• Journal of Korea Water Resources Association
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• v.33 no.5
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• pp.593-601
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• 2000

Analyzing monthly inflows of the Chung-Ju Dam associated with EI Nino Southern Oscillation (ENSO), Kim and Lee(2000) reported that the fall and winter inflows in EI Nino years tended to be low while those in La Nina years tended to be high. This study proposes a methodology of employing such a teleconnection between ENSO and inflow in reservoir operations. The ENSO information is used as a hydrologic state variable in stochastic dynamic programming (SDP) to derive a monthly optimal rule for operating the Chung- Ju Dam. An alternative operating rule is also derived with the SDP with no hydrologic state variable. Both of the SDP operating rules are simulated and compared to examine the value of using the ENSO information in operations of the Chung-Ju Dam. The simulation results show that the operating rule using the ENSO information increases energy generation and reliability of water supply as well as reduces spill. spill.

• Journal of Korean Society on Water Environment
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• v.36 no.5
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• pp.353-363
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• 2020

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.

• Journal of Korea Water Resources Association
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• v.34 no.3
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• pp.275-285
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• 2001

Since the majority of streamflow during dry periods is provided by groundwater storage, the streamflow depends on a basin moisture state recharged from rainfall during wet periods. This hydrologic characteristics dives good condition to predict long-term streamflow if the basin state like groundwater recharge is known in advance. The objective of this study is to examine groundwater recharge effect to monthly streamflow, and to attempt monthly streamflow prediction using estimated groundwater recharge. The ground water recharge is used as an independent variable with streamflow and precipitation to construct multiple regression models for the prediction. Correlation analysis was performed to assess the effect of groundwater carry-over to streamflow and to establish the associations among independent variables. The predicted streamflow shows that the multiple regression model involved groundwater recharge gives improved results comparing to the model only using streamflow and precipitation as independent variables. In addition, this paper shows that the prediction model with the effect of groundwater carry-over taken into account can be developed using only precipitation.