Junchao Gong
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Junchao Gong has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Cheng Yao
College of Hydrology and Water Resources, Hohai University, Nanjing, 210024, China
1 shared publication - Jingbing Li
Hydrology Bureau of Anhui Province, Hefei 230022, China
1 shared publication - Junfu Gong
College of Agricultural Science and Engineering, Hohai University, Nanjing, 210024, China
1 shared publication - Mingkun Sun
Yellow River Engineering Consulting Co., Ltd., Zhengzhou 450003, China
1 shared publication - Yanan Ma
Hydrology Bureau (Information Center) of Huaihe River Commission, Bengbu 233001, China
1 shared publication - Youbing Hu
Hydrology Bureau of the Huaihe Water Conservancy Commission, Bengbu 233001, China
1 shared publication
Latest Publications
- Open Access
The WRF-Driven Grid-Xin’anjiang Model and Its Application in Small and Medium Catchments of China
The distributed Grid-Xin’anjiang (Grid-XAJ) model is very sensitive to the spatial and temporal distribution of data when used in humid and semi-humid small and medium catchments. We used the successive correction method to merge the gauged rainfall with rainfall forecasted by the Weather Research and Forecasting (WRF) model to enhance the spatiotemporal accuracy of rainfall distribution. And we used the Penman–Monteith equation to calculate the potential evapotranspiration (PEPM). Then, we designed two forcing scenarios (WRF-driven rainfall (Wr) + PEPM, WRF-merged rainfall (Wm) + PEPM) to drive the Grid-XAJ model for flood forecasting. We found the WRF-driven Grid-XAJ model held significant potential in flood forecasting. The Grid-XAJ model provided only an approximation of flood hygrographs when driven by scenario Wr + PEPM. The results in scenario Wm + PEPM showed a high degree-of-fit with observed floods with mean Nash–Sutcliffe efficiency coefficient (NSE) values of 0.94 and 0.68 in two catchments. Additionally, scenario Wm + PEPM performed better flood hygrographs than scenario Wr + PEPM. The flood volumes and flow peaks in scenario Wm + PEPM had an obvious improvement compare to scenario Wr + PEPM. Finally, we observed that the model exhibited superior performance in forecasting flood hydrographs, flow peaks, and flood volumes in humid catchments compared with semi-humid catchments.
Open AccessThe WRF-Driven Grid-Xin’anjiang Model and Its Application in Small and Medium Catchments of China
The distributed Grid-Xin’anjiang (Grid-XAJ) model is very sensitive to the spatial and temporal distribution of data when used in humid and semi-humid small and medium catchments. We used the successive correction method to merge the gauged rainfall with rainfall forecasted by the Weather Research and Forecasting (WRF) model to enhance the spatiotemporal accuracy of rainfall distribution. And we used the Penman–Monteith equation to calculate the potential evapotranspiration (PEPM). Then, we designed two forcing scenarios (WRF-driven rainfall (Wr) + PEPM, WRF-merged rainfall (Wm) + PEPM) to drive the Grid-XAJ model for flood forecasting. We found the WRF-driven Grid-XAJ model held significant potential in flood forecasting. The Grid-XAJ model provided only an approximation of flood hygrographs when driven by scenario Wr + PEPM. The results in scenario Wm + PEPM showed a high degree-of-fit with observed floods with mean Nash–Sutcliffe efficiency coefficient (NSE) values of 0.94 and 0.68 in two catchments. Additionally, scenario Wm + PEPM performed better flood hygrographs than scenario Wr + PEPM. The flood volumes and flow peaks in scenario Wm + PEPM had an obvious improvement compare to scenario Wr + PEPM. Finally, we observed that the model exhibited superior performance in forecasting flood hydrographs, flow peaks, and flood volumes in humid catchments compared with semi-humid catchments.