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웹기반의 유량 군집화 EI 평가시스템을 이용한 SWAT 직접유출과 기저유출 평가

SWAT Direct Runoff and Baseflow Evaluation using Web-based Flow Clustering EI Estimation System

  • 장원석 (강원대학교 지역건설공학과) ;
  • 문종필 (농촌진흥청 국립농업과학원) ;
  • 김남원 (한국건설기술연구원) ;
  • 유동선 (강원대학교 지역건설공학과) ;
  • 금동혁 (강원대학교 지역건설공학과) ;
  • 김익재 (한국환경정책.평가연구원) ;
  • 문유리 (한국환경정책.평가연구원) ;
  • 임경재 (강원대학교 지역건설공학과)
  • Jang, Won Seok (Department of Regional Infrastructures Engineering, Kangwon National University) ;
  • Moon, Jong Pil (National Academy of Agricultural Science, Rural Development Administration) ;
  • Kim, Nam Won (Korea Institute of Construction Technology) ;
  • Yoo, Dong Sun (Department of Regional Infrastructures Engineering, Kangwon National University) ;
  • Kum, Dong Hyuk (Department of Regional Infrastructures Engineering, Kangwon National University) ;
  • Kim, Ik Jae (Korea Environment Institute) ;
  • Mun, Yuri (Korea Environment Institute) ;
  • Lim, Kyoung Jae (Department of Regional Infrastructures Engineering, Kangwon National University)
  • 투고 : 2010.11.15
  • 심사 : 2010.12.23
  • 발행 : 2011.01.30

초록

유역 단위 수문 및 수질 평가 모형인 SWAT 모형을 이용한 유역 내 정확한 수문과 비점오염원 거동을 평가하기 위해서는 유역 적용에 앞서 모형의 정확성 평가가 우선시 되어야 한다. SWAT 모형의 수문 보정및 검정 시, Nash-Sutcliffe의 효율계수(EI)가 널리 사용되고 있다. 그러나 이러한 EI 값은 비교되어지는 값들의 범위 중 큰 값 즉, 수문 분석에 있어 고유량에 대해 민감하게 영향을 받는 것으로 알려져 있다. 그리하여 본 연구에서는 보다 정확한 수문 분석을 위해 K-means 군집화 알고리즘을 이용한 웹기반의 EI 평가시스템을 개발하였고, 이를 SWAT 모형의 수문 평가에 적용하였다. 본 연구의 결과 전체 유량의 EI 값은 높았지만, 수문성분에 따른 EI 값은 높지 않았다. SWAT 모형의 수문 보정 및 검정에 널리 활용되고 있는 SWAT auto-calibration tool은 전체 유량에 대해서는 높은 EI 값을 산정하는 것으로 보이지만, 직접유출과 기저유출 각각에 대한 유량 그룹 I 과 II 에 대해서는 대부분 음수(-)의 EI 값을 보였다. 그리하여 본 연구 결과를 통해 SWAT 모형의 수문성분 평가에 있어 보다 정확한 평가를 위해서는 직접유출과 기저유출에 대한 각각의 유량 그룹에 대해 양수(+)의 EI 값이 산정되도록 모형 보정 및 검정의 수행 필요할 것으로 사료된다.

In order to assess hydrologic and nonpoint source pollutant behaviors in a watershed with Soil and Water Assessment Tool (SWAT) model, the accuracy evaluation of SWAT model should be conducted prior to the application of it to a watershed. When calibrating and validating hydrological components of SWAT model, the Nash-Sutcliffe efficiency coefficient (EI) has been widely used. However, the EI value has been known as it is affected sensitively by big numbers among the range of numbers. In this study, a Web-based flow clustering EI estimation system using K-means clustering algorithm was developed and used for SWAT hydrology evaluation. Even though the EI of total streamflow was high, the EI values of hydrologic components (i.e., direct runoff and baseflow) were not high. Also when the EI values of flow group I and II (i.e., low and high value group) clustered from direct runoff and baseflow were computed, respectively, the EI values of them were much lower with negative EI values for some flow group comparison. The SWAT auto-calibration tool estimated values also showed negative EI values for most flow group I and II of direct runoff and baseflow although EI value of total streamflow was high. The result obtained in this study indicates that the SWAT hydrology component should be calibrated until all four positive EI values for each flow group of direct runoff and baseflow are obtained for better accuracy both in direct runoff and baseflow.

