한국수자원학회논문집 (Journal of Korea Water Resources Association)

한국수자원학회 (Korea Water Resources Association)
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하천유지유량 추가 댐방류에 따른 한강유역의 수질 및 수생태계 건강성 변화 평가

Assessment of changes on water quality and aquatic ecosystem health in Han river basin by additional dam release of stream maintenance flow

  • 우소영 (건국대학교 대학원 사회환경플랜트공학과) ;
  • 김성준 (건국대학교 공과대학 사회환경플랜트공학부) ;
  • 황순진 (건국대학교 상허생명과학대학 환경보건과학과) ;
  • 정충길
  • Woo, So Young (Graduate School of Civil, Environmental and Plant Engineering, Konkuk University) ;
  • Kim, Seong Joon (School of Civil and Environmental Engineering, Konkuk University) ;
  • Hwang, Sun Jin (Department of Environmental Health Science, Konkuk University) ;
  • Jung, Chung Gil (Agricultural and Water Resources Engineering, Texas A&M AgriLife Research Center)
  • 투고 : 2019.07.15
  • 심사 : 2019.09.30
  • 발행 : 2019.10.31
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초록

본 연구에서는 SWAT (Soil and Water Assessment Tool)을 이용하여 한강유역 ($34,148km^2$)내 다목적 댐(소양강댐, 횡성댐, 충주댐)의 하천유지유량 추가 방류 모의를 통한 유역의 수질 및 수생태계 건강성 변화를 평가하였다. 추가 방류기간은 수생태계 건강성 조사가 수행되는 봄(4-6월), 가을(8-10월)로 산정하였으며, 방류량은 댐의 기존 방류량에 비례하며 총 방류량이 댐별 고시된 하천유지유량을 초과하지 않도록 산정하였다. 하천 유지유량 방류에 따른 수질(T-N, $NH_4$, $NO_3-N$, T-P, $PO_4-P$) 농도는 봄철에 감소하지만 가을철에는 오히려 증가하는 것으로 모의되었다. 변화한 수질농도 데이터를 기존에 구축한 Random Forest 알고리즘에 적용하여 수생태계 건강성을 평가하였을 때, 유역의 하류에서 모든 수생태계 건강성 지수(FAI, TDI, BMI) 등급이 개선되는 것으로 분석되었다. 가을보다 봄에 하천유지유량 방류에 따른 수생태계 개선의 효과가 큰 것으로 나타났다.

The purpose of this study is to evaluate changes in water quality and aquatic ecosystem health by additional dam release of stream maintenance flow from multipurpose dams in Han river basin ($34,148km^2$) using SWAT (Soil and Water Assessment Tool). The period of additional release was spring (April to June) and autumn (August to October) to evaluate the changes with the data of aquatic ecosystem health survey. The amount of additional release was set proportional to the present dam release, and the maximum release amount was controlled not to exceed the officially notified stream maintenance flow from dam. The 10 percent to 50 percent additional releases showed that the stream water quality (T-N, $NH_4$, T-P, and $PO_4-P$) concentrations except $NO_3-N$ decreased in spring while increased in autumn period. Using the stream water quality results and applying with Random Forest algorithm, the grade of aquatic ecosystem health index (FAI, TDI, and BMI) was improved for both periods especially in the downstream of basin. This study showed that the additional release of stream maintenance flow was more effective in spring than autumn period for the improvement of water quality and aquatic ecosystem.

