Rainfall-induced landslide early warning system based on corrected mesoscale numerical models: an application for the southern Andes

• Published in: Natural hazards and earth system sciences Vol. 22; no. 6; pp. 2169 - 2183
• Main Authors: Fustos-Toribio, Ivo, Manque-Roa, Nataly, Vásquez Antipan, Daniel, Hermosilla Sotomayor, Mauricio, Letelier Gonzalez, Viviana
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 28-06-2022; Copernicus Publications
• Subjects: Atmospheric models; Atmospheric precipitations; Bias; Climate change; Climate models; Damage; Decision making; Early warning systems; Economics; Extreme weather; Geomorphology; Landslide warnings; Landslides; Mathematical models; Mesoscale phenomena; Meteorological research; Modelling; Numerical models; Numerical weather forecasting; Precipitation; Precipitation variability; Rain; Rain and rainfall; Rainfall; Spatial variability; Spatial variations; Topography; Warning systems; Weather; Weather forecasting; Weather stations; Central Valley
• ISSN: 1684-9981; 1561-8633; 1684-9981
• DOI: 10.5194/nhess-22-2169-2022

Rainfall-induced landslides (RILs) are an issue in the southern Andes nowadays. RILs cause loss of life and damage to critical infrastructure. Rainfall-induced landslide early warning systems (RILEWSs) can reduce and mitigate economic and social damages related to RIL events. The southern Andes do not have an operational-scale RILEWS yet. In this contribution, we present a pre-operational RILEWS based on the Weather and Research Forecast (WRF) model and geomorphological features coupled to logistic models in the southern Andes. The models have been forced using precipitation simulations. We correct the precipitation derived from WRF using 12 weather stations through a bias correction approach. The models were trained using 57 well-characterized RILs and validated by ROC analysis. We show that WRF has strong limitations in representing the spatial variability in the precipitation. Therefore, accurate precipitation needs a bias correction in the study zone. We used accurate precipitation simulation and slope, demonstrating a high predicting capacity (area under the curve, AUC, of 0.80). We conclude that our proposal could be suitable at an operational level under determined conditions. A reliable RIL database and operational weather networks that allow real-time correction of the mesoscale model in the implemented zone are needed. The RILEWSs could become a support to decision-makers during extreme-precipitation events related to climate change in the south of the Andes.