Articles | Volume 18, issue 8
https://doi.org/10.5194/nhess-18-2183-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-18-2183-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Aug 2018
Research article |
| 16 Aug 2018
| 16 Aug 2018
Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology
Department of Geography and Regional Research, University of Vienna,
Universitätsstraße 7, 1010 Vienna, Austria
Martin Mergili
Department of Geography and Regional Research, University of Vienna,
Universitätsstraße 7, 1010 Vienna, Austria
Institute of Applied Geology, University of Natural Resources and
Life Sciences, Gregor-Mendel-Straße 33, 1180 Vienna, Austria
Benni Thiebes
Department of Geography and Regional Research, University of Vienna,
Universitätsstraße 7, 1010 Vienna, Austria
German Committee for Disaster Reduction (DKKV),
Kaiser-Friedrich-Straße 13, 53113 Bonn, Germany
Thomas Glade
Department of Geography and Regional Research, University of Vienna,
Universitätsstraße 7, 1010 Vienna, Austria
Related authors
No articles found.
Martin Mergili, Hanna Pfeffer, Andreas Kellerer-Pirklbauer, Christian Zangerl, and Shiva Prasad Pudasaini
EGUsphere, https://doi.org/10.5194/egusphere-2025-213, https://doi.org/10.5194/egusphere-2025-213, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
We present a new version of the landslide model r.avaflow. It includes a model where different materials move on top of each other instead of mixing; a model supporting the entire range from block sliding to flowing; a model for slow-moving processes; and an interface for virtual reality visualization. Based on the results for four case studies we conclude that, at the moment, our enhancements are very useful for visualization of landslides for awareness building and environmental education.
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
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
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.
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
Short summary
Short summary
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.
Adam Emmer, Simon K. Allen, Mark Carey, Holger Frey, Christian Huggel, Oliver Korup, Martin Mergili, Ashim Sattar, Georg Veh, Thomas Y. Chen, Simon J. Cook, Mariana Correas-Gonzalez, Soumik Das, Alejandro Diaz Moreno, Fabian Drenkhan, Melanie Fischer, Walter W. Immerzeel, Eñaut Izagirre, Ramesh Chandra Joshi, Ioannis Kougkoulos, Riamsara Kuyakanon Knapp, Dongfeng Li, Ulfat Majeed, Stephanie Matti, Holly Moulton, Faezeh Nick, Valentine Piroton, Irfan Rashid, Masoom Reza, Anderson Ribeiro de Figueiredo, Christian Riveros, Finu Shrestha, Milan Shrestha, Jakob Steiner, Noah Walker-Crawford, Joanne L. Wood, and Jacob C. Yde
Nat. Hazards Earth Syst. Sci., 22, 3041–3061, https://doi.org/10.5194/nhess-22-3041-2022, https://doi.org/10.5194/nhess-22-3041-2022, 2022
Short summary
Short summary
Glacial lake outburst floods (GLOFs) have attracted increased research attention recently. In this work, we review GLOF research papers published between 2017 and 2021 and complement the analysis with research community insights gained from the 2021 GLOF conference we organized. The transdisciplinary character of the conference together with broad geographical coverage allowed us to identify progress, trends and challenges in GLOF research and outline future research needs and directions.
Christian Zangerl, Annemarie Schneeberger, Georg Steiner, and Martin Mergili
Nat. Hazards Earth Syst. Sci., 21, 2461–2483, https://doi.org/10.5194/nhess-21-2461-2021, https://doi.org/10.5194/nhess-21-2461-2021, 2021
Short summary
Short summary
The Köfels rockslide in the Ötztal Valley (Austria) represents the largest known extremely rapid rockslide in metamorphic rock masses in the Alps and was formed in the early Holocene. Although many hypotheses for the conditioning and triggering factors were discussed in the past, until now no scientifically accepted explanatory model has been found. This study provides new data and numerical modelling results to better understand the cause and triggering factors of this gigantic natural event.
Guoxiong Zheng, Martin Mergili, Adam Emmer, Simon Allen, Anming Bao, Hao Guo, and Markus Stoffel
The Cryosphere, 15, 3159–3180, https://doi.org/10.5194/tc-15-3159-2021, https://doi.org/10.5194/tc-15-3159-2021, 2021
Short summary
Short summary
This paper reports on a recent glacial lake outburst flood (GLOF) event that occurred on 26 June 2020 in Tibet, China. We find that this event was triggered by a debris landslide from a steep lateral moraine. As the relationship between the long-term evolution of the lake and its likely landslide trigger revealed by a time series of satellite images, this case provides strong evidence that it can be plausibly linked to anthropogenic climate change.
Charlotte Heinzlef, Bruno Barocca, Mattia Leone, Thomas Glade, and Damien Serre
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2020-217, https://doi.org/10.5194/nhess-2020-217, 2020
Preprint withdrawn
Johnnatan Palacio Cordoba, Martin Mergili, and Edier Aristizábal
Nat. Hazards Earth Syst. Sci., 20, 815–829, https://doi.org/10.5194/nhess-20-815-2020, https://doi.org/10.5194/nhess-20-815-2020, 2020
Short summary
Short summary
Landslides triggered by rainfall are very common phenomena in complex tropical environments such as the Colombian Andes. In this work, we perform probabilistic analyses with r.slope.stability for landslide susceptibility analysis. We test the model in the La Arenosa catchment, northern Colombian Andes. The results are compared to those yielded with the corresponding deterministic analyses and with other physically based models applied in the same catchment.
Martin Mergili, Michel Jaboyedoff, José Pullarello, and Shiva P. Pudasaini
Nat. Hazards Earth Syst. Sci., 20, 505–520, https://doi.org/10.5194/nhess-20-505-2020, https://doi.org/10.5194/nhess-20-505-2020, 2020
Short summary
Short summary
Computer simulations of complex landslide processes in mountain areas are important for informing risk management but are at the same time challenging in terms of parameterization and physical and numerical model implementation. Using the tool r.avaflow, we highlight the progress and the challenges with regard to such simulations on the example of the Piz Cengalo–Bondo landslide cascade in Switzerland, which started as an initial rockslide–rockfall and finally evolved into a debris flow.
Martin Mergili, Shiva P. Pudasaini, Adam Emmer, Jan-Thomas Fischer, Alejo Cochachin, and Holger Frey
Hydrol. Earth Syst. Sci., 24, 93–114, https://doi.org/10.5194/hess-24-93-2020, https://doi.org/10.5194/hess-24-93-2020, 2020
Short summary
Short summary
In 1941, the glacial lagoon Lake Palcacocha in the Cordillera Blanca (Peru) drained suddenly. The resulting outburst flood/debris flow consumed another lake and had a disastrous impact on the town of Huaraz 23 km downstream. We reconstuct this event through a numerical model to learn about the possibility of prediction of similar processes in the future. Remaining challenges consist of the complex process interactions and the lack of experience due to the rare occurrence of such process chains.
