Using Multi-Scale Uncertainty Information And Specific Forecast Skill To Improve Reservoir Operations

Optimization of reservoir operations to time series of forecasted inflows are constrained by a set of multiple objectives that span many time scales, however the temporally evolving skill of the forecasts are usually not considered in the objective functions. For example, a flow forecast time series extending from 1 day to 6 months consists of a medium range flow forecast that draws its skill from initial conditions and weather forecasts and a seasonal flow forecast that relies on the initial conditions only. The skill of the medium range flow forecast is the daily and aggregated values with a range of uncertainties that increases with lead time, while the seasonal flow forecasts only have skill in the monthly volumetric values with a range of uncertainties that is large, but predictable. Unfortunately, the impacts of temporally evolving skill and uncertainty on reservoir operations and operational risk is unknown, which may leave significant room for improvement. To explore this question we conduct a set of optimization experiments of reservoir operations at the snowmelt dominated Gunnison River Basin in Colorado and the snow-rain transition Feather River Basin in California. Each optimization experiment uses the same ensemble flow forecast, which is an ensemble medium range forecast merged with an ensemble seasonal forecast, but utilizes a different set of weights that are applied to the medium and seasonal scale objectives (which are to maximize revenue and envrionmental performance). By comparing the weighted set of optimizations to a non-weighted optimization, we are able to isolate the impact of the skill and uncertainty of the forecasts on reservoir operations. We conclude by using the results to develop a functional relationship between the skill and uncertainty in the forecasts to the objective weights and as a basis to calculate operational risk