Journal of Korean Society on Water Environment (한국물환경학회지)

Korean Society on Water Environment (한국물환경학회)
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Future Development Direction of Water Quality Modeling Technology to Support National Water Environment Management Policy

국가 물환경관리정책 지원을 위한 수질모델링 기술의 발전방향

  • Chung, Sewoong (Deparment of Environmental Engineering, Chungbuk National University) ;
  • Kim, Sungjin (Deparment of Environmental Engineering, Chungbuk National University) ;
  • Park, Hyungseok (Deparment of Environmental Engineering, Chungbuk National University) ;
  • Seo, Dongil (Deparment of Environmental Engineering, Chungnam National University)
  • Received : 2020.09.18
  • Accepted : 2020.11.16
  • Published : 2020.11.30
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Abstract

Water quality models are scientific tools that simulate and interpret the relationship between physical, chemical and biological reactions to external pollutant loads in water systems. They are actively used as a key technology in environmental water management. With recent advances in computational power, water quality modeling technology has evolved into a coupled three-dimensional modeling of hydrodynamics, water quality, and ecological inputs. However, there is uncertainty in the simulated results due to the increasing model complexity, knowledge gaps in simulating complex aquatic ecosystem, and the distrust of stakeholders due to nontransparent modeling processes. These issues have become difficult obstacles for the practical use of water quality models in the water management decision process. The objectives of this paper were to review the theoretical background, needs, and development status of water quality modeling technology. Additionally, we present the potential future directions of water quality modeling technology as a scientific tool for national environmental water management. The main development directions can be summarized as follows: quantification of parameter sensitivities and model uncertainty, acquisition and use of high frequency and high resolution data based on IoT sensor technology, conjunctive use of mechanistic models and data-driven models, and securing transparency in the water quality modeling process. These advances in the field of water quality modeling warrant joint research with modeling experts, statisticians, and ecologists, combined with active communication between policy makers and stakeholders.

Keywords

Acknowledgement

이 논문은 2020학년도 충북대학교 연구년제 사업의 연구비 지원에 의하여 연구되었음.

