[논문]베이지안 네트워크와 코플라 함수의 결합을 통한 가뭄전이 발생확률의 정량적 분석

신지예; 유재희; 권현한; 김태웅

doi:10.3741/jkwra.2021.54.7.523

베이지안 네트워크와 코플라 함수의 결합을 통한 가뭄전이 발생확률의 정량적 분석
Quantitative analysis of drought propagation probabilities combining Bayesian networks and copula function 원문보기

Journal of Korea Water Resources Association = 한국수자원학회논문집, v.54 no.7, 2021년, pp.523 - 534

신지예 (한양대학교(ERICA) 공학기술연구소) , 유재희 (한양대학교 대학원 건설환경시스템공학과) , 권현한 (세종대학교 건설환경공학과) , 김태웅 (한양대학교(ERICA) 건설환경공학과)

초록
AI-Helper

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.

Abstract ▼ AI-Helper

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.

주제어

표/그림 (13)

그림 Fig. 1. The study are of Han River basin in South Korea
표 Table 1. Drought condition based on the SPI and SRI
그림 Fig. 2. A schematic framework of the calculation of drought propagation probability
그림 Fig. 3. Three types of Bayesian networks
그림 Fig. 4. Framework of Bayesian network for drought propagation
표 Table 2. Copula functions used in this study
그림 Fig. 5. Weekly SPI and SRI time series (for example, 1001 sub-basin)
그림 Fig. 6. Theoretical PDFs for the SPI and SRI (for example, 1001 sub-basin)
그림 Fig. 7. The probabilities of drought proagation for specific SPI values (for example, 1001 sub-basin)
표 Table 3. Propagation probabilities of different classes SPI into SRI
그림 Fig. 8. Seasonal propagation probabilities
그림 Fig. 9. Box-plots of the seasonal propagation probabilities
그림 Fig. 10. Comparison of the seasonal SPI and SRI

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