Preprints
https://doi.org/10.5194/nhess-2021-317
https://doi.org/10.5194/nhess-2021-317

  08 Nov 2021

08 Nov 2021

Review status: this preprint is currently under review for the journal NHESS.

Rainfall-Induced Landslide Early Warning System based on corrected mesoscale numerical models: an application for the Southern Andes

Ivo Fustos1, Nataly Manque2, Daniel Vásquez1,3, Mauricio Hermosilla1, and Viviana Letelier1 Ivo Fustos et al.
  • 1Department of Civil Engineering, University of La Frontera, Temuco, Chile
  • 2Universidad Adolfo Ibañez, Santiago, Chile
  • 3Master on Engineering Sciences, University of La Frontera, Temuco, Chile

Abstract. Rainfall-Induced Landslide Early Warning Systems (RILEWS) are critical tools for reducing and mitigating economic and social damages related to landslides. Despite this critical need, the Southern Andes does not yet possess an operational-scale system to support decision-makers. We propose RILEWS using a logistic regression system in the Southern Andes. The models were forced by corrected simulations of precipitation and geomorphological features. We evaluated the precipitation using the Weather and Research Forecast (WRF) model on an hourly scale. The precipitation was corrected using bias correction approaches with daily data from 12 meteorological stations. Four logistic and probabilistic models were then calibrated using Logit and Probit distributions. The predictor variables used were combinations of the slope, corrected daily precipitation and data preceding the events (7 and 30 days previous) for 57 Rainfall-Induced Landslides (RIL); validation was by ROC analysis. Our results showed that WRF does not represent the spatial variability of the precipitation. This situation was resolved by bias correcting. Specifically, the PP_M4a method with Bernoulli distribution for the occurrence and Gamma for the intensity produced lower MAE and RMSE values and higher correlation values. Finally, our RILEWS had a high predicting capacity with an AUC of 0.80 using daily precipitation data and slope. We conclude that our methodology is suitable at an operational level in the Southern Andes. Our contribution could become a useful tool in the mitigation of impacts related to climate change.

Ivo Fustos et al.

Status: open (until 20 Dec 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-317', Anonymous Referee #1, 17 Nov 2021 reply
  • RC2: 'Review of nhess-2021-317', Anonymous Referee #2, 30 Nov 2021 reply

Ivo Fustos et al.

Ivo Fustos et al.

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Short summary
We develop for the first time a Rainfall-Induced Landslide Early Warning System for the South of Chile. We used forecast precipitation values at different scales using mesoscale models to evaluate the probability of landslides using statistical models. We showed the feasibility of implementing these models in future, supporting stakeholders and decision-makers.
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