Articles | Volume 22, issue 3
https://doi.org/10.5194/nhess-22-1129-2022
© Author(s) 2022. 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-22-1129-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Apr 2022
Research article |
| 01 Apr 2022
| 01 Apr 2022
Insights from the topographic characteristics of a large global catalog of rainfall-induced landslide event inventories
Robert Emberson
CORRESPONDING AUTHOR
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center,
Greenbelt, MD, USA
Goddard Earth Sciences Technology and Research II, Greenbelt, MD, USA
University of Maryland, Baltimore County, 1000 Hilltop Cir, Baltimore, MD, USA
Dalia B. Kirschbaum
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center,
Greenbelt, MD, USA
Pukar Amatya
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center,
Greenbelt, MD, USA
Goddard Earth Sciences Technology and Research II, Greenbelt, MD, USA
University of Maryland, Baltimore County, 1000 Hilltop Cir, Baltimore, MD, USA
Hakan Tanyas
ITC, University of Twente, Twente, the Netherlands
Odin Marc
Géosciences Environnement Toulouse (GET), UMR 5563,
CNRS/IRD/CNES/UPS, Observatoire Midi-Pyrénées, Toulouse, France
Related authors
Robert A. Emberson
Hydrol. Earth Syst. Sci., 27, 3547–3563, https://doi.org/10.5194/hess-27-3547-2023, https://doi.org/10.5194/hess-27-3547-2023, 2023
Short summary
Short summary
Soil can be eroded by rainfall, and this is a major threat to agricultural sustainability. Estimating the erosivity of rainfall is essential as a first step to determine how much soil might be lost. Until recently, satellite data have not been used to estimate rainfall erosivity, but the data quality is now sufficient to do so. In this study, I test several methods to calculate rainfall erosivity using satellite rainfall data and contrast this with ground-based estimates.
Robert Emberson, Dalia Kirschbaum, and Thomas Stanley
Nat. Hazards Earth Syst. Sci., 20, 3413–3424, https://doi.org/10.5194/nhess-20-3413-2020, https://doi.org/10.5194/nhess-20-3413-2020, 2020
Short summary
Short summary
Landslides cause thousands of fatalities and cost billions of dollars of damage worldwide every year, but different inventories of landslide events can have widely diverging completeness. This can lead to spatial biases in our understanding of the impacts. Here we use a globally homogeneous model of landslide hazard and exposure to provide consistent estimates of where landslides are most likely to cause damage to people, roads and other critical infrastructure at 1 km resolution.
Rafael Jan Pablo Schmitt, Shikshita Bhandari, Adrian Vogl, and Odin Marc
EGUsphere, https://doi.org/10.5194/egusphere-2025-3733, https://doi.org/10.5194/egusphere-2025-3733, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
Landslides put humans at risk in the world's mountain regions. Many of these regions are data scarce, and models to evaluate hazards and plan for adaptation do consider for some key aspects of landslides. Here, we propose a simple method that maps both where a landslide can occur and how far debris may travel. Our work was motivated by the need of planners to better site infrastructure and plan for mitigation options, such as reforestation, that can cut risk and protect mountain communities.
Hunter N. Jimenez, Erkan Istanbulluoglu, Tolga Gorum, Thomas A. Stanley, Pukar M. Amatya, Hakan Tanyas, Mehmet C. Demirel, Aykut Akgun, and Deniz Bozkurt
EGUsphere, https://doi.org/10.5194/egusphere-2025-3011, https://doi.org/10.5194/egusphere-2025-3011, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
After a major earthquake struck near the Türkiye/Syria border in February 2023, a powerful storm brought intense rainfall to the region, triggering additional landslides. We used satellite data and a physics-based model to map probabilistic landslide hazard using both coseismic and hydrologic drivers. We also explored how the sequence of these disasters affected landslide risk. Finally, we offer a method for seasonal forecasting of landslide hazard in at-risk areas using the historic climate.
Gregory A. Ruetenik, Ken L. Ferrier, and Odin Marc
Earth Surf. Dynam., 12, 863–881, https://doi.org/10.5194/esurf-12-863-2024, https://doi.org/10.5194/esurf-12-863-2024, 2024
Short summary
Short summary
Fluvial sediment fluxes increased dramatically in Taiwan during Typhoon Morakot in 2009, which produced some of the heaviest landsliding on record. We analyzed fluvial discharge and suspended sediment concentration data at 87 gauging stations across Taiwan to quantify fluvial sediment responses since Morakot. In basins heavily impacted by landsliding, rating curve coefficients sharply increased during Morakot and then declined exponentially with a characteristic decay time of <10 years.
