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WRF-Hydro模式结合不同降水产品模拟清江流域径流的效果分析

Effect analysis of WRF-Hydro model combined with different precipitation products to simulate runoff in the Qingjiang River Basin

  • 摘要: 以2016—2017年清江流域2次大径流事件和3次小径流事件为研究对象,首先,分析了CMORPH卫星-地面自动站-雷达三源融合降水产品(CMPAS)、中国全球大气再分析产品(CRA)和雨量站降雨资料(Gauge)3种产品的降水时空分布特征;然后,基于径流事件实况与不同降水产品的特点,设计了两种径流模拟试验方案,对3种产品的降水数据输入WRF-Hydro模式的径流模拟结果进行分析。最后,结合降水时空分布差异,探讨3种降水产品在径流模拟中的应用效果。结果表明: (1)5次径流事件中,3种降水产品探测的降雨中心、雨带位置和走向大致相同,流域内面雨量随时间变化趋势较为一致。(2)两种试验方案下,3种降水产品均能模拟出各次径流事件。对大径流事件,CMPAS的模拟效果最优,相关系数均在0.76以上、纳什效率系数均在0.63以上;对小径流事件,Gauge的模拟效果最优,相关系数均在0.75以上,纳什效率系数均在0.48以上;CRA无论对大、小径流事件,其模拟效果相对都较差,但参数经重新率定后,其模拟效果明显改善。(3)3种降水产品经重新率定参数后(方案2),其在峰现、涨水、退水各时段的径流模拟效果改善不同。对小径流事件,相对涨水和退水时段,各产品在峰现时段的模拟效果改善较为明显,而对大径流事件,3种降水产品在各时段的模拟效果均无明显改善。

     

    Abstract: Taking two large runoff events and three small runoff events in the Qingjiang River Basin from 2016 to 2017 as the research objects, we analyze the spatial and temporal distributions of precipitation from the three products of CMORPH satellite-gauge-radar merged precipitation product (CMPAS), China global atmospheric reanalysis product (CRA), and rain gauge precipitation (Gauge). Then, based on the characteristics of runoff events and different precipitation products, the two experiments are designed, and the runoff simulations of WRF-Hydro model driven by the precipitation data from the three precipitation products are analyzed. Finally, combined with the spatiotemporal variations of precipitation, we explore the effectiveness of the three precipitation products in the runoff simulations. The main results are as follow. (1) The rainfall centers and the rainband locations and orientations detected with the three precipitation products are similar for the five runoff events, and the temporal trends of rainfall averaged over the Qingjiang River Basin are consistent. (2) All three precipitation products can produce the runoff events in both experiments. For large runoff events, CMPAS performs best, with correlation coefficients above 0.76 and Nash efficiency coefficients above 0.63. For small runoff events, Gauge performs best, with correlation coefficients above 0.75 and Nash efficiency coefficients above 0.48. CRA has a relatively poor performance for both large and small runoff events, but its simulations are significantly improved after calibrating parameters. (3) After calibrating the parameters in the second experiment, the simulation results of runoff are improved to different extents for the peaking, rising, and falling periods of each event. For the small runoff events, compared with the rising and falling periods, the simulations driven by each precipitation product for the peaking period is significantly improved, while, for the large runoff events, the simulation results driven by the three precipitation products for each period is not significantly improved.

     

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