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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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
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
Characteristics of debris flows recorded in the Shenmu area of central Taiwan between 2004 and 2021
Optimization strategy for flexible barrier structures: Investigation and back analysis of a rockfall disaster case in southwestern China
Semi-automatic mapping of shallow landslides using free Sentinel-2 images and Google Earth Engine
The role of thermokarst evolution in debris flow initiation (Hüttekar Rock Glacier, Austrian Alps)
Accounting for the effect of forest and fragmentation in probabilistic rockfall hazard
Comprehensive landslide susceptibility map of Central Asia
The influence of large woody debris on post-wildfire debris flow sediment storage
Statistical modeling of sediment supply in torrent catchments of the northern French Alps
A data-driven evaluation of post-fire landslide susceptibility
Deciphering seasonal effects of triggering and preparatory precipitation for improved shallow landslide prediction using generalized additive mixed models
Brief communication: The northwest Himalaya towns slipping towards potential disaster
Space-time landslide hazard modeling via Ensemble Neural Networks
Dynamic response and breakage of trees subject to a landslide-induced air blast
Debris-flow surges of a very active alpine torrent: a field database
Rainfall thresholds estimation for shallow landslides in Peru from gridded daily data
Instantaneous limit equilibrium back analyses of major rockslides triggered during the 2016–2017 central Italy seismic sequence
Deadly disasters in southeastern South America: flash floods and landslides of February 2022 in Petrópolis, Rio de Janeiro
Multi-event assessment of typhoon-triggered landslide susceptibility in the Philippines
Antecedent rainfall as a critical factor for the triggering of debris flows in arid regions
Sensitivity analysis of a built environment exposed to the synthetic monophasic viscous debris flow impacts with 3-D numerical simulations
Simulation analysis of 3D stability of a landslide with a locking segment: A case study of Tizicao landslide in Maoxian County, Southwest China
Characteristics and causes of natural and human-induced landslides in a tropical mountainous region: the rift flank west of Lake Kivu (Democratic Republic of the Congo)
Spatio-temporal analysis of slope-type debris flow activity in Horlachtal, Austria, based on orthophotos and lidar data since 1947
Assessing the relationship between weather conditions and rockfall using terrestrial laser scanning to improve risk management
Using principal component analysis to incorporate multi-layer soil moisture information in hydrometeorological thresholds for landslide prediction: an investigation based on ERA5-Land reanalysis data
Assessing uncertainties in landslide susceptibility predictions in a changing environment (Styrian Basin, Austria)
Numerical model derived intensity-duration thresholds for early warning of rainfall-induced debris flows in the Himalayas
Brief communication: An autonomous UAV for catchment-wide monitoring of a debris flow torrent
How volcanic stratigraphy constrains headscarp collapse scenarios: the Samperre cliff case study (Martinique island, Lesser Antilles)
Landslide susceptibility assessment in the rocky coast subsystem of Essaouira, Morocco
Landsifier v1.0: a Python library to estimate likely triggers of mapped landslides
Timing landslide and flash flood events from SAR satellite: a regionally applicable methodology illustrated in African cloud-covered tropical environments
Potential of satellite-derived hydro-meteorological information for landslide initiation thresholds in Rwanda
Earthquake-induced landslides in Haiti: analysis of seismotectonic and possible climatic influences
Pre-collapse motion of the February 2021 Chamoli rock–ice avalanche, Indian Himalaya
Physically based modeling of co-seismic landslide, debris flow, and flood cascade
Finite-hillslope analysis of landslides triggered by excess pore water pressure: the roles of atmospheric pressure and rainfall infiltration during typhoons
Estimating global landslide susceptibility and its uncertainty through ensemble modeling
Terrain visibility impact on the preparation of landslide inventories: a practical example in Darjeeling district (India)
Using Sentinel-1 radar amplitude time series to constrain the timings of individual landslides: a step towards understanding the controls on monsoon-triggered landsliding
Introducing SlideforMAP: a probabilistic finite slope approach for modelling shallow-landslide probability in forested situations
Augmentation of WRF-Hydro to simulate overland-flow- and streamflow-generated debris flow susceptibility in burn scars
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.