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.
Luke A. McGuire, Scott W. McCoy, Odin Marc, William Struble, and Katherine R. Barnhart
Earth Surf. Dynam., 11, 1117–1143, https://doi.org/10.5194/esurf-11-1117-2023, https://doi.org/10.5194/esurf-11-1117-2023, 2023
Short summary
Short summary
Debris flows are mixtures of mud and rocks that can travel at high speeds across steep landscapes. Here, we propose a new model to describe how landscapes are shaped by debris flow erosion over long timescales. Model results demonstrate that the shapes of channel profiles are sensitive to uplift rate, meaning that it may be possible to use topographic data from steep channel networks to infer how erosion rates vary across a landscape.
Robert A. Emberson
Hydrol. Earth Syst. Sci., 27, 3547–3563, https://doi.org/10.5194/hess-27-3547-2023, https://doi.org/10.5194/hess-27-3547-2023, 2023
Short summary
Short summary
Soil can be eroded by rainfall, and this is a major threat to agricultural sustainability. Estimating the erosivity of rainfall is essential as a first step to determine how much soil might be lost. Until recently, satellite data have not been used to estimate rainfall erosivity, but the data quality is now sufficient to do so. In this study, I test several methods to calculate rainfall erosivity using satellite rainfall data and contrast this with ground-based estimates.
Ionut Cristi Nicu, Letizia Elia, Lena Rubensdotter, Hakan Tanyaş, and Luigi Lombardo
Earth Syst. Sci. Data, 15, 447–464, https://doi.org/10.5194/essd-15-447-2023, https://doi.org/10.5194/essd-15-447-2023, 2023
Short summary
Short summary
Thaw slumps and thermo-erosion gullies are cryospheric hazards that are widely encountered in Nordenskiöld Land, the largest and most compact ice-free area of the Svalbard Archipelago. By statistically analysing the landscape characteristics of locations where these processes occurred, we can estimate where they may occur in the future. We mapped 562 thaw slumps and 908 thermo-erosion gullies and used them to create the first multi-hazard susceptibility map in a high-Arctic environment.
Katy Burrows, Odin Marc, and Dominique Remy
Nat. Hazards Earth Syst. Sci., 22, 2637–2653, https://doi.org/10.5194/nhess-22-2637-2022, https://doi.org/10.5194/nhess-22-2637-2022, 2022
Short summary
Short summary
The locations of triggered landslides following a rainfall event can be identified in optical satellite images. However cloud cover associated with the rainfall means that these images cannot be used to identify landslide timing. Timings of landslides triggered during long rainfall events are often unknown. Here we present methods of using Sentinel-1 satellite radar data, acquired every 12 d globally in all weather conditions, to better constrain the timings of rainfall-triggered landslides.
Alexander L. Handwerger, Mong-Han Huang, Shannan Y. Jones, Pukar Amatya, Hannah R. Kerner, and Dalia B. Kirschbaum
Nat. Hazards Earth Syst. Sci., 22, 753–773, https://doi.org/10.5194/nhess-22-753-2022, https://doi.org/10.5194/nhess-22-753-2022, 2022
Short summary
Short summary
Rapid detection of landslides is critical for emergency response and disaster mitigation. Here we develop a global landslide detection tool in Google Earth Engine that uses satellite radar data to measure changes in the ground surface properties. We find that we can detect areas with high landslide density within days of a triggering event. Our approach allows the broader hazard community to utilize these state-of-the-art data for improved situational awareness of landslide hazards.
Odin Marc, Jens M. Turowski, and Patrick Meunier
Earth Surf. Dynam., 9, 995–1011, https://doi.org/10.5194/esurf-9-995-2021, https://doi.org/10.5194/esurf-9-995-2021, 2021
Short summary
Short summary
The size of grains delivered to rivers is an essential parameter for understanding erosion and sediment transport and their related hazards. In mountains, landslides deliver these rock fragments, but few studies have analyzed the landslide properties that control the resulting sizes. We present measurements on 17 landslides from Taiwan and show that their grain sizes depend on rock strength, landslide depth and drop height, thereby validating and updating a previous theory on fragmentation.