Heidi Kreibich, Thomas Thaler, Thomas Glade, and Daniela Molinari
Nat. Hazards Earth Syst. Sci., 19, 551–554, https://doi.org/10.5194/nhess-19-551-2019, https://doi.org/10.5194/nhess-19-551-2019, 2019
Sven Fuchs, Margreth Keiler, and Thomas Glade
Nat. Hazards Earth Syst. Sci., 17, 1203–1206, https://doi.org/10.5194/nhess-17-1203-2017, https://doi.org/10.5194/nhess-17-1203-2017, 2017
Martin Mergili, Jan-Thomas Fischer, Julia Krenn, and Shiva P. Pudasaini
Geosci. Model Dev., 10, 553–569, https://doi.org/10.5194/gmd-10-553-2017, https://doi.org/10.5194/gmd-10-553-2017, 2017
Short summary
Short summary
r.avaflow represents a GIS-based, multi-functional open-source tool for the simulation of debris flows, rock avalanches, snow avalanches, or two-phase (solid and fluid) process chains. It further facilitates parameter studies and validation of the simulation results against observed patterns. r.avaflow shall inform strategies to reduce the risks related to the interaction of mass flow processes with society.
Stefan Steger, Alexander Brenning, Rainer Bell, and Thomas Glade
Nat. Hazards Earth Syst. Sci., 16, 2729–2745, https://doi.org/10.5194/nhess-16-2729-2016, https://doi.org/10.5194/nhess-16-2729-2016, 2016
Short summary
Short summary
This study investigates the propagation of landslide inventory-based positional errors into statistical landslide susceptibility models by artificially introducing such spatial inaccuracies. The findings highlight that (i) an increasing positional error is related to increasing distortions of modelling and validation results, (ii) interrelations between inventory-based errors and modelling results are complex, and (iii) inventory-based errors can be counteracted by adapting the study design.
M. Mergili, J. Krenn, and H.-J. Chu
Geosci. Model Dev., 8, 4027–4043, https://doi.org/10.5194/gmd-8-4027-2015, https://doi.org/10.5194/gmd-8-4027-2015, 2015
Short summary
Short summary
r.randomwalk is a flexible and multi-functional open-source GIS tool for simulating the propagation of mass movements. Mass points are routed from given release pixels through a digital elevation model until a defined break criterion is reached. In contrast to existing tools, r.randomwalk includes functionalities to account for parameter uncertainties, and it offers built-in functions for validation and visualization. We show the key functionalities of r.randomwalk for three test areas.
M. Mergili and H.-J. Chu
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhessd-3-5677-2015, https://doi.org/10.5194/nhessd-3-5677-2015, 2015
Revised manuscript not accepted
Short summary
Short summary
We propose a procedure to compute an integrated spatial landslide probability, combining release and propagation. The zonal release probability is introduced to correct the pixel-based release probability for the size of the release zone relevant for a pixel. For a test area in Taiwan we observe that the model performs moderately well in predicting the observed landslides and that the size of the release zone influences the result to a much higher degree than the pixel-based release probability.
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.
H. Petschko, A. Brenning, R. Bell, J. Goetz, and T. Glade
Nat. Hazards Earth Syst. Sci., 14, 95–118, https://doi.org/10.5194/nhess-14-95-2014, https://doi.org/10.5194/nhess-14-95-2014, 2014
F. E. Gruber and M. Mergili
Nat. Hazards Earth Syst. Sci., 13, 2779–2796, https://doi.org/10.5194/nhess-13-2779-2013, https://doi.org/10.5194/nhess-13-2779-2013, 2013
B. Schwendtner, M. Papathoma-Köhle, and T. Glade
Nat. Hazards Earth Syst. Sci., 13, 2195–2207, https://doi.org/10.5194/nhess-13-2195-2013, https://doi.org/10.5194/nhess-13-2195-2013, 2013
Related subject area
Landslides and Debris Flows Hazards
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
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
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
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
Is higher resolution always better? Open-access DEM comparison for Slope Units delineation and regional landslide prediction
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
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
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 on GLOF modelling results using the state-of-the-art modelling code, r.avaflow
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
A regional analysis of paraglacial landslide activation in southern coastal Alaska
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
Brief Communication: Weak correlation between building damage and loss of life from landslides
Space–time landslide hazard modeling via Ensemble Neural Networks
Optimization strategy for flexible barrier structures: investigation and back analysis of a rockfall disaster case in southwestern China
Numerical-model-derived intensity–duration thresholds for early warning of rainfall-induced debris flows in a Himalayan catchment
Slope Unit Maker (SUMak): an efficient and parameter-free algorithm for delineating slope units to improve landslide modeling
Probabilistic Hydrological Estimation of LandSlides (PHELS): global ensemble landslide hazard modelling
A new analytical method for stability analysis of rock blocks with basal erosion in sub-horizontal strata by considering the eccentricity effect
Rockfall monitoring with a Doppler radar on an active rockslide complex in Brienz/Brinzauls (Switzerland)
Landslide initiation thresholds in data-sparse regions: application to landslide early warning criteria in Sitka, Alaska, USA
Lessons learnt from a rockfall time series analysis: data collection, statistical analysis, and applications
The concept of event-size-dependent exhaustion and its application to paraglacial rockslides
Coastal earthquake-induced landslide susceptibility during the 2016 Mw 7.8 Kaikōura earthquake, New Zealand
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.
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.
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.
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.
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
Short summary
Short summary
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
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.
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
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.
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
Short summary
Short summary
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
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.
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
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 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
Short summary
Short summary
We propose a modeling approach capable of recognizing slopes that may generate landslides, as well as how large these mass movements may be. This protocol is implemented, tested, and validated with data that change in both space and time via an Ensemble Neural Network architecture.
Li-Ru Luo, Zhi-Xiang Yu, Li-Jun Zhang, Qi Wang, Lin-Xu Liao, and Li Peng
Nat. Hazards Earth Syst. Sci., 24, 631–649, https://doi.org/10.5194/nhess-24-631-2024, https://doi.org/10.5194/nhess-24-631-2024, 2024
Short summary
Short summary
We performed field investigations on a rockfall near Jiguanshan National Forest Park, Chengdu. Vital information was obtained from an unmanned aerial vehicle survey. A finite element model was created to reproduce the damage evolution. We found that the impact kinetic energy was below the design protection energy. Improper member connections prevent the barrier from producing significant deformation to absorb energy. Damage is avoided by improving the ability of the nets and ropes to slide.
