Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
권호 표지
DOI QR코드

DOI QR Code

Quantitative analysis of drought propagation probabilities combining Bayesian networks and copula function

베이지안 네트워크와 코플라 함수의 결합을 통한 가뭄전이 발생확률의 정량적 분석

  • Shin, Ji Yae (Research Institute of Engineering Technology, Hanyang University) ;
  • Ryu, Jae Hee (Department of Civil and Environmental System Engineering, Hanyang University) ;
  • Kwon, Hyun-Han (Department of Civil and Environmental Engineering, Sejong University) ;
  • Kim, Tae-Woong (Department of Civil and Environmental Engineering, Hanyang University)
  • 신지예 (한양대학교(ERICA) 공학기술연구소) ;
  • 유재희 (한양대학교 대학원 건설환경시스템공학과) ;
  • 권현한 (세종대학교 건설환경공학과) ;
  • 김태웅 (한양대학교(ERICA) 건설환경공학과)
  • Received : 2021.03.20
  • Accepted : 2021.06.03
  • Published : 2021.07.31
Download PDF
⟨ Previous Next ⟩

Abstract

Meteorological drought originates from a precipitation deficiency and propagates to agricultural and hydrological droughts through the hydrological cycle. Comparing with the meteorological drought, agricultural and hydrological droughts have more direct impacts on human society. Thus, understanding how meteorological drought evolves to agricultural and hydrological droughts is necessary for efficient drought preparedness and response. In this study, meteorological and hydrological droughts were defined based on the observed precipitation and the synthesized streamflow by the land surface model. The Bayesian network model was applied for probabilistic analysis of the propagation relationship between meteorological and hydrological droughts. The copula function was used to estimate the joint probability in the Bayesian network. The results indicated that the propagation probabilities from the moderate and extreme meteorological droughts were ranged from 0.41 to 0.63 and from 0.83 to 0.98, respectively. In addition, the propagation probabilities were highest in autumn (0.71 ~ 0.89) and lowest in winter (0.41 ~ 0.62). The propagation probability increases as the meteorological drought evolved from summer to autumn, and the severe hydrological drought could be prevented by appropriate mitigation during that time.

수문순환 과정에서 강수량의 부족으로 발생된 기상학적 가뭄은 농업 및 수문학적 가뭄으로 전이된다. 기상학적 가뭄과 다르게 농업적 가뭄과 수문학적 가뭄의 발생은 인간 생활에 직접적으로 영향을 미치기 때문에 가뭄의 전이과정을 분석하는 것은 가뭄의 효과적 대비와 대응을 위하여 필요한 일이다. 본 연구에서는 관측된 강수량과 수문모형으로 모의된 자연유출량 자료를 바탕으로 기상학적 가뭄과 수문학적 가뭄을 정의하고, 베이지안 네트워크 모형을 활용하여 기상학적 가뭄과 수문학적 가뭄 간의 전이 관계를 확률론적으로 분석하였다. 베이지안 네트워크의 결합확률을 산정하기 위하여 코플라함수를 활용하였다. 분석 결과, 기상학적 가뭄이 보통가뭄 상황에서는 수문학적 가뭄으로 전이될 확률은 0.41 ~ 0.63, 기상학적 가뭄이 극심한 경우에는 수문학적 가뭄으로 전이될 확률은 0.83 ~ 0.98이었다. 즉, 기상학적 가뭄이 심화됨에 따라 수문학적 가뭄으로 전이될 확률은 증가되었다. 계절별로는 가을에 가뭄전이 발생확률이 0.71 ~ 0.89로 가장 높으며, 겨울에는 0.41 ~ 0.62의 값으로 낮게 산정되었다. 결론적으로 여름에서 가을까지 기상학적 가뭄 심도가 심화됨에 따라서 수문학적 가뭄으로의 전이 확률이 높아지며, 해당 시기 동안 가뭄에 적절한 대응을 수행하면 가뭄의 예방이 가능할 것이다.

Keywords

Acknowledgement

본 연구는 한국연구재단(NRF-2019R1I1A1A01059865)의 지원으로 수행되었습니다.

