Articles | Volume 6, issue 4
https://doi.org/10.5194/esurf-6-903-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-6-903-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Initial insights from a global database of rainfall-induced landslide inventories: the weak influence of slope and strong influence of total storm rainfall
École et Observatoire des Sciences de la Terre,
Institut de Physique du Globe de Strasbourg, Centre National de la Recherche
Scientifique UMR 7516, University of Strasbourg, 67084 Strasbourg CEDEX, France
André Stumpf
École et Observatoire des Sciences de la Terre,
Institut de Physique du Globe de Strasbourg, Centre National de la Recherche
Scientifique UMR 7516, University of Strasbourg, 67084 Strasbourg CEDEX, France
Jean-Philippe Malet
École et Observatoire des Sciences de la Terre,
Institut de Physique du Globe de Strasbourg, Centre National de la Recherche
Scientifique UMR 7516, University of Strasbourg, 67084 Strasbourg CEDEX, France
Marielle Gosset
Géoscience Environnement Toulouse, Toulouse, France
Taro Uchida
National Institute for Land and Infrastructure Management, Research Center for Disaster Risk Management, Tsukuba, Japan
Shou-Hao Chiang
Center for Space and Remote Sensing Research, National Central University, Taoyuan City 32001, Taiwan
Related authors
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
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.
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.
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.
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.
Robert Emberson, Dalia B. Kirschbaum, Pukar Amatya, Hakan Tanyas, and Odin Marc
Nat. Hazards Earth Syst. Sci., 22, 1129–1149, https://doi.org/10.5194/nhess-22-1129-2022, https://doi.org/10.5194/nhess-22-1129-2022, 2022
Short summary
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.
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.
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
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.
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.
Floriane Provost, Dimitri Zigone, Emmanuel Le Meur, Jean-Philippe Malet, and Clément Hibert
The Cryosphere, 18, 3067–3079, https://doi.org/10.5194/tc-18-3067-2024, https://doi.org/10.5194/tc-18-3067-2024, 2024
Short summary
Short summary
The recent calving of Astrolabe Glacier in November 2021 presents an opportunity to better understand the processes leading to ice fracturing. Optical-satellite imagery is used to retrieve the calving cycle of the glacier ice tongue and to measure the ice velocity and strain rates in order to document fracture evolution. We observed that the presence of sea ice for consecutive years has favoured the glacier extension but failed to inhibit the growth of fractures that accelerated in June 2021.
Clément Hibert, François Noël, David Toe, Miloud Talib, Mathilde Desrues, Emmanuel Wyser, Ombeline Brenguier, Franck Bourrier, Renaud Toussaint, Jean-Philippe Malet, and Michel Jaboyedoff
Earth Surf. Dynam., 12, 641–656, https://doi.org/10.5194/esurf-12-641-2024, https://doi.org/10.5194/esurf-12-641-2024, 2024
Short summary
Short summary
Natural disasters such as landslides and rockfalls are mostly difficult to study because of the impossibility of making in situ measurements due to their destructive nature and spontaneous occurrence. Seismology is able to record the occurrence of such events from a distance and in real time. In this study, we show that, by using a machine learning approach, the mass and velocity of rockfalls can be estimated from the seismic signal they generate.
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.
Axel A. J. Deijns, Olivier Dewitte, Wim Thiery, Nicolas d'Oreye, Jean-Philippe Malet, and François Kervyn
Nat. Hazards Earth Syst. Sci., 22, 3679–3700, https://doi.org/10.5194/nhess-22-3679-2022, https://doi.org/10.5194/nhess-22-3679-2022, 2022
Short summary
Short summary
Landslides and flash floods are rainfall-induced processes that often co-occur and interact, generally very quickly. In mountainous cloud-covered environments, determining when these processes occur remains challenging. We propose a regional methodology using open-access satellite radar images that allow for the timing of landslide and flash floods events, in the contrasting landscapes of tropical Africa, with an accuracy of up to a few days. The methodology shows potential for transferability.
François Noël, Michel Jaboyedoff, Andrin Caviezel, Clément Hibert, Franck Bourrier, and Jean-Philippe Malet
Earth Surf. Dynam., 10, 1141–1164, https://doi.org/10.5194/esurf-10-1141-2022, https://doi.org/10.5194/esurf-10-1141-2022, 2022
Short summary
Short summary
Rockfall simulations are often performed to make sure infrastructure is safe. For that purpose, rockfall trajectory data are needed to calibrate the simulation models. In this paper, an affordable, flexible, and efficient trajectory reconstruction method is proposed. The method is tested by reconstructing trajectories from a full-scale rockfall experiment involving 2670 kg rocks and a flexible barrier. The results highlight improvements in precision and accuracy of the proposed method.