Sudhanshu Dixit, Srikrishnan Siva Subramanian, Piyush Srivastava, Ali P. Yunus, Tapas Ranjan Martha, and Sumit Sen
Nat. Hazards Earth Syst. Sci., 24, 465–480, https://doi.org/10.5194/nhess-24-465-2024, https://doi.org/10.5194/nhess-24-465-2024, 2024
Short summary
Short summary
Rainfall intensity–duration (ID) thresholds can aid in the prediction of natural hazards. Large-scale sediment disasters like landslides, debris flows, and flash floods happen frequently in the Himalayas because of their propensity for intense precipitation events. We provide a new framework that combines the Weather Research and Forecasting (WRF) model with a regionally distributed numerical model for debris flows to analyse and predict intense rainfall-induced landslides in the Himalayas.
Jacob B. Woodard, Benjamin B. Mirus, Nathan J. Wood, Kate E. Allstadt, Benjamin A. Leshchinsky, and Matthew M. Crawford
Nat. Hazards Earth Syst. Sci., 24, 1–12, https://doi.org/10.5194/nhess-24-1-2024, https://doi.org/10.5194/nhess-24-1-2024, 2024
Short summary
Short summary
Dividing landscapes into hillslopes greatly improves predictions of landslide potential across landscapes, but their scaling is often arbitrarily set and can require significant computing power to delineate. Here, we present a new computer program that can efficiently divide landscapes into meaningful slope units scaled to best capture landslide processes. The results of this work will allow an improved understanding of landslide potential and can help reduce the impacts of landslides worldwide.
Anne Felsberg, Zdenko Heyvaert, Jean Poesen, Thomas Stanley, and Gabriëlle J. M. De Lannoy
Nat. Hazards Earth Syst. Sci., 23, 3805–3821, https://doi.org/10.5194/nhess-23-3805-2023, https://doi.org/10.5194/nhess-23-3805-2023, 2023
Short summary
Short summary
The Probabilistic Hydrological Estimation of LandSlides (PHELS) model combines ensembles of landslide susceptibility and of hydrological predictor variables to provide daily, global ensembles of hazard for hydrologically triggered landslides. Testing different hydrological predictors showed that the combination of rainfall and soil moisture performed best, with the lowest number of missed and false alarms. The ensemble approach allowed the estimation of the associated prediction uncertainty.
Xushan Shi, Bo Chai, Juan Du, Wei Wang, and Bo Liu
Nat. Hazards Earth Syst. Sci., 23, 3425–3443, https://doi.org/10.5194/nhess-23-3425-2023, https://doi.org/10.5194/nhess-23-3425-2023, 2023
Short summary
Short summary
A 3D stability analysis method is proposed for biased rockfall with external erosion. Four failure modes are considered according to rockfall evolution processes, including partial damage of underlying soft rock and overall failure of hard rock blocks. This method is validated with the biased rockfalls in the Sichuan Basin, China. The critical retreat ratio from low to moderate rockfall susceptibility is 0.33. This method could facilitate rockfall early identification and risk mitigation.
Marius Schneider, Nicolas Oestreicher, Thomas Ehrat, and Simon Loew
Nat. Hazards Earth Syst. Sci., 23, 3337–3354, https://doi.org/10.5194/nhess-23-3337-2023, https://doi.org/10.5194/nhess-23-3337-2023, 2023
Short summary
Short summary
Rockfalls and their hazards are typically treated as statistical events based on rockfall catalogs, but only a few complete rockfall inventories are available today. Here, we present new results from a Doppler radar rockfall alarm system, which has operated since 2018 at a high frequency under all illumination and weather conditions at a site where frequent rockfall events threaten a village and road. The new data set is used to investigate rockfall triggers in an active rockslide complex.
Annette I. Patton, Lisa V. Luna, Joshua J. Roering, Aaron Jacobs, Oliver Korup, and Benjamin B. Mirus
Nat. Hazards Earth Syst. Sci., 23, 3261–3284, https://doi.org/10.5194/nhess-23-3261-2023, https://doi.org/10.5194/nhess-23-3261-2023, 2023
Short summary
Short summary
Landslide warning systems often use statistical models to predict landslides based on rainfall. They are typically trained on large datasets with many landslide occurrences, but in rural areas large datasets may not exist. In this study, we evaluate which statistical model types are best suited to predicting landslides and demonstrate that even a small landslide inventory (five storms) can be used to train useful models for landslide early warning when non-landslide events are also included.
Sandra Melzner, Marco Conedera, Johannes Hübl, and Mauro Rossi
Nat. Hazards Earth Syst. Sci., 23, 3079–3093, https://doi.org/10.5194/nhess-23-3079-2023, https://doi.org/10.5194/nhess-23-3079-2023, 2023
Short summary
Short summary
The estimation of the temporal frequency of the involved rockfall processes is an important part in hazard and risk assessments. Different methods can be used to collect and analyse rockfall data. From a statistical point of view, rockfall datasets are nearly always incomplete. Accurate data collection approaches and the application of statistical methods on existing rockfall data series as reported in this study should be better considered in rockfall hazard and risk assessments in the future.
Stefan Hergarten
Nat. Hazards Earth Syst. Sci., 23, 3051–3063, https://doi.org/10.5194/nhess-23-3051-2023, https://doi.org/10.5194/nhess-23-3051-2023, 2023
Short summary
Short summary
Rockslides are a major hazard in mountainous regions. In formerly glaciated regions, the disposition mainly arises from oversteepened topography and decreases through time. However, little is known about this decrease and thus about the present-day hazard of huge, potentially catastrophic rockslides. This paper presents a new theoretical framework that explains the decrease in maximum rockslide size through time and predicts the present-day frequency of large rockslides for the European Alps.
Colin K. Bloom, Corinne Singeisen, Timothy Stahl, Andrew Howell, Chris Massey, and Dougal Mason
Nat. Hazards Earth Syst. Sci., 23, 2987–3013, https://doi.org/10.5194/nhess-23-2987-2023, https://doi.org/10.5194/nhess-23-2987-2023, 2023
Short summary
Short summary
Landslides are often observed on coastlines following large earthquakes, but few studies have explored this occurrence. Here, statistical modelling of landslides triggered by the 2016 Kaikōura earthquake in New Zealand is used to investigate factors driving coastal earthquake-induced landslides. Geology, steep slopes, and shaking intensity are good predictors of landslides from the Kaikōura event. Steeper slopes close to the coast provide the best explanation for a high landslide density.
Cited articles
Alfieri, L., Salamon, P., Pappenberger, F., Wetterhall, F., and Thielen, J.:
Operational early warning systems for water-related hazards in Europe,
Environ. Sci. Pol., 21, 35–49,
https://doi.org/10.1016/j.envsci.2012.01.008, 2012a.
