Articles | Volume 24, issue 11
https://doi.org/10.5194/nhess-24-3815-2024
© Author(s) 2024. 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-24-3815-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Nov 2024
Research article |
| 08 Nov 2024
| 08 Nov 2024
Size scaling of large landslides from incomplete inventories
Oliver Korup
CORRESPONDING AUTHOR
Institute of Environmental Sciences and Geography, University of Potsdam, Potsdam, Germany
Institute of Geosciences, University of Potsdam, Potsdam, Germany
Lisa V. Luna
Institute of Environmental Sciences and Geography, University of Potsdam, Potsdam, Germany
Potsdam Institute for Climate Impact Research PIK, Potsdam, Germany
Joaquin V. Ferrer
Institute of Environmental Sciences and Geography, University of Potsdam, Potsdam, Germany
Potsdam Institute for Climate Impact Research PIK, Potsdam, Germany
Related authors
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.
Natalie Lützow, Bretwood Higman, Martin Truffer, Bodo Bookhagen, Friedrich Knuth, Oliver Korup, Katie E. Hughes, Marten Geertsema, John J. Clague, and Georg Veh
EGUsphere, https://doi.org/10.5194/egusphere-2024-2812, https://doi.org/10.5194/egusphere-2024-2812, 2024
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.
Annette I. Patton, Lisa V. Luna, Joshua J. Roering, Aaron Jacobs, Oliver Korup, and Benjamin B. Mirus
Nat. Hazards Earth Syst. Sci., 23, 3261–3284, https://doi.org/10.5194/nhess-23-3261-2023, https://doi.org/10.5194/nhess-23-3261-2023, 2023
Short summary
Short summary
Landslide warning systems often use statistical models to predict landslides based on rainfall. They are typically trained on large datasets with many landslide occurrences, but in rural areas large datasets may not exist. In this study, we evaluate which statistical model types are best suited to predicting landslides and demonstrate that even a small landslide inventory (five storms) can be used to train useful models for landslide early warning when non-landslide events are also included.
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.
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.
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.
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
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.
Natalie Lützow, Bretwood Higman, Martin Truffer, Bodo Bookhagen, Friedrich Knuth, Oliver Korup, Katie E. Hughes, Marten Geertsema, John J. Clague, and Georg Veh
EGUsphere, https://doi.org/10.5194/egusphere-2024-2812, https://doi.org/10.5194/egusphere-2024-2812, 2024
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.
Annette I. Patton, Lisa V. Luna, Joshua J. Roering, Aaron Jacobs, Oliver Korup, and Benjamin B. Mirus
Nat. Hazards Earth Syst. Sci., 23, 3261–3284, https://doi.org/10.5194/nhess-23-3261-2023, https://doi.org/10.5194/nhess-23-3261-2023, 2023
Short summary
Short summary
Landslide warning systems often use statistical models to predict landslides based on rainfall. They are typically trained on large datasets with many landslide occurrences, but in rural areas large datasets may not exist. In this study, we evaluate which statistical model types are best suited to predicting landslides and demonstrate that even a small landslide inventory (five storms) can be used to train useful models for landslide early warning when non-landslide events are also included.
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.
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.
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.
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
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
Brief communication: Monitoring impending slope failure with very high-resolution spaceborne synthetic aperture radar
InSAR-informed in situ monitoring for deep-seated landslides: insights from El Forn (Andorra)
A coupled hydrological and hydrodynamic modeling approach for estimating rainfall thresholds of debris-flow occurrence
More than one landslide per road kilometer – surveying and modeling mass movements along the Rishikesh–Joshimath (NH-7) highway, Uttarakhand, India
An integrated method for assessing vulnerability of buildings caused by debris flows in mountainous areas
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
Unravelling Landslide Failure Mechanisms with Seismic Signal Analysis for Enhanced Pre-Survey Understanding
Predicting Deep-Seated Landslide Displacements in Mountains through the Integration of Convolutional Neural Networks and Age of Exploration-Inspired Optimizer
Addressing class imbalance in soil movement predictions
Assessing the impact of climate change on landslides near Vejle, Denmark, using public data
Predicting the thickness of shallow landslides in Switzerland using machine learning
Analysis of three-dimensional slope stability combined with rainfall and earthquake
Assessing landslide damming susceptibility in Central Asia
Invited Perspectives: Integrating hydrologic information into the next generation of landslide early warning systems
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
Characterizing the scale of regional landslide triggering from storm hydrometeorology
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
Comparison of conditioning factors classification criteria in large scale statistically based landslide susceptibility models
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
Exploratory analysis of the annual risk to life from debris flows
A new analytical method for stability analysis of rock blocks with basal erosion in sub-horizontal strata by considering the eccentricity effect
Rockfall monitoring with a Doppler radar on an active rockslide complex in Brienz/Brinzauls (Switzerland)
Landslide initiation thresholds in data-sparse regions: application to landslide early warning criteria in Sitka, Alaska, USA
Lessons learnt from a rockfall time series analysis: data collection, statistical analysis, and applications
The concept of event-size-dependent exhaustion and its application to paraglacial rockslides
Coastal earthquake-induced landslide susceptibility during the 2016 Mw 7.8 Kaikōura earthquake, New Zealand
Characteristics of debris flows recorded in the Shenmu area of central Taiwan between 2004 and 2021
Semi-automatic mapping of shallow landslides using free Sentinel-2 images and Google Earth Engine
The role of thermokarst evolution in debris flow initiation (Hüttekar Rock Glacier, Austrian Alps)
Accounting for the effect of forest and fragmentation in probabilistic rockfall hazard
Comprehensive landslide susceptibility map of Central Asia
The influence of large woody debris on post-wildfire debris flow sediment storage
Statistical modeling of sediment supply in torrent catchments of the northern French Alps
A data-driven evaluation of post-fire landslide susceptibility
Deciphering seasonal effects of triggering and preparatory precipitation for improved shallow landslide prediction using generalized additive mixed models
Brief communication: The northwest Himalaya towns slipping towards potential disaster
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.
