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Quantifying uncertainty in rainfall-runoff models due to design losses using Monte Carlo simulation: a case study in New South Wales, Australia

机译:使用蒙特卡洛模拟对设计损失造成的降雨径流模型的不确定性进行量化:以澳大利亚新南威尔士州为例

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摘要

With the potentially devastating consequences of flooding, it is crucial that uncertainties in the modelling process are quantified in flood simulations. In this paper, the impact of uncertainties in design losses on peak flow estimates is investigated. Simulations were carried out using a conceptual rainfall-runoff model called RORB in four catchments along the east coast of New South Wales, Australia. Monte Carlo simulation was used to evaluate parameter uncertainty in design losses, associated with three loss models (initial loss-continuing loss, initial loss-proportional loss and soil water balance model). The results show that the uncertainty originating from each loss model differs and can be quite significant in some cases. The uncertainty in the initial loss-proportional loss model was found to be the highest, with estimates up to 2.2 times the peak flow, whilst the uncertainty in the soil water balance model was significantly less, with up to 60 % variability in peak flows for an annual exceedance probability of 0.02. Through applying Monte Carlo simulation a better understanding of the predicted flows is achieved, thus providing further support for planning and managing river systems.
机译:由于洪水可能带来毁灭性的后果,因此至关重要的是,在洪水模拟中量化建模过程中的不确定性。在本文中,研究了设计损失的不确定性对峰值流量估计的影响。在澳大利亚新南威尔士州东海岸的四个流域中,使用称为RORB的概念性降雨径流模型进行了模拟。蒙特卡罗模拟用于评估设计损失的参数不确定性,该不确定性与三种损失模型(初始损失-持续损失,初始损失-比例损失和土壤水分平衡模型)相关。结果表明,源自每种损失模型的不确定性有所不同,并且在某些情况下可能非常显着。发现初始损失-比例损失模型中的不确定性最高,估计高达峰值流量的2.2倍,而土壤水平衡模型中的不确定性则要小得多,峰值损失的波动性高达60%。年超出概率为0.02。通过应用蒙特卡洛模拟,可以更好地了解预测流量,从而为规划和管理河流系统提供进一步的支持。

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