Robert Emberson, Dalia Kirschbaum, and Thomas Stanley
Nat. Hazards Earth Syst. Sci., 20, 3413–3424, https://doi.org/10.5194/nhess-20-3413-2020, https://doi.org/10.5194/nhess-20-3413-2020, 2020
Short summary
Short summary
Landslides cause thousands of fatalities and cost billions of dollars of damage worldwide every year, but different inventories of landslide events can have widely diverging completeness. This can lead to spatial biases in our understanding of the impacts. Here we use a globally homogeneous model of landslide hazard and exposure to provide consistent estimates of where landslides are most likely to cause damage to people, roads and other critical infrastructure at 1 km resolution.
Cited articles
Adriano, B., Yokoya, N., Miura, H., Matsuoka, M., and Koshimura, S.: A semiautomatic pixel-object method for detecting landslides using multitemporal ALOS-2 intensity images, Remote Sens., 12, 561, https://doi.org/10.3390/rs12030561, 2020.
Amatya, P., Kirschbaum, D., and Stanley, T.: Use of very high-resolution optical data for landslide mapping and susceptibility analysis along the
Karnali highway, Nepal, Remote Sens., 11, 2284, https://doi.org/10.3390/rs11192284, 2019.
Amatya, P., Kirschbaum, D., Stanley, T., and Tanyas, H.: Landslide mapping
using object-based image analysis and open source tools, Eng. Geol., 282, 106000, https://doi.org/10.1016/j.enggeo.2021.106000, 2021.
Badoux, A., Andres, N., and Turowski, J. M.: Damage costs due to bedload
transport processes in Switzerland, Nat. Hazards Earth Syst. Sci., 14, 279–294, https://doi.org/10.5194/nhess-14-279-2014, 2014.
Behling, R., Roessner, S., Segl, K., Kleinschmit, B., and Kaufmann, H.: Robust automated image co-registration of optical multi-sensor time series data: Database generation for multi-temporal landslide detection, Remote Sens., 6, 2572–2600, https://doi.org/10.3390/rs6032572, 2014.
Bekaert, D. P., Handwerger, A. L., Agram, P., and Kirschbaum, D. B.: InSAR-based detection method for mapping and monitoring slow-moving landslides in remote regions with steep and mountainous terrain: An application to Nepal, Remote Sens. Environ., 249, 111983,
https://doi.org/10.1016/j.rse.2020.111983, 2020.
Beven, K. J. and Kirkby, M. J.: A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d'appel variable de l'hydrologie du bassin versant, Hydrolog. Sci. J., 24, 43–69, https://doi.org/10.1080/02626667909491834, 1979.
Bogaard, T. A. and Greco, R.: Landslide hydrology: from hydrology to pore
pressure, Wiley Interdisciplin. Rev.: Water, 3, 439–459, https://doi.org/10.1002/wat2.1126, 2016.
Bookhagen, B. and Strecker, M. R.: Spatiotemporal trends in erosion rates
across a pronounced rainfall gradient: Examples from the southern Central
Andes, Earth Planet. Sc. Lett., 327–328, 97–110, https://doi.org/10.1016/j.epsl.2012.02.005, 2012.
Broeckx, J., Maertens, M., Isabirye, M., Vanmaercke, M., Namazzi, B., Deckers, J., Tamale, J., Jacobs, L., Thiery, W., Kervyn, M., Vranken, L., and Poesen, J.: Landslide susceptibility and mobilization rates in the Mount Elgon region, Uganda, (October 2018), Landslides, 16, 571–584, https://doi.org/10.1007/s10346-018-1085-y, 2019.
Budimir, M. E. A., Atkinson, P. M., and Lewis, H. G.: A systematic review of
landslide probability mapping using logistic regression, Landslides, 12, 419–436, https://doi.org/10.1007/s10346-014-0550-5, 2015.
Burrows, K., Walters, R. J., Milledge, D., and Densmore, A. L.: A systematic
exploration of satellite radar coherence methods for rapid landslide
detection, Nat. Hazards Earth Syst. Sci., 20, 3197–3214, https://doi.org/10.5194/nhess-20-3197-2020, 2020.
