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A hybrid Monte Carlo—possibilistic method to evaluate soil erosion in an Alpine valley

机译:一种杂交蒙特卡罗可能主义方法,以评估高山山谷土壤侵蚀的方法

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The high number of complex processes involved in soil erosion and sediment delivery make their understanding and reproduction a difficult task. Alpine valleys, characterized by high slopes, are particularly susceptible to severe soil erosion. Two of the main consequences are silting of water reservoirs and fostering of shallow landslides. In the last decades several models for the evaluation of sediment production and delivery have been proposed. Different approaches can be split into two main categories: bottom-up and top-down models. Bottom-up models are designed to reproduce the main physical processes involved in soil erosion; these methods are really complicated from a computational point of view. Instead top-down models, like the Gavrilovic one, reproduce the phenomenon at the basin scale with a low number of parameters. In this paper the authors present a hybrid Monte Carlo and possibilistic approach to the Gavrilovic method, designed to take into account uncertainties on input data. An example of application on a test basin situated in the Italian Alps is used to show the potential of the proposed method. The basin was split into sub-areas to reduce the subjectivity of the choice of empirical coefficients. A quantitative comparison between measures of average sediment yield and results obtained with the proposed method was performed.
机译:涉及土壤侵蚀和沉积物交付的大量复杂过程使其理解和复制成为一项艰巨的任务。特征在于高斜坡的高山谷特别容易受到严重的土壤侵蚀的影响。两种主要后果是淤泥水库和浅层滑坡的培养。在过去的几十年中,已经提出了一些用于评估泥沙生产和交付的模型。不同的方法可以分为两个主要类别:自下而上和自上而下的模型。自下而上的模型旨在再现土壤侵蚀所涉及的主要物理过程;这些方法与计算的观点来说真的很复杂。相反,像gavrilovic一个一样自上而下的模型,以较少数量的参数再现盆地刻度的现象。本文提交了一种混合蒙特卡罗和可能的方法,用于Gavrilovic方法,旨在考虑输入数据的不确定性。用于在意大利阿尔卑斯山的测试盆地上的应用示例用于显示所提出的方法的潜力。盆地被分成子区域,以降低经验系数的选择的主观性。进行了平均沉积物产量和用所提出的方法获得的结果之间的定量比较。

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