Articles | Volume 19, issue 11
https://doi.org/10.5194/nhess-19-2477-2019
© Author(s) 2019. 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-19-2477-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A new approach to mapping landslide hazards: a probabilistic integration of empirical and physically based models in the North Cascades of Washington, USA
Ronda Strauch
CORRESPONDING AUTHOR
Seattle City Light, Seattle, WA, USA
Civil & Environmental Engineering, College of Engineering, University of Washington, Seattle, WA, USA
Erkan Istanbulluoglu
Civil & Environmental Engineering, College of Engineering, University of Washington, Seattle, WA, USA
Jon Riedel
National Park Service, US Department of the Interior, Sedro-Woolley, WA, USA
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Ronda Strauch, Erkan Istanbulluoglu, Sai Siddhartha Nudurupati, Christina Bandaragoda, Nicole M. Gasparini, and Gregory E. Tucker
Earth Surf. Dynam., 6, 49–75, https://doi.org/10.5194/esurf-6-49-2018, https://doi.org/10.5194/esurf-6-49-2018, 2018
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We develop a model of annual probability of shallow landslide initiation triggered by soil water from a hydrologic model. Our physically based model accommodates data uncertainty using a Monte Carlo approach. We found elevation-dependent patterns in probability related to the stabilizing effect of forests and soil and slope limitation at high elevations. We demonstrate our model in Washington, USA, but it is designed to run elsewhere with available data for risk planning using the Landlab.
Hunter N. Jimenez, Erkan Istanbulluoglu, Tolga Gorum, Thomas A. Stanley, Pukar M. Amatya, Hakan Tanyas, Mehmet C. Demirel, Aykut Akgun, and Deniz Bozkurt
EGUsphere, https://doi.org/10.5194/egusphere-2025-3011, https://doi.org/10.5194/egusphere-2025-3011, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
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After a major earthquake struck near the Türkiye/Syria border in February 2023, a powerful storm brought intense rainfall to the region, triggering additional landslides. We used satellite data and a physics-based model to map probabilistic landslide hazard using both coseismic and hydrologic drivers. We also explored how the sequence of these disasters affected landslide risk. Finally, we offer a method for seasonal forecasting of landslide hazard in at-risk areas using the historic climate.
Jeffrey Keck, Erkan Istanbulluoglu, Benjamin Campforts, Gregory Tucker, and Alexander Horner-Devine
Earth Surf. Dynam., 12, 1165–1191, https://doi.org/10.5194/esurf-12-1165-2024, https://doi.org/10.5194/esurf-12-1165-2024, 2024
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MassWastingRunout (MWR) is a new landslide runout model designed for sediment transport, landscape evolution, and hazard assessment applications. MWR is written in Python and includes a calibration utility that automatically determines best-fit parameters for a site and empirical probability density functions of each parameter for probabilistic model implementation. MWR and Jupyter Notebook tutorials are available as part of the Landlab package at https://github.com/landlab/landlab.
Katherine R. Barnhart, Eric W. H. Hutton, Gregory E. Tucker, Nicole M. Gasparini, Erkan Istanbulluoglu, Daniel E. J. Hobley, Nathan J. Lyons, Margaux Mouchene, Sai Siddhartha Nudurupati, Jordan M. Adams, and Christina Bandaragoda
Earth Surf. Dynam., 8, 379–397, https://doi.org/10.5194/esurf-8-379-2020, https://doi.org/10.5194/esurf-8-379-2020, 2020
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Landlab is a Python package to support the creation of numerical models in Earth surface dynamics. Since the release of the 1.0 version in 2017, Landlab has grown and evolved: it contains 31 new process components, a refactored model grid, and additional utilities. This contribution describes the new elements of Landlab, discusses why certain backward-compatiblity-breaking changes were made, and reflects on the process of community open-source software development.
Ronda Strauch, Erkan Istanbulluoglu, Sai Siddhartha Nudurupati, Christina Bandaragoda, Nicole M. Gasparini, and Gregory E. Tucker
Earth Surf. Dynam., 6, 49–75, https://doi.org/10.5194/esurf-6-49-2018, https://doi.org/10.5194/esurf-6-49-2018, 2018
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We develop a model of annual probability of shallow landslide initiation triggered by soil water from a hydrologic model. Our physically based model accommodates data uncertainty using a Monte Carlo approach. We found elevation-dependent patterns in probability related to the stabilizing effect of forests and soil and slope limitation at high elevations. We demonstrate our model in Washington, USA, but it is designed to run elsewhere with available data for risk planning using the Landlab.
Jordan M. Adams, Nicole M. Gasparini, Daniel E. J. Hobley, Gregory E. Tucker, Eric W. H. Hutton, Sai S. Nudurupati, and Erkan Istanbulluoglu
Geosci. Model Dev., 10, 1645–1663, https://doi.org/10.5194/gmd-10-1645-2017, https://doi.org/10.5194/gmd-10-1645-2017, 2017
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OverlandFlow is a 2-dimensional hydrology component contained within the Landlab modeling framework. It can be applied in both hydrology and geomorphology applications across real and synthetic landscape grids, for both short- and long-term events. This paper finds that this non-steady hydrology regime produces different landscape characteristics when compared to more traditional steady-state hydrology and geomorphology models, suggesting that hydrology regime can impact resulting morphologies.
Daniel E. J. Hobley, Jordan M. Adams, Sai Siddhartha Nudurupati, Eric W. H. Hutton, Nicole M. Gasparini, Erkan Istanbulluoglu, and Gregory E. Tucker
Earth Surf. Dynam., 5, 21–46, https://doi.org/10.5194/esurf-5-21-2017, https://doi.org/10.5194/esurf-5-21-2017, 2017
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Many geoscientists use computer models to understand changes in the Earth's system. However, typically each scientist will build their own model from scratch. This paper describes Landlab, a new piece of open-source software designed to simplify creation and use of models of the Earth's surface. It provides off-the-shelf tools to work with models more efficiently, with less duplication of effort. The paper explains and justifies how Landlab works, and describes some models built with it.
Gregory E. Tucker, Daniel E. J. Hobley, Eric Hutton, Nicole M. Gasparini, Erkan Istanbulluoglu, Jordan M. Adams, and Sai Siddartha Nudurupati
Geosci. Model Dev., 9, 823–839, https://doi.org/10.5194/gmd-9-823-2016, https://doi.org/10.5194/gmd-9-823-2016, 2016
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This paper presents a new Python-language software library, called CellLab-CTS, that enables rapid creation of continuous-time stochastic (CTS) cellular automata models. These models are quite useful for simulating the behavior of natural systems, but can be time-consuming to program. CellLab-CTS allows users to set up models with a minimum of effort, and thereby focus on the science rather than the software.
