Articles | Volume 23, issue 10
https://doi.org/10.5194/nhess-23-3261-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-23-3261-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Oct 2023
Research article |
| 18 Oct 2023
| 18 Oct 2023
Landslide initiation thresholds in data-sparse regions: application to landslide early warning criteria in Sitka, Alaska, USA
Annette I. Patton
CORRESPONDING AUTHOR
Sitka Sound Science Center, Sitka, Alaska, USA
Department of Earth Sciences, University of Oregon, Eugene, Oregon, USA
Institute of Environmental Science and Geography, University of Potsdam, Potsdam, Germany
Institute of Geosciences, University of Potsdam, Potsdam, Germany
Potsdam Institute for Climate Impact Research, Potsdam, Germany
Joshua J. Roering
Department of Earth Sciences, University of Oregon, Eugene, Oregon, USA
Aaron Jacobs
NOAA National Weather Service Forecast Office Juneau, Juneau, Alaska, USA
Oliver Korup
Institute of Environmental Science and Geography, University of Potsdam, Potsdam, Germany
Institute of Geosciences, University of Potsdam, Potsdam, Germany
Benjamin B. Mirus
Geologic Hazards Science Center, US Geological Survey, Golden, Colorado, USA
Related authors
No articles found.
Maria Isabel Arango-Carmona, Paul Voit, Marcel Hürlimann, Edier Aristizábal, and Oliver Korup
EGUsphere, https://doi.org/10.5194/egusphere-2025-1698, https://doi.org/10.5194/egusphere-2025-1698, 2025
Short summary
Short summary
We studied 22 cascading landslide and torrential events in tropical mountains to understand how rainfall, slopes, and soil types interact to trigger them. We found that extreme rainfall alone is not always the cause, but long wet periods and sediment type also play a role. Our findings can help improve warning systems and reduce disaster risks in vulnerable regions.
Lisa V. Luna, Jacob B. Woodard, Janice L. Bytheway, Gina M. Belair, and Benjamin B. Mirus
EGUsphere, https://doi.org/10.5194/egusphere-2025-947, https://doi.org/10.5194/egusphere-2025-947, 2025
Short summary
Short summary
Landslide frequency (how often landslides occur) is needed to assess landslide hazard and risk but has rarely been quantified at near continental scales. Here, we used statistical models to estimate landslide frequency across the United States while addressing gaps in landslide reporting. Our results showed strong variations in landslide frequency that followed topography, earthquake probability, and ecological region and highlighted areas with potential for widespread landsliding.
Ian D. Wachino, Joshua J. Roering, Reuben Cash, and Annette I. Patton
EGUsphere, https://doi.org/10.5194/egusphere-2025-1168, https://doi.org/10.5194/egusphere-2025-1168, 2025
Short summary
Short summary
Rockfalls are a common hazard in steep mountain valleys, especially near Skagway, Alaska, where recent events have threatened public safety and infrastructure. This study identifies zones prone to rockfall by analyzing rock formations, past rockfall deposits, and computer models predicting how rocks travel downslope. Our findings highlight high-risk areas and provide insights to improve hazard mitigation, helping protect communities and tourism in the region.
Natalie Lützow, Bretwood Higman, Martin Truffer, Bodo Bookhagen, Friedrich Knuth, Oliver Korup, Katie E. Hughes, Marten Geertsema, John J. Clague, and Georg Veh
The Cryosphere, 19, 1085–1102, https://doi.org/10.5194/tc-19-1085-2025, https://doi.org/10.5194/tc-19-1085-2025, 2025
Short summary
Short summary
As the atmosphere warms, thinning glacier dams impound smaller lakes at their margins. Yet, some lakes deviate from this trend and have instead grown over time, increasing the risk of glacier floods to downstream populations and infrastructure. In this article, we examine the mechanisms behind the growth of an ice-dammed lake in Alaska. We find that the growth in size and outburst volumes is more controlled by glacier front downwaste than by overall mass loss over the entire glacier surface.
Benjamin B. Mirus, Thom Bogaard, Roberto Greco, and Manfred Stähli
Nat. Hazards Earth Syst. Sci., 25, 169–182, https://doi.org/10.5194/nhess-25-169-2025, https://doi.org/10.5194/nhess-25-169-2025, 2025
Short summary
Short summary
Early warning of increased landslide potential provides situational awareness to reduce landslide-related losses from major storm events. For decades, landslide forecasts relied on rainfall data alone, but recent research points to the value of hydrologic information for improving predictions. In this paper, we provide our perspectives on the value and limitations of integrating subsurface hillslope hydrologic monitoring data and mathematical modeling for more accurate landslide forecasts.
Oliver Korup, Lisa V. Luna, and Joaquin V. Ferrer
Nat. Hazards Earth Syst. Sci., 24, 3815–3832, https://doi.org/10.5194/nhess-24-3815-2024, https://doi.org/10.5194/nhess-24-3815-2024, 2024
Short summary
Short summary
Catalogues of mapped landslides are useful for learning and forecasting how frequently they occur in relation to their size. Yet, rare and large landslides remain mostly uncertain in statistical summaries of these catalogues. We propose a single, consistent method of comparing across different data sources and find that landslide statistics disclose more about subjective mapping choices than trigger types or environmental settings.
Amalie Skålevåg, Oliver Korup, and Axel Bronstert
Hydrol. Earth Syst. Sci., 28, 4771–4796, https://doi.org/10.5194/hess-28-4771-2024, https://doi.org/10.5194/hess-28-4771-2024, 2024
Short summary
Short summary
We present a cluster-based approach for inferring sediment discharge event types from suspended sediment concentration and streamflow. Applying it to a glacierised catchment, we find event magnitude and shape complexity to be the key characteristics separating event types, while hysteresis is less important. The four event types are attributed to compound rainfall–melt extremes, high snowmelt and glacier melt, freeze–thaw-modulated snow-melt and precipitation, and late-season glacier melt.
Greg Balco, Alan J. Hidy, William T. Struble, and Joshua J. Roering
Geochronology, 6, 71–76, https://doi.org/10.5194/gchron-6-71-2024, https://doi.org/10.5194/gchron-6-71-2024, 2024
Short summary
Short summary
We describe a new method of reconstructing the long-term, pre-observational frequency and/or intensity of wildfires in forested landscapes using trace concentrations of the noble gases helium and neon that are formed in soil mineral grains by cosmic-ray bombardment of the Earth's surface.
Jacob B. Woodard, Benjamin B. Mirus, Nathan J. Wood, Kate E. Allstadt, Benjamin A. Leshchinsky, and Matthew M. Crawford
Nat. Hazards Earth Syst. Sci., 24, 1–12, https://doi.org/10.5194/nhess-24-1-2024, https://doi.org/10.5194/nhess-24-1-2024, 2024
Short summary
Short summary
Dividing landscapes into hillslopes greatly improves predictions of landslide potential across landscapes, but their scaling is often arbitrarily set and can require significant computing power to delineate. Here, we present a new computer program that can efficiently divide landscapes into meaningful slope units scaled to best capture landslide processes. The results of this work will allow an improved understanding of landslide potential and can help reduce the impacts of landslides worldwide.
Z. Xiong, D. Stober, M. Krstić, O. Korup, M. I. Arango, H. Li, and M. Werner
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-5-W1-2023, 75–81, https://doi.org/10.5194/isprs-annals-X-5-W1-2023-75-2023, https://doi.org/10.5194/isprs-annals-X-5-W1-2023-75-2023, 2023
Melanie Fischer, Jana Brettin, Sigrid Roessner, Ariane Walz, Monique Fort, and Oliver Korup
Nat. Hazards Earth Syst. Sci., 22, 3105–3123, https://doi.org/10.5194/nhess-22-3105-2022, https://doi.org/10.5194/nhess-22-3105-2022, 2022
Short summary
Short summary
Nepal’s second-largest city has been rapidly growing since the 1970s, although its valley has been affected by rare, catastrophic floods in recent and historic times. We analyse potential impacts of such floods on urban areas and infrastructure by modelling 10 physically plausible flood scenarios along Pokhara’s main river. We find that hydraulic effects would largely affect a number of squatter settlements, which have expanded rapidly towards the river by a factor of up to 20 since 2008.
Adam Emmer, Simon K. Allen, Mark Carey, Holger Frey, Christian Huggel, Oliver Korup, Martin Mergili, Ashim Sattar, Georg Veh, Thomas Y. Chen, Simon J. Cook, Mariana Correas-Gonzalez, Soumik Das, Alejandro Diaz Moreno, Fabian Drenkhan, Melanie Fischer, Walter W. Immerzeel, Eñaut Izagirre, Ramesh Chandra Joshi, Ioannis Kougkoulos, Riamsara Kuyakanon Knapp, Dongfeng Li, Ulfat Majeed, Stephanie Matti, Holly Moulton, Faezeh Nick, Valentine Piroton, Irfan Rashid, Masoom Reza, Anderson Ribeiro de Figueiredo, Christian Riveros, Finu Shrestha, Milan Shrestha, Jakob Steiner, Noah Walker-Crawford, Joanne L. Wood, and Jacob C. Yde
Nat. Hazards Earth Syst. Sci., 22, 3041–3061, https://doi.org/10.5194/nhess-22-3041-2022, https://doi.org/10.5194/nhess-22-3041-2022, 2022
Short summary
Short summary
Glacial lake outburst floods (GLOFs) have attracted increased research attention recently. In this work, we review GLOF research papers published between 2017 and 2021 and complement the analysis with research community insights gained from the 2021 GLOF conference we organized. The transdisciplinary character of the conference together with broad geographical coverage allowed us to identify progress, trends and challenges in GLOF research and outline future research needs and directions.
