hazards Earth Syst. Sci., 25, 1037–1056, https://doi.org/10.5194/nhess-25-1037-2025, https://doi.org/10.5194/nhess-25-1037-2025, 2025
Short summary
Short summary
Rainfall-induced landslides result in deaths and economic losses annually across the globe. However, it is unclear how storm severity relates to landslide severity across large regions. Here we develop a method to dynamically map landslide-affected areas, and we compare this to meteorological estimates of storm severity. We find that preconditioning by earlier storms and the location of rainfall bursts, rather than atmospheric storm strength, dictate landslide magnitude and pattern.
Zhenkui Gu, Xin Yao, and Xuchao Zhu
EGUsphere, https://doi.org/10.5194/egusphere-2024-4164, https://doi.org/10.5194/egusphere-2024-4164, 2025
Short summary
Short summary
Debris flow susceptibility was assessed using erosion intensity, connectivity, and erodibility; A Bayesian model integrated precipitation and surface conditions to evaluate debris flow risks; Quantitative metrics elucidated debris flow likelihood across diverse spatiotemporal scales; The model accurately predicted a recent debris flow event, validating its disaster assessment.
Ellen B. Robson, Bhim Kumar Dahal, and David G. Toll
Nat. Hazards Earth Syst. Sci., 25, 949–973, https://doi.org/10.5194/nhess-25-949-2025, https://doi.org/10.5194/nhess-25-949-2025, 2025
Short summary
Short summary
Slopes excavated alongside roads in Nepal frequently fail (a landslide), resulting in substantial losses. Our participatory approach study with road engineers aimed to assess how road slope design guidelines in Nepal can be improved. Our study revealed inconsistent guideline adherence due to a lack of user-friendliness and inadequate training. We present general recommendations to enhance road slope management, as well as technical recommendations to improve the guidelines.
Chenchen Qiu and Xueyu Geng
Nat. Hazards Earth Syst. Sci., 25, 709–726, https://doi.org/10.5194/nhess-25-709-2025, https://doi.org/10.5194/nhess-25-709-2025, 2025
Short summary
Short summary
We propose an integrated method using a combination of a physical vulnerability matrix and a machine learning model to estimate the potential physical damage and associated economic loss caused by future debris flows based on collected historical data on the Qinghai–Tibet Plateau region.
Mark Bloomberg, Tim Davies, Elena Moltchanova, Tom Robinson, and David Palmer
Nat. Hazards Earth Syst. Sci., 25, 647–656, https://doi.org/10.5194/nhess-25-647-2025, https://doi.org/10.5194/nhess-25-647-2025, 2025
Short summary
Short summary
Debris flows occur infrequently, with average recurrence intervals (ARIs) ranging from decades to millennia. Consequently, they pose an underappreciated hazard. We describe how to make a preliminary identification of debris-flow-susceptible catchments, estimate threshold ARIs for debris flows that pose an unacceptable risk to life, and identify the “window of non-recognition” where debris flows are infrequent enough that their hazard is unrecognised yet frequent enough to pose a risk to life.
Christoph Schaller, Luuk Dorren, Massimiliano Schwarz, Christine Moos, Arie C. Seijmonsbergen, and E. Emiel van Loon
Nat. Hazards Earth Syst. Sci., 25, 467–491, https://doi.org/10.5194/nhess-25-467-2025, https://doi.org/10.5194/nhess-25-467-2025, 2025
Short summary
Short summary
We developed a machine-learning-based approach to predict the potential thickness of shallow landslides to generate improved inputs for slope stability models. We selected 21 explanatory variables, including metrics on terrain, geomorphology, vegetation height, and lithology, and used data from two Swiss field inventories to calibrate and test the models. The best-performing machine learning model consistently reduced the mean average error by at least 20 % compared to previous models.
Jui-Ming Chang, Che-Ming Yang, Wei-An Chao, Chin-Shang Ku, Ming-Wan Huang, Tung-Chou Hsieh, and Chi-Yao Hung
Nat. Hazards Earth Syst. Sci., 25, 451–466, https://doi.org/10.5194/nhess-25-451-2025, https://doi.org/10.5194/nhess-25-451-2025, 2025
Short summary
Short summary
The study on the Cilan landslide (CL) demonstrates the utilization of seismic analysis results as preliminary data for geologists during field surveys. Spectrograms revealed that the first event of CL consisted of four sliding failures accompanied by a gradual reduction in landslide volume. The second and third events were minor toppling and rockfalls. Then combining the seismological-based knowledge and field survey results, the spatiotemporal variation in landslide evolution is proposed.