Camilo, D. C., Lombardo, L., Mai, P. M., Dou, J., and Huser, R.: Environmental Modelling & Software Handling high predictor dimensionality
in slope-unit-based landslide susceptibility models through LASSO-penalized
Generalized Linear Model, Environ. Model. Softw., 97, 145–156, https://doi.org/10.1016/j.envsoft.2017.08.003, 2017.
Casagli, N., Frodella, W., Morelli, S., Tofani, V., Ciampalini, A., Interieri, C., Raspini, F., Rossi, G., Tanteri, L., and Lu, P.: Spaceborne, UAV and ground-based remote sensing techniques for landslide mapping, monitoring and early warning, Geoenviron. Disast., 4, 1–23, https://doi.org/10.1186/s40677-017-0073-1, 2017.
Chang, K., Chiang, S., Chen, Y., and Mondini, A. C.: Modeling the spatial occurrence of shallow landslides triggered by typhoons, Geomorphology, 208,
137–148, https://doi.org/10.1016/j.geomorph.2013.11.020, 2014.
Chen, Y., Chang, K., Chiu, Y., Lau, S., Lee, H., and County, T.: Quantifying
rainfall controls on catchment-scale landslide erosion in Taiwan, Earth Surf. Proc. Land., 382, 372–382, https://doi.org/10.1002/esp.3284, 2013.
Conrad, J. L., Morphew, M. D., Baum, R. L., and Mirus, B. B.: HydroMet: A New Code for Automated Objective Optimization of Hydrometeorological Thresholds for Landslide Initiation, Water, 13, 1752, https://doi.org/10.3390/w13131752, 2021.
Costanzo, D., Rotigliano, E., Irigaray, C., Jiménez-Perálvarez, J. D., and Chacón, J.: Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: Application to the
river Beiro basin (Spain), Nat. Hazards Earth Syst. Sci., 12, 327–340, https://doi.org/10.5194/nhess-12-327-2012, 2012.
Densmore, A. L. and Hovius, N.: Topographic fingerprints of bedrock landslides, Geology, 28, 371–374, https://doi.org/10.1130/0091-7613(2000)28<371:TFOBL>2.0.CO;2, 2000.
Dietrich, W. E., Reiss, R., Hsu, M. L., and Montgomery, D. R.: A process-based model for colluvial soil depth and shallow landsliding using digital elevation data, Hydrol. Process., 9, 383–400, https://doi.org/10.1002/hyp.3360090311, 1995.
Domej, G., Bourdeau, C., Lenti, L., Martino, S., and Piuta, K.: Mean landslide geometries inferred from a global database of earthquake-and non-earthquake-triggered landslides, Ital. J. Eng. Geol. Environ., 17, 87–107, https://doi.org/10.4408/IJEGE.2017-02.O-05, 2017.
Emberson, R., Kirschbaum, D., and Stanley, T.: New global characterisation of landslide exposure, Nat. Hazards Earth Syst. Sci., 20, 3413–3424,
https://doi.org/10.5194/nhess-20-3413-2020, 2020.
Emberson, R. A., Kirschbaum, D. B., and Stanley, T.: Landslide hazard and exposure modelling in data-poor regions: the example of the Rohingya refugee camps in Bangladesh, Earth's Future, 9, e2020EF001666, https://doi.org/10.1029/2020EF001666, 2021.
Friedman, J., Hastie, T., Tibshirani, R., Narasimhan, B., Tay, K., Simon, N., and Qian, J.: Lasso and Elastic-Net Regularized Generalized Linear Models, CRAN, https://doi.org/10.18637/jss.v033.i01, 2021.
Froude, M. J. and Petley, D. N.: Global fatal landslide occurrence from 2004 to 2016, Nat. Hazards Earth Syst. Sci., 18, 2161–2181, https://doi.org/10.5194/nhess-18-2161-2018, 2018.
García-Rodríguez, M. J., Malpica, J. A., Benito, B., and Díaz, M.: Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression, Geomorphology, 95, 172–191,
https://doi.org/10.1016/j.geomorph.2007.06.001, 2008.
Geiger, R.: Klassifikation der Klimate nach W. Köppen, in: Landolt-Börnstein – Zahlenwerte und Funktionen aus Physik, Chemie,
Astronomie, Geophysik und Technik, alte Serie, Springer, Berlin, 603–607,
1954.
Goetz, J. N., Brenning, A., Petschko, H., and Leopold, P.: Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling, Comput. Geosci., 81, 1–11, https://doi.org/10.1016/j.cageo.2015.04.007, 2015.