Related subject area
Landslides and Debris Flows Hazards
Landslide activation during deglaciation in a fjord-dominated landscape: observations from southern Alaska (1984–2022)
Brief communication: Weak correlation between building damage and loss of life from landslides
Comparative analysis of μ(I) and Voellmy-type grain flow rheologies in geophysical mass flows: insights from theoretical and real case studies
Exploring implications of input parameter uncertainties in glacial lake outburst flood (GLOF) modelling results using the modelling code r.avaflow
From rockfall source area identification to susceptibility zonation: a proposed workflow tested on El Hierro (Canary Islands, Spain)
Brief communication: Visualizing uncertainties in landslide susceptibility modelling using bivariate mapping
Constraining landslide frequency across the United States to inform county-level risk reduction
Topographic controls on landslide mobility: modeling hurricane-induced landslide runout and debris-flow inundation in Puerto Rico
Characterizing the scale of regional landslide triggering from storm hydrometeorology
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
The Parraguirre ice-rock avalanche 1987, semi-arid Andes, Chile – A holistic revision
Comparison of conditioning factor classification criteria in large-scale statistically based landslide susceptibility models
Invited perspectives: Integrating hydrologic information into the next generation of landslide early warning systems
Predicting deep-seated landslide displacement on Taiwan's Lushan through the integration of convolutional neural networks and the Age of Exploration-Inspired Optimizer
Limit analysis of earthquake-induced landslides considering two strength envelopes
The vulnerability of buildings to a large-scale debris flow and outburst flood hazard cascade that occurred on 30 August 2020 in Ganluo, southwest China
Optimizing rainfall-triggered landslide thresholds for daily landslide hazard warning in the Three Gorges Reservoir area
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)
Brief Communication: AI-driven rapid landslides mapping following the 2024 Hualien City Earthquake in Taiwan
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
Transformations in Exposure to Debris Flows in Post-Earthquake Sichuan, China
Large-scale assessment of rainfall-induced landslide hazard based on hydrometeorological information: application to Partenio Massif (Italy)
Shaping shallow landslide susceptibility as a function of rainfall events
Temporal clustering of precipitation for detection of potential landslides
Shallow-landslide stability evaluation in loess areas according to the Revised Infinite Slope Model: a case study of the 7.25 Tianshui sliding-flow landslide events of 2013 in the southwest of the Loess Plateau, China
Probabilistic assessment of postfire debris-flow inundation in response to forecast rainfall
Evaluating post-wildfire debris-flow rainfall thresholds and volume models at the 2020 Grizzly Creek Fire in Glenwood Canyon, Colorado, USA
Addressing class imbalance in soil movement predictions
Assessing the impact of climate change on landslides near Vejle, Denmark, using public data
Analysis of three-dimensional slope stability combined with rainfall and earthquake
Assessing landslide damming susceptibility in Central Asia
Assessing locations susceptible to shallow landslide initiation during prolonged intense rainfall in the Lares, Utuado, and Naranjito municipalities of Puerto Rico
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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
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Simulation analysis of 3D stability of a landslide with a locking segment: a case study of the Tizicao landslide in Maoxian County, southwest China
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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
Jane Walden, Mylène Jacquemart, Bretwood Higman, Romain Hugonnet, Andrea Manconi, and Daniel Farinotti
Nat. Hazards Earth Syst. Sci., 25, 2045–2073, https://doi.org/10.5194/nhess-25-2045-2025, https://doi.org/10.5194/nhess-25-2045-2025, 2025
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We studied eight glacier-adjacent landslides in Alaska and found that slope movement increased at four sites as the glacier retreated past the landslide area. Movement at other sites may be due to heavy precipitation or increased glacier thinning, and two sites showed little to no motion. We suggest that landslides near waterbodies may be especially vulnerable to acceleration, which we guess is due to faster retreat rates of water-terminating glaciers and changing water flow in the slope.
Maximillian Van Wyk de Vries, Alexandre Dunant, Amy L. Johnson, Erin L. Harvey, Sihan Li, Katherine Arrell, Jeevan Baniya, Dipak Basnet, Gopi K. Basyal, Nyima Dorjee Bhotia, Simon J. Dadson, Alexander L. Densmore, Tek Bahadur Dong, Mark E. Kincey, Katie Oven, Anuradha Puri, and Nick J. Rosser
Nat. Hazards Earth Syst. Sci., 25, 1937–1942, https://doi.org/10.5194/nhess-25-1937-2025, https://doi.org/10.5194/nhess-25-1937-2025, 2025
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Mapping exposure to landslides is necessary to mitigate risk and reduce vulnerability. In this study, we show that there is a poor correlation between building damage and deaths from landslides, such that the deadliest landslides do not always destroy the most buildings and vice versa. This has important implications for our management of landslide risk.
Yu Zhuang, Brian W. McArdell, and Perry Bartelt
Nat. Hazards Earth Syst. Sci., 25, 1901–1912, https://doi.org/10.5194/nhess-25-1901-2025, https://doi.org/10.5194/nhess-25-1901-2025, 2025
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The experimentally based μ(I) rheology, widely used for gravitational mass flows, is reinterpreted as a Voellmy-type relationship to highlight its link to grain flow theory. Through block modeling and case studies, we establish its equivalence to μ(R) rheology. μ(I) models shear thinning but fails to capture acceleration and deceleration processes and deposit structure. Incorporating fluctuation energy in μ(R) improves accuracy, refining mass flow modeling and revealing practical challenges.
Sonam Rinzin, Stuart Dunning, Rachel Joanne Carr, Ashim Sattar, and Martin Mergili
Nat. Hazards Earth Syst. Sci., 25, 1841–1864, https://doi.org/10.5194/nhess-25-1841-2025, https://doi.org/10.5194/nhess-25-1841-2025, 2025
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We modelled multiple glacial lake outburst flood (GLOF) scenarios (84 simulations) and tested the effect of nine key input parameters on the modelling results using r.avaflow. Our results highlight that GLOF modelling results are subject to uncertainty from the multiple input parameters. The variation in the volume of mass movement entering the lake causes the highest uncertainty in the modelled GLOF, followed by the DEM dataset and the origin of mass movement.
Roberto Sarro, Mauro Rossi, Paola Reichenbach, and Rosa María Mateos
Nat. Hazards Earth Syst. Sci., 25, 1459–1479, https://doi.org/10.5194/nhess-25-1459-2025, https://doi.org/10.5194/nhess-25-1459-2025, 2025
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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
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Communicating uncertainties is a crucial yet challenging aspect of spatial modelling – especially in applied research, where results inform decisions. In disaster risk reduction, susceptibility maps for natural hazards guide planning and risk assessment, yet their uncertainties are often overlooked. We present a new type of landslide susceptibility map that visualizes both susceptibility and associated uncertainty alongside guidelines for creating such maps using free and open-source software.
Lisa V. Luna, Jacob B. Woodard, Janice L. Bytheway, Gina M. Belair, and Benjamin B. Mirus
EGUsphere, https://doi.org/10.5194/egusphere-2025-947, https://doi.org/10.5194/egusphere-2025-947, 2025
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Landslide frequency (how often landslides occur) is needed to assess landslide hazard and risk but has rarely been quantified at near continental scales. Here, we used statistical models to estimate landslide frequency across the United States while addressing gaps in landslide reporting. Our results showed strong variations in landslide frequency that followed topography, earthquake probability, and ecological region and highlighted areas with potential for widespread landsliding.
Dianne L. Brien, Mark E. Reid, Collin Cronkite-Ratcliff, and Jonathan P. Perkins
Nat. Hazards Earth Syst. Sci., 25, 1229–1253, https://doi.org/10.5194/nhess-25-1229-2025, https://doi.org/10.5194/nhess-25-1229-2025, 2025
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Landslide runout zones are the areas downslope or downstream of landslide initiation. People often live and work in these areas, leading to property damage and deaths. Landslide runout may occur on hillslopes or in channels, requiring different modeling approaches. We develop methods to identify potential runout zones and apply these methods to identify susceptible areas for three municipalities in Puerto Rico.
Jonathan Perkins, Nina S. Oakley, Brian D. Collins, Skye C. Corbett, and W. Paul Burgess
Nat. Hazards Earth Syst. Sci., 25, 1037–1056, https://doi.org/10.5194/nhess-25-1037-2025, https://doi.org/10.5194/nhess-25-1037-2025, 2025
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Rainfall-induced landslides result in deaths and economic losses annually across the globe. However, it is unclear how storm severity relates to landslide severity across large regions. Here we develop a method to dynamically map landslide-affected areas, and we compare this to meteorological estimates of storm severity. We find that preconditioning by earlier storms and the location of rainfall bursts, rather than atmospheric storm strength, dictate landslide magnitude and pattern.