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.
Robert Emberson, Dalia B. Kirschbaum, Pukar Amatya, Hakan Tanyas, and Odin Marc
Nat. Hazards Earth Syst. Sci., 22, 1129–1149, https://doi.org/10.5194/nhess-22-1129-2022, https://doi.org/10.5194/nhess-22-1129-2022, 2022
Short summary
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.
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.
Cited articles
Ardizzone, F., Basile, G., Cardinali, M., Casagli, N., Conte, S. D.,
Ventisette, C. D., Fiorucci, F., Garfagnoli, F., Gigli, G., Guzzetti, F.,
Iovine, G., Mondini, A. C., Moretti, S., Panebianco, M., Raspini, F.,
Reichenbach, P., Rossi, M., Tanteri, L., and Terranova, O.: Landslide
inventory map for the Briga and the Giampilieri catchments, NE
Sicily, Italy, J. Maps, 8, 176–180,
https://doi.org/10.1080/17445647.2012.694271,
2012. a
Arnone, E., Noto, L. V., Lepore, C., and Bras, R. L.: Physically-based and
distributed approach to analyze rainfall-triggered landslides at watershed
scale, Geomorphology, 133, 121–131, https://doi.org/10.1016/j.geomorph.2011.03.019,
2011. a, b
Baum, R. L., Godt, J. W., and Savage, W. Z.: Estimating the timing and location
of shallow rainfall-induced landslides using a model for transient,
unsaturated infiltration, J. Geophys. Res.-Earth,
115, F03013, https://doi.org/10.1029/2009JF001321,
2010. a, b
Blodgett, T. A. and Isacks, B. L.: Landslide Erosion Rate in the Eastern
Cordillera of Northern Bolivia, Earth Interact., 11, 1–30,
https://doi.org/10.1175/2007EI222.1,
2007. a
Bucknam, R. C., Coe, J. A., Chavarria, M. M., Godt, J. W., Tarr, A. C.,
Bradley, L.-A., Rafferty, S. A., Hancock, D., Dart, R. L., and Johnson,
M. L.: Landslides triggered by Hurricane Mitch in Guatemala –
inventory and discussion, USGS Numbered Series 2001-443,
https://doi.org/10.3133/ofr01443, 2001. a
Caine, N.: The Rainfall Intensity: Duration Control of Shallow
Landslides and Debris Flows, Geogr. Ann. A, 62, 23–27, https://doi.org/10.2307/520449,
1980. a
Camargo, L. P.: Análise integrada no meio físico dos ribeirões Braço
Serafim e Máximo com ênfase nas áreas de fragilidade estrutural,
Luís Alves, (SC), PhD thesis, Universidade Federal de Santa
Catarina, Florianopolis,
available at: https://repositorio.ufsc.br/handle/123456789/157291
(last access: 2 October 2018), 2015. a, b, c
Cannon, S. H., Haller, K. M., Ekstrom, I., Schweig, E. S., Devoli, G., Moore,
D. W., Rafferty, S. A., and Tarr, A. C.: Landslide response to Hurricane
Mitch rainfall in seven study areas in Nicaragua, USGS Numbered
Series 2001-412-A,
https://doi.org/10.3133/ofr01412A, 2001. a, b
Cardinali, M., Galli, M., Guzzetti, F., Ardizzone, F., Reichenbach, P., and Bartoccini, P.: Rainfall induced
landslides in December 2004 in south-western Umbria, central Italy: types, extent, damage and risk assessment,
Nat. Hazards Earth Syst. Sci., 6, 237–260, https://doi.org/10.5194/nhess-6-237-2006, 2006. a
Chang, K.-T., Chiang, S.-H., Chen, Y.-C., 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. a
Chen, Y.-C., Chang, K.-T., Lee, H.-Y., and Chiang, S.-H.: Average landslide
erosion rate at the watershed scale in southern Taiwan estimated from
magnitude and frequency of rainfall, Geomorphology, 228, 756–764,
https://doi.org/10.1016/j.geomorph.2014.07.022,
2015. a
Clark, K. E., West, A. J., Hilton, R. G., Asner, G. P., Quesada, C. A., Silman, M. R., Saatchi, S. S., Farfan-Rios, W.,
Martin, R. E., Horwath, A. B., Halladay, K., New, M., and Malhi, Y.: Storm-triggered landslides in the Peruvian Andes and
implications for topography, carbon cycles, and biodiversity, Earth Surf. Dynam., 4, 47–70, https://doi.org/10.5194/esurf-4-47-2016, 2016. a
Crone, A. J., Baum, R. L., Lidke, D. J., Sather, D. N., Bradley, L.-A., and
Tarr, A. C.: Landslides induced by Hurricane Mitch in El Salvador –
