Catani, Filippo

Fanti, Riccardo

Martelloni, Gianluca

Segoni, Samuele

English

Florence Research

Geophysical Research AbstractsVol. 12, EGU2010-13868, 2010EGU General Assembly 2010© Author(s) 2010Snow melt modeling for landslide prediction: applications in the NorthernApennines (Italy)Filippo Catani, Gianluca Martelloni, Samuele Segoni, and Riccardo FantiUniversity of Firenze, Earth Sciences Dept., Firenze, Italy (filippo.catani@unifi.it, +39 055 2756296)Climate change driven landslide forecasting implementation in early warning activities is strictly dependent onthe quality and timing of rainfall measurements and on an accurate empirical or deterministic relationship linkingwater infiltration to slope stability. The scientific literature offers a wide range of choices based on distributedphysically based models or on empirical rainfall thresholds but a common weakness of all those approaches is thatthey are, for quite obvious reasons, very sensitive to errors in the rainfall estimation. This is especially true fortemperate or cold climate regions in which a not negligible part of precipitation may be withhold in a snow coveruntil a temperature change that triggers a sudden water input in the ground.Northern Apennines of Italy are no exception to this rule as demonstrated by the very recent event of Christmas2009 in which several hundred landslides have been triggered by a rapid snow melt in Tuscany and Emila-Romagna regions.In order to integrate for snow precipitation the existing models for landslide prediction, usually based on rainfallthresholds in Tuscany and Emilia-Romagna, a simple, physically based model for the snow melting is proposed.Landslides induced from rainfall or snowfall are frequent in Northern Apennines: the proposed model is dynamicand takes into account the buildup and melting of the snow cover in time. This model is based on two equations:the conservation of mass (input-output balance) and an empirical equation for modeling the snow density variation.From the conservation of mass a differential equation of snow cover depth, depending on density and averagetemperature of the air, can be obtained. The second equation is an empirical function for the density variation anddepends on the snow cover depth (gravity effect) and the temperature of air, variable in time. We consider 0.5 °Cas a threshold temperature between snowfall and rainfall. In the present form, this model depends on six empiricalparameters: we calibrate their values employing empirical data of rainfall, depth of snow cover and temperatureof the rain gauges in the study area. Then we use an algorithm (simplex optimized) to deduce the optimal valuesof the six parameters of the model. The final objective of the work is to increase the predictive capacity of thestatistical models for landslide prediction.

Snow melt modeling for landslide prediction: applications in the Northern Apennines (Italy)

https://flore.unifi.it/bitstream/2158/384875/1/Catani%20et%20al%20GRA%20vol%2012%20EGU%202010.pdf

Snow melt modeling for landslide prediction: applications in the Northern Apennines (Italy)

Abstract

Similar works

Full text

Available Versions

Florence Research