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Modeling ensemble streamflow: Application to the Senegal River upper the Manantali Dam

机译:集成流建模:在塞内加尔河Manantali大坝上的应用

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This study presents an ensemble stream flow simulation with probability of occurrence accounting for errors of the GR4J turned to the Senegal River upper the Manantali dam. Through this approach, probability is associated to simulation at each time, with step and reliability of issues depending on probability scores. Results including the reliability of the results permit decision-makers to judge the reliability of their simulations. Past errors pattern of the model is used to perturb the model for issuing ensemble scenarios rather than classical single deterministic. Basic and reverse Box-Cox transformation is carried out to allow treatment of the errors through weighted multivariate Gaussian distribution. Statistic errors are then used to perturb the hydrological model to obtain ensemble issues (RAW-Ens). Furthermore, a post-processing method (affine kernel dressing) is performed to produce a dressed ensemble (AKD-Ens). Ensembles are evaluated at deterministic and probabilistic scale. Diagrams (attribute and ROC) are also used for this purpose. Evaluating methods reveal through scores that the system is reliable and that dressing method (AKD) improves quality of the raw ensemble drawn from the perturbed model.
机译:这项研究提出了一个整体流模拟,其发生概率考虑了转向Manantali大坝上方塞内加尔河的GR4J的误差。通过这种方法,概率每次都与仿真相关联,问题的步骤和可靠性取决于概率分数。结果(包括结果的可靠性)使决策者可以判断其仿真的可靠性。该模型的过去错误模式用于扰动用于发布整体方案的模型,而不是经典的单一确定性模型。进行基本的和反向的Box-Cox转换,以允许通过加权多元高斯分布处理误差。然后使用统计误差扰动水文模型以获得整体问题(RAW-Ens)。此外,执行后处理方法(仿射内核修整)以生成修整合奏(AKD-Ens)。合奏是在确定性和概率尺度上进行评估的。图表(属性和ROC)也用于此目的。评估方法通过分数显示系统是可靠的,并且修整方法(AKD)可以提高从受扰模型得出的原始合奏的质量。

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