Ellen B. Robson, Bhim Kumar Dahal, and David G. Toll
Nat. Hazards Earth Syst. Sci., 25, 949–973, https://doi.org/10.5194/nhess-25-949-2025, https://doi.org/10.5194/nhess-25-949-2025, 2025
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Slopes excavated alongside roads in Nepal frequently fail (a landslide), resulting in substantial losses. Our participatory approach study with road engineers aimed to assess how road slope design guidelines in Nepal can be improved. Our study revealed inconsistent guideline adherence due to a lack of user-friendliness and inadequate training. We present general recommendations to enhance road slope management, as well as technical recommendations to improve the guidelines.
Chenchen Qiu and Xueyu Geng
Nat. Hazards Earth Syst. Sci., 25, 709–726, https://doi.org/10.5194/nhess-25-709-2025, https://doi.org/10.5194/nhess-25-709-2025, 2025
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We propose an integrated method using a combination of a physical vulnerability matrix and a machine learning model to estimate the potential physical damage and associated economic loss caused by future debris flows based on collected historical data on the Qinghai–Tibet Plateau region.
Mark Bloomberg, Tim Davies, Elena Moltchanova, Tom Robinson, and David Palmer
Nat. Hazards Earth Syst. Sci., 25, 647–656, https://doi.org/10.5194/nhess-25-647-2025, https://doi.org/10.5194/nhess-25-647-2025, 2025
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Debris flows occur infrequently, with average recurrence intervals (ARIs) ranging from decades to millennia. Consequently, they pose an underappreciated hazard. We describe how to make a preliminary identification of debris-flow-susceptible catchments, estimate threshold ARIs for debris flows that pose an unacceptable risk to life, and identify the “window of non-recognition” where debris flows are infrequent enough that their hazard is unrecognised yet frequent enough to pose a risk to life.
Christoph Schaller, Luuk Dorren, Massimiliano Schwarz, Christine Moos, Arie C. Seijmonsbergen, and E. Emiel van Loon
Nat. Hazards Earth Syst. Sci., 25, 467–491, https://doi.org/10.5194/nhess-25-467-2025, https://doi.org/10.5194/nhess-25-467-2025, 2025
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We developed a machine-learning-based approach to predict the potential thickness of shallow landslides to generate improved inputs for slope stability models. We selected 21 explanatory variables, including metrics on terrain, geomorphology, vegetation height, and lithology, and used data from two Swiss field inventories to calibrate and test the models. The best-performing machine learning model consistently reduced the mean average error by at least 20 % compared to previous models.
Jui-Ming Chang, Che-Ming Yang, Wei-An Chao, Chin-Shang Ku, Ming-Wan Huang, Tung-Chou Hsieh, and Chi-Yao Hung
Nat. Hazards Earth Syst. Sci., 25, 451–466, https://doi.org/10.5194/nhess-25-451-2025, https://doi.org/10.5194/nhess-25-451-2025, 2025
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The study on the Cilan landslide (CL) demonstrates the utilization of seismic analysis results as preliminary data for geologists during field surveys. Spectrograms revealed that the first event of CL consisted of four sliding failures accompanied by a gradual reduction in landslide volume. The second and third events were minor toppling and rockfalls. Then combining the seismological-based knowledge and field survey results, the spatiotemporal variation in landslide evolution is proposed.
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
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The 1987 Parraguirre ice-rock avalanche developed into a devastating debris-flow causing loss of many lives and inflicting severe damage near Santiago, Chile. Here, we revise this event combining various observational records with modelling techniques. In this year, important snow cover coincided with warm days in spring. We further quantify the total solid volume, and forward important upward corrections for the trigger and flood volumes. Finally, river damming was key for high flow mobility.
Marko Sinčić, Sanja Bernat Gazibara, Mauro Rossi, and Snježana Mihalić Arbanas
Nat. Hazards Earth Syst. Sci., 25, 183–206, https://doi.org/10.5194/nhess-25-183-2025, https://doi.org/10.5194/nhess-25-183-2025, 2025
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The paper focuses on classifying continuous landslide conditioning factors for susceptibility modelling, which resulted in 54 landslide susceptibility models that tested 11 classification criteria in combination with 5 statistical methods. The novelty of the research is that using stretched landslide conditioning factor values results in models with higher accuracy and that certain statistical methods are more sensitive to the landslide conditioning factor classification criteria than others.
Benjamin B. Mirus, Thom Bogaard, Roberto Greco, and Manfred Stähli
Nat. Hazards Earth Syst. Sci., 25, 169–182, https://doi.org/10.5194/nhess-25-169-2025, https://doi.org/10.5194/nhess-25-169-2025, 2025
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Early warning of increased landslide potential provides situational awareness to reduce landslide-related losses from major storm events. For decades, landslide forecasts relied on rainfall data alone, but recent research points to the value of hydrologic information for improving predictions. In this paper, we provide our perspectives on the value and limitations of integrating subsurface hillslope hydrologic monitoring data and mathematical modeling for more accurate landslide forecasts.
Jui-Sheng Chou, Hoang-Minh Nguyen, Huy-Phuong Phan, and Kuo-Lung Wang
Nat. Hazards Earth Syst. Sci., 25, 119–146, https://doi.org/10.5194/nhess-25-119-2025, https://doi.org/10.5194/nhess-25-119-2025, 2025
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This study enhances landslide prediction using advanced machine learning, including new algorithms inspired by historical explorations. The research accurately forecasts landslide movements by analyzing 8 years of data from Taiwan's Lushan, improving early warning and potentially saving lives and infrastructure. This integration marks a significant advancement in environmental risk management.
Di Wu, Yuke Wang, and Xin Chen
Nat. Hazards Earth Syst. Sci., 24, 4617–4630, https://doi.org/10.5194/nhess-24-4617-2024, https://doi.org/10.5194/nhess-24-4617-2024, 2024
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This paper proposes a 3D limit analysis for seismic stability of soil slopes to address the influence of earthquakes on slope stabilities with nonlinear and linear criteria. Comparison results illustrate that the use of a linear envelope leads to the non-negligible overestimation of steep-slope stability, and this overestimation will be significant with increasing earthquakes. Earthquakes have a smaller influence on slope slip surfaces with a nonlinear envelope than those with a linear envelope.
Li Wei, Kaiheng Hu, Shuang Liu, Lan Ning, Xiaopeng Zhang, Qiyuan Zhang, and Md. Abdur Rahim
Nat. Hazards Earth Syst. Sci., 24, 4179–4197, https://doi.org/10.5194/nhess-24-4179-2024, https://doi.org/10.5194/nhess-24-4179-2024, 2024
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The damage patterns of the buildings were classified into three types: (I) buried by primary debris flow, (II) inundated by secondary dam-burst flood, and (III) sequentially buried by debris flow and inundated by dam-burst flood. The threshold of the impact pressures in Zones (II) and (III) where vulnerability is equal to 1 is 84 kPa and 116 kPa, respectively. Heavy damage occurs at an impact pressure greater than 50 kPa, while slight damage occurs below 30 kPa.
Bo Peng and Xueling Wu
Nat. Hazards Earth Syst. Sci., 24, 3991–4013, https://doi.org/10.5194/nhess-24-3991-2024, https://doi.org/10.5194/nhess-24-3991-2024, 2024
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Our research enhances landslide prevention using advanced machine learning to forecast heavy-rainfall-triggered landslides. By analyzing regions and employing various models, we identified optimal ways to predict high-risk rainfall events. Integrating multiple factors and models, including a neural network, significantly improves landslide predictions. Real data validation confirms our approach's reliability, aiding communities in mitigating landslide impacts and safeguarding lives and property.
Mahnoor Ahmed, Giacomo Titti, Sebastiano Trevisani, Lisa Borgatti, and Mirko Francioni
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-211, https://doi.org/10.5194/nhess-2024-211, 2024
Revised manuscript accepted for NHESS
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Elevation models are compared with a true dataset for terrain characteristics which selects a better ranking model to test with different parameters for partitioning the terrain. The partitioning of the terrain is measured by how well a partitioned unit can support the mapped landslide area and number of landslides. The effect of this relationship is reflected with different metrics in the susceptibility maps.
