한국수자원학회논문집 (Journal of Korea Water Resources Association)

  • 제57권10호
  • /
  • Pages.769-783
  • /
  • 2024
  • /
  • 2799-8746(pISSN)
  • /
  • 2799-8754(eISSN)
한국수자원학회 (Korea Water Resources Association)
권호 표지
DOI QR코드

DOI QR Code

섬진강 옥정호 유역의 수자원 관리를 위한 SWAT 모델과 HSPF 모델의 비교 분석

Evaluation of SWAT model and HSPF model predictions for water resource management in the Okjeong Lake watershed of the Seomjin River

  • 이어진 (충남대학교 환경 IT 융합공학과) ;
  • 이승문 (충남대학교 환경 IT 융합공학과) ;
  • 서동일 (충남대학교 환경공학과)
  • Lee, Eojin (Department of Environmental and IT Convergence, Chungnam National University) ;
  • Lee, Seungmoon (Department of Environmental and IT Convergence, Chungnam National University) ;
  • Seo, Dongil (Department of Environmental Engineering, Chungnam national University)
  • 투고 : 2024.09.04
  • 심사 : 2024.10.08
  • 발행 : 2024.10.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 연구에서는 섬진강 상류 옥정호의 유역에서 유입하는 유량. 총인(TP), 총질소(TN) 및 총부유물질(TSS) 유입량을 산정하기 위해 널리 사용되는 SWAT (Soil and Water Assessment Tool) 모델 및 HSPF (Hydrological Simulation Program-Fortran)를 동시에 적용한 결과를 비교 분석하였다. 환경부에서 제공하는 2012년부터 2021년까지의 현장의 각종 자료를 위 두 가지 모델의 입력자료와 보정자료로 사용하였으며 결정계수(R2), Nash-Sutcliffe Efficiency (NSE) 및 Percent Bias (PBIAS)등을 정확도의 평가지표로 이용하였다. 유량 의 경우 SWAT 모델은 본 연구의 실측자료에 대해 평균 R2 0.82 및 NSE 0.72로, HSPF 모델의 R2 0.76 및 NSE 0.67의 경우보다 약간 더 정확하거나 유사한 성능을 보였다. 그러나 수질 모의의 경우 SWAT 모델은 TN 13.2%, TP 19.1%, TSS 31.5%의 평균 PBIAS를 보인 반면, HSPF 모델은 TN 17.2%, TP 23.2%, TSS 25.9%의 평균 PBIAS를 나타냈다. 이러한 결과는 두 모델 모두 실측 수질을 정확하게 모의하는 데에는 한계가 있음을 시사한다. 본 연구에서는 두 가지 모델의 예측 결과를 토대로 오차의 원인을 분석하고 비점오염 부하 저감 등 옥정호의 수질관리를 위해 적절한 유역모델을 선택하는 데에 유용한 사례를 제공할 수 있을 것으로 판단된다.

This study conducted a comparative analysis by simultaneously applying the widely used SWAT (Soil and Water Assessment Tool) and HSPF (Hydrological Simulation Program-Fortran) models to estimate the inflow of discharge, total phosphorus (TP), total nitrogen (TN), and total suspended solids (TSS) into Okjeong Lake, located in the upper reaches of the Seomjin River. Data provided by the Ministry of Environment from 2012 to 2021 were used as input and calibration data for both models, and performance evaluation metrics such as the coefficient of determination (R2), Nash-Sutcliffe Efficiency (NSE), and Percent Bias (PBIAS) were utilized to assess model accuracy. For flow calibration, the SWAT model showed slightly better performance, with an average R2 of 0.82 and NSE of 0.72 across all stations, compared to the HSPF model's R2 of 0.76 and NSE of 0.67. However, for water quality calibration, the SWAT model had an average PBIAS of 13.2% for TN, 19.1% for TP, and 31.5% for TSS, while the HSPF model had an average PBIAS of 17.2% for TN, 23.2% for TP, and 25.9% for TSS. These results suggest that both models are limited in their ability to accurately simulate real world water quality. Based on the predicted results of the two models, this study analyzed the causes of the errors and provided useful examples for selecting an appropriate watershed model for water quality management of Okjeong Lake, including non-point source pollution load reduction.

키워드

과제정보

본 성과는 환경부의 재원을 지원받아 한국환경산업기술원 "신기후체제 대응 환경기술개발사업"의 연구개발을 통해 창출되었습니다(2022003570007).