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.
Rachael Lau, Carolina Seguí, Tyler Waterman, Nathaniel Chaney, and Manolis Veveakis
Nat. Hazards Earth Syst. Sci., 24, 3651–3661, https://doi.org/10.5194/nhess-24-3651-2024, https://doi.org/10.5194/nhess-24-3651-2024, 2024
Short summary
Short summary
This work examines the use of interferometric synthetic-aperture radar (InSAR) alongside in situ borehole measurements to assess the stability of deep-seated landslides for the case study of El Forn (Andorra). Comparing InSAR with borehole data suggests a key trade-off between accuracy and precision for various InSAR resolutions. Spatial interpolation with InSAR informed how many remote observations are necessary to lower error in a remote sensing re-creation of ground motion over the landslide.
Zhen Lei Wei, Yue Quan Shang, Qiu Hua Liang, and Xi Lin Xia
Nat. Hazards Earth Syst. Sci., 24, 3357–3379, https://doi.org/10.5194/nhess-24-3357-2024, https://doi.org/10.5194/nhess-24-3357-2024, 2024
Short summary
Short summary
The initiation of debris flows is significantly influenced by rainfall-induced hydrological processes. We propose a novel framework based on an integrated hydrological and hydrodynamic model and aimed at estimating intensity–duration (ID) rainfall thresholds responsible for triggering debris flows. In comparison to traditional statistical approaches, this physically based framework is particularly suitable for application in ungauged catchments where historical debris flow data are scarce.
Jürgen Mey, Ravi Kumar Guntu, Alexander Plakias, Igo Silva de Almeida, and Wolfgang Schwanghart
Nat. Hazards Earth Syst. Sci., 24, 3207–3223, https://doi.org/10.5194/nhess-24-3207-2024, https://doi.org/10.5194/nhess-24-3207-2024, 2024
Short summary
Short summary
The Himalayan road network links remote areas, but fragile terrain and poor construction lead to frequent landslides. This study on the NH-7 in India's Uttarakhand region analyzed 300 landslides after heavy rainfall in 2022 . Factors like slope, rainfall, rock type and road work influence landslides. The study's model predicts landslide locations for better road maintenance planning, highlighting the risk from climate change and increased road use.
Chenchen Qiu and Xueyu Geng
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-156, https://doi.org/10.5194/nhess-2024-156, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
We proposed an interated method with the combination of a physical vulnerability matric and a machine learning model to estimate the potential physical damage and associated economic loss caused by future debris flows based on the collected historical data on the Qinghai-Tibet Plateau regions.
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.
Jui-Ming Chang, Che-Ming Yang, Wei-An Chao, Chin-Shang Ku, Ming-Wan Huang, Tung-Chou Hsieh, and Chi-Yao Hung
EGUsphere, https://doi.org/10.5194/egusphere-2024-1267, https://doi.org/10.5194/egusphere-2024-1267, 2024
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 1st event of CL consisted of 4 sliding failures, accompanied by a gradual reduction in landslide volume. The 2nd and 3rd events were minor topplings and rockfalls. Then combining the seismological-based knowledge and field survey results, the temporal-spatial variation of landslide evolution is proposed.
Jui-Sheng Chou, Hoang-Minh Nguyen, Huy-Phuong Phan, and Kuo-Lung Wang
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-86, https://doi.org/10.5194/nhess-2024-86, 2024
Revised manuscript accepted for NHESS
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 eight years of data from Taiwan's Lushan Mountain, improving early warnings and potentially saving lives and infrastructure. This integration marks a significant advancement in environmental risk management.
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.
Christoph Schaller, Luuk Dorren, Massimiliano Schwarz, Christine Moos, Arie C. Seijmonsbergen, and E. Emiel van Loon
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-76, https://doi.org/10.5194/nhess-2024-76, 2024
Revised manuscript accepted for NHESS
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 least 17 % compared to previously existing models.
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.
Benjamin B. Mirus, Thom A. Bogaard, Roberto Greco, and Manfred Stähli
EGUsphere, https://doi.org/10.5194/egusphere-2024-1219, https://doi.org/10.5194/egusphere-2024-1219, 2024
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 article, we provide our perspectives on the value and limitations of integrating subsurface hillslope hydrologic monitoring data and mathematical modeling for more accurate landslide forecasts.
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.
Jonathan P. Perkins, Nina S. Oakley, Brian D. Collins, Skye C. Corbett, and W. Paul Burgess
EGUsphere, https://doi.org/10.5194/egusphere-2024-873, https://doi.org/10.5194/egusphere-2024-873, 2024
Short summary
Short summary
Landslides are a global issue that results in deaths and economic losses annually. However, it is not clear how storm severity relates to landslide severity across large regions. Here we develop a method to estimate the footprint of landslide area and compare this to meteorologic estimates of storm severity. We find that total storm strength does not clearly relate to landslide area. Rather, landslide area depends on soil wetness and smaller storm structures that can produce intense rainfall.
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.
Marko Sinčić, Sanja Bernat Gazibara, Mauro Rossi, and Snježana Mihalić Arbanas
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-29, https://doi.org/10.5194/nhess-2024-29, 2024
Revised manuscript accepted for NHESS
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 five 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.
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.
Mark Bloomberg, Tim Davies, Elena Moltchanova, Tom Robinson, and David Palmer
EGUsphere, https://doi.org/10.5194/egusphere-2023-2695, https://doi.org/10.5194/egusphere-2023-2695, 2023
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 which 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.
