Search

To study on a regional basis, the relation between fluvial sediment delivery and coastal erosion, the historical record of coastline migration of Sicily was analyzed with respect to the estimated sediment delivery to the coast obtained from the spatially distributed sediment delivery WaTEM/SEDEM model. The latter was directly acquired from the ESDAC database as a 25 m pixel layers, being based on the combination between the RUSLE model and a transport capacity routing algorithm. At the same time, the coastline-evolution (accretion/retreatment) data for 1960/1994 and 1994/2012 intervals were processed. This dataset, provided by ISPRA (Italian Institute for Environmental Protection and Research), is made by vectorial polygons, corresponding to erosion or accretion areas obtained by the intersection between two coastlines. The dataset contains polygons related to the 1960-1994 and 1994-2012 periods. Once a common baseline was extracted from 2019 satellite images, 22 Physiographic Units (PU) were identified. The PU was defined based on geomorphologic criteria and by assuming a null net sediment budget (null sediment transport between two PU neighboring). Each coastal PU was connected to its contributing fluvial basins, also assigning the expected sediment delivery at the coastline. To perform the analysis, cross profiles along the coastline were generated and intersected with the polygons, calculating a response value, in terms of retreatment or accretion, to each of the cross-profile centroids. Finally, for each PU, the cumulated variations were computed. PUs with significant cumulative variations (more than 2 km) in at least one of the two epochs were identified and three different patterns were detected: accretion/retreatment, retreatment/accretion, and retreatment/retreatment. The response observed for the different PUs was then analyzed considering estimated sediment delivery, recognizing coherent (large sediment delivery = accretion) and incoherent (large sediment delivery = retreatment) behaviors, which have been interpreted as controlled by the history of soil/coastal erosion management practices. In particular, in spite of a very high expected sediment delivery, more than three-quarters of the Powered by TCPDF (www.tcpdf.org) Tyrrhenian coast resulted as affected by a marked retreat in 60-94 (same tens of meters) and a moderate accretion in 94-12, as the result of extensive coastal works which have been realized to mitigate coastal erosion.

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&rsquo, 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.

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 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.

In the studies of landslide susceptibility assessment, which have been developed in recent years, statistical methods have increasingly been applied. Among all, the BLR (Binary Logistic Regression) certainly finds a more extensive application while MARS (Multivariate Adaptive Regression Splines), despite the good performance and the innovation of the strategies of analysis, only recently began to be employed as a statistical tool for predicting landslide occurrence. The purpose of this research was to evaluate the predictive performance and identify possible drawbacks of the two statistical techniques mentioned above, focusing in particular on the prediction of debris flows. To this aim, an inventory of debris flows triggered by the passage of the hurricane IDA and the low-pressure system associated with it 96E, on 7 th and 8 th November 2009, in an area of about 26 km 2 close to the Caldera Ilopango, El Salvador (CA), was employed. Two validation strategies have been applied to both statistical techniques, thus obtaining four models – BLR (I), MARS (I), BLR (II) and MARS (II) – to be compared in pairs. Model performance was assessed in terms of AUC (area under the receiver operating characteristic (ROC) curve), Sensitivity, Specificity, Positive Prediction Value and Negative Prediction Value. Moreover, to evaluate the robustness of the modelling procedure, 50 replicates were created for each model and standard deviation was calculated for each of them. The results show that both techniques allow for obtaining good or excellent performances so that it is not possible to define one of the two techniques as absolutely better. However, the validation procedure reveals slightly better performance of the MARS models, with greater sensitivity and greater discrimination among True Negatives (TNs).