an inventory and descriptions of selected features, USGS Numbered
Series 2001-444,
https://doi.org/10.3133/ofr01444, 2001. a
Dahal, R. K. and Hasegawa, S.: Representative rainfall thresholds for
landslides in the Nepal Himalaya, Geomorphology, 100, 429–443,
https://doi.org/10.1016/j.geomorph.2008.01.014,
2008. a
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. a
Domej, G., Bourdeau, C., and Lenti, L.: Mean Landslide Geometries
Inferred from a Global Database of Earthquake- and
Non-Earthquake-Triggered Landslides, Italian Journal of Engineering
Geology and Environment, 87–107, https://doi.org/10.4408/IJEGE.2017-02.O-05,
2017. a
Farr, T. G. and Kobrick, M.: Shuttle radar topography mission produces a wealth of
data,
Eos, 81, 583–585, https://doi.org/10.1029/EO081i048p00583, 2000.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E.,
Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D. E.:
The shuttle radar topography mission, Rev. Geophys., 45, n RG2004, https://doi.org/10.1029/2005RG000183, 2007.
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, b, c, d
Gabet, E. J., Burbank, D. W., Putkonen, J. K., Pratt-Sitaula, B. A., and Ojha,
T.: Rainfall thresholds for landsliding in the Himalayas of Nepal,
Geomorphology, 63, 131–143, https://doi.org/10.1016/j.geomorph.2004.03.011,
2004. a, b, c
Gallen, S. F., Clark, M. K., and Godt, J. W.: Coseismic landslides reveal
near-surface rock strength in a high-relief, tectonically active setting,
Geology, 43, 11–14, https://doi.org/10.1130/G36080.1,
2015. a
Gariano, S. L. and Guzzetti, F.: Landslides in a changing climate,
Earth-Sci. Rev., 162, 227–252, https://doi.org/10.1016/j.earscirev.2016.08.011,
2016. a
Godt, J. W. and Coe, J. A.: Map showing alpine debris flows triggered by a July 28, 1999 thunderstorm in the central
Front Range of Colorado, USGS Open-File Report, https://doi.org/10.3133/ofr0350, 2003.
Godt, J. W., Baum, R. L., and Chleborad, A. F.: Rainfall characteristics for
shallow landsliding in Seattle, Washington, USA, Earth Surf.
Proc. Land., 31, 97–110, https://doi.org/10.1002/esp.1237,
2006. a
Gorum, T., van Westen, C. J., Korup, O., van der Meijde, M., Fan, X., and
van der Meer, F. D.: Complex rupture mechanism and topography control
symmetry of mass-wasting pattern, 2010 Haiti earthquake, Geomorphology,
184, 127–138, https://doi.org/10.1016/j.geomorph.2012.11.027,
2013. a
Gorum, T., Korup, O., van Westen, C. J., van der Meijde, M., Xu, C., and
van der Meer, F. D.: Why so few? Landslides triggered by the 2002 Denali
earthquake, Alaska, Quaternary Sci. Rev., 95, 80–94,
https://doi.org/10.1016/j.quascirev.2014.04.032,
2014. a
Guzzetti, F., Cardinali, M., Reichenbach, P., Cipolla, F., Sebastiani, C.,
Galli, M., and Salvati, P.: Landslides triggered by the 23 November 2000
rainfall event in the Imperia Province, Western Liguria, Italy,
Eng. Geol., 73, 229–245, https://doi.org/10.1016/j.enggeo.2004.01.006,
2004. a
Guzzetti, F., Peruccacci, S., Rossi, M., and Stark, C. P.: The rainfall
intensity-duration control of shallow landslides and debris flows: an
update, Landslides, 5, 3–17, https://doi.org/10.1007/s10346-007-0112-1,
2008. a, b
Harp, E. and Jibson, R.: Landslides triggered by the 1994 Northridge,
California, earthquake, B. Seismol. Soc. Am.,
86, S319–S332, 1996. a
Harp, E. L., Hagaman, K. W., Held, M. D., and McKenna, J. P.: Digital inventory
of landslides and related deposits in Honduras triggered by Hurricane
Mitch, USGS Numbered Series 2002-61, U.S. Geological Survey, Reston,
VA, https://doi.org/10.3133/ofr0261, 2002. a
Harp, 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 Numbered Series 2004-1348,
https://doi.org/10.3133/ofr20041348, 2004. a, b, c
Houze, R. A.: Orographic effects on precipitating clouds, Rev.
