Reservoir Operation Incorporating Hedging Rules and Operational Inflow Forecasts

Reservoir operation incorporating a naïve hedging strategy and operational inflow forecasting is studied in this paper. Gridded precipitation forecasts from climate model, ECHAM4.5, are used as potential predictors for reservoir inflow forecasting. In building a statistical predicting model, principal component analysis (PCA) is used to reduce the dimension of the regression model. Performance evaluation indices, including water supply satisfaction ratio, environmental flow satisfaction ratio, end-of-month storage satisfaction ratio and flood prevention capacity index, are defined. Three scenarios where a naïve hedging operation rule under different set of reservoir inflow are investigated. These are evaluated for a water supply reservoir, Falls Lake Reservoir, at Neuse River in the southeast United State. Reservoir simulation with monthly average inflow serves as a benchmark. The utility of operational inflow forecasts is quantified by the improvements of performance indices. Results show that reservoir operation under perfect inflow forecasting has the highest values for most indices. Compared to climatology, operational inflow forecasts result in higher index values. Among all the performance indices, end-of-month storage satisfaction ratio is the most sensitive index to inflow information. Limitation of this study and further work is also discussed.