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
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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
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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
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Landslides are often observed on coastlines following large earthquakes, but few studies have explored this occurrence. Here, statistical modelling of landslides triggered by the 2016 Kaikōura earthquake in New Zealand is used to investigate factors driving coastal earthquake-induced landslides. Geology, steep slopes, and shaking intensity are good predictors of landslides from the Kaikōura event. Steeper slopes close to the coast provide the best explanation for a high landslide density.
Yi-Min Huang
Nat. Hazards Earth Syst. Sci., 23, 2649–2662, https://doi.org/10.5194/nhess-23-2649-2023, https://doi.org/10.5194/nhess-23-2649-2023, 2023
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Debris flows are common hazards in Taiwan, and debris-flow early warning is important for disaster responses. The rainfall thresholds of debris flows are analyzed and determined in terms of rainfall intensity, accumulated rainfall, and rainfall duration, based on case histories in Taiwan. These thresholds are useful for disaster management, and the cases in Taiwan are useful for global debris-flow databases.
Li-Ru Luo, Zhi-Xiang Yu, Qi Wang, Li-Jun Zhang, Lin-Xu Liao, and Li Peng
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-136, https://doi.org/10.5194/nhess-2023-136, 2023
Revised manuscript accepted for NHESS
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Field investigations were done on a rockfall near Jiguanshan National Forest Park, Chengdu. Vital information was presumed from UAV survey. A FEM model, including the barrier and rocks, was created to reproduce the damage evolution. It was found that the impact kinetic energy was below the design protection energy. The improper member connections prevent the barrier from producing significant deformation to absorb energy. Damages are avoided by improving the nets’ and ropes’ ability to slide.
Davide Notti, Martina Cignetti, Danilo Godone, and Daniele Giordan
Nat. Hazards Earth Syst. Sci., 23, 2625–2648, https://doi.org/10.5194/nhess-23-2625-2023, https://doi.org/10.5194/nhess-23-2625-2023, 2023
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We developed a cost-effective and user-friendly approach to map shallow landslides using free satellite data. Our methodology involves analysing the pre- and post-event NDVI variation to semi-automatically detect areas potentially affected by shallow landslides (PLs). Additionally, we have created Google Earth Engine scripts to rapidly compute NDVI differences and time series of affected areas. Datasets and codes are stored in an open data repository for improvement by the scientific community.
Simon Seelig, Thomas Wagner, Karl Krainer, Michael Avian, Marc Olefs, Klaus Haslinger, and Gerfried Winkler
Nat. Hazards Earth Syst. Sci., 23, 2547–2568, https://doi.org/10.5194/nhess-23-2547-2023, https://doi.org/10.5194/nhess-23-2547-2023, 2023
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A rapid sequence of cascading events involving thermokarst lake outburst, rock glacier front failure, debris flow development, and river blockage hit an alpine valley in Austria during summer 2019. We analyze the environmental conditions initiating the process chain and identify the rapid evolution of a thermokarst channel network as the main driver. Our results highlight the need to account for permafrost degradation in debris flow hazard assessment studies.
Camilla Lanfranconi, Paolo Frattini, Gianluca Sala, Giuseppe Dattola, Davide Bertolo, Juanjuan Sun, and Giovanni Battista Crosta
Nat. Hazards Earth Syst. Sci., 23, 2349–2363, https://doi.org/10.5194/nhess-23-2349-2023, https://doi.org/10.5194/nhess-23-2349-2023, 2023
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This paper presents a study on rockfall dynamics and hazard, examining the impact of the presence of trees along slope and block fragmentation. We compared rockfall simulations that explicitly model the presence of trees and fragmentation with a classical approach that accounts for these phenomena in model parameters (both the hazard and the kinetic energy change). We also used a non-parametric probabilistic rockfall hazard analysis method for hazard mapping.
Ascanio Rosi, William Frodella, Nicola Nocentini, Francesco Caleca, Hans Balder Havenith, Alexander Strom, Mirzo Saidov, Gany Amirgalievich Bimurzaev, and Veronica Tofani
Nat. Hazards Earth Syst. Sci., 23, 2229–2250, https://doi.org/10.5194/nhess-23-2229-2023, https://doi.org/10.5194/nhess-23-2229-2023, 2023
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This work was carried out within the Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia (SFRARR) project and is focused on the first landslide susceptibility analysis at a regional scale for Central Asia. The most detailed available landslide inventories were implemented in a random forest model. The final aim was to provide a useful tool for reduction strategies to landslide scientists, practitioners, and administrators.
