Broad-scale studies of climate change effects on freshwater
species have focused mainly on temperature, ignoring critical
drivers such as flow regime and biotic interactions. We use
downscaled 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.

Hydropower represents approximately 20% of the world’s energy supply, is viewed as both vulnerable to global climate warming and an asset to reduce climate altering emissions, and is increasingly the target of improved regulation to meet multiple ecosystem service benefits. It is within this context that the recent decision by the United States Federal Energy Regulatory Commission to reject studies of climate change in its consideration of reoperation of the Yuba-Bear Drum-Spaulding hydroelectric facilities in northern California is shown to be poorly reasoned and risky. Given the rapidity of climate warming, and its anticipated impacts to natural and human communities, future long-term fixed licenses of hydropower operation will be ill prepared to adapt if science-based approaches to incorporating reasonable and foreseeable hydrologic changes into study plans are not included. The licensing of hydroelectricity generation can no longer be issued in isolation due to downstream contingencies such as domestic water use, irrigated agricultural production, ecosystem maintenance, and general socioeconomic well-being. At minimum, if the Federal Energy Regulatory Commission is to establish conditions of operation for 30-50 years, licensees should be required to anticipate changing climatic and hydrologic conditions for a similar period of time.

California’s hydropower system is composed of high and low elevation power plants. There are more than 150 high-elevation power plants, at elevations above 1,000 feet (300 m). Most have modest reservoir storage capacities, but supply roughly 74% of California’s in-state hydropower. The expected shift of runoff peak from spring to winter due to climate warming, resulting in snowpack reduction and increased snowmelt, might have important effects on power generation and revenues in California. The large storage capacities at low-elevation power plants provide flexibility to operations of these units under climate warming. However, with climate warming, the adaptability of the high-elevation hydropower system is in question as this system was designed to take advantage of snowpack, a natural reservoir.With so many high-elevation hydropower plants in California, estimation of climate warming effects by conventional simulation or optimization methods would be tedious and expensive. An Energy-Based Hydropower Optimization Model (EBHOM) was developed to facilitate practical climate change and other low-resolution system-wide hydropower studies, based on the historical generation data of 137 high-elevation hydropower plants for which the data were complete for 14 years. Employing recent historical hourly energy prices, the model was used to explore energy generation in California for three climate warming scenarios (dry warming, wet warming, and warming-only) over 14 years, representing a range of hydrologic conditions. The system is sensitive to the quantity and timing of inflows. While dry warming and warming-only climate changes reduce average hydropower revenues, wet warming could increase revenue. Re-operation of available storage and generation capacities help compensate for snowpack losses to some extent. Storage capacity expansion and to a lesser extent generation capacity expansion both increase revenues, although such expansions might not be cost-effective.

Hydropower dams play a critical role in the health of river ecosystems throughout the United States, and hundreds of these dams will be relicensed by the Federal Energy Regulatory Commission (FERC) in the coming years. Such licenses lock in the operating and environmental protection requirements of such dams for periods of up to 50 years. Given the complex, dynamic nature of river ecosystems, as well as the impacts of climate change, there is pervasive scientific uncertainty about how to best manage dams for power production while protecting and enhancing environmental values such as water quality and fisheries. Unless dams are managed adaptively, with licenses that provide pathways for gathering and applying new knowledge and responding to changing conditions, we run the risk of locking in mistaken approaches and stymieing environmental improvements on our rivers for the next half century.

Climate and hydrologic fluctuations in the Pacific Northwest lead to large year-to-year variations in the strength of the Columbia River hydropower resource. We describe and present results from a seasonal hydrologic prediction system for the Columbia River basin that gives insight into the seasons-ahead behavior of this resource starting near the beginning of each water year. The forecast system is based on the real-time application of a state-of-the-science, macroscale hydrologic model coupled with ensemble climate forecasts. Estimates of initial land surface conditions, primarily in the form of snow water equivalent, are improved via the assimilation of snowpack observations, and forecast biases are reduced through statistical forecast calibration. The forecast system produces graphical forecast products designed to help water and energy managers understand the current state of the Columbia River basin, the climate outlook for the water year, and the implications of both for future streamflow.

Climate strongly affects energy supply and demand in the Pacific Northwest (PNW) and Washington State (WA). We evaluate potential changes in the seasonality and annual amount of PNW hydropower production and changes in energy demand in a warming climate by linking simulated streamflow scenarios produced by a hydrology model to a simulation model of the Columbia River hydro system. Energy demand, and potential changes therein, are assessed estimates of heating degree days (HDD) and cooling degree days (CDD) for both the 20th century climate and projections of climate in three future periods (2010-2039, 2030-2059, and 2070-2099) and two emissions scenarios (IPCC A1B and B1). The gridded HDD and CDD values are then combined with population projections to create energy demand indices that respond both to climate, future population, and changes in air conditioning market penetration. We find that substantial changes in the amount and seasonality of energy supply and demand in the PNW are likely to occur over the next century in response to warming, precipitation changes, and population growth. In the 2020s, regional hydropower production increases by 0.5-4% in winter, decreases by 9-11% in summer, with annual reductions of 1-4%. Slightly larger increases in winter, and summer decreases, are projected for the 2040s and 2080s. In the absence of warming, population growth is projected to result in considerable increases in heating energy demand, however, the combined effects of warming and population growth are projected to result in net increases that are approximately one-half those associated with population growth alone. On the other hand, population growth combined with warming greatly increases the projected demand for cooling energy, notwithstanding that by the 2080s, total cooling energy requirements will still be substantially lower than heating energy demand.

Global climate models do not have sufficient spatial resolution to represent the atmospheric and land surface processes that determine the unique regional heterogeneity of the climate of the State of Washington. If future large-scale weather patterns interact differently with the local terrain and coastlines than current weather patterns, local changes in temperature and precipitation could be quite different from the coarse-scale changes projected by global models. Regional climate models explicitly simulate the interactions between the large-scale weather patterns simulated by a global model and the local terrain. We have performed two 100-year climate simulations using the Weather and Research Forecasting (WRF) model developed at the National Center for Atmospheric Research (NCAR). One simulation is forced by the NCAR Community Climate System Model version 3 (CCSM3) and the second is forced by a simulation of the Max Plank Institute, Hamburg, global model(ECHAM5). The mesoscale simulations produce regional changes in snow cover, cloudiness, and circulation patterns associated with interactions between the large-scale climate change and the regional topography and land-water contrasts. These changes substantially alter the temperature and precipitation trends over the region relative to the global model result or statistical down scaling. To illustrate this effect, we analyze the changes from the current climate (1970-1999) to the mid 21st century (2030-2059). Changes in seasonal-mean temperature, precipitation, and snowpack are presented. Several climatological indices of extreme daily weather are also presented: precipitation intensity, fraction of precipitation occurring in extreme daily events, heat wave frequency, growing season length, and frequency of warm nights. Despite somewhat different changes in seasonal precipitation and temperature from the two regional simulations, consistent results for changes in snowpack and extreme precipitation are found in both simulations.