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Comparative Analysis of SWAT Generated Streamflow and Stream Water Quality Using Different Spatial Resolution Data

SWAT모형에서 공간 입력자료의 다양한 해상도에 따른 수문-수질 모의결과의 비교분석

  • Park, Jong-Yoon (Dept. of Civil and Environmental System Engineering, Konkuk University) ;
  • Lee, Mi-Seon (Dept. of Rural Engineering, Konkuk University) ;
  • Park, Geun-Ae (Dept. of Civil and Environmental System Engineering, Konkuk University) ;
  • Kim, Seong-Joon (Dept. of Civil and Environmental System Engineering, Konkuk University)
  • 박종윤 (건국대학교 대학원 사회환경시스템공학과) ;
  • 이미선 (건국대학교 대학원 지역건설환경공학과) ;
  • 박근애 (건국대학교 대학원 사회환경시스템공학과) ;
  • 김성준 (건국대학교 생명환경과학대학 사회환경시스템공학과)
  • Published : 2008.11.30

Abstract

This study is to evaluate the impact of varying spatial resolutions on the uncertainty of Soil and Water Assessment Tool (SWAT) predicted streamflow, non-point source (NPS) pollution loads transport in a small agricultural watershed (1.21 $km^2$) for three cases of model input; Case A is the combination of 2 m DEM, QuickBird land use, Case B is the combination of 10 m DEM, 1/25,000 land use, and Case C is the combination of 30 m DEM, Landsat land use, soil data is used 1/25,000 for three cases respectively. The model was calibrated for 2 years (1999-2000) using daily streamflow and monthly water quality records, and verified for another 2 years (2001-2002). The average Nash and Sutcliffe model efficiency was 0.59 for streamflow and RMSE were 2.08, 4.30 and 0.70 tons/yr for sediment, T-N and T-P respectively. The model was run for a small agricultural watershed with three cases of spatial input data. The hydrological results showed that output uncertainty was biggest by spatial resolution of land use. Streamflow increase the watershed average CN value of QucikBird land use was 0.4 and 1.8 higher than those of 1/25,000 and Landsat land use caused increase of streamflow. On the other hand, The NPS loadings from the model prediction showed that the sediment, T-N and T-P of QuickBird land use (Case A) showed 23.7 %, 43.3 % and 48.4 % higher value than 1/25,000 land use (Case B) and 50.6 %, 50.8 % and 56.9 % higher value than Landsat land use (Case C) respectively.

본 연구는 농촌소유역(1.21 $km^2$)에서 다양한 공간입력자료의 해상도가 SWAT(Soil and Water Assessment Tool) 모형의 수문-수질 모의결과에 미치는 영향을 분석하고자 Case A(2 m DEM, QucikBird 토지이용도, 1/25,000 토양도), Case B(10 m DEM, 1/25,000 토지이용도, 1/25,000 토양도), Case C(30 m DEM, Landsat 토지이용도, 1/25,000 토양도)에 해당하는 해상도별 공간입력자료를 구축하였다. 모형의 적용성 평가는 경안천유역(255.44 $km^2$) 출구점에서 일별 유출량 및 월별 수질자료를 이용하여 보정($1999{\sim}2000$)하였으며, $2001{\sim}2002$년 자료를 이용하여 검증하였다. 유출량에 대한 Nash-Sutcliffe 모형효율은 평균 0.59의 결과를 얻었으며, Sediment, T-N, T-P 부하량은 각각 2.08, 4.30, 0.70 tons/yr의 RMSE 오차로 검보정되었다. 농촌소유역을 대상으로 다양한 공간자료(Case A, B, C)를 적용하여 수문, 수질모의를 실시한 결과, 유출량은 토지이용도 해상도에 의한 모의결과의 불확실성이 가장 큰 것으로 분석되었다. QuickBird 토지이용도의 유역평균 CN값이 1/25,000과 Landsat 토지이용에 비해 0.4, 1.8 더 크게 분석됨으로서 총유출량도 증가하였다. 한편, 유사량과 영영물질 오염부하량에 대한 수질모의 결과는 QuickBird(Case A) 토지이용도의 유사량 및 T-N, T-P 부하량이 1/25,000(Case B) 토지이용도에 비해 23.7 %, 43.3 %, 48.4 %, Landsat(Case C) 토지이용도에 비해 50.6 %, 50.8 %, 56.9 % 높게 평가되는 것으로 분석되었다.