Xushan Shi, Bo Chai, Juan Du, Wei Wang, and Bo Liu
Nat. Hazards Earth Syst. Sci., 23, 3425–3443, https://doi.org/10.5194/nhess-23-3425-2023, https://doi.org/10.5194/nhess-23-3425-2023, 2023
Short summary
Short summary
A 3D stability analysis method is proposed for biased rockfall with external erosion. Four failure modes are considered according to rockfall evolution processes, including partial damage of underlying soft rock and overall failure of hard rock blocks. This method is validated with the biased rockfalls in the Sichuan Basin, China. The critical retreat ratio from low to moderate rockfall susceptibility is 0.33. This method could facilitate rockfall early identification and risk mitigation.
Marius Schneider, Nicolas Oestreicher, Thomas Ehrat, and Simon Loew
Nat. Hazards Earth Syst. Sci., 23, 3337–3354, https://doi.org/10.5194/nhess-23-3337-2023, https://doi.org/10.5194/nhess-23-3337-2023, 2023
Short summary
Short summary
Rockfalls and their hazards are typically treated as statistical events based on rockfall catalogs, but only a few complete rockfall inventories are available today. Here, we present new results from a Doppler radar rockfall alarm system, which has operated since 2018 at a high frequency under all illumination and weather conditions at a site where frequent rockfall events threaten a village and road. The new data set is used to investigate rockfall triggers in an active rockslide complex.
Annette I. Patton, Lisa V. Luna, Joshua J. Roering, Aaron Jacobs, Oliver Korup, and Benjamin B. Mirus
Nat. Hazards Earth Syst. Sci., 23, 3261–3284, https://doi.org/10.5194/nhess-23-3261-2023, https://doi.org/10.5194/nhess-23-3261-2023, 2023
Short summary
Short summary
Landslide warning systems often use statistical models to predict landslides based on rainfall. They are typically trained on large datasets with many landslide occurrences, but in rural areas large datasets may not exist. In this study, we evaluate which statistical model types are best suited to predicting landslides and demonstrate that even a small landslide inventory (five storms) can be used to train useful models for landslide early warning when non-landslide events are also included.
Sandra Melzner, Marco Conedera, Johannes Hübl, and Mauro Rossi
Nat. Hazards Earth Syst. Sci., 23, 3079–3093, https://doi.org/10.5194/nhess-23-3079-2023, https://doi.org/10.5194/nhess-23-3079-2023, 2023
Short summary
Short summary
The estimation of the temporal frequency of the involved rockfall processes is an important part in hazard and risk assessments. Different methods can be used to collect and analyse rockfall data. From a statistical point of view, rockfall datasets are nearly always incomplete. Accurate data collection approaches and the application of statistical methods on existing rockfall data series as reported in this study should be better considered in rockfall hazard and risk assessments in the future.
Stefan Hergarten
Nat. Hazards Earth Syst. Sci., 23, 3051–3063, https://doi.org/10.5194/nhess-23-3051-2023, https://doi.org/10.5194/nhess-23-3051-2023, 2023
Short summary
Short summary
Rockslides are a major hazard in mountainous regions. In formerly glaciated regions, the disposition mainly arises from oversteepened topography and decreases through time. However, little is known about this decrease and thus about the present-day hazard of huge, potentially catastrophic rockslides. This paper presents a new theoretical framework that explains the decrease in maximum rockslide size through time and predicts the present-day frequency of large rockslides for the European Alps.
Colin K. Bloom, Corinne Singeisen, Timothy Stahl, Andrew Howell, Chris Massey, and Dougal Mason
Nat. Hazards Earth Syst. Sci., 23, 2987–3013, https://doi.org/10.5194/nhess-23-2987-2023, https://doi.org/10.5194/nhess-23-2987-2023, 2023
Short summary
Short summary
Landslides are often observed on coastlines following large earthquakes, but few studies have explored this occurrence. Here, statistical modelling of landslides triggered by the 2016 Kaikōura earthquake in New Zealand is used to investigate factors driving coastal earthquake-induced landslides. Geology, steep slopes, and shaking intensity are good predictors of landslides from the Kaikōura event. Steeper slopes close to the coast provide the best explanation for a high landslide density.
Yi-Min Huang
Nat. Hazards Earth Syst. Sci., 23, 2649–2662, https://doi.org/10.5194/nhess-23-2649-2023, https://doi.org/10.5194/nhess-23-2649-2023, 2023
Short summary
Short summary
Debris flows are common hazards in Taiwan, and debris-flow early warning is important for disaster responses. The rainfall thresholds of debris flows are analyzed and determined in terms of rainfall intensity, accumulated rainfall, and rainfall duration, based on case histories in Taiwan. These thresholds are useful for disaster management, and the cases in Taiwan are useful for global debris-flow databases.
Davide Notti, Martina Cignetti, Danilo Godone, and Daniele Giordan
Nat. Hazards Earth Syst. Sci., 23, 2625–2648, https://doi.org/10.5194/nhess-23-2625-2023, https://doi.org/10.5194/nhess-23-2625-2023, 2023
Short summary
Short summary
We developed a cost-effective and user-friendly approach to map shallow landslides using free satellite data. Our methodology involves analysing the pre- and post-event NDVI variation to semi-automatically detect areas potentially affected by shallow landslides (PLs). Additionally, we have created Google Earth Engine scripts to rapidly compute NDVI differences and time series of affected areas. Datasets and codes are stored in an open data repository for improvement by the scientific community.
Simon Seelig, Thomas Wagner, Karl Krainer, Michael Avian, Marc Olefs, Klaus Haslinger, and Gerfried Winkler
Nat. Hazards Earth Syst. Sci., 23, 2547–2568, https://doi.org/10.5194/nhess-23-2547-2023, https://doi.org/10.5194/nhess-23-2547-2023, 2023
Short summary
Short summary
A rapid sequence of cascading events involving thermokarst lake outburst, rock glacier front failure, debris flow development, and river blockage hit an alpine valley in Austria during summer 2019. We analyze the environmental conditions initiating the process chain and identify the rapid evolution of a thermokarst channel network as the main driver. Our results highlight the need to account for permafrost degradation in debris flow hazard assessment studies.