References

  1. Ambrose, R. B., Wool, T. A., and Martin, J. L. (1993). The water quality analysis simulation program, WASP5, Part A: Model documentation version 5.1, Environmental Research Laboratoy, USA. 1-210.
  2. An, I. K., Park, H. S., Chung, S. W., Ryu, I. G., Choi, J. K., and Kim, J. W. (2020). Analysis of organic carbon cycle and mass balance in Daecheong reservoir using three-dimensional hydrodynamic and water quality model, Journal of Korean Society on Water Environment, 36(4), 284-299. [Korean Literature] https://doi.org/10.15681/KSWE.2020.36.4.284
  3. Arhonditsis, G. B., Neumann, A., Shimoda. Y., Kim, D. K., Dong, F., Onandia, G., Yang, C., Javed, A., Brady, M., Visha, A., Ni, F., and Cheng, V. (2019). Castles built on sand or predictive limnology in action? Part A: Evaluation of an integrated modelling framework to guide adaptive management implementation in Lake Erie, Ecological Informatics, 53, 1-26.
  4. Bae, S. and Seo, D. (2018). Analysis and modeling of algal bloom occurrences in the Nakdong river, Ecological Modelling, 372, 53-63. https://doi.org/10.1016/j.ecolmodel.2018.01.019
  5. Brown, L. C. and Barnwell, Jr. T. O. (1987). The enhanced stream water quality models QUAL2E and QUAL2E-UNCAD: Documentation and user manual, US Environmental Protection Agency, Environmental Protection Agency, Athens, Ga, USA. 1-189.
  6. Cerco, C. F. and Cole, T. (1995). User's guide to the CE-QUAL-ICM three-dimensional eutrophication model, Release Version 1.0, Technical Report EL-95-15, US Army Engineer Waterways Experiment Station, Vicksburg, MS, USA.
  7. Chang, G. C. and Dickey, T. D. (2007). Interdisciplinary sampling strategies for detection and characterization of harmful algal blooms, Real-time Coastal Observing Systems for Marine Ecosystem Dynamics and Harmful Algal Blooms, 43-84.
  8. Chapra, S. C. (1997). Surface water-quality modeling, McGraw-Hill Companies, Inc., 14-16.
  9. Chapra, S. C., Pelletier, G. J., and Tao, H. (2012). QUAL2K: A modeling framework for simulating river and stream water quality, Version 2.12, Documentation and Users Manual, Civil and Environmental Engineering Dept., Tufts University, Medford, MA, USA.
  10. Cho, E., Arhonditsis, G. B., Khim, J., Chung, S. W., and Heo, T. (2016). Modeling metal-sediment interaction processes: Parameter sensitivity assessment and uncertainty analysis, Environmental Modelling & Software, 80, 159-174. https://doi.org/10.1016/j.envsoft.2016.02.026
  11. Cho, H. Y., Jun, K. S., and Lee, K. S. (1993). Water quality modeling by the WASP4 Model, Korean Society of Coastal and Ocean Engineers, 5(3), 221-231. [Korean Literature]
  12. Chung, S. W, Chong, S. A., and Park, H. S. (2016). Development and applications of a predictive model for geosmin in north Han river, Korea, Procedia Engineering, 154, 521-528. https://doi.org/10.1016/j.proeng.2016.07.547
  13. Chung, S. W. (2004). Density flow regime of turbidity current into a stratified Reservoir and vertical two-dimensional modeling, Journal of Korean Society of Environmental Engineers, 26(9), 970-978. [Korean Literature]
  14. Chung, S. W. and Oh, J. K. (2006). Calibration of CE-QUAL-W2 for a monomictic reservoir in monsoon climate area, Water Science and Technology, 54(12), 29-37. https://doi.org/10.2166/wst.2006.841
  15. Chung, S. W., Hipsey, M. R., and Imberger, J. (2009). Modelling the propagation of turbid density inflows into a stratified lake: Daecheong Reservoir, Korea, Environmental Modeling and Software, 24, 1462-1482.
  16. Chung, S. W., Imberger, J., Hipsey, M. R., and Lee, H. S. (2014). The influence of physical and physiological processes on the spatial heterogeneity of a Microcystis bloom in a stratified reservoir, Ecological Modelling, 289, 133-149. https://doi.org/10.1016/j.ecolmodel.2014.07.010
  17. Chung, S. W., Lee, H. S., and Jung, Y. R. (2008). The effect of hydrodynamic flow regimes on the algal bloom in a monomictic reservoir, Water Science and Technology, 58(6), 1291-1298. https://doi.org/10.2166/wst.2008.482
  18. Chung, S. W., Oh, J. K., and Ko, I. H. (2005). Simulations of temporal and spatial distributions of rainfall-induced turbidity flow in a Reservoir using CE-QUAL-W2, Journal of Korea Water Resources Association, 38(8), 655-664. [Korean Literature] https://doi.org/10.3741/JKWRA.2005.38.8.655
  19. Chung, S. W., Park, J. H., Kim, Y. K., and Yoon, S. W. (2007). Application of CE-QUAL-W2 to Daecheong reservoir for the eutrophication simulation, Journal of Korean Society on Water Environment, 23(1), 52-63. [Korean Literature]
