Effect of Watershed Delineation and Climate Datasets Density on Runoff Predictions for the Upper Mississippi River Basin Using SWAT within HAWQS
Effect of Watershed Delineation and Climate Datasets Density on Runoff Predictions for the Upper Mississippi River Basin Using SWAT within HAWQS
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DOI：
10.3390/w13040422
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发表时间：
2021-02
期刊：
影响因子：
3.4
通讯作者：
Manyu Chen;Yuanlai Cui;P. Gassman;R. Srinivasan
中科院分区：
文献类型：
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作者：
Manyu Chen;Yuanlai Cui;P. Gassman;R. Srinivasan
输入数据的质量和流域划分的过程会影响流域模拟中径流预测的准确性。选择上密西西比河流域来评估子流域和/或水文响应单元（HRU）划定以及气候数据集密度对使用水文和水质系统（HAWQS）平台模拟的径流和水平衡分量的影响。五个方案进行了审查，相同的参数集，包括8位和12位水文单位代码，两个层次的HRU阈值和两个气候数据密度。结果表明，1983年至2005年的月径流统计评价是令人满意的，在一些测量站点，但在其他人相对较差时，从8位到12位的子流域，揭示了水文响应划定方案可以在一个大的流域。平均渠道坡度和排水密度显着增加，从8位到12位子流域。这导致了较高的侧流和地下水流量估计，特别是侧流。此外，一个更好的HRU划定往往会产生更多的径流，因为它捕捉了一个精细的流域空间变异水平。对气候数据集的分析表明，更密集的气候数据产生更高的预测径流，特别是在夏季。
The quality of input data and the process of watershed delineation can affect the accuracy of runoff predictions in watershed modeling. The Upper Mississippi River Basin was selected to evaluate the effects of subbasin and/or hydrologic response unit (HRU) delineations and the density of climate dataset on the simulated streamflow and water balance components using the Hydrologic and Water Quality System (HAWQS) platform. Five scenarios were examined with the same parameter set, including 8- and 12-digit hydrologic unit codes, two levels of HRU thresholds and two climate data densities. Results showed that statistic evaluations of monthly streamflow from 1983 to 2005 were satisfactory at some gauge sites but were relatively worse at others when shifting from 8-digit to 12-digit subbasins, revealing that the hydrologic response to delineation schemes can vary across a large basin. Average channel slope and drainage density increased significantly from 8-digit to 12-digit subbasins. This resulted in higher lateral flow and groundwater flow estimates, especially for the lateral flow. Moreover, a finer HRU delineation tends to generate more runoff because it captures a refined level of watershed spatial variability. The analysis of climate datasets revealed that denser climate data produced higher predicted runoff, especially for summer months.