Thiago Dutra dos Santos and Taro Uchida
EGUsphere, https://doi.org/10.5194/egusphere-2024-2255, https://doi.org/10.5194/egusphere-2024-2255, 2025
Short summary
Short summary
Five federal sediment-related disaster risk assessments have been conducted in Brazil, each with distinct objectives and methodologies. To evaluate their effectiveness and identify issues, we analyzed the methods, the outcome data, and reviewed the status of disaster prevention initiatives based on the assessment results. Our findings revealed persistent problems across all methods. Consequently, we recommended improvements to enhance their efficacy and reliability.
Johannes Jakob Fürst, David Farías-Barahona, Thomas Bruckner, Lucia Scaff, Martin Mergili, Santiago Montserrat, and Humberto Peña
EGUsphere, https://doi.org/10.5194/egusphere-2024-3103, https://doi.org/10.5194/egusphere-2024-3103, 2025
Short summary
Short summary
The 1987 Parraguirre ice-rock avalanche developed into a devastating debris-flow causing loss of many lives and inflicting severe damage near Santiago, Chile. Here, we revise this event combining various observational records with modelling techniques. In this year, important snow cover coincided with warm days in spring. We further quantify the total solid volume, and forward important upward corrections for the trigger and flood volumes. Finally, river damming was key for high flow mobility.
Marko Sinčić, Sanja Bernat Gazibara, Mauro Rossi, and Snježana Mihalić Arbanas
Nat. Hazards Earth Syst. Sci., 25, 183–206, https://doi.org/10.5194/nhess-25-183-2025, https://doi.org/10.5194/nhess-25-183-2025, 2025
Short summary
Short summary
The paper focuses on classifying continuous landslide conditioning factors for susceptibility modelling, which resulted in 54 landslide susceptibility models that tested 11 classification criteria in combination with 5 statistical methods. The novelty of the research is that using stretched landslide conditioning factor values results in models with higher accuracy and that certain statistical methods are more sensitive to the landslide conditioning factor classification criteria than others.
Benjamin B. Mirus, Thom Bogaard, Roberto Greco, and Manfred Stähli
Nat. Hazards Earth Syst. Sci., 25, 169–182, https://doi.org/10.5194/nhess-25-169-2025, https://doi.org/10.5194/nhess-25-169-2025, 2025
Short summary
Short summary
Early warning of increased landslide potential provides situational awareness to reduce landslide-related losses from major storm events. For decades, landslide forecasts relied on rainfall data alone, but recent research points to the value of hydrologic information for improving predictions. In this paper, we provide our perspectives on the value and limitations of integrating subsurface hillslope hydrologic monitoring data and mathematical modeling for more accurate landslide forecasts.
Jui-Sheng Chou, Hoang-Minh Nguyen, Huy-Phuong Phan, and Kuo-Lung Wang
Nat. Hazards Earth Syst. Sci., 25, 119–146, https://doi.org/10.5194/nhess-25-119-2025, https://doi.org/10.5194/nhess-25-119-2025, 2025
Short summary
Short summary
This study enhances landslide prediction using advanced machine learning, including new algorithms inspired by historical explorations. The research accurately forecasts landslide movements by analyzing 8 years of data from Taiwan's Lushan, improving early warning and potentially saving lives and infrastructure. This integration marks a significant advancement in environmental risk management.
Di Wu, Yuke Wang, and Xin Chen
Nat. Hazards Earth Syst. Sci., 24, 4617–4630, https://doi.org/10.5194/nhess-24-4617-2024, https://doi.org/10.5194/nhess-24-4617-2024, 2024
Short summary
Short summary
This paper proposes a 3D limit analysis for seismic stability of soil slopes to address the influence of earthquakes on slope stabilities with nonlinear and linear criteria. Comparison results illustrate that the use of a linear envelope leads to the non-negligible overestimation of steep-slope stability, and this overestimation will be significant with increasing earthquakes. Earthquakes have a smaller influence on slope slip surfaces with a nonlinear envelope than those with a linear envelope.
Li Wei, Kaiheng Hu, Shuang Liu, Lan Ning, Xiaopeng Zhang, Qiyuan Zhang, and Md. Abdur Rahim
Nat. Hazards Earth Syst. Sci., 24, 4179–4197, https://doi.org/10.5194/nhess-24-4179-2024, https://doi.org/10.5194/nhess-24-4179-2024, 2024
Short summary
Short summary
The damage patterns of the buildings were classified into three types: (I) buried by primary debris flow, (II) inundated by secondary dam-burst flood, and (III) sequentially buried by debris flow and inundated by dam-burst flood. The threshold of the impact pressures in Zones (II) and (III) where vulnerability is equal to 1 is 84 kPa and 116 kPa, respectively. Heavy damage occurs at an impact pressure greater than 50 kPa, while slight damage occurs below 30 kPa.
