Perennial streams in Idaho have been modeled using regression equations for 7-day, 2-year low flows (7Q2) described in Wood and others (2009, U.S. Geological Survey Scientific Investigations Report 2009-5015). The model produces "synthetic" streams based on 10-meter resolution digitial elevation models that have been processed to agree closely with 1:24,000-scale National Hydrography Dataset flowlines. Lines having attribute PerCode = 1 represent model grid cells having 7Q2 flow estimates between 0.1 cubic feet per second (ft3/s) and 0.1 minus the standard error for the applicable regional regression equation. Lines having attribute PerCode = 2 represent model grid cells having 7Q2 flow estimates between 0.1 ft3/s and 0.1 plus the standard error for the applicable regional regression equation. Lines having attribute PerCode = 3 represent model grid cells having 7Q2 flow estimates greater than 0.1 ft3/s plus the standard error for the applicable regional regression equation. These three categories of lines indicate approximate levels of uncertainty in the model results, with lines having PerCode = 1 representing the most uncertainty, and lines having PerCode = 3 representing the least uncertainty with respect to representing a perennial stream.

We projected future streamflow outcomes arising from climate change for the southwestern United States during the 21st century due to climate change under two possible greenhouse gas concentration pathways (RCP4.5 and 8.5). The results inform water managers about the future risks of drought in their water resource regions by providing bounds on the possible locations and extents of streamflow loss. To get to these results, we used downscaled future and historical climate data from seven models to drive a new, calibrated SPAtially Referenced Regression On Watershed attributes (SPARROW) streamflow model (Wise and others, 2019, Miller and others, 2020). Temperature and precipitation data come from the NASA Earth Exchange (NEX) Downscaled Climate Projections (NEX-DCP30, Thrasher and others, 2013 and Thrasher and others, 2015), and actual and potential evapotranspiration come from the NEX-DCP30 temperature and precipitation used in the Monthly Water Balance Model (MWBM, Hostetler and Alder, 2016 and Alder, 2017a,b,c). This data set comprises climate data preprocessing code to convert the gridded, monthly-scale climate data to reach scale multidecadal averages for the intervals 1975-2005, 2020-2049, 2040-2069 and 2070-2099, the model input (data1) and model control files, the model code, model results files, and code to post-process and analyze the streamflow model results. The raw climate data (NEX-DCP30, MWBM), and SPARROW model calibration documentation are publicly available elsewhere and are cross linked with this data release (see crossref section). The full data preparation, modeling, and analysis methods, as well as results are described in Miller and others, (2021)

The continental United States (CONUS) was modeled to produce simulations of historical and potential future streamflow using the Precipitation-Runoff Modeling System (PRMS) application of the USGS National Hydrologic Model infrastructure (NHM; Regan and others, 2018). This child page specifically contains a suite of 52 streamflow metrics. These metrics were computed using daily outputs of runoff from HRUs (PRMS variable hru_outflow) and streamflow from the model stream segments (PRMS variable seg_outflow) for all historical and future simulations (table1_GCMs_used.csv) with both static and dynamic land cover parameters. These streamflow statistics describe the duration, frequency, magnitude, rate of change, and timing of streamflow computed for historical and future simulation periods (streamflow_statistics_description_table.csv).

The continental United States (CONUS) was modeled to produce simulations of historical and potential future streamflow using the Precipitation-Runoff Modeling System (PRMS) application of the USGS National Hydrologic Model infrastructure (NHM; Regan and others, 2018). This child page specifically contains a suite of 52 streamflow metrics. These metrics were computed using daily outputs of runoff from HRUs (PRMS variable hru_outflow) and streamflow from the model stream segments (PRMS variable seg_outflow) for all historical and future simulations (table1_GCMs_used.csv) with both static and dynamic land cover parameters. These streamflow statistics describe the duration, frequency, magnitude, rate of change, and timing of streamflow computed for historical and future simulation periods (streamflow_statistics_description_table.csv).

