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http://dx.doi.org/10.3741/JKWRA.2022.55.12.1137

A development of stochastic simulation model based on vector autoregressive model (VAR) for groundwater and river water stages  

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)
Publication Information
Journal of Korea Water Resources Association / v.55, no.12, 2022 , pp. 1137-1147 More about this Journal
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.
Keywords
Autoregressive model (AR); Vector autoregressive model (VAR); Bayesian; Groundwater level; River water stage; Deviance information criterion (DIC);
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Times Cited By KSCI : 1  (Citation Analysis)
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