Bo Peng and Xueling Wu
Nat. Hazards Earth Syst. Sci., 24, 3991–4013, https://doi.org/10.5194/nhess-24-3991-2024, https://doi.org/10.5194/nhess-24-3991-2024, 2024
Short summary
Short summary
Our research enhances landslide prevention using advanced machine learning to forecast heavy-rainfall-triggered landslides. By analyzing regions and employing various models, we identified optimal ways to predict high-risk rainfall events. Integrating multiple factors and models, including a neural network, significantly improves landslide predictions. Real data validation confirms our approach's reliability, aiding communities in mitigating landslide impacts and safeguarding lives and property.
Andrea Manconi, Yves Bühler, Andreas Stoffel, Johan Gaume, Qiaoping Zhang, and Valentyn Tolpekin
Nat. Hazards Earth Syst. Sci., 24, 3833–3839, https://doi.org/10.5194/nhess-24-3833-2024, https://doi.org/10.5194/nhess-24-3833-2024, 2024
Short summary
Short summary
Our research reveals the power of high-resolution satellite synthetic-aperture radar (SAR) imagery for slope deformation monitoring. Using ICEYE data over the Brienz/Brinzauls instability, we measured surface velocity and mapped the landslide event with unprecedented precision. This underscores the potential of satellite SAR for timely hazard assessment in remote regions and aiding disaster mitigation efforts effectively.
Oliver Korup, Lisa V. Luna, and Joaquin V. Ferrer
Nat. Hazards Earth Syst. Sci., 24, 3815–3832, https://doi.org/10.5194/nhess-24-3815-2024, https://doi.org/10.5194/nhess-24-3815-2024, 2024
Short summary
Short summary
Catalogues of mapped landslides are useful for learning and forecasting how frequently they occur in relation to their size. Yet, rare and large landslides remain mostly uncertain in statistical summaries of these catalogues. We propose a single, consistent method of comparing across different data sources and find that landslide statistics disclose more about subjective mapping choices than trigger types or environmental settings.
Rachael Lau, Carolina Seguí, Tyler Waterman, Nathaniel Chaney, and Manolis Veveakis
Nat. Hazards Earth Syst. Sci., 24, 3651–3661, https://doi.org/10.5194/nhess-24-3651-2024, https://doi.org/10.5194/nhess-24-3651-2024, 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). Comparing InSAR with borehole data suggests a key trade-off between accuracy and precision for various InSAR resolutions. Spatial interpolation with InSAR informed how many remote observations are necessary to lower error in a remote sensing re-creation of ground motion over the landslide.
Zhen Lei Wei, Yue Quan Shang, Qiu Hua Liang, and Xi Lin Xia
Nat. Hazards Earth Syst. Sci., 24, 3357–3379, https://doi.org/10.5194/nhess-24-3357-2024, https://doi.org/10.5194/nhess-24-3357-2024, 2024
Short summary
Short summary
The initiation of debris flows is significantly influenced by rainfall-induced hydrological processes. We propose a novel framework based on an integrated hydrological and hydrodynamic model and aimed at estimating intensity–duration (ID) rainfall thresholds responsible for triggering debris flows. In comparison to traditional statistical approaches, this physically based framework is particularly suitable for application in ungauged catchments where historical debris flow data are scarce.
Jürgen Mey, Ravi Kumar Guntu, Alexander Plakias, Igo Silva de Almeida, and Wolfgang Schwanghart
Nat. Hazards Earth Syst. Sci., 24, 3207–3223, https://doi.org/10.5194/nhess-24-3207-2024, https://doi.org/10.5194/nhess-24-3207-2024, 2024
Short summary
Short summary
The Himalayan road network links remote areas, but fragile terrain and poor construction lead to frequent landslides. This study on the NH-7 in India's Uttarakhand region analyzed 300 landslides after heavy rainfall in 2022 . 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.
Bart Strijker and Matthijs Kok
EGUsphere, https://doi.org/10.5194/egusphere-2024-1495, https://doi.org/10.5194/egusphere-2024-1495, 2024
Short summary
Short summary
This study examines how hydraulic head levels in canal dikes respond to heavy rainfall, potentially causing instabilities and flooding. Using time series models and simulating long-term head levels, we identified clusters of dikes where head peaks are driven by similar rainfall events. Statistical analyses show that extreme and yearly conditions are close. However, extreme conditions are expected to become more frequent due to climate change, though some dikes will be less affected than others.
