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Development of daily spatio-temporal downscaling model with conditional Copula based bias-correction of GloSea5 monthly ensemble forecasts

조건부 Copula 함수 기반의 월단위 GloSea5 앙상블 예측정보 편의보정 기법과 연계한 일단위 시공간적 상세화 모델 개발

  • Kim, Yong-Tak (Department of Civil and Environmental Engineering, Sejong University) ;
  • Kim, Min Ji (Hydrometeorological and Meteorological Drought Team, Climate Science Bureau, Korea Meteorological Administration) ;
  • Kwon, Hyun-Han (Department of Civil and Environmental Engineering, Sejong University)
  • 김용탁 (세종대학교 건설환경공학과) ;
  • 김민지 (기상청 기후과학국 수문기상팀) ;
  • 권현한 (세종대학교 건설환경공학과)
  • Received : 2021.10.18
  • Accepted : 2021.11.29
  • Published : 2021.12.31

Abstract

This study aims to provide a predictive model based on climate models for simulating continuous daily rainfall sequences by combining bias-correction and spatio-temporal downscaling approaches. For these purposes, this study proposes a combined modeling system by applying conditional Copula and Multisite Non-stationary Hidden Markov Model (MNHMM). The GloSea5 system releases the monthly rainfall prediction on the same day every week, however, there are noticeable differences in the updated prediction. It was confirmed that the monthly rainfall forecasts are effectively updated with the use of the Copula-based bias-correction approach. More specifically, the proposed bias-correction approach was validated for the period from 1991 to 2010 under the LOOCV scheme. Several rainfall statistics, such as rainfall amounts, consecutive rainfall frequency, consecutive zero rainfall frequency, and wet days, are well reproduced, which is expected to be highly effective as input data of the hydrological model. The difference in spatial coherence between the observed and simulated rainfall sequences over the entire weather stations was estimated in the range of -0.02~0.10, and the interdependence between rainfall stations in the watershed was effectively reproduced. Therefore, it is expected that the hydrological response of the watershed will be more realistically simulated when used as input data for the hydrological model.

본 연구에서는 예측 모델의 정확성이 비교적 높은 월단위의 GloSea5 자료를 기반으로 예측강수량을 편의보정 및 시공간적으로 상세화하여 연속된 일단위 강우량을 모의하고자 하였다. 이를 위하여 GloSea5를 입력자료로 조건부 Copula와 MNHMM 모형을 적용하여 일단위 시계열 강우량 예측정보를 생산할 수 있는 모델링 체계를 제시하였다. 모의결과 동기간의 자료라도 매주 생산되는 결과가 큰 차이를 나타내는 예측강수량의 변동성이 유의하게 개선되었다. 모형 검증에서 모의된 일강수량, 연속강우확률, 연속무강우확률 및 강우일수가 관측자료와 유사한 값으로 모의되는 등 수문모형의 입력자료로써 활용성이 클 것으로 판단된다. 유역 단위에서의 모의된 강수량 계열간의 상관성 차이가 최소 -0.02에서 최대 0.10로 유역의 강우관측소간 상호종속성을 효과적으로 복원되는 등 수문모형의 입력자료로 활용 시 유역의 수문기상학적 반응을 보다 현실적으로 모의가 가능할 것으로 기대된다.

Keywords

Acknowledgement

본 연구는 환경부/한국환경산업기술원의 지원으로 수행되었음(과제번호 127568).

