Landslide Hazard Is Projected to Increase Across High Mountain Asia

dc.contributor.authorStanley, Thomas
dc.contributor.authorSoobitsky, Rachel B.
dc.contributor.authorAmatya, Pukar
dc.contributor.authorKirschbaum, Dalia B.
dc.date.accessioned2024-11-14T15:18:36Z
dc.date.available2024-11-14T15:18:36Z
dc.date.issued2024-10-03
dc.description.abstractHigh Mountain Asia has long been known as a hotspot for landslide risk, and studies have suggested that landslide hazard is likely to increase in this region over the coming decades. Extreme precipitation may become more frequent, with a nonlinear response relative to increasing global temperatures. However, these changes are geographically varied. This article maps probable changes to landslide hazard, as shown by a landslide hazard indicator (LHI) derived from downscaled precipitation and temperature. In order to capture the nonlinear response of slopes to extreme precipitation, a simple machine-learning model was trained on a database of landslides across High Mountain Asia to develop a regional LHI. This model was applied to statistically downscaled data from the 30 members of the Seamless System for Prediction and Earth System Research large ensembles to produce a range of possible outcomes under the Shared Socioeconomic Pathways 2-4.5 and 5-8.5. The LHI reveals that landslide hazard will increase in most parts of High Mountain Asia. Absolute increases will be highest in already hazardous areas such as the Central Himalaya, but relative change is greatest on the Tibetan Plateau. Even in regions where landslide hazard declines by year 2100, it will increase prior to the mid-century mark. However, the seasonal cycle of landslide occurrence will not change greatly across High Mountain Asia. Although substantial uncertainty remains in these projections, the overall direction of change seems reliable. These findings highlight the importance of continued analysis to inform disaster risk reduction strategies for stakeholders across High Mountain Asia.
dc.description.sponsorshipThe authors thank Fadji Maina for providing easy access to and information about the historical temperature and precipitation data. The authors also thank Efthymios Nikolopoulos and Diogo Stalinde Alcantara e Alcantara Araujo for providing the downscaled precipitation data. This research has been funded by the NASA High Mountain Asia program(solicitation # NNH19ZDA001N?HMA).The model was trained on computing nodes provided by the NASA High?End Computing (HEC) Program through the NASA Center for Climate Simulation(NCCS) at Goddard Space Flight Center.
dc.description.urihttps://onlinelibrary.wiley.com/doi/abs/10.1029/2023EF004325
dc.format.extent15 pages
dc.genrejournal articles
dc.identifierdoi:10.13016/m2mlpi-5oom
dc.identifier.citationStanley, Thomas A., Rachel B. Soobitsky, Pukar M. Amatya, and Dalia B. Kirschbaum. “Landslide Hazard Is Projected to Increase Across High Mountain Asia.” Earth’s Future 12, no. 10 (2024): e2023EF004325. https://doi.org/10.1029/2023EF004325.
dc.identifier.urihttps://doi.org/10.1029/2023EF004325
dc.identifier.urihttp://hdl.handle.net/11603/36940
dc.language.isoen_US
dc.publisherAGU
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC GESTAR II
dc.rightsThis work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
dc.rightsPublic Domain
dc.rights.urihttps://creativecommons.org/publicdomain/mark/1.0/
dc.subjectclimate change
dc.subjectrainfall
dc.subjectmachine learning
dc.subjectprecipitation
dc.subjectmass movements
dc.subjectHimalaya
dc.titleLandslide Hazard Is Projected to Increase Across High Mountain Asia
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0003-2288-0363
dcterms.creatorhttps://orcid.org/0000-0001-8008-4475

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