Mapping Susceptibility to Debris Flows Triggered by Tropical Storms: A Case Study of the San Vicente Volcano Area (El Salvador, CA)

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.

Investigating Limits in Exploiting Assembled Landslide Inventories for Calibrating Regional Susceptibility Models: A Test in Volcanic Areas of El Salvador

“…Floods and landslides lashed these areas, causing around 200 deaths and huge economic losses [9], with damages to cropland, rural houses, and roads. In particular, the most devastating debris flows were triggered from the north-western flank of the San Vicente Volcano, hitting the villages of Verapaz and Guadalupe [5,7].…”

“…The selection of a set of geo-environmental variables potentially expressing the landslide preparatory causes (Table 1) was based on widely adopted geomorphological criteria [5][6][7][34][35][36][37]. In particular, outcropping lithology (GEO) and soil use (USE) were derived from an available thematic map [38] and a remote survey, respectively.…”

“…The same grid cell structure was then adopted as the susceptibility mapping unit, assigning a stable/unstable status depending on the intersection of LIPs. In fact, according to a number of debris flow susceptibility assessment studies (e.g., [7,22,29,31,32,36,37,[44][45][46][47][48][49][50]), we considered the instability conditions of each inventoried landslides to be effectively captured in the highest crown 10 × 10 m pixel. In order to optimize the final selected predictors that were included in the MARS modelling procedure, the variance inflation factor (VIF) [51] test was performed for multicollinearity analysis through the continuous variables.…”

“…In particular, the tropical-humid climate setting of El Salvador [3], with a mean annual rainfall above 1846 mm and a temperature between 20 and 30 • C [4], is responsible for the intense weathering of the topsoil, resulting in poor geotechnical properties. At the same time, intense rainfall events associated with recurrent hurricanes frequently result in water saturation and neutral pression increasing in the regolithic mantle, causing the triggering of slope failures [5][6][7]. As a consequence, the volcanoes and the caldera's inner flanks are very frequently affected by landslides of a debris slide/flow type.…”

“…More recently, a regional landslide susceptibility scenario with a 30-arcsecond resolution was also proposed by applying a fuzzy-based heuristic approach [17]. Rotigliano et al [5,6] and Mercurio et al [7] extensively applied logistic regression and MARS to assess landslide susceptibility in two limited volcanic sectors (Ilopango caldera and San Vicente, respectively). Regarding the civil protection authorities, MARN (Ministerio de Medio Ambiente y Recursos Naturales) adopted a 1:50,000 scale landslide susceptibility map for the whole country [18] by applying the heuristic approach of [19,20].…”