Alfieri, L., Thielen, J., and Pappenberger, F.: Ensemble hydro-meteorological
simulation for flash flood early detection in southern Switzerland, J. Hydrol., 424–425, https://doi.org/10.1016/j.jhydrol.2011.12.038, 2012b.
Althuwaynee, O. F., Pradhan, B., Park, H.-J., and Lee, J. H.: A novel
ensemble bivariate statistical evidential belief function with
knowledge-based analytical hierarchy process and multivariate statistical
logistic regression for landslide susceptibility mapping, Catena, 114,
21–36, https://doi.org/10.1016/j.catena.2013.10.011, 2014a.
Althuwaynee, O. F., Pradhan, B., Park, H.-J., and Lee, J. H.: A novel
ensemble decision tree-based CHi-squared Automatic Interaction Detection
(CHAID) and multivariate logistic regression models in landslide
susceptibility mapping, Landslides, 11, 1063–1078,
https://doi.org/10.1007/s10346-014-0466-0, 2014b.
Alvioli, M. and Baum, R. L.: 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.
Arnone, E., Dialynas, Y. G., Noto, L. V., and Bras, R. L.: Parameter
Uncertainty in Shallow Rainfall-triggered Landslide Modeling at Basin Scale:
A Probabilistic Approach, Procedia Earth Planet. Sci., 9, 101–111,
https://doi.org/10.1016/j.proeps.2014.06.003, 2014.
Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and Reinhardt, T.:
Operational Convective-Scale Numerical Weather Prediction
with the COSMO Model: Description and Sensitivities, Mon. Weather Rev.,
139, 3887–3905, https://doi.org/10.1175/MWR-D-10-05013.1, 2011.
Bartholmes and Todini: Coupling meteorological and hydrological models for flood
forecasting, Hydrol. Earth Syst. Sci., 9, 333–346, https://doi.org/10.5194/hess-9-333-2005, 2005.
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical
weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956,
2015.
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, 2008.
Baum, R. L. and Godt, J. W.: Early warning of rainfall-induced shallow
landslides and debris flows in the USA, Landslides, 7, 259–272,
https://doi.org/10.1007/s10346-009-0177-0, 2010.
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.
Beven, K.: Equifinality and Uncertainty in Geomorphological Modelling, in:
The Scientific Nature of Geomorphology: Proceedings of the 27th Binghamton
Symposium in Geomorphology, held 27–29 September, 1996, John Wiley &
Sons, 27, 289–313, 1996.
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.
Bivand, R., Keitt, T., Rowlingson, B., Pebesma, E., Sumner, M., Hijmans, R.,
and Rouault, E.: rgdal: Geospatial Data Abstraction Library, available at:
https://cran.r-project.org/package=rgdal, last access: 30 November 2017.
Bogaard, T. and Greco, R.: Invited perspectives: Hydrological perspectives on
precipitation intensity-duration thresholds for landslide initiation:
proposing hydro-meteorological thresholds, Nat. Hazards Earth Syst. Sci., 18, 31–39,
https://doi.org/10.5194/nhess-18-31-2018, 2018.
Bordoni, M., Meisina, C., Valentino, R., Bittelli, M., and Chersich, S.:
Site-specific to local-scale shallow landslides triggering zones assessment
using TRIGRS, Nat. Hazards Earth Syst. Sci., 15, 1025–1050,
https://doi.org/10.5194/nhess-15-1025-2015, 2015.
Canli, E., Loigge, B., and Glade, T.: Spatially distributed rainfall
information and its potential for regional landslide early warning systems,
Nat. Hazards, 103, https://doi.org/10.1007/s11069-017-2953-9, 2017.
Challinor, A., Martre, P., Asseng, S., Thornton, P., and Ewert, F.: Making
the most of climate impacts ensembles, Nat. Clim. Change, 4, 77–80,
https://doi.org/10.1038/nclimate2117, 2014.
Chen, H. X. and Zhang, L. M.: A physically-based distributed cell model for
predicting regional rainfall-induced shallow slope failures, Eng. Geol., 176, 79–92, https://doi.org/10.1016/j.enggeo.2014.04.011, 2014.
Chen, Y., Chen, D., Li, Z., Wu, Y., and Huang, J.: An ensemble prediction
model for rainfall-induced landslides and its preliminary application,
Chin. J. Atmos. Sci., 40, 515–527,
https://doi.org/10.3878/j.issn.1006-9895.1503.15120, 2016.
Cho, S. E.: Effects of spatial variability of soil properties on slope
stability, Eng. Geol., 92, 97–109, 2007.
Ciurleo, M., Cascini, L., and Calvello, M.: A comparison of statistical and
deterministic methods for shallow landslide susceptibility zoning in clayey
soils, Eng. Geol., 223, 71–81, https://doi.org/10.1016/j.enggeo.2017.04.023, 2017.
Cloke, H. L. and Pappenberger, F.: Ensemble flood forecasting: A review,
J. Hydrol., 375, 613–626,
https://doi.org/10.1016/j.jhydrol.2009.06.005, 2009.
Collier, C. G.: Flash flood forecasting: What are the limits of
predictability?, Q. J. Roy. Meteor. Soc.,
133, 3–23, https://doi.org/10.1002/qj.29, 2007.
Corominas, J., van Westen, C., Frattini, P., Cascini, L., Malet, J.-P.,
Fotopoulou, S., Catani, F., van ven Eeckhaut, M., Mavrouli, O., Agliardi,
F., Pitilakis, K., Winter, M. G., Pastor, M., Ferlisi, S., Tofani, V.,
Hervás, J., and Smith, J. T.: Recommendations for the quantitative
analysis of landslide risk, Bull. Eng. Geol. Environ., 73, 209–263, https://doi.org/10.1007/s10064-013-0538-8, 2014.
Crozier, M. J.: Deciphering the effect of climate change on landslide
activity: A review, Geomorphol., 124, 260–267,
https://doi.org/10.1016/j.geomorph.2010.04.009, 2010.
Devoli, G., Kleivane, I., Sund, M., Orthe, N., Ekker, R., and Johnsen, E.:
Landslide Early Warning System and Web Tools for Real-Time Scenarios and for
Distribution of Warning Messages in Norway, in Engineering Geology for
Society and Territory, Springer, Cham., 2, 625–629, 2015.
Devoli, G., Tiranti, D., Cremonini, R., Sund, M., and Boje, S.: Comparison of landslide
forecasting services in Piedmont (Italy) and Norway, illustrated by events in
late spring 2013, Nat. Hazards Earth Syst. Sci., 18, 1351–1372, https://doi.org/10.5194/nhess-18-1351-2018, 2018.
