Error adjustment of TMPA satellite precipitation estimates and assessment of their hydrological utility in the middle and upper Yangtze River Basin, China

Autor: Xiaofan Zeng, Sun Ao, Dongwei Gui, Jie Xue, Huaiwei Sun, Weihong Liao, Zhang Yiran, Na Zhao, Dong Yan
Rok vydání: 2019
Předmět:
Zdroj: Atmospheric Research. 216:52-64
ISSN: 0169-8095
DOI: 10.1016/j.atmosres.2018.09.021
Popis: Accurate and continuous precipitation amount and spatiotemporal distribution data are essential for water resource management, flood forecasting, and simulation of hydrological processes. Satellite-based precipitation products (SPPs) with high spatio-temporal resolution have been shown to represent a potential alternative data source for ungauged regions. The objective of this study was to evaluate the performance and the hydrological utility of a SPP (TMPA 3B42V7) driving two different hydrological models (Xinanjiang and Tank models) for runoff monitoring in the Yangtze River Basin in China. The results indicate that the TMPA precipitation data can be applied for runoff simulation compared to the ground-gauged data, and the adjustment of TMPA data can be helpful to improve the accuracy of rainfall data at the point and region scale. Two adjustment equations were proposed in this study (the geographical adjustment model and the additive error adjustment model), and the Bias had significantly improved, which solve the problem of data undervaluation. In addition, the TMPA precipitation data show an acceptable accuracy for driving the Xinanjiang and Tank models for the purpose of runoff monitoring. By utilizing the measured runoff data from the four main stations of the Yangtze River Basin, an excellent performance was shown in Xinanjiang model with the determined coefficient of 0.945–0.966 and 0.900–0.922 for the calibration and validation periods, respectively. This was similar for the Tank model with values of 0.943–0.963 and 0.917–0.920 for the calibration and validation periods, respectively. Based on these results, TMPA data would be helpful not only for understanding the characteristics of precipitation at high spatial and temporal resolution, but also for estimating near-real time runoff patterns.
Databáze: OpenAIRE
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