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A coupled stochastic rainfall–evapotranspiration model for hydrological impact analysis

机译:水文影响分析的随机降雨-蒸散耦合模型

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A hydrological impact analysis concerns the study of the consequences of certain scenarios on one or more variables or fluxes in the hydrological cycle. In such an exercise, discharge is often considered, as floods originating from extremely high discharges often cause damage. Investigating the impact of extreme discharges generally requires long time series of precipitation and evapotranspiration to be used to force a rainfall-runoff model. However, such kinds of data may not be available and one should resort to stochastically generated time series, even though the impact of using such data on the overall discharge, and especially on the extreme discharge events, is not well studied. In this paper, stochastically generated rainfall and corresponding evapotranspiration time series, generated by means of vine copulas, are used to force a simple conceptual hydrological model. The results obtained are comparable to the modelled discharge using observed forcing data. Yet, uncertainties in the modelled discharge increase with an increasing number of stochastically generated time series used. Notwithstanding this finding, it can be concluded that using a coupled stochastic rainfall–evapotranspiration model has great potential for hydrological impact analysis.
机译:水文影响分析涉及对某些情景对水文循环中一个或多个变量或流量的后果的研究。在这样的练习中,通常会考虑排放,因为源自极高排放的洪水经常会造成破坏。要研究极端排放的影响,通常需要长时间的降水和蒸散量序列,以使用降雨径流模型。但是,即使没有充分研究使用此类数据对整体放电,尤其是对极端放电事件的影响,也可能无法获得此类数据,应该使用随机生成的时间序列。在本文中,利用葡萄树copulas随机产生的降雨和相应的蒸散时间序列被用来推动一个简单的概念性水文模型。所获得的结果与使用观察到的强迫数据模拟的排放量相当。但是,随着使用的随机生成时间序列数量的增加,建模放电中的不确定性也会增加。尽管有这一发现,但可以得出结论,使用随机降雨-​​蒸散耦合模型具有进行水文影响分析的巨大潜力。

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