한국물환경학회지 (Journal of Korean Society on Water Environment)

  • 제39권1호
  • /
  • Pages.9-19
  • /
  • 2023
  • /
  • 2289-0971(pISSN)
  • /
  • 2289-098X(eISSN)
한국물환경학회 (Korean Society on Water Environment)
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기상학적 가뭄이 하천 BOD 수질에 미치는 영향의 확률론적 모니터링

Probabilistic Monitoring of Effect of Meteorological Drought on Stream BOD Water Quality

  • 서지유 (부경대학교 지구환경시스템과학부 환경공학전공) ;
  • 이정훈 (부경대학교 지구환경시스템과학부 환경공학전공) ;
  • 이호선 (한국수자원공사 국가가뭄정보분석센터) ;
  • 김상단 (부경대학교 지구환경시스템과학부 환경공학전공)
  • Jiyu Seo (Division of Earth Environmental System Science (Major of Environmental Engineering), Pukyong National University) ;
  • Jeonghoon Lee (Division of Earth Environmental System Science (Major of Environmental Engineering), Pukyong National University) ;
  • Hosun Lee (Drought Information Analysis Center, Korea Water Resources Corporation National) ;
  • Sangdan Kim (Division of Earth Environmental System Science (Major of Environmental Engineering), Pukyong National University)
  • 투고 : 2022.08.30
  • 심사 : 2022.12.23
  • 발행 : 2023.01.30
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초록

Drought is a natural disaster that can have serious social impacts. Drought's impact ranges from water supply for humans to ecosystems, but the impact of drought on river water quality requires careful investigation. In general, drought occurs meteorologically and is classified as agricultural drought, hydrological drought, and environmental drought. In this study, the BOD environmental drought is defined using the bivariate copula joint probability distribution model between the meteorological drought index and the river BOD, and based on this, the environmental drought condition index (EDCI-BOD) was proposed. The results of examining the proposed index using past precipitation and BOD observation data showed that EDCI-BOD expressed environmental drought well in terms of river BOD water quality. In addition, by classifying the calculated EDCI-BOD into four levels, namely, 'attention', 'caution', 'alert', and 'seriousness', a practical monitoring stage for environmental drought of BOD was constructed. We further estimated the sensitivity of the stream BOD to meteorological drought, and through this, we could identify the stream section in which the stream BOD responded relatively more sensitively to the occurrence of meteorological drought. The results of this study are expected to provide information necessary for river BOD management in the event of meteorological droughts.

키워드

과제정보

본 연구는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행되었음 (NRF-2022R1A2B5B01001750).

