| Title |
Stochastic Simple Hydrologic Partitioning Model Associated with Markov Chain Monte Carlo and Ensemble Kalman Filter |
| Authors |
최정현 ( Choi Jeonghyeon ) ; 이옥정 ( Lee Okjeong ) ; 원정은 ( Won Jeongeun ) ; 김상단 ( Kim Sangdan ) |
| DOI |
https://doi.org/10.15681/KSWE.2020.36.5.353 |
| Keywords |
Data assimilation; Ensemble Kalman filter; Markov chain Monte Carlo; Stochastic hydrologic model; Uncertainty |
| 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 watershedaveraged soil moisture. |