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Modelling and sensitivity analysis of river flow in the Upper Indus Basin, Pakistan

机译:巴基斯坦上印度河流域河流径流建模与敏感性分析

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>Undoubtedly, it is important to model the average and extreme phenomena in earth sciences disciplines such as hydrology under uncertain and changing climate conditions. The issues become more important when we deal with reservoir management, flood forecasting and irrigation. In this paper, we model the average and extreme river flow in the Indus River at the Upper Indus Basin. For modelling average river flow, we utilised the popular classes of time series models including the autoregressive integrated moving average and autoregressive conditional heteroscedasticity models. For modelling the extremes, preference is given to probability distributions dealing with extremes in the tails. Starting with different models and distributions we finally choose the one which performs best among the competing models and distributions, respectively. Finally, when modelling extremes we noted that different probability distributions may be used for the same data, depending on whether interest is in lower or higher order moments.
机译:>毫无疑问,重要的是在不确定和变化的气候条件下对水文学等地球科学学科的平均现象和极端现象进行建模。当我们处理水库管理,洪水预报和灌溉时,这些问题变得更加重要。在本文中,我们对印度河上游的印度河的平均和极端河流量进行了建模。为了模拟平均河流量,我们利用了流行的时间序列模型,包括自回归综合移动平均值和自回归条件异方差模型。为了建模极端,优先考虑处理尾部极端的概率分布。从不同的模型和分布开始,我们最终选择一种分别在竞争模型和分布中表现最好的模型。最后,在对极端进行建模时,我们注意到对于相同的数据,可能会使用不同的概率分布,具体取决于兴趣是处于低阶矩还是高阶矩。

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