Original Articles

Effects of Spatial Resolution of Soil Data on Hydrological Processes Modeling

  • 1. Nanjing Institute of Geography and Limnology, State Key Laboratory of Lake Science and Environment|Nanjing 210008, China; 2. Graduate University of Chinese Academy of Sciences|Beijing 100049, China

Online published: 2009-07-25


The prediction accuracy of a distributed hydrological model depends on how well the model input spatial data describe the characteristics of the watershed. Especially, in a large scale catchment, could a higher resolution of input data contribute to a more accurate result? As an important component of input data, soil information directly impacts the accuracy of the simulation of hydrologic model. In this study, surveyed soil data with two different spatial resolutions were used as input data for a SWAT model simulation in a large scale catchment of Xinjiang River basin (15535km2) in China. Simulations of stream flow, soil water storage and evapotranspiration using the two soil datasets were compared, and the applicability of fine resolution of soil data was analysed. The results indicate that the different resolutions of soil data have a great impact on the distribution of hydrological response units in the SWAT model, but show no obvious differences in stream flow simulation and evapotranspiration (ET). Our observations also show that the lower resolution data improved slightly in average monthly stream flow simulation before and after calibration, but there is no substantial difference. The finer resolution data produced a higher monthly soil water storage (SW) simulation than the lower resolution data across the whole watershed during the simulating period. Results also show that the evapotranspiration calculation method in the SWAT model is insensitive to soil resolution. The implications of this study are that improvement of the resolution of soil data does not necessarily contribute to a more accurate prediction of streamflow in large scale catchments. In practical studies, modellers need to select an appropriate resolution of soil data depending on the scale of watershed and the level of accuracy required, and also need to consider the principle of the model and the physical meanings of some key parameters to explain the simulation result.

Cite this article

YE Xuchun1,2, ZHANG Qi1, LIU Jian1,2, LI Lijiao1,2, ZUO Haijun1,2 . Effects of Spatial Resolution of Soil Data on Hydrological Processes Modeling[J]. PROGRESS IN GEOGRAPHY, 2009 , 28(4) : 575 -583 . DOI: 10.11820/dlkxjz.2009.04.013


[1]  Helge Bormann. Sensitivity of a soil-vegetation-atmosphere-transfer scheme to input data resolution and data classification. Journal of Hydrology, 2008, 351∶ 154-169.

[2]  Romanowicz A A, Vanclooster M, Rounsevell M, et al. Sensitivity of the SWAT model to the soil and land use data parameterization: A case study in the Thyle catchment, Belgium. Ecological Modeling, 2005,187:27-39.

[3]  Chaplot V. Impact of DEM mesh size and soil map precision for the prediction of water, sediment and NO3 loads in a watershed. Journal of Hydrology, 2005, 312: 207-222.

[4]  Band L E, Moore I D. Scale: landscape attributes and geographical information systems. Hydrological Processes, 1995, 9: 401-422.

[5]  Seyfried M. Spatial variability constructions to modeling soil water at different scales. Geoderma, 1998, 85:231-254.

[6]  Muttiah R S, Wurbs R A. Scale-dependent soil and climate variability effects on watershed water balance of the SWAT mode1.Journal of Hydrology, 2003, 256(3-4):264-285.

[7]  Zhu A X, Mackay D S. Effects of spatial detail of soil information on watershed modeling. Journal of Hydrology, 2001, 248(1-4): 54-77.

[8]  Peck A J, Luxmoore R J, Stolzy J L. Effects of spatial variability of soil hydraulic properties in water modeling. Water Resoure, 1997, 13(2): 348-354.

[9]  Peschel J M, Haan K P, Lacey R E. A SSURGO preprocessing extension for the ArcView Soil and Water Assessment too1. ASAE Meeting Presentation, Paper No.032123, Las Vegas, NV, 2003, July: 27-30.

[10] Wang X, Melesse A M. Effects of STATSGO and SSURGO as inputs on SWAT model’s snowmelt simulation. Journal of American Water Resources Association, 2006, 42 (5): 1217-1236.

[11] Geza Mengistu, McCray J E. Effects of soil data resolution on SWAT model stream flow and water quality predictions. Journal of Environmental Management, 2008, 88:393-406

[12] Guo H, Hu Q, Jiang T. Annual and seasonal stream flow responses to climate and land-cover changes in the Poyang Lake basin, China. Journal of Hydrology, 2008, 355: 106-122.

[13]史学正, 于东升, 高鹏, 等. 中国土壤信息系统(SISChin)及其应用基础研究. 土壤, 2007, 39(3):329-333.

[14] Shi X Z, Yu D S, Warner E D, et al. Cross-Reference System for translating between genetic soil classification of China and soil taxonomy. Soil Sci. Soc. Am.J. 2006, 70(1):78-83.

[15] Shi X Z, Yu D S, Warner E D, et al. Soil Database of 1∶1,000,000 digital soil survey and reference system of the Chinese genetic soil classification system. Soil Survey Horizons, 2004, 45(4):129-136.

[16] Levick L R, Semmens D J, Guertin D P, et al. Adding global soils data to the automated geospatial watershed assessment tool (AGWA)
[C/CD]. Second International Symposium on Transboundary Waters Management, Tucson, AZ, 2004, November 16-19: 8.

[17] 李润奎, 朱阿兴, Peter, 等. SWAT模型对高精度土壤信息的敏感性研究. 地球信息科学, 2007, 9(3):72-90.

[18] Zhang W, Montgomery D R. Digital elevation model grid size, landscape representation, and hydrologic simulations. Water Resources Research, 1994, 30: 1019-1028.

[19] 吴军, 张万昌. DEM分辨率对AVSWAT2000径流模拟的敏感性分析. 遥感信息, 2007, 3: 8-13.

[20] 郝芳华, 程红光, 杨胜天. 非点源污染模型: 理论方法与应用. 北京: 中国环境科学出版社, 2006:62-118.

[21] 王中根, 刘昌明, 黄友波. SWAT模型的原理、结果及应用研究. 地理科学进展, 2003,22(1):79-86.