References

  1. Barker, L.J., Hannaford, J., Chiverton, A., and Svensson, C. (2016). "From meteorological to hydrological drought using standardised indicators." Hydrology and Earth System Sciences, Vol. 20, No. 6, pp. 2483-2505. https://doi.org/10.5194/hess-20-2483-2016
  2. Chen, L., Singh, V.P., Guo, S., Zhou, J., and Zhang, J. (2015). "Copulabased method for multisite monthly and daily streamflow simulation." Journal of Hydrology, Vol. 528, pp. 369-384. https://doi.org/10.1016/j.jhydrol.2015.05.018
  3. Chen, N., Li, R., Zhang, X., Yang, C., Wang, X., Zeng, L., Tang, S., Wang, W., Li, D., and Niyogi, D. (2020). "Drought propagation in Northern China Plain: a comparative analysis of GLDAS and MERRA-2 Datasets." Journal of Hydrology, Vol. 588, 125026. https://doi.org/10.1016/j.jhydrol.2020.125026
  4. Fang, W., Huang, S., Huang, Q., Huang, G., Wang, H., Leng, G., Wang, L., and Guo, Y. (2019a). "Probabilistic assessment of remote sensing-based terrestrial vegetation vulnerability to drought stress of the Loess Plateau in China." Remote Sensing of Environment, Vol. 232, 111290. https://doi.org/10.1016/j.rse.2019.111290
  5. Fang, W., Huang, S., Huang, Q., Huang, G., Wang, H., Leng, G., Wang, L., and Ma, L. (2019b). "Bivariate probabilistic quantification of drought impacts on terrestrial vegetation dynamics in mainland China." Journal of Hydrology, Vol. 577, 123980. https://doi.org/10.1016/j.jhydrol.2019.123980
  6. 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, Vol. 712, 136502. https://doi.org/10.1016/j.scitotenv.2020.136502
  7. Hao, Z., and Singh, V.P. (2015). "Drought characterization from a multivariate perspective: A review." Journal of Hydrology, Vol. 527, pp. 668-678. https://doi.org/10.1016/j.jhydrol.2015.05.031
  8. Haslinger, K., Koffler, D., Schoner, W., and Laaha, G. (2014). "Exploring the link between meteorological drought and streamflow: Effects of climate-catchment interaction." Water Resources Research, Vol. 50, No. 3, pp. 2468-2487. https://doi.org/10.1002/2013WR015051
  9. Huang, S., Li, P., Huang, Q., Leng, G., Hou, B., and Ma, L. (2017). "The propagation from meteorological to hydrological drought and its potential influence factors." Journal of Hydrology, No. 547, pp. 184-195. https://doi.org/10.1016/j.jhydrol.2017.01.041
  10. Jehanzaib, M., Sattar, M.N., Lee, J.H., and Kim, T.W. (2020). "Investigating effect of climate change on drought propagation from meteorological to hydrological drought using multimodel ensemble projections." Stochastic Environmental Research and Risk Assessment, Vol. 34, No. 1, pp. 7-21. https://doi.org/10.1007/s00477-019-01760-5
  11. Joseph, S., Sahai, A.K., and Goswami, B.N. (2009). "Eastward propagating MJO during boreal summer and Indian monsoon droughts." Climate Dynamics, Vol. 32, No. 7-8, pp. 1139-1153. https://doi.org/10.1007/s00382-008-0412-8
  12. Konapala, G., and Mishra, A. (2020). "Quantifying climate and catchment control on hydrological drought in the continental United States." Water Resources Research, Vol. 56, No. 1, e2018WR 024620.
  13. Madadgar, S., and Moradkhani, H. (2013). "A Bayesian framework for probabilistic seasonal drought forecasting." Journal of Hydrometeorology, Vol. 14, No. 6, pp. 1685-1705. https://doi.org/10.1175/JHM-D-13-010.1
  14. Mckee, T.B., Doesken, N.J., and Kleist, J. (1993) "The relationship of drought frequency and duration to time scales." Proceedings of the Eighth Conference on Applied Climatology, Vol. 17, No. 22. pp. 179-184.
  15. Nelsen, R.B. (2007). An introduction to copulas. Springer Science & Business Media, U.S.
  16. Pearl, J. (1988). Probabilistic reasoning in intelligence systems. Morgan Kaufmann, San Mateo, CA.
  17. Peter, E. (2003). Propagation of drought through groundwater systems Illustrated in the Pang (UK) and Upper-Guadiana (ES) catchments, Dissertation Wageningen Universiteit, Wageningen, Netherlands.
  18. Salvadori, G., and De Michele, C. (2015). "Multivariate real-time assessment of droughts via copula-based multi-site hazard trajectories and fans." Journal of Hydrology, Vol. 526, pp. 101-115. https://doi.org/10.1016/j.jhydrol.2014.11.056
  19. Sattar, M.N., Lee, J.Y., Shin, J.Y., and Kim, T.W. (2019). "Probabilistic characteristics of drought propagation from meteorological to hydrological drought in South Korea." Water Resources Management, Vol. 33, No. 7, pp. 2439-2452. https://doi.org/10.1007/s11269-019-02278-9