Geophys., 50, RG1001, https://doi.org/10.1029/2011RG000365,
2012. a
Hovius, N., Stark, C. P., and Allen, P. A.: Sediment flux from a mountain belt
derived by landslide mapping, Geology, 25, 231–234,
https://doi.org/10.1130/0091-7613(1997)025<0231:SFFAMB>2.3.CO;2,
1997. a, b, c
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu,
G., Hong, Y., Bowman, K. P., and Stocker, E. F.: The TRMM Multisatellite
Precipitation Analysis (TMPA): Quasi-Global, Multiyear,
Combined-Sensor Precipitation Estimates at Fine Scales, J. Hydrometeorol., 8, 38–55, https://doi.org/10.1175/JHM560.1,
2007. a
Iverson, R. M.: Landslide triggering by rain infiltration, Water Resour.
Res., 36, 1897–1910, https://doi.org/10.1029/2000WR900090,
2000. a, b, c, d
Jiang, H., Halverson, J. B., Simpson, J., and Zipser, E. J.: Hurricane
“Rainfall Potential” Derived from Satellite Observations Aids
Overland Rainfall Prediction, J. Appl. Meteorol. Clim., 47, 944–959, https://doi.org/10.1175/2007JAMC1619.1,
2008. a, b
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. a
Katz, O., Morgan, J. K., Aharonov, E., and Dugan, B.: Controls on the size and
geometry of landslides: Insights from discrete element numerical
simulations, Geomorphology, 220, 104–113,
https://doi.org/10.1016/j.geomorph.2014.05.021,
2014. a
Keefer, D. K., Wilson, R. C., Mark, R. K., Brabb, E. E., Brown, W. M., Ellen,
S. D., Harp, E. L., Wieczorek, G. F., Alger, C. S., and Zatkin, R. S.:
Real-Time Landslide Warning During Heavy Rainfall, Science, 238,
921–925, https://doi.org/10.1126/science.238.4829.921,
1987. a
Kirschbaum, D., Adler, R., Adler, D., Peters-Lidard, C., and Huffman, G.:
Global Distribution of Extreme Precipitation and High-Impact
Landslides in 2010 Relative to Previous Years, J.
Hydrometeorol., 13, 1536–1551, https://doi.org/10.1175/JHM-D-12-02.1,
2012. a, b
Kirschbaum, D. B., Adler, R., Hong, Y., Hill, S., and Lerner-Lam, A.: A global
landslide catalog for hazard applications: method, results, and limitations,
Nat. Hazards, 52, 561–575, https://doi.org/10.1007/s11069-009-9401-4,
2009. a
Kubota, T., Shige, S., Hashizume, H., Ushio, T., Aonashi, K., Kachi, M., and
Okamoto, K.: Global Precipitation Map using Satelliteborne Microwave
Radiometers by the GSMaP Project: Production and Validation, in:
2006 IEEE MicroRad, 290–295, https://doi.org/10.1109/MICRAD.2006.1677106,
2006. a
Lafore, J. P., Stein, J., Asencio, N., Bougeault, P., Ducrocq, V., Duron, J.,
Fischer, C., Héreil, P., Mascart, P., Masson, V., Pinty, J. P.,
Redelsperger, J. L., Richard, E., and Arellano, J. V.-G. D.: The Meso-NH
Atmospheric Simulation System. Part I: adiabatic formulation and
control simulations, Ann. Geophys., 16, 90–109,
https://doi.org/10.1007/s00585-997-0090-6,
1997. a
Lehmann, P. and Or, D.: Hydromechanical triggering of landslides: From
progressive local failures to mass release, Water Resour. Res., 48,
W03535, https://doi.org/10.1029/2011WR010947,
2012. a
Liao, H.-W. and Lee, C.: Landslides triggered by Chi-Chi earthquake, in:
Proceedings of the 21st Asian conference on remote sensing, 1,
383–388, 2000. 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, b
Marc, O., Hovius, N., Meunier, P., Uchida, T., and Hayashi, S.: Transient
changes of landslide rates after earthquakes, Geology, 43, 883–886,
https://doi.org/10.1130/G36961.1,
2015. a
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. a, b, c
Meunier, P., Hovius, N., and Haines, A. J.: Regional patterns of
earthquake-triggered landslides and their relation to ground motion,
Geophys. Res. Lett., 34, L20408, https://doi.org/10.1029/2007GL031337,
2007. a
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. 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., McKean, J. A., Densmore, A. L., and Dietrich,
W. E.: A multidimensional stability model for predicting shallow landslide
size and shape across landscapes, J. Geophys. Res.-Earth, 119, 2014JF003135, https://doi.org/10.1002/2014JF003135,
2014. a
