Assessing the Value of Improved Hydrologic Forecasting for Hydropower in the Sierra Nevada at Multiple Spatial Scales

Monday, 15 December 2014
David E Rheinheimer1, Roger C Bales1,2, Jay R Lund3 and Joshua H Viers1, (1)University of California Merced, Merced, CA, United States, (2)University of California Berkeley, Berkeley, CA, United States, (3)University of California Davis, Davis, CA, United States
Increased accuracy of snowpack measurements can potentially greatly improve planning for rest-of-year water allocations, particularly when coupled with improvements in hydrologic modeling skill. In California, the degree to which investments in improved hydrologic information systems, such as wireless sensor networks, is worth the cost remains poorly quantified. We conducted a numerical study to assess the value of improvements in snowpack measurements and hydrologic prediction for a single representative hydropower system in the Sierra Nevada considering different measurement and prediction accuracy levels and prediction intervals. Additionally, we examined the value of these improvements for a range of different infrastructure, operational, and hydrologic characteristics, such as reservoir capacity, hydropower capacity, electricity demand, instream flow requirements, and hydrologic regime. Operations were represented with a linear programming model using hydrologic information inaccuracy mimicked by perturbing assumed known hydrologic conditions. Results demonstrate that under current system physical and operational characteristics, improved snowpack estimation and hydrologic prediction generally improve optimal hydropower management compared to current estimation capabilities, particularly in drier years. However, improvements vary by infrastructure characteristics, as well as by management and hydrologic assumptions, such as energy demand and hydrologic stationarity. This approach and the specific results presented will help resource managers understand where investments in hydrologic sensor network and prediction systems will be most beneficial.