Andrea Manconi, Yves Bühler, Andreas Stoffel, Johan Gaume, Qiaoping Zhang, and Valentyn Tolpekin
Nat. Hazards Earth Syst. Sci., 24, 3833–3839, https://doi.org/10.5194/nhess-24-3833-2024, https://doi.org/10.5194/nhess-24-3833-2024, 2024
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Our research reveals the power of high-resolution satellite synthetic-aperture radar (SAR) imagery for slope deformation monitoring. Using ICEYE data over the Brienz/Brinzauls instability, we measured surface velocity and mapped the landslide event with unprecedented precision. This underscores the potential of satellite SAR for timely hazard assessment in remote regions and aiding disaster mitigation efforts effectively.
Oliver Korup, Lisa V. Luna, and Joaquin V. Ferrer
Nat. Hazards Earth Syst. Sci., 24, 3815–3832, https://doi.org/10.5194/nhess-24-3815-2024, https://doi.org/10.5194/nhess-24-3815-2024, 2024
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Catalogues of mapped landslides are useful for learning and forecasting how frequently they occur in relation to their size. Yet, rare and large landslides remain mostly uncertain in statistical summaries of these catalogues. We propose a single, consistent method of comparing across different data sources and find that landslide statistics disclose more about subjective mapping choices than trigger types or environmental settings.
Rachael Lau, Carolina Seguí, Tyler Waterman, Nathaniel Chaney, and Manolis Veveakis
Nat. Hazards Earth Syst. Sci., 24, 3651–3661, https://doi.org/10.5194/nhess-24-3651-2024, https://doi.org/10.5194/nhess-24-3651-2024, 2024
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This work examines the use of interferometric synthetic-aperture radar (InSAR) alongside in situ borehole measurements to assess the stability of deep-seated landslides for the case study of El Forn (Andorra). Comparing InSAR with borehole data suggests a key trade-off between accuracy and precision for various InSAR resolutions. Spatial interpolation with InSAR informed how many remote observations are necessary to lower error in a remote sensing re-creation of ground motion over the landslide.
Lorenzo Nava, Alessandro Novellino, Chengyong Fang, Kushanav Bhuyan, Kathryn Leeming, Itahisa Gonzalez Alvarez, Claire Dashwood, Sophie Doward, Rahul Chahel, Emma McAllister, Sansar Raj Meena, and Filippo Catani
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-146, https://doi.org/10.5194/nhess-2024-146, 2024
Revised manuscript accepted for NHESS
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On April 2, 2024, a Mw 7.4 earthquake hit Taiwan’s eastern coast, causing extensive landslides and damage. We used automated methods combining Earth Observation (EO) data with Artificial Intelligence (AI) to quickly inventory the landslides. This approach identified 7,090 landslides over 75 km2 within 3 hours of acquiring the EO imagery. The study highlights AI’s role in improving landslide detection and understanding earthquake-landslide interactions for better hazard mitigation.
Zhen Lei Wei, Yue Quan Shang, Qiu Hua Liang, and Xi Lin Xia
Nat. Hazards Earth Syst. Sci., 24, 3357–3379, https://doi.org/10.5194/nhess-24-3357-2024, https://doi.org/10.5194/nhess-24-3357-2024, 2024
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The initiation of debris flows is significantly influenced by rainfall-induced hydrological processes. We propose a novel framework based on an integrated hydrological and hydrodynamic model and aimed at estimating intensity–duration (ID) rainfall thresholds responsible for triggering debris flows. In comparison to traditional statistical approaches, this physically based framework is particularly suitable for application in ungauged catchments where historical debris flow data are scarce.
Jürgen Mey, Ravi Kumar Guntu, Alexander Plakias, Igo Silva de Almeida, and Wolfgang Schwanghart
Nat. Hazards Earth Syst. Sci., 24, 3207–3223, https://doi.org/10.5194/nhess-24-3207-2024, https://doi.org/10.5194/nhess-24-3207-2024, 2024
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The Himalayan road network links remote areas, but fragile terrain and poor construction lead to frequent landslides. This study on the NH-7 in India's Uttarakhand region analyzed 300 landslides after heavy rainfall in 2022 . Factors like slope, rainfall, rock type and road work influence landslides. The study's model predicts landslide locations for better road maintenance planning, highlighting the risk from climate change and increased road use.
Isabelle Utley, Tristram Hales, Ekbal Hussain, and Xuanmei Fan
EGUsphere, https://doi.org/10.5194/egusphere-2024-2277, https://doi.org/10.5194/egusphere-2024-2277, 2024
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We analysed debris flows in Sichuan, China, using satellite data and simulations to assess check dam efficacy. Our study found whilst check dams can mitigate smaller flows, they may increase exposure to extreme events, with up to 40 % of structures in some areas affected. Urban development and reliance on check dams can create a false sense of security, raising exposure during large debris flows and highlights the need for risk management and infrastructure planning in hazard-prone areas.
Daniel Camilo Roman Quintero, Pasquale Marino, Abdullah Abdullah, Giovanni Francesco Santonastaso, and Roberto Greco
EGUsphere, https://doi.org/10.5194/egusphere-2024-2329, https://doi.org/10.5194/egusphere-2024-2329, 2024
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Local thresholds for landslide forecasting, combining hydrologic predisposing factors and rainfall features, are developed from a physically based model of a slope. To extend their application to a wide area, uncertainty due to spatial variability of geomorphological and hydrologic variables is introduced. The obtained hydrometeorological thresholds, integrating root zone soil moisture and aquifer water level with rainfall depth, outperform thresholds based on rain intensity and duration.
Micol Fumagalli, Alberto Previati, Paolo Frattini, and Giovanni B. Crosta
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-140, https://doi.org/10.5194/nhess-2024-140, 2024
Revised manuscript accepted for NHESS
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Shallow landslides are mass movements of limited thickness, mainly triggered by extreme rainfalls, that can pose a serious risk to the population. This study uses statistical methods to analyse and simulate the relationship between shallow landslides and rainfalls, showing that in the studied area shallow landslides are modulated by rainfall but controlled by lithology. A new classification method considering the costs associated with a misclassification of the susceptibility is also proposed.
Fabiola Banfi, Emanuele Bevacqua, Pauline Rivoire, Sérgio C. Oliveira, Joaquim G. Pinto, Alexandre M. Ramos, and Carlo De Michele
Nat. Hazards Earth Syst. Sci., 24, 2689–2704, https://doi.org/10.5194/nhess-24-2689-2024, https://doi.org/10.5194/nhess-24-2689-2024, 2024
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Landslides are complex phenomena causing important impacts in vulnerable areas, and they are often triggered by rainfall. Here, we develop a new approach that uses information on the temporal clustering of rainfall, i.e. multiple events close in time, to detect landslide events and compare it with the use of classical empirical rainfall thresholds, considering as a case study the region of Lisbon, Portugal. The results could help to improve the prediction of rainfall-triggered landslides.
Jianqi Zhuang, Jianbing Peng, Chenhui Du, Yi Zhu, and Jiaxu Kong
Nat. Hazards Earth Syst. Sci., 24, 2615–2631, https://doi.org/10.5194/nhess-24-2615-2024, https://doi.org/10.5194/nhess-24-2615-2024, 2024
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The Revised Infinite Slope Model (RISM) is proposed using the equal differential unit method and correcting the deficiency of the safety factor increasing with the slope increasing when the slope is larger than 40°, as calculated using the Taylor slope infinite model. The intensity–duration (I–D) prediction curve of the rainfall-induced shallow loess landslides with different slopes was constructed and can be used in forecasting regional shallow loess landslides.
