The Application of a Meteo-hydrological Forecasting System with Rainfall Bias Correction in a Small and Medium-sized Catchment |
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| Authors: | GAO Yu-fang WU Yu-qing CHEN Yao-deng YU Wei GU Tian-wei and WU Ya-zhen |
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| Institution: | School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044 China,School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044 China,School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China,Weather Tech Services, LLC, Longmont, CO 80503 USA,School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China;
LEIHUA Electronic and Technology Research Institute, Aviation Industry Corporation of China, Wuxi, Jiangsu 214000 China and School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044 China |
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| Abstract: | 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. |
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| Keywords: | streamflow forecast bias correction meteo-hydrological forecasting model WRF WRF-Hydro |
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