基于分布式水文模型的小流域山洪预报方法与应用

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。

     

The Application of a Meteo-hydrological Forecasting System with Rainfall Bias Correction in a Small and Medium-sized Catchment

Meteo-hydrological forecasting models are an effective way to generate high-resolution gridded rainfall data for water source research and flood forecast. The quality of rainfall data in terms of both intensity and distribution is very important for establishing a reliable meteo-hydrological forecasting model. To improve the accuracy of rainfall data, the successive correction method is introduced to correct the bias of rainfall, and a meteo-hydrological forecasting model based on WRF and WRF-Hydro is applied for streamflow forecast over the Zhanghe River catchment in China. The performance of WRF rainfall is compared with the China Meteorological Administration Multi-source Precipitation Analysis System (CMPAS), and the simulated streamflow from the model is further studied. It shows that the corrected WRF rainfall is more similar to the CMPAS in both temporal and spatial distribution than the original WRF rainfall. By contrast, the statistical metrics of the corrected WRF rainfall are better. When the corrected WRF rainfall is used to drive the WRF-Hydro model, the simulated streamflow of most events is significantly improved in both hydrographs and volume than that of using the original WRF rainfall. Among the studied events, the largest improvement of the NSE is from -0.68 to 0.67. It proves that correcting the bias of WRF rainfall with the successive correction method can greatly improve the performance of streamflow forecast. In general, the WRF / WRF-Hydro meteo-hydrological forecasting model based on the successive correction method has the potential to provide better streamflow forecast in the Zhanghe River catchment.     

基于WRF模式的清江流域降雨-径流模拟研究

基于WRF模式,采用4层嵌套方案,选取3种积云参数化方案和7种微物理方案组成21种组合,对清江流域2016—2018年6—10月6次典型降雨事件进行数值预报,结合CMORPH卫星-地面自动站-雷达三源融合降水产品,采用TS评分和FSS评分,分析不同分辨率和云微物理方案的降雨预报效果;基于较优组合方案的WRF模式与WRF-Hydro水文模式耦合进行径流模拟,分析WRF模式在水文模拟中的应用效果。结果表明:3 km和1 km分辨率对降雨中心位置及强度预报的差别不大,对降雨落区都有较好的预报能力;在积云参数化方案中,KF方案和BMJ方案的降雨预报效果优于GF方案;在微物理方案中,WSM3、WSM5、WSM6、Thompson方案的预报结果与融合数据有较好的一致性;基于较优组合方案BMJ_WSM3,将WRF模式与WRF-Hydro模式耦合,耦合模式能较好地模拟洪水过程,径流模拟相关系数都在0.67以上,且NSE最高可达0.79。