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.
Highlights
Precipitation data are generally recognized as the most important driving data for hydrologic models
Bao et al [11] successfully evaluated four different data (NCEP-NCAR reanalysis, NCEP-CFSR, ERA40, and ERA-Interim) products based on an enhanced observed network, and the results showed that the performance of NCEPCFSR and ERA-Interim data were superior for the Tibetan Plateau
The sensitive rank in the Puli river basin show that the gridded datasets presents more consistent results compared with the monitoring data, especially for the CH_K2 and CH_N2, the ranks are different in monitoring data and gridded data
Summary
Precipitation data are generally recognized as the most important driving data for hydrologic models. With development of modern observation and massive computing technologies, the estimation of precipitation based on combination of multi-source data (historical observed, radar and satellite) has become a feasible means for extending model applications. Chappell [4] used TRMM data, station observed data and a kernel-based statistical blending algorithm to produce 5-km resolution gridded precipitation data for Australia. Li et al [5] and Huang et al [6] used spline interpolation and the trend surface methods to generate 5-km resolution gridded precipitation data for New Zealand and China. With the expanded application requirements, these data have been widely used for hydrologic modeling in various studies and regions [7,8,9]
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