Calibration and validation of the CMADS-Driven SWAT model in the distributed hydrological simulation of the Baihe River Basin in Nanyang

The China Meteorological Assimilation Driving Dataset for the SWAT model (CMADS) has gained widespread use for its accuracy. This study focuses on the Baihe River Basin in Nanyang, using the SWAT tool and CMADS dataset to construct two distributed hydrological models of different scales. The accurac...

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Vydáno v:ISH journal of hydraulic engineering Ročník 31; číslo 1; s. 97 - 107
Hlavní autoři: BiYing, Xu, Abd Rahman, Nor Faiza, Tai, Vin Cent
Médium: Journal Article
Jazyk:angličtina
Vydáno: Taylor & Francis 01.01.2025
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ISSN:0971-5010, 2164-3040, 2164-3040
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Shrnutí:The China Meteorological Assimilation Driving Dataset for the SWAT model (CMADS) has gained widespread use for its accuracy. This study focuses on the Baihe River Basin in Nanyang, using the SWAT tool and CMADS dataset to construct two distributed hydrological models of different scales. The accuracy and effectiveness of these models were evaluated. Parameter uncertainty analysis on a monthly and annual scale was conducted usingSUFI-2 algorithm. The goal was to assess the applicability of the CMADS-driven SWAT model in theBasin by calibrating and validating flow data at both monthly and annual scales. The results form the calibration period (2009-2013) showedmonthly R 2 and NSE values were 0.81 and 0.8, while the annual values were 0.91 and 0.9. During validation(2014-2016), the annual R 2 and NSE values were 0.89 and 0.86, and the monthly values were 0.76 and 0.76, with reasonable values for the P-factor, R-factor, and percentage bias (PBIAS), indicating good model applicability. Seasonal variations in rainfall, surface runoff, water yield, evapotranspiration (ET), and potential evapotranspiration (PET) were analyzed. The results demonstrate that surface runoff and water yield is closely related to precipitation, while ET is largely controlled by land use types, providing insights for water resource management
Bibliografie:ObjectType-Article-1
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ISSN:0971-5010
2164-3040
2164-3040
DOI:10.1080/09715010.2024.2437172