Fan, L., Lehmann, P., and Or, D.: Effects of soil spatial variability at the
hillslope and catchment scales on characteristics of rainfall-induced
landslides, Water Resour. Res., 52, 1781–1799,
https://doi.org/10.1002/2015WR017758, 2016.
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.
Gariano, S. L., Brunetti, M. T., Iovine, G., Melillo, M., Peruccacci, S.,
Terranova, O., Vennari, C., and Guzzetti, F.: Calibration and validation of
rainfall thresholds for shallow landslide forecasting in Sicily, southern
Italy, Geomorphology, 228, 653–665, https://doi.org/10.1016/j.geomorph.2014.10.019,
2015.
Gariano, S. L., Rianna, G., Petrucci, O., and Guzzetti, F.: Assessing future
changes in the occurrence of rainfall-induced landslides at a regional
scale, Sci. Total Environ., 596–597, https://doi.org/10.1016/j.scitotenv.2017.03.103, 2017.
GDAL Development Team: GDAL – Geospatial Data Abstraction Library, Version
2.1.3, Open Source Geospatial Foundation, available at:
http://www.gdal.org, last access: 30 November 2017.
Gebhardt, C., Theis, S. E., Paulat, M., and Ben Bouallègue, Z.:
Uncertainties in COSMO-DE precipitation forecasts introduced by model
perturbations and variation of lateral boundaries, Atmos. Res.,
100, 168–177, https://doi.org/10.1016/j.atmosres.2010.12.008, 2011.
Glade, T. and Crozier, M. J.: A review of scale dependency in landslide
hazard and risk analysis, edited by: Glade, T., Anderson, M., and Crozier, M.:
Landslide hazard and risk, Wiley, Chichester 75–138, 2015.
Golding, B., Roberts, N., Leoncini, G., Mylne, K., and Swinbank, R.:
MOGREPS-UK Convection-Permitting Ensemble Products for Surface Water Flood
Forecasting: Rationale and First Results, J. Hydrometeorol.,
17, 1383–1406, https://doi.org/10.1175/JHM-D-15-0083.1, 2016.
Griffiths, D. V., Huang, J., and deWolfe, G. F.: Numerical and analytical
observations on long and infinite slopes, Int. J. Numer. Anal. Methods
Geomech., 35, 569–585, https://doi.org/10.1002/nag.909, 2011.
Greco, R. and Pagano, L.: Basic features of the predictive tools of early
warning systems for water-related natural hazards: examples for shallow landslides,
Nat. Hazards Earth Syst. Sci., 17, 2213–2227, https://doi.org/10.5194/nhess-17-2213-2017, 2017.
Guzzetti, F., Peruccacci, S., Rossi, M., and Stark, C. P.: Rainfall
thresholds for the initiation of landslides in central and southern Europe,
Meteorol. Atmos. Phys., 98, 239–267,
https://doi.org/10.1007/s00703-007-0262-7, 2007.
Haneberg, W. C.: A rational probabilistic method for spatially distributed
landslide hazard assessment, Environ. Eng. Geosci.,
10, 27–43, https://doi.org/10.2113/10.1.27, 2004.
Hapuarachchi, H. A. P., Wang, Q. J., and Pagano, T. C.: A review of advances
in flash flood forecasting, Hydrol. Proc., 25, 2771–2784,
https://doi.org/10.1002/hyp.8040, 2011.
Hesse, R.: Rhenodanubian Flyschzone, Bavarian Alps, in Geological Field
Trips in Central Western Europe: Fragile Earth International Conference,
Munich, September 2011, edited by: Carena, S., Friedrich, A. M., and Lammerer, B., Geol. Soc. Am., Boulder, USA, 51–73, 2011.
Ikeda, K., Steiner, M., Pinto, J., and Alexander, C.: Evaluation of
Cold-Season Precipitation Forecasts Generated by the Hourly Updating
High-Resolution Rapid Refresh Model, Weather Forecast., 28,
921–939, https://doi.org/10.1175/WAF-D-12-00085.1, 2013.
Intrieri, E., Gigli, G., Casagli, N., and Nadim, F.: Brief communication “Landslide Early Warning System:
toolbox and general concepts”, Nat. Hazards Earth Syst. Sci., 13, 85–90,
https://doi.org/10.5194/nhess-13-85-2013, 2013.
Iverson, R. M.: Landslide triggering by rain infiltration, Water Resour. Res., 36, 1897–1910, https://doi.org/10.1029/2000WR900090, 2000.
Klimeš, J., Stemberk, J., Blahut, J., Krejčí, V.,
Krejčí, O., Hartvich, F., and Kycl, P.: Challenges for landslide
hazard and risk management in “low-risk” regions, Czech
Republic–landslide occurrences and related costs (IPL project no. 197),
Landslides, 14, 771–780, https://doi.org/10.1007/s10346-017-0798-7, 2017.
Krzysztofowicz, R.: The case for probabilistic forecasting in hydrology,
J. Hydrol., 249, https://doi.org/10.1016/S0022-1694(01)00420-6,
2001.
Kuriakose, S. L., van Beek, L. P. H., and van Westen, C. J.: Parameterizing a
physically based shallow landslide model in a data poor region, Earth
Surf. Proc. Land., 34, 867–881, https://doi.org/10.1002/esp.1794,
2009.
Lari, S., Frattini, P., and Crosta, G. B.: A probabilistic approach for
landslide hazard analysis, Eng. Geol., 182, 3–14,
https://doi.org/10.1016/j.enggeo.2014.07.015, 2014.
Lee, S. and Oh, H.-J.: Ensemble-Based Landslide Susceptibility Maps in Jinbu
Area, Korea, in Terrigenous Mass Movements, edited by: Pradhan, B. and Buchroithner, M.,
Springer Berlin Heidelberg, 193–220, 2012.
Lee, J. H. and Park, H. J.: Assessment of shallow landslide susceptibility
using the transient infiltration flow model and GIS-based probabilistic
approach, Landslides, 13, 885–903, https://doi.org/10.1007/s10346-015-0646-6, 2016.
Liao, Z., Hong, Y., Kirschbaum, D., Adler, R. F., Gourley, J. J., and Wooten,
R.: Evaluation of TRIGRS (transient rainfall infiltration and grid-based
regional slope-stability analysis)'s predictive skill for
hurricane-triggered landslides: a case study in Macon County, North
Carolina, Natural Hazards, 58, 325–339, https://doi.org/10.1007/s11069-010-9670-y,
2011.