Isabelle Utley, Tristram Hales, Ekbal Hussain, and Xuanmei Fan
EGUsphere, https://doi.org/10.5194/egusphere-2024-2277, https://doi.org/10.5194/egusphere-2024-2277, 2024
Short summary
Short summary
We analysed debris flows in Sichuan, China, using satellite data and simulations to assess check dam efficacy. Our study found whilst check dams can mitigate smaller flows, they may increase exposure to extreme events, with up to 40 % of structures in some areas affected. Urban development and reliance on check dams can create a false sense of security, raising exposure during large debris flows and highlights the need for risk management and infrastructure planning in hazard-prone areas.
Daniel Camilo Roman Quintero, Pasquale Marino, Abdullah Abdullah, Giovanni Francesco Santonastaso, and Roberto Greco
EGUsphere, https://doi.org/10.5194/egusphere-2024-2329, https://doi.org/10.5194/egusphere-2024-2329, 2024
Short summary
Short summary
Local thresholds for landslide forecasting, combining hydrologic predisposing factors and rainfall features, are developed from a physically based model of a slope. To extend their application to a wide area, uncertainty due to spatial variability of geomorphological and hydrologic variables is introduced. The obtained hydrometeorological thresholds, integrating root zone soil moisture and aquifer water level with rainfall depth, outperform thresholds based on rain intensity and duration.
Micol Fumagalli, Alberto Previati, Paolo Frattini, and Giovanni B. Crosta
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-140, https://doi.org/10.5194/nhess-2024-140, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
Shallow landslides are mass movements of limited thickness, mainly triggered by extreme rainfalls, that can pose a serious risk to the population. This study uses statistical methods to analyse and simulate the relationship between shallow landslides and rainfalls, showing that in the studied area shallow landslides are modulated by rainfall but controlled by lithology. A new classification method considering the costs associated with a misclassification of the susceptibility is also proposed.
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.
Cited articles
Alvioli, M. and Baum, R.: Parallelization of the TRIGRS model for rainfall-induced landslides using the message passing interface, Environ. Modell. Softw., 81, 122–135, https://doi.org/10.1016/j.envsoft.2016.04.002, 2016.
Amann, G., Schennach, R., Kessler, J., Maier, B., and Terzer, S.: Handbuch der Vorarlberger Waldgesellschaften, vol. 2, Amt der Vorarlberger Landesregierung, Abteilung Forstwesen, 2014.
Andrecs, P., Markart, G., Lang, E., Hagen, K., Kohl, B., and Bauer, W.: Untersuchung der Rutschungsprozesse vom Mai 1999 im Laternsertal (Vorarlberg), BFW Berichte, 127, 55–87, 2002.
Bathurst, J. C., Moretti, G., El-Hames, A., Moaven-Hashemi, A., and Burton, A.: Scenario modelling of basin-scale, shallow landslide sediment yield, Valsassina, Italian Southern Alps, Nat. Hazards Earth Syst. Sci., 5, 189–202, https://doi.org/10.5194/nhess-5-189-2005, 2005.
Baum, R. L., Savage, W. Z., and Godt, J. W.: TRIGRS- A Fortran Program for Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Analysis, Version 2.0, Tech. rep., US Geological Survey, 2008.
Baum, R. L., Godt, J. W., and Savage, W. Z.: Estimating the timing and location of shallow rainfall-induced landslides using a model for transient, unsaturated infiltration, J. Geophys. Res., 115, F03013, https://doi.org/10.1029/2009JF001321, 2010.
Begueria, S.: Validation and Evaluation of Predictive Models in Hazard Assessment and Risk Management, Nat. Hazards, 37, 315–329, https://doi.org/10.1007/s11069-005-5182-6, 2006.
Berti, M. and Simoni, A.: Field evidence of pore pressure diffusion in clayey soils prone to landsliding, J. Geophys. Res.-Earth, 115, F04006, https://doi.org/10.1029/2009JF001463, 2010.
Beven, K. and Freer, J.: Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology, J. Hydrol., 249, 11–29, https://doi.org/10.1016/S0022-1694(01)00421-8, 2001.
Beven, K. and Kirkby, M.: A physically based, variable contributing area model of basin hydrology, Hydrol. Sci. Bull., 24, 43–69, https://doi.org/10.1080/02626667909491834, 1979.
Bischetti, G., Chiaradia, E., Epis, T., and Morlotti, E.: Root cohesion of forest species in the Italian Alps, Plant Soil, 324, 71–89, https://doi.org/10.1007/s11104-009-9941-0, 2009.
Bischetti, G. B., Chiaradia, E. A., Simonato, T., Speziali, B., Vitali, B., Vullo, P., and Zocco, A.: Root strength and root area ratio of forest species in Lombardy (Northern Italy), Plant Soil, 278, 11–22, https://doi.org/10.1007/s11104-005-0605-4, 2005.
