Journal of Wetlands Research (한국습지학회지)

Korean Wetlands Society (한국습지학회)
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Non-stationary frequency analysis of monthly maximum daily rainfall in summer season considering surface air temperature and dew-point temperature

지표면 기온 및 이슬점 온도를 고려한 여름철 월 최대 일 강수량의 비정상성 빈도해석

  • Lee, Okjeong (Division of Earth Environmental System Science (Major of Environmental Engineering), Pukyong National University) ;
  • Sim, Ingyeong (Division of Earth Environmental System Science (Major of Environmental Engineering), Pukyong National University) ;
  • Kim, Sangdan (Department of Environmental Engineering, Pukyong National University)
  • 이옥정 (부경대학교 지구환경시스템과학부 환경공학전공) ;
  • 심인경 (부경대학교 지구환경시스템과학부 환경공학전공) ;
  • 김상단 (부경대학교 환경공학과)
  • Received : 2018.08.15
  • Accepted : 2018.10.16
  • Published : 2018.11.30
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Abstract

In this study, the surface air temperature (SAT) and the dew-point temperature (DPT) are applied as the covariance of the location parameter among three parameters of GEV distribution to reflect the non-stationarity of extreme rainfall due to climate change. Busan station is selected as the study site and the monthly maximum daily rainfall depth from May to October is used for analysis. Various models are constructed to select the most appropriate co-variate(SAT and DPT) function for location parameter of GEV distribution, and the model with the smallest AIC(Akaike Information Criterion) is selected as the optimal model. As a result, it is found that the non-stationary GEV distribution with co-variate of exp(DPT) is the best. The selected model is used to analyze the effect of climate change scenarios on extreme rainfall quantile. It is confirmed that the design rainfall depth is highly likely to increase as the future DPT increases.

본 연구에서는 기후변화에 따른 극한 강우의 비정상성을 반영하기 위하여 GEV 분포의 3개 매개변수 중 위치매개변수를 공변량으로 적용하여, 지표면 기온(Surface air temperature, SAT) 및 이슬점 온도(Dew point temperature, DPT)을 고려한 비정상성 빈도해석이 실시된다. 부산 지점이 연구대상지점으로 선정되었으며, 5월부터 10월까지의 월 최대 일강수량을 이용하여 분석을 수행하였다. GEV 분포의 위치 매개변수를 위한 가장 적절한 공변량(기온과 이슬점 온도) 함수를 선택하기 위하여 다양한 모델을 구성하였으며, 구성된 모델 중 AIC(Akaike Information Criterion)가 가장 작은 모델을 최적 모델로 선정하였다. 분석 결과, exp(DPT)가 공변량인 비정상성 GEV 분포가 가장 적합한 것으로 나타났다. 선택된 모델을 이용하여 기후변화 시나리오에 따른 확률강우량의 영향을 분석하였으며, 부산지점의 경우 미래 이슬점 온도가 증가함에 따라 확률강우량이 증가할 가능성이 매우 높음을 살펴볼 수 있었다.

Keywords

HKSJBV_2018_v20n4_338_f0001.png 이미지

Fig. 1. Annual precipitation time series at Busan.

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Fig. 2. Standardized monthly maximum values for surface air and dew-point temperature at Busan.

HKSJBV_2018_v20n4_338_f0003.png 이미지

Fig. 3. Relationship between extreme daily rainfall and the daily temperatures during wet days in summer at Busan.

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Fig. 4. GEV distribution.

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Fig. 5. 99th quantile estimates of monthly maximum daily rainfall depth for stationary model and non-stationary model.

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Fig. 6. Distributions for stationary model (stationary) and non-stationary model for low (NS(08/1976)) and high (NS(07/2000)) daily rainfall depth in summer months.

HKSJBV_2018_v20n4_338_f0007.png 이미지

Fig. 7. Distributions of 99th quantile value from non-stationary model under present scenario, DPT 2 ℃-up scenario, and DPT 5 ℃-up scenario.

