Improving long-range hydropower forecasts in snowmelt dominated basins
Mark T. Jelinek, Georgia Tech, Atlanta, GA; and R. D. Garreaud
With growing demand for the creation of energy from renewable sources comes a need for improved forecasting of variables that impact production from these sources. Increased dependency on renewable based energy also amplifies the need for extending the lead time knowledge of potential impacts to the production cycle. An example of this reliance is seen in hydropower generation on streams primarily feed by annual snowpack melt. Hydropower production levels in these seasonally driven river basins are influenced by both the timing of and volume during the primary streamflow season. Recent analysis utilizing new predictors for snowpack melt behavior and total snow water equivalent suggests there is an opportunity to improve forecasts on monthly and seasonal time scales in portions of both the northern and southern hemispheres. Results show potential in addressing two key deficiencies in current long range forecasts; the ability to improve forecasts for the timing of critical spring snow melt and the ability to improve seasonal streamflow volume estimates in basins with limited availability of in situ measurements. Improving the forecast of both streamflow volume and timing can allow for better preparation in meeting overall energy needs when forecasts indicate a call for alternate source planning, giving both producers and users an opportunity to reduce risks associated with hydropower production shortfalls.
Joint Session 6, Energy Supply and Demand
Tuesday, 19 January 2010, 1:30 PM-3:00 PM, B202
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