Keywords

References

  1. 김남원, 정일문, 원유승 (2006). "완전연동형 SWATMODFLOW 모형을 이용한 지표수-지하수 통합 유출모의." 대한토목학회논문집, 대한토목학회, 제26권, 제5B호, pp. 481-488
  2. 김성준 (2001). "원격탐사와 지리정보시스템에서 규모와 해상도 효과의 이해." 한국수자원학회논문집, 한국수자원학회, 제34권, 제3호, pp. 76-86
  3. 김성준, 이용준 (2007). "면적규모 및 공간해상도가 CA-Markov 기법에 의한 미래 토지이용 예측결과에 미치는 영향." 한국지리정보학회논문집, 한국지리정보학회, 제10권, 제2호, pp. 58-70
  4. 박경훈 (2003). "GIS 및 RUSLE 기법을 활용한 금호강유역의 토양침식위험도 평가." 한국지리정보학회논문집, 한국지리정보학회, 제6권, 제2호, pp. 24-36
  5. 박종윤, 이미선, 이용준, 김성준 (2008). “SWAT 모형을 이용한 미래 토지이용변화가 수문-수질에 미치는 영향 분석.” 대한토목학회논문집, 대한토목학회, 제28권, 제2B호, pp. 187-197
  6. 채효석, 김성준, 고덕구 (2004). “원격탐사 자료를 이용한 미계측 유역의 수문정보 추출.” 한국수자원학회지, 한국수자원학회, 제37권, 제3호, pp. 44-49
  7. 최규석, 김환식 (1991). "팔당 상수원에 유입하는 오염 물질 부하량과 유출 특성에 관한 연구." 환경과학논문집, 제12권, pp. 151-171
  8. 한국건설기술연구원 (1992). 댐설계를 위한 유역단위 비유사량 조사연구
  9. Arnold, J.G., Srinivasan, R., Muttiah, R.S., and Allen, P.M. (1999). "Continental-scale simulation of the hydrologic balance." Journal of American Water Resources Association, JAWRA, Vol. 35, No. 5, pp. 1037-1052 https://doi.org/10.1111/j.1752-1688.1999.tb04192.x
  10. 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, JAWRA, Vol. 34, No. 1, pp. 73-89 https://doi.org/10.1111/j.1752-1688.1998.tb05961.x
  11. Bedient, P.B., Huber, W.C., and Vieux, B.E. (2008). Hydrology and floodplain analysis, 4th Ed., Prentice-Hall, Inc., Upper Saddle River, NJ 07458
  12. Brown, L.C., and Barnell, T.O. Jr. (1987). The enhanced water quality models QUAL2E and QUAL2E-UNCAS documentation and user manual. EPA document EPA/600/3-87/007, USEPA, Athens, GA
  13. Chaplot, V. (2005). "Impact of DEM mesh size and soil map scale on SWAT runoff, sediment, and $NO_3$-N loads predictions." Journal of Hydrology, Vol. 312, pp. 207-222 https://doi.org/10.1016/j.jhydrol.2005.02.017
  14. Chen, E., and Mackay, D.S. (2004). "Effects of distribution-based parameter aggregation on a spatially distributed agricultural nonpoint source pollution model." Journal of Hydrology, Vol. 295, pp. 211-224 https://doi.org/10.1016/j.jhydrol.2004.03.029
  15. Cotter, A.S., Chaubey, I., Costello, T.A., Soerens, T.S., and Nelson, M.A. (2003). “Water quality model output uncertainty as affected by spatial resolution of input data.” Journal of American Water Resources Association, JAWRA, Vol. 39, No. 4, pp. 977-986 https://doi.org/10.1111/j.1752-1688.2003.tb04420.x
  16. FitzHugh, T.W., and Mackay, D.S. (2000). "Impacts of input parameter spatial aggregation on an agricultural nonpoint source pollution model." Journal of Hydrology, Vol. 236, pp. 35-53 https://doi.org/10.1016/S0022-1694(00)00276-6
  17. Fontaine, T.A., Cruickshank, T.S., Arnold, J.G., and Hotchkiss, R.H. (2002). "Development of a snowfall-snowmelt routine for mountainous terrain for the soil water assessment tool (SWAT)." Journal of Hydrology, Vol. 262, pp. 209-223 https://doi.org/10.1016/S0022-1694(02)00029-X
  18. Goodchild, M.F., and Quattrochi, D.A. (1997). Introduction; scale, multiscale, remote sesing and GIS. In D. A. Quattrochi & M. F. Goodchild (eds), Scale in remote sensing and GIS, Lewis Publishers. Boca Raton, pp. 1-11
  19. Joao, E.M., and Walsh, S.J. (1992). "GIS implications for hydrologic modeling: simulation of nonpoint pollution generated as a consequence of watershed development scenarios." Computer Environment and Urban Systems, Vol. 16, pp. 43-63 https://doi.org/10.1016/0198-9715(92)90052-S
  20. Kalin, L., Govindarajua, R.S., and Hantush, M.M. (2003). "Effect of geomorphologic resolution on modeling of runoff hydrograph and sedimentograph over small watersheds." Journal of Hydrology, Vol. 276, pp. 89-111 https://doi.org/10.1016/S0022-1694(03)00072-6