Alexander B. Prescott, Luke A. McGuire, Kwang-Sung Jun, Katherine R. Barnhart, and Nina S. Oakley
Nat. Hazards Earth Syst. Sci., 24, 2359–2374, https://doi.org/10.5194/nhess-24-2359-2024, https://doi.org/10.5194/nhess-24-2359-2024, 2024
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Fire can dramatically increase the risk of debris flows to downstream communities with little warning, but hazard assessments have not traditionally included estimates of inundation. We unify models developed by the scientific community to create probabilistic estimates of inundation area in response to rainfall at forecast lead times (≥ 24 h) needed for decision-making. This work takes an initial step toward a near-real-time postfire debris-flow inundation hazard assessment product.
Francis K. Rengers, Samuel Bower, Andrew Knapp, Jason W. Kean, Danielle W. vonLembke, Matthew A. Thomas, Jaime Kostelnik, Katherine R. Barnhart, Matthew Bethel, Joseph E. Gartner, Madeline Hille, Dennis M. Staley, Justin K. Anderson, Elizabeth K. Roberts, Stephen B. DeLong, Belize Lane, Paxton Ridgway, and Brendan P. Murphy
Nat. Hazards Earth Syst. Sci., 24, 2093–2114, https://doi.org/10.5194/nhess-24-2093-2024, https://doi.org/10.5194/nhess-24-2093-2024, 2024
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Every year the U.S. Geological Survey produces 50–100 postfire debris-flow hazard assessments using models for debris-flow likelihood and volume. To refine these models they must be tested with datasets that clearly document rainfall, debris-flow response, and debris-flow volume. These datasets are difficult to obtain, but this study developed and analyzed a postfire dataset with more than 100 postfire storm responses over a 2-year period. We also proposed ways to improve these models.
Praveen Kumar, Priyanka Priyanka, Kala Venkata Uday, and Varun Dutt
Nat. Hazards Earth Syst. Sci., 24, 1913–1928, https://doi.org/10.5194/nhess-24-1913-2024, https://doi.org/10.5194/nhess-24-1913-2024, 2024
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Our study focuses on predicting soil movement to mitigate landslide risks. We develop machine learning models with oversampling techniques to address the class imbalance in monitoring data. The dynamic ensemble model with K-means SMOTE (synthetic minority oversampling technique) achieves high precision, high recall, and a high F1 score. Our findings highlight the potential of these models with oversampling techniques to improve soil movement predictions in landslide-prone areas.
Kristian Svennevig, Julian Koch, Marie Keiding, and Gregor Luetzenburg
Nat. Hazards Earth Syst. Sci., 24, 1897–1911, https://doi.org/10.5194/nhess-24-1897-2024, https://doi.org/10.5194/nhess-24-1897-2024, 2024
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In our study, we analysed publicly available data in order to investigate the impact of climate change on landslides in Denmark. Our research indicates that the rising groundwater table due to climate change will result in an increase in landslide activity. Previous incidents of extremely wet winters have caused damage to infrastructure and buildings due to landslides. This study is the first of its kind to exclusively rely on public data and examine landslides in Denmark.
Jiao Wang, Zhangxing Wang, Guanhua Sun, and Hongming Luo
Nat. Hazards Earth Syst. Sci., 24, 1741–1756, https://doi.org/10.5194/nhess-24-1741-2024, https://doi.org/10.5194/nhess-24-1741-2024, 2024
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With a simplified formula linking rainfall and groundwater level, the rise of the phreatic surface within the slope can be obtained. Then, a global analysis method that considers both seepage and seismic forces is proposed to determine the safety factor of slopes subjected to the combined effect of rainfall and earthquakes. By taking a slope in the Three Gorges Reservoir area as an example, the safety evolution of the slope combined with both rainfall and earthquake is also examined.
Carlo Tacconi Stefanelli, William Frodella, Francesco Caleca, Zhanar Raimbekova, Ruslan Umaraliev, and Veronica Tofani
Nat. Hazards Earth Syst. Sci., 24, 1697–1720, https://doi.org/10.5194/nhess-24-1697-2024, https://doi.org/10.5194/nhess-24-1697-2024, 2024
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Central Asia regions are marked by active tectonics, high mountains with glaciers, and strong rainfall. These predisposing factors make large landslides a serious threat in the area and a source of possible damming scenarios, which endanger the population. To prevent this, a semi-automated geographic information system (GIS-)based mapping method, centered on a bivariate correlation of morphometric parameters, was applied to give preliminary information on damming susceptibility in Central Asia.
Rex L. Baum, Dianne L. Brien, Mark E. Reid, William H. Schulz, and Matthew J. Tello
Nat. Hazards Earth Syst. Sci., 24, 1579–1605, https://doi.org/10.5194/nhess-24-1579-2024, https://doi.org/10.5194/nhess-24-1579-2024, 2024
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We mapped potential for heavy rainfall to cause landslides in part of the central mountains of Puerto Rico using new tools for estimating soil depth and quasi-3D slope stability. Potential ground-failure locations correlate well with the spatial density of landslides from Hurricane Maria. The smooth boundaries of the very high and high ground-failure susceptibility zones enclose 75 % and 90 %, respectively, of observed landslides. The maps can help mitigate ground-failure hazards.
Katherine R. Barnhart, Christopher R. Miller, Francis K. Rengers, and Jason W. Kean
Nat. Hazards Earth Syst. Sci., 24, 1459–1483, https://doi.org/10.5194/nhess-24-1459-2024, https://doi.org/10.5194/nhess-24-1459-2024, 2024
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Debris flows are a type of fast-moving landslide that start from shallow landslides or during intense rain. Infrastructure located downstream of watersheds susceptible to debris flows may be damaged should a debris flow reach them. We present and evaluate an approach to forecast building damage caused by debris flows. We test three alternative models for simulating the motion of debris flows and find that only one can forecast the correct number and spatial pattern of damaged buildings.
Luke A. McGuire, Francis K. Rengers, Ann M. Youberg, Alexander N. Gorr, Olivia J. Hoch, Rebecca Beers, and Ryan Porter
Nat. Hazards Earth Syst. Sci., 24, 1357–1379, https://doi.org/10.5194/nhess-24-1357-2024, https://doi.org/10.5194/nhess-24-1357-2024, 2024
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Runoff and erosion increase after fire, leading to a greater likelihood of floods and debris flows. We monitored debris flow activity following a fire in western New Mexico, USA, and observed 16 debris flows over a <2-year monitoring period. Rainstorms with recurrence intervals of approximately 1 year were sufficient to initiate debris flows. All debris flows initiated during the first several months following the fire, indicating a rapid decrease in debris flow susceptibility over time.
Ken'ichi Koshimizu, Satoshi Ishimaru, Fumitoshi Imaizumi, and Gentaro Kawakami
Nat. Hazards Earth Syst. Sci., 24, 1287–1301, https://doi.org/10.5194/nhess-24-1287-2024, https://doi.org/10.5194/nhess-24-1287-2024, 2024
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Morphological conditions of drainage basins that classify the presence or absence of debris flow fans were analyzed in areas with different rock strength using decision tree analysis. The relief ratio is the most important morphological factor regardless of the geology. However, the thresholds of morphological parameters needed for forming debris flow fans differ depending on the geology. Decision tree analysis is an effective tool for evaluating the debris flow risk for each geology.
Daniel Bolliger, Fritz Schlunegger, and Brian W. McArdell
Nat. Hazards Earth Syst. Sci., 24, 1035–1049, https://doi.org/10.5194/nhess-24-1035-2024, https://doi.org/10.5194/nhess-24-1035-2024, 2024
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We analysed data from the Illgraben debris flow monitoring station, Switzerland, and we modelled these flows with a debris flow runout model. We found that no correlation exists between the grain size distribution, the mineralogical composition of the matrix, and the debris flow properties. The flow properties rather appear to be determined by the flow volume, from which most other parameters can be derived.