Camilla Lanfranconi, Paolo Frattini, Gianluca Sala, Giuseppe Dattola, Davide Bertolo, Juanjuan Sun, and Giovanni Battista Crosta
Nat. Hazards Earth Syst. Sci., 23, 2349–2363, https://doi.org/10.5194/nhess-23-2349-2023, https://doi.org/10.5194/nhess-23-2349-2023, 2023
Short summary
Short summary
This paper presents a study on rockfall dynamics and hazard, examining the impact of the presence of trees along slope and block fragmentation. We compared rockfall simulations that explicitly model the presence of trees and fragmentation with a classical approach that accounts for these phenomena in model parameters (both the hazard and the kinetic energy change). We also used a non-parametric probabilistic rockfall hazard analysis method for hazard mapping.
Ascanio Rosi, William Frodella, Nicola Nocentini, Francesco Caleca, Hans Balder Havenith, Alexander Strom, Mirzo Saidov, Gany Amirgalievich Bimurzaev, and Veronica Tofani
Nat. Hazards Earth Syst. Sci., 23, 2229–2250, https://doi.org/10.5194/nhess-23-2229-2023, https://doi.org/10.5194/nhess-23-2229-2023, 2023
Short summary
Short summary
This work was carried out within the Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia (SFRARR) project and is focused on the first landslide susceptibility analysis at a regional scale for Central Asia. The most detailed available landslide inventories were implemented in a random forest model. The final aim was to provide a useful tool for reduction strategies to landslide scientists, practitioners, and administrators.
Francis K. Rengers, Luke A. McGuire, Katherine R. Barnhart, Ann M. Youberg, Daniel Cadol, Alexander N. Gorr, Olivia J. Hoch, Rebecca Beers, and Jason W. Kean
Nat. Hazards Earth Syst. Sci., 23, 2075–2088, https://doi.org/10.5194/nhess-23-2075-2023, https://doi.org/10.5194/nhess-23-2075-2023, 2023
Short summary
Short summary
Debris flows often occur after wildfires. These debris flows move water, sediment, and wood. The wood can get stuck in channels, creating a dam that holds boulders, cobbles, sand, and muddy material. We investigated how the channel width and wood length influenced how much sediment is stored. We also used a series of equations to back calculate the debris flow speed using the breaking threshold of wood. These data will help improve models and provide insight into future field investigations.
Maxime Morel, Guillaume Piton, Damien Kuss, Guillaume Evin, and Caroline Le Bouteiller
Nat. Hazards Earth Syst. Sci., 23, 1769–1787, https://doi.org/10.5194/nhess-23-1769-2023, https://doi.org/10.5194/nhess-23-1769-2023, 2023
Short summary
Short summary
In mountain catchments, damage during floods is generally primarily driven by the supply of a massive amount of sediment. Predicting how much sediment can be delivered by frequent and infrequent events is thus important in hazard studies. This paper uses data gathered during the maintenance operation of about 100 debris retention basins to build simple equations aiming at predicting sediment supply from simple parameters describing the upstream catchment.
Elsa S. Culler, Ben Livneh, Balaji Rajagopalan, and Kristy F. Tiampo
Nat. Hazards Earth Syst. Sci., 23, 1631–1652, https://doi.org/10.5194/nhess-23-1631-2023, https://doi.org/10.5194/nhess-23-1631-2023, 2023
Short summary
Short summary
Landslides have often been observed in the aftermath of wildfires. This study explores regional patterns in the rainfall that caused landslides both after fires and in unburned locations. In general, landslides that occur after fires are triggered by less rainfall, confirming that fire helps to set the stage for landslides. However, there are regional differences in the ways in which fire impacts landslides, such as the size and direction of shifts in the seasonality of landslides after fires.
Stefan Steger, Mateo Moreno, Alice Crespi, Peter James Zellner, Stefano Luigi Gariano, Maria Teresa Brunetti, Massimo Melillo, Silvia Peruccacci, Francesco Marra, Robin Kohrs, Jason Goetz, Volkmar Mair, and Massimiliano Pittore
Nat. Hazards Earth Syst. Sci., 23, 1483–1506, https://doi.org/10.5194/nhess-23-1483-2023, https://doi.org/10.5194/nhess-23-1483-2023, 2023
Short summary
Short summary
We present a novel data-driven modelling approach to determine season-specific critical precipitation conditions for landslide occurrence. It is shown that the amount of precipitation required to trigger a landslide in South Tyrol varies from season to season. In summer, a higher amount of preparatory precipitation is required to trigger a landslide, probably due to denser vegetation and higher temperatures. We derive dynamic thresholds that directly relate to hit rates and false-alarm rates.
Yaspal Sundriyal, Vipin Kumar, Neha Chauhan, Sameeksha Kaushik, Rahul Ranjan, and Mohit Kumar Punia
Nat. Hazards Earth Syst. Sci., 23, 1425–1431, https://doi.org/10.5194/nhess-23-1425-2023, https://doi.org/10.5194/nhess-23-1425-2023, 2023
Short summary
Short summary
The NW Himalaya has been one of the most affected terrains of the Himalaya, subject to disastrous landslides. This article focuses on two towns (Joshimath and Bhatwari) of the NW Himalaya, which have been witnessing subsidence for decades. We used a slope stability simulation to determine the response of the hillslopes accommodating these towns under various loading conditions. We found that the maximum displacement in these hillslopes might reach up to 20–25 m.
