Abstract

In this study, an inventory of storm-triggered debris flows performed in the area of the San Vicente volcano (El Salvador, CA) was used to calibrate predictive models and prepare a landslide susceptibility map. The storm event struck the area in November 2009 as the result of the simultaneous action of low-pressure system 96E and Hurricane Ida. Multivariate Adaptive Regression Splines (MARS) was employed to model the relationships between a set of environmental variables and the locations of the debris flows. Validation of the models was performed by splitting 100 random samples of event and non-event 10 m pixels into training and test subsets. The validation results revealed an excellent (area under the receiver operating characteristic (ROC) curve (AUC) = 0.80) and stable (AUC std. dev. = 0.01) ability of MARS to predict the locations of the debris flows which occurred in the study area. However, when using the Youden’s index as probability threshold to discriminate between pixels predicted as positives and negatives, MARS exhibits a moderate ability to identify stable cells (specificity = 0.66). The final debris flow susceptibility map, which was prepared by averaging for each pixel the score of the 100 MARS repetitions, shows where future debris flows are more likely to occur, and thus may help in mitigating the risk associated with these landslides.

Highlights

  • Landslides are among the main causes of natural hazards in El Salvador [1]

  • Almost half (i.e., 2753) of the landslide identification point (LIP) are located over the hard soil unit (Figure 6), which includes acid pyroclastic rocks and volcanic epiclastites, being the class with the greatest areal extension (Table 1)

  • Susceptibility to debris flow initiation in the area of the San Vicente volcano (El Salvador, CA) was evaluated by preparing an inventory including thousands of landslides triggered by heavy rainfalls due to the Hurricane Ida and the 96E low-pressure system (November 2009) and by modelling the relationship between the location of these landslides and the spatial variability of a set of environmental variables by using Multivariate Adaptive Regression Splines (MARS) as a modelling technique

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Summary

Introduction

The intense rainfall events that frequently affect the country are responsible for the activation of gravitational phenomena consisting of shallow and fast-moving flow landslides that may cause severe economic damage and even victims [2]. The occurrence of these landslides is related to the outcropping of unconsolidated material on steep slopes, which can be rapidly moved by gravity and travel hundreds of meters to several kilometers from its origin [3].

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