Yuntao Zhou, Xiaoyan Zhao, Guangze Zhang, Bernd Wünnemann, Jiajia Zhang, and Minghui Meng
Nat. Hazards Earth Syst. Sci., 24, 891–906, https://doi.org/10.5194/nhess-24-891-2024, https://doi.org/10.5194/nhess-24-891-2024, 2024
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We developed three rock bridge models to analyze 3D stability and deformation behaviors of the Tizicao landslide and found that the contact surface model with high strength parameters combines advantages of the intact rock mass model in simulating the deformation of slopes with rock bridges and the modeling advantage of the Jennings model. The results help in choosing a rock bridge model to simulate landslide stability and reveal the influence laws of rock bridges on the stability of landslides.
Ashok Dahal, Hakan Tanyas, Cees van Westen, Mark van der Meijde, Paul Martin Mai, Raphaël Huser, and Luigi Lombardo
Nat. Hazards Earth Syst. Sci., 24, 823–845, https://doi.org/10.5194/nhess-24-823-2024, https://doi.org/10.5194/nhess-24-823-2024, 2024
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We propose a modeling approach capable of recognizing slopes that may generate landslides, as well as how large these mass movements may be. This protocol is implemented, tested, and validated with data that change in both space and time via an Ensemble Neural Network architecture.
Li-Ru Luo, Zhi-Xiang Yu, Li-Jun Zhang, Qi Wang, Lin-Xu Liao, and Li Peng
Nat. Hazards Earth Syst. Sci., 24, 631–649, https://doi.org/10.5194/nhess-24-631-2024, https://doi.org/10.5194/nhess-24-631-2024, 2024
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We performed field investigations on a rockfall near Jiguanshan National Forest Park, Chengdu. Vital information was obtained from an unmanned aerial vehicle survey. A finite element model was created to reproduce the damage evolution. We found that the impact kinetic energy was below the design protection energy. Improper member connections prevent the barrier from producing significant deformation to absorb energy. Damage is avoided by improving the ability of the nets and ropes to slide.
Sudhanshu Dixit, Srikrishnan Siva Subramanian, Piyush Srivastava, Ali P. Yunus, Tapas Ranjan Martha, and Sumit Sen
Nat. Hazards Earth Syst. Sci., 24, 465–480, https://doi.org/10.5194/nhess-24-465-2024, https://doi.org/10.5194/nhess-24-465-2024, 2024
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Rainfall intensity–duration (ID) thresholds can aid in the prediction of natural hazards. Large-scale sediment disasters like landslides, debris flows, and flash floods happen frequently in the Himalayas because of their propensity for intense precipitation events. We provide a new framework that combines the Weather Research and Forecasting (WRF) model with a regionally distributed numerical model for debris flows to analyse and predict intense rainfall-induced landslides in the Himalayas.
Jacob B. Woodard, Benjamin B. Mirus, Nathan J. Wood, Kate E. Allstadt, Benjamin A. Leshchinsky, and Matthew M. Crawford
Nat. Hazards Earth Syst. Sci., 24, 1–12, https://doi.org/10.5194/nhess-24-1-2024, https://doi.org/10.5194/nhess-24-1-2024, 2024
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Dividing landscapes into hillslopes greatly improves predictions of landslide potential across landscapes, but their scaling is often arbitrarily set and can require significant computing power to delineate. Here, we present a new computer program that can efficiently divide landscapes into meaningful slope units scaled to best capture landslide processes. The results of this work will allow an improved understanding of landslide potential and can help reduce the impacts of landslides worldwide.
Anne Felsberg, Zdenko Heyvaert, Jean Poesen, Thomas Stanley, and Gabriëlle J. M. De Lannoy
Nat. Hazards Earth Syst. Sci., 23, 3805–3821, https://doi.org/10.5194/nhess-23-3805-2023, https://doi.org/10.5194/nhess-23-3805-2023, 2023
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The Probabilistic Hydrological Estimation of LandSlides (PHELS) model combines ensembles of landslide susceptibility and of hydrological predictor variables to provide daily, global ensembles of hazard for hydrologically triggered landslides. Testing different hydrological predictors showed that the combination of rainfall and soil moisture performed best, with the lowest number of missed and false alarms. The ensemble approach allowed the estimation of the associated prediction uncertainty.
Cited articles
Agee, J. K. and Kertis, J.: Forest types of the north Cascades National Park Service complex, Can. J. Bot., 65, 1520–1530, 1987.
Aleotti, P. and Chowdhury, R.: Landslide hazard assessment: summary review
and new perspectives, B. Eng. Geol. Environ., 58, 21–44, 1999.
Anagnostopoulos, G. G., Fatichi, S., and Burlando, P.: An advanced
process-based distributed model for the investigation of rainfall-induced
landslides: The effect of process representation and boundary conditions,
Water Resour. Res., 51, 7501–7523, 2015.
Ayalew, L., Yamagishi, H., and Ugawa, N.: Landslide susceptibility mapping
using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan, Landslides, 1, 73–81, 2004.
Baum, R. L., Galloway, D. L., and Harp, E. L.: Landslide and land subsidence
hazards to pipelines, US Geological Survey Open-File Report 2008-1164, US Geological Survey, Reston, Virginia, 192 pp., 2008.
Beaty, C. B.: Landslides and Slope Exposure, J. Geol., 64, 70–74, 1956.
Bellugi, D., Milledge, D. G., Dietrich, W. E., Perron, J. T., and McKean, J.:
Predicting shallow landslide size and location across a natural landscape:
Application of a spectral clustering search algorithm, J. Geophys.
Res.-Earth, 120, 2552–2585, 2015.
Beven, K. J. and Kirkby, M. J.: A physically based, variable contributing area model of basin hydrology, Hydrolog. Sci. J., 24, 43–69, 1979.
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.
Borga, M., Fontana, G. D., and Cazorzi, F.: Analysis of topographic and climatic control on rainfall-triggered shallow landsliding using a quasi-dynamic wetness index, J. Hydrol., 268, 56–71, 2002.
Carrara, A., Cardinali, M., Guzzetti, F., and Reichenback, P.: GIS technology in mapping landslide hazards, in: Geographical Information System in Assessing Natural Hazard, edited by: Carrara, A. and Guzzetti, F., Springer, Dordrecht, 135–175, 1995.
Carson, M. A. and Kirkby, M. J.: Hillslope Form and Process, Cambridge University Press, Cambridge, UK, 475 pp., 1972.
Cevasco, A., Pepe, G., and Brandolini, P.: The influences of geological and
land use settings on shallow landslides triggered by an intense rainfall event in a coastal terraced environment, B. Eng. Geol. Environ., 73,
859–875, 2014.
Chalkias, C., Ferentinou, M., and Polykretis, C.: GIS-based landslide
susceptibility mapping on the Peloponnese Peninsula, Greece, Geosciences, 4, 176–190, 2014.
Chung, C. J. and Fabbri, A. G.: Modeling the conditional probability of the occurrence of future landslides in a study area characterized by spatial data, Int. Arch. Photogram. Remote Sens. Spat. Inform. Sci., 34, 124–131, 2002.
Coe, J. A.: Landslide hazards and climate change: A perspective from the United States, in: Slope safety preparedness for impact of climate change,
Chapter: 14, edited by: Ho, K., Lacasse, S., and Picarelli, L., CRC Press,
Boca Raton, FL, 479–523, 2016.
Collins, B. D. and Montgomery, D. R.: The legacy of Pleistocene glaciation and the organization of lowland alluvial process domains in the Puget Sound
region, Geomorphology, 126, 174–185, 2011.
Corominas, J., Van Westen, C., Frattini, P., Cascini, L., Malet, J. P., Fotopoulou, S., Catani, F., Van Den Eeckhaut, M., Mavrouli, O., Agliardi, F., and Pitilakis, K.: Recommendations for the quantitative analysis of landslide risk, B. Eng. Geol. Environ., 73, 209–263, 2014.
