An enhanced rainfall-induced landslide catalogue in Italy.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-05 DOI:10.1038/s41597-025-04551-6

Maria Teresa Brunetti, Stefano Luigi Gariano, Massimo Melillo, Mauro Rossi, Silvia Peruccacci

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Abstract

With the increasing use of data-driven landslide prediction models also based on artificial intelligence, the availability of accurate information on the occurrence of landslides and the rigorous reconstruction of their triggering rainfall conditions are crucial. To this end, an enhanced rainfall-induced landslide catalogue, e-ITALICA, is presented here. e-ITALICA contains spatial and temporal information on 6312 rainfall-induced landslides that occurred in Italy between 1996 and 2021 (already listed in the previous ITALICA catalogue published in 2023), with the addition of their rainfall triggering conditions in terms of rainfall duration D (h) and cumulative event rainfall E (mm). The triggering conditions are calculated using hourly rainfall measurements from 4033 rain gauges and applying a rigorous and reproducible method. In addition, topographic and land cover information is also provided. e-ITALICA can be used to analyse rainfall conditions capable of triggering landslides, to calibrate and validate physically based landslide prediction models, and to define empirical rainfall thresholds from local to national scales in Italy, thus contributing to landslide risk reduction.

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.