Francis K. Rengers, Luke A. McGuire, Katherine R. Barnhart, Ann M. Youberg, Daniel Cadol, Alexander N. Gorr, Olivia J. Hoch, Rebecca Beers, and Jason W. Kean
Nat. Hazards Earth Syst. Sci., 23, 2075–2088, https://doi.org/10.5194/nhess-23-2075-2023, https://doi.org/10.5194/nhess-23-2075-2023, 2023
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Debris flows often occur after wildfires. These debris flows move water, sediment, and wood. The wood can get stuck in channels, creating a dam that holds boulders, cobbles, sand, and muddy material. We investigated how the channel width and wood length influenced how much sediment is stored. We also used a series of equations to back calculate the debris flow speed using the breaking threshold of wood. These data will help improve models and provide insight into future field investigations.
Maxime Morel, Guillaume Piton, Damien Kuss, Guillaume Evin, and Caroline Le Bouteiller
Nat. Hazards Earth Syst. Sci., 23, 1769–1787, https://doi.org/10.5194/nhess-23-1769-2023, https://doi.org/10.5194/nhess-23-1769-2023, 2023
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In mountain catchments, damage during floods is generally primarily driven by the supply of a massive amount of sediment. Predicting how much sediment can be delivered by frequent and infrequent events is thus important in hazard studies. This paper uses data gathered during the maintenance operation of about 100 debris retention basins to build simple equations aiming at predicting sediment supply from simple parameters describing the upstream catchment.
Elsa S. Culler, Ben Livneh, Balaji Rajagopalan, and Kristy F. Tiampo
Nat. Hazards Earth Syst. Sci., 23, 1631–1652, https://doi.org/10.5194/nhess-23-1631-2023, https://doi.org/10.5194/nhess-23-1631-2023, 2023
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Landslides have often been observed in the aftermath of wildfires. This study explores regional patterns in the rainfall that caused landslides both after fires and in unburned locations. In general, landslides that occur after fires are triggered by less rainfall, confirming that fire helps to set the stage for landslides. However, there are regional differences in the ways in which fire impacts landslides, such as the size and direction of shifts in the seasonality of landslides after fires.
Stefan Steger, Mateo Moreno, Alice Crespi, Peter James Zellner, Stefano Luigi Gariano, Maria Teresa Brunetti, Massimo Melillo, Silvia Peruccacci, Francesco Marra, Robin Kohrs, Jason Goetz, Volkmar Mair, and Massimiliano Pittore
Nat. Hazards Earth Syst. Sci., 23, 1483–1506, https://doi.org/10.5194/nhess-23-1483-2023, https://doi.org/10.5194/nhess-23-1483-2023, 2023
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We present a novel data-driven modelling approach to determine season-specific critical precipitation conditions for landslide occurrence. It is shown that the amount of precipitation required to trigger a landslide in South Tyrol varies from season to season. In summer, a higher amount of preparatory precipitation is required to trigger a landslide, probably due to denser vegetation and higher temperatures. We derive dynamic thresholds that directly relate to hit rates and false-alarm rates.
Yaspal Sundriyal, Vipin Kumar, Neha Chauhan, Sameeksha Kaushik, Rahul Ranjan, and Mohit Kumar Punia
Nat. Hazards Earth Syst. Sci., 23, 1425–1431, https://doi.org/10.5194/nhess-23-1425-2023, https://doi.org/10.5194/nhess-23-1425-2023, 2023
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The NW Himalaya has been one of the most affected terrains of the Himalaya, subject to disastrous landslides. This article focuses on two towns (Joshimath and Bhatwari) of the NW Himalaya, which have been witnessing subsidence for decades. We used a slope stability simulation to determine the response of the hillslopes accommodating these towns under various loading conditions. We found that the maximum displacement in these hillslopes might reach up to 20–25 m.
Ashok Dahal, Hakan Tanyas, Cees van Westen, Mark van der Meijde, Paul Martin Mai, Raphaël Huser, and Luigi Lombardo
EGUsphere, https://doi.org/10.31223/X5B075, https://doi.org/10.31223/X5B075, 2023
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We propose a modelling 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 both in space and in time via an Ensemble Neural Network architecture.
