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1000 Titel
  • A global-scale applicable framework of landslide dam formation susceptibility
1000 Autor/in
  1. Wu, Hang |
  2. Trigg, Mark |
  3. Murphy, William |
  4. Fuentes, Raul |
  5. MARTINO, Salvatore |
  6. Esposito, Carlo |
  7. MARMONI, GIAN MARCO |
  8. SCARASCIA-MUGNOZZA, Gabriele |
1000 Verlag
  • Springer Berlin Heidelberg
1000 Erscheinungsjahr 2024
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2024-07-12
1000 Erschienen in
1000 Quellenangabe
  • 21(10):2399-2416
1000 Copyrightjahr
  • 2024
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1007/s10346-024-02306-9 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • <jats:title>Abstract</jats:title><jats:p>The formation and failure of landslide dams is an important and understudied, multi-hazard topic. A framework of landslide dam formation susceptibility evaluation was designed for large-scale studies to avoid the traditional dependence on landslide volume calculations based on empirical relationships, which requires comprehensive local inventories of landslides and landslide dams. The framework combines logistic regression landslide susceptibility models and global fluvial datasets and was tested in Italy and Japan based on landslide and landslide dam inventories collected globally. The final landslide dam formation susceptibility index identifies which river reach is most prone to landslide dam formation, based on the river width and the landslide susceptibility in the adjacent delineated slope drainage areas. The logistic regression models showed good performances with area under the receiver operating characteristics curve values of 0.89 in Italy and 0.74 in Japan. The index effectively identifies the probability of landslide dam formation for specific river reaches, as demonstrated by the higher index values for river reaches with past landslide dam records. The framework is designed to be applied globally or for other large-scale study regions, especially for less studied data-scarce regions. It also provides a preliminary evaluation result for smaller catchments and has the potential to be applied at a more detailed scale with local datasets.</jats:p>
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0002-6709-2398|https://orcid.org/0000-0002-8412-9332|https://orcid.org/0000-0002-7392-1527|https://orcid.org/0000-0001-8617-7381|https://orcid.org/0000-0003-1277-7784|https://orcid.org/0000-0001-5429-2959|https://orcid.org/0000-0002-0443-4389|https://orcid.org/0000-0002-2917-0324
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