DEWS: A QGIS tool pack for the automatic selection of reference rain gauges for landslide-triggering rainfall thresholds

Al-Thuwaynee, Omar F.; Melillo, Massimo; Gariano, Stefano Luigi; Park, Hyuck Jin; Kim, Sang-Wan; Lombardo, Luigi; Hader, Paulo; Mohajane, Meriame; Quevedo, Renata Pacheco; Catani, Filippo; Aydda, Ali

Abstract

Several studies on empirical rainfall thresholds for landslide occurrence depend on the measurements of nearest rain gauges to the landslides, without taking in consideration the morphological and hydrological settings of the areas. Therefore, we introduce the DEWS (Distance, Elevation, Watershed, and Slope unit) QGIS software tool, for selecting representative rain gauges, a relevant step in the definition of empirical rainfall threshold models. DEWS set with default parameter values for non-expert users. DEWS employs four filters: Distance, Elevation, Watershed, Slope unit, and requires only three data inputs (digital elevation model, landslides inventory, rain gauge locations). Reliability was tested using 223 landslides and 328 rain gauges with the CTRL-T (Calculation of Thresholds for Rainfall-induced Landslides) tool applied in South Korea. Consequently, the amount of rain gauges used was optimized and reduced by 33% using DEWS from using CTRL-T alone, while the shape of the threshold curve and uncertainty values were maintained.

Citation

Al-Thuwaynee, O. F., Melillo, M., Gariano, S. L., Park, H. J., Kim, S.-W., Lombardo, L., Hader, P., Mohajane, M., Quevedo, R. P., Catani, F., & Aydda, A. (2023). DEWS: A QGIS tool pack for the automatic selection of reference rain gauges for landslide-triggering rainfall thresholds. Environmental Modelling and Software, 162, Article 105657. https://doi.org/10.1016/j.envsoft.2023.105657

• Journal Article Type: Article
• Acceptance Date: Jan 21, 2023
• Online Publication Date: Feb 18, 2023
• Publication Date: 2023-04
• Deposit Date: Nov 9, 2024
• Journal: Environmental Modelling & Software
• Print ISSN: 1364-8152
• Electronic ISSN: 1873-6726
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 162
• Article Number: 105657
• DOI: https://doi.org/10.1016/j.envsoft.2023.105657
• Public URL: https://durham-repository.worktribe.com/output/3090623