The paper analyses how rainfall-induced landslide risks may be mitigated by implementing early warning systems in sloping silty volcanic covers. Their answer to rainfall and other atmospheric variables seems to contain features and factors such to provide generality to the topic. The work firstly investigates the factors influencing the hydrological response of a silty volcanic cover referring to results provided by two physical models. Experimental results evidence the importance of accounting for evaporation (or evapotranspiration) and a realistic lowermost boundary condition when the evolution of the hydrological behaviour has to be predicted for early warning purposes. All findings are used to develop a physically-based model suitable for early warning prediction and provide an example of application. © Springer Nature Switzerland AG 2020.
Hydrological Characterization of Silty Volcanic Slopes and Physically-Based Early Warning Systems / Coppola, L.; Pagano, L.; Reder, A.; Rianna, G.. - 40:(2020), pp. 174-183. (Intervento presentato al convegno Geotechnical Research for Land Protection and Development, Proceedings of CNRIG 2019 tenutosi a Lecco nel 3-5 luglio 2019) [10.1007/978-3-030-21359-6_19].
Hydrological Characterization of Silty Volcanic Slopes and Physically-Based Early Warning Systems
Pagano L.;Reder A.
;Rianna G.
2020
Abstract
The paper analyses how rainfall-induced landslide risks may be mitigated by implementing early warning systems in sloping silty volcanic covers. Their answer to rainfall and other atmospheric variables seems to contain features and factors such to provide generality to the topic. The work firstly investigates the factors influencing the hydrological response of a silty volcanic cover referring to results provided by two physical models. Experimental results evidence the importance of accounting for evaporation (or evapotranspiration) and a realistic lowermost boundary condition when the evolution of the hydrological behaviour has to be predicted for early warning purposes. All findings are used to develop a physically-based model suitable for early warning prediction and provide an example of application. © Springer Nature Switzerland AG 2020.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
