The dataset contains long-term and short-term summaries of streamflow alteration and measures of biological condition (fish multi-metric index). Streamflow alteration metrics include the magnitude, duration, frequency, and seasonality of high and low flow streamflow. Biological condition was estimated from the National Rivers and Streams Assessment and National Water Quality Assessment fish sampling programs. Using fish samples, a fish multi-metric index was calculated and categorized into altered versus non-altered fish communities.

Detecting trends in biological attributes is central to many stream monitoring programs; however, understanding how natural variability in environmental factors affects trend results is not well understood. We evaluated the influence of antecedent streamflow and sample timing (covariates) on trend estimates for fish, invertebrate, and diatom taxa richness and biological condition from 2002 to 2012 at 51 sites distributed across the conterminous United States. This data release contains all of the input and output files necessary to reproduce the results presented and discussed in the associated journal article.

Detecting trends in biological attributes is central to many stream monitoring programs; however, understanding how natural variability in environmental factors affects trend results is not well understood. We evaluated the influence of antecedent streamflow and sample timing (covariates) on trend estimates for fish, invertebrate, and diatom taxa richness and biolgical condition from 2002 to 2012 at 51 sites distributed across the conterminous United States. This data release contains all of the input and output files necessary to reproduce the results presented and discussed in the associated journal article.

Detecting trends in biological attributes is central to many stream monitoring programs; however, understanding how natural variability in environmental factors affects trend results is not well understood. We evaluated the influence of antecedent streamflow and sample timing (covariates) on trend estimates for fish, invertebrate, and diatom taxa richness and biolgical condition from 2002 to 2012 at 51 sites distributed across the conterminous United States. This data release contains all of the input and output files necessary to reproduce the results presented and discussed in the associated journal article.

Empirical models described in previous publications were developed and applied to estimate the probability of streamflow modification for every stream segment in the conterminous United States from 1980-2015. This metadata record documents 6 comma separated tables populated with predictions of streamflow modification (please see the Supplemental Element for citations or please refer to the cross-reference section). These data are based on watershed attributes computed for each NHDPlus v2.1 reach that were subsequently applied to previously published (and herein described) machine-learning models.

Empirical models described in previous publications were developed and applied to estimate the probability of streamflow modification for every stream segment in the conterminous United States from 1980-2015. This metadata record documents 6 comma separated tables populated with predictions of streamflow modification (please see the Supplemental Element for citations or please refer to the cross-reference section). These data are based on watershed attributes computed for each NHDPlus v2.1 reach that were subsequently applied to previously published (and herein described) machine-learning models.

Detecting trends in biological attributes is central to many stream monitoring programs; however, understanding how natural variability in environmental factors affects trend results is not well understood. We evaluated the influence of antecedent streamflow and sample timing (covariates) on trend estimates for fish, invertebrate, and diatom taxa richness and biological condition from 2002 to 2012 at 51 sites distributed across the conterminous United States. This data release contains all of the input and output files necessary to reproduce the results presented and discussed in the associated journal article.

Detecting trends in biological attributes is central to many stream monitoring programs; however, understanding how natural variability in environmental factors affects trend results is not well understood. We evaluated the influence of antecedent streamflow and sample timing (covariates) on trend estimates for fish, invertebrate, and diatom taxa richness and biological condition from 2002 to 2012 at 51 sites distributed across the conterminous United States. This data release contains all of the input and output files necessary to reproduce the results presented and discussed in the associated journal article.

This data release contains inputs for and outputs from hydrologic simulations of the southeastern U.S. using the Monthly Water Balance Model, the Precipitation Runoff Modeling System (PRMS), and statistically-based methods. These simulations were developed to provide estimates of water availability and statistics of streamflow for historical and potential future conditions for an area of approximately 1.16 million square miles. These model input and output data are intended to accompany a U.S. Geological Survey Scientific Investigations Report (LaFontaine and others, 2019); they include four types of data: 1) model input parameters, 2) model output statistics, 3) GIS files of the model hydrologic response units and stream segments, and 4) statistically-based streamflow estimates for headwater watersheds. LaFontaine, J.H., Hart, R.M., Hay, L.E., Farmer, W.H., Bock, A.R., Viger, R.J., Markstrom, S.L., Regan, R.S., and Driscoll, J.M., 2019, Simulation of Water Availability in the Southeastern United States for Historical and Potential Future Climate and Land-Cover Conditions: U.S. Geological Survey Scientific Investigations Report, 2019-5039, 83 p., https://doi.org/10.3133/sir20195039.

This data release contains inputs for and outputs from hydrologic simulations of the southeastern U.S. using the Monthly Water Balance Model, the Precipitation Runoff Modeling System (PRMS), and statistically-based methods. These simulations were developed to provide estimates of water availability and statistics of streamflow for historical and potential future conditions for an area of approximately 1.16 million square miles. These model input and output data are intended to accompany a U.S. Geological Survey Scientific Investigations Report (LaFontaine and others, 2019); they include four types of data: 1) model input parameters, 2) model output statistics, 3) GIS files of the model hydrologic response units and stream segments, and 4) statistically-based streamflow estimates for headwater watersheds. LaFontaine, J.H., Hart, R.M., Hay, L.E., Farmer, W.H., Bock, A.R., Viger, R.J., Markstrom, S.L., Regan, R.S., and Driscoll, J.M., 2019, Simulation of Water Availability in the Southeastern United States for Historical and Potential Future Climate and Land-Cover Conditions: U.S. Geological Survey Scientific Investigations Report, 2019-5039, 83 p., https://doi.org/10.3133/sir20195039.