참고문헌

  1. Abbaspour, K.C. (2008). SWAT-CUP2: SWAT calibration and uncertainty programs - A user manual. Swiss Federal Institute of Aquatic Science and Technology, Duebendorf, Switzerland.
  2. Abbaspour, K.C., Johnson, A., van Genuchten, M.Th. (2004). "Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure." Vadose Zone Journal, Vol. 3, No. 4, pp. 1340-1352. https://doi.org/10.2136/vzj2004.1340
  3. Abbaspour, K.C., Yang, I., Maximov, I., Siber, R., Bogner, K., Mieleitner, J., Zobrist, J., Srinivasan, R. (2007). "Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT." Journal of Hydrology, Vol. 333, pp. 413-430. https://doi.org/10.1016/j.jhydrol.2006.09.014
  4. Aggarwal, S., Rallapalli, S., and Thinagaran, N., (2024). "Agricultural watershed conservation and optimization using a participatory hydrological approach." Environmental Science and Pollution Research, Vol. 31, pp. 48590-48607.
  5. Arnold, J.G., Srinivasan, R., Muttiah, R.S., and Williams, J.R. (1998). "Large area hydrologic modeling and assessment part I: Model development." JAWRA Journal of the American Water Resources Association, Vol. 34. pp. 73-89. https://doi.org/10.1111/j.1752-1688.1998.tb05961.x
  6. Beven, K., and Binley, A. (1992). "The future of distributed models - Model calibration and uncertainty prediction." Hydrological Processes, Vol. 6, No. 3, pp. 279-298.
  7. Bicknell, B.R., Imhoff, J.C., Kittle, J.L., and Donigian, A.S. (1996). Hydrological simulation program - fortran user manual. National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Athens, GA, U.S.
  8. Cho, H.L., Jeong, E., and Koo, B.K. (2015). "Development of a hybrid watershed model STREAM: Model structures and theories." Journal of Korean Society on Water Environment, Vol. 31, No. 5, pp. 491-506. https://doi.org/10.15681/KSWE.2015.31.5.491
  9. Cho, Y.C., Choi, J.W., Noh, C., Kwon, P.S., Kim, S., and Yu, S. (2018). "Evaluation of discharge-water quality characteristics and river grade classification of Jinwi River unit basin." Journal of Environmental Impact Assessment, Vol. 27, No. 6, pp. 704-716. https://doi.org/10.14249/EIA.2018.27.6.704
  10. Cole, T.M., and Wells, S.A. (2016). CE-QUAL-W2: A two-dimensional, laterally averaged, hydrodynamic and water quality model, version 4.0 user's manual. U.S. Army Corps of Engineers, Vicksburg, MS, U.S.
  11. Cronshey, R., McCuen, R.H., Miller, N., Rawls, W., Robbins, S., and Woodward, D. (1986). Urban hydrology for small watersheds (Technical release 55). U.S. Department of Agriculture, Natural Resources Conservation Service, Washington, DC, U.S.
  12. Dile, Y., Srinivasan, R., and George, C. (2024). QGIS interface for SWAT (QSWAT) version 2.0 user manual. Swiss Federal Institute of Aquatic Science and Technology, Duebendorf, Switzerland.
  13. Goh, N., Kim, J., and Seo, D. (2022). "Evaluation of water quality in the Sangsa Lake under climate change by combined application of HSPF and AEM3D." Journal of Korea Water Resources Association, Vol. 55, No. 11, pp. 877-886. https://doi.org/10.3741/JKWRA.2022.55.11.877
  14. Han, M., Son, J., Ryu, J., Ahn, K., and Kim, Y. (2014). "The effects of pollutants into sub-basin on the water quality and loading of receiving streams." Journal of Korean Society of Environmental Engineers, Vol. 36, No. 9, pp. 648-660. https://doi.org/10.4491/KSEE.2014.36.9.648
  15. Harmel, R.D., and Smith, P.L. (2007). "Consideration of measurement uncertainty in the evaluation of goodness-of-fit in hydrologic and water quality modeling." Journal of Hydrology, Vol. 337, No. 3-4, pp. 326-336. https://doi.org/10.1016/j.jhydrol.2007.01.043
  16. Harmel, R.D., Smith, P.K., and Migliaccio, K.W. (2010). "Modifying goodness-of-fit indicators to incorporate both measurement and model uncertainty in model calibration and validation." Transactions of the ASABE, Vol. 53, No. 1, pp. 55-63. https://doi.org/10.13031/2013.29502
  17. Hodges, B., and Dallimore, C. (2019). Aquatic ecosystem model: AEM3D v1.2 user manual. HydroNumerics, Victoria, Australia.
  18. Jeon, S.W. (2009). A comparative study on the runoff in Idongreservoir watershed using HSPF and SWAT, Master Thesis, University of Konkuk, pp. 60-61.
  19. Kim, N.W., Kim, H.S., Jung, Y.M., Kim, C.R., Lee, J.U., Lee, J.H., Bae, Y.J., Lee, G.W., Sol, R., Ha, S.G., Son, Y.W., Heo, S.O., Jung, K.H., Lee, M.H., and Lee, D.S. (2007). Development of a hydrological analysis system for surface water Phase 2 Research Report. Korea Institute of Civil Engineering and Building Technology, p. 384.
