Rainfall thresholds estimation for shallow landslides in Peru from gridded daily data

• Autores: Millán-Arancibia, Carlos, Lavado-Casimiro, Waldo
• Formato: artículo
• Fecha de Publicación: 2023
• Institución: Servicio Nacional de Meteorología e Hidrología del Perú
• Repositorio: SENAMHI-Institucional
• Lenguaje: español
• OAI Identifier: oai:repositorio.senamhi.gob.pe:20.500.12542/2715
• Enlace del recurso: https://hdl.handle.net/20.500.12542/2715; https://doi.org/10.5194/nhess-23-1191-2023
• Nivel de acceso: acceso abierto
• Materia: Rainfall Monitoring; Landslide Datasets; Precipitación; Deslizamiento de Tierras; Lluvia; Gestión de Riesgo; Sistema de Alerta; https://purl.org/pe-repo/ocde/ford#1.05.09; https://purl.org/pe-repo/ocde/ford#1.05.11; sistemas de alerta temprana ante emergencias - Gestión de Riesgos y Desastres

This work aims to generate and evaluate regional rainfall thresholds obtained from a combination of high-resolution gridded rainfall data, developed by the National Service of Meteorology and Hydrology of Peru, and information from observed shallow landslide events. The landslide data were associated with rainfall data, determining triggering and non-triggering rainfall events with rainfall properties from which rainfall thresholds are determined. The validation of the performance of the thresholds is carried out with events that occurred during 2020 and focuses on evaluating the operability of these thresholds in landslide warning systems in Peru. The thresholds are determined for 11 rainfall regions. The method of determining the thresholds is based on an empirical–statistical approach, and the predictive performance of the thresholds is evaluated with true skill statistics. The best predictive performance is the mean daily intensity–duration (Imean−D) threshold curve, followed by accumulated rainfall E. This work is the first estimation of regional thresholds on a country scale to better understand landslides in Peru, and the results obtained reveal the potential of using thresholds in the monitoring and forecasting of shallow landslides caused by intense rainfall and in supporting the actions of disaster risk management.