Author(s): Krittikorn Thanawisitsawat; Chun Zhao
Linked Author(s):
Keywords: SWAT model; Runoff simulation; Reanalysis; Yangtze River
Abstract: Data scarcity is a key obstacle that often limits the potential of hydrological modeling around the world. In particular, in remote and isolated areas that remain largely ungauged, ground observations or reanalysis products could be used to replace missing data. Reanalysis datasets provide a reliable reanalysis of climate input data for hydrological modeling. However, the performance of different datasets needs to be thoroughly tested. This study evaluates the performance of the Climate Research Unit Timeseries (CRU), the NASA Prediction of Worldwide Energy Resources (NASA Power), and the China Meteorological Administration (CMA) by using monthly precipitation datasets from 3 meteorological stations in the headwater area of the Yangtze River over the Qinghai-Tibetan Plateau (QTP), which were used as input data for the SWAT model for runoff simulation and evaluation, respectively. The best convergence of the precipitation aspect was achieved for a combination of CRU and CMA, compared with the coefficient of determination (R2) of 0.93 and the Nash-Sutcliffe coefficient (NSE) of 0.91. The runoff simulation results from the three different datasets provide corresponding performance indicators. The CRU runoff reanalysis dataset compared with the observed data can adequately fulfill the data requirements for runoff simulations, while CMA and NASA Power have slightly poorer performance than CRU, respectively. It can be inferred that the accuracy of runoff simulations depends on the accuracy of precipitation. This research can provide a reference for selecting meteorological data products and optimization methods for hydrological process simulation in areas with few meteorological stations.
Year: 2024