• Journal of Korean Society on Water Environment
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• v.30 no.1
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• pp.31-43
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• 2014

Recently, Andong Reservoir and Imha Reservoir located in Nakdong River basin (Korea) are being connected by a tunnel (length 2km, diameter 5.5m) for a conjunctive use. The objectives of this study were to construct a two dimensional(2D) laterally-averaged model for two reservoirs, and examine the effects of connection on the water transportation and temperature stratification in the reservoirs. The 2D models for each reservoir were calibrated using field data obtained in 2006, and applied to the linked system for the year of 2002 when a severe flood intruded into Imha Reservoir during the typhoon Rusa. Simulation results showed that 364 million $m^3$ of water can be conveyed from Imha to Andong, while 291 million $m^3$ of water from Andong to Imha after connection. It resulted in 1.38 m increase of annual averaged water level in Andong Reservoir, whereas 3.75 m decrease in Imha Reservoir. The structures of thermal stratification in both reservoirs were influenced in line with the flow exchanges. In Andong Reservoir, the location of thermocline moved upward about 10 m compared to an independent operation. The results imply that the persistent turbidity issue of Imha Reservoir might be shifted to Andong Reservoir during a severe flood event after connection.

• Journal of Korea Water Resources Association
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• v.36 no.2
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• pp.195-209
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• 2003

Spatial-Stochastic Neural Networks Model(SSNNM) is used to estimate long-term streamflow in the parallel reservoir groups. SSNNM employs two kinds of backpropagation algorithms, based on LMBP and BFGS-QNBP separately. SSNNM has three layers, input, hidden, and output layer, in the structure and network configuration consists of 8-8-2 nodes one by one. Nodes in input layer are composed of streamflow, precipitation, pan evaporation, and temperature with the monthly average values collected from Andong and Imha reservoir. But some temporal differences apparently exist in their time series. For the SSNNM training procedure, the training sets in input layer are generated by the PARMA(1,1) stochastic model and they covers insufficient time series. Generated data series are used to train SSNNM and the model parameters, optimal connection weights and biases, are estimated during training procedure. They are applied to evaluate model validation using observed data sets. In this study, the new approaches give outstanding results by the comparison of statistical analysis and hydrographs in the model validation. SSNNM will help to manage and control water distribution and give basic data to develop long-term coupled operation system in parallel reservoir groups of the Upper Nakdong River.