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http://dx.doi.org/10.15681/KSWE.2020.36.5.353

Stochastic Simple Hydrologic Partitioning Model Associated with Markov Chain Monte Carlo and Ensemble Kalman Filter  

Choi, Jeonghyeon (Division of Earth Environmental System Science (Major of Environmental Engineering), Pukyong National University)
Lee, Okjeong (Department of Environmental Engineering, Pukyong National University)
Won, Jeongeun (Division of Earth Environmental System Science (Major of Environmental Engineering), Pukyong National University)
Kim, Sangdan (Department of Environmental Engineering, Pukyong National University)
Publication Information
Journal of Korean Society on Water Environment / v.36, no.5, 2020 , pp. 353-363 More about this Journal
Abstract
Hydrologic models can be classified into two types: those for understanding physical processes and those for predicting hydrologic quantities. This study deals with how to use the model to predict today's stream flow based on the system's knowledge of yesterday's state and the model parameters. In this regard, for the model to generate accurate predictions, the uncertainty of the parameters and appropriate estimates of the state variables are required. In this study, a relatively simple hydrologic partitioning model is proposed that can explicitly implement the hydrologic partitioning process, and the posterior distribution of the parameters of the proposed model is estimated using the Markov chain Monte Carlo approach. Further, the application method of the ensemble Kalman filter is proposed for updating the normalized soil moisture, which is the state variable of the model, by linking the information on the posterior distribution of the parameters and by assimilating the observed steam flow data. The stochastically and recursively estimated stream flows using the data assimilation technique revealed better representation of the observed data than the stream flows predicted using the deterministic model. Therefore, the ensemble Kalman filter in conjunction with the Markov chain Monte Carlo approach could be a reliable and effective method for forecasting daily stream flow, and it could also be a suitable method for routinely updating and monitoring the watershed-averaged soil moisture.
Keywords
Data assimilation; Ensemble Kalman filter; Markov chain Monte Carlo; Stochastic hydrologic model; Uncertainty;
Citations & Related Records
Times Cited By KSCI : 7  (Citation Analysis)
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