Analysis and Prediction of Rainfall-Induced Landslides in a Changing Environment

Dear Colleagues,

Rainfall-induced landslides are frequent and widespread natural phenomena that cause damage to humans and goods worldwide. About 90% of the landslides that caused casualties worldwide are triggered by rainfall. Therefore, the prediction of rainfall-induced landslides constitutes a key scientific question with significant social implications.

To model the relationship between rainfall and slope stability and to predict the occurrence of landslides, two approaches are generally adopted. The first, “physically-based”, attempts to determine the influence of water (rainfall, infiltration, superficial flow) on slope stability by modelling its effects in terms of pore water pressure and related balance between shear stress and resistance. In this regard, numerical models are employed, and a notable number of detailed data is commonly required. The second approach, “empirical”, is based on a statistical–probabilistic analysis of rainfall time series and of past dates of known occurrences of slope movement. In this context, rainfall thresholds are the most commonly used empirical tools to predict the possible occurrence of a single landslide or a population of landslides within a homogeneous geo-environmental setting in a given study area.

This Special Issue will collect contributions about recent research advances and/or well-documented applications in the prediction of rainfall-induced landslides. Contributions regarding the definition and the application of both empirical and physically-based methods and procedures to single phenomena or a population of landslides are welcome. Given the strong relationship between rainfall and landslides, variations in rainfall regimes are supposed to have effects on slope stability and on landslide characteristics. Therefore, contributions regarding the evaluation of the impact of observed and expected climatic and environmental (e.g. land use/cover) changes on landslide activity, frequency, and distribution are also welcome. Finally, we encourage the submission of contributions concerning operative applications, with the validation and performance evaluation of the models.

Dr. Stefano Luigi Gariano
Dr. Samuele Segoni
Dr. Guido Rianna
Guest Editors

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