Journal of Korean Society on Water Environment (한국물환경학회지)

Korean Society on Water Environment (한국물환경학회)
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Major Watershed Characteristics Influencing Spatial Variability of Stream TP Concentration in the Nakdong River Basin

낙동강 유역에서 하천 TP 농도의 공간적 변동성에 영향을 미치는 주요 유역특성

  • Seo, Jiyu (Division of Earth Environmental System Science(Major of Environmental Engineering), Pukyong National University) ;
  • Won, Jeongeun (Division of Earth Environmental System Science(Major of Environmental Engineering), Pukyong National University) ;
  • Choi, Jeonghyeon (Division of Earth Environmental System Science(Major of Environmental Engineering), Pukyong National University) ;
  • Kim, Sangdan (Department of Environmental Engineering, Pukyong National University)
  • 서지유 (부경대학교 지구환경시스템과학부(환경공학전공)) ;
  • 원정은 (부경대학교 지구환경시스템과학부(환경공학전공)) ;
  • 최정현 (부경대학교 지구환경시스템과학부(환경공학전공)) ;
  • 김상단 (부경대학교 환경공학과)
  • Received : 2021.04.09
  • Accepted : 2021.05.26
  • Published : 2021.05.30
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Abstract

It is important to understand the factors influencing the temporal and spatial variability of water quality in order to establish an effective customized management strategy for contaminated aquatic ecosystems. In this study, the spatial diversity of the 5-year (2015 - 2019) average total phosphorus (TP) concentration observed in 40 Total Maximum Daily Loads unit-basins in the Nakdong River watershed was analyzed using 50 predictive variables of watershed characteristics, climate characteristics, land use characteristics, and soil characteristics. Cross-correlation analysis, a two-stage exhaustive search approach, and Bayesian inference were applied to identify predictors that best matched the time-averaged TP. The predictors that were finally identified included watershed altitude, precipitation in fall, precipitation in winter, residential area, public facilities area, paddy field, soil available phosphate, soil magnesium, soil available silicic acid, and soil potassium. Among them, it was found that the most influential factors for the spatial difference of TP were watershed altitude in watershed characteristics, public facilities area in land use characteristics, and soil available silicic acid in soil characteristics. This means that artificial factors have a great influence on the spatial variability of TP. It is expected that the proposed statistical modeling approach can be applied to the identification of major factors affecting the spatial variability of the temporal average state of various water quality parameters.

Keywords

Acknowledgement

본 연구는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행되었음 (NRF-2019R1A2C1003114).

