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An enhanced rainfall-induced landslide catalogue in Italy
Maria Teresa Brunetti1, Stefano Luigi Gariano2, Massimo Melillo2
1Istituto di Ricerca per la Protezione Idrogeologica, Consiglio Nazionale delle Ricerche, Perugia, 06128, Italy. mariateresa.brunetti@cnr.it.
Scientific Data
|February 5, 2025
Summary
The e-ITALICA catalogue provides crucial rainfall data for 6312 landslides in Italy (1996-2021). This enhanced dataset aids in developing accurate artificial intelligence landslide prediction models and risk reduction strategies.
Area of Science:
- Geosciences
- Environmental Science
- Data Science
Background:
- Accurate landslide prediction models, especially AI-driven ones, require precise data on landslide occurrences and triggering rainfall.
- Existing landslide catalogues need enhancement with detailed rainfall information for improved model calibration and validation.
Purpose of the Study:
- To present the enhanced rainfall-induced landslide catalogue, e-ITALICA, for Italy.
- To provide spatial, temporal, and detailed rainfall triggering conditions (duration and cumulative rainfall) for 6312 landslides.
- To offer topographic and land cover data to support landslide analysis and prediction.
Main Methods:
- Compilation of spatial and temporal data for 6312 rainfall-induced landslides in Italy (1996-2021).
- Calculation of triggering rainfall conditions (duration D, cumulative event rainfall E) using hourly data from 4033 rain gauges.
- Integration of topographic and land cover information into the catalogue.
Main Results:
- The e-ITALICA catalogue includes 6312 rainfall-induced landslides with detailed triggering rainfall data.
- A rigorous and reproducible method was applied to calculate rainfall conditions using extensive rain gauge data.
- The catalogue provides essential data for analysing landslide-triggering rainfall and defining empirical thresholds.
Conclusions:
- The e-ITALICA catalogue is a valuable resource for advancing landslide prediction research and applications in Italy.
- It facilitates the calibration and validation of physically based and AI-driven landslide models.
- The dataset contributes to improved landslide risk assessment and reduction strategies at various scales.
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