Bollinger, D., Hegg, C., Keusen, H., and Lateltin, O.: Ursachenanalyse der Hanginstabilitäten 1999. Arbeitsgruppe Geologie und Naturgefahren (AGN) Teil A: Allgemeiner Teil: Aspekte der Hangstabilität, in: Bulletin für angewandte Geologie, 5/1, 5–38, AGN, 2000.
Carrara, A., Cardinali, M., Detti, R., Guzzetti, F., Pasqui, V., and Reichenbach, P.: GIS techniques and statistical models in evaluating landslide hazard, Earth Surf. Proc. Land., 16, 427–445, https://doi.org/10.1002/esp.3290160505, 1991.
Casadei, M., Dietrich, W. E., and Miller, N. L.: Testing a model for predicting the timing and location of shallow landslide initiation in soil-mantled landscapes, Earth Surf. Proc. Land., 28, 925–950, https://doi.org/10.1002/esp.470, 2003.
Catani, F., Segoni, S., and Falorni, G.: An empirical geomorphology-based approach to the spatial prediction of soil thickness at catchment scale, Water Resour. Res., 46, W05508, https://doi.org/10.1029/2008WR007450, 2010.
Crosta, G. B. and Frattini, P.: Distributed modelling of shallow landslides triggered by intense rainfall, Nat. Hazards Earth Syst. Sci., 3, 81–93, https://doi.org/10.5194/nhess-3-81-2003, 2003.
Cruden, D. M. and Varnes, D. J.: Landslide types and processes, in: Landslides, Investigation and Mitigation, edited by: Turner, A. K. and Schuster, R. L., Transportation Research Board Special Report 247, Washington D.C., 36–75, 1996.
Davis, J. L. and Annan, A. P.: Ground-penetrating radar for high-resolution mapping of soil and rock stratigraphy, Geophys. Prospect., 37, 531–551, https://doi.org/10.1111/j.1365-2478.1989.tb02221.x, 1989.
Dietrich, W. and Montgomery, D.: Shalstab: a digital terrain model for mapping shallow landslide potential, WWW document, http://socrates.berkeley.edu/~geomorph/shalstab/ (last access: 20 May 2015), 1998.
Dietrich, W. E., Reiss, R., Hsu, M.-L., and Montgomery, D. R.: A process-based model for colluvial soil depth and shallow landsliding using digital elevation data, Hydrol. Process., 9, 383–400, https://doi.org/10.1002/hyp.3360090311, 1995.
Dobler, C. and Pappenberger, F.: Global sensitivity analyses for a complex hydrological model applied in an Alpine watershed, Hydrol. Process., 27, 3922–3940, https://doi.org/10.1002/hyp.9520, 2013.
Fairbridge, R. W.: The Encyclopedia of Geomorphology. Encyclopedia of Earth Sciences Series, vol. 3, Dowden, Hutchinson & Ross Stroudsburg, Pennsylvania, 1968.
Formayer, H. and Kromp-Kolb, H.: Hochwasser und Klimawandel. Auswirkungen des Klimawandels auf Hochwasserereignisse in Österreich, BOKU-Met, Wien, https://meteo.boku.ac.at/klima/berichte/ (last access: 14 September 2016), 2009.
Formetta, G., Capparelli, G., and Versace, P.: Evaluating performance of simplified physically based models for shallow landslide susceptibility, Hydrol. Earth Syst. Sci., 20, 4585–4603, https://doi.org/10.5194/hess-20-4585-2016, 2016.
Friebe, J.: Vorarlberg. Geologie der Österreichischen Bundesländer, Verlag der Geologischen Bundesanstalt (GBA), Wien, 2007.
Gardner, W.: Some steady-state solutions of the unsaturated moisture flow equation with application to evaporation from a water table, Soil Sci., 85, 228–232, 1958.
Gioia, E., Speranza, G., Ferretti, M., Godt, J. W., Baum, R. L., and Marincioni, F.: Application of a process-based shallow landslide hazard model over a broad area in Central Italy, Landslides, 13, 1197–1214, https://doi.org/10.1007/s10346-015-0670-6, 2016.
GRASS Development Team: Geographic Resources Analysis Support System (GRASS GIS) Software, Open Source Geospatial Foundation, USA, http://grass.osgeo.org (last access: 8 July 2016), 2014.
Hammond, C., Hall, D., Miller, S., and Swetik, P.: Level I stability analysis (LISA) documentation for version 2.0, Intermountain Research Station, Ogden, 1992.
Heimsath, A. M., Dietrich, W. E., Nishiizumi, K., and Finkel, R. C.: The soil production function and landscape equilibrium, Nature, 388, 358–361, https://doi.org/10.1038/41056, 1997.