Guzzetti, F., Cardinali, M., and Reichenbach, P.: The Influence of Structural Setting and Lithology on Landslide Type and Pattern, Environ. Eng. Geosci., II, 531–555, https://doi.org/10.2113/gseegeosci.II.4.531, 1996.
Guzzetti, F., Cesare, A., Cardinali, M., Fiorucci, F., Santangelo, M., and
Chang, K.: 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.
Handwerger, A. L., Fielding, E. J., Huang, M., Bennett, G., Liang, C., and
Schulz, W. H.: Widespread Initiation, Reactivation , and Acceleration of
Landslides in the Northern California Coast Ranges due to Extreme Rainfall, J. Geophys. Res.-Earth, 124, 1782–1797, https://doi.org/10.1029/2019JF005035, 2019.
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., and Townshend, J. R. G.: High-Resolution Global Maps of 21st-Century Forest Cover Change, Science, 342, 850–853, https://doi.org/10.1126/science.1244693, 2013.
Harp, B. E. L., Reid, M. E., and Michael, J. A.: Hazard Analysis of Landslides Triggered by Typhoon Chata'an on July 2, 2002, in Chuuk State, Federated States of Micronesia, USGS Open-File Report 2004-1348, USGS, https://doi.org/10.3133/ofr20041348, 2004.
Harp, E. L., Keefer, D. K., Sato, H. P., and Yagi, H.: Landslide inventories: The essential part of seismic landslide hazard analyses, Eng. Geol., 122, 9–21, https://doi.org/10.1016/j.enggeo.2010.06.013, 2011.
Hartmann, J. and Moosdorf, N.: The new global lithological map database GLiM: A representation of rock properties at the Earth surface, Geochem. Geophy. Geosy., 13, 1–37, https://doi.org/10.1029/2012GC004370, 2012.
Hencher, S. R.: Preferential flow paths through soil and rock and their
association with landslides, Hydrol. Process., 24, 1610–1630, https://doi.org/10.1002/hyp.7721, 2010.
Hosmer, D. and Lemeshow, S: Applied Logistic Regression, 2nd Edn., Wiley, New York, ISBN 978-0-470-58247-3, 2000.
Hu, X., Bürgmann, R., Lu, Z., Handwerger, A. L., Wang, T., and Miao, R.:
Mobility, Thickness, and Hydraulic Diffusivity of the Slow – Moving Monroe
Landslide in California Revealed by L – Band Satellite Radar Interferometry
J. Geophys. Res.-Solid, 124, 7504–7518, https://doi.org/10.1029/2019JB017560, 2019.
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K.-L., Joyce, R. J., Kidd, C., Nelkin, E. J., Sorooshian, S., Stocker, E. F., Tan, J., Wolff, D. B., and Xie, P.: Integrated Multi-Satellite Retrievals for the Global Precipitation Measurement (GPM) Mission (IMERG), in: Advances in Global Change Research, Springer, 343–353, https://doi.org/10.1007/978-3-030-24568-9_19, 2020.
Iida, T.: A stochastic hydro-geomorphological model for shallow landsliding
due to rainstorm, Catena, 34, 293–313, https://doi.org/10.1016/S0341-8162(98)00093-9, 1999.
Iida, T.: Theoretical research on the relationship between return period of
rainfall and shallow landslides, Hydrol. Process., 18, 739–756, https://doi.org/10.1002/hyp.1264, 2004.
Iverson, R. M.: Landslide triggering by rain infiltration, Water Resour. Res., 36, 1897–1910, https://doi.org/10.1029/2000WR900090, 2000.
Jibson, R. W., Harp, E. L., and Michael, J. A.: A method for producing digital probabilistic seismic landslide hazard maps, Eng. Geol., 58, 271–289, https://doi.org/10.1016/S0013-7952(00)00039-9, 2000.
Jung, J. and Yun, S. H.: Evaluation of coherent and incoherent landslide detection methods based on synthetic aperture radar for rapid response: A case study for the 2018 Hokkaido landslides, Remote Sens., 12, 265,
https://doi.org/10.3390/rs12020265, 2020.
Kirschbaum, D. B. 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.
Kirschbaum, D. B., Stanley, T., and Zhou, Y.: Spatial and temporal analysis of a global landslide catalog, Geomorphology, 249, 4–15, https://doi.org/10.1016/j.geomorph.2015.03.016, 2015.