William T. Struble and Joshua J. Roering
Earth Surf. Dynam., 9, 1279–1300, https://doi.org/10.5194/esurf-9-1279-2021, https://doi.org/10.5194/esurf-9-1279-2021, 2021
Short summary
Short summary
We used a mathematical technique known as a wavelet transform to calculate the curvature of hilltops in western Oregon, which we used to estimate erosion rate. We find that this technique operates over 1000 times faster than other techniques and produces accurate erosion rates. We additionally built artificial hillslopes to test the accuracy of curvature measurement methods. We find that at fast erosion rates, curvature is underestimated, raising questions of measurement accuracy elsewhere.
Melanie Fischer, Oliver Korup, Georg Veh, and Ariane Walz
The Cryosphere, 15, 4145–4163, https://doi.org/10.5194/tc-15-4145-2021, https://doi.org/10.5194/tc-15-4145-2021, 2021
Short summary
Short summary
Glacial lake outburst floods (GLOFs) in the greater Himalayan region threaten local communities and infrastructure. We assess this hazard objectively using fully data-driven models. We find that lake and catchment area, as well as regional glacier-mass balance, credibly raised the susceptibility of a glacial lake in our study area to produce a sudden outburst. However, our models hardly support the widely held notion that rapid lake growth increases GLOF susceptibility.
David Jon Furbish, Joshua J. Roering, Tyler H. Doane, Danica L. Roth, Sarah G. W. Williams, and Angel M. Abbott
Earth Surf. Dynam., 9, 539–576, https://doi.org/10.5194/esurf-9-539-2021, https://doi.org/10.5194/esurf-9-539-2021, 2021
Short summary
Short summary
Sediment particles skitter down steep hillslopes on Earth and Mars. Particles gain speed in going downhill but are slowed down and sometimes stop due to collisions with the rough surface. The likelihood of stopping depends on the energetics of speeding up (heating) versus slowing down (cooling). Statistical physics predicts that particle travel distances are described by a generalized Pareto distribution whose form varies with the Kirkby number – the ratio of heating to cooling.
David Jon Furbish, Sarah G. W. Williams, Danica L. Roth, Tyler H. Doane, and Joshua J. Roering
Earth Surf. Dynam., 9, 577–613, https://doi.org/10.5194/esurf-9-577-2021, https://doi.org/10.5194/esurf-9-577-2021, 2021
Short summary
Short summary
The generalized Pareto distribution of particle travel distances on steep hillslopes, as described in a companion paper (Furbish et al., 2021a), is entirely consistent with measurements of travel distances obtained from laboratory and field-based experiments, supplemented with high-speed imaging and audio recordings that highlight the effects of bumpety-bump particle motions. Particle size and shape, in concert with surface roughness, strongly influence particle energetics and deposition.
Guilherme S. Mohor, Annegret H. Thieken, and Oliver Korup
Nat. Hazards Earth Syst. Sci., 21, 1599–1614, https://doi.org/10.5194/nhess-21-1599-2021, https://doi.org/10.5194/nhess-21-1599-2021, 2021
Short summary
Short summary
We explored differences in the damaging process across different flood types, regions within Germany, and six flood events through a numerical model in which the groups can learn from each other. Differences were found mostly across flood types, indicating the importance of identifying them, but there is great overlap across regions and flood events, indicating either that socioeconomic or temporal information was not well represented or that they are in fact less different within our cases.
Susan L. Brantley, David M. Eissenstat, Jill A. Marshall, Sarah E. Godsey, Zsuzsanna Balogh-Brunstad, Diana L. Karwan, Shirley A. Papuga, Joshua Roering, Todd E. Dawson, Jaivime Evaristo, Oliver Chadwick, Jeffrey J. McDonnell, and Kathleen C. Weathers
Biogeosciences, 14, 5115–5142, https://doi.org/10.5194/bg-14-5115-2017, https://doi.org/10.5194/bg-14-5115-2017, 2017
Short summary
Short summary
This review represents the outcome from an invigorating workshop discussion that involved tree physiologists, geomorphologists, ecologists, geochemists, and hydrologists and developed nine hypotheses that could be tested. We argue these hypotheses point to the essence of issues we must explore if we are to understand how the natural system of the earth surface evolves, and how humans will affect its evolution. This paper will create discussion and interest both before and after publication.
Karolina Korzeniowska, Yves Bühler, Mauro Marty, and Oliver Korup
Nat. Hazards Earth Syst. Sci., 17, 1823–1836, https://doi.org/10.5194/nhess-17-1823-2017, https://doi.org/10.5194/nhess-17-1823-2017, 2017
Short summary
Short summary
In this study, we have focused on automatically detecting avalanches and classifying them into release zones, tracks, and run-out zones based on aerial imagery using an object-based image analysis (OBIA) approach. We compared the results with manually mapped avalanche polygons, and obtained a user’s accuracy of > 0.9 and a Cohen’s kappa of 0.79–0.85. Testing the method for a larger area of 226.3 km2, we estimated producer’s and user’s accuracies of 0.61 and 0.78, respectively.
N. K. Meyer, W. Schwanghart, O. Korup, and F. Nadim
Nat. Hazards Earth Syst. Sci., 15, 985–995, https://doi.org/10.5194/nhess-15-985-2015, https://doi.org/10.5194/nhess-15-985-2015, 2015
Short summary
Short summary
In the past decades the importance of and reliance on all kinds of transport networks has grown extensively making them more vulnerable to any kind of hazard. The linear structure of road networks is especially sensitive to debris flows, a process frequently occurring in the mountainous area of Norway. The paper quantifies the functional risk associated with these processes. The results reveal that the costs related to route closures are strongly related to the information status of drivers.
O. Korup and C. Rixen
The Cryosphere, 8, 651–658, https://doi.org/10.5194/tc-8-651-2014, https://doi.org/10.5194/tc-8-651-2014, 2014
C. H. Mohr, A. Zimmermann, O. Korup, A. Iroumé, T. Francke, and A. Bronstert
Earth Surf. Dynam., 2, 117–125, https://doi.org/10.5194/esurf-2-117-2014, https://doi.org/10.5194/esurf-2-117-2014, 2014
Related subject area
Landslides and Debris Flows Hazards
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
A regional analysis of paraglacial landslide activation in southern coastal Alaska
Analysis of three-dimensional slope stability combined with rainfall and earthquake
Assessing landslide damming susceptibility in Central Asia
Assessing locations susceptible to shallow landslide initiation during prolonged intense rainfall in the Lares, Utuado, and Naranjito municipalities of Puerto Rico
Evaluation of debris-flow building damage forecasts
Characteristics of debris-flow-prone watersheds and debris-flow-triggering rainstorms following the Tadpole Fire, New Mexico, USA
Morphological characteristics and conditions of drainage basins contributing to the formation of debris flow fans: an examination of regions with different rock strength using decision tree analysis
Comparison of debris flow observations, including fine-sediment grain size and composition and runout model results, at Illgraben, Swiss Alps
Simulation analysis of 3D stability of a landslide with a locking segment: a case study of the Tizicao landslide in Maoxian County, southwest China
Space–time landslide hazard modeling via Ensemble Neural Networks
Optimization strategy for flexible barrier structures: investigation and back analysis of a rockfall disaster case in southwestern China
Numerical-model-derived intensity–duration thresholds for early warning of rainfall-induced debris flows in a Himalayan catchment
Slope Unit Maker (SUMak): an efficient and parameter-free algorithm for delineating slope units to improve landslide modeling
Probabilistic Hydrological Estimation of LandSlides (PHELS): global ensemble landslide hazard modelling
Maximillian Van Wyk de Vries, Alexandre Dunant, Amy L. Johnson, Erin L. Harvey, Sihan Li, Katherine Arrell, Jeevan Baniya, Dipak Basnet, Gopi K. Basyal, Nyima Dorjee Bhotia, Simon J. Dadson, Alexander L. Densmore, Tek Bahadur Dong, Mark E. Kincey, Katie Oven, Anuradha Puri, and Nick J. Rosser
Nat. Hazards Earth Syst. Sci., 25, 1937–1942, https://doi.org/10.5194/nhess-25-1937-2025, https://doi.org/10.5194/nhess-25-1937-2025, 2025
Short summary
Short summary
Mapping exposure to landslides is necessary to mitigate risk and reduce vulnerability. In this study, we show that there is a poor correlation between building damage and deaths from landslides, such that the deadliest landslides do not always destroy the most buildings and vice versa. This has important implications for our management of landslide risk.
Yu Zhuang, Brian W. McArdell, and Perry Bartelt
Nat. Hazards Earth Syst. Sci., 25, 1901–1912, https://doi.org/10.5194/nhess-25-1901-2025, https://doi.org/10.5194/nhess-25-1901-2025, 2025
Short summary
Short summary
The experimentally based μ(I) rheology, widely used for gravitational mass flows, is reinterpreted as a Voellmy-type relationship to highlight its link to grain flow theory. Through block modeling and case studies, we establish its equivalence to μ(R) rheology. μ(I) models shear thinning but fails to capture acceleration and deceleration processes and deposit structure. Incorporating fluctuation energy in μ(R) improves accuracy, refining mass flow modeling and revealing practical challenges.
Sonam Rinzin, Stuart Dunning, Rachel Joanne Carr, Ashim Sattar, and Martin Mergili
Nat. Hazards Earth Syst. Sci., 25, 1841–1864, https://doi.org/10.5194/nhess-25-1841-2025, https://doi.org/10.5194/nhess-25-1841-2025, 2025
Short summary
Short summary
We modelled multiple glacial lake outburst flood (GLOF) scenarios (84 simulations) and tested the effect of nine key input parameters on the modelling results using r.avaflow. Our results highlight that GLOF modelling results are subject to uncertainty from the multiple input parameters. The variation in the volume of mass movement entering the lake causes the highest uncertainty in the modelled GLOF, followed by the DEM dataset and the origin of mass movement.
