This dataset contains input parameter and data files, as well as output files for simulations prior to (pre-calibration) and after calibration (post-calibration) of streamflow parameters for nine near-native subbasins. Simulated and observed streamflow for nine near-native subbasins are included for parts of the Upper Rio Grande Basin in Colorado, New Mexico, Texas, and northern Mexico using the Precipitation-Runoff Modeling System (PRMS). Input data include pre-calibration input parameters for the entire Upper Rio Grande Basin. Pre-calibrated parameters used as input to PRMS for step 2 are the post-calibration parameters in Step 1-Solar Radiation and Potential ET calibration. Post-calibration model parameters include parameters after calibration of streamflow in selected nine near-native subbasins. Output files include daily PRMS simulated streamflow (seg_outflow) and observed streamflow at USGS streamgages for each near-native subbasin. These PRMS model input and output data are intended to accompany a U.S. Geological Survey Scientific Investigations Report (Chavarria and others, 2020).

This dataset contains input parameter and data files, as well as output files for simulations prior to the distribution of parameters from near-native subbasins to uncalibrated hydrologic response units (HRUs) (pre-distribution) and after parameters are distributed to HRUs (post-distribution). Simulated and observed streamflow for sites along the mainstem of the Rio Grande River are included for parts of the Upper Rio Grande Basin in Colorado, New Mexico, Texas, and northern Mexico using the Precipitation-Runoff Modeling System (PRMS). Input data include pre-distribution input parameters for the entire Upper Rio Grande Basin. Pre-distribution parameters used as input to PRMS for step 3 are the post-calibration parameters in Step 2-Calibration Near-Native subbasins. Post-distribution model parameters include interpolated parameters from the calibrated near-native subbasins. Output files include daily PRMS simulated streamflow (seg_outflow) and observed streamflow (runoff) at five USGS streamgages along the mainstem Rio Grande. These PRMS model input and output data are intended to accompany a U.S. Geological Survey Scientific Investigations Report (Chavarria and others, 2020).

This dataset contains input parameter and data files, as well as output files for simulations prior to the distribution of parameters from near-native subbasins to uncalibrated hydrologic response units (HRUs) (pre-distribution) and after parameters are distributed to HRUs (post-distribution). Simulated and observed streamflow for sites along the mainstem of the Rio Grande River are included for parts of the Upper Rio Grande Basin in Colorado, New Mexico, Texas, and northern Mexico using the Precipitation-Runoff Modeling System (PRMS). Input data include pre-distribution input parameters for the entire Upper Rio Grande Basin. Pre-distribution parameters used as input to PRMS for step 3 are the post-calibration parameters in Step 2-Calibration Near-Native subbasins. Post-distribution model parameters include interpolated parameters from the calibrated near-native subbasins. Output files include daily PRMS simulated streamflow (seg_outflow) and observed streamflow (runoff) at five USGS streamgages along the mainstem Rio Grande. These PRMS model input and output data are intended to accompany a U.S. Geological Survey Scientific Investigations Report (Chavarria and others, 2020).

This data release contains input and output data from hydrologic simulations of naturalized or near-native streamflow conditions in the Upper Rio Grande Basin (URGB) in Colorado, New Mexico, Texas, and northern Mexico by using the Precipitation-Runoff Modeling System (PRMS). The Upper Rio Grande Basin PRMS model was calibrated in a three step process by (1) calibrating solar radiation and potential evapotranspiration parameters by subarea for hydrologic response units (HRU) in the model domain, (2) calibrating streamflow parameters in nine subbasins identified to be “near-native” subbasins, or basins with low anthropogenic disturbance, and (3) distributing calibrated parameters from near-native subbasins to uncalibrated HRUs in the model domain. The data release contains the pre- and post-calibrated input and output data, for each of the three steps, needed to run PRMS to achieve the results presented in a U.S. Geological Survey Scientific Investigations Report (Chavarria and others, 2020).

