Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
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Uncertainty decomposition in water resources projection considering interaction effects

교호작용 효과를 고려한 수자원 전망의 불확실성 분해

  • Ohn, Ilsang (Department of Statistics, Seoul National University) ;
  • Kim, Yongdai (Department of Statistics, Seoul National University) ;
  • Kim, Young-Oh (Department of Civil & Environmental Engineering, Seoul National University)
  • 온일상 (서울대학교 자연과학대학 통계학과) ;
  • 김용대 (서울대학교 자연과학대학 통계학과) ;
  • 김영오 (서울대학교 공과대학 건설환경공학부)
  • Received : 2018.07.30
  • Accepted : 2018.11.13
  • Published : 2018.11.30
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Abstract

Water resources projection typically consists of several stages including emission scenarios, global circulation models (GCMs), downscaling techniques, and hydrological models, and each stage is a source of total uncertainty in water resources projection. Several studies proposed methods to quantify the relative contribution of each stage to total uncertainty, and we call such analysis uncertainty decomposition. Uncertainty decomposition enables us to investigate the stages yielding large uncertainties and to establish the uncertainty reduction plan that reflects them. Interactions between stages is one of the important issues to be considered in uncertainty decomposition. This study suggests a new uncertainty decomposition method considering interaction effect. The proposed method has an advantage of decomposing the total uncertainty to the uncertainty from each stage considering both the main and interactions effects. We apply the proposed method to streamflow projection for Chungju Dam basin. The results show that the uncertainties from the main effects are larger than the uncertainties from interaction effects in both summer and winter. Using the proposed uncertainty decomposition method, we show that the GCM stage is the largest source of the total uncertainty in summer and the downscaling technique stage is the one in winter among the following four stages: emission scenarios, GCMs, downscaling techniques, and hydrological models.

기후변화로 인한 수자원 전망은 배출 시나리오, 전지구적 순환모형, 상세화 기법, 수문 모형 등 여러 전망 단계를 거쳐 이루어지며, 각 단계는 수자원 전망의 총 불확실성의 원천이 된다. 몇몇 연구를 통해 개별 전망 단계의 총 불확실성에 대한 상대적인 기여를 계량화하는 방법이 제안되었으며며, 이러한 분석을 불확실성 분해라고 한다. 불확실성 분해 분석은 큰 불확실성을 발생시키는 단계를 진단하고, 이를 반영한 불확실성 저감 계획을 수립할 수 있게 한다. 전망 단계 간의 교호작용은 불확실성 분해 시 고려되어야 하는 중요한 문제 중 하나이다. 본 연구는 교호작용 효과로 인한 불확실성을 계량화하고 이를 불확실성 분해에 반영하는 새로운 방법을 제안한다. 제안한 방법은 전망 단계별 불확실성을 주효과와 교호작용 효과를 모두 고려하여 계량화함과 동시에 총 불확실성에서 개별 전망 단계가 차지하는 상대적인 비중을 제시할 수 있다는 장점이 있다. 제안한 방법을 충주댐 유량 전망의 불확실성 분석에 적용하였다. 충주댐 유역의 불확실성 분석 결과 여름과 겨울 두 계절 모두에서 교호작용 효과의 불확실성은 주효과의 불확실성에 비해 그 크기가 작은 것으로 나타났다. 교호작용 효과를 고려하여 불확실성을 분해한 결과 배출 시나리오, 전지구적 순환모형, 상세화 기법, 수문 모형의 네 단계 중 여름철은 전지구적 순환모형의 불확실성이, 겨울철은 상세화 기법의 불확실성이 가장 큰 것으로 분석되었다.

Keywords

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Fig. 1. An example of the streamflow projection consisting of four stages

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Fig. 2. Example of estimation results based on two ANOVA models

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Fig. 3. Chungju Dam in the study basin

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Fig. 4. Streamflow projection values based on scenarios for each stage in two seasons. ES stands for emission scenario, GCM for global circulation model, DS for downscaling technique and HM for hydrological model.

Table 1. Scenarios, models and methods used in the study

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Table 2. Uncertainties from main and interaction effects of four stages and from internal variability for two seasons. The percentage in the bracket indicates the proportion of the uncertainty of each stage contributed to the total uncertainty. ES stands for emission scenario, GCM for global circulation model, DS for downscaling technique and HM for hydrological model. `:’ notation indicates interaction between two stages.

SJOHCI_2018_v51nspc_1067_t0002.png 이미지

Table 3. Uncertainties from four stages and internal variability for two seasons using the three methods. The percentage in the bracket indicates the proportion of the uncertainty of each stage contributed to the total uncertainty.

SJOHCI_2018_v51nspc_1067_t0003.png 이미지

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