Roberto Sarro, Mauro Rossi, Paola Reichenbach, and Rosa María Mateos
Nat. Hazards Earth Syst. Sci., 25, 1459–1479, https://doi.org/10.5194/nhess-25-1459-2025, https://doi.org/10.5194/nhess-25-1459-2025, 2025
Short summary
Short summary
This study proposes a novel systematic workflow that integrates source area identification, deterministic runout modelling, the classification of runout outputs to derive susceptibility zonation, and robust procedures for validation and comparison. The proposed approach enables the integration and comparison of different modelling, introducing a robust and consistent workflow/methodology that allows us to derive and verify rockfall susceptibility zonation, considering different steps.
Matthias Schlögl, Anita Graser, Raphael Spiekermann, Jasmin Lampert, and Stefan Steger
Nat. Hazards Earth Syst. Sci., 25, 1425–1437, https://doi.org/10.5194/nhess-25-1425-2025, https://doi.org/10.5194/nhess-25-1425-2025, 2025
Short summary
Short summary
Communicating uncertainties is a crucial yet challenging aspect of spatial modelling – especially in applied research, where results inform decisions. In disaster risk reduction, susceptibility maps for natural hazards guide planning and risk assessment, yet their uncertainties are often overlooked. We present a new type of landslide susceptibility map that visualizes both susceptibility and associated uncertainty alongside guidelines for creating such maps using free and open-source software.
Lisa V. Luna, Jacob B. Woodard, Janice L. Bytheway, Gina M. Belair, and Benjamin B. Mirus
EGUsphere, https://doi.org/10.5194/egusphere-2025-947, https://doi.org/10.5194/egusphere-2025-947, 2025
Short summary
Short summary
Landslide frequency (how often landslides occur) is needed to assess landslide hazard and risk but has rarely been quantified at near continental scales. Here, we used statistical models to estimate landslide frequency across the United States while addressing gaps in landslide reporting. Our results showed strong variations in landslide frequency that followed topography, earthquake probability, and ecological region and highlighted areas with potential for widespread landsliding.
Dianne L. Brien, Mark E. Reid, Collin Cronkite-Ratcliff, and Jonathan P. Perkins
Nat. Hazards Earth Syst. Sci., 25, 1229–1253, https://doi.org/10.5194/nhess-25-1229-2025, https://doi.org/10.5194/nhess-25-1229-2025, 2025
Short summary
Short summary
Landslide runout zones are the areas downslope or downstream of landslide initiation. People often live and work in these areas, leading to property damage and deaths. Landslide runout may occur on hillslopes or in channels, requiring different modeling approaches. We develop methods to identify potential runout zones and apply these methods to identify susceptible areas for three municipalities in Puerto Rico.
Jonathan Perkins, Nina S. Oakley, Brian D. Collins, Skye C. Corbett, and W. Paul Burgess
Nat. Hazards Earth Syst. Sci., 25, 1037–1056, https://doi.org/10.5194/nhess-25-1037-2025, https://doi.org/10.5194/nhess-25-1037-2025, 2025
Short summary
Short summary
Rainfall-induced landslides result in deaths and economic losses annually across the globe. However, it is unclear how storm severity relates to landslide severity across large regions. Here we develop a method to dynamically map landslide-affected areas, and we compare this to meteorological estimates of storm severity. We find that preconditioning by earlier storms and the location of rainfall bursts, rather than atmospheric storm strength, dictate landslide magnitude and pattern.
Ellen B. Robson, Bhim Kumar Dahal, and David G. Toll
Nat. Hazards Earth Syst. Sci., 25, 949–973, https://doi.org/10.5194/nhess-25-949-2025, https://doi.org/10.5194/nhess-25-949-2025, 2025
Short summary
Short summary
Slopes excavated alongside roads in Nepal frequently fail (a landslide), resulting in substantial losses. Our participatory approach study with road engineers aimed to assess how road slope design guidelines in Nepal can be improved. Our study revealed inconsistent guideline adherence due to a lack of user-friendliness and inadequate training. We present general recommendations to enhance road slope management, as well as technical recommendations to improve the guidelines.
Chenchen Qiu and Xueyu Geng
Nat. Hazards Earth Syst. Sci., 25, 709–726, https://doi.org/10.5194/nhess-25-709-2025, https://doi.org/10.5194/nhess-25-709-2025, 2025
Short summary
Short summary
We propose an integrated method using a combination of a physical vulnerability matrix and a machine learning model to estimate the potential physical damage and associated economic loss caused by future debris flows based on collected historical data on the Qinghai–Tibet Plateau region.
Mark Bloomberg, Tim Davies, Elena Moltchanova, Tom Robinson, and David Palmer
Nat. Hazards Earth Syst. Sci., 25, 647–656, https://doi.org/10.5194/nhess-25-647-2025, https://doi.org/10.5194/nhess-25-647-2025, 2025
Short summary
Short summary
Debris flows occur infrequently, with average recurrence intervals (ARIs) ranging from decades to millennia. Consequently, they pose an underappreciated hazard. We describe how to make a preliminary identification of debris-flow-susceptible catchments, estimate threshold ARIs for debris flows that pose an unacceptable risk to life, and identify the “window of non-recognition” where debris flows are infrequent enough that their hazard is unrecognised yet frequent enough to pose a risk to life.
Christoph Schaller, Luuk Dorren, Massimiliano Schwarz, Christine Moos, Arie C. Seijmonsbergen, and E. Emiel van Loon
Nat. Hazards Earth Syst. Sci., 25, 467–491, https://doi.org/10.5194/nhess-25-467-2025, https://doi.org/10.5194/nhess-25-467-2025, 2025
Short summary
Short summary
We developed a machine-learning-based approach to predict the potential thickness of shallow landslides to generate improved inputs for slope stability models. We selected 21 explanatory variables, including metrics on terrain, geomorphology, vegetation height, and lithology, and used data from two Swiss field inventories to calibrate and test the models. The best-performing machine learning model consistently reduced the mean average error by at least 20 % compared to previous models.
Jui-Ming Chang, Che-Ming Yang, Wei-An Chao, Chin-Shang Ku, Ming-Wan Huang, Tung-Chou Hsieh, and Chi-Yao Hung
Nat. Hazards Earth Syst. Sci., 25, 451–466, https://doi.org/10.5194/nhess-25-451-2025, https://doi.org/10.5194/nhess-25-451-2025, 2025
Short summary
Short summary
The study on the Cilan landslide (CL) demonstrates the utilization of seismic analysis results as preliminary data for geologists during field surveys. Spectrograms revealed that the first event of CL consisted of four sliding failures accompanied by a gradual reduction in landslide volume. The second and third events were minor toppling and rockfalls. Then combining the seismological-based knowledge and field survey results, the spatiotemporal variation in landslide evolution is proposed.
Johannes Jakob Fürst, David Farías-Barahona, Thomas Bruckner, Lucia Scaff, Martin Mergili, Santiago Montserrat, and Humberto Peña
EGUsphere, https://doi.org/10.5194/egusphere-2024-3103, https://doi.org/10.5194/egusphere-2024-3103, 2025
Short summary
Short summary
The 1987 Parraguirre ice-rock avalanche developed into a devastating debris-flow causing loss of many lives and inflicting severe damage near Santiago, Chile. Here, we revise this event combining various observational records with modelling techniques. In this year, important snow cover coincided with warm days in spring. We further quantify the total solid volume, and forward important upward corrections for the trigger and flood volumes. Finally, river damming was key for high flow mobility.
Marko Sinčić, Sanja Bernat Gazibara, Mauro Rossi, and Snježana Mihalić Arbanas
Nat. Hazards Earth Syst. Sci., 25, 183–206, https://doi.org/10.5194/nhess-25-183-2025, https://doi.org/10.5194/nhess-25-183-2025, 2025
Short summary
Short summary
The paper focuses on classifying continuous landslide conditioning factors for susceptibility modelling, which resulted in 54 landslide susceptibility models that tested 11 classification criteria in combination with 5 statistical methods. The novelty of the research is that using stretched landslide conditioning factor values results in models with higher accuracy and that certain statistical methods are more sensitive to the landslide conditioning factor classification criteria than others.
Benjamin B. Mirus, Thom Bogaard, Roberto Greco, and Manfred Stähli
Nat. Hazards Earth Syst. Sci., 25, 169–182, https://doi.org/10.5194/nhess-25-169-2025, https://doi.org/10.5194/nhess-25-169-2025, 2025
Short summary
Short summary
Early warning of increased landslide potential provides situational awareness to reduce landslide-related losses from major storm events. For decades, landslide forecasts relied on rainfall data alone, but recent research points to the value of hydrologic information for improving predictions. In this paper, we provide our perspectives on the value and limitations of integrating subsurface hillslope hydrologic monitoring data and mathematical modeling for more accurate landslide forecasts.
Jui-Sheng Chou, Hoang-Minh Nguyen, Huy-Phuong Phan, and Kuo-Lung Wang
Nat. Hazards Earth Syst. Sci., 25, 119–146, https://doi.org/10.5194/nhess-25-119-2025, https://doi.org/10.5194/nhess-25-119-2025, 2025
Short summary
Short summary
This study enhances landslide prediction using advanced machine learning, including new algorithms inspired by historical explorations. The research accurately forecasts landslide movements by analyzing 8 years of data from Taiwan's Lushan, improving early warning and potentially saving lives and infrastructure. This integration marks a significant advancement in environmental risk management.