This data release contains input and output data from hydrologic simulations of naturalized or near-native streamflow conditions in the Upper Rio Grande Basin (URGB) in Colorado, New Mexico, Texas, and northern Mexico by using the Precipitation-Runoff Modeling System (PRMS). The Upper Rio Grande Basin PRMS model was calibrated in a three step process by (1) calibrating solar radiation and potential evapotranspiration parameters by subarea for hydrologic response units (HRU) in the model domain, (2) calibrating streamflow parameters in nine subbasins identified to be “near-native” subbasins, or basins with low anthropogenic disturbance, and (3) distributing calibrated parameters from near-native subbasins to uncalibrated HRUs in the model domain. The data release contains the pre- and post-calibrated input and output data, for each of the three steps, needed to run PRMS to achieve the results presented in a U.S. Geological Survey Scientific Investigations Report (Chavarria and others, 2020).

This dataset contains projected climate data (precipitation, maximum temperature, minimum temperature) from 27 climate scenarios used as input to the Precipitation-Runoff Modeling System (PRMS), and baseline PRMS simulated streamflow at 63 sites in the Upper Rio Grande Basin under each of the 27 scenarios. Projected climate data, obtained from the USGS South Central Climate Adaptation Science Center (Wooten, 2020), were generated using three general circulation models, run under three emission scenarios (RCP 2.6, RCP 4.5, RCP 8.5), and downscaled using three different methods (delta SD, equidistant quantile mapping, piecewise asynchronous regression). Together, the three models, RCPs, and downscaling methods resulted in a set of 27 climate projections. Each input climate data file includes precipitation, maximum temperature, and minimum temperature for each hydrologic response unit in the PRMS model. Model output includes 27 files of PRMS simulated projected daily streamflow at 63 sites in the Upper Rio Grande Basin in Colorado, New Mexico, and Texas for the years 1981-2099.

This data release contains the input and output used to assess the potential effect of climate on streamflow and salinity (measured as total dissolved solids [TDS]) yields across the Upper Colorado River Basin from water years 1986 to 2021. This analysis included estimation of the spatiotemporal variability in mean annual climatic variables (air temperature, snow water equivalent, precipitation and antecedent precipitation), mean annual streamflow yields, and mean annual TDS yields at 34 sites within the basin. Generalized Additive Models (GAMs) were used to look at non-linear trends in streamflow and TDS yields in the Upper Colorado River Basin. GAMs were also used to create attribution models that explain temporal variability in streamflow and TDS using climate variables (precipitation, snow, and air temperature). A detailed description of the analysis is provided in the associated journal article.

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)

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)

Modeling streamflow is an important approach for understanding landscape-scale drivers of flow and estimating flows where there are no streamgage records. In this study conducted by the U.S. Geological Survey in cooperation with Colorado State University, the objectives were to model streamflow metrics on small, ungaged streams in the Upper Colorado River Basin and identify streams that are potentially threatened with becoming intermittent under drier climate conditions. The Upper Colorado River Basin is a region that is critical for water resources and also projected to experience large future climate shifts toward a drying climate. A random forest modeling approach was used to model the relationship between streamflow metrics and environmental variables. Flow metrics were then projected to ungaged reaches in the Upper Colorado River Basin using environmental variables for each stream, represented as raster cells, in the basin. Last, the projected random forest models of minimum flow coefficient of variation and specific mean daily flow were used to highlight streams that had greater than 61.84 percent minimum flow coefficient of variation and less than 0.096 specific mean daily flow and suggested that these streams will be most threatened to shift to intermittent flow regimes under drier climate conditions. Map projection products can help scientists, land managers, and policymakers understand current hydrology in the Upper Colorado River Basin and make informed decisions regarding water resources. With knowledge of which streams are likely to undergo significant drying in the future, managers and sci- entists can plan for stream-dependent ecosystems and human water users.