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Assessment of CFSR and CMADS Weather Data for Capturing Extreme Hydrologic Events in the Fuhe River Basin of the Poyang Lake
Author(s) -
Lu Jianzhong,
Liu Zixuan,
Liu Weizhe,
Chen Xiaoling,
Zhang Ling
Publication year - 2020
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/1752-1688.12866
Subject(s) - soil and water assessment tool , climate forecast system , environmental science , streamflow , precipitation , climatology , swat model , hydrological modelling , drainage basin , data assimilation , hydrology (agriculture) , meteorology , geology , geography , cartography , geotechnical engineering
Evaluating the accuracy of gridded weather data is important because these data significantly affect the results of hydrologic simulations. In this study, we evaluated the applicability of Climate Forecast System Reanalysis (CFSR) and China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) datasets for capturing the hydrologic extreme events in the Fuhe River Basin (FRB) of the Poyang Lake, a typical humid area in eastern China. First, both the CFSR and CMADS temperature and precipitation data obtained from 2008 to 2013 were validated using ground‐based meteorological station data. Then, the SWAT model was driven by the CFSR and CMADS datasets for hydrologic predictions. The results show that both CFSR and CMADS temperature data are of high quality. The CMADS data underestimate precipitation, whereas CFSR data overestimate precipitation. Both datasets have their own advantage and disadvantage to detect extreme rainfall events. For the FRB case study, SWAT models driven by two datasets yield good streamflow simulation results. The CMADS‐driven model performs slightly better than CFSR‐driven model in predicting extreme streamflow events in the simulation time‐period (2009–2013). In general, both the CFSR and CMADS data can be used to obtain satisfactory simulation results for hydrologic predictions. They provide alternatives that enable quickly implementing a hydrologic model in data‐scarce areas.