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.

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.