Research Data Leeds Repository

Citation

Wu, Hang and Trigg, Mark and Murphy, William and Fuentes, Raul (2024) River Augmented Global Landslide Dams (RAGLAD). University of Leeds. [Dataset] https://doi.org/10.5518/1477

Dataset description

To address the current data and understanding knowledge gap in landslide dam inventories related to geomorphological parameters, a new global-scale landslide dam dataset named River Augmented Global Landslide Dams (RAGLAD) was created. RAGLAD is a collection of landslide dam records from multiple data sources published in various languages and many of these records we have been able to precisely geolocate. In total, 779 landslide dam records were compiled from 34 countries/regions. The spatial distribution, time trend, triggers, and geomorphological characteristic of the landslides and catchments where landslide dams formed are summarized. The relationships between geomorphological characteristics for landslides that form river dams are discussed and compared with those of landslides more generally. Additionally, a potential threshold for landslide dam formation is proposed, based on the relationship of landslide volume to river width. Our findings from our analysis of the value of the use of additional fluvial datasets to augment the database parameters indicate that they can be applied as a reliable supplemental data source, when the landslide dam records were accurately and precisely geolocated, although location precision in smaller river catchment areas can result in some uncertainty at this scale. This newly collected and supplemented dataset will allow the analysis and development of new relationships between landslides located near rivers and their actual propensity to block those particular rivers based on their geomorphology.

Subjects:	F000 - Physical sciences > F600 - Geology > F680 - Geohazards
Divisions:	Faculty of Engineering and Physical Sciences > School of Civil Engineering
Related resources:	Location Type https://etheses.whiterose.ac.uk/34362/ Ethesis https://doi.org/10.1007/s10346-021-01817-z Publication https://eprints.whiterose.ac.uk/182345/ Publication
License:	Creative Commons Attribution 4.0 International (CC BY 4.0)
Date deposited:	07 Feb 2024 09:02
URI:	https://archive.researchdata.leeds.ac.uk/id/eprint/1237
Additional details Additional details Creators or authors: Creators ORCID Other ID Email Primary affiliation Primary affiliation ID Primary affiliation ID type Secondary affiliation Secondary affiliation ID Secondary affiliation ID type Wu, Hang https://orcid.org/0000-0002-6709-2398 University of Leeds https://ror.org/024mrxd33 ror Trigg, Mark https://orcid.org/0000-0002-8412-9332 University of Leeds https://ror.org/024mrxd33 ror Murphy, William https://orcid.org/0000-0002-7392-1527 University of Leeds https://ror.org/024mrxd33 ror Fuentes, Raul https://orcid.org/0000-0001-8617-7381 RWTH Aachen University https://ror.org/04xfq0f34 ror Type of data: Dataset Research funders: University of Leeds, China Scholarship Council Grant numbers: 201906400001 Publication date: 2024 Resource language: English Metadata language: English Publisher: University of Leeds Contact email: cnhwu@leeds.ac.uk; hangwu94@foxmail.com; m.trigg@leeds.ac.uk Last Modified: 02 Dec 2024 12:29

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Documentation

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Data

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