| Title: | Evaluation of gridded precipitation data for driving SWAT Model in area upstream of Three Gorges Reservoir |
| Authors: | Yang, Y., G. Wang, L. Wang, J. Yu and Z. Xu |
| Year: | 2014 |
| Journal: | PLoS ONE |
| Volume (Issue): | 9(11) |
| Pages: | 1-15 |
| Article ID: | |
| DOI: | 10.1371/journal.pone.0112725 |
| URL (non-DOI journals): | |
| Model: | SWAT |
| Broad Application Category: | hydrologic only |
| Primary Application Category: | climate data effects |
| Secondary Application Category: | hydrologic assessment |
| Watershed Description: | 1,098 km^2 Dong River and 366 km^2 Puli River, located in the upstream portion of the Three Gorges Reservoir region in central China |
| Calibration Summary: | |
| Validation Summary: | |
| General Comments: | |
| Abstract: | Gridded precipitation data are becoming an important source for driving hydrologic models to achieve stable and valid
simulation results in different regions. Thus, evaluating different sources of precipitation data is important for improving the applicability of gridded data. In this study, we used three gridded rainfall datasets: 1) National Centers for Environmental Prediction - Climate Forecast System Reanalysis (NCEP-CFSR); 2) Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE); and 3) China trend - surface reanalysis (trend surface) data. These are compared with monitoring precipitation data for driving the Soil and Water Assessment Tool in two basins upstream of Three Gorges Reservoir (TGR) in China. The results of one test basin with significant topographic influence indicates that all the gridded data have poor abilities in reproducing hydrologic processes with the topographic influence on precipitation quantity and distribution. However, in a relatively flat test basin, the APHRODITE and trend surface data can give stable and desirable results. The results of this study suggest that precipitation data for future applications should be considered comprehensively in the TGR area, including the influence of data density and topography. |
| Language: | English |
| Keywords: | |