Weather scenarios associated with rainfall-induced landslides in the Liguria Region, Italy

Maria Teresa Brunetti,Stefano Luigi Gariano, Monica Solimano,Massimo Melillo,Silvia Peruccacci, Pietro Gabriele De Stefanis, Michele Cicoria

crossref(2024)

引用 0|浏览0
暂无评分
摘要
Liguria is an Italian region bordered on the North by the Alps and on the South by the Thyrrenian Sea. This geographical location and its topography lead to the occurrence of numerous weather scenarios. The rainfall pattern and the steep topography give rise to frequent landslide events in the region. This work aims to investigate the relationship between the occurrence of landslides and different weather scenarios. A catalogue with detailed spatial and temporal information on 475 rainfall-induced landslides that occurred in Liguria region in 2019 and 2020 is available and is used to perform the analysis. Forecasts of local atmospheric conditions for the Liguria region are calculated daily by the Regional Agency for the Environmental Protection of Liguria region (ARPAL), and are used to issue regional weather vigilance bulletins to be used by the civil protection authority to give geo-hydrological alerts. The atmospheric conditions are classified in 7 weather scenarios and 8 sub-scenarios by ARPA. The classification is based on both the synoptic circulation and the types of precipitation and antecedent conditions. We observe that the most frequent scenario associated with landslide occurrences in the region is the “West-Southern weather pattern”, whereas the most frequent sub-scenario is the “Intense rainfall and rainstorms”. Temporal analyses are carried out to assess variations in the monthly distribution of the weather scenarios. In addition, the characteristics of the rainfall conditions responsible for the failures are evaluated to search for peculiarities related to different weather scenarios.   This work was supported by the 2021-2023 Cooperation Agreement between CNR-IRPI and the Regional Agency for the Environmental Protection of Liguria region (ARPAL), Italy, and the PRIN-ITALERT project (PRIN2022 call - grant number: 202248MN7N, CUP: B53D23006720006) funded by NextGenerationEU.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要