Gridded precipitation datasets are becoming a convenient substitute for gauge measurements in hydrological modeling; however, these data have not been fully evaluated across a range of conditions. We compared four gridded datasets (Daily Surface Weather and Climatological Summaries [DAYMET], North American Land Data Assimilation System [NLDAS], Global Land Data Assimilation System [GLDAS], and Parameter‐elevation Regressions on Independent Slopes Model [PRISM]) as precipitation data sources and evaluated how they affected hydrologic model performance when compared with a gauged dataset, Global Historical Climatology Network‐Daily (GHCN‐D). Analyses were performed for the Delaware Watershed at Perry Lake in eastern Kansas. Precipitation indices for DAYMET and PRISM precipitation closely matched GHCN‐D, whereas NLDAS and GLDAS showed weaker correlations. We also used these precipitation data as input to the Soil and Water Assessment Tool (SWAT) model that confirmed similar trends in streamflow simulation. For stations with complete data, GHCN‐D based SWAT‐simulated streamflow variability better than gridded precipitation data. During low flow periods we found PRISM performed better, whereas both DAYMET and NLDAS performed better in high flow years. Our results demonstrate that combining gridded precipitation sources with gauge‐based measurements can improve hydrologic model performance, especially for extreme events.

中文翻译：

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基于SWAT的堪萨斯农业流域网格降水数据集的比较与评价。

网格化降水数据集正在成为水文模拟中水位测量的便捷替代方法。但是，这些数据尚未在各种条件下得到全面评估。我们比较了四个栅格化数据集（每日地面天气和气候摘要[DAYMET]，北美土地数据同化系统[NLDAS]，全球土地数据同化系统[GLDAS]以及独立坡度模型[PRISM]的参数高程回归）作为降水量数据源，并与全球历史气候学网络日报（GHCN‐D）相比，评估了它们如何影响水文模型性能。对堪萨斯州东部佩里湖的特拉华流域进行了分析。DAYMET和PRISM降水的降水指数非常接近GHCN‐D，而NLDAS和GLDAS显示出较弱的相关性。我们还将这些降水数据用作土壤和水评估工具（SWAT）模型的输入，该模型证实了流量模拟中的类似趋势。对于具有完整数据的台站，基于GHCN‐D的SWAT模拟的水流变异性要好于网格降水数据。在低流量期间，我们发现PRISM的性能更好，而DAYMET和NLDAS在高流量年份中的性能都更好。我们的结果表明，结合网格化降水源与基于仪表的测量值可以改善水文模型的性能，尤其是在极端事件中。基于GHCN‐D的SWAT模拟的流变率要优于网格降水数据。在低流量期间，我们发现PRISM的性能更好，而DAYMET和NLDAS在高流量年份中的性能都更好。我们的结果表明，结合网格化降水源与基于仪表的测量值可以改善水文模型的性能，尤其是在极端事件中。基于GHCN‐D的SWAT模拟的流变率要优于网格降水数据。在低流量期间，我们发现PRISM的性能更好，而DAYMET和NLDAS在高流量年份中的性能都更好。我们的结果表明，结合网格化降水源与基于仪表的测量值可以改善水文模型的性能，尤其是在极端事件中。