In January and February 2001, El Salvador was hit by two strong earthquakes that triggered thousands of landslides, causing 1,259 fatalities and extensive damage. The analysis of aerial and SPOT-4 satellite images taken a few days after the events allowed us to map 6,491 coesismic landslides, which occurred in 14 study areas extending for about 400 km2. Four different Multivariate Adaptive Regression Splines (MARS) models were produced by using different covariate sets and landslide inventories, the latter containing the slope failures triggered by an extreme rainfall event of November 2009 and those induced by the earthquakes of 2001. Moreover, two validation scenarios were employed, including the 25% and 95% of the mapped landslides, respectively. The results of our experiment revealed that: (i) MARS algorithm provides reliable predictions of coesismic landslides; (ii) models calibrated with rainfall-induced landslides predict with acceptable accuracy landslides caused by deep earthquakes and distributed over vast areas; (iii) the best accuracy is achieved by models trained with both preparatory and trigger variables; (iv) a small portion of the landslides produced by an earthquake can be used to calibrate MARS predictive models that help to identify slopes where yet unreported landslides may have occurred.

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.

In recent decades, coastal erosion phenomena have increased due to climate change. The increased frequency and intensity of extreme events and the poor sediment supply by anthropized river basins (dams, river weirs, culverts, etc.) have a crucial role in coastal erosion. Therefore, an integrated analysis of coastal erosion is crucial to produce detailed and accurate coastal erosion vulnerability information to support mitigation strategies. This research aimed to assess the erosion vulnerability of the Sicilian coast, also including a validation procedure of the obtained scenario. The coastal vulnerability was computed by means of the CeVI (Coastal Erosion Vulnerability Index) approach, which considers physical indicators such as geomorphology and geology, coastal slope, sea storms, wave maxima energy flux and sediment supply to river mouths. Each indicator was quantified using indexes which were assessed considering transects orthogonal to the coastline in 2020. These transects were clustered inside natural compartments called littoral cells. Each cell was assumed to contain a complete cycle of sedimentation and not to have sediment exchange with the near cells. Physical parameters were identified to define a new erosion vulnerability index for the Sicilian coast. By using physical indexes (geological/geomorphological, erosion/sediment supply, sea storms, etc.), the CeVI was calculated both for each littoral cell and for the transects that fall into retreating/advancing coastal areas. The vulnerability index was then validated by comparing CeVI values and the coastline change over time. The validation study showed a direct link between the coastline retreat and high values of CeVI. The proposed method allowed for a detailed mapping of the Sicilian coastal vulnerability, and it will be useful for coastal erosion risk management purposes.

<p>On January 13th, 2001, El Salvador was hit by an earthquake of magnitude 7.7 which triggered thousands of landslides, causing 1259 fatalities and extensive damage to infrastructures. The analysis of aerial images provided by the CNR (Centro Nacional de Registros de El Salvador), which were taken a few days after the event, allowed us to map 1005 seismically-induced landslides that occurred in a study area extended for 92 km<sup>2</sup>. The objective of this experiment was to verify whether it is possible to predict the spatial distribution of these landslides through a stochastic approach that combines a rainfall-induced landslide susceptibility (SUSC) model, which is based on preparatory factors, and an earthquake-triggered landslide predictive (TRIGGER) model, which is based on seismic parameters such as peak ground acceleration (<em>PGA</em>) and distance to the epicenter (<em>ED</em>). The SUSC model was calibrated by using an inventory of 5609 landslides that occurred in November 2009 in the area of the San Vicente volcano, due to the simultaneous action of low-pressure system 96E and Hurricane Ida. The TRIGGER model was instead trained with the 20% of the earthquake-triggered landslides, whereas the remaining 80% was used to validate both the SUSC and TRIGGER models, as well as an ensemble model obtained by using as predictors <em>PGA</em>, <em>ED</em> and the landslide probability calculated by the SUSC model. In order to evaluate the robustness of the results, ten calibration and validation samples were randomly extracted from the 2001 landslide inventory. Multivariate adaptive regression splines (MARS) was used as modelling technique. The predictive performance of the models was evaluated by using receiver operating characteristics (ROC) curves and the area under the ROC curve (AUC).</p><p>The validation results revealed a slightly better performance of the SUSC model (average AUC = 0.719; AUC st.dev. = 0.008) with respect to the TRIGGER model (average AUC = 0.707; AUC st.dev. = 0.009). Moreover, the analysis highlighted that the best predictive ability is achieved by the ensemble model (average AUC = 0.743; AUC st.dev. = 0.006). These results suggest that, in the event that only some of the landslides triggered by an earthquake are known, as usually happens shortly after the event, it is possible to use the approach proposed in this study to identify those sites where the other landslides are more likely to have occurred.</p><p>This work is a part of the CASTES project, which is funded by the Italian Agency for Development Cooperation (AICS) and focuses on promoting research and training activities in the field of earth sciences in El Salvador (CA).</p>

<p>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.</p><p>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.</p><p>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.</p><p>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.</p><p>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.</p><p>In particular, in spite of a very high expected sediment delivery, more than three-quarters of the 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. </p>