While only about 30 percent of California’s usable water storage capacity lies at higher elevations, high‐elevation hydropower units generate, on average, 74 percent of California’s instate hydroelectricity. In general, high‐elevation plants have small man‐made reservoirs and rely mainly on snowpack. Their low built‐in storage capacity is a concern with regard to climate warming. Snowmelt is expected to shift to earlier in the year, and the system may not be able to store sufficient water for release in high‐demand periods. Previous studies have explored the climate warming effects on California’s high‐elevation hydropower system by focusing on the supply side (exploring the effects of hydrological changes on generation and revenues) but they have ignored the warming effects on hydropower demand and pricing. This study extends the previous work by simultaneous consideration of climate change effects on high‐elevation hydropower supply and demand in California. Artificial Neural Network models were developed as long‐term price estimation tools, to investigate the impact of climate warming on energy prices. California’s Energy‐Based Hydropower Optimization Model (EBHOM) was then applied, to estimate the adaptability of California’s high‐elevation hydropower system to climate warming, considering the warming effects on hydropower supply and demand. The model was run for dry and wet warming scenarios, representing a range of hydrological changes under climate change. The model’s results relative to energy generation, energy spills, reservoir energy storage, and average shadow prices of energy generation and storage capacity expansion are examined and discussed. The modeling results are compared with previous studies to emphasize the need to consider climate change effects on hydroelectricity demand and pricing when exploring the effects of climate change on California’s hydropower system.
PNAS- Proceedings of the National Academy of Sciences
Broad-scale studies of climate change effects on freshwaterspecies have focused mainly on temperature, ignoring criticaldrivers such as flow regime and biotic interactions. We usedownscaled outputs from general circulation models coupled with a hydrologic model to forecast the effects of altered flows andincreased temperatures on four interacting species of trout across the interior western United States (1.01 million km2), based onempirical statistical models built from fish surveys at 9,890 sites. Projections under the 2080s A1B emissions scenario forecast amean 47% decline in total suitable habitat for all trout, a groupof fishes of major socioeconomic and ecological significance. We project that native cutthroat trout Oncorhynchus clarkii, already excluded from much of its potential range by nonnative species, will lose a further 58% of habitat due to an increase in temperatures beyond the species’ physiological optima and continuednegative biotic interactions. Habitat for nonnative brook troutSalvelinus fontinalis and brown trout Salmo trutta is predictedto decline by 77% and 48%, respectively, driven by increases in temperature and winter flood frequency caused by warmer, rainier winters. Habitat for rainbow trout, Oncorhynchus mykiss, isprojected to decline the least (35%) because negative temperature effects are partly offset by flow regime shifts that benefit the species. These results illustrate how drivers other than temperature influence species response to climate change. Despite some uncertainty, large declines in trout habitat are likely, but our findings point to opportunities for strategic targeting of mitigation efforts to appropriate stressors and locations.
Seth J. Wengera, Daniel J. Isaak, Charles H. Luce, Helen M. Neville, Kurt D. Fausch, Jason B. Dunham,Daniel C. Dauwalter, Michael K. Young, Marketa M. Elsner, Bruce E. Rieman, Alan F. Hamlet, and Jack E. Williams