Stream flow is a controlling element in the ecology of rivers and streams. Knowledge of the natural flow regime facilitates the assessment of whether specific hydrologic attributes have been altered by humans in a particular stream and the establishment of specific goals for stream‐flow restoration. Because most streams are ungaged or have been altered by human influences, characterizing the natural flow regime is often only possible by estimating flow characteristics based on nearby stream gages of reference quality, i.e., gaged locations that are least disturbed by human influences. The ability to evaluate natural stream flow, that which is not altered by human activities, would be enhanced by the existence of a nationally consistent and up‐to‐date database of gages in relatively undisturbed watersheds. As part of a national effort to characterize stream‐flow effects on ecological condition, data for 6785 U.S. Geological Survey (USGS) stream gages and their upstream watersheds were compiled. The sites comprise all USGS stream gages in the conterminous United States with at least 20 years of complete‐year flow record from 1950–2007, and for which watershed boundaries could reliably be delineated (median size = 578 km2). Several hundred watershed and site characteristics were calculated or compiled from national data sources, including environmental features (e.g., climate, geology, soils, topography) and anthropogenic influences (e.g., land use, roads, presence of dams, or canals). In addition, watersheds were assessed for their reference quality within nine broad regions for use in studies intended to characterize stream flows under conditions minimally influenced by human activities. Three primary criteria were used to assess reference quality: (1) a quantitative index of anthropogenic modification within the watershed based on GIS‐derived variables, (2) visual inspection of every stream gage and drainage basin from recent high‐resolution imagery and topographic maps, and (3) information about man‐made influences from USGS Annual Water Data Reports. From the set of 6785 sites, we identified 1512 as reference‐quality stream gages. All data derived for these watersheds as well as the reference condition evaluation are provided as an online data set termed GAGES (geospatial attributes of gages for evaluating stream flow).
Understanding the extent to which natural streamflow characteristics have been altered is an important consideration for ecological assessments of streams. Assessing hydrologic condition requires that we quantify the attributes of the flow regime that would be expected in the absence of anthropogenic modifications. The objective of this study was to evaluate whether selected streamflow characteristics could be predicted at regional and national scales using geospatial data. Long-term, gaged river basins distributed throughout the contiguous US that had streamflow characteristics representing least disturbed or near pristine conditions were identified. Thirteen metrics of the magnitude, frequency, duration, timing and rate of change of streamflow were calculated using a 20-50 year period of record for each site. We used random forests (RF), a robust statistical modelling approach, to develop models that predicted the value for each streamflow metric using natural watershed characteristics. We compared the performance (i.e. bias and precision) of national-and regional-scale predictive models to that of models based on landscape classifications, including major river basins, ecoregions and hydrologic landscape regions (HLR). For all hydrologic metrics, landscape stratification models produced estimates that were less biased and more precise than a null model that accounted for no natural variability. Predictive models at the national and regional scale performed equally well, and substantially improved predictions of all hydrologic metrics relative to landscape stratification models. Prediction error rates ranged from 15 to 40%, but were 25% for most metrics. We selected three gaged, non-reference sites to illustrate how predictive models could be used to assess hydrologic condition. These examples show how the models accurately estimate predisturbance conditions and are sensitive to changes in streamflow variability associated with long-term land-use change. We also demonstrate how the models can be applied to predict expected natural flow characteristics at ungaged sites.
Water security is a top concern for social well-being, and dramatic changes in the availability of freshwater have occurred as a result of human uses and landscape management. Elevated nutrient loading and perturbations to major ion composition have resulted from human activities and have degraded freshwater resources. This study addresses the emerging nature of streamwater quality in the 21st century through analysis of concentrations and trends in a wide variety of constituents in streams and rivers of the U.S. Concentrations of 15 water quality constituents including nutrients, major ions, sediment, and specific conductance were analyzed over the period 1982−2012 and a targeted trend analysis was performed from 1992 to 2012. Although environmental policy is geared toward addressing the longstanding problem of nutrient overenrichment, these efforts have had uneven success, with decreasing nutrient concentrations at urbanized sites and little to no change at agricultural sites. Additionally, freshwaters are being salinized rapidly in all human-dominated land use types. While efforts to control nutrients are ongoing, rapid salinity increases are ushering in a new set of poorly defined issues. Increasing salinity negatively affects biodiversity, mobilizes sediment-bound contaminants, and increases lead contamination of drinking water, but its effects are not well integrated into current paradigms of water management.
We developed and evaluated empirical models to predict biological condition of wadeable streams in a large portion of the eastern USA, with the ultimate goal of prediction for unsampled basins. Previous work had classified (i.e., altered vs. unaltered) the biological condition of 920 streams based on a biological assessment of macroinvertebrate assemblages. Predictor variables were limited to widely available geospatial data, which included land cover, topography, climate, soils, societal infrastructure, and potential hydrologic modification. We compared the accuracy of predictions of biological condition class based on models with continuous and binary responses. We also evaluated the relative importance of specific groups and individual predictor variables, as well as the relationships between the most important predictors and biological condition. Prediction accuracy and the relative importance of predictor variables were different for two subregions for which models were created. Predictive accuracy in the highlands region improved by including predictors that represented both natural and human activities. Riparian land cover and road-stream intersections were the most important predictors. In contrast, predictive accuracy in the lowlands region was best for models limited to predictors representing natural factors, including basin topography and soil properties. Partial dependence plots revealed complex and nonlinear relationships between specific predictors and the probability of biological alteration. We demonstrate a potential application of the model by predicting biological condition in 552 unsampled basins across an ecoregion in southeastern Wisconsin (USA). Estimates of the likelihood of biological condition of unsampled streams could be a valuable tool for screening large numbers of basins to focus targeted monitoring of potentially unaltered or altered stream segments.
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