Liu, Y., Weerts, A. H., Clark, M., Hendricks Franssen, H.-J., Kumar, S., Moradkhani,
H., Seo, D.-J., Schwanenberg, D., Smith, P., van Dijk, A. I. J. M., van Velzen, N., He,
M., Lee, H., Noh, S. J., Rakovec, O., and Restrepo, P.: Advancing data assimilation in
operational hydrologic forecasting: progresses, challenges, and emerging opportunities,
Hydrol. Earth Syst. Sci., 16, 3863–3887, https://doi.org/10.5194/hess-16-3863-2012, 2012.
Martelloni, G., Segoni, S., Fanti, R., and Catani, F.: Rainfall thresholds
for the forecasting of landslide occurrence at regional scale, Landslides,
9, 485–495, https://doi.org/10.1007/s10346-011-0308-2, 2012.
Melillo, M., Brunetti, M. T., Peruccacci, S., Gariano, S. L., and Guzzetti,
F.: Rainfall thresholds for the possible landslide occurrence in Sicily
(Southern Italy) based on the automatic reconstruction of rainfall events,
Landslides, 13, 165–172, https://doi.org/10.1007/s10346-015-0630-1, 2016.
Melchiorre, C. and Frattini, P.: Modelling probability of rainfall-induced
shallow landslides in a changing climate, Otta, Central Norway, Clim.
Change, 113, 413–436, https://doi.org/10.1007/s10584-011-0325-0, 2012.
Mergili, M., Marchesini, I., Alvioli, M., Metz, M., Schneider-Muntau, B., Rossi, M.,
and Guzzetti, F.: A strategy for GIS-based 3-D slope stability modelling over large
areas, Geosci. Model Dev., 7, 2969–2982, https://doi.org/10.5194/gmd-7-2969-2014, 2014a.
Mergili, M., Marchesini, I., Rossi, M., Guzzetti, F., and Fellin, W.:
Spatially distributed three-dimensional slope stability modelling in a
raster GIS, Geomorphology, 206, 178–195,
https://doi.org/10.1016/j.geomorph.2013.10.008, 2014b.
Milledge, D. G., Griffiths, D. V., Lane, S. N., and Warburton, J.: Limits on
the validity of infinite length assumptions for modelling shallow
landslides: testing the infinite length assumption in landslide models,
Earth Surf. Process. Land., 37, 1158–1166, https://doi.org/10.1002/esp.3235, 2012.
Mulligan, R. and Take, A.: Momentum transfer during landslide tsunami wave
generation, 19, 11065, available at: http://adsabs.harvard.edu/abs/2017EGUGA..1911065M
last access: 25 May 2018, 2017.
Neves Seefelder, C., Koide, S., and Mergili, M.: Does parameterization
influence the performance of slope stability model results? A case study in
Rio de Janeiro, Brazil, Landslides, https://doi.org/10.1007/s10346-016-0783-6, 2016.
Oreskes, N., Shrader-Frechette, K., Belitz, K.: Verification,
validation, and confirmation of numerical models in the earth sciences,
Science, 263, 641–646, https://doi.org/10.1126/science.263.5147.641, 1994.
Pagano, T. C., Wood, A. W., Ramos, M.-H., Cloke, H. L., Pappenberger, F.,
Clark, M. P., Cranston, M., Kavetski, D., Mathevet, T., Sorooshian, S., and
Verkade, J. S.: Challenges of Operational River Forecasting, J. Hydrometeorol., 15, 1692–1707, https://doi.org/10.1175/JHM-D-13-0188.1, 2014.
Papathoma-Köhle, M. and Glade, T.: The role of vegetation cover change for
landslide hazard and risk, edited by: Renaud, G., Sudmeier-Rieux, K., and Estrella, M.:
The Role of Ecosystems in Disaster Risk Reduction, UNU-Press,
Tokyo, 293–320, 2013.
Park, H. J., Lee, J. H., and Woo, I.: Assessment of rainfall-induced shallow
landslide susceptibility using a GIS-based probabilistic approach,
Eng. Geol., 161, 1–15, https://doi.org/10.1016/j.enggeo.2013.04.011, 2013.
Pebesma, E. J. and Bivand, R. S.: Classes and methods for spatial data in R,
R News, 5, 9–13, 2005.
Peres, D. J., Cancelliere, A., Greco, R., and Bogaard, T. A.: Influence of uncertain
identification of triggering rainfall on the assessment of landslide early
warning thresholds, Nat. Hazards Earth Syst. Sci., 18, 633–646, https://doi.org/10.5194/nhess-18-633-2018, 2018.
Petschko, H., Brenning, A., Bell, R., Goetz, J., and Glade, T.: Assessing the quality of
landslide susceptibility maps – case study Lower Austria,
Nat. Hazards Earth Syst. Sci., 14, 95–118, https://doi.org/10.5194/nhess-14-95-2014, 2014.
Petschko, H., Bell, R., and Glade, T.: Effectiveness of visually analyzing
LiDAR DTM derivatives for earth and debris slide inventory mapping for
statistical susceptibility modeling, Landslides, 13, 857–872, https://doi.org/10.1007/s10346-015-0622-1, 2015.
Piciullo, L., Calvello, M., and Cepeda, J. M.: Territorial early warning
systems for rainfall-induced landslides, Earth-Sci. Rev., 179, 228–247,
https://doi.org/10.1016/j.earscirev.2018.02.013, 2018.
Piciullo, L., Gariano, S. L., Melillo, M., Brunetti, M. T., Peruccacci, S.,
Guzzetti, F., and Calvello, M.: Definition and performance of a
threshold-based regional early warning model for rainfall-induced
landslides, Landslides, 14, 995–1008, https://doi.org/10.1007/s10346-016-0750-2,
2017.
Pradhan, A. M. S., Kang, H.-S., Lee, J.-S., and Kim, Y.-T.: An ensemble
landslide hazard model incorporating rainfall threshold for Mt. Umyeon,
South Korea, Bull. Eng. Geol. Environ., 1–16,
https://doi.org/10.1007/s10064-017-1055-y, 2017.
R Core Team: R: A Language and Environment for Statistical Computing, R
Foundation for Statistical Computing, Vienna, Austria, available at:
https://www.R-project.org, last access: 30 November 2017.
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.
Reichenbach, P., Cardinali, M., De Vita, P., and Guzzetti, F.: Regional
hydrological thresholds for landslides and floods in the Tiber River Basin
(central Italy), Environ. Geol., 35, 146–159,
https://doi.org/10.1007/s002540050301, 1998.
Reichle, R. H.: Data assimilation methods in the Earth sciences, Adv. Water Res., 31, 1411–1418, https://doi.org/10.1016/j.advwatres.2008.01.001,
2008.