키워드

참고문헌

  1. Arnold, J. G., Srinivasan, R., Muttiah, R. S., and Williams, J. R. (1998). Large area hydrologic modeling and assessment: part I: model development. Journal of American Water Resources Association, 34(1), pp. 73-89. https://doi.org/10.1111/j.1752-1688.1998.tb05961.x
  2. Bicknell, B. R., Imhoff, J. C., Donigian, A. S., and Johanson, R. C. (1997). Hydrological Simulation Program-FOTRAN (HSPF). User's Manual for release 11(EPA-600/R-97/080), United States Environmental Protection Agency, Athens, GA.
  3. Cao, F., Liang, J., and Jiang, G. (2009). An initialization method for the Web-based K-means algorithm using neighborhood model. Computers and Mathematics with Applications, 58, pp. 474-483. https://doi.org/10.1016/j.camwa.2009.04.017
  4. Cho, J., Park, S., and Lm, S. (2008). Evaluation of Agricultural Nonpoint Source (AGNPS) model for small watersheds in Korea applying irregular cell delineation. Agricultural Water Management, 95(4), pp. 400-408. https://doi.org/10.1016/j.agwat.2007.11.001
  5. Chow, V. T., Maidment, D. R., and Mays, L. W. (1988). Applied Hydrology, McGraw-Hill, New York.
  6. Cryer, S. A. and Havens, P. L. (1999). Regional sensitivity analysis using a fractional factorial method for the USDA model GLEAMS. Environmental Modeling and Software, 14(6), pp. 613-624. https://doi.org/10.1016/S1364-8152(99)00003-1
  7. Green, C. H. and Van Griensven, A. (2008). Autocalibration in hydrologic modeling: Using SWAT 2005 in small-scale watersheds. Environmental Modelling & Software, 23, pp. 422-434. https://doi.org/10.1016/j.envsoft.2007.06.002
  8. Gupta, H. V., Sorooshian, S., and Yapo, P. O. (1999). Status of automatic calibration for hydrologic models: comparison with multilevel except calibration. Journal of Hydrologic Engineering, 4(2), pp. 135-143. https://doi.org/10.1061/(ASCE)1084-0699(1999)4:2(135)
  9. Kim, J., Park, Y., Yoo, D., Kim, N., Engel, B. A., Kim, S., Kim, K., and Lim, K. J. (2009). Development of a SWAT patch for better estimation of sediment yield in steep sloping watersheds. Journal of the American Water Resources Association, 45(4), pp. 963-972. https://doi.org/10.1111/j.1752-1688.2009.00339.x
  10. Lim, K. J., Engel, B. A., Tang, Z., Choi, J., Kim, K., Muthukrishnan, S., and Tripathy, D. (2005). Automated Web GIS-based Hydrograph Analysis Tool, WHAT. Journal of the American Water Recourse Association, 41(6), pp. 1407-1416. https://doi.org/10.1111/j.1752-1688.2005.tb03808.x
  11. Lloyd, S. P. (1957). Least square quantization in PCM. Bell Telephone Laboratories Paper. Published in journal much later: S. P. Lloyd (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2). pp. 129-137. https://doi.org/10.1109/TIT.1982.1056489
  12. MacQueen, J. B. (1967). Some Methods for classification and Analysis of Multivariate Observations. Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, University of California Press, 1, pp. 281-297.
  13. McCuen, R. H., Knight, Z., and Cutter, A. G. (2006). Evaluation of the Nash-Sutcliffe Efficiency Index, Journal of Hydrologic Engineering, 11(6), p. 597. https://doi.org/10.1061/(ASCE)1084-0699(2006)11:6(597)
  14. Nash, J. E. and Sutcliffe, J. V. (1970). River flow forecasting through conceptual models. Part 1: A discussion of principles. Journal of Hydrology, 10(3), pp. 282-290. https://doi.org/10.1016/0022-1694(70)90255-6
  15. Neitsch, S. L., Arnold, J. G., Kiniry, J. R., Srinivasan, R., and Williams, J. R. (2005). Soil and Water Assessment Tool (SWAT) Users Manual. BRC(02-06), Blackland Research Center, Texas Agricultural Experiment Station, Temple, Texas.
  16. Qi, C. and Grunwald, S. (2005). GIS-Based hydrologic modeling in the Sandusky watershed using swat. American Society of Agricultural Engineers, 48(1), pp. 169-180. https://doi.org/10.13031/2013.17960
  17. Rossman, L. A. (2009). Storm Water Management Model User's Manual Version 5.0. EPA/600/R-05/040, National Risk Management Research Laboratory, United States Environmental Protection Agency, Cincinnati, Ohio.
  18. Steinhaus, H. (1956). Sur la division des corps matériels en parties, Bull. Acad. Polon. Sci., 1, pp. 801-804.
  19. Van Grienven, A., Francos, A., and Bauwens, W. (2002). Sensitivity analysis and auto-calibration of an integral dynamic model for river water quality. Water Science and Technology, 45(9), pp. 325-332.
  20. Van Griensven, A., Meixner, T., Grunwald, S., Bishop, T., Di luzio, M., and Srinivasan, R. (2006). A global sensitivity analysis tool for the parameters of multi-variable catchment models. Journal Hydrology, 324, pp. 10-23. https://doi.org/10.1016/j.jhydrol.2005.09.008
  21. Walling, D. E., He, Q., and Whelan, P. A. (2003). Using 137Cs Measurements to validate the application of the AGNPS and ANSWERS erosion and sediment yield modles in two small Devon Catchments. Soil and Tillage Research, 69(1-2), pp. 27-43. https://doi.org/10.1016/S0167-1987(02)00126-5
  22. Zhou, H., and Liu, Y. (2008). Accurate integration of multiview range image using k-means clustering. The Journal of the Pattern Recognition Society, 41(1), pp. 152-175. https://doi.org/10.1016/j.patcog.2007.06.006