Di Wu, Yuke Wang, and Xin Chen
Nat. Hazards Earth Syst. Sci., 24, 4617–4630, https://doi.org/10.5194/nhess-24-4617-2024, https://doi.org/10.5194/nhess-24-4617-2024, 2024
Short summary
Short summary
This paper proposes a 3D limit analysis for seismic stability of soil slopes to address the influence of earthquakes on slope stabilities with nonlinear and linear criteria. Comparison results illustrate that the use of a linear envelope leads to the non-negligible overestimation of steep-slope stability, and this overestimation will be significant with increasing earthquakes. Earthquakes have a smaller influence on slope slip surfaces with a nonlinear envelope than those with a linear envelope.
Li Wei, Kaiheng Hu, Shuang Liu, Lan Ning, Xiaopeng Zhang, Qiyuan Zhang, and Md. Abdur Rahim
Nat. Hazards Earth Syst. Sci., 24, 4179–4197, https://doi.org/10.5194/nhess-24-4179-2024, https://doi.org/10.5194/nhess-24-4179-2024, 2024
Short summary
Short summary
The damage patterns of the buildings were classified into three types: (I) buried by primary debris flow, (II) inundated by secondary dam-burst flood, and (III) sequentially buried by debris flow and inundated by dam-burst flood. The threshold of the impact pressures in Zones (II) and (III) where vulnerability is equal to 1 is 84 kPa and 116 kPa, respectively. Heavy damage occurs at an impact pressure greater than 50 kPa, while slight damage occurs below 30 kPa.
Bo Peng and Xueling Wu
Nat. Hazards Earth Syst. Sci., 24, 3991–4013, https://doi.org/10.5194/nhess-24-3991-2024, https://doi.org/10.5194/nhess-24-3991-2024, 2024
Short summary
Short summary
Our research enhances landslide prevention using advanced machine learning to forecast heavy-rainfall-triggered landslides. By analyzing regions and employing various models, we identified optimal ways to predict high-risk rainfall events. Integrating multiple factors and models, including a neural network, significantly improves landslide predictions. Real data validation confirms our approach's reliability, aiding communities in mitigating landslide impacts and safeguarding lives and property.
Mahnoor Ahmed, Giacomo Titti, Sebastiano Trevisani, Lisa Borgatti, and Mirko Francioni
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-211, https://doi.org/10.5194/nhess-2024-211, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
Elevation models are compared with a true dataset for terrain characteristics which selects a better ranking model to test with different parameters for partitioning the terrain. The partitioning of the terrain is measured by how well a partitioned unit can support the mapped landslide area and number of landslides. The effect of this relationship is reflected with different metrics in the susceptibility maps.
Andrea Manconi, Yves Bühler, Andreas Stoffel, Johan Gaume, Qiaoping Zhang, and Valentyn Tolpekin
Nat. Hazards Earth Syst. Sci., 24, 3833–3839, https://doi.org/10.5194/nhess-24-3833-2024, https://doi.org/10.5194/nhess-24-3833-2024, 2024
Short summary
Short summary
Our research reveals the power of high-resolution satellite synthetic-aperture radar (SAR) imagery for slope deformation monitoring. Using ICEYE data over the Brienz/Brinzauls instability, we measured surface velocity and mapped the landslide event with unprecedented precision. This underscores the potential of satellite SAR for timely hazard assessment in remote regions and aiding disaster mitigation efforts effectively.
Oliver Korup, Lisa V. Luna, and Joaquin V. Ferrer
Nat. Hazards Earth Syst. Sci., 24, 3815–3832, https://doi.org/10.5194/nhess-24-3815-2024, https://doi.org/10.5194/nhess-24-3815-2024, 2024
Short summary
Short summary
Catalogues of mapped landslides are useful for learning and forecasting how frequently they occur in relation to their size. Yet, rare and large landslides remain mostly uncertain in statistical summaries of these catalogues. We propose a single, consistent method of comparing across different data sources and find that landslide statistics disclose more about subjective mapping choices than trigger types or environmental settings.
Rachael Lau, Carolina Seguí, Tyler Waterman, Nathaniel Chaney, and Manolis Veveakis
Nat. Hazards Earth Syst. Sci., 24, 3651–3661, https://doi.org/10.5194/nhess-24-3651-2024, https://doi.org/10.5194/nhess-24-3651-2024, 2024
Short summary
Short summary
This work examines the use of interferometric synthetic-aperture radar (InSAR) alongside in situ borehole measurements to assess the stability of deep-seated landslides for the case study of El Forn (Andorra). Comparing InSAR with borehole data suggests a key trade-off between accuracy and precision for various InSAR resolutions. Spatial interpolation with InSAR informed how many remote observations are necessary to lower error in a remote sensing re-creation of ground motion over the landslide.
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
Short summary
Short summary
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
Short summary
Short summary
The initiation of debris flows is significantly influenced by rainfall-induced hydrological processes. We propose a novel framework based on an integrated hydrological and hydrodynamic model and aimed at estimating intensity–duration (ID) rainfall thresholds responsible for triggering debris flows. In comparison to traditional statistical approaches, this physically based framework is particularly suitable for application in ungauged catchments where historical debris flow data are scarce.
Jürgen Mey, Ravi Kumar Guntu, Alexander Plakias, Igo Silva de Almeida, and Wolfgang Schwanghart
Nat. Hazards Earth Syst. Sci., 24, 3207–3223, https://doi.org/10.5194/nhess-24-3207-2024, https://doi.org/10.5194/nhess-24-3207-2024, 2024
Short summary
Short summary
The Himalayan road network links remote areas, but fragile terrain and poor construction lead to frequent landslides. This study on the NH-7 in India's Uttarakhand region analyzed 300 landslides after heavy rainfall in 2022 . Factors like slope, rainfall, rock type and road work influence landslides. The study's model predicts landslide locations for better road maintenance planning, highlighting the risk from climate change and increased road use.
Isabelle Utley, Tristram Hales, Ekbal Hussain, and Xuanmei Fan
EGUsphere, https://doi.org/10.5194/egusphere-2024-2277, https://doi.org/10.5194/egusphere-2024-2277, 2024
Short summary
Short summary
We analysed debris flows in Sichuan, China, using satellite data and simulations to assess check dam efficacy. Our study found whilst check dams can mitigate smaller flows, they may increase exposure to extreme events, with up to 40 % of structures in some areas affected. Urban development and reliance on check dams can create a false sense of security, raising exposure during large debris flows and highlights the need for risk management and infrastructure planning in hazard-prone areas.
Daniel Camilo Roman Quintero, Pasquale Marino, Abdullah Abdullah, Giovanni Francesco Santonastaso, and Roberto Greco
EGUsphere, https://doi.org/10.5194/egusphere-2024-2329, https://doi.org/10.5194/egusphere-2024-2329, 2024
Short summary
Short summary
Local thresholds for landslide forecasting, combining hydrologic predisposing factors and rainfall features, are developed from a physically based model of a slope. To extend their application to a wide area, uncertainty due to spatial variability of geomorphological and hydrologic variables is introduced. The obtained hydrometeorological thresholds, integrating root zone soil moisture and aquifer water level with rainfall depth, outperform thresholds based on rain intensity and duration.
Micol Fumagalli, Alberto Previati, Paolo Frattini, and Giovanni B. Crosta
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-140, https://doi.org/10.5194/nhess-2024-140, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
Shallow landslides are mass movements of limited thickness, mainly triggered by extreme rainfalls, that can pose a serious risk to the population. This study uses statistical methods to analyse and simulate the relationship between shallow landslides and rainfalls, showing that in the studied area shallow landslides are modulated by rainfall but controlled by lithology. A new classification method considering the costs associated with a misclassification of the susceptibility is also proposed.
Fabiola Banfi, Emanuele Bevacqua, Pauline Rivoire, Sérgio C. Oliveira, Joaquim G. Pinto, Alexandre M. Ramos, and Carlo De Michele
Nat. Hazards Earth Syst. Sci., 24, 2689–2704, https://doi.org/10.5194/nhess-24-2689-2024, https://doi.org/10.5194/nhess-24-2689-2024, 2024
Short summary
Short summary
Landslides are complex phenomena causing important impacts in vulnerable areas, and they are often triggered by rainfall. Here, we develop a new approach that uses information on the temporal clustering of rainfall, i.e. multiple events close in time, to detect landslide events and compare it with the use of classical empirical rainfall thresholds, considering as a case study the region of Lisbon, Portugal. The results could help to improve the prediction of rainfall-triggered landslides.
Jianqi Zhuang, Jianbing Peng, Chenhui Du, Yi Zhu, and Jiaxu Kong
Nat. Hazards Earth Syst. Sci., 24, 2615–2631, https://doi.org/10.5194/nhess-24-2615-2024, https://doi.org/10.5194/nhess-24-2615-2024, 2024
Short summary
Short summary
The Revised Infinite Slope Model (RISM) is proposed using the equal differential unit method and correcting the deficiency of the safety factor increasing with the slope increasing when the slope is larger than 40°, as calculated using the Taylor slope infinite model. The intensity–duration (I–D) prediction curve of the rainfall-induced shallow loess landslides with different slopes was constructed and can be used in forecasting regional shallow loess landslides.
Alexander B. Prescott, Luke A. McGuire, Kwang-Sung Jun, Katherine R. Barnhart, and Nina S. Oakley
Nat. Hazards Earth Syst. Sci., 24, 2359–2374, https://doi.org/10.5194/nhess-24-2359-2024, https://doi.org/10.5194/nhess-24-2359-2024, 2024
Short summary
Short summary
Fire can dramatically increase the risk of debris flows to downstream communities with little warning, but hazard assessments have not traditionally included estimates of inundation. We unify models developed by the scientific community to create probabilistic estimates of inundation area in response to rainfall at forecast lead times (≥ 24 h) needed for decision-making. This work takes an initial step toward a near-real-time postfire debris-flow inundation hazard assessment product.
