• Journal of Korea Water Resources Association
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• v.55 no.7
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• pp.515-529
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• 2022

Identifying the available water resources amount is an essential process in establishing a sustainable water resources management plan. Dam facility is a major infrastructure storing and supplying water during the dry season, and the water supply yield of the dam varies depending on dam inflow conditions or operation rule. In South Korea, water supply yield of dam is calculated by reservoir simulation based on observed historical dam inflow data. However, the water supply capacity of a dam can be underestimated or overestimated depending on the existence of historical drought events during the simulation period. In this study, probabilistic inflow data was generated and used to estimate the appropriate range of the water supply yield of hydropower dams. That is, a method for estimating the probabilistic dam inflow that fluctuates according to climatic and socio-economic conditions and the range of water supply yield for hydropower dams was presented, and applied to hydropower dams located in the Han river in South Korea. It is expected that the understanding water supply yield of the hydropower dams will become more important to respond to climate change in the future, and this study will contribute to national water resources management planning by providing potential range of water supply yield of hydropower dams.

• Journal of Environmental Science International
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• v.22 no.8
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• pp.965-977
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• 2013

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. It is necessary to get probabilistic forecasts to establish risk-based reservoir operation policies. Probabilistic forecasts may be useful for the users who assess and manage risks according to decision-making responding forecasting results. Probabilistic forecasting of seasonal inflow to Andong dam is performed and assessed using selected predictors from sea surface temperature and 500 hPa geopotential height data. Categorical probability forecast by Piechota's method and logistic regression analysis, and probability forecast by conditional probability density function are used to forecast seasonal inflow. Kernel density function is used in categorical probability forecast by Piechota's method and probability forecast by conditional probability density function. The results of categorical probability forecasts are assessed by Brier skill score. The assessment reveals that the categorical probability forecasts are better than the reference forecasts. The results of forecasts using conditional probability density function are assessed by qualitative approach and transformed categorical probability forecasts. The assessment of the forecasts which are transformed to categorical probability forecasts shows that the results of the forecasts by conditional probability density function are much better than those of the forecasts by Piechota's method and logistic regression analysis except for winter season data.

• Journal of Korea Water Resources Association
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• v.49 no.7
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• pp.607-614
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• 2016

The well-timed water management is required to reduce drought damages. It is also necessary to induce residents in drought-affected areas to save water. Information on future storage is important in managing water resources based on the current and future states of drought. This study employed a kernel function to develop a probabilistic model for predicting dam storage considering inflow uncertainty. This study also investigated the application of the proposed probabilistic model during the extreme drought. This model can predict a probability of temporal variation of storage. Moreover, the model can be used to make a long-term plan since it can identify a temporal change of storage and estimate a required reserving volume of water to achieve the target storage.

• Journal of Korea Water Resources Association
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• v.49 no.10
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• pp.835-849
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• 2016

Accurate drought outlook and drought monitoring have been preceded recently to mitigate drought damages that further deepen. This study improved the limitations of the previous MSWSI (Modified Surface Water Supply Index) used in Korea and carried out probabilistic drought forecasts based on ensemble technique with the improved MSWSI. This study investigated available hydrometeorological components in Geum river basin and supplemented appropriate components (dam water level, dam release discharge) in addition to the four components (streamflow, groundwater, precipitation, dam inflow) usedin the previous MSWSI to each sub-basin. Although normal distribution was fitted in the previous MSWSI, the most suitable probabilistic distributions to each meteorological component were estimated in this study, including Gumbel distribution for precipitation and streamflow data; 2-parameter log-normal distribution for dam inflow, water level, and release discharge data; 3-parameter log-normal distribution for groundwater. To verify the improved MSWSI results using historical precipitation and streamflow, simulated drought situations were used. Results revealed that the improved MSWSI results were closer to actual drought than previous MSWSI results. The probabilistic forecasts based on ensemble technique with improved MSWSI were performed and evaluated in 2006 and 2014. The accuracy of the improved MSWSI was better than the previous MSWSI. Moreover, the drought index of actual drought was included in ranges of drought forecasts using the improved MSWSI.

• Water Engineering Research
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• v.5 no.2
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• pp.101-109
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• 2004

Aggregating information by combining forecasts from two or more forecasting methods is an alternative to using forecasts from just a single method to improve forecast accuracy. This paper describes the development and use of a monthly inflow forecast model based on an optimal linear combination (OLC) of forecasts derived from naive, persistence, and Ensemble Streamflow Prediction (ESP) forecasts. Using the cross-validation technique, the OLC model made 1-month ahead probabilistic forecasts for the Chungju multi-purpose dam inflows for 15 years. For most of the verification months, the skill associated with the OLC forecast was superior to those drawn from the individual forecast techniques. Therefore this study demonstrates that OLC can improve the accuracy of the ESP forecast, especially during the dry season. This study also examined the value of the OLC forecasts in reservoir operations. Stochastic Dynamic Programming (SDP) derived the optimal operating policy for the Chungju multi-purpose dam operation and the derived policy was simulated using the 15-year observed inflows. The simulation results showed the SDP model that updated its probability from the new OLC forecast provided more efficient operation decisions than the conventional SDP model.

• Journal of Korea Water Resources Association
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• v.47 no.10
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• pp.853-865
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• 2014

Hydrologic dam risk analysis depends on complex hydrologic analyses in that probabilistic relationship need to be established to quantify various uncertainties associated modeling process and inputs. However, the systematic approaches to uncertainty analysis for hydrologic risk analysis have not been addressed yet. In this paper, two major innovations are introduced to address this situation. The first is the use of a Hierarchical Bayesian model based regional frequency analysis to better convey uncertainties associated with the parameters of probability density function to the dam risk analysis. The second is the use of Bayesian model coupled HEC-1 rainfall-runoff model to estimate posterior distributions of the model parameters. A reservoir routing analysis with the existing operation rule was performed to convert the inflow scenarios into water surface level scenarios. Performance functions for dam risk model was finally employed to estimate hydrologic dam risk analysis. An application to the Dam in South Korea illustrates how the proposed approach can lead to potentially reliable estimates of dam safety, and an assessment of their sensitivity to the initial water surface level.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.1
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• pp.231-238
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• 2017

Water supply safety index plays an important role on assessing the water supply capacity of hydrologic system. Due to the absence of consistent guidance, however, practical problems have been brought up on data period used for dam design and performance evaluation. Therefore, this study employed bivariate drought frequency analysis which is able to consider drought severity and duration simultaneously, in order to evaluate water supply capacity of multi-purpose dams. Drought characteristics were analyzed based on the probabilistic approach, and water supply capacity of five multi-purpose dams in Korea (Soyang River, Chungju, Andong, Daecheong, Seomjin River) were evaluated under the specific drought conditions. As a result, it would be possible to have stable water supply with their own inflow during summer and fall, whereas water shortage would occur even under the 1-year return period drought event during spring and winter due to low rainfall.