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참고문헌

  1. Ahn, S. R., and Kim, S. J. (2017). "Assessment of integrated watershed health based on the natural environment, hydrology, water quality, and aquatic ecology." Hydrology and Earth System Sciences, Vol. 21, No. 1, pp. 5583-5602. https://doi.org/10.5194/hess-21-5583-2017
  2. Ahn, S. R., Lee, J. W., Jang, S. S., and Kim, S. J. (2016). "Large scale SWAT watershed modeling considering multi-purpose dams and multi-function weirs operation-for Namhan river basin." Journal of the Korean Society of Agricultural Engineers, Vol. 58, No. 4, pp. 21-35. https://doi.org/10.5389/KSAE.2016.58.4.021
  3. An, K., Lee, J., and Jang, H. (2005). "Ecological health assessments and water quality patterns in Youdeung Stream." Korean Journal of Limnology, Vol. 38, No. 3, p. 341.
  4. Arnold, J. G., Williams, J. R., Srinivasan, R., and King, K. W. (1996). SWAT manual. USDA. Agricultural Research Service and Blackland Research Center, Texas.
  5. Breiman, L. (2001). "Random Forests." Machine learning, Vol. 45, No. 1, pp. 5-32. https://doi.org/10.1023/A:1010933404324
  6. Cordier, T., Esling, P., Lejzerowicz, F., Visco, J., Ouadahi, A., Martins, C., Cedhagen, T., and Pawlowski, J. (2017). "Predicting the ecological quality status of marine environments from eDNA metabarcoding data using supervised machine learning." Environmental science & technology, Vol. 51, No. 16, pp. 9118-9126. https://doi.org/10.1021/acs.est.7b01518
  7. Dudoit, S., Fridlyand, J., and Speed, T. P. (2002). "Comparison of discrimination methods for the classification of tumors using gene expression data." Journal of the American Statistical Association, Vol. 97, No. 457, pp. 77-87. https://doi.org/10.1198/016214502753479248
  8. Duro, D. C., Franklin, S. E., and Dube, M. G. (2012). "Multi-scale object-based image analysis and feature selection of multi-sensor earth observation imagery using random forests." International Journal of Remote Sensing, Vol. 33, No. 14, pp. 4502-4526. easyDataBt.do?menuIdx=3_1_2>. https://doi.org/10.1080/01431161.2011.649864
  9. FernAndez, D. M., Cernadas, E., Barro, S., and Amorim, D. (2014). "Do we need hundreds of classifiers to solve real world classification problems?" The Journal of Machine Learning Research, Vol. 15, No. 1, pp. 3133-3181.
  10. Hahm, C. H., and Kim, G. H. (2010). "A study of ecological flow assessment for environmental development in natural river." Journal of the Korean Society for Geo-spatial Information Science, Vol. 18, No. 1, pp. 47-53.
  11. Jung, C. G., Lee, J. W., Ahn, S. R., Hwang, S. J., and Kim, S. J. (2016). "Assessment of ecological streamflow for maintaining good ecological water environment." Journal of The Korean Society of Agricultural Engineers, Vol. 58, No. 3, pp. 1-12. https://doi.org/10.5389/KSAE.2016.58.3.001
  12. Kang, B., and Son, J. K. (2011). "The study on the evaluation of environment function at small stream-In the case of Hongdong stream in Hongsung-gun." Journal of the Korea Society of Environmental Restoration Technology, Vol. 14, No. 5, pp. 81-101. https://doi.org/10.13087/KOSERT.2011.14.5.081
  13. Kim, S., Noh, H. S., Hong, S. J., Kwak, J. W., and Kim, H. S. (2013). "Impact of climate change on habitat of the Rhynchocypris kumgangensis in Pyungchang river." Journal of Wetlands Research, Vol. 15, No. 2, pp. 271-280. https://doi.org/10.17663/JWR.2013.15.2.271
  14. Kim, Y. J., Shin, K., and Lee, O. M. (2009). "Water quality assessed by DAIpo and TDI of Bokha stream and Dal stream in South-Han river." Korean Journal of Environmental Biology, Vol. 27, No. 4, pp. 414-424.
  15. Lee, J. W., Jung, C. G., Kim, D. R., and Kim, S. J. (2018). "Assessment of future climate change impact on groundwater level behavior in Geum river basin using SWAT." Journal of Korea Water Resources Association, Vol. 51, No. 3, pp. 247-261. https://doi.org/10.3741/JKWRA.2018.51.3.247
  16. Ministry of Environment (MOE) (2015). Nationwide aquatic ecological monitoring program. National Institute of Environmental Research, Incheon, South Korea.
  17. Mkhwanazi, M., Chavez, J. L., and Rambikur, E. H. (2012). "Comparison of Large aperture scintillometer and satellite-based energy balance models in sensible heat flux and crop evapotranspiration determination." International Journal of Remote Sensing Applications, Vol. 12, pp. 24-30.
  18. Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., and Veith, T. L. (2007). "Model evaluation guidelines for systematic quantification of accuracy in watershed simulations." Transactions of the ASABE, Vol. 50, No. 3, pp. 885-900. https://doi.org/10.13031/2013.23153
  19. Muller, A. C., and Guido, S. (2016). Introduction to machine learning with Python: a guide for data scientists. O'Reilly Media, Inc., USA.
  20. Na, X., Zhang, S., Li, X., Yu, H., and Liu, C. (2010). "Improved land cover mapping using random forests combined with landsat thematic mapper imagery and ancillary geographic data." Photogrammetric Engineering and Remote Sensing, Vol. 76, No. 7, pp. 833-840. https://doi.org/10.14358/PERS.76.7.833
  21. Nash, J. E., and Sutcliffe, J. V. (1970). "River flow forecasting though 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
  22. Neitsch, S. L., Arnold, J. G., Kiniry, J. R., and Williams, J. R. (2001). Soil and water assessment tool: Theoretical documentation. U.S Agricultural Research Service, 340-367, Temple, Texas.
  23. Park, C. Y., Kim, Y. D., Kim, J. S., Song, J. W., and Choi, H. S. (2011). R data mining. Kyowoo, Seoul.
  24. Prasad, A. M., Iverson, L. R., and Liaw, A. (2006). "Newer classification and regression tree techniques: bagging and random forests for ecological prediction." Ecosystems, Vol. 9, No. 2, pp. 181-199. https://doi.org/10.1007/s10021-005-0054-1
  25. Probst, P., Boulesteix, A. L., and Bischl, B. (2019). "Tunability: importance of hyperparameters of machine learning algorithms." Journal of Machine Learning Research, Vol. 20, No. 53, pp. 1-32.
  26. Recknagel, F. (2001). "Applications of machine learning to ecological modelling." Ecological Modelling, Vol. 146, No. 1-3, pp. 303-310. https://doi.org/10.1016/S0304-3800(01)00316-7
  27. Santhi, C., Arnold, J. G., Williams, J. R., Dugas, W. A., Srinivasan, R., and Hauck, L. M. (2001). "Validation of the swat model on a large RWER basin with point and nonpoint sources." JAWRA Journal of the American Water Resources Association, Vol. 37, No. 5, pp. 1169-1188. https://doi.org/10.1111/j.1752-1688.2001.tb03630.x
  28. Sung, Y. D., Park, B. J., Joo, G. J., and Jung, K. S. (2005). "The estimation of ecological flow recommendations for fish habitat." Journal of Korea Water Resources Association, Vol. 38, No. 7, pp. 545-554. https://doi.org/10.3741/JKWRA.2005.38.7.545
  29. Water Environment Information System (2018). accessed 31 December 2018,
  30. Woo, S. Y., Jung, C. G., Lee, J. W., and Kim, S. J. (2019). "Evaluation of watershed scale aquatic ecosystem health by SWAT Modeling and Random forest technique." Sustainability, Vol. 11, No. 12, p. 3397. https://doi.org/10.3390/su11123397