References

  1. Camp, J., Roberts, M., MacLachlan, C., Wallace, E., Hermanson, L., Brookshaw, A., Arribas, A., and Scaife, A.A. (2015). "Seasonal forecasting of tropical storms using the Met Office GloSea5 seasonal forecast system." Quarterly Journal of the Royal Meteorological Society, Vol. 141, No. 691, pp. 2206-2219. https://doi.org/10.1002/qj.2516
  2. Chen, L., Singh, V.P., Guo, S., Mishra, A.K., and Guo, J. (2013). "Drought analysis using copulas." Journal of Hydrologic Engineering, Vol. 18, pp. 797-808. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000697
  3. Choi, B.K., Oh, T.S., Park, R.G., and Moon, Y.I. (2008). "Derivation of intensity-duration-frequency and flood frequency curve by simulation of hourly precipitation using Nonhomogeneous Markov Chain Model." Journal of Korea Water Resources Association, Vol. 41, No. 3, pp. 251-264. https://doi.org/10.3741/JKWRA.2008.41.3.251
  4. Favre, A.C., El Adlouni, S., Perreault, L., Thiemonge, N., and Bobee, B. (2004). "Multivariate hydrological frequency analysis using copulas." Water Resources Research, Vol. 40, No. 1.
  5. Gyalistras, D. (2003). "Development and validation of a high-resolution monthly gridded temperature and precipitation data set for Switzerland (1951-2000)." Climate Research, Vol. 25, No. 1, pp. 55-83. https://doi.org/10.3354/cr025055
  6. Haan, C.T., Allen, D.M., and Street, J.O. (1976). "A Markov Chain Model of daily rainfall." Water Resources Research, Vol. 12, No. 3, pp. 443-449. https://doi.org/10.1029/WR012i003p00443
  7. Holsclaw, T., Greene, A.M., Robertson, A.W., and Smyth, P. (2017). "Bayesian nonhomogeneous Markov models via Polya-Gamma data augmentation with applications to rainfall modeling." The Annals of Applied Statistics, Vol. 11, No. 1, pp. 93-426.
  8. Jung, M.I., Son, S.W., Choi, J., and Kang, H.S. (2015). "Assessment of 6-month lead prediction skill of the GloSea5 hindcast experiment." Atmosphere, Vol. 25, No, 2, pp. 323-337. https://doi.org/10.14191/Atmos.2015.25.2.323
  9. Kim, Y.T., Lee, M.S., Chae, B.S., and Kwon, H.H. (2018). "A development of summer seasonal rainfall and extreme rainfall outlook using Bayesian beta model and climate information." Journal of The Korean Society of Civil Engineers, Vol. 38, No. 5, pp. 655-669. https://doi.org/10.12652/Ksce.2018.38.5.0655
  10. Kim, Y.T., Park, M., and Kwon, H.H. (2020). "Spatio-temporal summer rainfall pattern in 2020 from a rainfall frequency perspective." Journal of Korean Society of Disaster and Security, Vol. 13, No. 4, pp. 93-104. https://doi.org/10.21729/KSDS.2020.13.4.93
  11. Kumar, D., Arya, D.S., Murumkar, A.R., and Rahman, M.M. (2014). "Impact of climate change on rainfall in Northwestern Bangladesh using multi-GCM ensembles." International Journal of Climatology, Vol. 34, No. 5, pp. 1395-1404. https://doi.org/10.1002/joc.3770
  12. Kwak, J., Kim, Y.S., Lee, J.S., and Kim, H.S. (2012). Drought severity-duration-frequency analysis of hydrological drought based on copula theory. Ph. D. dissertation, Colorado State University, CO, U.S.
  13. Kwon, H.H., and Kim, B.S. (2009). "Development of statistical downscaling model using nonstationary Markov chain." Journal of Korea Water Resources Association, Vol. 42, No. 3, pp. 213-225. https://doi.org/10.3741/JKWRA.2009.42.3.213
  14. Lim, Y.H., Hassell, J., and Teng, W. (2010). "Modeling hydrologic regime of a Terminal Lake Basin with GCM down-scaled scenarios." 5th International Congress on Environmental Modelling and Software, iEMSs, Ottawa, Ontarion, Canada.
  15. MacLachlan, C., Arribas, A., Peterson, K.A., Maidens, A., Fereday, D., Scaife, A.A., Gordon, M., Vellinga, M., Williams, A., Comer, R.E., Camp, J., Xaviera, P., and Madec, G. (2015). "Global Seasonal forecast system version 5 (GloSea5): A high-resolution seasonal forecast system." Quarterly Journal of the Royal Meteorological Society, Vol. 141, No. 689, pp. 1072-1084. https://doi.org/10.1002/qj.2396
  16. Mearns, L.O., Schneider, S.H., Thompson, S.L., and McDaniel, L.R. (1990). "Analysis of climate variability in General Circulation Models: Comparison with observations and changes in variability in 2xCO2 experiments." Journal of Geophysical Research: Atmospheres, Vol. 95, No. D12, pp. 20469-20490. https://doi.org/10.1029/JD095iD12p20469
  17. Noor, M., Ismail, T., Chung, E.S., Shahid, S., and Sung, J.H. (2018). "Uncertainty in rainfall intensity duration frequency curves of peninsular Malaysia under changing climate scenarios." Water, Vol. 10, No. 12, 1750. https://doi.org/10.3390/w10121750
  18. Nord, J. (1975). "Some applications of Markov chains." Proceedings Fourth Conference on Probability and Statistics in Atmospheric Science, AMS, Tallahas, FL, U.S., pp. 125-130.
  19. Qiu, J., Shen, Z., Leng, G., and Wei, G. (2021). "Synergistic effect of drought and rainfall events of different patterns on watershed systems." Scientific Reports, Vol. 11, No. 1, pp. 1-18. https://doi.org/10.1038/s41598-020-79139-8
  20. Scott, S.L. (2002). "Bayesian methods for hidden Markov models: Recursive computing in the 21st century." Journal of the American Statistical Association, Vol. 97, No. 457, pp. 337-351. https://doi.org/10.1198/016214502753479464
  21. Shin, J., Lee, H.S., Kwon, W.T., and Kim, M. (2009). "A study on statistical downscaling for projection of future temperature change simulated by ECHO-G/S over the Korean peninsula." Atmosphere, Vol. 19, No. 2, pp. 107-125.
  22. So, B.J., Kwon, H.H., Kim, D., and Lee, S.O. (2015). "Modeling of daily rainfall sequence and extremes based on a semiparametric Pareto tail approach at multiple locations." Journal of Hydrology, Vol. 529, pp. 1442-1450. https://doi.org/10.1016/j.jhydrol.2015.08.037
  23. Wilby, R.L., Hay, L.E., Gutowski Jr, W.J., Arritt, R.W., Takle, E.S., Pan, Z., Leavesley, G.H., and Clark, M.P. (2000). "Hydrological responses to dynamically and statistically downscaled climate model output." Geophysical Research Letters, Vol. 27, No. 8, pp. 1199-1202. https://doi.org/10.1029/1999GL006078
  24. Willems, P., and Vrac, M. (2011) "Statistical precipitation downscaling for small-scale hydrological impact investigations of climate change." Journal of Hydrology, Vol. 402, pp. 193-205. https://doi.org/10.1016/j.jhydrol.2011.02.030
  25. Willmott, C.J., and Matsuura, K. (1995). "Smart interpolation of annually averaged air temperature in the United States." Journal of Applied Meteorology and Climatology, Vol. 34, No. 12, pp. 2577-2586. https://doi.org/10.1175/1520-0450(1995)034<2577:SIOAAA>2.0.CO;2