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

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

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 n...

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Bibliographic Details
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
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Summary: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.
ISSN:1684-9981
1561-8633
1684-9981
DOI:10.5194/nhess-22-2169-2022