Croke, J. C. and Hairsine, P. B.: Sediment delivery in managed forests: a
review, Environ. Rev., 14, 59–87, 2006.
Crozier, M. J.: Deciphering the effect of climate change on landslide activity: A review, Geomorphology, 124, 260–267, 2010.
Dai, F. C. and Lee, C. F.: Landslide characteristics and slope instability
modeling using GIS, Lantau Island, Hong Kong, Geomorphology, 42, 213–228, 2002.
Densmore, A. L., Anderson, R. S., McAdoo, B. G., and Ellis, M. A.: Hillslope
evolution by bedrock landslides, Science, 275, 369–372, 1997.
DOI-NPS – United States Department of the Interior, National Park Service:
Foundation Document, North Cascades National Park Complex, Washington,
available at:
https://www.nps.gov/noca/learn/management/upload/North-Cascades-NP-Complex-Foundation-Document_small.pdf, (last access: 23 January 2017), 2012.
El-Ramly, H., Morgenstern, N. R., and Cruden, D. M.: Probabilistic slope stability analysis for practice, Can. Geotech. J., 39, 665–683, 2002.
Ercanoglu, M. and Sonmez, H.: General Trends and New Perspectives on Landslide Mapping and Assessment Methods. In Environmental Information
Systems: Concepts, Methodologies, Tools, and Applications, IGI Global, Hershey, Pennsylvania, 64–93, https://doi.org/10.4018/978-1-5225-7033-2.ch004, 2019.
Evans, R. D. and Fonda, R. W.: The influence of snow on subalpine meadow
community pattern, North Cascades, Washington, Can. J. Bot., 68, 212–220, 1990.
Fawcett, T.: An introduction to ROC analysis, Pattern Recogn. Lett., 27, 861–874, 2006.
Fischer, L., Kääb, A., Huggel, C., and Noetzli, J.: Geology, glacier retreat and permafrost degradation as controlling factors of slope instabilities in a high-mountain rock wall: the Monte Rosa east face, Nat. Hazards Earth Syst. Sci., 6, 761–772, https://doi.org/10.5194/nhess-6-761-2006, 2006.
Gabet, E. J.: Sediment transport by dry ravel, J. Geophys. Res.-Solid,
108, 2049, https://doi.org/10.1029/2001JB001686, 2003.
Geroy, I. J., Gribb, M. M., Marshall, H. P., Chandler, D. G., Benner, S. G., and McNamara, J. P.: Aspect influences on soil water retention and storage,
Hydrol. Process., 25, 3836–3842, https://doi.org/10.1002/hyp.8281, 2011.
Ghirotti, M.: The 1963 Vaiont landslide, Italy, in: Landslides: Types,
mechanisms and modeling, edited by: Claque, J. J. and Stead, D., Cambridge
University Press, New York, NY, 359 pp., 2012.
Goetz, J. N., Guthrie, R. H., and Brenning, A.: Integrating physical and empirical landslide susceptibility models using generalized additive models, Geomorphology, 129, 376–386, https://doi.org/10.1016/j.geomorph.2011.03.001, 2011.
Gokceoglu, C., Sonmez, H., Nefeslioglu, H. A., Duman, T. Y., and Can, T.:
The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide-susceptibility map of its near vicinity, Eng. Geol., 81, 65–83,
2005.
Gupta, R. P. and Joshi, B. C.: Landslide hazard zoning using the GIS approach – a case study from the Ramganga catchment, Himalayas, Eng. Geol., 28, 119–131, 1990.
Haeberli, W., Schaub, Y., and Huggel, C.: Increasing risks related to landslides from degrading permafrost into new lakes in de-glaciating mountain ranges, Geomorphology, 293, 405–417, https://doi.org/10.1016/j.geomorph.2016.02.009, 2017.
Hales, T. C., Ford, C. R., Hwang, T., Vose, J. M., and Band, L. E.: Topographic and ecologic controls on root reinforcement, J. Geophys. Res.,
114, F03013, https://doi.org/10.1029/2008JF001168, 2009.
Hamlet, A. F., Elsner, M. M., Mauger, G., Lee, S., and Tohver, I.: An Overview of the Columbia Basin Climate Change Scenarios Project: Approach,
Methods, and Summary of Key Results, Atmos. Ocean., 51, 392–415, 2013.
Hanley, J. A. and McNeil, B. J.: The meaning and use of the area under a
receiver operating characteristic (ROC) curve, Radiology, 143, 29–36, 1982.
Haugerud, R. A. and Tabor, R. W.: Geologic map of the North Cascade Range,
Washington, US Department of the Interior, US Geological Survey, Denver, Colorado, 29 pp., 2009.
Hobley, D. E. J., Adams, J. M., Nudurupati, S. S., Hutton, E. W. H., Gasparini, N. M., Istanbulluoglu, E., and Tucker, G. E.: Creative computing
with Landlab: an open-source toolkit for building, coupling, and exploring
two-dimensional numerical models of Earth-surface dynamics, Earth Surf. Dynam., 5, 21–46, https://doi.org/10.5194/esurf-5-21-2017, 2017.
Hong, H., Chen, W., Xu, C., Youssef, A. M., Pradhan, B., and Tien Bui, D.:
Rainfall-induced landslide susceptibility assessment at the Chongren area
(China) using frequency ratio, certainty factor, and index of entropy,
Geocarto Int., 32, 139–154, 2017.
Hungr, O.: A review of landslide hazard and risk assessment methodology,
in: Landslides and engineered slopes. Experience, theory and practice, edited
by: Aversa, S., Cascini, L., Picarelli, L., and Scavia, C., CRC Press, Boca
Raton, FL, 3–27, 2018.
Hungr, O., Leroueil, S., and Picarelli, L.: The Varnes classification of
landslide types, an update. Landslides, 11, 167–194, 2014.
Jin, S., Yang, L., Danielson, P., Homer, C., Fry, J., and Xian, G.: A comprehensive change detection method for updating the National Land Cover Database to circa 2011, Remote Sens. Environ., 132, 159–175, 2013.
Kelsey, H. M.: Formation of inner gorges, Catena, 15, 433–458, 1988.
Kirschbaum, D. B., Adler, R., Hong, Y., Kumar, S., Peters-Lidard, C., and
Lerner-Lam, A.: Advances in landslide nowcasting: evaluation of global and
regional modeling approach, Environ. Earth. Sci., 66, 1683–1696, 2012.
LaHusen, S. R., Duvall, A. R., Booth, A. M., and Montgomery, D. R.: Surface
roughness dating of long-runout landslides near Oso, Washington (USA),
reveals persistent postglacial hillslope instability, Geology, 44, 111–114, 2016.
Lee, S. and Pradhan, B.: Landslide hazard mapping at Selangor, Malaysia using
frequency ratio and logistic regression models, Landslides, 4, 33–41, 2007.
Lee, S., Ryu, J. H., and Kim, I. S.: Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial
neural network models: Case study of Youngin, Korea, Landslides, 4, 327–338,
2007.
Lepore, C., Kamal, S. A., Shanahan, P., and Bras, R. L.: Rainfall-induced
landslide susceptibility zonation of Puerto Rico, Environ. Earth Sci., 66, 1667–1681, 2012.
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple
hydrologically based model of land surface water and energy fluxes for GSMs,
J. Geophys. Res., 99, 14415–14428, 1994.
Mancini, F., Ceppi, C., and Ritrovato, G.: GIS and statistical analysis for landslide susceptibility mapping in the Daunia area, Italy, Nat. Hazards Earth Syst. Sci., 10, 1851–1864, https://doi.org/10.5194/nhess-10-1851-2010, 2010.
