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Modeling regional precipitation over the Indus River basin of Pakistan using statistical downscaling
Theoretical and Applied Climatology ( IF 3.4 ) Pub Date : 2020-06-23 , DOI: 10.1007/s00704-020-03246-9
Muhammad Saleem Pomee , Moetasim Ashfaq , Bashir Ahmad , Elke Hertig

Complex processes govern spatiotemporal distribution of precipitation within the high-mountainous headwater regions (commonly known as the upper Indus basin (UIB)), of the Indus River basin of Pakistan. Reliable precipitation simulations particularly over the UIB present a major scientific challenge due to regional complexity and inadequate observational coverage. Here, we present a statistical downscaling approach to model observed precipitation of the entire Indus basin, with a focus on UIB within available data constraints. Taking advantage of recent high altitude (HA) observatories, we perform precipitation regionalization using K-means cluster analysis to demonstrate effectiveness of low-altitude stations to provide useful precipitation inferences over more uncertain and hydrologically important HA of the UIB. We further employ generalized linear models (GLM) with gamma and Tweedie distributions to identify major dynamic and thermodynamic drivers from a reanalysis dataset within a robust cross-validation framework that explain observed spatiotemporal precipitation patterns across the Indus basin. Final statistical models demonstrate higher predictability to resolve precipitation variability over wetter southern Himalayans and different lower Indus regions, by mainly using different dynamic predictors. The modeling framework also shows an adequate performance over more complex and uncertain trans-Himalayans and the northwestern regions of the UIB, particularly during the seasons dominated by the westerly circulations. However, the cryosphere-dominated trans-Himalayan regions, which largely govern the basin hydrology, require relatively complex models that contain dynamic and thermodynamic circulations. We also analyzed relevant atmospheric circulations during precipitation anomalies over the UIB, to evaluate physical consistency of the statistical models, as an additional measure of reliability. Overall, our results suggest that such circulation-based statistical downscaling has the potential to improve our understanding towards distinct features of the regional-scale precipitation across the upper and lower Indus basin. Such understanding should help to assess the response of this complex, data-scarce, and climate-sensitive river basin amid future climatic changes, to serve communal and scientific interests.



中文翻译:

使用统计降尺度模拟巴基斯坦印度河流域的区域降水

复杂的过程控制着巴基斯坦印度河河流域的高山区源头地区(通常称为印度河上游盆地(UIB))内降水的时空分布。可靠的降水模拟,特别是在UIB上,由于区域复杂性和观测范围不足而面临重大科学挑战。在这里,我们提出了一种统计缩减方法来模拟整个印度河盆地的降水量,重点是在可用数据限制内的UIB。利用最近的高空(HA)观测站,我们使用K-均值聚类分析对降水进行分区,以证明低空站的有效性,从而可以为UIB的不确定性和水文重要性HA提供有用的降水推断。我们进一步采用具有γ和Tweedie分布的广义线性模型(GLM),从可靠的交叉验证框架内的再分析数据集中识别出主要的动力学和热力学驱动因素,该框架解释了整个印度河盆地观测到的时空降水模式。最终的统计模型表明,通过主要使用不同的动态预测因子,可以解决南部喜马拉雅湿润地区和印度河下游不同地区降水变化的更高可预测性。该建模框架还显示了在更复杂和不确定的跨喜马拉雅山脉和UIB西北地区的性能,特别是在以西风环流为主的季节。但是,以冰冻圈为主的跨喜马拉雅山地区(主要控制流域水文学),需要包含动态和热力学循环的相对复杂的模型。我们还分析了UIB降水异常期间的相关大气环流,以评估统计模型的物理一致性,以此作为可靠性的附加度量。总体而言,我们的结果表明,这种基于环流的统计缩减有可能增进我们对印度河上下游地区区域降水的不同特征的理解。这种理解应该有助于评估在未来的气候变化中这种复杂,数据稀少且对气候敏感的流域的响应,以服务于公共和科学利益。评估统计模型的物理一致性,作为可靠性的附加度量。总体而言,我们的结果表明,这种基于环流的统计缩减有可能增进我们对印度河上下游地区区域降水的不同特征的理解。这种理解应该有助于评估在未来的气候变化中这种复杂,数据稀少且对气候敏感的流域的响应,以服务于公共和科学利益。评估统计模型的物理一致性,作为可靠性的附加度量。总体而言,我们的结果表明,这种基于环流的统计缩减有可能增进我们对印度河上下游地区区域降水的不同特征的理解。这种理解应该有助于评估在未来的气候变化中这种复杂,数据稀少且对气候敏感的流域的响应,以服务于公共和科学利益。

更新日期:2020-06-23
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