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地理学报(英文版)  2015, Vol. 25 Issue (2): 177-195    DOI: 10.1007/s11442-015-1161-3
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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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Abstract

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.

Key wordsrainfall    TRMM    distributed hydrological model    DTVGM    hydrological simulation    Weihe River catchment
收稿日期: 2014-01-21      出版日期: 2015-06-24
作者简介: Zhao Haigen (1983-), PhD Candidate, specialized in hydrological simulation and remote sensing. E-mail: zhaohaigen1983@163.com
引用本文:   
. [J]. 地理学报(英文版), 2015, 25(2): 177-195.
Haigen ZHAO,Shengtian YANG,Zhiwei WANG,Xu ZHOU,Ya LUO,Linna WU. Evaluating the suitability of TRMM satellite rainfall data for hydrological simulation using a distributed hydrological model in the Weihe River catchment in China. Journal of Geographical Sciences, 2015, 25(2): 177-195.
链接本文:  
http://www.geogsci.com/CN/10.1007/s11442-015-1161-3      或      http://www.geogsci.com/CN/Y2015/V25/I2/177
Figure 1  
Figure 2  
Figure 3  
Figure 4  
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  
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  
Figure 5  
Figure 6  
Precipitation
products
Scenario I Scenario II
NSCE WBE (%) CC NSCE WBE (%) CC
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  
Figure 7  
Figure 8  
Precipitation
products
Scenario I Scenario II
NSCE WBE (%) CC NSCE WBE (%) CC
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  
Figure 9  
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  
Figure 10  
1 Allen R G, Pereira L S, Raes Det al., 1998. Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements. FAO Irrigation and Drainage.
2 Aston A R, 1979. Rainfall interception by eight small trees.Journal of Hydrology, 42: 383-396.
3 Beskow S, Mello C R, Norton L Det al., 2011. Performance of a distributed semi-conceptual hydrological model under tropical watershed conditions.Catena, 86: 160-171.
4 Beven K J, Freer J, 2001. Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology.Journal of Hydrology, 249(1-4): 11-29.
5 Bitew M M, Gebremichael M, Gebremichael L Tet al., 2011. Evaluation of high-resolution satellite rainfall products through streamflow simulation in a hydrological modeling of a small mountainous watershed in Ethiopia.Journal of Hydrometeorology, 13(1): 338-350.
6 Chen Y, Ebert E E, Walsh K J Eet al., 2013. Evaluation of TRMM 3B42 precipitation estimates of tropical cyclone rainfall using PACRAIN Data.Journal of Geophysical Research - Atmospheres, 118(5): 2184-2196.
7 Chiu L S, North G R, McConnell Aet al., 1990. Rain estimation from satellites: Effect of finite field of view.J. Geophys. Res., 95: 2177-2186.
8 Eckhardt K, Fohrer N, Frede H, 2005. Automatic model calibration.Hydrological Processes, 19(3): 651-658.
9 Gourley J J, Vieux B E, 2006. A method for identifying sources of model uncertainty in rainfall-runoff simulations.Journal of Hydrology, 327(1/2): 68-80.
10 Guillermo Q, Tabios III G Q, Salas J D, 1985. A comparative analysis of techniques for spatial interpolation of precipitation.Journal of the American Water Resources Association, 21: 365-380.
11 Huffman G J, Adler R F, Bolvin D Tet al., 2007. The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales.Journal of Hydrometeorology, 8: 38-55.
12 Jiang S, Ren L L, Hong Yet al., 2012. Comprehensive evaluation of multi-satellite precipitation products with a dense rain gauge network and optimally merging their simulated hydrological flows using the Bayesian model averaging method.Journal of Hydrology, 452/453: 213-225.
13 Joyce R J, Janowiak J E, Arkin P Aet al., 2004. CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution.Journal of Hydrometeorology, 5(3): 487-503.