Yu Zhuang, Aiguo Xing, Perry Bartelt, Muhammad Bilal, and Zhaowei Ding
Nat. Hazards Earth Syst. Sci., 23, 1257–1266, https://doi.org/10.5194/nhess-23-1257-2023, https://doi.org/10.5194/nhess-23-1257-2023, 2023
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Tree destruction is often used to back calculate the air blast impact region and to estimate the air blast power. Here we established a novel model to assess air blast power using tree destruction information. We find that the dynamic magnification effect makes the trees easier to damage by a landslide-induced air blast, but the large tree deformation would weaken the effect. Bending and overturning are two likely failure modes, which depend heavily on the properties of trees.
Suzanne Lapillonne, Firmin Fontaine, Frédéric Liebault, Vincent Richefeu, and Guillaume Piton
Nat. Hazards Earth Syst. Sci., 23, 1241–1256, https://doi.org/10.5194/nhess-23-1241-2023, https://doi.org/10.5194/nhess-23-1241-2023, 2023
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Debris flows are fast flows most often found in torrential watersheds. They are composed of two phases: a liquid phase which can be mud-like and a granular phase, including large boulders, transported along with the flow. Due to their destructive nature, accessing features of the flow, such as velocity and flow height, is difficult. We present a protocol to analyse debris flow data and results of the Réal torrent in France. These results will help experts in designing models.
Carlos Millán-Arancibia and Waldo Lavado-Casimiro
Nat. Hazards Earth Syst. Sci., 23, 1191–1206, https://doi.org/10.5194/nhess-23-1191-2023, https://doi.org/10.5194/nhess-23-1191-2023, 2023
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This study is the first approximation of regional rainfall thresholds for shallow landslide occurrence in Peru. This research was generated from a gridded precipitation data and landslide inventory. The analysis showed that the threshold based on the combination of mean daily intensity–duration variables gives the best results for separating rainfall events that generate landslides. Through this work the potential of thresholds for landslide monitoring at the regional scale is demonstrated.
Luca Verrucci, Giovanni Forte, Melania De Falco, Paolo Tommasi, Giuseppe Lanzo, Kevin W. Franke, and Antonio Santo
Nat. Hazards Earth Syst. Sci., 23, 1177–1190, https://doi.org/10.5194/nhess-23-1177-2023, https://doi.org/10.5194/nhess-23-1177-2023, 2023
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Stability analyses in static and seismic conditions were performed on four rockslides that occurred during the main shocks of the 2016–2017 central Italy seismic sequence. These results also indicate that specific structural features of the slope must carefully be accounted for in evaluating potential hazards on transportation infrastructures in mountainous regions.
Enner Alcântara, José A. Marengo, José Mantovani, Luciana R. Londe, Rachel Lau Yu San, Edward Park, Yunung Nina Lin, Jingyu Wang, Tatiana Mendes, Ana Paula Cunha, Luana Pampuch, Marcelo Seluchi, Silvio Simões, Luz Adriana Cuartas, Demerval Goncalves, Klécia Massi, Regina Alvalá, Osvaldo Moraes, Carlos Souza Filho, Rodolfo Mendes, and Carlos Nobre
Nat. Hazards Earth Syst. Sci., 23, 1157–1175, https://doi.org/10.5194/nhess-23-1157-2023, https://doi.org/10.5194/nhess-23-1157-2023, 2023
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The municipality of Petrópolis (approximately 305 687 inhabitants) is nestled in the mountains 68 km outside the city of Rio de Janeiro. On 15 February 2022, the city of Petrópolis in Rio de Janeiro, Brazil, received an unusually high volume of rain within 3 h (258 mm). This resulted in flash floods and subsequent landslides that caused 231 fatalities, the deadliest landslide disaster recorded in Petrópolis. This work shows how the disaster was triggered.
Joshua N. Jones, Georgina L. Bennett, Claudia Abancó, Mark A. M. Matera, and Fibor J. Tan
Nat. Hazards Earth Syst. Sci., 23, 1095–1115, https://doi.org/10.5194/nhess-23-1095-2023, https://doi.org/10.5194/nhess-23-1095-2023, 2023
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We modelled where landslides occur in the Philippines using landslide data from three typhoon events in 2009, 2018, and 2019. These models show where landslides occurred within the landscape. By comparing the different models, we found that the 2019 landslides were occurring all across the landscape, whereas the 2009 and 2018 landslides were mostly occurring at specific slope angles and aspects. This shows that landslide susceptibility must be considered variable through space and time.
