Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
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A development of stochastic simulation model based on vector autoregressive model (VAR) for groundwater and river water stages

벡터자기회귀(VAR) 모형을 이용한 지하수위와 하천수위의 추계학적 모의기법 개발

  • Kwon, Yoon Jeong (Department of Civil and Environmental Engineering, Sejong University) ;
  • Won, Chang-Hee (National Integrated Drought Center, National Disaster Management Research Institute, Ulsan, Korea) ;
  • Choi, Byoung-Han (Rural Research Institute, Korea Rural Community Corporation) ;
  • Kwon, Hyun-Han (Department of Civil & Environmental Engineering, Sejong University)
  • 권윤정 (세종대학교 건설환경공학과) ;
  • 원창희 (국립재난안전연구원 국가통합가뭄센터) ;
  • 최병한 (한국농어촌공사 농어촌연구원) ;
  • 권현한 (세종대학교 건설환경공학과)
  • Received : 2022.11.08
  • Accepted : 2022.12.06
  • Published : 2022.12.31
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Abstract

River and groundwater stages are the main elements in the hydrologic cycle. They are spatially correlated and can be used to evaluate hydrological and agricultural drought. Stochastic simulation is often performed independently on hydrological variables that are spatiotemporally correlated. In this setting, interdependency across mutual variables may not be maintained. This study proposes the Bayesian vector autoregression model (VAR) to capture the interdependency between multiple variables over time. VAR models systematically consider the lagged stages of each variable and the lagged values of the other variables. Further, an autoregressive model (AR) was built and compared with the VAR model. It was confirmed that the VAR model was more effective in reproducing observed interdependency (or cross-correlation) between river and ground stages, while the AR generally underestimated that of the observed.

하천수위와 지하수위는 수문학적 순환과정에서 나타나는 수문학적 요소로 상호 연관성이 높으며 이러한 수문학적 요소에 대해 확률적 시뮬레이션을 독립적으로 수행하는 경우 상호 관련 정보손실과 같은 문제가 발생할 수 있다. 하천수위와 지하수위는 수문학적·농업적 가뭄을 평가하는 중요한 지표로 활용되지만 하천수위의 경우 건기 중에는 정확한 관측을 얻기가 매우 어려우며, 지하수위의 경우 데이터 기간이 상대적으로 짧아 이를 활용한 가뭄지수 사용이 제한적이다. 이와 관련하여 손실 없이 자료를 최대한 이용하기 위해 본 연구는 각 변수의 시간 의존성을 고려하는 동시에 상호 연관된 변수의 시간 의존성을 고려하는 벡터자기회 모형VAR)을 구성했다. 하천수위와 지하수위 사이의 자기 상관 및 상관관계를 확인하고, 정보 손실을 최소화하는 하천수위 및 지하수위를 예측할 수 있는지 여부를 결정하기 위해 벡터 자기 회귀 모델의 최적 순서 결정과 매개변수를 결정하였다. 또한, 두 변수 간의 상관관계를 반영하지 않는 자기회귀모형(AR)을 구축하고 모의에 대한 DIC와 상관계수를 VAR 모형과 비교하여 VAR 모형 더 적합함을 보이고 하천수위와 지하수위의 간의 상호관계성을 효과적으로 반영함을 확인하였다.

Keywords

Acknowledgement

본 결과물은 행정안전부 재난안전 공동연구 기술개발사업의 지원을 받아 연구되었습니다(2022-MOIS63-001).

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