This data release contains results of a study investigating changes in streamflow seasonality associated with hydroclimatic variability in the north-central United States, including nine States (Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin). Peak-flow records from unregulated U.S. Geological Survey streamgages were used to evaluate changes in streamflow seasonality over 75-, 50-, and 30-year trend periods through water year 2020. The streamgages in each of the nine states used in the analysis and the results of the seasonal characteristics and statistical analyses are provided in tabular form (in csv file format) in file "Results.zip" under "Attached Files" below.

This data release contains results of a study investigating changes in streamflow seasonality associated with hydroclimatic variability in the north-central United States, including nine States (Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin). Peak-flow records from unregulated U.S. Geological Survey streamgages were used to evaluate changes in streamflow seasonality over 75-, 50-, and 30-year trend periods through water year 2020. The streamgages in each of the nine states used in the analysis and the results of the seasonal characteristics and statistical analyses are provided in tabular form (in csv file format) in file "Results.zip" under "Attached Files" below.

These tabular data sets represent mean monthly temperature (degrees Celsius) data from 800 meter resolution PRISM for the years 2016 and 2017 compiled for two spatial components of the NHDPlus version 2.1 data suite (NHDPlusv2) for the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. This dataset can be linked to the NHDPlus version 2 data suite by the unique identifier COMID. The source data for mean monthly temperature (degrees Celsius) from 800 meter resolution resolution PRISM data was produced by the PRISM Group at Oregon State University. Units are degrees degrees Celsius. Reach catchment information characterizes data at the local scale. Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions. Network-accumulated values are computed using two methods, 1) divergence-routed and 2) total cumulative drainage area. Both approaches use a modified routing database to navigate the NHDPlus reach network to aggregate (accumulate) the metrics derived from the reach catchment scale. (Schwarz and Wieczorek, 2018).

These tabular data sets represent mean monthly precipitation (millimeters) data from 800-meter PRISM data for the years 2016 and 2017 compiled for two spatial components of the NHDPlus version 2 data suite (NHDPlusv2) for the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. This dataset can be linked to the NHDPlus version 2 data suite by the unique identifier COMID. The source data for mean monthly precipitation (Celsius) from 800-meter PRISM data was produced by the PRISM Group at Oregon State University. Units are degrees Celsius. Reach catchment information characterizes data at the local scale. Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions. Network-accumulated values are computed using two methods, 1) divergence-routed and 2) total cumulative drainage area. Both approaches use a modified routing database to navigate the NHDPlus reach network to aggregate (accumulate) the metrics derived from the reach catchment scale. (Schwarz and Wieczorek, 2018).

These tabular data sets represent mean monthly precipitation (millimeters) data from 800-meter PRISM data for the years 2016 and 2017 compiled for two spatial components of the NHDPlus version 2 data suite (NHDPlusv2) for the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. This dataset can be linked to the NHDPlus version 2 data suite by the unique identifier COMID. The source data for mean monthly precipitation (Celsius) from 800-meter PRISM data was produced by the PRISM Group at Oregon State University. Units are degrees Celsius. Reach catchment information characterizes data at the local scale. Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions. Network-accumulated values are computed using two methods, 1) divergence-routed and 2) total cumulative drainage area. Both approaches use a modified routing database to navigate the NHDPlus reach network to aggregate (accumulate) the metrics derived from the reach catchment scale. (Schwarz and Wieczorek, 2018).

These tabular data sets represent mean 30 year (1971-2000) monthly temperature (degrees Celsius) data from 800 meter resolution PRISM for two spatial components of the NHDPlus version 2.1 data suite (NHDPlusv2) for the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. This dataset can be linked to the NHDPlus version 2 data suite by the unique identifier COMID. The source data for mean monthly temperature (degrees Celsius) from 800 meter resolution resolution PRISM data was produced by the PRISM Group at Oregon State University. Units are degrees degrees Celsius. Reach catchment information characterizes data at the local scale. Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions. Network-accumulated values are computed using two methods, 1) divergence-routed and 2) total cumulative drainage area. Both approaches use a modified routing database to navigate the NHDPlus reach network to aggregate (accumulate) the metrics derived from the reach catchment scale. (Schwarz and Wieczorek, 2018).