Shalev Siman-Tov and Francesco Marra
Nat. Hazards Earth Syst. Sci., 23, 1079–1093, https://doi.org/10.5194/nhess-23-1079-2023, https://doi.org/10.5194/nhess-23-1079-2023, 2023
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Debris flows represent a threat to infrastructure and the population. In arid areas, they are observed when heavy rainfall hits steep slopes with sediments. Here, we use digital surface models and radar rainfall data to detect and characterize the triggering and non-triggering rainfall conditions. We find that rainfall intensity alone is insufficient to explain the triggering. We suggest that antecedent rainfall could represent a critical factor for debris flow triggering in arid regions.
Xun Huang, Zhijian Zhang, and Guoping Xiang
Nat. Hazards Earth Syst. Sci., 23, 871–889, https://doi.org/10.5194/nhess-23-871-2023, https://doi.org/10.5194/nhess-23-871-2023, 2023
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A sensitivity analysis on the building impact force resulting from the representative built environment parameters is executed through the FLOW-3D model. The surrounding buildings' properties, especially the azimuthal angle, have been confirmed to play significant roles in determining the peak impact forces. The single and combined effects of built environments are analyzed in detail. This will improve understanding of vulnerability assessment and migration design against debris flow hazards.
Yuntao Zhou, Xiaoyan Zhao, Guangze Zhang, Bernd Wünnemann, Jiajia Zhang, and Minghui Meng
EGUsphere, https://doi.org/10.5194/egusphere-2023-28, https://doi.org/10.5194/egusphere-2023-28, 2023
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We developed three rock bridge models to analyze 3D stability and deformation behaviors of the Tizicao landslide. We found that the CSM-HSP combines the advantages of the IRMM model in simulating the actual deformation of slopes with rock bridges and the modeling advantage of the JM model. The research results are helpful to choose an appropriate rock bridge model to simulate the 3D landslide stability and to reveal the influence laws of rock bridges on the 3D stability of landslides.
Jean-Claude Maki Mateso, Charles L. Bielders, Elise Monsieurs, Arthur Depicker, Benoît Smets, Théophile Tambala, Luc Bagalwa Mateso, and Olivier Dewitte
Nat. Hazards Earth Syst. Sci., 23, 643–666, https://doi.org/10.5194/nhess-23-643-2023, https://doi.org/10.5194/nhess-23-643-2023, 2023
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This research highlights the importance of human activities on the occurrence of landslides and the need to consider this context when studying hillslope instability patterns in regions under anthropogenic pressure. Also, this study highlights the importance of considering the timing of landslides and hence the added value of using historical information for compiling an inventory.
Jakob Rom, Florian Haas, Tobias Heckmann, Moritz Altmann, Fabian Fleischer, Camillo Ressl, Sarah Betz-Nutz, and Michael Becht
Nat. Hazards Earth Syst. Sci., 23, 601–622, https://doi.org/10.5194/nhess-23-601-2023, https://doi.org/10.5194/nhess-23-601-2023, 2023
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In this study, an area-wide slope-type debris flow record has been established for Horlachtal, Austria, since 1947 based on historical and recent remote sensing data. Spatial and temporal analyses show variations in debris flow activity in space and time in a high-alpine region. The results can contribute to a better understanding of past slope-type debris flow dynamics in the context of extreme precipitation events and their possible future development.
Tom Birien and Francis Gauthier
Nat. Hazards Earth Syst. Sci., 23, 343–360, https://doi.org/10.5194/nhess-23-343-2023, https://doi.org/10.5194/nhess-23-343-2023, 2023
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On highly fractured rockwalls such as those found in northern Gaspésie, most rockfalls are triggered by weather conditions. This study highlights that in winter, rockfall frequency is 12 times higher during a superficial thaw than during a cold period in which temperature remains below 0 °C. In summer, rockfall frequency is 22 times higher during a heavy rainfall event than during a mainly dry period. This knowledge could be used to implement a risk management strategy.
