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Evaluation of Hydrological Application of CMADS in Jinhua River Basin, China
Evaluating the hydrological application of reanalysis datasets is of practical importance for the design of water resources management and flood controlling facilities in regions with sparse meteorological data. This paper compared a new reanalysis dataset named CMADS with gauge observations and investigated the performance of the hydrological application of CMADS on daily streamflow, evapotranspiration, and soil moisture content simulations. The results show that: CMADS can represent meteorological elements including precipitation, temperature, relative humidity, and wind speed reasonably for both daily and monthly temporal scales while underestimates precipitation compared with gauge observations slightly (<15%). The hydrological model using CMADS dataset as meteorological inputs can capture the daily streamflow chracteristics well overall (with a NS value of 0.56 during calibration period and 0.61 during validation period) but underestimates streamflow obviously (with a BIAS of − 42.42 % during calibration period and a BIAS of − 33.29 % during validation period). The underestimation of streamflow simulated with CMADS dataset is more seriously in dry seasons ( − 48.40 %) than that in wet seasons ( − 39.41 %) for calibration period. The model driven by CMADS estimates evapotranspiration and soil moisture content well compared with the model driven by gauge observations.
Evaluation of Hydrological Application of CMADS in Jinhua River Basin, China
Evaluating the hydrological application of reanalysis datasets is of practical importance for the design of water resources management and flood controlling facilities in regions with sparse meteorological data. This paper compared a new reanalysis dataset named CMADS with gauge observations and investigated the performance of the hydrological application of CMADS on daily streamflow, evapotranspiration, and soil moisture content simulations. The results show that: CMADS can represent meteorological elements including precipitation, temperature, relative humidity, and wind speed reasonably for both daily and monthly temporal scales while underestimates precipitation compared with gauge observations slightly (<15%). The hydrological model using CMADS dataset as meteorological inputs can capture the daily streamflow chracteristics well overall (with a NS value of 0.56 during calibration period and 0.61 during validation period) but underestimates streamflow obviously (with a BIAS of − 42.42 % during calibration period and a BIAS of − 33.29 % during validation period). The underestimation of streamflow simulated with CMADS dataset is more seriously in dry seasons ( − 48.40 %) than that in wet seasons ( − 39.41 %) for calibration period. The model driven by CMADS estimates evapotranspiration and soil moisture content well compared with the model driven by gauge observations.
Evaluation of Hydrological Application of CMADS in Jinhua River Basin, China
Zhenghui Zhou (author) / Xichao Gao (author) / Zhiyong Yang (author) / Jie Feng (author) / Chao Meng (author) / Zhi Xu (author)
2019
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
| DOAJ | 2019
Evaluation of Potential Evapotranspiration Based on CMADS Reanalysis Dataset over China
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