Thermal regimes are fundamental determinants of aquatic ecosystems, which makes description and prediction of temperatures critical during a period of rapid global change. The advent of inexpensive temperature sensors dramatically increased monitoring in recent decades, and although most monitoring is done by individuals for agency‐specific purposes, collectively these efforts constitute a massive distributed sensing array that generates an untapped wealth of data. Using the framework provided by the National Hydrography Dataset, we organized temperature records from dozens of agencies in the western U.S. to create the NorWeST database that hosts >220,000,000 temperature recordings from >22,700 stream and river sites. Spatial‐stream‐network models were fit to a subset of those data that described mean August water temperatures (AugTw) during 63,641 monitoring site‐years to develop accurate temperature models (r2 = 0.91; RMSPE = 1.10°C; MAPE = 0.72°C), assess covariate effects, and make predictions at 1 km intervals to create summer climate scenarios. AugTw averaged 14.2°C (SD = 4.0°C) during the baseline period of 1993–2011 in 343,000 km of western perennial streams but trend reconstructions also indicated warming had occurred at the rate of 0.17°C/decade (SD = 0.067°C/decade) during the 40 year period of 1976–2015. Future scenarios suggest continued warming, although variation will occur within and among river networks due to differences in local climate forcing and stream responsiveness. NorWeST scenarios and data are available online in user‐friendly digital formats and are widely used to coordinate monitoring efforts among agencies, for new research, and for conservation planning.

The last few decades have seen an increased reliance on the use of stream attributes to monitor stream conditions. The use of stream attributes has been criticized because of variation in how observers evaluate them, inconsistent protocol application, lack of consistent training, and the difficulty in using them to detect change caused by management activity. In this paper, we evaluate the effect of environmental heterogeneity and observer variation on the use of physical stream attributes as monitoring tools. For most stream habitat attributes evaluated, difference among streams accounted for greater than 80 percent of the total survey variation. To minimize the effect that variation among streams has on evaluating stream conditions, it may be necessary to design survey protocols and analysis that include stratification, permanent sites, and/or analysis of covariance. Although total variation was primarily due to differences among streams, observers also differed in their evaluation of stream attributes. This study suggests that if trained observers conducting a study that is designed to account for environmental heterogeneity can objectively evaluate defined stream attributes, results should prove valuable in monitoring differences in reach scale stream conditions. The failure to address any of these factors will likely lead to the failure of stream attributes as effective monitoring tools.

1. The statistical rigour and interpretability of ecological assessments is strongly affected by how well we predict the biological assemblages expected to occur in the absence of human-caused stress, i.e. the reference condition. In this study, we examined how the specific method used to predict the reference condition affected the performance of two commonly used types of ecological index: RIVPACS-based O ⁄ E indices and multimetric indices (MMIs). 2. These two types of index have generally relied on different approaches to predicting the reference condition. For MMIs, some type of regionalisation is typically used to describe the range of metric values among reference sites and hence the expected range at assessed sites. For O ⁄ E indices, continuous modelling is used to predict how the biota varies among sites both among and within regions. Because the prediction method differs for these two types of index, it has been impossible to judge if differences in index performance (accuracy, precision, responsiveness and sensitivity) are caused by differences in the way reference condition biota are predicted or by differences in what the indices measure. 3. We used a common data set of 94 reference sites and 255 managed sites and the same potential set of predictor variables to compare the performance of five different MMIs and three O ⁄ E indices that were derived from different prediction methods: null models, multiple linear regression (MLR), classification and regression trees, Random Forests (RF) and linear discriminant functions models (LDM). We then calculated values of these indices for samples collected from the managed catchments as well as samples collected from 13 reference sites that were progressively altered in known ways by a simulation programme. 4. Both the type of predictor used and the type of index affected overall index performance. Modelled indices generally had the greatest sensitivity in assessing managed sites as biologically different from reference. Index sensitivity was determined by both an aspect of index precision (10th percentile of reference condition values) and responsiveness. The O ⁄ E indices showed the best scope of response to known biological alteration. All three O ⁄ E indices decreased linearly in response to simulated alteration in both overall assemblage structure (Bray-Curtis dissimilarity) and taxa loss. The MMIs declined linearly from low to intermediate levels of assemblage alteration but were less responsive between intermediate and high levels of biological alteration. 5. Insights gained from simulations can aid in testing assumptions regarding index response to stress and help ensure that we select indices that are ecologically interpretable and most useful to resource managers.