Verification of water environment monitoring network representativeness under estuary backwater effects

  • Jung Min Ahn
  • Kang Young Jung
  • Deuk Seok Yang
  • Dong-seok Shin


The multi-functional weirs constructed as part of the Four Major River Restoration Project in Korea are operated for water level management and may have a backwater effect in estuaries. If the main channel of the Nakdong River flows backward and affects the estuary water, the water quality in the estuaries may not be representative of the tributary water quality. In this study, we confirmed the representativeness of the existing water quality monitoring networks using spatiotemporally disperse electrical conductivity observations, self-organizing maps (SOMs) for monthly pattern analysis, and the LOcally WEighted Scatter plot Smoother (LOWESS) technique for trend analysis. The results show that the Namgang 4-1 site, which is located in the Nam River estuary, is not affected by the Nakdong River, while the Baekcheon (Sunwongyo) site in the Baekcheon estuary is always affected by the Nakdong River. Therefore, it is necessary to relocate the existing monitoring network or establish a new monitoring network for locations affected by mainstream backflow, as is seen in Baekcheon (Sunwongyo). The methods proposed in this study, including spatiotemporally diverse electrical conductivity measurement, dimensionless fluctuation values, SOMs, and LOWESS, can be used to verify the representativeness of water quality measurement networks in other regions.


Tributary drainage Water quality Backwater effect SOM LOWESS 


Funding information

This research was supported by a grant (NIER-2017-01-01-081) from the National Institute of Environmental Research (NIER), which is funded by the Ministry of Environment (MOE) of the Republic of Korea.


  1. Davie, T. (2002). Fundamentals of hydrology (2nd ed.). London and New York: Routledge.Google Scholar
  2. Garcia, H. L., & Gonzalez, I. M. (2003). Self-organizing map and clustering for wastewater treatment monitoring. Engineering Applications of Artificial Intelligence, 17, 215–225.CrossRefGoogle Scholar
  3. Hayashi, M. (2004). Temperature-electrical conductivity relation of water for environmental monitoring and geophysical data inversion. Environmental Monitoring and Assessment, 96(1–3), 119–128.CrossRefGoogle Scholar
  4. Helsel, D. R., & Hirsch, R. M. (2002). Statistical methods in water resources techniques of water-resources investigations. Book 4, chapter A3, U.S. Geological Survey, 226–230, 329–335.Google Scholar
  5. Hong, Y. S., & Rosen, M. R. (2001). Intelligent characterisation and diagnosis of the groundwater quality in an urban fractured-rock aquifer using an artificial neural network. Urban Water, 3, 193–204.CrossRefGoogle Scholar
  6. Jung, K. Y., Lee, I. J., Lee, K. L., Cheon, S. U., Hong, J. Y., & Ahn, J. M. (2016). Long-term trend analysis and exploratory data analysis of Geumho River based on seasonal Mann-Kendall test. Journal of Environmental Science International, 25(2), 217–229.CrossRefGoogle Scholar
  7. Kadlec, R. H., & Wallace, S. (2009). Treatment wetlands (2nd ed.). USA: CRC Press.Google Scholar
  8. Kim, J. T. (2014). Lowess and outlier analysis of biological oxygen demand on Nakdong main stream river. Journal of the Korean Data and Information Science Society, 25(1), 119–130.CrossRefGoogle Scholar
  9. Kim, E. S., & Lee, S. H. (2015). Hydraulic analysis using a two-dimensional model (II): bridge backwater analysis. Journal of the Korea Academia-Industrial Cooperation Society, 16(8), 5716–5720.CrossRefGoogle Scholar
  10. Kim, Y. G., Jin, Y. H., Park, S. C., & Kim, J. M. (2010). Analysis of non-point source pollution discharge characteristics in leisure facilities areas for pattern classification. Journal of Korea Water Resource Association, 43(12), 1029–1038.CrossRefGoogle Scholar
  11. Kim, E. J., Kim, Y. S., Rhew, D. H., Ryu, J. C., & Park, B. K. (2014). A study on the water quality changes of TMDL unit watershed in Guem River basin using a nonparametric trend analysis. Journal of Korean Society on Water Environment, 30(2), 148–158.CrossRefGoogle Scholar
  12. Kim, J., Kim W., & Kim S. H. (2015a). Discharge estimation at monitoring station affected by backwater effects in a junction. 2015 Proceedings of the Korea Water Resource Association, 393.Google Scholar
  13. Kim, S., Kim, H., & Yoon, K. S. (2015b). Analysis of flood control effect by applying the connecting channel in estuary area including the confluence of two rivers. Journal of Korea Water Resource Association, 48(12), 1065–1075.CrossRefGoogle Scholar
  14. Kohonen, T. (2002). Self-organizing maps. Berlin: Springer.Google Scholar
  15. Lizotte Jr., R. E., Shields Jr., F. D., Knight, S. S., Cooper, C. M., Testa III, S., & Bryant, C. T. (2011). Effects of artificial flooding on water quality of a floodplain backwater. River Research and Applications, 28(10), 1644–1657.CrossRefGoogle Scholar
  16. Lu, R. S., & Lo, S. L. (2002). Diagnosing reservoir water quality using self-organizing maps and fuzzy theory. Water Research, 36, 2265–2274.CrossRefGoogle Scholar
  17. Lu, H., Bryant, R. B., Buda, A. R., Collick, A. S., Folmar, G. J., & Kleinman, P. J. A. (2015). Long-term trends in climate and hydrology in an agricultural, headwater watershed of central Pennsylvania. Journal of Hydrology: Regional Studies, 4, 713–731.Google Scholar
  18. Ministry of Environment. (2017). Water environmental monitoring network operation plan. Sejong, Korea: Ministry of Environment.Google Scholar
  19. Ministry of Land, Infrastructure, and Transport. (2009). Nakdonggang river maintenance basic plan. Sejong, Korea: Ministry of Land, Infrastructure, and Transport.Google Scholar
  20. Paik, B. C., Kim, C. K., & Kim, T. R. (2011). A study on the pattern analysis of correlation between the river flow and water quality using a SOM technique. Journal of Korea Society for the Urban Environment, 11(2), 153–160.Google Scholar
  21. Rodriguez-Murillo, J. C., & Filella, M. (2015). Temporal evolution of organic carbon concentrations in Swiss lakes: trends of allochthonous and autochthonous organic carbon. Science of the Total Environment, 520, 13–22.CrossRefGoogle Scholar
  22. Sha, Y., Wei, Y., Li, W., Fan, J., & Cheng, G. (2015). Artificial tide generation and its effects on the water environment in the backwater of Three Gorges Reservoir. Journal of Hydrology, 528, 230–237.CrossRefGoogle Scholar
  23. SOM Toolbox Team. (2000). SOM toolbox for Matlab 5. Helsinki: Helsinki University.Google Scholar
  24. Water Environment Information System. (2017). Available online at: (accessed on 12. 2017).
  25. Wu, M. L., Zhang, Y. Y., Dong, J. D., Wang, Y. S., & Cai, C. H. (2011). Identification of coastal water quality by self-organizing map in Sanya Bay, South China Sea. Aquatic Ecosystem Health & Management, 14(3), 291–297.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Jung Min Ahn
    • 1
  • Kang Young Jung
    • 1
  • Deuk Seok Yang
    • 1
  • Dong-seok Shin
    • 1
  1. 1.National Institute of Environmental Research (NIER)Nakdong River Environment Research CenterGoryeong-gunRepublic of Korea

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