Francis K. Rengers, Samuel Bower, Andrew Knapp, Jason W. Kean, Danielle W. vonLembke, Matthew A. Thomas, Jaime Kostelnik, Katherine R. Barnhart, Matthew Bethel, Joseph E. Gartner, Madeline Hille, Dennis M. Staley, Justin K. Anderson, Elizabeth K. Roberts, Stephen B. DeLong, Belize Lane, Paxton Ridgway, and Brendan P. Murphy
Nat. Hazards Earth Syst. Sci., 24, 2093–2114, https://doi.org/10.5194/nhess-24-2093-2024, https://doi.org/10.5194/nhess-24-2093-2024, 2024
Short summary
Short summary
Every year the U.S. Geological Survey produces 50–100 postfire debris-flow hazard assessments using models for debris-flow likelihood and volume. To refine these models they must be tested with datasets that clearly document rainfall, debris-flow response, and debris-flow volume. These datasets are difficult to obtain, but this study developed and analyzed a postfire dataset with more than 100 postfire storm responses over a 2-year period. We also proposed ways to improve these models.
Praveen Kumar, Priyanka Priyanka, Kala Venkata Uday, and Varun Dutt
Nat. Hazards Earth Syst. Sci., 24, 1913–1928, https://doi.org/10.5194/nhess-24-1913-2024, https://doi.org/10.5194/nhess-24-1913-2024, 2024
Short summary
Short summary
Our study focuses on predicting soil movement to mitigate landslide risks. We develop machine learning models with oversampling techniques to address the class imbalance in monitoring data. The dynamic ensemble model with K-means SMOTE (synthetic minority oversampling technique) achieves high precision, high recall, and a high F1 score. Our findings highlight the potential of these models with oversampling techniques to improve soil movement predictions in landslide-prone areas.
Kristian Svennevig, Julian Koch, Marie Keiding, and Gregor Luetzenburg
Nat. Hazards Earth Syst. Sci., 24, 1897–1911, https://doi.org/10.5194/nhess-24-1897-2024, https://doi.org/10.5194/nhess-24-1897-2024, 2024
Short summary
Short summary
In our study, we analysed publicly available data in order to investigate the impact of climate change on landslides in Denmark. Our research indicates that the rising groundwater table due to climate change will result in an increase in landslide activity. Previous incidents of extremely wet winters have caused damage to infrastructure and buildings due to landslides. This study is the first of its kind to exclusively rely on public data and examine landslides in Denmark.
Jane Walden, Mylène Jacquemart, Bretwood Higman, Romain Hugonnet, Andrea Manconi, and Daniel Farinotti
EGUsphere, https://doi.org/10.5194/egusphere-2024-1086, https://doi.org/10.5194/egusphere-2024-1086, 2024
Short summary
Short summary
In a study of eight landslides adjacent to glaciers in Alaska, we found that landslide movement increased as the glacier retreated past the landslide at four sites. Movement at other sites coincided with heavy precipitation or increased glacier thinning, and two sites showed little-to-no motion. We suggest that landslides next to water-terminating glaciers may be especially vulnerable to acceleration, which we guess is due to faster retreat rates and water replacing ice at the landslide edge.
Jiao Wang, Zhangxing Wang, Guanhua Sun, and Hongming Luo
Nat. Hazards Earth Syst. Sci., 24, 1741–1756, https://doi.org/10.5194/nhess-24-1741-2024, https://doi.org/10.5194/nhess-24-1741-2024, 2024
Short summary
Short summary
With a simplified formula linking rainfall and groundwater level, the rise of the phreatic surface within the slope can be obtained. Then, a global analysis method that considers both seepage and seismic forces is proposed to determine the safety factor of slopes subjected to the combined effect of rainfall and earthquakes. By taking a slope in the Three Gorges Reservoir area as an example, the safety evolution of the slope combined with both rainfall and earthquake is also examined.
Carlo Tacconi Stefanelli, William Frodella, Francesco Caleca, Zhanar Raimbekova, Ruslan Umaraliev, and Veronica Tofani
Nat. Hazards Earth Syst. Sci., 24, 1697–1720, https://doi.org/10.5194/nhess-24-1697-2024, https://doi.org/10.5194/nhess-24-1697-2024, 2024
Short summary
Short summary
Central Asia regions are marked by active tectonics, high mountains with glaciers, and strong rainfall. These predisposing factors make large landslides a serious threat in the area and a source of possible damming scenarios, which endanger the population. To prevent this, a semi-automated geographic information system (GIS-)based mapping method, centered on a bivariate correlation of morphometric parameters, was applied to give preliminary information on damming susceptibility in Central Asia.
Rex L. Baum, Dianne L. Brien, Mark E. Reid, William H. Schulz, and Matthew J. Tello
Nat. Hazards Earth Syst. Sci., 24, 1579–1605, https://doi.org/10.5194/nhess-24-1579-2024, https://doi.org/10.5194/nhess-24-1579-2024, 2024
Short summary
Short summary
We mapped potential for heavy rainfall to cause landslides in part of the central mountains of Puerto Rico using new tools for estimating soil depth and quasi-3D slope stability. Potential ground-failure locations correlate well with the spatial density of landslides from Hurricane Maria. The smooth boundaries of the very high and high ground-failure susceptibility zones enclose 75 % and 90 %, respectively, of observed landslides. The maps can help mitigate ground-failure hazards.
Katherine R. Barnhart, Christopher R. Miller, Francis K. Rengers, and Jason W. Kean
Nat. Hazards Earth Syst. Sci., 24, 1459–1483, https://doi.org/10.5194/nhess-24-1459-2024, https://doi.org/10.5194/nhess-24-1459-2024, 2024
Short summary
Short summary
Debris flows are a type of fast-moving landslide that start from shallow landslides or during intense rain. Infrastructure located downstream of watersheds susceptible to debris flows may be damaged should a debris flow reach them. We present and evaluate an approach to forecast building damage caused by debris flows. We test three alternative models for simulating the motion of debris flows and find that only one can forecast the correct number and spatial pattern of damaged buildings.
Luke A. McGuire, Francis K. Rengers, Ann M. Youberg, Alexander N. Gorr, Olivia J. Hoch, Rebecca Beers, and Ryan Porter
Nat. Hazards Earth Syst. Sci., 24, 1357–1379, https://doi.org/10.5194/nhess-24-1357-2024, https://doi.org/10.5194/nhess-24-1357-2024, 2024
Short summary
Short summary
Runoff and erosion increase after fire, leading to a greater likelihood of floods and debris flows. We monitored debris flow activity following a fire in western New Mexico, USA, and observed 16 debris flows over a <2-year monitoring period. Rainstorms with recurrence intervals of approximately 1 year were sufficient to initiate debris flows. All debris flows initiated during the first several months following the fire, indicating a rapid decrease in debris flow susceptibility over time.
Ken'ichi Koshimizu, Satoshi Ishimaru, Fumitoshi Imaizumi, and Gentaro Kawakami
Nat. Hazards Earth Syst. Sci., 24, 1287–1301, https://doi.org/10.5194/nhess-24-1287-2024, https://doi.org/10.5194/nhess-24-1287-2024, 2024
Short summary
Short summary
Morphological conditions of drainage basins that classify the presence or absence of debris flow fans were analyzed in areas with different rock strength using decision tree analysis. The relief ratio is the most important morphological factor regardless of the geology. However, the thresholds of morphological parameters needed for forming debris flow fans differ depending on the geology. Decision tree analysis is an effective tool for evaluating the debris flow risk for each geology.
Daniel Bolliger, Fritz Schlunegger, and Brian W. McArdell
Nat. Hazards Earth Syst. Sci., 24, 1035–1049, https://doi.org/10.5194/nhess-24-1035-2024, https://doi.org/10.5194/nhess-24-1035-2024, 2024
Short summary
Short summary
We analysed data from the Illgraben debris flow monitoring station, Switzerland, and we modelled these flows with a debris flow runout model. We found that no correlation exists between the grain size distribution, the mineralogical composition of the matrix, and the debris flow properties. The flow properties rather appear to be determined by the flow volume, from which most other parameters can be derived.
Yuntao Zhou, Xiaoyan Zhao, Guangze Zhang, Bernd Wünnemann, Jiajia Zhang, and Minghui Meng
Nat. Hazards Earth Syst. Sci., 24, 891–906, https://doi.org/10.5194/nhess-24-891-2024, https://doi.org/10.5194/nhess-24-891-2024, 2024
Short summary
Short summary
We developed three rock bridge models to analyze 3D stability and deformation behaviors of the Tizicao landslide and found that the contact surface model with high strength parameters combines advantages of the intact rock mass model in simulating the deformation of slopes with rock bridges and the modeling advantage of the Jennings model. The results help in choosing a rock bridge model to simulate landslide stability and reveal the influence laws of rock bridges on the stability of landslides.
Ashok Dahal, Hakan Tanyas, Cees van Westen, Mark van der Meijde, Paul Martin Mai, Raphaël Huser, and Luigi Lombardo
Nat. Hazards Earth Syst. Sci., 24, 823–845, https://doi.org/10.5194/nhess-24-823-2024, https://doi.org/10.5194/nhess-24-823-2024, 2024
Short summary
Short summary
We propose a modeling approach capable of recognizing slopes that may generate landslides, as well as how large these mass movements may be. This protocol is implemented, tested, and validated with data that change in both space and time via an Ensemble Neural Network architecture.
