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

机译:用于综合使用的雪积累/熔化模型(SAMM)在区域规模滑坡预警系统中使用

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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.
机译:我们提出了一种简单的积雪/熔化模型(SAMM),以基于经验降雨阈值与山体滑坡警告系统一起应用。 SAMM基于两个模块,建模积雪和散雪过程。每个模块由两个等式组成:质量方程的守恒来解决积雪厚度和雪密度的经验方程。该模型取决于13个经验参数,其最佳值用使用积雪厚度的校准测量来定义具有优化算法(Simplex Flexible)的优化值。从操作的角度来看,SAMM用作仅限输入数据的温度和降雨测量,带来相对容易实现的额外益处。在执行交叉验证和与两个更简单的温度指数模型的比较之后,我们模拟了区域规模滑坡预警系统(EWS)的运营就业,我们发现与SAMM一起使用时,EWS预测有效性显着提高。

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