Nunziarita Palazzolo, David J. Peres, Enrico Creaco, and Antonino Cancelliere
Nat. Hazards Earth Syst. Sci., 23, 279–291, https://doi.org/10.5194/nhess-23-279-2023, https://doi.org/10.5194/nhess-23-279-2023, 2023
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We propose an approach exploiting PCA to derive hydrometeorological landslide-triggering thresholds using multi-layered soil moisture data from ERA5-Land reanalysis. Comparison of thresholds based on single- and multi-layered soil moisture information provides a means to identify the significance of multi-layered data for landslide triggering in a region. In Sicily, the proposed approach yields thresholds with a higher performance than traditional precipitation-based ones (TSS = 0.71 vs. 0.50).
Raphael Knevels, Helene Petschko, Herwig Proske, Philip Leopold, Aditya N. Mishra, Douglas Maraun, and Alexander Brenning
Nat. Hazards Earth Syst. Sci., 23, 205–229, https://doi.org/10.5194/nhess-23-205-2023, https://doi.org/10.5194/nhess-23-205-2023, 2023
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In summer 2009 and 2014, rainfall events occurred in the Styrian Basin (Austria), triggering thousands of landslides. Landslide storylines help to show potential future changes under changing environmental conditions. The often neglected uncertainty quantification was the aim of this study. We found uncertainty arising from the landslide model to be of the same order as climate scenario uncertainty. Understanding the dimensions of uncertainty is crucial for allowing informed decision-making.
Srikrishnan Siva Subramanian, Piyush Srivastava, Ali Pulpadan Yunus, Tapas Ranjan Martha, and Sumit Sen
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2022-297, https://doi.org/10.5194/nhess-2022-297, 2023
Revised manuscript accepted for NHESS
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Rainfall intensity-duration (ID) thresholds can aid in the prediction of natural disasters. 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 model (WRF) with a regionally distributed numerical model for debris flows to analyse and predict intense rainfall-induced landslides in the Himalayas.
Fabian Walter, Elias Hodel, Erik S. Mannerfelt, Kristen Cook, Michael Dietze, Livia Estermann, Michaela Wenner, Daniel Farinotti, Martin Fengler, Lukas Hammerschmidt, Flavia Hänsli, Jacob Hirschberg, Brian McArdell, and Peter Molnar
Nat. Hazards Earth Syst. Sci., 22, 4011–4018, https://doi.org/10.5194/nhess-22-4011-2022, https://doi.org/10.5194/nhess-22-4011-2022, 2022
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Debris flows are dangerous sediment–water mixtures in steep terrain. Their formation takes place in poorly accessible terrain where instrumentation cannot be installed. Here we propose to monitor such source terrain with an autonomous drone for mapping sediments which were left behind by debris flows or may contribute to future events. Short flight intervals elucidate changes of such sediments, providing important information for landscape evolution and the likelihood of future debris flows.
Marc Peruzzetto, Yoann Legendre, Aude Nachbaur, Thomas J. B. Dewez, Yannick Thiery, Clara Levy, and Benoit Vittecoq
Nat. Hazards Earth Syst. Sci., 22, 3973–3992, https://doi.org/10.5194/nhess-22-3973-2022, https://doi.org/10.5194/nhess-22-3973-2022, 2022
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Volcanic edifices result from successive construction and dismantling phases. Thus, the geological units forming volcanoes display complex geometries. We show that such geometries can be reconstructed thanks to aerial views, topographic surveys and photogrammetric models. In our case study of the Samperre cliff (Martinique, Lesser Antilles), it allows us to link destabilizations from a rocky cliff to the existence of a filled paleo-valley and estimate a potentially unstable volume.
Abdellah Khouz, Jorge Trindade, Sérgio C. Oliveira, Fatima El Bchari, Blaid Bougadir, Ricardo A. C. Garcia, and Mourad Jadoud
Nat. Hazards Earth Syst. Sci., 22, 3793–3814, https://doi.org/10.5194/nhess-22-3793-2022, https://doi.org/10.5194/nhess-22-3793-2022, 2022
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The aim of this study was to assess the landslide susceptibility of the rocky coast of Essaouira using the information value model. The resulting susceptibility maps could be used for both environmental protection and general planning of future development activities.
