RONALD VAN OVERLOOP MICHIGAN SERIAL

This study was to investigate improvement in water resources systems management through the use of seasonal climate forecasts. Considering most water supply systems are multipurpose, operating these systems to meet increasing demand under the growing stresses of climate variability and climate change, population and economic growth, and environmental concerns could be very challenging. Ultimately, it is hoped that use of the model will improve the LCRA's operational practices by helping to identify flexible policies that appropriately hedge against unfavorable inflow scenarios.Īccurate seasonal to interannual streamflow forecasts based on climate information are critical for optimal management and operation of water resources systems.

RONALD VAN OVERLOOP MICHIGAN SERIAL

Sensitivity analysis indicates that model results can be improved by using a scenario generation technique which better preserves the serial correlation of flows. Thirty-scenario instances of the model are solved using both a primal simplex method and Benders decomposition, and results show that the first-stage (‘here and now’) decision of how much interruptible water to contract for the coming year is highly dependent on the initial (current) reservoir storage levels.

Input data can be represented by a scenario tree, built empirically from a segment of the historical flow record. The model explicitly considers two objectives: (1) maximize the expected revenue from the sale of interruptible water while reliably maintaining firm water supply, and (2) maximize recreational benefits. A scenario-based, multistage stochastic programming model is developed for the management of the Highland Lakes by the Lower Colorado River Authority (LCRA) in Central Texas.

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