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Evaluation of Flood Prediction Capability of the WRF-Hydro Model Based on Multiple Forcing Scenarios

1
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
2
Information Center (Hydrology Monitor and Forecast Center), Ministry of Water Resources, Beijing 100000, China
3
School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA
*
Authors to whom correspondence should be addressed.
Water 2020, 12(3), 874; https://doi.org/10.3390/w12030874
Received: 18 January 2020 / Revised: 22 February 2020 / Accepted: 18 March 2020 / Published: 20 March 2020
(This article belongs to the Section Hydrology)
The Weather Research and Forecasting (WRF)-Hydro model as a physical-based, fully-distributed, multi-parameterization modeling system easy to couple with numerical weather prediction model, has potential for operational flood forecasting in the small and medium catchments (SMCs). However, this model requires many input forcings, which makes it difficult to use it for the SMCs without adequate observed forcings. The Global Land Data Assimilation System (GLDAS), the WRF outputs and the ideal forcings generated by the WRF-Hydro model can provide all forcings required in the model for these SMCs. In this study, seven forcing scenarios were designed based on the products of GLDAS, WRF and ideal forcings, as well as the observed and merged rainfalls to assess the performance of the WRF-Hydro model for flood simulation. The model was applied to the Chenhe catchment, a typical SMC located in the Midwestern China. The flood prediction capability of the WRF-Hydro model was also compared to that of widely used Xinanjiang model. The results show that the three forcing scenarios, including the GLDAS forcings with observed rainfall, the WRF forcings with observed rainfall and GLDAS forcings with GLDAS-merged rainfall, are optimal input forcings for the WRF-Hydro model. Their mean root mean square errors (RMSE) are 0.18, 0.18 and 0.17 mm/h, respectively. The performance of the WRF-Hydro model driven by these three scenarios is generally comparable to that of the Xinanjiang model (RMSE = 0.17 mm/h). View Full-Text
Keywords: the WRF-Hydro modeling system; flood prediction capability; multiple forcing scenarios; small and medium catchments; distributed hydrological model; the Xinanjiang model the WRF-Hydro modeling system; flood prediction capability; multiple forcing scenarios; small and medium catchments; distributed hydrological model; the Xinanjiang model
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Sun, M.; Li, Z.; Yao, C.; Liu, Z.; Wang, J.; Hou, A.; Zhang, K.; Huo, W.; Liu, M. Evaluation of Flood Prediction Capability of the WRF-Hydro Model Based on Multiple Forcing Scenarios. Water 2020, 12, 874.

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