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.
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.
Liang, W. and Chan, M.: Spatial and temporal variations in the effects of soil depth and topographic wetness index of bedrock topography on subsurface saturation generation in a steep natural forested headwater catchment, J. Hydrol., 546, 405–418, https://doi.org/10.1016/j.jhydrol.2017.01.033, 2017.
Lin, C.-W., Chang, W.-S., Liu, S.-H., Tsai, T.-T., Lee, S.-P., Tsang, Y.-C., Shieh, C.-L., and Tseng, C.-M.: Landslides triggered by the 7 August 2009 Typhoon Morakot in southern Taiwan, Eng. Geol., 123, 3–12, https://doi.org/10.1016/j.enggeo.2011.06.007, 2011.
Lombardo, L. and Tanyas, H.: Chrono-validation of near-real-time landslide
susceptibility models via plug-in statistical simulations, Eng. Geol., 278,
105818, https://doi.org/10.1016/j.enggeo.2020.105818, 2020.
Lombardo, L., Optiz, T., and Huser, R.: Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster, Stoch. Environ. Res. Risk Assess., 32, 2179–2198, https://doi.org/10.1007/s00477-018-1518-0, 2018.
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.
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.
Marc, O., Hovius, N., Meunier, P., Gorum, T., and Uchida, T.: A seismologically consistent expression for the total area and volume of
earthquake-triggered landsliding, J. Geophys. Res.-Earth, 121, 640–663, https://doi.org/10.1002/2015JF003732, 2016.
Marc, O., Meunier, P., and Hovius, N.: Prediction of the area affected by
earthquake-induced landsliding based on seismological parameters, Nat.
Hazards Earth Syst. Sci., 17, 1159–1175, https://doi.org/10.5194/nhess-17-1159-2017, 2017.
Marc, O., Stumpf, A., Malet, J.-P., Gosset, M., Uchida, T., and Chiang, S.-H.: Initial insights from a global database of rainfall-induced landslide inventories: the weak influence of slope and strong influence of total storm rainfall, Earth Surf. Dynam., 6, 903–922, https://doi.org/10.5194/esurf-6-903-2018, 2018.
Marc, O., Gosset, M., Saito, H., Uchida, T., and Malet, J.-P.: Spatial Patterns of Storm-Induced Landslides and Their Relation to Rainfall Anomaly
Maps, Geophys. Res. Lett., 46, 11167–11177, https://doi.org/10.1029/2019GL083173, 2019.
Martha, T. R., Kerle, N., Van Westen, C. J., Jetten, V., and Kumar, K. V.:
Object-oriented analysis of multi-temporal panchromatic images for creation
of historical landslide inventories, ISPRS J. Photogram. Remote Sens., 67, 105–119, https://doi.org/10.1016/j.isprsjprs.2011.11.004, 2012.
Meunier, P., Hovius, N., and Haines, J. A.: Topographic site effects and the
location of earthquake induced landslides, Earth Planet. Sc. Lett., 275, 221–232, https://doi.org/10.1016/j.epsl.2008.07.020, 2008.
Milledge, D. G., Densmore, A. L., Bellugi, D., Rosser, N. J., Watt, J., Li,
G., and Oven, K. J.: Simple rules to minimise exposure to coseismic landslide hazard, Nat. Hazards Earth Syst. Sci., 19, 837–856, https://doi.org/10.5194/nhess-19-837-2019, 2019.
Milliman, J. and Syvitski, J. P. M.: Geomorphic Tectonic Control of Sediment Discharge to Ocean – The Importance of Small Mountainous Rivers Geomorphic/Tectonic Control of Sediment Discharge to the Ocean: The Importance of Small, J. Geol., 100, 525–544, https://doi.org/10.1086/629606, 1991.
Mirus, B., Jones, E. S., Baum, R. L., Godt, J. W., Slaughter, S., Crawford, M. M., Lancaster, J., Stanley, T., Kirschbaum, D. B., Burns, W. J., Schmitt, R. G., Lindsey, K. O., and McCoy, K. M.: Landslides across the USA: occurrence, susceptibility, and data limitations, Landslides, 17, 2271–2285, 2020.
