The U.S. Geological Survey (USGS) and the Minnesota Pollution Control Agency (MPCA) conducted a cooperative study to develop linear regression models that quantify relations among 173 hydrologic explanatory metrics in five categories (duration, frequency, magnitude, rate-of-change, and timing) computed from streamgage records and 132 biological response metrics in six categories (composition, habitat, life history, reproductive, tolerance, trophic) computed from fish community samples collected from the 1996-2015 water years (WYs). In addition, linear relations were quantified between hydrologic metrics, fish-based indices of biotic integrity (FIBI) scores, and FIBI scores normalized to the impairment threshold of the corresponding stream class (FIBI_BCG4), resulting in a total of 134 regression equations per hydrologic dataset. Three hydrologic datasets were used to examine relations between altered hydrology and fish community responses at different temporal scales. First, the period-of-record (POR) dataset was created by computing hydrologic metrics using all complete WYs of streamflow record (1945WY and later) and ending with the WY of corresponding biological sample collection. Next, datasets representing long-term changes (LTC) and short-term changes (STC) in hydrology were created using ratios of hydrologic metrics computed for different time periods. The LTC ratios were obtained by dividing hydrologic metrics computed from available streamflow records from the 1981WY through the WY of biological sample collection by hydrologic metrics computed from available streamflow records during the 1945-79WYs. The STC ratios were obtained by dividing hydrologic metrics computed from the last 10 water years up to the WY of biological sample collection by hydrologic metrics computed from the POR for each streamgage. The POR, LTC, and STC datasets included 54, 39, and 48 hydrologic and biological site pairs, respectively. Results of regression analyses are described in a companion publication (https://doi.org/TBD). A subset of the best regression models based on pseudo-R2 values is published in the companion publication, but all 134 final regression models for each of the three datasets are published in this data release. Model archives of best subset and left-censored linear regression models are provided and include readme files, raw data files, R scripts used to compute regression analyses, and model outputs. Daily streamflow data were retrieved from the National Water Information System (NWIS; at https://waterdata.usgs.gov/nwis). A minimum of 10 years of complete daily streamflow record was required for computing hydrologic metrics to pair with biological metrics. RStudio (version 3.5.0) and the EflowStats (version 5.0.0) and NWCCompare (version 5.0) packages were used to compute hydrologic metrics. Biological metrics used in described datasets were computed by and obtained from the MPCA (MPCA, 2016). Similar hydrologic statistics were computed using the EflowStats package and published in a previous data release (https://doi.org/10.5066/P9ND1NPT).

Hydrologic indicator statistics were computed for 82 selected surface water sites located throughout Minnesota using daily streamflow data from the U.S. Geological Survey (USGS) National Water Information System (NWIS). The 187 hydrologic indicator statistics were computed in RStudio version 3.5.0 using the EflowStats version 5.0.0 (Mills and Blodgett, 2017) and NWCCompare version 5.0 (Blodgett, 2017). The computed hydrologic indicator statistics encompass the five components of hydrologic conditions: magnitude, frequency, duration, timing, and rate of change. Magnitude is the amount of water moving past a fixed location in a given unit of time. Frequency refers to how often streamflows above a given magnitude recur over a specified time interval. Duration is the period of time associated with a specific streamflow condition. Timing refers to the regularity with which streamflows of a given magnitude occur, and rate of change refers to how quickly the magnitude of streamflow changes (Poff and others, 1997). Site selection was based on sites previously selected in three other studies evaluating long-term streamflow records for trends (Novatny and Stefan, 2007; Peterson, Nieber, and Kanivetsky, 2011; Ziegeweid et.al, 2015). Nontrend sites were shown to not have trends in streamflow that were not related to precipitation. Hydrologic indicator statistics were computed for two periods: 1) the pre-period from 10-1-1944 through 9-30-1979 and 2) the post-period from 10-1-1980 through 9-30-2015. Exact dates of the start of trends varied among sites, but 1980 was the selected cutoff period based on an approximation of the largest cluster and on other anecdotal evidence of changes in farming practices. Both categories also had at least 10 water years with complete streamflow data. Blodgett, D., 2017, NWCCompare: Returns NWC comparison stats for two daily data sets version 5.0, https://github.com/USGS-R/NWCCompare. Mills, J., and Blodgett, D., 2017, EflowStats: Hydrologic Indicator and Alterations Stats version 5.0.0, https://github.com/USGS-R/EflowStats. Novotny, E.V., and Stefan, H.G., 2007, Stream flow in Minnesota: Indicator of climate change, Journal of Hydrology 334: 319-333. Peterson, H.M., Nieber, J.L., and Kanivetsky, R., 2011, Hydrologic regionalization to assess anthropogenic changes, Journal of Hydrology 408: 212-225. Ziegeweid, J.R., Lorenz, D.L., Sanocki, C.A., and Czuba, C.R., 2015, Methods for estimating flow-duration curve and low-flow frequency statistics for ungaged locations on small streams in Minnesota: U.S. Geological Survey Scientific Investigations Report 2015–5170, 23 p., http://dx.doi.org/10.3133/sir20155170.