  20. Kim, N.W., Shin, A.H., and Kim, C.G. (2009). "Comparison of SWAT-K and HSPF for hydrological components modeling in the Chungju Dam watershed." Journal of Environmental Science International, Vol. 18, No. 6, pp. 609-619. https://doi.org/10.5322/JES.2009.18.6.609
  21. Knoben, W.J.M., Freer, J.E., and Woods, R.A. (2019). "Technical note: Inherent benchmark or not? Comparing Nash - Sutcliffe and Kling - Gupta efficiency scores." Hydrology and Earth System Sciences, Vol. 23, pp. 4323-4331. https://doi.org/10.5194/hess-23-4323-2019
  22. Krause, P., Boyle, D.P., and Base, F. (2005). "Comparison of different efficiency criteria for hydrological model assessment." Advances in Geosciences, Vol. 5, pp. 89-97. https://doi.org/10.5194/adgeo-5-89-2005
  23. Kuczera, G., and Parent, E. (1998). "Monte Carlo assessment of parameter uncertainty in conceptual catchment models: The metropolis algorithm." Journal of Hydrology, Vol. 211, No. 1-4, pp. pp. 69-85. https://doi.org/10.1016/S0022-1694(98)00198-X
  24. Lee, E., and Seo, D. (2011). "Flow calibration and uncertainty analysis of the Daechung Lake watershed, Korea using SWAT-CUP." Journal of Korean Water Resources Association, Vol. 44, No. 9, pp. 711-720. https://doi.org/10.3741/JKWRA.2011.44.9.711
  25. Legates, D.R., and McCabe, G.J. (1999). "Evaluating the use of "goodness-of-fit" measures in hydrologic and hydroclimatic model validation." Water Resources Research, Vol. 35, No. 1, pp. 233-241. https://doi.org/10.1029/1998WR900018
  26. Marshall, L., Nott, D., and Sharma, A. (2004). "A comparative study of Markov chain Monte Carlo methods for conceptual rainfall-runoff modeling." Water Resources Research, Vol. 40, W02501.
  27. Ministry of Environment (ME) (2017). 2017 White paper of environment. p. 630.
  28. Moriasi, D.N., Gitau, M.W., Pai, N., and Daggupati, P. (2015). "Hydrologic and water quality models: Performance measures and evaluation criteria." Transactions of the ASABE, Vol. 58, No. 6, pp. 1763-1785. https://doi.org/10.13031/trans.58.10715
  29. Neitsch, S.L., Arnold J.G., Kinry, J.R., and Williams, J.R. (2011). Soil and water analysis tool theoretical documentation version 2009. Texas Water Resource Institute Technical Report No. 406, Texas A&M University System, TX, U.S.
  30. Park, J., Jang, Y., and Seo, D. (2017). "Water quality prediction of inflow of the Yongdam Dam basin and its reservoir using SWAT and CE-QUAL-W2 models in series to climate change scenarios." Journal of Korea Water Resources Association, Vol. 50, No. 10, pp. 703-714. https://doi.org/10.3741/JKWRA.2017.50.10.703
  31. Park, Y.S., Ryu, J., Kim, J., Kum, D., and Lim, K.J. (2020). "Review of features and applications of watershed-scale modeling, and improvement strategies of it in South-Korea." Journal of Korean Society on Water Environment, Vol. 36, No. 6, pp. 592-610. https://doi.org/10.15681/KSWE.2020.36.6.592
  32. Sloan, P.G., Morre, I.D., Coltharp, G.B., and Eigel, J.D. (1983). "Modeling surface and subsurface stromflow on steeply-sloping forested watersheds." Water Resources Research Institute Report 142, University Kentucky, Lexington, KY, U.S.
  33. Van, G.A., and Bauwens, W. (2003). "Multi-objective auto-calibration for semi-distributed water quality models." Water Resources Research, Vol. 39, No. 12, 1348.
  34. Vrugt, J.A., Gupta, H.V., Bouten, W., and Sorooshian, S. (2003). "A shuffled complex evolution metropolis algorithm for estimating posterior distribution of watershed model parameters." Water Resources Research, Vol. 39, No. 8, pp 1201-1219.
  35. Wool, T., Ambrose, R.B. Jr., Martin, J.L., and Comer, A. (2020a). "WASP 8: The next generation in the 50-year evolution of USEPA's water quality model." Water, Vol. 12, No. 5, pp. 1398-1431. https://doi.org/10.3390/w12051398
  36. Wool, T.A., Ambrose, R.B. Jr., and Martin, J.L. (2020b). WASP8 multiple algae - model theory and user's guide: Supplement to Water quality Analysis Simulation Program (WASP) user documentation. U.S. Environmental Protection Agency, Office of Research and Development, Washington, DC, U.S.
  37. Yang, J., Reichert, P., Abbaspour, K.C., Yang, H. (2007). "Hydrological modelling of the Chaohe Basin in China: statistical model formulation and bayesian inference." Journal of Hydrology, Vol. 340, pp. 167-182. https://doi.org/10.1016/j.jhydrol.2007.04.006
  38. Zhang, X., Arnold, J.G., Williams, J.R., and Srinivasan, R. (2022). SWAT-carbon user manual. agricultural research service. United States Department of Agriculture. Beltsville, MD, U.S.