Heissel, W., Oberhauser, R., and Schmidegg, O.: Geologische Karte des Walgaues, Vorarlberg 1 : 25.000, Verlag der Geologischen Bundesanstalt (GBA), Wien, 1967.
Heumader, J.: Die Katastrophenlawinen von Galtür und Valzur am 23. und 24.2.1999 im Paznauntal/Tirol, in: Proc. of the IX International Congress Interpraevent 2000, Villach, vol. 1, 397–409, 2000.
Horn, B.: Hill shading and the reflectance map, P. IEEE, 69, 14–47, https://doi.org/10.1109/PROC.1981.11918, 1981.
Iverson, R. M.: Landslide triggering by rain infiltration, Water Resour. Res., 36, 1897–1910, https://doi.org/10.1029/2000WR900090, 2000.
Keijsers, J. G. S., Schoorl, J. M., Chang, K. T., Chiang, S. H., Claessens, L., and Veldkamp, A.: Calibration and resolution effects on model performance for predicting shallow landslide locations in Taiwan, Geomorphology, 133, 168–177, https://doi.org/10.1016/j.geomorph.2011.03.020, 2011.
Kienholz, H.: Kombinierte geomorphologische Gefahrenkarte 1:10 000 von Grindelwald, Catena, 3, 265–294, https://doi.org/10.1016/0341-8162(77)90034-0, 1977.
Kim, D., Im, S., Lee, C., and Woo, C.: Modeling the contribution of trees to shallow landslide development in a steep, forested watershed, Ecol. Eng., 61, 658–668, https://doi.org/10.1016/j.ecoleng.2013.05.003, 2013.
Kromp-Kolb, H., Nakicenovic, N., Seidl, R., Steininger, K., Ahrens, B., Auer, I., Baumgarten, A., Bednar-Friedl, B., Eitzinger, J., Foelsche, U., Formayer, H., Geitner, C., Glade, T., Gobiet, A., Grabherr, G., Haas, R., Haberl, H., Haimberger, L., Hitzenberger, R., König, M., Köppl, A., Lexer, M., Loibl, W., Molitor, R., Moshammer, H., Nachtnebel, H.-P., Prettenthaler, F., Rabitsch, W., Radunsky, K., Schneider, L., Schnitzer, H., Schöner, W., Schulz, N., Seibert, P., Stagl, S., Steiger, R., Stötter, H., Streicher, W., and Winiwarter, W.: Österreichischer Sachstandsbericht Klimawandel 2014 (AAR14). Austrian Panel on Climate Change (APCC), Verlag der Österreichischen Akademie der Wissenschaften, Wien, Österreich, 2014.
Kutschera, L. and Lichtenegger, E.: Wurzelatlas europäischer Waldbäume, Leopold Stocker Verlag, Graz, 2002.
Lanni, C., Borga, M., Rigon, R., and Tarolli, P.: Modelling shallow landslide susceptibility by means of a subsurface flow path connectivity index and estimates of soil depth spatial distribution, Hydrol. Earth Syst. Sci., 16, 3959–3971, https://doi.org/10.5194/hess-16-3959-2012, 2012.
Lanni, C., McDonnell, J., Hopp, L., and Rigon, R.: Simulated effect of soil depth and bedrock topography on near-surface hydrologic response and slope stability, Earth Surf. Proc. Land., 38, 146–159, https://doi.org/10.1002/esp.3267, 2013.
Lateltin, O., Haemmig, C., Raetzo, H., and Bonnard, C.: Landslide risk management in Switzerland, Landslides, 2, 313–320, https://doi.org/10.1007/s10346-005-0018-8, 2005.
Lu, N. and Godt, J. W.: Hillslope hydrology and stability, Cambridge University Press, New York, 2013.
Markart, G., Perzl, F., Kohl, B., Luzian, R., Kleemayr, K., Ess, B., and Mayerl, J.: Schadereignisse 22./23. August 2005 – Ereignisdokumentation und -analyse in ausgewählten Gemeinden Vorarlbergs, in: BFW-Dokumentation, vol. 5, Bundesforschungs-und Ausbildungszentrum für Wald, Naturgefahren und Landschaft, Wien, 2007.
Mergili, M., Fischer, J.-T., Krenn, J., and Pudasaini, S. P.: r.avaflow v1, an advanced open-source computational framework for the propagation and interaction of two-phase mass flows, Geosci. Model Dev., 10, 553–569, https://doi.org/10.5194/gmd-10-553-2017, 2017.