참고문헌

  1. Anderson, P. W. and Faust, S. D. (1972). Impact of drought on quality in a New Jersey water supply system 1, Journal of the American Water Resources Association, 8(4), 750-760. https://doi.org/10.1111/j.1752-1688.1972.tb05217.x
  2. Caruso, B. S. (2001). Regional river flow, water quality, aquatic ecological impacts and recovery from drought, Hydrological Sciences Journal, 46(5), 677-699. https://doi.org/10.1080/02626660109492864
  3. Chessman, B. C. and Robinson, D. P. (1987). Some effects of the 1982-83 drought on water quality and macroinvertebrate fauna in the lower La Trobe river, Australian Journal of Marine and Freshwater Research, 38(2), 289-299. https://doi.org/10.1071/MF9870289
  4. Crausbay, S., Ramirez, A., Carter, S., Cross, M., Hall, K., Bathke, D., Betancourt, J., Colt, S., Cravens, A., Dalton, M., Dunham, J., Hay, L., Hayes, M., McEvoy, J., McNutt, C., Moritz, M., Nislow, K., Raheem, N., and Sanford, T. (2017). Defining ecological drought for the twenty-first century, Bulletin of the American Meteorological Society, 98(12), 2543-2550. https://doi.org/10.1175/BAMS-D-16-0292.1
  5. Dai, A. (2013). Increasing drought under global warming in observations and models, Nature Climate Change, 3(1), 52-58. https://doi.org/10.1038/nclimate1633
  6. Esfahanian, E., Nejadhashemi, A. P., Abouali, M., Adhikari, U., Zhang, Z., Daneshvar, F., and Herman, M. R. (2017). Development and evaluation of a comprehensive drought index, Journal of Environmental Management, 185, 31-43. https://doi.org/10.1016/j.jenvman.2016.10.050
  7. Fang, W., Huang, S., Huang, Q., Huang, G., Wang, H., Leng, G., Leng, L., Wang, Y., and Guo, Y. (2019). Probabilistic assessment of remote sensing-based terrestrial vegetation vulnerability to drought stress of the Loess Plateau in China, Remote Sensing Environmental, 232, 111290.
  8. Goldyn, B., Kowalczewska-Madura, K., and Celewicz-Goldyn, S. (2015). Drought and deluge: influence of environmental factors on water quality of kettle holes in two subsequent years with different precipitation, Limnologica, 54, 14-22. https://doi.org/10.1016/j.limno.2015.07.002
  9. Guo, Y., Huang, S., Huang, Q., Leng, G., Fang, W., Wang, L., and Wang, H. (2020). Propagation thresholds of meteorological drought for triggering hydrological drought at various levels, Science of the Total Environment, 712, 136502.
  10. Hashemi, S. J., Khan, F., and Ahmed, S. (2016). Multivariate probabilistic safety analysis of process facilities using the Copula Bayesian Network model, Computers & Chemical Engineering, 93, 128-142.
  11. Heudorfer, B. and Stahl, K. (2016). Comparison of different threshold level methods for drought propagation analysis in Germany, Hydrology Research, 48(5), 1311-1326. https://doi.org/10.2166/nh.2016.258
  12. Hobbins, M. T., Wood, A., McEvoy, D. J., Huntington, J. L., Morton, C., Anderson, M., and Hain, C. (2016). The evaporative demand drought index. Part I: Linking drought evolution to variations in evaporative demand, Journal of Hydrometeorology, 17(6), 1745-1761. https://doi.org/10.1175/JHM-D-15-0121.1
  13. Hudson, L. D., Schaeffer, D. J., Tucker, W. J., and Ettinger, W. H. (1978). 1976 Illinois drought: Evidence for improved water quality, Environmental Management, 2(6), 555-559.
  14. Hyun, Y., Kang, H., Lee, J. M., and Hwang, H. T. (2017). Evaluation of environmental ecological drought considering stream baseflow, Korea Environment Institute, Sejong, Korea, 1-109. [Korean Literature]
  15. Kim, E., Choi, H. I., Park, M. J., Cho, S. J., and Kim, S. (2011). The effect of climate change on Korean drought occurrences using a stochastic soil water balance model, Scientific Research and Essays, 6, 2771-2783.
  16. Kim, J. S., Jain, S., Lee, J. H., Chen, H., and Park, S. Y. (2019). Quantitative vulnerability assessment of water quality to extreme drought in a changing climate, Ecological Indicators, 103, 688-697. https://doi.org/10.1016/j.ecolind.2019.04.052
  17. Korea Meteorological Administration (KMA). (2015). Open MET Data Portal, http://data.kma.go.kr (accessed August. 2020).
  18. Li, C., Leal Filho, W., Yin, J., Hu, R., Wang, J., Yang, C., Yin, S., Bao, Y., and Ayal, D. (2018). Assessing vegetation response to multi-time-scale drought across inner Mongolia plateau, Journal of Cleaner Production, 179, 210-216. https://doi.org/10.1016/j.jclepro.2018.01.113