Hydrologic indicator statistics were computed for 82 selected surface water sites located throughout Minnesota using daily streamflow data from the U.S. Geological Survey (USGS) National Water Information System (NWIS). The 187 hydrologic indicator statistics were computed in RStudio version 3.5.0 using the EflowStats version 5.0.0 (Mills and Blodgett, 2017) and NWCCompare version 5.0 (Blodgett, 2017). The computed hydrologic indicator statistics encompass the five components of hydrologic conditions: magnitude, frequency, duration, timing, and rate of change. Magnitude is the amount of water moving past a fixed location in a given unit of time. Frequency refers to how often streamflows above a given magnitude recur over a specified time interval. Duration is the period of time associated with a specific streamflow condition. Timing refers to the regularity with which streamflows of a given magnitude occur, and rate of change refers to how quickly the magnitude of streamflow changes (Poff and others, 1997). Site selection was based on sites previously selected in three other studies evaluating long-term streamflow records for trends (Novatny and Stefan, 2007; Peterson, Nieber, and Kanivetsky, 2011; Ziegeweid et.al, 2015). Nontrend sites were shown to not have trends in streamflow that were not related to precipitation. Hydrologic indicator statistics were computed for two periods: 1) the pre-period from 10-1-1944 through 9-30-1979 and 2) the post-period from 10-1-1980 through 9-30-2015. Exact dates of the start of trends varied among sites, but 1980 was the selected cutoff period based on an approximation of the largest cluster and on other anecdotal evidence of changes in farming practices. Both categories also had at least 10 water years with complete streamflow data. Blodgett, D., 2017, NWCCompare: Returns NWC comparison stats for two daily data sets version 5.0, https://github.com/USGS-R/NWCCompare. Mills, J., and Blodgett, D., 2017, EflowStats: Hydrologic Indicator and Alterations Stats version 5.0.0, https://github.com/USGS-R/EflowStats. Novotny, E.V., and Stefan, H.G., 2007, Stream flow in Minnesota: Indicator of climate change, Journal of Hydrology 334: 319-333. Peterson, H.M., Nieber, J.L., and Kanivetsky, R., 2011, Hydrologic regionalization to assess anthropogenic changes, Journal of Hydrology 408: 212-225. Ziegeweid, J.R., Lorenz, D.L., Sanocki, C.A., and Czuba, C.R., 2015, Methods for estimating flow-duration curve and low-flow frequency statistics for ungaged locations on small streams in Minnesota: U.S. Geological Survey Scientific Investigations Report 2015–5170, 23 p., http://dx.doi.org/10.3133/sir20155170.