  20. Del Giudice, D., Matli, V. R. R., and Obenour, D. R. (2019). Bayesian mechanistic modeling characterizes Gulf of Mexico hypoxia: 1968-2016 and future scenarios, Ecological Applications, 30(2), 1-14.
  21. Deltares. (2019). D-Water Quality User Manual, https://www.deltares.nl/software (accessed September. 2020).
  22. Gal, G., Hipsey, M., Rinke, K., and Robson, B. (2014). Novel approaches to address challenges in modelling aquatic ecosystems, Environmetal Modelling & Software, 61, 246-248. https://doi.org/10.1016/j.envsoft.2014.08.008
  23. Hamrick, J. M. (1992). A three dimensional environmental fluid dynamics computer code; Theoretical and computational aspects. Special Report, The College of William and Mary, Virginia Institute of Marine Science, Gloceslter Point, VA., USA.
  24. Hanson, P. C., Stillman, A. B., Jia, X., Karpatne, A., Dugan, H. A., Carey, C. C., Stachelek, J., Ward, N. K., Zhang, Y., Read, J. S., and Kumar, V. (2020). Predicting lake surface water phosphorus dynamics using process-guided machine learning, Ecological Modelling, 430, 1-11.
  25. Hipsey, M. R. (2008). The CWR computational aquatic ecosystem dynamics model CAEDYM. User manual, Centre for Water Research, The University of Western Australia.
  26. Hodges, B. and Dallimore, C. (2019). Aquatic ecosystem model: AEM3D v1.0 User manual, Hydronumerics, Victroia, Australia.
  27. Hodges, B. R. and Dallimore, C. (2006). Estuary, lake and coastal ocean model: ELCOM. Users guide, Centre for Water Research, University of Western Australia technical Publication.
  28. Jeon, J. H., Chung, S. W., Park, H. S., and Jang, J. R. (2011). Evaluation of EFDC for the simulations of water quality in Saemangeum Reservoir, Journal of Korean Society on Water Environment, 27(4), 445-460. [Korean Literature]
  29. Jeong, D. I. and Kong, D. S. (2007). User manual on QUALKO2, National Institute of Environmental Research. [Korean Literature]
  30. Jia, X., Willard, J., Karpatne, A., Read, J. S., Zwart, J. A., Steinbach, M., and Kumar, V. (2020). Physics-guided machine learning for scientific discovery: An application in simulating lake temperature profiles, Computer Science, 1-25.
  31. Kim, D. M., Park, H. S., and Chung, S. W. (2017). Relationship of the thermal stratification and critical flow velocity near the Baekje weir in Geum river, Journal of Korean Society on Water Environment, 33(4), 449-459. [Korean Literature] https://doi.org/10.15681/KSWE.2017.33.4.449
  32. Kim, J., Lee, T., and Seo, D. (2017). Algal bloom prediction of the lower Han river, Korea using the EFDC hydrodynamic and water quality model, Ecological Modeling, 366, 27-36. https://doi.org/10.1016/j.ecolmodel.2017.10.015
  33. Kim, Y. H., Kim, B. C., Choi, K. S., and Seo, D. I. (2001). Modeling of thermal stratification and transport of density flow in Soyang Reservoir using the 2-D hydrodynamic water quality model, CE-QUAL-W2, Journal of the Korean Society of Water and Wastewater, 15(1), 40-49. [Korean Literature]
  34. Kim, Y. K., Chung, S. W., Lee, H. S., and Jung, Y. R. (2007). Numerical modeling effects of a skimmer weir method on the control of algal growth in Daecheong Reservoir, Journal of Korean Society on Water Environment, 23(5), 581-590. [Korean Literature]
  35. Knutti, R. (2008). Should we believe model predictions of future climate change?, Royal Society, 366 (1885), 4647-4664.
  36. Kong, D. S. (2014). Water quality modeling of the eutrotphic transition zone in a river-type reservoir Paldang, Journal of Korean Society on Water Environment, 30(4), 429-440. [Korean Literature] https://doi.org/10.15681/KSWE.2014.30.4.429
  37. Lee, H. S., Chung, S. W., Jeong, H. Y., and Min, B. H. (2010). Analysis the effects of curtain weir on the control of algal bloom according to installation location in Dacheong reservoir, Journal of Korean Society on Water Environment, 26(2), 231-242. [Korean Literature]
  38. Lee, S. W., Seo, D. I., and Chung, S. W. (2003). Modelling of thermal stratification in Daechung reservoir using the 2-D hydrodynamic water quality model, CE-QUAL-W2, Journal of Korea Water Association, 676-686. [Korean Literature]
  39. Li, H., Qin, C., He, W., Sun, F., and Du, P. (2020). Prototyping a numerical model coupled with remote sensing for tracking harmful algal blooms in shallow lakes, Global Ecology and Conservation, 22, 1-13.
  40. Ma, J., Wang, P., Ren, L., Wang, X., and Paerl, H. W. (2019). Using alkaline phosphatase activity as a supplemental index to optimize predicting algal blooms in phosphorus-deficient lakes: A case study of lake Taihu, China, Ecological Indicators, 103, 698-712. https://doi.org/10.1016/j.ecolind.2019.04.043
