Zhang, S.; Lang, Y.; Yang, F.; Qiao, X.; Li, X.; Gu, Y.; Yi, Q.; Luo, L.; Duan, Q. Hydrological Modeling in the Upper Lancang-Mekong River Basin Using Global and Regional Gridded Meteorological Re-Analyses. Water2023, 15, 2209.
Zhang, S.; Lang, Y.; Yang, F.; Qiao, X.; Li, X.; Gu, Y.; Yi, Q.; Luo, L.; Duan, Q. Hydrological Modeling in the Upper Lancang-Mekong River Basin Using Global and Regional Gridded Meteorological Re-Analyses. Water 2023, 15, 2209.
Zhang, S.; Lang, Y.; Yang, F.; Qiao, X.; Li, X.; Gu, Y.; Yi, Q.; Luo, L.; Duan, Q. Hydrological Modeling in the Upper Lancang-Mekong River Basin Using Global and Regional Gridded Meteorological Re-Analyses. Water2023, 15, 2209.
Zhang, S.; Lang, Y.; Yang, F.; Qiao, X.; Li, X.; Gu, Y.; Yi, Q.; Luo, L.; Duan, Q. Hydrological Modeling in the Upper Lancang-Mekong River Basin Using Global and Regional Gridded Meteorological Re-Analyses. Water 2023, 15, 2209.
Abstract
Multisource meteorological re-analyses are the most reliable forcing data for driving hydrological models to simulate streamflow. We aimed to assess the different hydrological responses through hydrological modeling in the upper Lancang-Mekong River Basin (LMRB) using the two gridded meteorological datasets, climate forecast system reanalysis (CFSR) and the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS). We selected the Pearson’s correlation coefficient (R), percent bias (PBIAS), and root mean square error (RMSE) indices to compare the six meteorological variables of the two datasets. The spatial distributions of the statistical indicators in the CFSR and CMADS, namely, the R, PBIAS, and RMSE values, were different. Furthermore, the soil and water assessment tool plus (SWAT+) model was used to do hydrological modeling based on CFSR and CMADS meteorological re-analyses in the upper LMRB. Different meteorological datasets resulted in significant differences of hydrological responses, which reflected by different sensitive parameters and their optimal value. These different calibrated optimal values of sensitive parameters further lead to the different simulated water balance components between CFSR- and CMADS-based SWAT+ model. These findings can help in a better understanding of the strengths and weaknesses of different meteorological re-analysis datasets and the roles on the hydrological modeling.
Environmental and Earth Sciences, Water Science and Technology
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