Regression diagnostics (including number of observations, residual variance, R squared, bias percentage, Akaike's information criterion (AIC), Nash-Sutcliffe efficiency) and coefficients (variable, estimate, standard error, Z score, p-value) for the tributary nutrient and sediment monitoring program on the Great Lakes, 2011-2013

Regression diagnostics (including number of observations, residual variance, R squared, bias percentage, Akaike's information criterion (AIC), Nash-Sutcliffe efficiency) and coefficients (variable, estimate, standard error, Z score, p-value) for the tributary nutrient and sediment monitoring program on the Great Lakes, 2011-2013

This data release contains data in support of "Regional Analysis of the Dependence of Peak-Flow Quantiles on Climate with Application to Adjustment to Climate Trends" (Over and others, 2025). It contains input and output data used to analyze the effect of climate changes on trends in floods using three regression approaches. The input consists of two files. The first, "station_list.csv," contains streamgage information for the 404 streamgages considered for use in Over and others (2025). Only 330 of the 404 streamgages were considered non-redundant and used in the final analysis; these streamgages have a value of "Non-redundant" in the "redundancy_status" column. This file includes calibrated Monthly Water Balance Model (MWBM) parameters and basin characteristics. The second, "regression_input.csv," contains regression input data, including observed peak streamflow and precipitation. MWBM-simulated streamflow data was created using two sets of MWBM parameters: at-site calibrated parameters and median calibrated parameters. At-site calibrated parameters varied by station and represent the best-performing set of parameters per station. These parameters can be found in "station_list.csv". The median calibrated parameters were obtained by taking the median of all at-site calibrated parameters for the 330 streamgage basins used in analysis. See the Entity and Attribute section for details. The output files consist of nine Comma Separated Value (CSV) files. "Kendall_cor.csv" contains Mann-Kendall trend analysis results by streamgage. The regression results for annual maximum streamflow from at-site calibrated MWBM parameters by streamgage are provided in "byStation-sqrt_ann_max_MWBM_Q.csv". The regression results for annual maximum streamflow from median calibrated MWBM parameters by streamgage are provided in "byStation-sqrt_ann_max_MWBM_Q-medianMWBM.csv". "FixedEffects-sqrt_ann_max_MWBM_Q.csv" contains fixed effects for annual maximum streamflow from at-site calibrated MWBM parameters by streamgage. "FixedEffects-sqrt_ann_max_MWBM_Q-medianMWBM.csv" contains fixed effects for annual maximum streamflow from median calibrated MWBM parameters by streamgage. "MMQR-sqrt_ann_max_MWBM_Q_adjusted_moments.csv" contains observed and adjusted peak discharge moments from the method-of-moments quantile-regression (MMQR) method. "MMQR-sqrt_ann_max_MWBM_Q_adjusted_quantiles.csv" contains observed and adjusted discharge quantiles from the MMQR method. "QR-sqrt_ann_max_MWBM_Q_adjusted_moments.csv" contains observed and adjusted moments from the single-station quantile regression (QR) method. "QR-sqrt_ann_max_MWBM_Q_adjusted_quantiles.csv" contains observed and adjusted discharge quantiles from the QR method. Also included is "ModelArchive.zip", which contains the R scripts used to create the data provided in this data release and in Over and others, 2025. It contains the input data necessary to run the scripts and readMe files with directions for running the scripts locally.

Rivers and Streams data for the National Aquatic Resource Surveys. This dataset is associated with the following publication: Herlihy, A., J. Sifneos, R. Hughes, D. Peck, and R. Mitchell. The Relation of Lotic Fish and Benthic Macroinvertebrate Condition Indices to Environmental Factors Across the Conterminous USA. ECOLOGICAL INDICATORS. Elsevier Science Ltd, New York, NY, USA, 112: 105958, (2020).

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.

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.

Hydrographic and Impairment Statistics (HIS) is a National Park Service (NPS) Water Resources Division (WRD) project established to track certain goals created in response to the Government Performance and Results Act of 1993 (GPRA). One water resources management goal established by the Department of the Interior under GRPA requires NPS to track the percent of its managed surface waters that are meeting Clean Water Act (CWA) water quality standards. This goal requires an accurate inventory that spatially quantifies the surface water hydrography that each bureau manages and a procedure to determine and track which waterbodies are or are not meeting water quality standards as outlined by Section 303(d) of the CWA. This project helps meet this DOI GRPA goal by inventorying and monitoring in a geographic information system for the NPS: (1) CWA 303(d) quality impaired waters and causes; and (2) hydrographic statistics based on the United States Geological Survey (USGS) National Hydrography Dataset (NHD). Hydrographic and 303(d) impairment statistics were evaluated based on a combination of 1:24,000 (NHD) and finer scale data (frequently provided by state GIS layers).