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Hydrological Modeling in the Upper Lancang-Mekong River Basin Using Global and Regional Gridded Meteorological Re-Analyses.
- Source :
- Water (20734441); Jun2023, Vol. 15 Issue 12, p2209, 24p
- Publication Year :
- 2023
-
Abstract
- Multisource meteorological re-analyses provide the most reliable forcing data for driving hydrological models to simulate streamflow. We aimed to assess different hydrological responses through hydrological modeling in the upper Lancang-Mekong River Basin (LMRB) using two gridded meteorological datasets, Climate Forecast System Re-analysis (CFSR) and the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (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 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 perform hydrological modeling based on CFSR and CMADS meteorological re-analyses in the upper LMRB. The different meteorological datasets resulted in significant differences in hydrological responses, reflected by variations in the sensitive parameters and their optimal values. The differences in the calibrated optimal values for the sensitive parameters led to differences in the simulated water balance components between the CFSR- and CMADS-based SWAT+ models. These findings could help improve the understanding of the strengths and weaknesses of different meteorological re-analysis datasets and their roles in hydrological modeling. [ABSTRACT FROM AUTHOR]
- Subjects :
- HYDROLOGIC models
STANDARD deviations
WATERSHEDS
PEARSON correlation (Statistics)
Subjects
Details
- Language :
- English
- ISSN :
- 20734441
- Volume :
- 15
- Issue :
- 12
- Database :
- Complementary Index
- Journal :
- Water (20734441)
- Publication Type :
- Academic Journal
- Accession number :
- 164684428
- Full Text :
- https://doi.org/10.3390/w15122209