Cited articles
Abancó, C., Bennett, G. L., Matthews, A. J., Matera, M. A. M., and Tan, F. J.: The role of geomorphology, rainfall and soil moisture in the occurrence of landslides triggered by 2018 Typhoon Mangkhut in the Philippines, Nat. Hazards Earth Syst. Sci., 21, 1531–1550, https://doi.org/10.5194/nhess-21-1531-2021, 2021. a
Alberti, S., Leshchinsky, B., Roering, J., Perkins, J., and Olsen, M. J.: Inversions of landslide strength as a proxy for subsurface weathering, Nat. Commun., 13, 6049, https://doi.org/10.1038/s41467-022-33798-5, 2022. a
Antinao, J. L. and Gosse, J.: Large rockslides in the Southern Central Andes of Chile (32–34.5° S): Tectonic control and significance for Quaternary landscape evolution, Geomorphology, 104, 117–133, https://doi.org/10.1016/j.geomorph.2008.08.008, 2009. a
Ardizzone, F., Bucci, F., Cardinali, M., Fiorucci, F., Pisano, L., Santangelo, M., and Zumpano, V.: Geomorphological landslide inventory map of the Daunia Apennines, southern Italy, Earth Syst. Sci. Data, 15, 753–767, https://doi.org/10.5194/essd-15-753-2023, 2023. a, b
Barlow, J., Lim, M., Rosser, N., Petley, D., Brain, M., Norman, E., and Geer, M.: Modeling cliff erosion using negative power law scaling of rockfalls, Geomorphology, 139–140, 416–424, https://doi.org/10.1016/j.geomorph.2011.11.006, 2012. a
Basharat, M., Rohn, J., Baig, M. S., and Khan, M. R.: Spatial distribution analysis of mass movements triggered by the 2005 Kashmir earthquake in the Northeast Himalayas of Pakistan, Geomorphology, 206, 203–214, https://doi.org/10.1016/j.geomorph.2013.09.025, 2014. a
Basharat, M., Ali, A., Jadoon, I. A. K., and Rohn, J.: Using PCA in evaluating event-controlling attributes of landsliding in the 2005 Kashmir earthquake region, NW Himalayas, Pakistan, Nat. Hazards, 81, 1999–2017, https://doi.org/10.1007/s11069-016-2172-9, 2016. a, b, c
Behling, R., Roessner, S., Golovko, D., and Kleinschmit, B.: Derivation of long-term spatiotemporal landslide activity—A multi-sensor time series approach, Remote Sens. Environ., 186, 88–104, https://doi.org/10.1016/j.rse.2016.07.017, 2016. a
Belair, G., Jones, E., Slaughter, S., and Mirus, B.: Landslide Inventories across the United States version 2, USGS, https://doi.org/10.5066/P9FZUX6N, 2022. a, b, c, d
Bellugi, D. G., Milledge, D. G., Cuffey, K. M., Dietrich, W. E. J., and Larsen, L. G.: Controls on the size distributions of shallow landslides, P. Natl. Acad. Sci. USA, 118, e2021855118, https://doi.org/10.1073/pnas.2021855118, 2021. a, b
Bernard, T. G., Lague, D., and Steer, P.: Beyond 2D landslide inventories and their rollover: synoptic 3D inventories and volume from repeat lidar data, Earth Surf. Dynam., 9, 1013–1044, https://doi.org/10.5194/esurf-9-1013-2021, 2021. a, b
Bessette-Kirton, E. K. and Coe, J. A.: A 36-Year Record of Rock Avalanches in the Saint Elias Mountains of Alaska, With Implications for Future Hazards, Front. Earth Sci., 8, 293, https://doi.org/10.3389/feart.2020.00293, 2020. a, b
Bhuyan, K., Tanyaş, H., Nava, L., Puliero, S., Meena, S. R., Floris, M., van Westen, C., and Catani, F.: Generating multi-temporal landslide inventories through a general deep transfer learning strategy using HR EO data, Sci. Rep.-UK, 13, 162, https://doi.org/10.1038/s41598-022-27352-y, 2023. a, b, c, d
Brardinoni, F., Slaymaker, O., and Hassan, M. A.: Landslide inventory in a rugged forested watershed: a comparison between air-photo and field survey data, Geomorphology, 54, 179–196, https://doi.org/10.1016/S0169-555X(02)00355-0, 2003. a
Burrows, K., Marc, O., and Remy, D.: Using Sentinel-1 radar amplitude time series to constrain the timings of individual landslides: a step towards understanding the controls on monsoon-triggered landsliding, Nat. Hazards Earth Syst. Sci., 22, 2637–2653, https://doi.org/10.5194/nhess-22-2637-2022, 2022. a
Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., and Riddell, A.: Stan: A Probabilistic Programming Language, J. Stat. Softw., 76, 1–32, https://doi.org/10.18637/jss.v076.i01, 2017. a
Casagli, N., Intrieri, E., Tofani, V., Gigli, G., and Raspini, F.: Landslide detection, monitoring and prediction with remote-sensing techniques, Nature Reviews Earth & Environment, 4, 51–64, https://doi.org/10.1038/s43017-022-00373-x, 2023. a
Castro-Camilo, D., Huser, R., and Rue, H.: Practical strategies for generalized extreme value-based regression models for extremes, Environmetrics, 33, e2742, https://doi.org/10.1002/env.2742, 2022. a
Clauset, A., Shalizi, C. R., and Newman, M. E. J.: Power-Law Distributions in Empirical Data, SIAM Rev., 51, 661–703, https://doi.org/10.1137/070710111, 2009. a, b, c
Dente, E., Katz, O., Crouvi, O., and Mushkin, A.: The Geomorphic Effectiveness of Landslides, J. Geophys. Res.-Earth, 128, e2023JF007191, https://doi.org/10.1029/2023JF007191, 2023. a