Metz, C. E.: Basic principles of ROC analysis, Sem. Nucl. Med., 8, 283–298, https://doi.org/10.1016/S0001-2998(78)80014-2, 1978.
Milledge, D. G., Griffiths, D. V., Lane, S. N., and Warburton, J.: Limits on the validity of infinite length assumptions for modelling shallow landslides, Earth Surf. Proc. Land., 37, 1158–1166, https://doi.org/10.1002/esp.3235, 2012.
Montgomery, D. R. and Dietrich, W. E.: A physically-based model for the topographic control on shallow landsliding, Water Resour. Res., 30, 1153–1171, https://doi.org/10.1029/93WR02979, 1994.
Oberhauser, R.: Neue Beiträge zur Geologie und Mikropaläontologie von Helvetikum und Flysch im Gebiet der Hohen Kugel (Vorarlberg), Verhandlungen der Geologischen Bundesanstalt, 1958/2, 121–140, 1958.
Oberhauser, R.: Geologische Karte St. Gallen Süd und Dornbirn Süd 1 : 25.000, Verlag der Geologischen Bundesanstalt (GBA), Wien, 1982.
Oberhauser, R.: Erläuterungen zur geologisch-tektonischen Übersichtskarte von Vorarlberg 1 : 200.000, Verlag der Geologischen Bundesanstalt (GBA), Wien, 1998.
ÖNORM EN ISO 22476-2:2012: Geotechnische Erkundung und Untersuchung – Felduntersuchungen – Teil 2: Rammsondierungen. Geotechnical investigation and testing – Field testing – Part 2: Dynamic probing, Austrian Standards, 2012.
Park, D. W., Nikhil, N. V., and Lee, S. R.: Landslide and debris flow susceptibility zonation using TRIGRS for the 2011 Seoul landslide event, Nat. Hazards Earth Syst. Sci., 13, 2833–2849, https://doi.org/10.5194/nhess-13-2833-2013, 2013.
Prinz, H. and Strauß, R.: Ingenieurgeologie, vol. 5, Springer, Heidelberg, 2011.
Python Software Foundation: Python Programming Language, Python Software Foundation, Hampton, New Hampshire, http://www.python.org/ (last access: 18 November 2016), 2016.
R Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (last access: 14 December 2016), 2016.
Raia, S., Alvioli, M., Rossi, M., Baum, R. L., Godt, J. W., and Guzzetti, F.: Improving predictive power of physically based rainfall-induced shallow landslide models: a probabilistic approach, Geosci. Model Dev., 7, 495–514, https://doi.org/10.5194/gmd-7-495-2014, 2014.
Rowe, P. W. and Barden, L.: A new consolidation cell, Géotechnique, 16, 162–170, https://doi.org/10.1680/geot.1966.16.2.162, 1966.
Salciarini, D., Godt, J. W., Savage, W. Z., Conversini, P., Baum, R. L., and Michael, J. A.: Modeling regional initiation of rainfall-induced shallow landslides in the eastern Umbria Region of central Italy, Landslides, 3, 181–194, https://doi.org/10.1007/s10346-006-0037-0, 2006.
Sass, O.: Bedrock detection and talus thickness assessment in the European Alps using geophysical methods, J. Appl. Geophys., 62, 254–269, https://doi.org/10.1016/j.jappgeo.2006.12.003, 2007.
Schmidt, H.-H., Buchmaier, R. F., and Vogt-Breyer, C.: Grundlagen der Geotechnik: Geotechnik nach Eurocode, Springer Fachmedien Wiesbaden, Heidelberg, 2014.
Schwarz, M., Preti, F., Giadrossich, F., Lehmann, P., and Or, D.: Quantifying the role of vegetation in slope stability: A case study in Tuscany (Italy), Ecol. Eng., 36, 285–291, https://doi.org/10.1016/j.ecoleng.2009.06.014, 2010.
Schwarz, M., Cohen, D., and Or, D.: Spatial characterization of root reinforcement at stand scale: Theory and case study, Geomorphology, 171–172, 190–200, https://doi.org/10.1016/j.geomorph.2012.05.020, 2012.
Segoni, S., Rossi, G., and Catani, F.: Improving basin scale shallow landslide modelling using reliable soil thickness maps, Nat. Hazards, 61, 85–101, https://doi.org/10.1007/s11069-011-9770-3, 2012.
Seibert, P., Frank, A., and Formayer, H.: Synoptic and regional patterns of heavy precipitation in Austria, Theor. Appl. Climatol., 87, 139–153, https://doi.org/10.1007/s00704-006-0198-8, 2007.
Srivastava, R. and Yeh, T. C. J.: Analytical Solutions For One-dimensional, Transient Infiltration Toward the Water-table In Homogeneous and Layered Soils, Water Resour. Res., 27, 753–762, https://doi.org/10.1029/90WR02772, 1991.