Kamal Rana, Nishant Malik, and Ugur Ozturk
Nat. Hazards Earth Syst. Sci., 22, 3751–3764, https://doi.org/10.5194/nhess-22-3751-2022, https://doi.org/10.5194/nhess-22-3751-2022, 2022
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The landslide hazard models assist in mitigating losses due to landslides. However, these models depend on landslide databases, which often have missing triggering information, rendering these databases unusable for landslide hazard models. In this work, we developed a Python library, Landsifier, consisting of three different methods to identify the triggers of landslides. These methods can classify landslide triggers with high accuracy using only a landslide polygon shapefile as an input.
Axel A. J. Deijns, Olivier Dewitte, Wim Thiery, Nicolas d'Oreye, Jean-Philippe Malet, and François Kervyn
Nat. Hazards Earth Syst. Sci., 22, 3679–3700, https://doi.org/10.5194/nhess-22-3679-2022, https://doi.org/10.5194/nhess-22-3679-2022, 2022
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Landslides and flash floods are rainfall-induced processes that often co-occur and interact, generally very quickly. In mountainous cloud-covered environments, determining when these processes occur remains challenging. We propose a regional methodology using open-access satellite radar images that allow for the timing of landslide and flash floods events, in the contrasting landscapes of tropical Africa, with an accuracy of up to a few days. The methodology shows potential for transferability.
Judith Uwihirwe, Alessia Riveros, Hellen Wanjala, Jaap Schellekens, Frederiek Sperna Weiland, Markus Hrachowitz, and Thom A. Bogaard
Nat. Hazards Earth Syst. Sci., 22, 3641–3661, https://doi.org/10.5194/nhess-22-3641-2022, https://doi.org/10.5194/nhess-22-3641-2022, 2022
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This study compared gauge-based and satellite-based precipitation products. Similarly, satellite- and hydrological model-derived soil moisture was compared to in situ soil moisture and used in landslide hazard assessment and warning. The results reveal the cumulative 3 d rainfall from the NASA-GPM to be the most effective landslide trigger. The modelled antecedent soil moisture in the root zone was the most informative hydrological variable for landslide hazard assessment and warning in Rwanda.
Hans-Balder Havenith, Kelly Guerrier, Romy Schlögel, Anika Braun, Sophia Ulysse, Anne-Sophie Mreyen, Karl-Henry Victor, Newdeskarl Saint-Fleur, Léna Cauchie, Dominique Boisson, and Claude Prépetit
Nat. Hazards Earth Syst. Sci., 22, 3361–3384, https://doi.org/10.5194/nhess-22-3361-2022, https://doi.org/10.5194/nhess-22-3361-2022, 2022
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We present a new landslide inventory for the 2021, M 7.2, Haiti, earthquake. We compare characteristics of this inventory with those of the 2010 seismically induced landslides, highlighting the much larger total area of 2021 landslides. This fact could be related to the larger earthquake magnitude in 2021, to the more central location of the fault segment ruptured in 2021 with respect to coastal zones, and/or to possible climatic preconditioning of slope failures in the 2021 affected area.
Maximillian Van Wyk de Vries, Shashank Bhushan, Mylène Jacquemart, César Deschamps-Berger, Etienne Berthier, Simon Gascoin, David E. Shean, Dan H. Shugar, and Andreas Kääb
Nat. Hazards Earth Syst. Sci., 22, 3309–3327, https://doi.org/10.5194/nhess-22-3309-2022, https://doi.org/10.5194/nhess-22-3309-2022, 2022
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On 7 February 2021, a large rock–ice avalanche occurred in Chamoli, Indian Himalaya. The resulting debris flow swept down the nearby valley, leaving over 200 people dead or missing. We use a range of satellite datasets to investigate how the collapse area changed prior to collapse. We show that signs of instability were visible as early 5 years prior to collapse. However, it would likely not have been possible to predict the timing of the event from current satellite datasets.
Bastian van den Bout, Chenxiao Tang, Cees van Westen, and Victor Jetten
Nat. Hazards Earth Syst. Sci., 22, 3183–3209, https://doi.org/10.5194/nhess-22-3183-2022, https://doi.org/10.5194/nhess-22-3183-2022, 2022
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Natural hazards such as earthquakes, landslides, and flooding do not always occur as stand-alone events. After the 2008 Wenchuan earthquake, a co-seismic landslide blocked a stream in Hongchun. Two years later, a debris flow breached the material, blocked the Min River, and resulted in flooding of a small town. We developed a multi-process model that captures the full cascade. Despite input and process uncertainties, probability of flooding was high due to topography and trigger intensities.