Mondini, A. C.: Measures of Spatial Autocorrelation Changes in
Multitemporal SAR Images for Event Landslides Detection, Remote
Sensing, 9, 554, https://doi.org/10.3390/rs9060554,
2017. a
Montgomery, D. R.: Slope Distributions, Threshold Hillslopes, and
Steady-state Topography, Am. J. Sci., 301, 432–454,
https://doi.org/10.2475/ajs.301.4-5.432,
2001. a
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. a, b
NASA JPL: NASA Shuttle Radar Topography Mission Global 1 arc second [Data set], NASA EOSDIS Land Processes DAAC,
https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL1.003, 2013.
Netto, A. L. C., Sato, A. M., Avelar, A. D. S., Vianna, L. G. G., Araújo,
I. S., Ferreira, D. L. C., Lima, P. H., Silva, A. P. A., and Silva, R. P.:
January 2011: The Extreme Landslide Disaster in Brazil, in:
Landslide Science and Practice, Springer, Berlin,
Heidelberg,
377–384, https://doi.org/10.1007/978-3-642-31319-6_51, 2013. a, b, c
Nowicki, M. A., Wald, D. J., Hamburger, M. W., Hearne, M., and Thompson, E. M.:
Development of a globally applicable model for near real-time prediction of
seismically induced landslides, Eng. Geol., 173, 54–65,
https://doi.org/10.1016/j.enggeo.2014.02.002,
2014. a
Ono, K., Kazama, S., and Ekkawatpanit, C.: Assessment of rainfall-induced
shallow landslides in Phetchabun and Krabi provinces, Thailand, Natural
Hazards, 74, 2089–2107, https://doi.org/10.1007/s11069-014-1292-3,
2014. a
Parise, M. and Jibson, R. W.: A seismic landslide susceptibility rating of
geologic units based on analysis of characteristics of landslides triggered
by the 17 January, 1994 Northridge, California earthquake, Eng.
Geol., 58, 251–270, https://doi.org/10.1016/S0013-7952(00)00038-7,
2000. a, b
Petley, D.: Global patterns of loss of life from landslides, Geology, 40,
927–930, https://doi.org/10.1130/G33217.1,
2012. a
Pozzobon, M.: Análise da suscetibilidade a deslizamentos no
município de blumenau/sc: uma abordagem probabilística
atravées da Aplicação da Técnica pesos de evidência,
PhD thesis, Universidade federal do paranã, Curitiba,
available at: http://www.floresta.ufpr.br/pos-graduacao/defesas/pdf_dr/2013/t342_0370-D.pdf (last access: 2 October 2018), 2013. a, b
Reid, L. M.: Calculation of average landslide frequency using climatic records,
Water Resour. Res., 34, 869–877, https://doi.org/10.1029/97WR02682,
1998. a
Reid, L. M. and Page, M. J.: Magnitude and frequency of landsliding in a large
New Zealand catchment, Geomorphology, 49, 71–88,
https://doi.org/10.1016/S0169-555X(02)00164-2,
2003. a
Saito, H. and Matsuyama, H.: Catastrophic Landslide Disasters Triggered
by Record-Breaking Rainfall in Japan: Their Accurate Detection
with Normalized Soil Water Index in the Kii Peninsula for the
Year 2011, Sola, 8, 81–84, https://doi.org/10.2151/sola.2012-021, 2012. a
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
Schmidt, K. M. and Montgomery, D. R.: Limits to Relief, Science, 270,
617–620, https://doi.org/10.1126/science.270.5236.617,
1995. a
Stark, C. P. and Guzzetti, F.: Landslide rupture and the probability
distribution of mobilized debris volumes, J. Geophys. Res.-Earth, 114, F00A02, https://doi.org/10.1029/2008JF001008,
2009. a, b, c, d
Stark, C. P. and Hovius, N.: The characterization of landslide size
distributions, Geophys. Res. Lett., 28, 1091–1094,
https://doi.org/10.1029/2000GL008527,
2001. a
Stumpf, A., Lachiche, N., Malet, J.-P., Kerle, N., and Puissant, A.: Active
Learning in the Spatial Domain for Remote Sensing Image
Classification, IEEE T. Geosci. Remote, 52,
2492–2507, https://doi.org/10.1109/TGRS.2013.2262052, 2014. a
Taniguchi, A., Shige, S., Yamamoto, M. K., Mega, T., Kida, S., Kubota, T.,
Kachi, M., Ushio, T., and Aonashi, K.: Improvement of High-Resolution
Satellite Rainfall Product for Typhoon Morakot (2009) over
Taiwan, J. Hydrometeorol., 14, 1859–1871,
https://doi.org/10.1175/JHM-D-13-047.1,
2013. 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, 2017JF004236, https://doi.org/10.1002/2017JF004236,
2017.
