In Central Italy a significant number of landslides occurrence have been triggered by rapid snow melt: recent seasonal events in the Northern Apennines, the study area, demonstrate that it is necessary to consider this phenomenon and to integrate snow precipitation within existing statistical models for landslide prediction. The proposed snow melt modeling (SMM) is divided in two modules depending on whether or not a threshold temperature is exceeded: the first one for the accumulation of solid rainfall in the snowpack and the latter for the snow melting. For the modeling identification we employ empirical data of depth of snow cover using an optimization algorithm to deduce the optimal values of the model parameters. This work is developed to increase the predictive capacity of the statistical models for landslide prediction based on rainfall thresholds. In the study area an improvement was achieved: several landslides, caused by snow melting, were correctly detected.

Snowmelt modelling for improving the forecasts of rainfall threshold-based landslide triggering / Martelloni G.; Segoni S.; Catani F.; Fanti R.. - STAMPA. - (2013), pp. 249-255. [10.1007/978-3-642-31337-0_32]

Snowmelt modelling for improving the forecasts of rainfall threshold-based landslide triggering

MARTELLONI, GIANLUCA;SEGONI, SAMUELE;CATANI, FILIPPO;FANTI, RICCARDO
2013

Abstract

In Central Italy a significant number of landslides occurrence have been triggered by rapid snow melt: recent seasonal events in the Northern Apennines, the study area, demonstrate that it is necessary to consider this phenomenon and to integrate snow precipitation within existing statistical models for landslide prediction. The proposed snow melt modeling (SMM) is divided in two modules depending on whether or not a threshold temperature is exceeded: the first one for the accumulation of solid rainfall in the snowpack and the latter for the snow melting. For the modeling identification we employ empirical data of depth of snow cover using an optimization algorithm to deduce the optimal values of the model parameters. This work is developed to increase the predictive capacity of the statistical models for landslide prediction based on rainfall thresholds. In the study area an improvement was achieved: several landslides, caused by snow melting, were correctly detected.
2013
9783642313363
9783642313370
Landslide Science and Practice - Volume 4: Global Environmental Change
249
255
Martelloni G.; Segoni S.; Catani F.; Fanti R.
File in questo prodotto:
File Dimensione Formato  
Martelloni et al LANDSLIDE SCIENCE AND PRACTICE 2013.pdf

Accesso chiuso

Tipologia: Versione finale referata (Postprint, Accepted manuscript)
Licenza: Open Access
Dimensione 363.33 kB
Formato Adobe PDF
363.33 kB Adobe PDF   Richiedi una copia

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/826275
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact