Climate warming is expected to affect the beneficial uses of water in the Sierra Nevada, impacting nearly every resident of California. This paper describes the development and results from an integrated water resource management model encompassing water operations and hydropower generation for the west slope Sierra Nevada spanning the Feather River basin in the north to the Kern River basin in the south at the weekly time step. This model application includes management of reservoirs, run-of-river hydropower plants, water supply demand locations, conveyances, and instream flow requirement. Model validation indicates that most major hydropower turbine flows were simulated well, with wetter years modeled more effectively than drier years. The results of this work indicated that hydropower generation will be reduced by approximately 8 percent with 6°C (10.8°F) warming, consistent with other studies, with a conservative parameterization of no change in precipitation. Reservoir operations adapt to capture earlier and greater runoff volumes that result from earlier and greater runoff due to climate warming. Seasonal compensation in operations is insufficient to overcome warming mediated losses.

We assessed the potential value of hydrologic forecasting improvements for a snow-dominated high-elevation hydropower system in the Sierra Nevada of California, using a hydropower optimization model. To mimic different forecasting skill levels for inflow time series, rest-of-year inflows from regression-based forecasts were blended in different proportions with representative inflows from a spatially distributed hydrologic model. The statistical approach mimics the simpler, historical forecasting approach that is still widely used. Revenue was calculated using historical electricity prices, with perfect price foresight assumed. With current infrastructure and operations, perfect hydrologic forecasts increased annual hydropower revenue by $0.14 to $1.6 million, with lower values in dry years and higher values in wet years, or about $0.8 million (1.2%) on average, representing overall willingness-to-pay for perfect information. A second sensitivity analysis found a wider range of annual revenue gain or loss using different skill levels in snow measurement in the regression-based forecast, mimicking expected declines in skill as the climate warms and historical snow measurements no longer represent current conditions. The value of perfect forecasts was insensitive to storage capacity for small and large reservoirs, relative to average inflow, and modestly sensitive to storage capacity with medium (current) reservoir storage. The value of forecasts was highly sensitive to powerhouse capacity, particularly for the range of capacities in the northern Sierra Nevada. The approach can be extended to multireservoir, multipurpose systems to help guide investments in forecasting.