HESS Hydrology and Earth System Sciences HESS Hydrol. Earth Syst. Sci. 1607-7938 Copernicus Publications Göttingen, Germany 10.5194/hess-17-1229-2013 Snow accumulation/melting model (SAMM) for integrated use in regional scale landslide early warning systems Martelloni G. 1 2 Segoni S. 1 Lagomarsino D. 1 Fanti R. 1 Catani F. https://orcid.org/0000-0001-5185-4725 1 University 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 20 03 2013 17 3 1229 1240 Copyright: © 2013 G. Martelloni et al. 2013 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://hess.copernicus.org/articles/17/1229/2013/hess-17-1229-2013.html The full text article is available as a PDF file from https://hess.copernicus.org/articles/17/1229/2013/hess-17-1229-2013.pdf

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. <br><br> 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. <br><br> 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. <br><br> 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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