Domènech, G., Yang, F., Guo, X., Fan, X., Scaringi, G., Dai, L., He, C., Xu, Q., and Huang, R.: Two multi-temporal datasets to track the enhanced landsliding after the 2008 Wenchuan earthquake, Zenodo [data set], https://doi.org/10.5281/zenodo.1484667, 2018. a
Emberson, R., Kirschbaum, D. B., Amatya, P., Tanyas, H., and Marc, O.: Insights from the topographic characteristics of a large global catalog of rainfall-induced landslide event inventories, Nat. Hazards Earth Syst. Sci., 22, 1129–1149, https://doi.org/10.5194/nhess-22-1129-2022, 2022. a, b, c
Fan, X., Scaringi, G., Korup, O., West, A. J., van Westen, C. J., Tanyas, H., Hovius, N., Hales, T. C., Jibson, R. W., Allstadt, K. E., Zhang, L., Evans, S. G., Xu, C., Li, G., Pei, X., Xu, Q., and Huang, R.: Earthquake-Induced Chains of Geologic Hazards: Patterns, Mechanisms, and Impacts, Rev. Geophys., 57, 421–503, https://doi.org/10.1029/2018RG000626, 2019. a
Franceschini, R., Rosi, A., Catani, F., and Casagli, N.: Exploring a landslide inventory created by automated web data mining: the case of Italy, Landslides, 19, 841–853, https://doi.org/10.1007/s10346-021-01799-y, 2022. a
Frattini, P. and Crosta, G. B.: The role of material properties and landscape morphology on landslide size distributions, Earth Planet. Sc. Lett., 361, 310–319, https://doi.org/10.1016/j.epsl.2012.10.029, 2013. a
Fusco, F., Tufano, R., De Vita, P., Di Martire, D., Di Napoli, M., Guerriero, L., Mileti, F. A., Terribile, F., and Calcaterra, D.: A revised landslide inventory of the Campania region (Italy), Scientific Data, 10, 355, https://doi.org/10.1038/s41597-023-02155-6, 2023. a, b, c, d
Gilham, J., Barlow, J., and Moore, R.: Marine control over negative power law scaling of mass wasting events in chalk sea cliffs with implications for future recession under the UKCP09 medium emission scenario, Earth Surf. Proc. Land., 43, 2136–2146, https://doi.org/10.1002/esp.4379, 2018. a
Guzzetti, F., Mondini, A. C., Cardinali, M., Fiorucci, F., Santangelo, M., and Chang, K.-T.: Landslide inventory maps: New tools for an old problem, Earth-Sci. Rev., 112, 42–66, https://doi.org/10.1016/j.earscirev.2012.02.001, 2012. a
Hao, L., Rajaneesh A., van Westen, C., Sajinkumar K. S., Martha, T. R., Jaiswal, P., and McAdoo, B. G.: Constructing a complete landslide inventory dataset for the 2018 monsoon disaster in Kerala, India, for land use change analysis, Earth Syst. Sci. Data, 12, 2899–2918, https://doi.org/10.5194/essd-12-2899-2020, 2020. a, b
Harp, E. L., Wilson, R. C., and Wieczorek, G. F.: Landslides from the February 4, 1976, Guatemala earthquake, Professional Paper 1204-A, USGS, https://doi.org/10.3133/pp1204A, 1981. a
Hergarten, S.: The concept of event-size-dependent exhaustion and its application to paraglacial rockslides, Nat. Hazards Earth Syst. Sci., 23, 3051–3063, https://doi.org/10.5194/nhess-23-3051-2023, 2023. a
Hergarten, S. and Neugebauer, H. J.: Self-organized criticality in a landslide model, Geophys. Res. Lett., 25, 801–804, https://doi.org/10.1029/98GL50419, 1998. a
Hibert, C., Michéa, D., Provost, F., Malet, J.-P., and Geertsema, M.: Exploration of continuous seismic recordings with a machine learning approach to document 20 yr of landslide activity in Alaska, Geophys. J. Int., 219, 1138–1147, https://doi.org/10.1093/gji/ggz354, 2019. a
Hu, K., Zhang, X., You, Y., Hu, X., Liu, W., and Li, Y.: Landslides and dammed lakes triggered by the 2017 Ms6.9 Milin earthquake in the Tsangpo gorge, Landslides, 16, 993–1001, https://doi.org/10.1007/s10346-019-01168-w, 2019. a
Jain, S., Khosa, R., and Gosain, A. K.: Impact of landslide size and settings on landslide scaling relationship: a study from the Himalayan regions of India, Landslides, 19, 373–385, https://doi.org/10.1007/s10346-021-01794-3, 2022. a
Jones, J. N., Boulton, S. J., Bennett, G. L., Stokes, M., and Whitworth, M. R. Z.: Temporal Variations in Landslide Distributions Following Extreme Events: Implications for Landslide Susceptibility Modeling, J. Geophys. Res.-Earth, 126, e2021JF006067, https://doi.org/10.1029/2021JF006067, 2021. a
Karakas, G., Nefeslioglu, H. A., Kocaman, S., Buyukdemircioglu, M., Yurur, T., and Gokceoglu, C.: Derivation of earthquake-induced landslide distribution using aerial photogrammetry: the January 24, 2020, Elazig (Turkey) earthquake, Landslides, 18, 2193–2209, https://doi.org/10.1007/s10346-021-01660-2, 2021. a
Katz, R. W., Parlange, M. B., and Naveau, P.: Statistics of extremes in hydrology, Adv. Water Resour., 25, 1287–1304, https://doi.org/10.1016/S0309-1708(02)00056-8, 2002. a, b, c
Kim, J., Coe, J. A., Lu, Z., Avdievitch, N. N., and Hults, C. P.: Spaceborne InSAR mapping of landslides and subsidence in rapidly deglaciating terrain, Glacier Bay National Park and Preserve and vicinity, Alaska and British Columbia, Remote Sens. Environ., 281, 113231, https://doi.org/10.1016/j.rse.2022.113231, 2022. a
