MODELOS DE SUSCEPTIBILIDAD PARA FLUJOS DE DETRITOS ACTIVADOS POR EL EVENTO EXTREMO DE LLUVIA PRODUCTO DE LAS TORMENTAS TROPICALES EP022020/AMANDA Y AL032020/CRISTOBAL EN EL SALVADOR

El Salvador se ha visto afectado por movimientos en masa desencadenados principalmente por eventos extremos de lluvia. Por consiguiente, la predicción de las áreas susceptibles a deslizamientos es el primordial insumo para una adecuada y futura gestión del riego. En este estudio se seleccionaron dos cuencas hidrográficas para predecir las áreas susceptibles las cuales se encuentran ubicadas en el costado norte del Lago de Ilopango en El Salvador. Para la predicción, se hizo uso de los deslizamientos desencadenados por el paso de las tormentas tropicales Amanda y Cristóbal en el año 2020. La identificación de estos movimientos en masa fue tanto puntal (LIP) como poligonal (BODY), definiendo el punto inicial del movimiento y el área de toda la zona afectada, respectivamente. Posteriormente, a través del proceso estocástico se generaron dos modelos de susceptibilidad de deslizamiento (BODY y LIP), empleando 10 variables predictoras (elevación, pendiente, curvatura de planta, curvatura de perfil, índice de humedad topográfica, clasificación de las formas del relieve, orientación norte-sur, orientación esteoeste, uso del suelo y litología). Esta estimación se realizó mediante el análisis de regresión adaptable a gran cantidad de variables conocido como “Multivariate Adaptive Regression Splines” (MARS). Los respectivos resultados se evaluaron a través del cálculo del área bajo las curvas ROC (Receiver Operating Characteristic), cuyos valores promedios para el modelo BODY fue de 0.91, y de 0.94 para el modelo LIP. Como resultado, las variables más importantes para la predicción de áreas susceptibles a deslizamientos son la elevación y la pendiente. Asimismo, se comprobó que el modelo LIP tiene una precisión mayor sobre el modelo BODY, estableciendo que, con solo el inventario puntual de los movimientos en masa, es posible llegar a un alto grado de predicción sobre el 93%.El Salvador country has been affected by landslides mainly triggered by extreme rain events. Therefore, the prediction of landslide susceptibility areas is a primary input for proper and future risk management. In this study, two catchments were selected to predict susceptible areas, in the northern part of Ilopango Lake. For prediction, were identified landslides triggered by the tropical storms Amanda and Cristóbal that occurred in 2020. The identification of these mass movements was both punctual (LIP) and polygonal (BODY), which define the initial point of motion and the entire area affected, respectively. Subsequently, two models (BODY and LIP) were generated through the stochastic process, employing 10 predictor variables (slope, plain curvature, profile curvature, land classification, topographic wetness index, aspect northness, aspect eastness, soil use, and lithology). This estimation was performed by “Multivariate Adaptive Regression Splines” (MARS). The respective results were evaluated through the calculation of the area under the Receiver Operating Characteristic curve (ROC), which mean values for BODY model was 0.91, and 0.94 for LIP model. As a result, the most important variables to predict landslide susceptibility were elevation and slope. Furthermore, LIP model has a greater precision over BODY model, establishing that with only the punctual landslide inventory is possible to reach a high degree of prediction over 93%

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

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

Susceptibility analysis for seismically-induced landslides: application to the 2001 earthquakes in El Salvador (C.A.)

The geodynamic context in which El Salvador is located, made of a convergent structure characterized by the interaction among six different plates, together with the lithological characteristics of the outcropping rocks and soils (mainly corresponding to deeply weathered acid pyroclastites, basic effusive rocks and volcanic ashes), are responsible for the very high seismically- induced landslide susceptibility of the country. These predisposing factors were decisive on the occurrence of thousands of seismically-induced landslides caused by two huge earthquakes on 13th January and 13th February 2001, which triggered thousands of landslides in the country. In particular, the February event (6.6M, onshore and intraplate at a depth of 10 km) triggered 5,371 landslides in an area of around 300km2. These gravitational phenomena took the form of debris slides, earth slides and debris flows and affected several inhabited areas damaging infrastructures and crops and causing, respectively 844 and 315 fatalities. Thanks to aerial photos taken soon after the days following both the two earthquakes and made available by the CNR (Centro Nacional de Registros - Instituto Geográfico y del Catastro Nacional), associated landslide maps have been prepared, where each phenomenon is represented by a landslide polygon and its LIP (Landslide Identification Point), located in the crown of the landslide. In particular, static landslide susceptibility models were prepared for the Ilopango (1594 landslides in an area of around 40km2) and the San Vicente (1602 landslides in an area of around 108 km2) sectors, by regressing the spatial distribution of the 13th February seismically-induced landslides on a set of explanatory variables obtained by a geologic map and a 10m pixel DTM (Digital Terrain Model). At the same time, shaking-dependent models were prepared by including also PGA (Peak Ground Acceleration) and the epicentral distance (ED) among the predictors. For both the two areas a marked increase of performance was observed (AUC from 0.70 to 0.75, for Ilopango, from 0.73 to 0.77, for San Vicente) from the static to the shaking-dependent models, highlighting the role of the seismic acceleration in the triggering of the landslides both in activating the susceptible sites and in lowering the score threshold for slope failures occurrences. Besides, for the Ilopango sector, a rainfall-induced susceptibility model was also prepared, exploiting a landslide inventory available for the 2009 IDA/12E storm events. The obtained score was then Powered by TCPDF (www.tcpdf.org) combined with PGA and ED to predict the spatial distribution of the seismically induced landslides, obtaining a higher performance than the relative basic model (AUC = 0.75). The results obtained from the research demonstrate suggest the possibility to couple the susceptibility scores obtained from static modelling to the expected mechanical shaking for the seismically-induced susceptibility assessment. The whole modelling was carried out by applying MARS (Multivariate Adaptive Regression Splines) analysis through RStudio and SAGA GIS freeware software

Predicting Earthquake-Induced Landslides by Using a Stochastic Modeling Approach: A Case Study of the 2001 El Salvador Coseismic Landslides

In January and February 2001, El Salvador was hit by two strong earthquakes that triggered thousands of landslides, causing 1259 fatalities and extensive damage. The analysis of aerial and SPOT-4 satellite images allowed us to map 6491 coseismic landslides, mainly debris slides and flows that occurred in volcanic epiclastites and pyroclastites. Four different multivariate adaptive regression splines (MARS) models were produced using different predictors and landslide inventories which contain slope failures triggered by an extreme rainfall event in 2009 and those induced by the earthquakes of 2001. In a predictive analysis, three validation scenarios were employed: the first and the second included 25% and 95% of the landslides, respectively, while the third was based on a k-fold spatial cross-validation. The results of our analysis revealed that: (i) the MARS algorithm provides reliable predictions of coseismic landslides; (ii) a better ability to predict coseismic slope failures was observed when including susceptibility to rainfall-triggered landslides as an independent variable; (iii) the best accuracy is achieved by models trained with both preparatory and trigger variables; (iv) an incomplete inventory of coseismic slope failures built just after the earthquake event can be used to identify potential locations of yet unreported landslides