Mondini, A. C., Santangelo, M., Rocchetti, M., Rossetto, E., Manconi, A., and Monserrat, O.: Sentinel-1 SAR amplitude imagery for rapid landslide detection, Remote Sens., 11, 760, https://doi.org/10.3390/rs11070760, 2019.
Montgomery, D. R. and Dietrich, W. E.: A physically based model for the
topographic control on shallow landsliding, Water Resour. Res., 30, 1153–1171, https://doi.org/10.1029/93WR02979, 1994.
Montgomery, D. R., Schmidt, K. M., Dietrich, W. E., and McKean, J.: Instrumental record of debris flow initiation during natural rainfall: Implications for modeling slope stability, J. Geophys. Res.-Earth, 114, F01031, https://doi.org/10.1029/2008JF001078, 2009.
Nowicki Jessee, M. A., Hamburger, M. W., Allstadt, K., Wald, D. J., Robeson, S. M., Tanyas, H., Hearne, M., and Thompson, E. M.: A Global Empirical Model for Near-Real-Time Assessment of Seismically Induced Landslides, J. Geophys. Res.-Earth, 123, 1835–1859, https://doi.org/10.1029/2017JF004494, 2018.
Pawluszek, K., Borkowski, A., and Tarolli, P.: Sensitivity analysis of automatic landslide mapping: numerical experiments towards the best solution, Landslides, 15, 1851–1865, https://doi.org/10.1007/s10346-018-0986-0, 2018.
Petley, D. Global patterns of loss of life from landslides, Geology, 40,
927–930, https://doi.org/10.1130/G33217.1, 2012.
Planet Team: Planet Application Program Interface: In Space for Life on Earth, Planet Team, San Francisco, CA, https://api.planet.com (last access: 31 March 2022), 2017.
Prancevic, J. P., Lamb, M. P., McArdell, B. W., Rickli, C., and Kirchner, J. W.: Decreasing landslide erosion on steeper slopes in soil-mantled landscapes, Geophys. Res. Lett., 47, e2020GL087505, https://doi.org/10.1029/2020GL087505, 2020.
Rault, C., Robert, A., Marc, O., Hovius, N., and Meunier, P.: Seismic and
geologic controls on spatial clustering of landslides in three large earthquakes, Earth Surf. Dynam., 7, 829–839, https://doi.org/10.5194/esurf-7-829-2019, 2019.
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: 31 March 2022), 2018.
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.
Riley, S. J., DeGloria, S. D., and Elliot, R.: A Terrain Ruggedness Index that Quantifies Topographic Heterogeneity, Intermount. J. Sci., 5, 23–27, 1999.
Roering, J. J., Kirchner, J. W., Sklar, L. S., and Dietrich, W. E.: Hillslope evolution by nonlinear creep and landsliding: An experimental study, Geology, 29, 143–146, https://doi.org/10.1130/0091-7613(2001)029<0143:HEBNCA>2.0.CO;2, 2001.
Rossi, G., Tanteri, L., Tofani, V., Vannocci, P., Moretti, S., and Casagli,
N.: Multitemporal UAV surveys for landslide mapping and characterization,
Landslides, 15, 1045–1052, https://doi.org/10.1007/s10346-018-0978-0, 2018.
Santangelo, M., Marchesini, I., Cardinali, M., Fiorucci, F., Rossi, M., Bucci, F., and Guzzetti, F.: A method for the assessment of the influence of
bedding on landslide abundance and types, Landslides, 12, 295–309,
https://doi.org/10.1007/s10346-014-0485-x, 2015.
Schmitt, R. G., Tanyas, H., Nowicki Jessee, M. A., Zhu, J., Biegel, K. M.,
Allstadt, K. E., Jibson, R. W., Thompson, E. M., van Westen, C. J., Sato, H. P., Wald, D. J., Godt, J. W., Gorum, T., Xu, C., Rathje, E. M., and Knudsen, K. L.: An open repository of earthquake-triggered ground-failure inventories, Data Series, USGS, Reston, VA, https://doi.org/10.3133/ds1064, 2017.
Selby, M. J.: Controls on the Stability and Inclinations of Hillslopes formed
on hard rock, Earth Surf. Proc. Land., 7, 449–467, https://doi.org/10.1002/esp.3290070506, 1982.
Sörensen, R., Zinko, U., and Seibert, J.: On the calculation of the topographic wetness index: evaluation of different methods based on field observations, Hydrol. Earth Syst. Sci., 10, 101–112, https://doi.org/10.5194/hess-10-101-2006, 2006.