Korup, O., Clague, J. J., Hermanns, R. L., Hewitt, K., Strom, A. L., and Weidinger, J. T.: Giant landslides, topography, and erosion, Earth Planet. Sc. Lett., 261, 578–589, https://doi.org/10.1016/j.epsl.2007.07.025, 2007. a, b
Korup, O., Görüm, T., and Hayakawa, Y.: Without power? Landslide inventories in the face of climate change, Earth Surf. Proc. Land., 37, 92–99, https://doi.org/10.1002/esp.2248, 2012. a
Lacroix, P., Handwerger, A. L., and Bièvre, G.: Life and death of slow-moving landslides, Nature Reviews Earth & Environment, 1, 404–419, https://doi.org/10.1038/s43017-020-0072-8, 2020. a
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, https://doi.org/10.1130/G37267.1, 2016. a
Larsen, I. J., Montgomery, D. R., and Korup, O.: Landslide erosion controlled by hillslope material, Nat. Geosci., 3, 247–251, https://doi.org/10.1038/ngeo776, 2010. a
Li, G., West, A. J., Densmore, A. L., Jin, Z., Parker, R. N., and Hilton, R. G.: Seismic mountain building: Landslides associated with the 2008 Wenchuan earthquake in the context of a generalized model for earthquake volume balance, Geochem. Geophy. Geosy., 15, 833–844, https://doi.org/10.1002/2013GC005067, 2014. a, b
Li, G. K. and Moon, S.: Topographic stress control on bedrock landslide size, Nat. Geosci., 14, 307–313, https://doi.org/10.1038/s41561-021-00739-8, 2021. a
Liu, J., Wu, Y., and Gao, X.: Increase in occurrence of large glacier-related landslides in the high mountains of Asia, Sci. Rep.-UK, 11, 1635, https://doi.org/10.1038/s41598-021-81212-9, 2021. a
Luetzenburg, G., Svennevig, K., Bjørk, A. A., Keiding, M., and Kroon, A.: A national landslide inventory for Denmark, Earth Syst. Sci. Data, 14, 3157–3165, https://doi.org/10.5194/essd-14-3157-2022, 2022. a, b
Luna, L. V. and Korup, O.: Seasonal Landslide Activity Lags Annual Precipitation Pattern in the Pacific Northwest, Geophys. Res. Lett., 49, e2022GL098506, https://doi.org/10.1029/2022GL098506, e2022GL098506 2022GL098506, 2022. a
Malamud, B. D., Turcotte, D. L., Guzzetti, F., and Reichenbach, P.: Landslide inventories and their statistical properties, Earth Surf. Proc. Land., 29, 687–711, https://doi.org/10.1002/esp.1064, 2004. a, b, c
Marc, O. and Hovius, N.: Amalgamation in landslide maps: effects and automatic detection, Nat. Hazards Earth Syst. Sci., 15, 723–733, https://doi.org/10.5194/nhess-15-723-2015, 2015. a
Marc, O., Behling, R., Andermann, C., Turowski, J. M., Illien, L., Roessner, S., and Hovius, N.: Long-term erosion of the Nepal Himalayas by bedrock landsliding: the role of monsoons, earthquakes and giant landslides, Earth Surf. Dynam., 7, 107–128, https://doi.org/10.5194/esurf-7-107-2019, 2019. a, b, c
McColl, S. T. and Cook, S. J.: A universal size classification system for landslides, Landslides, 21, 111–120, https://doi.org/10.1007/s10346-023-02131-6, 2024. a, b
McElreath, R.: Statistical Rethinking. A Bayesian Course with Examples in R and Stan, 1st Edn., Chapman and Hall/CRC, https://doi.org/10.1201/9781315372495, 2016. a, b
Medwedeff, W. G., Clark, M. K., Zekkos, D., and West, A. J.: Characteristic landslide distributions: An investigation of landscape controls on landslide size, Earth Planet. Sc. Lett., 539, 116203, https://doi.org/10.1016/j.epsl.2020.116203, 2020. a
Meunier, P., Uchida, T., and Hovius, N.: Landslide patterns reveal the sources of large earthquakes, Earth Planet. Sc. Lett., 363, 27–33, https://doi.org/10.1016/j.epsl.2012.12.018, 2013. a
Milledge, D. G., Bellugi, D. G., Watt, J., and Densmore, A. L.: Automated determination of landslide locations after large trigger events: advantages and disadvantages compared to manual mapping, Nat. Hazards Earth Syst. Sci., 22, 481–508, https://doi.org/10.5194/nhess-22-481-2022, 2022. a
Muñoz-Torrero, A.: Multi-temporal Landslide Inventory for the Far-Western region of Nepal, Zenodo [data set], https://doi.org/10.5281/zenodo.4290100, 2020. a, b
R Core Team: R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org. (last access: 7 November 2024), 2021. a
Roback, K., Clark, M. K., West, A. J., Zekkos, D., Li, G., Gallen, S. F., Chamlagain, D., and Godt, J. W.: The size, distribution, and mobility of landslides caused by the 2015 Mw7.8 Gorkha earthquake, Nepal, Geomorphology, 301, 121–138, https://doi.org/10.1016/j.geomorph.2017.01.030, 2018. a
RStudio Team: RStudio: Integrated Development for R, RStudio, Inc., Boston, MA, https://posit.co/ (last access: 7 November 2024), 2024. a
Safran, E., Anderson, S., Mills-Novoa, M., House, P., and Ely, L.: Controls on large landslide distribution and implications for the geomorphic evolution of the southern interior Columbia River basin, Geol. Soc. Am. Bull., 123, 1851–1862, https://doi.org/10.1130/B30061.1, 2011. a, b, c