References

  1. Allan, J. D. (2004). Influence of land use and landscape setting on the ecological status of rivers, Limnetica, 23(3-4), 187-197. https://doi.org/10.23818/limn.23.17
  2. Allen, R., Pereira, L., Raes, D., and Smith, M. (1998). Crop evapotranspiration - guidelines for computing crop water requirements - FAO Irrigation and Drainage Paper 56, Food and Agriculture Organization, Rome.
  3. Bozdogan, H. (1987). Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions, Psychometrika, 52(3), 345-370. https://doi.org/10.1007/BF02294361
  4. Burnham, K. P. and Anderson, D. R. (2002). Model selection and multimodel inference: A practical information-theoretic approach (2nd ed.), Springer.
  5. Cho, H. and Kim, S. (2020). Estimation of livestock pollutant sources reduction effect on water quality in Hapcheon dam watershed using HSPF model, Journal of Korean Society on Water Environment, 36(2), 98-108. [Korean Literature] https://doi.org/10.15681/KSWE.2020.36.2.98
  6. Choi, D., Jun, H., Shin, H. S., Yoon, Y. S., and Kim, S. (2010). The effect of climate change on Byeongseong stream's water quantity and quality, Desalination and Water Treatment, 19(1-3), 105-112. https://doi.org/10.5004/dwt.2010.1902
  7. Choi, J., Jang, S., and Kim, S. (2020). Parameter and modeling uncertainty analysis of semi-distributed hydrological model using Markov-Chain Monte Carlo technique, Journal of Korean Society on Water Environment, 36(5), 373-384. [Korean Literature] https://doi.org/10.15681/KSWE.2020.36.5.373
  8. Choi, J., Lee, O., Won, J., and Kim, S. (2020). Stochastic simple hydrologic partitioning model associated with Markov Chain Monte Carlo and ensemble Kalman filter, Journal of Korean Society on Water Environment, 36(5), 353-363. [Korean Literature] https://doi.org/10.15681/KSWE.2020.36.5.353
  9. Drewry, J. J., Newham, L. T. H., Greene, R. S. B., Jakeman, A. J., and Croke, B. F. W. (2006). A review of nitrogen and phosphorus export to waterways: context for catchment modelling, Marine and Freshwater Research, 57(8) 757-774. https://doi.org/10.1071/MF05166
  10. Gallagher, M. and Doherty, J. (2007). Parameter estimation and uncertainty analysis for a watershed model, Environmental Modelling & Software, 22(7), 1000-1020. https://doi.org/10.1016/j.envsoft.2006.06.007
  11. Giri, S. and Qiu, Z. (2016). Understanding the relationship of land uses and water quality in twenty first century: A review, Journal of environmental management, 173, 41-48. https://doi.org/10.1016/j.jenvman.2016.02.029
  12. Granger, S. J., Bol, R., Anthony, S., Owens, P. N., White, S. M., and Haygarth, P. M. (2010). Towards a holistic classification of diffuse agricultural water pollution from intensively managed grasslands on heavy soils, Advances in Agronomy, 105, 83-115. https://doi.org/10.1016/S0065-2113(10)05003-0
  13. Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F. (2009). Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, Journal of hydrology, 377(1-2), 80-91. https://doi.org/10.1016/j.jhydrol.2009.08.003
  14. Han, S., Kim, E., and Kim, S. (2009). The water quality management in the Nakdong river watershed using multivariate statistical techniques, KSCE Journal of Civil Engineering, 13(2), 97-105. https://doi.org/10.1007/s12205-009-0097-5
  15. Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications, Biometrika, 57(1), 97-103. https://doi.org/10.1093/biomet/57.1.97
  16. Hurvich, C. M. and Tsai, C. L. (1989). Regression and time series model selection in small samples, Biometrika, 76(2), 297-307. https://doi.org/10.1093/biomet/76.2.297
  17. Jiang, Y. (2009). China's water scarcity, Journal of Environmental Management, 90(11), 3185-3196. https://doi.org/10.1016/j.jenvman.2009.04.016
  18. Jung, K., Lee, K., Im, T., Lee, I., Kim, S., Han, K., and Ahn, J. (2016). Evaluation of water quality for the Nakdong river watershed using multivariate analysis, Environmental Technology &Innovation, 5, 67-82. https://doi.org/10.1016/j.eti.2015.12.001
  19. Kang, B., Kong, H., Park, I., and Shin, I. (2017). Measures to improve water quality for west Nakdong basin using EFDC model, Journal of Korea Water Resources Association, 95-99. [Korean Literature]
  20. Kim, K, Jung, M., Tsang, Y., and Kwon, H. (2020). Stochastic modeling of chlorophyll-a for probabilistic assessment and monitoring of algae blooms in the lower Nakdong river, South Korea, Journal of Hazardous Materials, 400, 123066. https://doi.org/10.1016/j.jhazmat.2020.123066
  21. Kim, R., Won, J., Choi, J., Lee, O., and Kim, S. (2020). Application of Bayesian approach to parameter estimation of TANK model: Comparison of MCMC and GLUE methods, Journal of Korean Society on Water Environment, 36(4), 300-313. [Korean Literature] https://doi.org/10.15681/KSWE.2020.36.4.300
  22. Korea Meteorological Administration (KMA). (2015). Open MET Data Portal, http://data.kma.go.kr (accessed August. 2020).
  23. Lee, A., Cho, S., Park, M. J., and Kim, S. (2013). Determination of standard target water quality in the Nakdong river basin for the total maximum daily load management system in Korea, KSCE Journal of Civil Engineering, 17(2), 309-319. https://doi.org/10.1007/s12205-013-1893-5