Li-Ru Luo, Zhi-Xiang Yu, Li-Jun Zhang, Qi Wang, Lin-Xu Liao, and Li Peng
Nat. Hazards Earth Syst. Sci., 24, 631–649, https://doi.org/10.5194/nhess-24-631-2024, https://doi.org/10.5194/nhess-24-631-2024, 2024
Short summary
Short summary
We performed field investigations on a rockfall near Jiguanshan National Forest Park, Chengdu. Vital information was obtained from an unmanned aerial vehicle survey. A finite element model was created to reproduce the damage evolution. We found that the impact kinetic energy was below the design protection energy. Improper member connections prevent the barrier from producing significant deformation to absorb energy. Damage is avoided by improving the ability of the nets and ropes to slide.
Sudhanshu Dixit, Srikrishnan Siva Subramanian, Piyush Srivastava, Ali P. Yunus, Tapas Ranjan Martha, and Sumit Sen
Nat. Hazards Earth Syst. Sci., 24, 465–480, https://doi.org/10.5194/nhess-24-465-2024, https://doi.org/10.5194/nhess-24-465-2024, 2024
Short summary
Short summary
Rainfall intensity–duration (ID) thresholds can aid in the prediction of natural hazards. Large-scale sediment disasters like landslides, debris flows, and flash floods happen frequently in the Himalayas because of their propensity for intense precipitation events. We provide a new framework that combines the Weather Research and Forecasting (WRF) model with a regionally distributed numerical model for debris flows to analyse and predict intense rainfall-induced landslides in the Himalayas.
Jacob B. Woodard, Benjamin B. Mirus, Nathan J. Wood, Kate E. Allstadt, Benjamin A. Leshchinsky, and Matthew M. Crawford
Nat. Hazards Earth Syst. Sci., 24, 1–12, https://doi.org/10.5194/nhess-24-1-2024, https://doi.org/10.5194/nhess-24-1-2024, 2024
Short summary
Short summary
Dividing landscapes into hillslopes greatly improves predictions of landslide potential across landscapes, but their scaling is often arbitrarily set and can require significant computing power to delineate. Here, we present a new computer program that can efficiently divide landscapes into meaningful slope units scaled to best capture landslide processes. The results of this work will allow an improved understanding of landslide potential and can help reduce the impacts of landslides worldwide.
Anne Felsberg, Zdenko Heyvaert, Jean Poesen, Thomas Stanley, and Gabriëlle J. M. De Lannoy
Nat. Hazards Earth Syst. Sci., 23, 3805–3821, https://doi.org/10.5194/nhess-23-3805-2023, https://doi.org/10.5194/nhess-23-3805-2023, 2023
Short summary
Short summary
The Probabilistic Hydrological Estimation of LandSlides (PHELS) model combines ensembles of landslide susceptibility and of hydrological predictor variables to provide daily, global ensembles of hazard for hydrologically triggered landslides. Testing different hydrological predictors showed that the combination of rainfall and soil moisture performed best, with the lowest number of missed and false alarms. The ensemble approach allowed the estimation of the associated prediction uncertainty.
Cited articles
Akaike, H.: Information Theory and an Extension of the Maximum Likelihood Principle, in: Breakthroughs in Statistics: Foundations and Basic Theory, edited by: Kotz, S. and Johnson, N. L., Springer, New York, NY, 610–624, https://doi.org/10.1007/978-1-4612-0919-5_38, 1992
Ashok, S. P. and Pekkat, S.: A systematic quantitative review on the performance of some of the recent short-term rainfall forecasting techniques, J. Water Clim. Change, 13, 3004–3029, https://doi.org/10.2166/wcc.2022.302, 2022.
Berti, M., Martina, M. L. V., Franceschini, S., Pignone, S., Simoni, A., and Pizziolo, M.: Probabilistic rainfall thresholds for landslide occurrence using a Bayesian approach, J. Geophys. Res.-Earth, 117, F04006, https://doi.org/10.1029/2012JF002367, 2012.
BeVille, S. H., Mirus, B. B., Ebel, B. A., Mader, G. G., and Loague, K.: Using simulated hydrologic response to revisit the 1973 Lerida Court landslide, Environ. Earth Sci., 61, 1249–1257, https://doi.org/10.1007/s12665-010-0448-z, 2010.
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.
Booth, A. M., Sifford, C., Vascik, B., Siebert, C., and Buma, B.: Large wood inhibits debris flow runout in forested southeast Alaska, Earth Surface Processes and Landforms, 45, 1555–1568, https://doi.org/10.1002/esp.4830, 2020.
Bozzolan, E., Holcombe, E., Pianosi, F., and Wagener, T.: Including informal housing in slope stability analysis – an application to a data-scarce location in the humid tropics, Nat. Hazards Earth Syst. Sci., 20, 3161–3177, https://doi.org/10.5194/nhess-20-3161-2020, 2020.
Brier, G. W.: Monthly weather review, Mon. Weather Rev., 78, 1–3, 1950.
Brunetti, M. T., Peruccacci, S., Rossi, M., Luciani, S., Valigi, D., and Guzzetti, F.: Rainfall thresholds for the possible occurrence of landslides in Italy, Nat. Hazards Earth Syst. Sci., 10, 447–458, https://doi.org/10.5194/nhess-10-447-2010, 2010.
Bürkner, P.-C.: brms: An R Package for Bayesian Multilevel Models Using Stan, J. Stat. Softw., 80, 1–28, https://doi.org/10.18637/jss.v080.i01, 2017.
Busch, L., O'Connell, V., Prussian, K., Foss, J., Landwehr, D. J., Hoffman, J., Becker, M., Gould, A., Wolken, G., Stevens, D. A., Whorton, E., Jacobs, A., Curtis, J., Holloway, E., Fielding, E., Buma, B., and Carter, B.: August 2015 Sitka Landslides, Sitka Geotask Force Summaries, 1, 1–32, 2016.
Busch, L., Lempert, R., Izenberg, M., and Patton, A.: Run Uphill for a Tsunami, Downhill for a Landslide, Issues Sci. Technol., 38, 40–46, 2021.
Chae, B.-G., Park, J.-J., Catani, F., Simoni, A., and Berti, M.: Landslide prediction, monitoring and early warning: a concise review of state-of-the-art, Geosci. J., 21, 1033–1070, 2017.
Chleborad, B. A. F., Baum, R. L., Godt, J. W., and Report, U. S. G. S. O.: Rainfall Thresholds for Forecasting Landslides in the Seattle, Washington, Area – Exceedance and Probability: USGS Open-File Report 2006-1064, 1–31, https://doi.org/10.3133/ofr20061064, 2006.
Chu, M., Patton, A., Roering, J., Siebert, C., Selker, J., Walter, C., and Udell, C.: SitkaNet: A low-cost, distributed sensor network for landslide monitoring and study, HardwareX, 9, e00191, https://doi.org/10.1016/j.ohx.2021.e00191, 2021.
Collins, B. D., Oakley, N. S., Perkins, J. P., East, A. E., Corbett, S. C., and Hatchett, B. J.: Linking Mesoscale Meteorology With Extreme Landscape Response: Effects of Narrow Cold Frontal Rainbands (NCFR), J. Geophys. Res.-Earth, 125, e2020JF005675, https://doi.org/10.1029/2020JF005675, 2020.
Cordeira, J. M., Stock, J., Dettinger, M. D., Young, A. M., Kalansky, J. F., and Ralph, F. M.: A 142-year climatology of northern California landslides and atmospheric rivers, B. Am. Meteorol. Soc., 100, 1499–1509, https://doi.org/10.1175/BAMS-D-18-0158.1, 2019.
Cutter, S. L. and Finch, C.: Temporal and spatial changes in social vulnerability to natural hazards, P. Natl. Acad. Sci. USA, 105, 2301–2306, https://doi.org/10.1073/pnas.0710375105, 2008.
Darrow, M. M., Nelson, V. A., Grilliot, M., Wartman, J., Jacobs, A., Baichtal, J. F., and Buxton, C.: Geomorphology and initiation mechanisms of the 2020 Haines, Alaska landslide, Landslides, 19, 2177–2188, https://doi.org/10.1007/s10346-022-01899-3, 2022.
Diamond, H. J., Karl, T. R., Palecki, M. A., Baker, C. B., Bell, J. E., Leeper, R. D., Easterling, D. R., Lawrimore, J. H., Meyers, T. P., Helfert, M. R., Goodge, G., and Thorne, P. W.: U.S. Climate Reference Network after One Decade of Operations: Status and Assessment, B. Am. Meteorol. Soc., 94, 485–498, https://doi.org/10.1175/BAMS-D-12-00170.1, 2013.
Elliott, J. and Freymueller, J. T.: A Block Model of Present-Day Kinematics of Alaska and Western Canada, J. Geophys. Res.-Sol. Ea., 125, 1–30, https://doi.org/10.1029/2019JB018378, 2020.
Fabry, F. and Seed, A. W.: Quantifying and predicting the accuracy of radar-based quantitative precipitation forecasts, Adv. Water Resour., 32, 1043–1049, https://doi.org/10.1016/j.advwatres.2008.10.001, 2009.
Gariano, S. L., Melillo, M., Peruccacci, S., and Brunetti, M. T.: How much does the rainfall temporal resolution affect rainfall thresholds for landslide triggering?, Nat. Hazards, 100, 655–670, https://doi.org/10.1007/s11069-019-03830-x, 2020.
Gelman, A., Jakulin, A., Grazia Pittau, M., and Su, Y.-S.: A weakly informative default prior distribution for logistic and other regression models, Ann. Appl. Stat., 2, 1360–1383, https://doi.org/10.1214/08-AOAS191, 2008.
