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Journal of Geographical Sciences    2015, Vol. 25 Issue (2) : 177-195     DOI: 10.1007/s11442-015-1161-3
Orginal Article |
Evaluating the suitability of TRMM satellite rainfall data for hydrological simulation using a distributed hydrological model in the Weihe River catchment in China
Haigen ZHAO1,2,3(),Shengtian YANG1,2,*(),Zhiwei WANG1,2,Xu ZHOU1,2,Ya LUO1,2,Linna WU1,2
1. State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing Applications of Chinese Academy of Sciences, Beijing 100875, China
2. Research Center for Remote Sensing and GIS, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, School of Geography, Beijing Normal University, Beijing100875, China
3. South China Institute of Environmental Sciences, MEP, Guangzhou 510655, China
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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 R2 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.

Keywords rainfall      TRMM      distributed hydrological model      DTVGM      hydrological simulation      Weihe River catchment     
Fund:National Key Technology P&D Program, No.2012BAB02B00;The Fundamental Research Funds for the Central Universities
Corresponding Authors: Shengtian YANG     E-mail:;
About author: Zhao Haigen (1983-), PhD Candidate, specialized in hydrological simulation and remote sensing. E-mail:
Issue Date: 24 June 2015
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Haigen ZHAO
Shengtian YANG
Zhiwei WANG
Linna WU
Cite this article:   
Haigen ZHAO,Shengtian YANG,Zhiwei WANG, et al. Evaluating the suitability of TRMM satellite rainfall data for hydrological simulation using a distributed hydrological model in the Weihe River catchment in China[J]. Journal of Geographical Sciences, 2015, 25(2): 177-195.
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Figure 1  Location of the Weihe River catchment in the Yellow River Basin and the distribution of stations
Figure 2  Map of land use in the study area
Figure 3  Soil types in the study area
Figure 4  Framework of the DTVGM (a) and vertical profile of hydrological processes (b) in a grid cell
Name Min Max Description Initial value
g1 0.20 0.50 Coefficient of time-variant gain factor 0.25
Ra 0.10 0.60 Initial ratio of soil water in the soil layer 0.20
Kr 0.0001 0.001 Storage-outflow coefficient for the soil layer 0.00015
C1 0.001 1.00 Dimensionless fitting parameter for ET 0.40
C2 0.60 0.80 Dimensionless fitting parameter for ET 0.60
C3 5.00 20.00 Dimensionless fitting parameter for ET 15.00
Table 1  Parameters optimized in the DTVGM hydrological simulation
Year Areal average (mm/d) Standard deviation (mm) Maximum daily rainfall (mm/d) Maximum 5-day rainfall (mm/5d) Annual rainfall (mm/y)
Gauge TRMM Gauge TRMM Gauge TRMM Gauge TRMM Gauge TRMM
2001 1.29 1.35 2.99 2.91 23.01 21.77 94.15 87.76 468.91 496.30
2002 1.26 1.24 3.36 2.90 36.12 23.91 105.36 94.05 458.57 456.12
2003 2.15 2.14 4.87 4.34 36.82 31.14 142.07 118.28 782.92 781.17
2004 1.31 1.35 3.19 2.94 23.92 27.73 90.46 86.92 480.03 493.15
2005 1.56 1.73 3.84 3.94 27.04 32.03 120.48 121.09 570.38 631.24
2006 1.52 1.36 3.32 2.68 21.62 18.82 94.00 77.42 555.04 497.86
2007 1.58 1.52 3.54 3.65 25.73 35.32 99.00 121.90 575.02 556.21
2008 1.26 1.44 3.09 3.42 22.67 25.64 88.16 109.00 462.70 525.65
2009 1.40 1.47 3.07 3.25 23.26 24.62 85.92 99.89 512.63 525.22
2010 1.61 1.55 4.27 3.42 44.29 22.65 133.07 95.49 589.15 565.08
Table 2  Comparison of statistical indices between averaged TRMM rainfall and rain gauge rainfall
Figure 5  Distribution of daily rainfall in different rainfall categories and relative contribution to total rainfall, 2001-2010
Figure 6  Scatter plots of monthly rainfall from TRMM and rain gauge data for the seven national meteorological stations and the areal average data
Scenario I Scenario II
Calibration period
Gauge 0.69 18.71 0.84 0.68 14.37 0.83
TRMM 0.59 21.96 0.78 0.60 18.02 0.78
Validation period
Gauge 0.55 -18.85 0.78 0.52 -23.85 0.74
TRMM 0.48 -15.38 0.71 0.50 -19.91 0.72
Table 3  Comparison of daily observed and simulated streamflow under two calibration scenarios
Figure 7  Comparison of observed and simulated hydrographs using daily rain gauge data at Xianyang hydrological station
Figure 8  Comparison of observed and simulated hydrographs using daily TRMM data at Xianyang hydrological station
Scenario I Scenario II
Gauge 0.64 -0.84 0.83 0.64 -5.58 0.82
TRMM 0.67 2.52 0.84 0.68 -1.78 0.84
Table 4  Comparison of monthly observed and simulated streamflow under two calibration scenarios
Figure 9  Comparison of observed and simulated discharge with monthly TRMM and rain gauge data at Xianyang hydrological station
Parameters Gauge TRMM
g1 0.23 0.22
Kr 0.0006 0.0002
C1 0.50 0.52
C2 0.78 0.66
C3 10.00 9.96
Ra 0.15 0.51
Table 5  DTVGM model parameter values calibrated with different rainfall inputs for the calibration period
Figure 10  Relative changes to water balance components in models based on rain gauge and TRMM rainfall data
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