  19. Ministry of Environment (ME). (2015). Water Environment Information System (WEIS), http://water.nier.go.kr (accessed August. 2020).
  20. Ministry of Environment (ME). (2016). Establishment of monitoring system and integrated model for estimating environmental ecological flow, TRKO201900000641, Bio-Monitoring Center, 155-221. [Korean Literature]
  21. Mosley, L. M. (2015). Drought impacts on the water quality of freshwater systems; Review and integration, Earth-Science Reviews, 140, 203-214. https://doi.org/10.1016/j.earscirev.2014.11.010
  22. Mulholland, P. J., Best, G. R., Coutant, C. C., Hornberger, G. M., Meyer, J. L., Robinson, P. J., Stenberg, J. R., Turner, R. E., Vera-Herrera, F., and Wetzel, R. G. (1997). Effects of climate change on freshwater ecosystems of the south eastern United States and the Gulf Coast of Mexico, Hydrological Processes, 11(8), 949-970. https://doi.org/10.1002/(SICI)1099-1085(19970630)11:8<949::AID-HYP513>3.0.CO;2-G
  23. Pena-Guerrero, M. D., Nauditt, A., Munoz-Robles, C., Ribbe, L., and Meza, F. (2020). Drought impacts on water quality and potential implications for agricultural production in the Maipo river basin, Central Chile, Hydrological Sciences Journal, 65(6), 1005-1021. https://doi.org/10.1080/02626667.2020.1711911
  24. Ryu, J., Ahn, J., and Kim, S. (2012). An application of drought severity-area-duration curves using copulas-based joint drought index, Journal of Korea Water Resource Association, 45(10), 1043-1050. [Korean Literature] https://doi.org/10.3741/JKWRA.2012.45.10.1043
  25. Seo, J., Won, J., Choi, J., Lee, J., and Kim, S. (2022). A copula model to identify the risk of river water temperature stress for meteorological drought, Journal of Environmental Management, 311, 114861.
  26. Sklar, M. (1959). Fonctions de repartition an dimensions et leurs marges, Publications de l'Institut Statistique de l'Universite de Paris, 8, 229-231.
  27. Van Loon, A. F. and Laaha, G. (2015). Hydrological drought severity explained by climate and catchment characteristics, Journal of Hydrology, 526, 3-14. https://doi.org/10.1016/j.jhydrol.2014.10.059
  28. Won, J. and Kim, S. (2020) Future drought analysis using SPI and EDDI to consider climate change in South Korea, Water Supply, 20(8), 3266-3280, ws2020209. https://doi.org/10.2166/ws.2020.209
  29. Won, J., Choi, J., Lee, O., and Kim, S. (2020). Copula-based Joint Drought Index using SPI and EDDI and its application to climate change, Science of The Total Environment, 744, 140701.
  30. Won, J., Jang, S., Kim, K., and Kim, S. (2018). Applicability of the evaporative demand drought index, Journal of the Korean Society of Hazard Mitigation, 18(6), 431-442. [Korean Literature] https://doi.org/10.9798/KOSHAM.2018.18.6.431
  31. Won, J., Seo, J., and Kim, S. (2022). A copula model integrating atmospheric moisture demand and supply for vegetation vulnerability mapping, Science of The Total Environment, 151464.
  32. Won, J., Seo, J., Lee, J., Lee, O., and Kim, S. (2021). Vegetation drought vulnerability mapping using a copula model of vegetation index and meteorological drought index, Remote Sensing, 13, 5103.
  33. Xu, Y., Zhang, X., Hao, Z., Singh, V. P., and Hao, F. (2021). Characterization of agricultural drought propagation over China based on bivariate probabilistic quantification, Journal of Hydrology, 598, 126194.
  34. Yee, K. C., Suhaila, J., Yusof, F., and Mean, F. H. (2014). Bivariate copula in fitting rainfall data. In AIP conference proceedings, American Institute of Physics, 1605(1), 986-990.
  35. Ylla, I., Sanpera-Calbet, I., Vazquez, E., Romani, A. M., Munoz, I., Butturini, A., and Sabater, S. (2010). Organic matter availability during pre-and post-drought periods in a Mediterranean stream, Global Change and River EcosystemsImplications for Structure, Function and Ecosystem Services, 217-232.
  36. Yoo, J., Ryu, J. H., Lee, J. H., and Kim, T. W. (2021). Probabilistic assessment of causal relationship between drought and water quality management in the Nakdong river basin using the Bayesian network model, Journal of Korea Water Resources Association, 54(10), 769-777. [Korean Literature]
  37. Zhang, L. and Singh, V. P. (2006). Bivariate flood frequency analysis using the copula method, Journal of Hydrology Engineering, 11(2), 150-164. https://doi.org/10.1061/(ASCE)1084-0699(2006)11:2(150)