a
Ushio, T., Sasashige, K., Kubota, T., Shige, S., Okamoto, K., Aonashi, K.,
Inoue, T., Takahashi, N., Iguchi, T., Kachi, M., Oki, R., Morimoto, T., and
Kawasaki, Z.-I.: A Kalman Filter Approach to the Global Satellite
Mapping of Precipitation (GSMaP) from Combined Passive Microwave
and Infrared Radiometric Data, J. Meteorol. Soc.
Jpn., 87A, 137–151, https://doi.org/10.2151/jmsj.87A.137, 2009. a, b
Uchida, T., Tamur, K., and Akiyama, K.: The role of grid cell size,
flow routing algolithm and spatial variability of soil
depth on shallow landslide prediction, in: 5th International
Conference on Debris-Flow Hazards Mitigation: Mechanics,
Prediction and Assessment, 149–157, Italian journal of engineering
geology and environment, Padua, Italy, 2011. a
Uchida, T., Sato, T., Mizuno, M., and Okamoto, A.: The role of rainfall
magnitude on landslide characteristics triggered by Typhoon Tales, 2011, Civil Engineering Journal, 54, 10–13, 2012 (in Japanese). a
Uchida, T., Okamoto, A., Kanbara, J. I., and Kuramoto, K.: RAINFALL
THRESHOLDS FOR DEEP-SEATED RAPID LANDSLIDES, in: International
Conference on Vajont 1963-2013/Proceedings – Thoughts and analyses
after 50 years since the catastrophic landslide, ITAlian journal of engineering geology and environment, Padua, Italy, 211–217, 2013. a
Van Asch, T. W. J., Buma, J., and Van Beek, L. P. H.: A view on some
hydrological triggering systems in landslides, Geomorphology, 30, 25–32,
https://doi.org/10.1016/S0169-555X(99)00042-2,
1999. a, b, c, d
von Ruette, J., Lehmann, P., and Or, D.: Rainfall-triggered shallow landslides
at catchment scale: Threshold mechanics-based modeling for abruptness and
localization, Water Resour. Res., 49, 6266–6285,
https://doi.org/10.1002/wrcr.20418,
2013. a, b
Wilson, R. C. and Wieczorek, G. F.: Rainfall Thresholds for the Initiation
of Debris Flows at La Honda, California, Environ.
Eng. Geosci., I, 11–27, https://doi.org/10.2113/gseegeosci.I.1.11,
1995. a
Yagi, H., Sato, G., Higaki, D., Yamamoto, M., and Yamasaki, T.: Distribution
and characteristics of landslides induced by the Iwate-Miyagi Nairiku
Earthquake in 2008 in Tohoku District, Northeast Japan, Landslides,
6, 335–344, 2009. a
Short summary
Rainfall-induced landslides cause significant damage and fatality worldwide, but we have few datasets constraining the impact of individual storms. We present and analyze 8 landslide inventories, with >150 to >150 00 landslides, comprehensively representing the landslide population caused by 8 storms from Asia and the Americas. We found that the total storm rainfall is a major control on total landsliding, landslide size, and that storms trigger landslides on less steep slopes than earthquakes.
Rainfall-induced landslides cause significant damage and fatality worldwide, but we have few...