Richwien, W. and Lesny, K.: Bodenmechanisches Praktikum, Auswahl und
Anwendung von bodenmechanischen Laborversuchen, 11 Edn., Verlag Gluckauf
GmbH, Essen,2004.
Rossi, M., Peruccacci, S., Brunetti, M. T., Marchesini, I., Luciani, S.,
Ardizzone, F., Balducci, V., Bianchi, C., Cardinali, M., Fiorucci, F.,
Mondini, A. C., Reichenbach, P., Salvati, P., Santangelo, M. A., Bartolini,
D., Gariano, S. L., Palladino, M., Vessia, G., Viero, A., Antronico, L.,
Borselli, L., Deganutti, A. M., Iovine, G., Luino, F., Parise, M., Polemio,
M., Guzzetti, F., Luciani, S., Fiorucci, F., Mondini, Santangelo, N., and
Tonellid, G.: SANF: National warning system for rainfall-induced landslides
in Italy, in: Proceedings of the 11th International & 2nd North American
Symposium on Landslides, Vol. 2, edited by: Eberhardt, E., Froese, C. R., Turner, A.
K., and Leroueil, S., 1895–1899, Taylor & Francis, London, 2012.
Rossi, G., Catani, F., Leoni, L., Segoni, S., and Tofani, V.: HIRESSS: a physically based
slope stability simulator for HPC applications, Nat. Hazards Earth Syst.
Sci., 13, 151–166, https://doi.org/10.5194/nhess-13-151-2013, 2013.
Rubio, E., Hall, J. W., and Anderson, M. G.: Uncertainty analysis in a slope
hydrology and stability model using probabilistic and imprecise information,
Comput. Geotech., 31, 529–536, https://doi.org/10.1016/j.compgeo.2004.09.002, 2004.
Salciarini, D., Fanelli, G., and Tamagnini, C.: A probabilistic model for
rainfall – induced shallow landslide prediction at the regional scale,
Landslides, https://doi.org/10.1007/s10346-017-0812-0, 2017.
Schaake, J. C., Hamill, T. M., Buizza, R., and Clark, M.: HEPEX: The
Hydrological Ensemble Prediction Experiment, Bull. Am. Meteorol. Soc., 88, 1541–1547, https://doi.org/10.1175/BAMS-88-10-1541,
2007.
Schmidt, J., Turek, G., Clark, M. P., Uddstrom, M., and Dymond, J. R.: Probabilistic forecasting of shallow,
rainfall-triggered landslides using real-time numerical weather predictions,
Nat. Hazards Earth Syst. Sci., 8, 349–357, https://doi.org/10.5194/nhess-8-349-2008, 2008.
Schweigl, J. and Hervás, J.: Landslide mapping in Austria, European
Commission Joint Research Centre, Institute for Environment and
Sustainability, Italy, available at:
https://esdac.jrc.ec.europa.eu/ESDB_Archive/eusoils_docs/Images/EUR23785EN.pdf, last access: 30
November 2017, 2009.
Schwenk, H.: Massenbewegungen in Niederösterreich 1953–1990, Jahrbuch
der Geologischen Bundesanstalt, 135, 597–660, 1992.
Segoni, S., Piciullo, L., and Gariano, S. L.: A review of the recent
literature on rainfall thresholds for landslide occurrence, Landslides,
15, 1483–1501, https://doi.org/10.1007/s10346-018-0966-4, 2018a.
Segoni, S., Rosi, A., Lagomarsino, D., Fanti, R., and Casagli, N.: Brief communication:
Using averaged soil moisture estimates to improve the performances of a regional-scale
landslide early warning system, Nat. Hazards Earth Syst. Sci., 18, 807–812, https://doi.org/10.5194/nhess-18-807-2018, 2018b.
Seity, Y., Brousseau, P., Malardel, S., Hello, G., Bénard, P., Bouttier,
F., Lac, C., and Masson, V.: The AROME-France Convective-Scale Operational
Model, Mon. Weather Rev., 139, 976–991, https://doi.org/10.1175/2010MWR3425.1,
2011.
Seyfried, M. S. and Wilcox, B. P.: Scale and the Nature of Spatial
Variability: Field Examples Having Implications for Hydrologic Modeling,
Water Resour. Res., 31, 173–184, https://doi.org/10.1029/94WR02025, 1995.
Shi, H., Li, T., Liu, R., Chen, J., Li, J., Zhang, A., and Wang, G.: A
service-oriented architecture for ensemble flood forecast from numerical
weather prediction, J. Hydrol., 527, 933–942,
https://doi.org/10.1016/j.jhydrol.2015.05.056, 2015.
Shute, J., Carriere, L., Duffy, D., Hoy, E., Peters, J., Shen, Y., and
Kirschbaum, D.: The Benefits and Complexities of Operating Geographic
Information Systems (GIS) in a High Performance Computing (HPC) Environment,
AGU Fall Meet. Abstr., 31, available at: http://adsabs.harvard.edu/abs/2017AGUFMIN31B0072S (last access: 25 May 2018),
2017.
Siccardi, F., Boni, G., Ferraris, L., and Rudari, R.: A hydrometeorological
approach for probabilistic flood forecast, J. Geophys. Res.-Atmos.,
110, D05101, https://doi.org/10.1029/2004JD005314, 2005.
Sing, T., Sander, O., Beerenwinkel, N., and Lengauer, T.: ROCR: visualizing
classifier performance in R, Bioinformatics, 21, 7881,
https://doi.org/10.1093/bioinformatics/bti623, 2005.
Smoltczyk, U.: Grundbau-Taschenbuch, Teil 1: Geotechnische Grundlagen,
6 Edn., Ernst & Sohn, Berlin, 2001.
Song, Y., Huang, D., and Zeng, B.: GPU-based parallel computation for
discontinuous deformation analysis (DDA) method and its application to
modelling earthquake-induced landslide, Comput. Geotech., 86, 80–94,
https://doi.org/10.1016/j.compgeo.2017.01.001, 2017.
Stensrud, D. J., Wicker, L. J., Kelleher, K. E., Xue, M., Foster, M. P.,
Schaefer, J. T., Schneider, R. S., Benjamin, S. G., Weygandt, S. S., Ferree,
J. T., and Tuell, J. P.: Convective-Scale Warn-on-Forecast System: A Vision
for 2020, Bull. Am. Meteorol. Soc., 90,
1487–1499, https://doi.org/10.1175/2009BAMS2795.1, 2009.
Thiebes, B.: Landslide Analysis and Early Warning Systems. Local and
Regional Case Study in the Swabian Alb, Germany, Springer Berlin Heidelberg,
Germany, 2012.