Table 1. Generalized extreme value parameters and AIC of summer monthly maximum daily rainfall

HKSJBV_2018_v20n4_338_t0001.png 이미지

Table 2. Probability of exceeding stationary quantiles for stationary model and for selected low and high maximum daily rainfall months for non-stationary model

HKSJBV_2018_v20n4_338_t0002.png 이미지

References

  1. Ali, H. and Mishra, V. (2017) Contrasting response of rainfall extremes to increase in surface air and dewpoint temperatures at urban locations in India, Nature Scientific Reports, 7, pp. 1228, [DOI:10.1038/s41598-017-01306-1.]
  2. Coles, S., Bawa, J., Trenner, L., and Dorazio, P. (2001). An introduction to statistical modeling of extreme values (Vol. 208). Springer, London.
  3. Cooley, D. (2009). Extreme value analysis and the study of climate change. Climatic change, 97(1-2), 77. [DOI : https://doi.org/10.1007/s10584-009-9627-x]
  4. El Adlouni, S., Ouarda, T., Zhang, X., Roy, R., and Bobee, B. (2007) Generalized maximum likelihood estimators for the nonstationary generalized extreme value model,
  5. Hosking, J. R. (1990). L-moments: analysis and estimation of distributions using linear combinations of order statistics. J. of the royal statistical society. Series B (Methodological), pp. 105-124. [DOI : 0035-9246/90.52105]
  6. Katz, R., Parlange, M. and Naveau, P. (2002), Statistics of extremes in hydrology, Adv. Water Resour., 25, pp. 1287-1304. [DOI : https://doi.org/10.1016/S0309-1708(02)00056-8]
  7. Lee, J. (2009) Hydrology, Goomibook, 727p.
  8. Lee, O. and Kim, S. (2018) Estimation of future probable maximum precipitation in Korea using multiple regional climate models, Water, 10, pp. 637, [DOI:10.3390/w10050637]
  9. Lenderink, G., Mok, H., Lee, T. and van Oldenborgh, G. (2011) Scaling and trends of hourly precipitation extremes in two different climate zones - Hong Kong and the Netherlands. Hydrol. Earth Syst. Sci. 15, pp. 3033-3041, [DOI:10.5194/hess-15-3033-2011]
  10. Milly, P., Betancourt, J., Falkenmark, M., Hirsch, R., Kundzewicz, Z., Lettenmaier, D. and Stouffer, R. (2008), Climate change: Stationarity is dead: Whither water management?, Science, 319, pp. 573-574. [DOI : 10.1126/science.1151915]
  11. Min, S., Zhang, X., Zwiers, F. and Hegerl, G. (2011) Human contribution to more-intense precipitation extremes. Nature 470(7334), 378, [DOI : https://doi.org/10.1038/nature09763]
  12. O'Gorman, P. (2012), Sensitivity of tropical precipitation extremes to climate change, Nat. Geosci., 5, pp. 697-700, [DOI : 10.1038/ngeo1568]
  13. O'gorman, P. A. and Schneider, T. (2009). Scaling of precipitation extremes over a wide range of climates simulated with an idealized GCM. J. of Climate, 22(21), pp. 5676-5685. [DOI : https://doi.org/10.1175/2009JCLI2701.1]
  14. O'Gorman, P. A. and Schneider, T. (2009). The physical basis for increases in precipitation extremes in simulations of 21st-century climate change. Proceedings of the National Academy of Sciences, 106(35), pp. 14773-14777. [DOI : https://doi.org/10.1073/pnas.0907610106]
  15. Pall, P., Aina, T., Stone, D. A., Stott, P. A., Nozawa, T., Hilberts, A. G., Lohmann. D and Allen, M. R. (2011). Anthropogenic greenhouse gas contribution to flood risk in England and Wales in autumn 2000. Nature, 470(7334), 382. [DOI : 10.1038/nature09762]
  16. Romps, D. (2011). Response of tropical precipitation to global warming. J. Atmos. Sci. 68, pp. 123-138. [DOI : https://doi.org/10.1175/2010JAS3542.1]
  17. Towler, E., Rajagopalan, B., Gilleland, E., Summers, R, Yates, D, and Katz, R. (2010), Modeling hydrologic and water quality extremes in a changing climate: A statistical approach based on extreme value theory, Water Resour. Res., 46, W11504, [DOI:10.1029/2009WR008876]
  18. Wasko, C. and Sharma, A. (2017) Continuous rainfall generation for a warmer climate using observed temperature sensitivities. J. of Hydrology 544, pp. 575-590, [DOI:10.1016/j.jhydrol.2016.12.002]