Author(s): G. Martelloni | S. Segoni | D. Lagomarsino | R. Fanti | F. Catani
Journal: Hydrology and Earth System Sciences Discussions
ISSN 1812-2108
Volume: 9;
Issue: 8;
Start page: 9391;
Date: 2012;
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Original page
ABSTRACT
We propose a simple snow accumulation-melting model (SAMM) to be applied at the regional scale in conjunction with landslide warning systems based on empirical rainfall thresholds. SAMM follows an intermediate approach between physically based models and empirical temperature index models. It 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 optimization 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 the additional advantage of a relatively easy implementation. The snow model validation gave satisfactory results; moreover we simulated an operational employment in a regional scale landslide early warning system (EWS) and found that the EWS forecasting effectiveness was substantially improved when used in conjunction with SAMM.
Journal: Hydrology and Earth System Sciences Discussions
ISSN 1812-2108
Volume: 9;
Issue: 8;
Start page: 9391;
Date: 2012;
VIEW PDF
DOWNLOAD PDF Original pageABSTRACT
We propose a simple snow accumulation-melting model (SAMM) to be applied at the regional scale in conjunction with landslide warning systems based on empirical rainfall thresholds. SAMM follows an intermediate approach between physically based models and empirical temperature index models. It 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 optimization 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 the additional advantage of a relatively easy implementation. The snow model validation gave satisfactory results; moreover we simulated an operational employment in a regional scale landslide early warning system (EWS) and found that the EWS forecasting effectiveness was substantially improved when used in conjunction with SAMM.
