Abstract. In this paper, we examine variations in climate characteristics near the area of Cortina d'Ampezzo (Dolomites, Eastern Italian Alps), with particular reference to the possible implications for debris-flow occurrence. The study area is prone to debris-flow release in response to summer high-intensity short-duration rainfalls and, therefore, it is of the utmost importance to investigate the potential increase in debris-flow triggering rainfall events. The critical rainfall threshold is agreed to be a crucial triggering factor for debris-flows. Data from a monitoring system, placed in a catchment near Cortina (Acquabona), show that debris-flows were triggered by rainfalls with peak rainfall intensities ranging from 4.9 to 17.4 mm/10 min.The analyses of meteorological data, collected from 1921 to 1994 at several stations in the study area, show a negative trend of annual rainfall, a considerable variation in the monthly rainfall distribution, and an increase in the temperature range, possibly related to global climate changes. Moreover, high-intensity and short-duration rainfall events, derived from data collected from 1990 and 2008, show an increase in exceptional rainfall events. The results obtained in a peak-over-threshold framework, applied to the rainfall data measured at the Faloria rain gauge station from 1990 to 2008, clearly show that the interarrival time of over-threshold events computed for different threshold values decreased in the last decade. This suggests that local climatic changes might produce an increase in the frequency of rainfall events, potentially triggering debris flows in the study area.
Land subsidence due to underground resources exploitation is a well-known problem that affects many cities in the world, especially the ones located along the coastal areas where the combined effect of subsidence and sea level rise increases the flooding risk. In this study, 25 years of land subsidence affecting the Municipality of Ravenna (Italy) are monitored using Advanced Differential Interferometric Synthetic Aperture Radar\ud
(A-DInSAR) techniques. In particular, the exploitation of the new Sentinel-1A SAR data allowed us to extend the monitoring period till 2016, giving a better understanding of the temporal evolution of the phenomenon in the area. Two statistical approaches are applied to fully exploit the informative potential of the A-DInSAR results in a fast and systematic way. Thanks to the applied analyses, we described the behavior of the subsidence during the monitored period along with the relationship between the occurrence of the displacement and its main driving factors
Abstract. In the domain of landslide risk science, landslide susceptibility mapping
(LSM) is very important, as it helps spatially identify potential
landslide-prone regions. This study used a statistical ensemble model
(frequency ratio and evidence belief function) and two machine learning (ML) models (random forest and XGBoost; eXtreme Gradient Boosting) for LSM in the province of Belluno (region of Veneto, northeastern Italy). The study investigated the importance of the conditioning factors in predicting landslide occurrences using the mentioned models. In this paper, we evaluated the importance of the conditioning factors in the overall prediction capabilities of the statistical and ML algorithms. By the trial-and-error method, we eliminated the least “important” features by using a common threshold of 0.30 for statistical and 0.03 for ML algorithms. Conclusively, we found that removing the least important features does not impact the overall accuracy of LSM for all three models. Based on the results of our study, the most commonly available features, for example, the topographic features, contributes to comparable results after removing the least important ones, namely the aspect plan and profile curvature, topographic wetness index (TWI), topographic roughness index (TRI), and normalized difference vegetation index (NDVI) in the case of the statistical model and the plan and profile curvature, TWI, and topographic position index (TPI) for ML algorithms. This confirms that the requirement for the important conditioning factor maps can be assessed based on the physiography of the region.
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