Giannecchini, R., Galanti, Y., Amato, G. D., and Barsanti, M.: Geomorphology Probabilistic rainfall thresholds for triggering debris flows in a human-modified landscape, Geomorphology, 257, 94–107, https://doi.org/10.1016/j.geomorph.2015.12.012, 2016.
Guzzetti, F.: Invited perspectives: Landslide populations – can they be predicted?, Nat. Hazards Earth Syst. Sci., 21, 1467–1471, https://doi.org/10.5194/nhess-21-1467-2021, 2021.
Guzzetti, F., Peruccacci, S., Rossi, M., and Stark, C. P.: The rainfall intensity-duration control of shallow landslides and debris flows: An update, Landslides, 5, 3–17, https://doi.org/10.1007/s10346-007-0112-1, 2008.
Guzzetti, F., Gariano, S. L., Peruccacci, S., Brunetti, M. T., Marchesini, I., Rossi, M., and Melillo, M.: Geographical landslide early warning systems, Earth-Sci. Rev., 200, 102973, https://doi.org/10.1016/j.earscirev.2019.102973, 2020.
Hamilton, T. D.: Correlation of quaternary glacial deposits in Alaska, Quaternary Sci. Rev., 5, 171–180, 1986.
Hennon, A., Paul, E., Amore, D., David, V., Dustin, T., and Melinda, B.: Influence of Forest Canopy and Snow on Microclimate in a Declining Yellow-Cedar Forest of Southeast Alaska, Northwest Sci., 84, 73–87, https://doi.org/10.3955/046.084.0108, 2010.
Izenberg, M., Brown, R., Siebert, C., Heinz, R., Rahmattalabi, A., and Vayanos, P.: A Community-Partnered Approach to Social Network Data Collection for a Large and Partial Network, Field Method., 35, 1–7, https://doi.org/10.1177/1525822X221074769, 2022.
Jacobs, A., Holloway, E., and Dixon, A.: Atmospheric Rivers in Alaska – Yes they do exist, and are usually tied to the biggest and most damaging rain-generated floods in Alaska, International Atmospheric Rivers Conference, La Jolla, CA, USA, 8–11 August 2016, http://cw3e.ucsd.edu/ARconf2016/Posters/Jacobs_IARC2016.pdf (last access: 11 October 2023), 2016.
Jakob, M., Holm, K., Lange, O., and Schwab, J. W.: Hydrometeorological thresholds for landslide initiation and forest operation shutdowns on the north coast of British Columbia, Landslides, 3, 228–238, https://doi.org/10.1007/s10346-006-0044-1, 2006.
Jakob, M., Owen, T., and Simpson, T.: A regional real-time debris-flow warning system for the District of North Vancouver, Canada, Landslides, 9, 165–178, https://doi.org/10.1007/s10346-011-0282-8, 2012.
Johnson, A. C., Swanston, D. N., and Mcgee, K. E.: Landslide Initiation, Runout, and Deposition within Clearcuts and Old-Growth Forests of Alaska, J. Am. Water Resour. As., 36, 17–30, 2000.
Johnson, G. L. and Hanson, C. L.: Topographic and Atmospheric Influences on Precipitation Variability over a Mountainous Watershed, J. Appl. Meteorol., 34, 68–87, 1994.
Johnston, E. C., Davenport, F. V., Wang, L., Caers, J. K., Muthukrishnan, S., Burke, M., and Diffenbaugh, N. S.: Quantifying the Effect of Precipitation on Landslide Hazard in Urbanized and Non-Urbanized Areas, Geophys. Res. Lett., 48, e2021GL094038, https://doi.org/10.1029/2021GL094038, 2021.
Khan, S., Kirschbaum, D. B., and Stanley, T.: Investigating the potential of a global precipitation forecast to inform landslide prediction, Weather Clim. Extrem., 33, 100364, https://doi.org/10.1016/j.wace.2021.100364, 2021.
King, G. and Zeng, L.: Explaining Rare Events in International Relations, Int. Organ., 55, 693–715, 2003.
Kirschbaum, D. and Stanley, T.: Satellite-Based Assessment of Rainfall-Triggered Landslide Hazard for Situational Awareness, Earth's Future, 6, 505–523, https://doi.org/10.1002/2017EF000715, 2018.
Kristensen, L., Czekirda, J., Penna, I., Etzelmüller, B., Nicolet, P., Pullarello, J. S., Blikra, L. H., Skrede, I., Oldani, S., and Abellan, A.: Movements, failure and climatic control of the Veslemannen rockslide, Western Norway, Landslides, 1963–1980, https://doi.org/10.1007/s10346-020-01609-x, 2021.
Kuha, J.: AIC and BIC: Comparisons of assumptions and performance, Sociol. Method. Res., 33, 188–229, https://doi.org/10.1177/0049124103262065, 2004.
Lee, S., Won, J. S., Jeon, S. W., Park, I., and Lee, M. J.: Spatial Landslide Hazard Prediction Using Rainfall Probability and a Logistic Regression Model, Math. Geosci., 47, 565–589, https://doi.org/10.1007/s11004-014-9560-z, 2015.
Leonarduzzi, E., Molnar, P., and McArdell, B.: Predictive performance of rainfall thresholds for shallow landslides in Switzerland from gridded daily data, J. Am. Water Resour. As., 5, 6612–6625, https://doi.org/10.1111/j.1752-1688.1969.tb04897.x, 2017.
Lesnek, A. J., Briner, J. P., Lindqvist, C., Baichtal, J. F., and Heaton, T. H.: Deglaciation of the Pacific coastal corridor directly preceded the human colonization of the Americas, Science Advances, 4, eaar5040, https://doi.org/10.1126/sciadv.aar5040, 2018.
Mann, D. H.: Wisconsin and Holocene Glaciation of Southeast Alaska, Glaciat. Alaska Geol. Rec., 64, 237–265, https://doi.org/10.4018/978-1-60566-198-8.ch103, 1986.
Marino, P., Peres, D. J., Cancelliere, A., Greco, R., and Bogaard, T. A.: Soil moisture information can improve shallow landslide forecasting using the hydrometeorological threshold approach, Landslides, 17, 2041–2054, https://doi.org/10.1007/s10346-020-01420-8, 2020.
McCullagh, P. and Nelder, J. A.: Generalized Linear Models, 2nd ed., Chapman and Hall, London New York, 532 pp., https://doi.org/10.2307/2347392, 1989.
McElreath, R.: Statistical Rethinking: A Bayesian Course with Exampels in R and STAN, Second Edn., Chapman and Hall/CRC, ISBN 978-0-367-13991-9, 2020.
Melillo, M., Brunetti, M. T., Peruccacci, S., Gariano, S. L., Roccati, A., and Guzzetti, F.: A tool for the automatic calculation of rainfall thresholds for landslide occurrence, Environ. Modell. Softw., 105, 230–243, https://doi.org/10.1016/j.envsoft.2018.03.024, 2018.
University of Utah, Department of Atmospheric Sciences: MesoWest, https://mesowest.utah.edu, last access: 12 October 2023.
Miller, D.: Modeling Susceptibility to Landslides and Debris Flows in Southeast Alaska, https://terrainworks.sharefile.com/share/view/s8532031dfcf44ab0a87bc8a83b2b3b1c (last access: 12 October 2023), 2019.
Mirus, B. B., Morphew, M. D., and Smith, J. B.: Developing Hydro-Meteorological Thresholds for Shallow Landslide Initiation and Early Warning, Water, 10, 1–19, https://doi.org/10.3390/w10091274, 2018a.
Mirus, B. B., Becker, R. E., Baum, R. L., and Smith, J. B.: Integrating real-time subsurface hydrologic monitoring with empirical rainfall thresholds to improve landslide early warning, Landslides, 15, 1909–1919, https://doi.org/10.1007/s10346-018-0995-z, 2018b.
Napoli, A., Crespi, A., Ragone, F., Maugeri, M., and Pasquero, C.: Variability of orographic enhancement of precipitation in the Alpine region, Sci. Rep., 9, 1–8, https://doi.org/10.1038/s41598-019-49974-5, 2019.
Patton, A. and Luna, L.: pattonai/sitka-lews: sitka-lews repository for manuscript submission 20210105, Zenodo [data set] and [code], https://doi.org/10.5281/zenodo.7508537, 2023.
Oakley, N. S., Lancaster, J. T., Kaplan, M. L., and Ralph, F. M.: Synoptic conditions associated with cool season post-fire debris flows in the Transverse Ranges of southern California, Nat. Hazards, 88, 327–354, https://doi.org/10.1007/s11069-017-2867-6, 2017.
Osanai, N., Shimizu, T., Kuramoto, K., Kojima, S., and Noro, T.: Japanese early-warning for debris flows and slope failures using rainfall indices with Radial Basis Function Network, Landslides, 7, 325–338, https://doi.org/10.1007/s10346-010-0229-5, 2010.
Patton, A. I., Roering, J. J., and Orland, E.: Debris flow initiation in postglacial terrain: Insights from shallow landslide initiation models and geomorphic mapping in Southeast Alaska, Earth Surf. Proc. Landf., 47, 1583–1598, https://doi.org/10.1002/esp.5336, 2022.
Peres, D. J. and Cancelliere, A.: Comparing methods for determining landslide early warning thresholds: potential use of non-triggering rainfall for locations with scarce landslide data availability, Landslides, 18, 3135–3147, https://doi.org/10.1007/s10346-021-01704-7, 2021.