  21. Kim, S.H., Lee, M.S., Park, G.A., and Kim, S.J. (2007). "Application of QuickBird satellite image to storm runoff modeling." Korean Journal of Remote Sensing, Vol. 23, No. 1, pp. 15-20 https://doi.org/10.7780/kjrs.2007.23.1.15
  22. Lohani, V., Kibler, D.F., and Chanat, J. (2002). "Constructing a problem solving environment tool for hydrologic assessment of land use change." Journal of the American Water Resources Association, JAWRA, Vol. 38, No. 2, pp. 439-452 https://doi.org/10.1111/j.1752-1688.2002.tb04328.x
  23. Mamillapalli, S., Srinivasan, R., Arnold, J.G., and Engel, B.A., (1996). "Effect of spatial variability on basin scale modeling." To be presented at the Third International NCGIA Conference/Workshop on Integrating GIS and Environmental Modeling, Sante Fe, New Mexico, January, 21-25
  24. Miller, S.N., Guertin, D.P., Kamran, H.S., and Goodrich, D.C., (1999). "Using high resolution synthetic aperture radar for terrain mapping: Influences on hydrologic and geomorphic investigation." Proceedings of Wildland Hydrology, AWRA Summer Specialty Conference, Bozeman, Montana
  25. Nash, J.E., and Sutcliffe, J.E. (1970). "River flow forecasting through conceptual models, Part I-A discussion of principles." Journal of Hydrology, Vol. 10, No. 3, pp. 282-290 https://doi.org/10.1016/0022-1694(70)90255-6
  26. Park, J.Y., Lee, M.S., and Kim, S.J. (2007). "Analysis of non-point source pollution loading in a small rural watershed using high spatial resolution image." Proceedings of the International Symposium on Remote Sensing (ISRS) 2007, Jeju Island, South Korea, 31 Oct. ~ 2 Nov., CD-ROM
  27. Rallison, R.E., and Miller, N. (1981). Past, present and future SCS runoff procedure. In V. P. Singh (ed.). Rainfall runoff relationship. Water Resources Publication, Littleton, CO., pp. 353-364
  28. Saxton, K.E., Rawls, W.J., Romberger, J.S., and Papendick, R.I. (1986). “Estimating generalized soil-water characteristics from texture.” Soil Science Society of America Journal, Vol. 50, No. 4, pp. 1031-1036 https://doi.org/10.2136/sssaj1986.03615995005000040039x
  29. Soil Survey Staff (1996). National soil survey handbook, title 430-VI, USDA Natural Resources Conservation Service, U.S. Government Printing Office, Washington, D.C.
  30. USDA Soil Conservation Service (1972). National Engineering Handbook. Section 4 Hydrology 1972 (Chapters 4-10)
  31. Vieux, B.E., and Needham, S. (1993). "Nonpointpollution model sensitivity to grid-cell size." Journal of Water Resources Planning and Management, Vol. 119, No. 2, pp. 141-157 https://doi.org/10.1061/(ASCE)0733-9496(1993)119:2(141)
  32. Williams, J.R. (1975). Sediment-yield prediction with universal equation using runoff energy factor. In present and prospective technology for predicting sediment yield and sources, ARS-S-40, USDA-ARS
  33. Williams, J.R. (1995). The EPIC model, In Computer models of watershed hydrology, Singh, V. P., (ed.), Chapter 25: pp. 909-1000, Water Resources Publications
  34. Wilson, J.P. (1996). "GIS-based Land Surface/Subsurface Modeling: New Potential for New Models?" NCGIA Third International Conference/Workshop on Integrating GIS and Environmental Modeling, Santa Fe, New Mexico, January 21-25
  35. Wischmeier, W.H., and Smith, D.D. (1965). Predicting rainfall-erosion losses from cropland east of the Roky Mountains, Agriculture Handbook 282, USDA-ARS
  36. Wischmeier, W.H., and Smith, D.D. (1978). Predicting rainfall erosion losses: a guide to conservation planning, Agriculture Handbook 282, USDA-ARS
  37. Zhang, X., Srinivassan, R., and Hao, F. (2007). "Predicting Hydrologic response to climate change in the Luohe river basin using the SWAT model." American Society of Agricultural and Biological Engineers, ASABE, Vol. 50, No. 3, pp. 901-910

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