Stanley, T. A. 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.
Tanyaş, H. and Lombardo, L.: Variation in landslide-affected area under
the control of ground motion and topography, Eng. Geol., 260, 105229, https://doi.org/10.1016/j.enggeo.2019.105229, 2019.
Tanyaş, H., van Westen, C. J., Allstadt, K. E., Nowicki Jessee, M. A., Gorum, T., Jibson, R. W., Godt, J. W., Sato, H. P., Schmitt, R. G., Marc, O., and Hovius, N.: Presentation and Analysis of Earthquake-Induced Landslide Inventories, J. Geophys. Res.-Earth, 122, 1991–2015, https://doi.org/10.1002/2017JF004236, 2017.
Tanyaş, H., Rossi, M., Alvioli, M., van Westen, C. J., and Marchesini, I.: A global slope unit-based method for the near real-time prediction of earthquake-induced landslides, Geomorphology, 327, 126–146, https://doi.org/10.1016/j.geomorph.2018.10.022, 2019.
The Association of Japanese Geographers: The 2018 July Heavy rain in West
Japan, http://ajg-disaster.blogspot.com/2018/07/3077.html, last access: 1 November 2019.
Tibshirani, R.: Regression Shrinkage and Selection via the Lasso, J. Roy. Stat. Soc. Ser. B, 58, 267–288, https://doi.org/10.1111/j.2517-6161.1996.tb02080.x, 1996.
Van Den Eeckhaut, M. and Hervás, J.: Geomorphology State of the art of
national landslide databases in Europe and their potential for assessing
landslide susceptibility, Hazard Risk, 140, 545–558, https://doi.org/10.1016/j.geomorph.2011.12.006, 2012.
Van Den Eeckhaut, M., Hervás, J., Jaedicke, C., Malet, J.-P., Montanarella, L., and Nadim, F.: Statistical modelling of Europe-wide landslide susceptibility using limited landslide inventory data, Landslides,
9, 357–369, https://doi.org/10.1007/s10346-011-0299-z, 2012.
van Westen, C. J. and Zhang, J.: Landslides and floods triggered by Hurricane Maria (18 September, 2017) in Dominica, Digital or Visual Products, UNITAR-UNOSAT, http://www.unitar.org/unosat/node/44/2762 (last access: 31 March 2022), 2018.
van Westen, C., Jetten, V., and Alkema, D.: Validating national landslide susceptibility and hazard maps for Caribbean island countries: the case of Dominica and tropical storm Erika, in: EGU General Assembly Conference Abstracts, April 2016, EPSC2016-4334, 2016.
Wasowski, J., Keefer, D. K., and Lee, C. T.: Toward the next generation of research on earthquake-induced landslides: current issues and future challenges, Eng. Geol., 122, 1–8, https://doi.org/10.1016/j.enggeo.2011.06.001, 2011.
Weiss, A.: Topographic Position and Landforms Analysis, in: ESRI User Conference, San Diego, CA, http://www.jennessent.com/downloads/TPI-poster-TNC_18x22.pdf (last access: 31 March 2022), 2001.
Williams, J. G., Rosser, N. J., Kincey, M. E., Benjamin, J., Oven, K. J.,
Densmore, A. L., Milledge, D. G., Robinson, T. R., Jordan, C. A., and Dijkstra, T. A.: Satellite-based emergency mapping using optical imagery:
experience and reflections from the 2015 Nepal earthquakes, Nat. Hazards
Earth Syst. Sci., 18, 185–205, https://doi.org/10.5194/nhess-18-185-2018, 2018.
Xu, C., Dai, F., Xu, X., and Hsi, Y.: GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang
River watershed, China, Geomorphology, 145–146, 70–80, https://doi.org/10.1016/j.geomorph.2011.12.040, 2012.
Short summary
Understanding where landslides occur in mountainous areas is critical to support hazard analysis as well as understand landscape evolution. In this study, we present a large compilation of inventories of landslides triggered by rainfall, including several that are described here for the first time. We analyze the topographic characteristics of the landslides, finding consistent relationships for landslide source and deposition areas, despite differences in the inventories' locations.
Understanding where landslides occur in mountainous areas is critical to support hazard analysis...
Altmetrics
Final-revised paper
Preprint