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.

The potential effects of climate change on the hydrology and water resources of the Columbia River Basin (CRB) were evaluated using simulations from the U.S. Department of Energy and National Center for Atmospheric Research Parallel Climate Model (DOE/NCAR PCM).

This study focuses on three climate projections for the 21st century based on a ‘business as usual' (BAU) global emissions scenario, evaluated with respect to a control climate scenario based on static 1995 emissions. Time-varying monthly PCM temperature and precipitation changes werestatistically downscaled and temporally disaggregated to produce daily forcings that drove a macroscale hydrologic simulation model of the Columbia River basin at 1/4-degree spatial resolution.

We evaluated sampling variability of stream habitat sampling methods used by the USDA Forest Service and the USDI Bureau of Land Management monitoring program for the upper Columbia River Basin. Three separate studies were conducted to describe the variability of individual measurement techniques, variability between crews, and temporal variation throughout the summer sampling season. We quantified the variability between crews and through time, and described the percent of the total variability attributed between crew and seasonal variability. We then estimated the number of samples needed to detect change between managed and reference sites. Differences among streams accounted for a larger share of the total variability than did differences among observers. Stream variability was greater than 80 percent of the total variability for 12 of the 16 variables measured. This is somewhat surprising given the similarities between the study streams. Observer variability was minimal for stream habitat methods describing reach, streambank, and cross-section variables. Conversely, variability was higher for pool, large woody debris, and substrate variables. Seasonal variation was minimal for stream channel variables with the exception of substrate particle sizes. Sample sizes derived from both observer and stream variability (type I error 0.1, type II error 0.9, minimum detectable change 10 percent) ranged from 10 to 3,502 sites to detect changes between two populations. We believe that these estimates represent an unambiguous and powerful way to display the consequences of variability to scientists and managers.