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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 17, issue 3
Hydrol. Earth Syst. Sci., 17, 1229–1240, 2013
https://doi.org/10.5194/hess-17-1229-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 17, 1229–1240, 2013
https://doi.org/10.5194/hess-17-1229-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 20 Mar 2013

Research article | 20 Mar 2013

Snow accumulation/melting model (SAMM) for integrated use in regional scale landslide early warning systems

G. Martelloni1,*, S. Segoni1, D. Lagomarsino1, R. Fanti1, and F. Catani1 G. Martelloni et al.
  • 1University of Firenze, Earth Sciences Department, Via La Pira 4, 50121 Firenze, Italy
  • *now at: University of Firenze, Department of Industrial Engineering (CSDC – Center of the Study of Complex Dynamics), Via Santa Marta 3, 50139 Firenze, Italy

Abstract. We propose a simple snow accumulation/melting model (SAMM) to be applied at regional scale in conjunction with landslide warning systems based on empirical rainfall thresholds.

SAMM is based on two modules modelling the snow accumulation and the snowmelt processes. Each module is composed by two equations: a conservation of mass equation is solved to model snowpack thickness and an empirical equation for the snow density. The model depends on 13 empirical parameters, whose optimal values were defined with an optimisation algorithm (simplex flexible) using calibration measures of snowpack thickness.

From an operational point of view, SAMM uses as input data only temperature and rainfall measurements, bringing about the additional benefit of a relatively easy implementation.

After performing a cross validation and a comparison with two simpler temperature index models, we simulated an operational employment in a regional scale landslide early warning system (EWS) and we found that the EWS forecasting effectiveness was substantially improved when used in conjunction with SAMM.

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