Data-Driven Landslide Nowcasting at the Global Scale

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dc.contributor.authorStanley, Thomas
dc.contributor.authorKirschbaum, Dalia B.
dc.contributor.authorBenz, Garrett
dc.contributor.authorEmberson, Robert A.
dc.contributor.authorAmatya, Pukar M.
dc.contributor.authorMedwedeff, William
dc.contributor.authorClark, Marin K.
dc.date.accessioned2022-10-03T21:25:33Z
dc.date.available2022-10-03T21:25:33Z
dc.date.issued2021-05-26
dc.description.abstractLandslides affect nearly every country in the world each year. To better understand this global hazard, the Landslide Hazard Assessment for Situational Awareness (LHASA) model was developed previously. LHASA version 1 combines satellite precipitation estimates with a global landslide susceptibility map to produce a gridded map of potentially hazardous areas from 60° North-South every 3 h. LHASA version 1 categorizes the world’s land surface into three ratings: high, moderate, and low hazard with a single decision tree that first determines if the last seven days of rainfall were intense, then evaluates landslide susceptibility. LHASA version 2 has been developed with a data-driven approach. The global susceptibility map was replaced with a collection of explanatory variables, and two new dynamically varying quantities were added: snow and soil moisture. Along with antecedent rainfall, these variables modulated the response to current daily rainfall. In addition, the Global Landslide Catalog (GLC) was supplemented with several inventories of rainfall-triggered landslide events. These factors were incorporated into the machine-learning framework XGBoost, which was trained to predict the presence or absence of landslides over the period 2015–2018, with the years 2019–2020 reserved for model evaluation. As a result of these improvements, the new global landslide nowcast was twice as likely to predict the occurrence of historical landslides as LHASA version 1, given the same global false positive rate. Furthermore, the shift to probabilistic outputs allows users to directly manage the trade-off between false negatives and false positives, which should make the nowcast useful for a greater variety of geographic settings and applications. In a retrospective analysis, the trained model ran over a global domain for 5 years, and results for LHASA version 1 and version 2 were compared. Due to the importance of rainfall and faults in LHASA version 2, nowcasts would be issued more frequently in some tropical countries, such as Colombia and Papua New Guinea; at the same time, the new version placed less emphasis on arid regions and areas far from the Pacific Rim. LHASA version 2 provides a nearly real-time view of global landslide hazard for a variety of stakeholders.en_US
dc.description.sponsorshipThis research was supported by NASA’s Disasters program through the solicitation for Earth Science Applications: Disaster Risk Reduction and Response (NNH18ZDA001N).en_US
dc.description.urihttps://www.frontiersin.org/articles/10.3389/feart.2021.640043/fullen_US
dc.format.extent15 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m2zaaa-qmhx
dc.identifier.citationStanley TA, Kirschbaum DB, Benz G, Emberson RA, Amatya PM, Medwedeff W and Clark MK (2021) Data-Driven Landslide Nowcasting at the Global Scale. Front. Earth Sci. 9:640043. doi: 10.3389/feart.2021.640043en_US
dc.identifier.urihttps://doi.org/10.3389/feart.2021.640043
dc.identifier.urihttp://hdl.handle.net/11603/26088
dc.language.isoen_USen_US
dc.publisherFrontiersen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC GESTAR II Collection
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.en_US
dc.rightsPublic Domain Mark 1.0*
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/*
dc.titleData-Driven Landslide Nowcasting at the Global Scaleen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0003-2288-0363en_US

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