  24. Lee, J., Lee, Y., Woo, S., Kim, W., and Kim, S. (2020). Evaluation of water quality interaction by dam and weir operation using SWAT in the Nakdong river Basin of South Korea, Sustainability, 12(17), 6845. https://doi.org/10.3390/su12176845
  25. Lim, J. S., Kim, Y. W., Lee, J. H., Park, T. J., and Byun, I. G. (2015). Evaluation of correlation between chlorophyll-a and multiple parameters by multiple linear regression analysis, Journal of Korean Society of Environmental Engineers, 37(5), 253-261. [Korean Literature] https://doi.org/10.4491/KSEE.2015.37.5.253
  26. Lintern, A., Webb, J. A., Ryu, D., Liu, S., Bende Michl, U., Waters, D., Leahy, P., Wilson, P., and Western, A. W. (2018). Key factors influencing differences in stream water quality across space, Wiley Interdisciplinary Reviews: Water, 5(1), e1260. https://doi.org/10.1002/wat2.1260
  27. Lintern, A., Webb, J. A., Ryu, D., Liu, S., Waters, D., Leahy, P., Bende-Michl, U., and Western, A. W. (2018). What are the key catchment characteristics affecting spatial differences in riverine water quality?, Water Resources Research, 54(10), 7252-7272. https://doi.org/10.1029/2017wr022172
  28. Loucks, D. P., van Beek, E., Stedinger, J. R., Dijkman, J. P. M., and Villars, M. T. (2005). Water resources systems planning and management: An introduction to methods, models and applications, Paris, UNESCO.
  29. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E. (1953). Equation of state calculations by fast computing machines, The journal of chemical physics, 21(6), 1087-1092. https://doi.org/10.1063/1.1699114
  30. Meybeck, M., Chapman, D., and Helmer, R. (1989). Global freshwater quality: A first assessment, WHO and UNEP/Blackwell Ltd, New York.
  31. Ministry of Environment (ME). (2007). Environmental Geographic Information Service (EGIS), https://egis.me.go.kr (accessed August 2020).
  32. Ministry of Environment (ME). (2010). Korean Soil Information System, http://soil.rda.go.kr (accessed August 2020).
  33. Ministry of Environment (ME). (2015a). National Spatial Data Infrastructure Portal (NSDI), http://data.nsdi.go.kr (accessed August 2020).
  34. Ministry of Environment (ME). (2015b). Water Environment Information System (WEIS), http://water.nier.go.kr (accessed August 2020).
  35. Ministry of Environment (ME). (2016). Total Pollution Load Management System, http://tmdlms.nier.go.kr (accessed August 2020).
  36. Muleta, M. K. and Nicklow, J. W. (2005). Sensitivity and uncertainty analysis coupled with automatic calibration for a distributed watershed model, Journal of hydrology, 306(1-4), 127-145. https://doi.org/10.1016/j.jhydrol.2004.09.005
  37. Park, S. and Lee, Y. (2002). A water quality modeling study of the Nakdong river, Korea, Ecological Modelling, 152(1), 65-75. https://doi.org/10.1016/S0304-3800(01)00489-6
  38. Paul, M. J. and Meyer, J. L. (2001). Streams in the urban landscape, Annual review of Ecology and Systematics, 32(1), 333-365. https://doi.org/10.1146/annurev.ecolsys.32.081501.114040
  39. Saft, M., Peel, M. C., Western, A. W., and Zhang, L. (2016). Predicting shifts in rainfall runoff partitioning during multiyear drought: Roles of dry period and catchment characteristics, Water Resources Research, 52(12), 9290-9305. https://doi.org/10.1002/2016WR019525
  40. Schwarzenbach, R. P., Egli, T., Hofstetter, T. B., Von Gunten, U., and Wehrli, B. (2010). Global water pollution and human health, Annual review of environment and resources, 35, 109-136. https://doi.org/10.1146/annurev-environ-100809-125342
  41. Smith, V. H., Tilman, G. D., and Nekola, J. C. (1999). Eutrophication: impacts of excess nutrient inputs on freshwater, marine, and terrestrial ecosystems, Environmental pollution, 100(1-3), 179-196. https://doi.org/10.1016/S0269-7491(99)00091-3
  42. Suarez, J. and Puertas, J. (2005). Determination of COD, BOD, and suspended solids loads during combined sewer overflow (CSO) events in some combined catchments in Spain, Ecological Engineering, 24(3), 199-217. https://doi.org/10.1016/j.ecoleng.2004.11.005
  43. Tramblay, Y., Ouarda, T. B., St-Hilaire, A., and Poulin, J. (2010). Regional estimation of extreme suspended sediment concentrations using watershed characteristics, Journal of Hydrology, 380(3-4), 305-317. https://doi.org/10.1016/j.jhydrol.2009.11.006
  44. Vorosmarty, C. J., McIntyre, P. B., Gessner, M. O., Dudgeon, D., Prusevich, A., Green, P., Glidden. S., Bunn. S., Sulivan, C., Reidy Liermann. C., and Davies, P. M. (2010). Global threats to human water security and river biodiversity, Nature, 467(7315), 555-561. https://doi.org/10.1038/nature09440
  45. Young, R. G., Quarterman, A. J., Eyles, R. F., Smith, R. A., and Bowden, W. B. (2005). Water quality and thermal regime of the Motueka River: influences of land cover, geology and position in the catchment, New Zealand Journal of Marine and Freshwater Research, 39(4), 803-825. https://doi.org/10.1080/00288330.2005.9517354