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
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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Preprint withdrawn
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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Evaluating post-wildfire debris-flow rainfall thresholds and volume models at the 2020 Grizzly Creek Fire in Glenwood Canyon, Colorado, USA
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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
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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
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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.
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
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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
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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.
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
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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
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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
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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
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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
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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.
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
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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
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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
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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
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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
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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
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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.
Mahnoor Ahmed, Giacomo Titti, Sebastiano Trevisani, Lisa Borgatti, and Mirko Francioni
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-211, https://doi.org/10.5194/nhess-2024-211, 2024
Revised manuscript accepted for NHESS
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Elevation models are compared with a true dataset for terrain characteristics which selects a better ranking model to test with different parameters for partitioning 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.
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
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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
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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
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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.
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. Discuss., https://doi.org/10.5194/nhess-2024-146, https://doi.org/10.5194/nhess-2024-146, 2024
Revised manuscript accepted for NHESS
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On April 2, 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 Artificial Intelligence (AI) to quickly inventory the landslides. This approach identified 7,090 landslides over 75 km2 within 3 hours of acquiring the EO imagery. The study highlights AI’s role in improving landslide detection and understanding earthquake-landslide interactions for better hazard mitigation.
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
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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
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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.
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
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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
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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.
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
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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
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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.
Yu Zhuang, Brian W. McArdell, and Perry Bartelt
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-87, https://doi.org/10.5194/nhess-2024-87, 2024
Revised manuscript accepted for NHESS
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This study reformulates the μ(I) rheology into a Voellmy-type relationship to elucidate its physical implications. The μ(I) rheology, incorporating a dimensionless inertial number, mimics granular temperature effects, reflecting shear thinning behavior of mass flows. However, its constant Coulomb friction coefficient limits accuracy in modeling deposition. Comparing μ(I) with Voellmy-type rheologies reveals strengths and limitations, enhancing mass flow modeling and engineering applications.
Sonam Rinzin, Stuart Dunning, Rachel Carr, Ashim Sattar, and Martin Mergili
EGUsphere, https://doi.org/10.5194/egusphere-2024-1819, https://doi.org/10.5194/egusphere-2024-1819, 2024
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We evaluated the sensitivity of model outputs to input parameter uncertainties by performing multiple GLOF simulations using the r.avaflow model. We found out that GLOF modelling outputs are highly sensitive to six parameters: volume of mass movements entering lakes, DEM datasets, origin of mass movements, mesh size, basal frictional angle, and entrainment coefficient. Future modelling should carefully consider the output uncertainty from these sensitive parameters.
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
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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
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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
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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
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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.
Jane Walden, Mylène Jacquemart, Bretwood Higman, Romain Hugonnet, Andrea Manconi, and Daniel Farinotti
EGUsphere, https://doi.org/10.5194/egusphere-2024-1086, https://doi.org/10.5194/egusphere-2024-1086, 2024
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In a study of eight landslides adjacent to glaciers in Alaska, we found that landslide movement increased as the glacier retreated past the landslide at four sites. Movement at other sites coincided with heavy precipitation or increased glacier thinning, and two sites showed little-to-no motion. We suggest that landslides next to water-terminating glaciers may be especially vulnerable to acceleration, which we guess is due to faster retreat rates and water replacing ice at the landslide edge.
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
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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
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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
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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
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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
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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
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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
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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
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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.
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. Discuss., https://doi.org/10.5194/nhess-2024-40, https://doi.org/10.5194/nhess-2024-40, 2024
Revised manuscript accepted for NHESS
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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 on landslide risk.
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
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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
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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.
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
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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
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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
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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
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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.
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
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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.
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
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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.
Cited articles
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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...
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