Zhao Haigen等:Evaluating the suitability of TRMM satellite rainfall data for hydrological simulation using a distributed hydrological model in the Weihe River catchment in China
被阅读 1140 次
2015-02-02

Evaluating the suitability of TRMM satellite rainfall data for hydrological simulation using a distributed hydrological model in the Weihe River catchment in China
作者:Zhao, HG (Zhao Haigen)[ 1,2,3,4 ] ; Yang, ST (Yang Shengtian)[ 1,2,3 ] ; Wang, ZW (Wang Zhiwei)[ 1,2,3 ] ; Zhou, X (Zhou Xu)[ 1,2,3 ] ; Luo, Y (Luo Ya)[ 1,2,3 ] ; Wu, LN (Wu Linna)[ 1,2,3 ]
JOURNAL OF GEOGRAPHICAL SCIENCES
卷: 25  期: 2  页: 177-195
DOI: 10.1007/s11442-015-1161-3
出版年: FEB 2015
摘要
The objective of this study is to quantitatively evaluate Tropical Rainfall Measuring Mission (TRMM) data with rain gauge data and further to use this TRMM data to drive a Distributed Time-Variant Gain Model (DTVGM) to perform hydrological simulations in the semi-humid Weihe River catchment in China. Before the simulations, a comparison with a 10-year (2001-2010) daily rain gauge data set reveals that, at daily time step, TRMM rainfall data are better at capturing rain occurrence and mean values than rainfall extremes. On a monthly time scale, good linear relationships between TRMM and rain gauge rainfall data are found, with determination coefficients R (2) varying between 0.78 and 0.89 for the individual stations. Subsequent simulation results of seven years (2001-2007) of data on daily hydrological processes confirm that the DTVGM when calibrated by rain gauge data performs better than when calibrated by TRMM data, but the performance of the simulation driven by TRMM data is better than that driven by gauge data on a monthly time scale. The results thus suggest that TRMM rainfall data are more suitable for monthly streamflow simulation in the study area, and that, when the effects of recalibration and the results for water balance components are also taken into account, the TRMM 3B42-V7 product has the potential to perform well in similar basins.
通讯作者地址: Yang, ST (通讯作者)
Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.
地址:
[ 1 ] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
[ 2 ] Chinese Acad Sci, Inst Remote Sensing Applicat, Beijing 100875, Peoples R China
[ 3 ] Beijing Normal Univ, Sch Geog, Beijing Key Lab Remote Sensing Environm & Digital, Res Ctr Remote Sensing & GIS, Beijing 100875, Peoples R China
[ 4 ] MEP, South China Inst Environm Sci, Guangzhou 510655, Guangdong, Peoples R China