14 Karaseva M O, Prakash S, Gairola R M, 2012. Validation of high-resolution TRMM-3B43 precipitation product using rain gauge measurements over Kyrgyzstan.Theoretical and Applied Climatology, 108: 147-157.
15 Kidd C, Kniveton D R, Todd M C, 2003. Satellite rainfall estimation using combined passive microwave and infrared algorithms.Journal of Hydrometeorology, 4(6): 1088-1104.
16 Kirstetter P E, Hong Y, Gourley J Jet al., 2013. Comparison of TRMM 2A25 products, Version 6 and Version 7, with NOAA/NSSL ground radar-based national mosaic QPE.Journal of Hydrometeorology, 14: 661-669.
17 Kummerow C, Simpson J, Thiele Oet al., 2000. The status of the Tropical Rainfall Measuring Mission (TRMM) after two years in orbit.Journal of Applied Meteorology, 39(1): 1965-1982.
18 Kurtzman D, Navon S, Morin E, 2009. Improving interpolation of daily precipitation for hydrologic modeling: Spatial patterns of preferred interpolators.Hydrological Processes, 23: 3281-3291.
19 Li L, Xia J, Xu C Yet al., 2010. Evaluation of the subjective factors of the GLUE method and comparison with the formal Bayesian method in uncertainty assessment of hydrological models.Journal of Hydrology, 390: 210-221.
20 Li X H, Zhang Q, Xu C X, 2012. Suitability of the TRMM satellite rainfalls in driving a distributed hydrological model for water balance computations in Xinjiang catchment, Poyang Lake Basin.Journal of Hydrology, 426/427: 27-28.
21 Liu Y, Hu A Y, 2006. Changes of precipitation characters along Weihe Basin in 50 years and its influence on water resources.Journal of Arid Land Resources and Environment, 20(1): 85-87. (in Chinese)
22 Moriasi D N, Arnold J G, Van Lew M Wet al., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations.ASABE, 50(3): 885-900.
23 Nair S, Srinivasan G, Nemani R, 2009. Evaluation of multi-satellite TRMM derived rainfall estimates over a western state of India.Journal of the Meteorological Society of Japan, 87(6): 927-939.
24 Narayanan M S, Shah S, Kishtawal C Met al., 2005. Validation of TRMM merge daily rainfall with IMD rain gauge analysis over Indian land mass. Technical Report, Space Applications Centre, Ahmedabad, India.
25 Nijssen B, Lettenmaier D P, 2004. Effect of precipitation sampling error on simulated hydrological fluxes and states: Anticipating the global precipitation measurement satellites.J. Geophys. Res., 109, D02103, doi:
doi: 10.1029/2003JD003497
26 Nilson T, 1971. A theoretical analysis of the frequency of gaps in plant stands.Journal of Agricultural Meteorology, 8: 25-38.
27 Rahman H, Sengupta D, 2007. Preliminary comparison of daily rainfall from satellites and Indian gauge data. CAOS Technical Report No. 2007AS1, Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bangalore-12.
28 Rozante J R, Moreira D S, Gustavo Let al, 2010. Combining TRMM and surface observations of precipitation: Technique and validation over South America.Weather and Forecasting, 25(3): 885-894.
29 Shin D B, North G R, 2000. Errors incurred in sampling a cyclostationary field.Journal of Atmospheric and Oceanic Technology, 17: 656-664.
30 Simon S, Jensen K H, Sandholt Iet al., 2008. A remote sensing driven distributed hydrological model of the Senegal River Basin.Journal of Hydrology, 354: 131-148.
31 Song X M, Zhan C S, Xia Jet al., 2012. An efficient global sensitivity analysis approach for distributed hydrological model.Journal of Geographical Sciences, 22(2): 209-222.
32 Sorooshian S, Hsu K L, Gao X Get al., 2000. Evaluation of PERSIANN system satellite-based estimates of tropical rainfall.Bulletin of the American Meteorological Society, 81(9): 2035-2046.