Thiebes, B., Bell, R., Glade, T., Jäger, S., Mayer, J., Anderson, M., and
Holcombe, L.: Integration of a limit-equilibrium model into a landslide
early warning system, Landslides, 11, 859–875,
https://doi.org/10.1007/s10346-013-0416-2, 2014.
Thiebes, B. and Glade, T.: Landslide Early Warning Systems–fundamental
concepts and innovative applications, in Landslides and Engineered Slopes.
Experience, Theory and Practice – Proceedings of the 12th International
Symposium on Landslides, 12–19 June 2016, edited by: Aversa, S., Cascini,
L., Picarelli, L., and Scavia, C., CRC Press, Napoli, 1903–1911, 2016.
Thiebes, B., Bai, S., Xi, Y., Glade, T., and Bell, R.: Combining landslide
susceptibility maps and rainfall thresholds using a matrix approach, 17,
https://doi.org/10.21094/rg.2017.003, 2017.
Thielen, J., Bartholmes, J., Ramos, M.-H., and de Roo, A.: The European Flood Alert
System – Part 1: Concept and development, Hydrol. Earth Syst. Sci., 13, 125–140,
https://doi.org/10.5194/hess-13-125-2009, 2009.
Tofani, V., Bicocchi, G., Rossi, G., Segoni, S., D'Ambrosio, M., Casagli, N.,
and Catani, F.: Soil characterization for shallow landslides modeling: a
case study in the Northern Apennines (Central Italy), Landslides, 14,
755–770, https://doi.org/10.1007/s10346-017-0809-8, 2017.
Tran, T. V., Alvioli, M., Lee, G., and An, H. U.: Three-dimensional,
time-dependent modeling of rainfall-induced landslides over a digital
landscape: a case study, Landslides, 15, 1071–1084,
https://doi.org/10.1007/s10346-017-0931-7, 2018.
Turke, H.: Statik im Erdbau, 3 Edn., Ernst & Sohn, Berlin, 1999.
UNEP: Early Warning Systems: A State of the Art Analysis and Future
Directions, Division of Early Warning and Assessment (DEWA), United Nations
Environment Programme (UNEP), Nairobi, Kenia, available at: http://na.unep.net/siouxfalls/publications/early_warning.pdf,
last access: 30 November 2017, 63 pp., 2012.
Vincendon, B., Ducrocq, V., Nuissier, O., and Vié, B.: Perturbation of
convection-permitting NWP forecasts for flash-flood ensemble forecasting, Nat.
Hazards Earth Syst. Sci., 11, 1529–1544, https://doi.org/10.5194/nhess-11-1529-2011, 2011.
van Asch, T. W., Buma, J., and van Beek, L. P. H.: A view on some
hydrological triggering systems in landslides, Geomorphology, 30, 25–32,
https://doi.org/10.1016/S0169-555X(99)00042-2, 1999.
van der Walt, S., Colbert, S. C., and Varoquaux, G.: The NumPy array: a
structure for efficient numerical computation, Comput. Sci. Eng., 13, 22–30, https://doi.org/10.1109/MCSE.2011.37, 2011.
van Westen, C. J., van Asch, T., and Soeters, R.: Landslide hazard and risk
zonation – why is it still so difficult?, Bull. Eng. Geol. Environ., 65, 167–184, https://doi.org/10.1007/s10064-005-0023-0, 2006.
van Westen, C. J., Castellanos, E., and Kuriakose, S. L.: Spatial data for
landslide susceptibility, hazard, and vulnerability assessment: An overview,
Eng. Geol., 102, 112–131, https://doi.org/10.1016/j.enggeo.2008.03.010,
2008.
Wang, Y., Zhao, T., and Cao, Z.: Site-specific probability distribution of
geotechnical properties, Comput. Geotech., 70, 159–168,
https://doi.org/10.1016/j.compgeo.2015.08.002, 2015.
Wieczorek, G. F. and Glade, T.: Climatic factors influencing occurrence of
debris flows, in Debris-flow Hazards and Related Phenomena, edited by: Jakob, M. and Hungr, O., Springer Berlin Heidelberg, 325–362, 2005.
WMO: Guidelines on Ensemble Prediction Systems and Forecasting, World
Meteorological Organization, WMO-No. 1091, Geneva, 23 pp., available at:
http://www.wmo.int/pages/prog/www/Documents/1091_en.pdf, (last
access: 30 November 2017), 2012.
Xie, M., Esaki, T., Zhou, G., and Mitani, Y.: Three-dimensional stability
evaluation of landslides and a sliding process simulation using a new
geographic information systems component, Environ. Geol., 43, 503–512,
https://doi.org/10.1007/s00254-002-0655-3, 2003.
Xie, M., Esaki, T., and Zhou, G.: GIS-Based Probabilistic Mapping of
Landslide Hazard Using a Three-Dimensional Deterministic Model, Nat.
Hazards, 33, 265–282, https://doi.org/10.1023/B:NHAZ.0000037036.01850.0d, 2004.
Xie, M., Esaki, T., Qiu, C., and Wang, C.: Geographical information
system-based computational implementation and application of spatial
three-dimensional slope stability analysis, Comput. Geotech., 33,
260–274, https://doi.org/10.1016/j.compgeo.2006.07.003, 2006.
Yu, W., Nakakita, E., Kim, S., and Yamaguchi, K.: Improvement of rainfall and
flood forecasts by blending ensemble NWP rainfall with radar prediction
considering orographic rainfall, J. Hydrol., 531, 494–507,
https://doi.org/10.1016/j.jhydrol.2015.04.055, 2015.
Zhang, S., Zhao, L., Delgado-Tellez, R., and Bao, H.: A physics-based probabilistic
forecasting model for rainfall-induced shallow landslides at regional scale, Nat.
Hazards Earth Syst. Sci., 18, 969–982, https://doi.org/10.5194/nhess-18-969-2018, 2018.
Zieher, T., Rutzinger, M., Schneider-Muntau, B., Perzl, F., Leidinger, D., Formayer, H.,
and Geitner, C.: Sensitivity analysis and calibration of a dynamic physically based
slope stability model, Nat. Hazards Earth Syst. Sci., 17, 971–992, https://doi.org/10.5194/nhess-17-971-2017, 2017.
Short summary
Regional-scale landslide forecasting traditionally strongly relies on empirical approaches and landslide-triggering rainfall thresholds. Today, probabilistic methods utilizing ensemble predictions are frequently used for flood forecasting. In our study, we specify how such an approach could also be applied for landslide forecasts and for operational landslide forecasting and early warning systems. To this end, we implemented a physically based landslide model in a probabilistic framework.
Regional-scale landslide forecasting traditionally strongly relies on empirical approaches and...
Altmetrics
Final-revised paper
Preprint