  41. National Institute of Environmental Research (NIER). (2005). User's guide on QUAL-NIER model, https://www.me.go.kr (accessed September. 2020).
  42. Park, H. S. (2020). Characterizing temporal and spatial variations of the CO2 net atmospheric flux and influence factors analysis in a stratified reservoir, Chungbuk National University Doctor's Thesis. [Korean Literature]
  43. Park, H. S., Chung, S. W., Cho, E. J., and Lim, K. J. (2018). Impact of climate change on the persistent turbidity issue of a large dam reservoir in the temperate monsoon region, Climatic Change, 151(3), 365-378. https://doi.org/10.1007/s10584-018-2322-z
  44. Read, J. S., Jia, X., Willard, J., Appling, A. P., Zwart, J. A., Oliver, S. K., Karpatne, A., Hanesn, G. J. A., Hanson, P. C., Watkins, W., Steinbach, M., and Kumar, V. (2019). Process-guided deep learning predictions of lake water temperature, Water Resources Research, 55(11), 9173-9190. https://doi.org/10.1029/2019wr024922
  45. Ryu, I. G., Yu, S. J., and Chung, S. W. (2020). Characterizing density flow regimes of three rivers with different physicochemical properties in a run-of-the-river reservoir, Water, 12(3), 717. https://doi.org/10.3390/w12030717
  46. Schuwirth, N., Borgwardt, F., Domisch, S., Friedrichs-Manthely, M., Kattwinkel, M., Kneis, D., Kuemmerlen, M., Langhans, S. D., Marinez-Lopez, J., and Vermeiren, P. (2019). How to make ecological models useful for environmental management, Ecological Modelling, 411, 1-11.
  47. Seo D. I., Seo M. J., Koo, M. S., and Woo, J. K. (2009). Serial use of hydrodynamic and water quality model of the Geum River using EFDC-Hydro and WASP7.2, Journal of Korean Society of Water and Wastewater, 23(1), 15-22. [Korean Literature]
  48. Seo, D. I. and Kim, S. J. (1997). Water quality modeling of Shiwa lake using WASP5, Proceedings of the Annual Meeting of Korean Society of Environmental Engineers, Korean Society of Environmental Engineers, 513-515. [Korean Literature]
  49. Seo, D. I. and Lee, E. H. (2002). Water quality modeling of Daechung lake-effect of Yongdam dam, Journal of Korea Water Resources Association, 35(6), 737-751. [Korean Literature] https://doi.org/10.3741/JKWRA.2002.35.6.737
  50. Seo, D. I., Park, I. H., Lee, Y. H., Lee, S. W., Kim J. S., and Cho, H. W. (2001). Water quality modeling of Yongdam Reservoir considering hydrological condition, Journal of Korean Society on Water Environment, 2001, 57-60. [Korean Literature]
  51. Seo, I. W. and Lee, M. E. (2006). Development of 2-D advection-dispersion model with dispersion tensor considering velocity field, Journal of the Korean Society of Civil Engineers B, 26(2B), 171-178. [Korean Literature]
  52. Shin, J. G. and Im, C. S. (2000). A study on the water quality simulation in the midstream and downstream of Guem-river, Journal of Korea Water Resources Association, 33(2), 145-157. [Korean Literature]
  53. Stow, C. A., Reckhow, K. H., Qian, S. S., Lamon III, E. C., Arhonditsis, G. B., Borsuk, M. E., and Seo, D. I. (2007). Approaches to evaluate water quality model parameter uncertainty for adaptive TMDL implementation, Journal of the American Water Resources Association, 43(6), 1499-1507. https://doi.org/10.1111/j.1752-1688.2007.00123.x
  54. United States Environmental Protection Agency (U. S. EPA). (2009). Guidance on the development, evaluation, and application of environmental models, Environmental Measurements and Modeling, EPA/100/K-09/00. Washington, DC, USA.
  55. Vodacek, A., Li, Y., and Garrett, A. J. (2008). Remote sensing data assimilation in environmental models, 37th IEEE Applied Imagery Pattern Recognition Workshop, Washington DC, 1-5.
  56. Wynne, T. T., Stumpf, R. P., Tomlison, M. C., Schwab, D. J., Watabayashi, G. Y., and Christensen, J. D. (2011). Estimating cyanobacterial bloom transport by coupling remotely sensed imagery and a hydrodynamic model, Ecological Applications, 21(7) 2709-2721. https://doi.org/10.1890/10-1454.1
  57. Yun, Y. J., Jang, E. J., Park, H. S., and Chung, S. W. (2018). Evaluation of eutrophication and control alternatives in Sejong weir using EFDC model, Journal of Environmental Impact Assessment, 27(6), 548-561. [Korean Literature] https://doi.org/10.14249/EIA.2018.27.6.548
  58. Yun, Y. J., Park, H. S., Chung, S. W., Kim, Y. D., Ohn, I. S., and Lee, S. R. (2020). Long-term simulation and uncertainty quantification of water temperature in Soyanggang reservoir due to climate change, Journal of Korean Society on Water Environment, 36(1), 14-28. [Korean Literature] https://doi.org/10.15681/KSWE.2020.36.1.14