33 Stisen S, Sandholt I, 2010. Evaluation of remote-sensing-based rainfall products through predictive capability in hydrological runoff modeling.Hydrological Processes, 24(7): 879-891.
34 Su F, Hong Y, Lettenmaier D P, 2008. Evaluation of TRMM multisatellite precipitation analysis (TMPA) and its utility in hydrologic prediction in the La Plata Basin.Journal of Hydrometeorology, 9(4): 622-640.
35 Varikoden H, Samah A A, Babu C A, 2010. Spatial and temporal characteristics of rain intensity in the peninsular Malaysia using TRMM rain rate.Journal of Hydrology, 387: 312-319.
36 Vázquez R F, Feyen J, 2003. Effect of potential evapotranspiration estimates on effective parameters and performance of the MIKE SHE-code applied to a medium-size catchment.Journal of Hydrology, 270: 309-327.
37 Xia J, 1991. Identification of a constrained nonlinear hydrological system described by Volterra functional series,Water Resources Research, 27(9): 2415-2420.
38 Xia J, 2002. A system approach to real time hydrological forecasts in watersheds.Water International, 27(1): 87-97.
39 Xia J, 2005. Development of distributed time-variant gain model for nonlinear hydrological systems. Science in China Series D: Earth Sciences, 48(6): 713-723.
40 Xia J, O'Connor K M, Kachroo P Ket al., 1997. A non-linear perturbation model considering catchment wetness and its application in river flow forecasting.Journal of Hydrology, 200: 164-178.
41 Xu Z X, Zuo D P, 2012. Assessment on blue and green water resources at different scales in the Wei River Basin.Special Issue of National Water Resources Rational Allocation and Optimal Scheduling Technology, 139-158. (in Chinese)
42 Xue X W, Hong Y, Limaye A Set al., 2013. Statistical and hydrological evaluation of TRMM-based multi-satellite precipitation analysis over the Wangchu Basin of Bhutan: Are the latest satellite precipitation products 3B42V7 ready for use in ungauged basins? Journal of Hydrology, 499: 91-99.
43 Yong B, Hong Y, Ren L Let al., 2012. Assessment of evolving TRMM-based multisatellite real-time precipitation estimation methods and their impacts on hydrologic prediction in a high latitude basin.Journal of Geophysical Research - Atmospheres, 117: D09108.
44 Yu M Y, Chen X, Li L Het al., 2011. Streamflow simulation by SWAT using different precipitation sources in large arid basins with scarce raingauges.Water Resources Management, 25: 2669-2681.
45 Zak S K, Beven K J, 1999. Equifinality, sensitivity and predictive uncertainty in the estimation of critical loads.Science of the Total Environment, 236(1-3): 191-124.
46 Zeng H W, Li L J, Hu J M, 2013. Accuracy validation of TRMM multisatellite precipitation analysis daily precipitation products in the Lancang River Basin of China.Theoretical and Applied Climatology, 112: 389-401.
47 Zeng H W, Li L J, Li J Y, 2012. The evaluation of TRMM Multisatellite Precipitation Analysis (TMPA) in drought monitoring in the Lancang River Basin.Journal of Geographical Sciences, 22(2): 273-282.
48 Zhan C S, Song X M, Xia Jet al., 2013. An efficient integrated approach for global sensitivity analysis of hydrological model parameters.Environmental Modelling and Software, 41: 39-52.
49 Zhang Y Q, Chiew F H S, Zhang L, 2009. Use of remotely sensed actual evapotranspiration to improve rainfall-runoff modeling in southeast Australia.Journal of Hydrometeorology, 10: 969-980.
50 Zhang Y Q, Martin W, 2007. Predicting runoff in ungauged catchments by using Xinanjiang model with MODIS leaf area index.Journal of Hydrology, 370: 155-162.
51 Zuo D P, Xu Z X, Yang Het al., 2012. Spatiotemporal variations and abrupt changes of potential evapotranspiration and its sensitivity to key meteorological variables in the Wei River Basin, China.Hydrological Processes, 26: 1149-1160.
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