Abstract. The definition of hydrological alert systems for rainfall-induced landslides is strongly related to a deep knowledge of the geological and geomorphological features of the territory. Climatic conditions, spatial and temporal evolution of the phenomena and characterization of landslide triggering, together with propagation mechanisms, are the key elements to be considered. Critical steps for the development of the systems consist of the identification of the hydrological variable related to landslide triggering and of the minimum rainfall threshold for landslide occurrence.
Abstract. In this paper, spatial data available in the Italian portals was used to evaluate the landslide susceptibility of the Euganean Hills Regional Park, located SW of Padua (northeastern Italy). Quality, applicability and possible analysis scales of the online data were investigated.After a brief overview on the WebGIS portals around the world, their contents and tools for natural risk analyses, a susceptibility analysis of the study area was carried out using a simple probabilistic approach that compared landslide distribution and influencing factors. The input factors used in the analysis depended on available data and included landslides, morphometric data (elevation, slope, curvature, profile and plan Curvature) and non-morphometric data (land use, distance to roads and distance to rivers). Great attention was paid to the pre-processing step, in particular the re-classification of continuous data that was performed following objective, geologic and geomorphologic criteria.The results of the study show that the simple probabilistic approach used for the susceptibility evaluation showed quite good accuracy and precision (repeatability). However, heuristic, statistical or deterministic methods could be applied to the online data to improve the prediction.The data available online for the Italian territory allows susceptibility assessment at medium and large scales. Morphometric factors, such as elevation and slope angle, are important because they implicitly include information that is not available, such as lithologic and structural data. The main drawback of the Italian online databases is the lack of information on the frequency of landslides; thus, a complete hazard analysis is not possible.Correspondence to: M. Floris (mario.floris@unipd.it) Despite the good results achieved to date, collection and sharing of data on natural risks must be improved in Italy and around the world. The creation of spatial data infrastructure and more WebGIS portals is desirable.
We analyze the climatic features of the Vicenza Province (NE Italy) and the characteristics of the exceptional rainfall event that hit the area in November 2010, triggering a huge number of landslides. Our analysis aims at identifying the hydrological variable related to the triggering of the recorded instabilities and the recent variation in the occurrence of rainfall events inducing landslides.\ud During the period 1920–2009, a negative trend in the annual rainfall and a marked positive trend of the mean annual temperature have been observed in the study area. Rainfall has become more concentrated during autumnal season (October–December) and the greatest increase in temperature has been registered in winter (January–March) and summer (June–August). As a consequence, the quantity of meteoric water available for infiltration and run off processes has increased in autumn, which has typically been the season with the maximum number of landslides. Moreover, the statistical analysis of annual rainfall maxima for durations of 1, 3, 6, 12 and 24 h, and 1, 2, 5, 10, 30, 60, 90, and 120 days, allowed us to highlight that the occurrence of intense rainfall events has increased in the last 20 years.\ud Our results show that the degree of rainfall-induced landslide hazard has increased in the study area, possibly due to recent climate changes. In the near future, such results should be taken into account for landslide forecasting, monitoring and mitigation
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.