Perica, S., Kane, D., Dietz, S., Maitaria, K., Martin, D., Pavlovic, S., Roy, I., Stuefer, S., Tidwell, A., Trypaluk, C., Unruh, D., Yekta, M., Betts, E., Bonnin, G., Heim, S., Hiner, L., Lilly, E., Narayanan, J., Yan, F., and Zhao, T.: NOAA Atlas 14 Precipitation-Frequency Atlas of the United States, US Department of Commerce, National Oceanic and Atmospheric Administraion, National Weather Service, and University of Fairbanks, Silver Spring, Maryland, v7-2.0, https://www.weather.gov/media/owp/oh/hdsc/docs/Atlas14_Volume7.pdf (last access: 13 October 2023), 2012.
Peruccacci, S., Brunetti, M. T., Gariano, S. L., Melillo, M., Rossi, M., and Guzzetti, F.: Rainfall thresholds for possible landslide occurrence in Italy, Geomorphology, 290, 39–57, https://doi.org/10.1016/j.geomorph.2017.03.031, 2017.
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.
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.
R Core Team: R: A languge and environment for statistical computing, https://www.r-project.org/ (last access: 10 October 2023), 2019.
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, https://doi.org/10.1016/j.earscirev.2018.03.001, 2018.
Riehle, J. R., Champion, D. E., Brew, D. A., and Lanphere, M. A.: Pyroclastic deposits of the Mount Edgecumbe volcanic field, southeast Alaska: eruptions of a stratified magma chamber, J. Volcanol. Geoth. Res., 53, 117–143, https://doi.org/10.1016/0377-0273(92)90078-R, 1992a.
Riehle, J. R., Mann, D. H., Peteet, D. M., Engstrom, D. R., Brew, D. A., and Meyer, C. E.: The Mount Edgecumbe tephra deposits, a marker horizon in southeastern Alaska near the Pleistocene-Holocene boundary, Quaternary Res., 37, 183–202, https://doi.org/10.1016/0033-5894(92)90081-S, 1992b.
Roth, A., Hock, R., Schuler, T. V., Bieniek, P. A., Pelto, M., and Aschwanden, A.: Modeling Winter Precipitation Over the Juneau Icefield, Alaska, Using a Linear Model of Orographic Precipitation, Front. Earth Sci., 6, 1–19, https://doi.org/10.3389/feart.2018.00020, 2018.
Saito, H., Nakayama, D., and Matsuyama, H.: Relationship between the initiation of a shallow landslide and rainfall intensity-duration thresholds in Japan, Geomorphology, 118, 167–175, https://doi.org/10.1016/j.geomorph.2009.12.016, 2010.
Saito, T. and Rehmsmeier, M.: The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets, PLOS ONE, 10, e0118432, https://doi.org/10.1371/journal.pone.0118432, 2015.
Sandberg, E.: A History of Alaska Population Settlement, Alaska Dep. Labor Work. Dev., 19, https://live.laborstats.alaska.gov/pop/estimates/pub/pophistory.pdf (last accessed: 10 October 2023), 2013.
Scheevel, C. R., Baum, R. L., Mirus, B. B., and Smith, J. B.: Precipitation thresholds for landslide occurrence near Seattle, Mukilteo, and Everett, Washington: U.S. Geological Survey Open-File Report 2017–1039, USGS Open-File Rep. 2017-1039, 51, https://doi.org/10.3133/ofr20171039, 2017.
Schwarz, G.: Estimating the Dimenson of a Model, Ann. Stat., 6, 461–464, 1978.
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, 2018.
Shanley, C. S., Pyare, S., Goldstein, M. I., Alaback, P. B., Albert, D. M., Beier, C. M., Brinkman, T. J., Edwards, R. T., Hood, E., MacKinnon, A., McPhee, M. V., Patterson, T. M., Suring, L. H., Tallmon, D. A., and Wipfli, M. S.: Climate change implications in the northern coastal temperate rainforest of North America, Climatic Change, 130, 155–170, https://doi.org/10.1007/s10584-015-1355-9, 2015.
Sharma, A. R. and Déry, S. J.: Variability and trends of landfalling atmospheric rivers along the Pacific Coast of northwestern North America, Int. J. Climatol., 40, 544–558, https://doi.org/10.1002/joc.6227, 2019.
Sharma, A. R. and Déry, S. J.: Contribution of atmospheric rivers to annual, seasonal, and extreme precipitation across British Columbia and southeastern Alaska, J. Geophys. Res.-Atmos., 125, e2019JD031823, https://doi.org/10.1029/2019jd031823, 2020.
Sidle, R. C.: Shallow groundwater fluctuations in unstable hillslopes of coastal Alaska, Zeitschrift für Gletscherkd. und Glazialgeol., 20, 79–95, 1984.
Sidle, R. C. and Swanston, D. N.: Analysis of a small debris slide in coastal Alaska, Can. Geotech. J., 19, 167–174, https://doi.org/10.1139/t82-018, 1981.
Staley, D. M., Negri, J. A., Kean, J. W., Laber, J. L., Tillery, A. C., and Youberg, A. M.: Prediction of spatially explicit rainfall intensity–duration thresholds for post-fire debris-flow generation in the western United States, Geomorphology, 278, 149–162, https://doi.org/10.1016/j.geomorph.2016.10.019, 2017.
Stan Development Team: Stan User's Guide 2.28, 1–9, https://www.mc-stan.org (last access: 10 February 2022), 2022.
Stanley, T. and Kirschbaum, D. B.: A heuristic approach to global landslide susceptibility mapping, Nat. Hazards, 87, 145–164, https://doi.org/10.1007/s11069-017-2757-y, 2017.
Swanston, D. N.: Mechanics of Debris Avalanching in Shallow Till Soils of Southeast Alaska, USDA Forest Service Research Paper PNW-103, Juneau, Alaska, https://doi.org/10.5962/bhl.title.87969, 1970.
Swanston, D. N. and Marion, D. A.: Landslide response to timber harvest in Southeast Alaska, in: Proceedings of the 5th Fed. Interag. Sediment. Conf., Las Vegas, NV, USA, 18–21 March 1991, 10, 49–56, https://pubs.usgs.gov/misc/FISC_1947-2006/pdf/1st-7thFISCs-CD/5thFISC/5Fisc-V2/5Fsc2-10.PDF (last access: 11 October 2023), 1991.
Synoptic: PASI Station Data Download, Synoptic [data set], https://download.synopticdata.com/#a/PASI, 2023.
Thomas, M. A., Mirus, B. B., Collins, B. D., Lu, N., and Godt, J. W.: Variability in soil-water retention properties and implications for physics-based simulation of landslide early warning criteria, Landslides, 15, 1265–1277, https://doi.org/10.1007/s10346-018-0950-z, 2018.
Tufano, R., Cesarano, M., Fusco, F., and Vita, P. De: Probabilistic approaches for assessing rainfall thresholds triggering shallow landslides. The study case of the peri-vesuvian area (Southern Italy), Ital. J. Eng. Geol. Environ., 2019, 105–110, https://doi.org/10.4408/IJEGE.2019-01.S-17, 2019.
Tullos, D., Byron, E., Galloway, G., Obeysekera, J., Prakash, O., and Sun, Y. H.: Review of challenges of and practices for sustainable management of mountain flood hazards, Nat. Hazards, 83, 1763–1797, https://doi.org/10.1007/s11069-016-2400-3, 2016.
US Census Bureau: QuickFacts for the Sitka city and borough, Alaska, https://www.census.gov/quickfacts/sitkacityandboroughalaska (last access: 12 October 2023), 2022.
US Forest Service: Tongass National Forest Landslide Areas, State of Alaska Open Data Portal, https://gis.data.alaska.gov/datasets/usfs::tongass-national-forest-landslide-areas/about (last access: 12 October 2023), 2019.
Vascik, B. A., Booth, A. M., Buma, B., and Berti, M.: Estimated Amounts and Rates of Carbon Mobilized by Landsliding in Old-Growth Temperate Forests of SE Alaska, J. Geophys. Res.-Biogeo., 126, 1–21, https://doi.org/10.1029/2021JG006321, 2021.
Vehtari, A., Gelman, A., and Gabry, J.: Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC, Stat. Comput., 27, 1413–1432, https://doi.org/10.1007/s11222-016-9696-4, 2017.
Wendler, G., Galloway, K., and Stuefer, M.: On the climate and climate change of Sitka, Southeast Alaska, Theor. Appl. Climatol., 126, 27–34, https://doi.org/10.1007/s00704-015-1542-7, 2016.
White, C., Gehrels, G. E., Pecha, M., Giesler, D., Yokelson, I., McClelland, W. C., and Butler, R. F.: U-Pb and Hf isotope analysis of detrital zircons from Paleozoic strata of the southern Alexander terrane (Southeast Alaska), Lithosphere, 8, 83–96, https://doi.org/10.1130/L475.1, 2016.
Wicki, A., Lehmann, P., Hauck, C., Seneviratne, S. I., Waldner, P., and Stähli, M.: Assessing the potential of soil moisture measurements for regional landslide early warning, Landslides, 17, 1881–1896, https://doi.org/10.1007/s10346-020-01400-y, 2020.
Wilks, D. S.: Forecast Verification, Int. Geophys., 100, 301–394, https://doi.org/10.1016/B978-0-12-385022-5.00008-7, 2011.
Short summary
Landslide warning systems often use statistical models to predict landslides based on rainfall. They are typically trained on large datasets with many landslide occurrences, but in rural areas large datasets may not exist. In this study, we evaluate which statistical model types are best suited to predicting landslides and demonstrate that even a small landslide inventory (five storms) can be used to train useful models for landslide early warning when non-landslide events are also included.
Landslide warning systems often use statistical models to predict landslides based on rainfall....
Altmetrics
Final-revised paper
Preprint