Steinacher, R., Medicus, G., Fellin, W., and Zangerl, C.: The Influence of Deforestation On Slope (in-) Stability, Aust. J. Earth Sci., 102, 90–99, 2009.
Tang, Y., Reed, P., Wagener, T., and van Werkhoven, K.: Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation, Hydrol. Earth Syst. Sci., 11, 793–817, https://doi.org/10.5194/hess-11-793-2007, 2007.
Tesfa, T. K., Tarboton, D. G., Chandler, D. G., and McNamara, J. P.: Modeling soil depth from topographic and land cover attributes, Water Resour. Res., 45, W10438, https://doi.org/10.1029/2008WR007474, 2009.
USGS: TRIGRS 2.0., http://pubs.usgs.gov/of/2008/1159/downloads/, last access: 2 November 2016.
van Bebber, W. J.: Die Zugstrassen der barometrischen Minima nach den Bahnenkarten der Deutschen Seewarte für den Zeitraum von 1870–1890, Meteorol. Z., 8, 361–366, 1891.
van Westen, C. J., van 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, https://doi.org/10.1007/s10064-005-0023-0, 2006.
Vieira, B. C., Fernandes, N. F., and Filho, O. A.: Shallow landslide prediction in the Serra do Mar, São Paulo, Brazil, Nat. Hazards Earth Syst. Sci., 10, 1829–1837, https://doi.org/10.5194/nhess-10-1829-2010, 2010.
Wagener, T. and Kollat, J.: Numerical and visual evaluation of hydrological and environmental models using the Monte Carlo analysis toolbox, Environ. Modell. Softw., 22, 1021–1033, https://doi.org/10.1016/j.envsoft.2006.06.017, 2007.
Werner, R. and Auer, I.: Klima von Vorarlberg: Eine anwendungsorientierte Klimatographie. 2. Niederschlag und Gewitter, Schnee und Gletscher, Verdunstung, Luftdruck, Wind, Umweltinstitut des Landes Vorarlberg, Bregenz, 2001a.
Werner, R. and Auer, I.: Klima von Vorarlberg: Eine anwendungsorientierte Klimatographie. 1. Lufttemperatur, Bodentemperatur, Wassertemperatur, Luftfeuchte, Bewölkung, Nebel, Umweltinstitut des Landes Vorarlberg, Bregenz, 2001b.
Wiedenhöft, A. and Vatslid, S.: LiDAR und RGB – Land Vorarlberg. Technischer Abschlussbericht, Tech. rep., Trimble Germany GmbH, 2014.
Wiegand, C., Kringer, K., Geitner, C., and Rutzinger, M.: Regolith structure analysis – A contribution to understanding the local occurrence of shallow landslides (Austrian Tyrol), Geomorphology, 183, 5–13, https://doi.org/10.1016/j.geomorph.2012.06.027, 2013.
Wood, J.: The geomorphological characterisation of digital elevation models, PhD thesis, University of Leicester, Leicester, http://hdl.handle.net/2381/34503 (last access: 27 June 2017), 1996.
Zieher, T., Perzl, F., Rössel, M., Rutzinger, M., Meißl, G., Markart, G., and Geitner, C.: A multi-annual landslide inventory for the assessment of shallow landslide susceptibility – Two test cases in Vorarlberg, Austria, Geomorphology, 259, 40–54, https://doi.org/10.1016/j.geomorph.2016.02.008, 2016.
Zieher, T., Markart, G., Ottowitz, D., Römer, A., Rutzinger, M., Meißl, G., and Geitner, C.: Water content dynamics at plot scale–comparison of time-lapse electrical resistivity tomography monitoring and pore pressure modelling, J. Hydrol., 544, 195–209, https://doi.org/10.1016/j.jhydrol.2016.11.019, 2017.
Zizioli, D., Meisina, C., Valentino, R., and Montrasio, L.: Comparison between different approaches to modeling shallow landslide susceptibility: a case history in Oltrepo Pavese, Northern Italy, Nat. Hazards Earth Syst. Sci., 13, 559–573, https://doi.org/10.5194/nhess-13-559-2013, 2013.
Short summary
At catchment scale, it is challenging to provide the required input parameters for physically based slope stability models. In the present study, the parameterization of such a model is optimized against observed shallow landslides during two triggering rainfall events. With the resulting set of parameters the model reproduces the location and the triggering timing of most observed landslides. Based on that, potential effects of increasing precipitation intensity on slope stability are assessed.
At catchment scale, it is challenging to provide the required input parameters for physically...
Altmetrics
Final-revised paper
Preprint