Lucas Pelascini, Philippe Steer, Maxime Mouyen, and Laurent Longuevergne
Nat. Hazards Earth Syst. Sci., 22, 3125–3141, https://doi.org/10.5194/nhess-22-3125-2022, https://doi.org/10.5194/nhess-22-3125-2022, 2022
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Landslides represent a major natural hazard and are often triggered by typhoons. We present a new 2D model computing the respective role of rainfall infiltration, atmospheric depression and groundwater in slope stability during typhoons. The results show rainfall is the strongest factor of destabilisation. However, if the slope is fully saturated, near the toe of the slope or during the wet season, rainfall infiltration is limited and atmospheric pressure change can become the dominant factor.
Anne Felsberg, Jean Poesen, Michel Bechtold, Matthias Vanmaercke, and Gabriëlle J. M. De Lannoy
Nat. Hazards Earth Syst. Sci., 22, 3063–3082, https://doi.org/10.5194/nhess-22-3063-2022, https://doi.org/10.5194/nhess-22-3063-2022, 2022
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In this study we assessed global landslide susceptibility at the coarse 36 km spatial resolution of global satellite soil moisture observations to prepare for a subsequent combination of the two. Specifically, we focus therefore on the susceptibility of hydrologically triggered landslides. We introduce ensemble techniques, common in, for example, meteorology but not yet in the landslide community, to retrieve reliable estimates of the total prediction uncertainty.
Txomin Bornaetxea, Ivan Marchesini, Sumit Kumar, Rabisankar Karmakar, and Alessandro Mondini
Nat. Hazards Earth Syst. Sci., 22, 2929–2941, https://doi.org/10.5194/nhess-22-2929-2022, https://doi.org/10.5194/nhess-22-2929-2022, 2022
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One cannot know if there is a landslide or not in an area that one has not observed. This is an obvious statement, but when landslide inventories are obtained by field observation, this fact is seldom taken into account. Since fieldwork campaigns are often done following the roads, we present a methodology to estimate the visibility of the terrain from the roads, and we demonstrate that fieldwork-based inventories are underestimating landslide density in less visible areas.
Katy Burrows, Odin Marc, and Dominique Remy
Nat. Hazards Earth Syst. Sci., 22, 2637–2653, https://doi.org/10.5194/nhess-22-2637-2022, https://doi.org/10.5194/nhess-22-2637-2022, 2022
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The locations of triggered landslides following a rainfall event can be identified in optical satellite images. However cloud cover associated with the rainfall means that these images cannot be used to identify landslide timing. Timings of landslides triggered during long rainfall events are often unknown. Here we present methods of using Sentinel-1 satellite radar data, acquired every 12 d globally in all weather conditions, to better constrain the timings of rainfall-triggered landslides.
Feiko Bernard van Zadelhoff, Adel Albaba, Denis Cohen, Chris Phillips, Bettina Schaefli, Luuk Dorren, and Massimiliano Schwarz
Nat. Hazards Earth Syst. Sci., 22, 2611–2635, https://doi.org/10.5194/nhess-22-2611-2022, https://doi.org/10.5194/nhess-22-2611-2022, 2022
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Shallow landslides pose a risk to people, property and infrastructure. Assessment of this hazard and the impact of protective measures can reduce losses. We developed a model (SlideforMAP) that can assess the shallow-landslide risk on a regional scale for specific rainfall events. Trees are an effective and cheap protective measure on a regional scale. Our model can assess their hazard reduction down to the individual tree level.
Chuxuan Li, Alexander L. Handwerger, Jiali Wang, Wei Yu, Xiang Li, Noah J. Finnegan, Yingying Xie, Giuseppe Buscarnera, and Daniel E. Horton
Nat. Hazards Earth Syst. Sci., 22, 2317–2345, https://doi.org/10.5194/nhess-22-2317-2022, https://doi.org/10.5194/nhess-22-2317-2022, 2022
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In January 2021 a storm triggered numerous debris flows in a wildfire burn scar in California. We use a hydrologic model to assess debris flow susceptibility in pre-fire and postfire scenarios. Compared to pre-fire conditions, postfire conditions yield dramatic increases in peak water discharge, substantially increasing debris flow susceptibility. Our work highlights the hydrologic model's utility in investigating and potentially forecasting postfire debris flows at regional scales.
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...
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