Saito, H., Korup, O., Uchida, T., Hayashi, S., and Oguchi, T.: Rainfall conditions, typhoon frequency, and contemporary landslide erosion in Japan, Geology, 42, 999–1002, https://doi.org/10.1130/G35680.1, 2014. a
Santangelo, M., Althuwaynee, O., Alvioli, M., Ardizzone, F., Bianchi, C., Bornaetxea, T., Brunetti, M. T., Bucci, F., Cardinali, M., Donnini, M., Esposito, G., Gariano, S. L., Grita, S., Marchesini, I., Melillo, M., Peruccacci, S., Salvati, P., Yazdani, M., and Fiorucci, F.: Inventory of landslides triggered by an extreme rainfall event in Marche-Umbria, Italy, on 15 September 2022, Scientific Data, 10, 427, https://doi.org/10.1038/s41597-023-02336-3, 2023. a, b
Schönfeldt, E., Winocur, D., Pánek, T., and Korup, O.: Deep learning reveals one of Earth's largest landslide terrain in Patagonia, Earth Planet. Sc. Lett., 593, 117642, https://doi.org/10.1016/j.epsl.2022.117642, 2022. a
Sepúlveda, S. A., Serey, A., Lara, M., Pavez, A., and Rebolledo, S.: Landslides induced by the April 2007 Aysén Fjord earthquake, Chilean Patagonia, Landslides, 7, 483–492, https://doi.org/10.1007/s10346-010-0203-2, 2010. a
Smith, W. D., Dunning, S. A., Ross, N., Telling, J., Jensen, E. K., Shugar, D. H., Coe, J. A., and Geertsema, M.: Revising supraglacial rock avalanche magnitudes and frequencies in Glacier Bay National Park, Alaska, Geomorphology, 425, 108591, https://doi.org/10.1016/j.geomorph.2023.108591, 2023. a, b
Song, C., Yu, C., Li, Z., Utili, S., Frattini, P., Crosta, G., and Peng, J.: Triggering and recovery of earthquake accelerated landslides in Central Italy revealed by satellite radar observations, Nat. Commun., 13, 7278, https://doi.org/10.1038/s41467-022-35035-5, 2022. a
Stan Development Team: Stan Modeling Language Users Guide and Reference Manual, Version 2.35, https://mc-stan.org/ (last access: 7 November 2024), 2024. a
Tanyaş, H., van Westen, C. J., Allstadt, K. E., Anna Nowicki Jessee, M., Görüm, T., Jibson, R. W., Godt, J. W., Sato, H. P., Schmitt, R. G., Marc, O., and Hovius, N.: Presentation and Analysis of a Worldwide Database of Earthquake-Induced Landslide Inventories, J. Geophys. Res.-Earth, 122, 1991–2015, https://doi.org/10.1002/2017JF004236, 2017. a, b, c
Tanyaş, H., van Westen, C. J., Allstadt, K. E., and Jibson, R. W.: Factors controlling landslide frequency–area distributions, Earth Surf. Proc. Land., 44, 900–917, https://doi.org/10.1002/esp.4543, 2019. a, b
Tanyaş, H., Görüm, T., Fadel, I., Yıldırım, C., and Lombardo, L.: An open dataset for landslides triggered by the 2016 Mw 7.8 Kaikōura earthquake, New Zealand, Landslides, 19, 1405–1420, https://doi.org/10.1007/s10346-022-01869-9, 2022a. a
Tanyaş, H., Hill, K., Mahoney, L., Fadel, I., and Lombardo, L.: The world's second-largest, recorded landslide event: Lessons learnt from the landslides triggered during and after the 2018 Mw 7.5 Papua New Guinea earthquake, Eng. Geol., 297, 106504, https://doi.org/10.1016/j.enggeo.2021.106504, 2022b. a
ten Brink, U. S., Barkan, R., Andrews, B. D., and Chaytor, J. D.: Size distributions and failure initiation of submarine and subaerial landslides, Earth Planet. Sc. Lett., 287, 31–42, https://doi.org/10.1016/j.epsl.2009.07.031, 2009. a, b
Valagussa, A., Marc, O., Frattini, P., and Crosta, G.: Seismic and geological controls on earthquake-induced landslide size, Earth Planet. Sc. Lett., 506, 268–281, https://doi.org/10.1016/j.epsl.2018.11.005, 2019. a
Valagussa, A., Frattini, P., Valbuzzi, E., and Crosta, G. B.: Role of landslides on the volume balance of the Nepal 2015 earthquake sequence, Sci. Rep.-UK, 11, 3434, https://doi.org/10.1038/s41598-021-83037-y, 2021. a
Van Den Eeckhaut, M., Poesen, J., Verstraeten, G., Vanacker, V., Moeyersons, J., Nyssen, J., and van Beek, L. P. H.: The effectiveness of hillshade maps and expert knowledge in mapping old deep-seated landslides, Geomorphology, 67, 351–363, https://doi.org/10.1016/j.geomorph.2004.11.001, 2005. a
Xu, C., Xu, X., Yao, X., and Dai, F.: Three (nearly) complete inventories of landslides triggered by the May 12, 2008 Wenchuan Mw 7.9 earthquake of China and their spatial distribution statistical analysis, Landslides, 11, 441–461, https://doi.org/10.1007/s10346-013-0404-6, 2014. a, b, c
Xu, Y., Allen, M. B., Zhang, W., Li, W., and He, H.: Landslide characteristics in the Loess Plateau, northern China, Geomorphology, 359, 107150, https://doi.org/10.1016/j.geomorph.2020.107150, 2020. a, b, c, d
Zhao, B., Li, W., Wang, Y., Lu, J., and Li, X.: Landslides triggered by the Ms 6.9 Nyingchi earthquake, China (18 November 2017): analysis of the spatial distribution and occurrence factors, Landslides, 16, 765–776, https://doi.org/10.1007/s10346-019-01146-2, 2019. a
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.
Catalogues of mapped landslides are useful for learning and forecasting how frequently they...
Altmetrics
Final-revised paper
Preprint