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Evaluation of Gridded Precipitation Data Products for Hydrological Applications in Complex Topography

机译:复杂地形中水文应用网格化降水数据产品的评估

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Accurate spatial and temporal representation of precipitation is of utmost importance for hydrological applications. Uncertainties in available data sets increase with spatial resolution due to small-scale processes over complex terrain. As previous studies revealed high regional differences in the performance of gridded precipitation data sets, it is important to assess the related uncertainties at the catchment scale, where these data sets are typically applied, e.g., for hydrological modeling. In this study, the uncertainty of eight gridded precipitation data sets from various sources is investigated over an alpine catchment. A high resolution reference data set is constructed from station data and applied to quantify the contribution of spatial resolution to the overall uncertainty. While the results demonstrate that the data sets reasonably capture inter-annual variability, they show large seasonal differences. These increase for daily indicators assessing dry and wet spells as well as heavy precipitation. Although the higher resolution data sets, independent of their source, show a better agreement, the coarser data sets showed great potential especially in the representation of the overall climatology. To bridge the gaps in data scarce areas and to overcome the issues with observational data sets (e.g., undercatch and station density) it is important to include a variety of data sets and select an ensemble for a robust representation of catchment precipitation. However, the study highlights the importance of a thorough assessment and a careful selection of the data sets, which should be tailored to the desired application.
机译:降水的准确时空表示对于水文应用至关重要。由于复杂地形上的小规模过程,可用数据集的不确定性随空间分辨率而增加。由于先前的研究表明,栅格化降水数据集的性能存在较大的区域差异,因此重要的是评估集水区规模的相关不确定性,这些数据集通常用于例如水文模型。在这项研究中,研究了来自高寒流域的八种不同来源的网格化降水数据集的不确定性。高分辨率参考数据集由测站数据构成,并用于量化空间分辨率对整体不确定性的贡献。虽然结果表明数据集合理地反映了年际变化,但它们显示出较大的季节性差异。这些增加的每日指标用于评估干湿两季以及强降水。尽管更高分辨率的数据集(与来源无关)显示出更好的一致性,但较粗糙的数据集显示出巨大的潜力,尤其是在总体气候学方面。为了弥合数据稀缺地区之间的差距并克服观测数据集(例如,雨量不足和站位密度)的问题,重要的是要包括各种数据集并选择一个集合来可靠地表示集水区降水。但是,该研究强调了彻底评估和仔细选择数据集的重要性,这些数据集应针对所需的应用而量身定制。

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