  20. Shi, H., Luo, G., Zheng, H., Chen, C., Bai, J., Liu, T., Ochege, F.U., and De Maeyer, P. (2020). "Coupling the water-energy-foodecology nexus into a Bayesian network for water resources analysis and management in the Syr Darya River basin." Journal of Hydrology, Vol. 581, 124387. https://doi.org/10.1016/j.jhydrol.2019.124387
  21. Shiau, J.T. (2006). "Fitting drought duration and severity with two dimensional copulas." Water Resources Management, Vol. 20, No. 5, pp. 795-815 https://doi.org/10.1007/s11269-005-9008-9
  22. Shin, J.Y., Chen, S., Lee, J.H., and Kim, T.W. (2018). "Investigation of drought propagation in South Korea using drought index and conditional probability." Terrestrial, Atmospheric & Oceanic Sciences, Vol. 29, No. 2, pp. 231-241. https://doi.org/10.3319/tao.2017.08.23.01
  23. Song, S., and Singh, V.P. (2010). "Meta-elliptical copulas for drought frequency analysis of periodic hydrologic data." Stochastic Environmental Research and Risk Assessment, Vol. 24, No. 3, pp. 425-444. https://doi.org/10.1007/s00477-009-0331-1
  24. Tallaksen, L.M., Hisdal, H., and Van Lanen, H.A. (2009). "Space-time modelling of catchment scale drought characteristics." Journal of Hydrology, Vol. 375, No. 3, pp. 363-372. https://doi.org/10.1016/j.jhydrol.2009.06.032
  25. Thomas, T., Jaiswal, R.K., Nayak, P.C., and Ghosh, N.C. (2015). "Comprehensive evaluation of the changing drought characteristics in Bundelkhand region of Central India." Meteorology and Atmospheric Physics, Vol. 127, No. 2, pp. 163-182. https://doi.org/10.1007/s00703-014-0361-1
  26. Van Loon, A.F. (2015). "Hydrological drought explained." Wiley Interdisciplinary Reviews: Water, Vol. 2, No. 4, pp. 359-392. https://doi.org/10.1002/wat2.1085
  27. Van Loon, A.F., and Van Lanen, H.A.J. (2012). "A process-based typology of hydrological drought." Hydrology and Earth System Sciences, Vol. 16, No. 7, pp. 1915-1946. https://doi.org/10.5194/hess-16-1915-2012
  28. Van Loon, A.F., Van Lanen, H.A., Tallaksen, L.M., Hanel, M., Fendekova, M., Machlica, M., Sapriza, G., Koutroulis, A., Van Huijgevoort., M.H.J., Bermudez, J.J., Tsanis, L., and Hisdal, H. (2011). Propagation of drought through the hydrological cycle. WATCH Technical Report, No. 32, European Commission, Brussel, Belgium, pp. 3-4.
  29. Vicente-Serrano, S.M., Begueria, S., and Lopez-Moreno, J.I. (2010). "A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index." Journal of Climate, No. 23, No. 7, pp. 1696-1718. https://doi.org/10.1175/2009JCLI2909.1
  30. Wong, G., Van Lanen, H.A.J., and Torfs, P.J.J.F. (2013). "Probabilistic analysis of hydrological drought characteristics using meteorological drought." Hydrological Sciences Journal, Vol. 58, No. 2, pp. 253-270. https://doi.org/10.1080/02626667.2012.753147
  31. Wu, J., Chen, X., Yao, H., Gao, L., Chen, Y., and Liu, M. (2017). "Non-linear relationship of hydrological drought responding to meteorological drought and impact of a large reservoir." Journal of Hydrology, Vol. 551, pp. 495-507. https://doi.org/10.1016/j.jhydrol.2017.06.029
  32. Zhai, J., Su, B., Krysanova, V., Vetter, T., Gao, C., and Jiang, T. (2010). "Spatial variation and trends in PDSI and SPI indices and their relation to streamflow in 10 large regions of China." Journal of Climate, Vol. 23, No. 3, pp. 649-663. https://doi.org/10.1175/2009JCLI2968.1
  33. Zhao, L., Lyu, A., Wu, J., Hayes, M., Tang, Z., He, B., Liu, J., and Liu, M. (2014). "Impact of meteorological drought on streamflow drought in Jinghe River Basin of China." Chinese Geographical Science, Vol. 24, No. 6, pp. 694-705. https://doi.org/10.1007/s11769-014-0726-x
  34. Zhu, Y., Liu, Y., Wang, W., Singh, V.P., Ma, X., and Yu, Z. (2019). "Three dimensional characterization of meteorological and hydrological droughts and their probabilistic links." Journal of Hydrology, Vol. 578, p. 124016. https://doi.org/10.1016/j.jhydrol.2019.124016
  35. Zhu, Y., Wang, W., Singh, V.P., and Liu, Y. (2016). "Combined use of meteorological drought indices at multi-time scales for improving hydrological drought detection." Science of the Total Environment, Vol. 571, pp. 1058-1068. https://doi.org/10.1016/j.scitotenv.2016.07.096
  36. Zscheischler, J., and Seneviratne, S.I. (2017). "Dependence of drivers affects risks associated with compound events." Science Advances, Vol. 3, No. 6, e1700263. https://doi.org/10.1126/sciadv.1700263