May, C. L., Pryor, B., Lisle, T. E., and Lang, M.: Coupling hydrodynamic modeling and empirical measures of bed mobility to predict the risk of scour and fill of salmon redds in a large regulated river, Water Resour. Res., 45, W05402, https://doi.org/10.1029/2007WR006498, 2009.
Megahan, W. F., Day, N. F., and Bliss, T. M.: Landslide occurrence in the
western and central Northern Rocky Mountain physiographic province in Idaho,
in: Forest Soils and Land Use: Proceedings of the Fifth North American Forest
Soils Conference, edited by: Youngberg, C. T., CSU, Ft. Collins, CO, 116–139, 1978.
Miller, D. J. and Burnett, K. M.: Effects of forest cover, topography, and
sampling extent on the measured density of shallow, translational landslides, Water Resour. Res., 43, W03433, https://doi.org/10.1029/2005WR004807, 2007.
Montgomery, D. R.: Road surface drainage, channel initiation, and slope
instability, Water Resour. Res., 30, 1925–1932, 1994.
Montgomery, D. R.: Slope distributions, threshold hillslopes, and steady-state topography, Am. J. Sci., 301, 432–454, 2001.
Mustoe, G. E. and Leopold, E. B.: Paleobotanical evidence for the post-Miocene uplift of the Cascade Range, Can. J. Earth Sci., 51, 809–824, 2014.
O'Loughlin, E. M.: Prediction of surface saturation zones in natural catchments by topographic analysis, Water Resour. Res., 22, 794–804, 1986.
Pachauri, A. K. and Pant, M.: Landslide hazard mapping based on geological
attributes, Eng. Geol., 32, 81–100, 1992.
Pack, R. T., Tarboton, D. G., and Goodwin, C. N.: The SINMAP approach to terrain stability mapping, in: Proceedings of the 8th international congress of the international association of engineering geology and the environment, vol. 2, 21–25 September 1998, Vancouver, British Columbia, Canada, AA Balkema, Rotterdam, 1157–1165, 1998.
Pelto, M. S. and Riedel, J.: Spatial and temporal variations in annual balance of North Cascade glaciers, Washington 1984–2000, Hydrol. Process.,
15, 3461–3472, 2001.
Pollock, M. M.: Biodiversity, in: River Ecology and Management: Lessons From
the Pacific Coastal Ecoregion, edited by: Naiman, R. J. and Bilby, R. E.,
Springer-Verlag, New York, 430–452, 1998.
Poulos, M. J., Pierce, J. L., Flores, A. N., and Benner, S. G.: Hillslope
asymmetry maps reveal widespread, multi-scale organization, Geophys. Res. Lett., 39, L06406, https://doi.org/10.1029/2012GL051283, 2012.
Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., and Guzzetti, F.: A
review of statistically-based landslide susceptibility models, Earth-Sci. Rev., 180, 60–91, 2018.
Riedel, J. L. and Probala, J.: Mapping ecosystems at the landform scale in Washington state, Park Science, 23, 37–42, 2005.
Riedel, J. L., Haugerud, R. A., and Clague, J. J.: Geomorphology of a Cordilleran Ice Sheet drainage network through breached divides in the North
Cascades Mountains of Washington and British Columbia, Geomorphology, 91, 1–18, 2007.
Riedel, J. L., Dorsch, S., and Wenger, J.: Geomorphology of the Stehekin River watershed: Landform mapping at North Cascades National Park Service Complex, Washington, Natural Resource Technical Report NPS/NCCN/NRTR-2012/566, National Park Service, Fort Collins, Colorado, 90 pp., 2012.
Roe, G. H.: Orographic Precipitation, Annu. Rev. Earth Planet. Sci., 33, 645–671, 2005.
Roering, J. J., Schmidt, K. M., Stock, J. D., Dietrich, W. E., and Montgomery, D. R.: Shallow landsliding, root reinforcement, and the spatial
distribution of trees in the Oregon Coast Range, Can. Geotech. J., 40,
237–253, 2003.
Sidle, R. C. and Ochiai, H.: Landslides: processes, prediction, and land use, Water Resources Monogram 18, American Geophysical Union, Washington, D.C., 2006.
Strauch, R., Istanbulluoglu, E., Nudurupati, S. S., Bandaragoda, C., Gasparini, N. M., and Tucker, G. E.: A hydro-climatological approach to
predicting regional landslide probability using Landlab, Earth Surf. Dynam.,
6, 49–75, https://doi.org/10.5194/esurf-6-49-2018, 2018.
Strauch, R., Istanbulluoglu, E., Riedel, J.: A New Approach to Mapping Landslide hazards: a probabilistic integration of empirical and physically based models in the North Cascades of Washington, USA – Research Data, HydroShare, https://doi.org/10.4211/hs.6d8c3c46f4c8422796f28584eb9bdfaa, 2019.
Swanson, F. J. and Dyrness, C . T .: Impact of clear-cutting and road construction on soil erosion by landslides in the western Cascade Range,
Oregon, Geology, 3, 393–396, 1975.
Tabor, R. W. and Haugerud, R. A.: Geology of the North Cascades: a mountain
mosaic, The Mountaineers Books, Seattle, WA, 1999.
Taylor, F. A. and Brabb, E. E.: Map showing landslides in California that have caused fatalities or at least $1,000,000 in damages from 1906 to 1984, US Geological Survey Miscellaneous Field Studies Map, MF-1867,
scale: 1:1 000 000, US Geological Survey, Reston, Virginia, https://doi.org/10.3133/ofr86100, 1986.
USGS – United States Geological Survey: National Elevation Data last modified March 6, 2014, National Map Viewer, available at: https://www.nrcs.usda.gov/wps/portal/nrcs/detail/national/?&cid=nrcs143_021626 (last access: 24 November 2014), 2014a.
USGS: National Land Cover Data (NLCD) version Marched 31, 2014, National Map
Viewer, available at: https://www.usgs.gov/core-science-systems/national-geospatial-program/land-cover (last access: 25 November 2014), 2014b.
Van Westen, C. J., Van Asch, T. W., and Soeters, R.: Landslide hazard and risk zonation – why is it still so difficult?, B. Eng. Geol. Environ., 65,
167–184, 2006.
WADNR – Washington State Department of Natural Resources:
Geologic_unit_poly_100k, Vector digital data, published June 2010, Division of Geology and Earth Resources, Olympia, WA, available at: https://www.dnr.wa.gov/programs-and-services/geology/publications-and-data/gis-data-and-databases, last access: 27 March 2014.
Wartman, J., Montgomery, D. R., Anderson, S. A., Keaton, J. R., Benoît,
J., dela Chapelle, J., and Gilbert, R.: The 22 March 2014 Oso landslide,
Washington, USA, Geomorphology, 253, 275–288, 2016.
Wooten, R. M., Witt, A. C., Miniat, C. F., Hales, T. C., and Aldred, J. L.:
Frequency and magnitude of selected historical landslide events in the
southern Appalachian Highlands of North Carolina and Virginia: relationships
to rainfall, geological and ecohydrological controls, and effects, in: Natural Disturbances and Historic Range of Variation, edited by: Greenberg,
C. H. and Collins, B. S., Springer International Publishing, Switzerland,
203–262, https://doi.org/10.1007/978-3-319-21527-3, 2016.
Wu, Z., Wu, Y., Yang, Y., Chen, F., Zhang, N., Ke, Y., and Li, W.: A comparative study on the landslide susceptibility mapping using logistic
regression and statistical index models, Arab. J. Geosci., 10, 187, 2017.
Short summary
Identifying landslide hazards is challenging but important for understanding risks to people and both built and natural resources. We use models to identify landslide hazards based on observed landslides and local site traits such as slope and on physical mechanisms such as soil moisture. Integrating both approaches improves hazard detection by accounting for processes not captured in the physically based model. Hazard maps are made for the North Cascades National Park Complex (Washington, USA).
Identifying landslide hazards is challenging but important for understanding risks to people and...
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