The U.S. Geological Survey (USGS), in cooperation with the Bureau of Land Management, used streamflow measurements at 11 partial-record sites and related them to nearby USGS or Idaho Power Company real-time streamgages (index sites) to provide daily mean streamflow values at ungaged (partial-record) sites within the Wild and Scenic River of the Owyhee Canyonlands Wilderness, Idaho. Daily mean streamflow was estimated by developing a regression relationship between streamflow at each partial-record site and the index site for the period of record of the index site. The regressions are then used to estimate annual and semimonthly 20-, 50-, and 80-percent exceedance probability streamflow statistics at each partial-record site. The streamflow statistics will support the Bureau of Land Management’s effort to develop and file a federal water rights claim for sustaining outstanding remarkable values for rivers designated as “Wild,” “Scenic,” or “Recreational”.

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)

This dataset consists of daily streamflow percentiles for 1981-10-01 to 2020-03-31 relevant to streamflow drought defined using two approaches: Percentiles accounting for flow seasonality (variable threshold percentiles) and those based on the full record of data for each site regardless of season (fixed threshold percentiles). Because of the size of this dataset (99,530,836 rows), it could not be provided as a .csv file, and is instead provided as a .parquet file. Instructions on reading this file using the R programming language are provided in the Processing Step section of this metadata. The daily streamflow percentiles were estimated for ungaged areas of the Colorado River Basin (CRB) using neural network models, specifically long short-term memory (LSTM) models, by scientists on the USGS Data-Driven Drought Prediction project. The models were trained on data from 391 streamgages in the CRB and surrounding region and then used to generate predictions at ungaged stream locations within the CRB. Data from 01-Oct-1981 to 31-Mar-2014 was used to train the model with validation over the period of record spanning 01-Apr-2014 to 31-Mar- 2020. The models use explanatory variable inputs described in Wieczorek (2023) (doi.org/10.5066/P98IG8LO) to predict daily streamflow and streamflow percentiles as described in Simeone (2022) (doi.org/10.5066/P92FAASD). Model predictions are provided for 3,539 ungaged area spatial units from the National Hydrologic Geospatial Fabric version 1.1 (Bock et al., 2020) across the CRB. A follow up set of predictions is planned, with those predictions based on a models using a greater number of predictor variables including variables quantifying human flow alteration.

This dataset consists of daily streamflow percentiles for 1981-10-01 to 2020-03-31 relevant to streamflow drought defined using two approaches: Percentiles accounting for flow seasonality (variable threshold percentiles) and those based on the full record of data for each site regardless of season (fixed threshold percentiles). Because of the size of this dataset (99,530,836 rows), it could not be provided as a .csv file, and is instead provided as a .parquet file. Instructions on reading this file using the R programming language are provided in the Processing Step section of this metadata. The daily streamflow percentiles were estimated for ungaged areas of the Colorado River Basin (CRB) using neural network models, specifically long short-term memory (LSTM) models, by scientists on the USGS Data-Driven Drought Prediction project. The models were trained on data from 391 streamgages in the CRB and surrounding region and then used to generate predictions at ungaged stream locations within the CRB. Data from 01-Oct-1981 to 31-Mar-2014 was used to train the model with validation over the period of record spanning 01-Apr-2014 to 31-Mar- 2020. The models use explanatory variable inputs described in Wieczorek (2023) (doi.org/10.5066/P98IG8LO) to predict daily streamflow and streamflow percentiles as described in Simeone (2022) (doi.org/10.5066/P92FAASD). Model predictions are provided for 3,539 ungaged area spatial units from the National Hydrologic Geospatial Fabric version 1.1 (Bock et al., 2020) across the CRB. A follow up set of predictions is planned, with those predictions based on a models using a greater number of predictor variables including variables quantifying human flow alteration.

To provide daily mean streamflow values at ungaged (partial-record) sites within the Yankee Fork Salmon River watershed, the U.S. Geological Survey (USGS), in cooperation with U.S. Bureau of Reclamation, used discharge measurements at three partial-record sites and related those measurements to a nearby USGS real-time streamgage (index site). Daily mean streamflow was estimated by developing a regression relationship between each partial-record site and the index site for water years 2012-2019. These data are intended to provide daily mean streamflow estimates at partial-record sites as part of a larger study (Clark and others, 2021) to estimate sediment loading for each site.

To provide daily mean streamflow values at ungaged (partial-record) sites within the Yankee Fork Salmon River watershed, the U.S. Geological Survey (USGS), in cooperation with U.S. Bureau of Reclamation, used discharge measurements at three partial-record sites and related those measurements to a nearby USGS real-time streamgage (index site). Daily mean streamflow was estimated by developing a regression relationship between each partial-record site and the index site for water years 2012-2019. These data are intended to provide daily mean streamflow estimates at partial-record sites as part of a larger study (Clark and others, 2021) to estimate sediment loading for each site.

Our objective was to model the risk of becoming intermittent under drier climate conditions on small, ungaged streams in the Upper Colorado River Basin. Modeling streamflows is an important tool for understanding landscape-scale drivers of flow and estimating flows where there are no gaged records. We focused our study in the Upper Colorado River Basin, a region that is not only critical for water resources but also projected to experience large future climate shifts toward a drier climate. We used a conditional inference modeling approach to model the relation between intermittency status on gaged streams (115 gages) and selected mean and minimum flow metrics. We then projected intermittency status and if a stream reach would be "threatened by intermittency" under a drier climate to ungaged reaches in the Upper Colorado River Basin using predicted minimum flow coefficient of variation (CV) and specific mean annual flow for each stream reach in the basin. This data layer shows modeled values of stream intermittency based on minimum flow CV and specific mean annual flow for each stream reach in the basin.

Our objective was to model the risk of becoming intermittent under drier climate conditions on small, ungaged streams in the Upper Colorado River Basin. Modeling streamflows is an important tool for understanding landscape-scale drivers of flow and estimating flows where there are no gaged records. We focused our study in the Upper Colorado River Basin, a region that is not only critical for water resources but also projected to experience large future climate shifts toward a drier climate. We used a conditional inference modeling approach to model the relation between intermittency status on gaged streams (115 gages) and selected mean and minimum flow metrics. We then projected intermittency status and if a stream reach would be "threatened by intermittency" under a drier climate to ungaged reaches in the Upper Colorado River Basin using predicted minimum flow coefficient of variation (CV) and specific mean annual flow for each stream reach in the basin. This data layer shows modeled values of stream intermittency based on minimum flow CV and specific mean annual flow for each stream reach in the basin.

The U.S. Geological Survey (USGS), in cooperation with the Wyoming Water Development Office, developed streamflow statistics for active (through September 30, 2021) and discontinued USGS streamgages in and near Wyoming with 10 or more years of daily mean streamflow record. The computation of streamflow statistics for USGS streamgages is part of a larger study to develop a StreamStats application (www.usgs.gov/streamstats) for the State of Wyoming (https://www.usgs.gov/centers/wyoming-montana-water-science-center/science/wyoming-streamstats). StreamStats is a web-based computer program that can be used to delineate drainage areas, determine basin characteristics, and compute streamflow statistics at locations with and without streamgages (https://streamstats.usgs.gov/ss/; Ries and others, 2024). Streamflow at each streamgage was assessed for degree of human alteration owing to dams and diversions before streamflow statistics were computed. Streamflow records from 631 streamgage periods of record were used to compute basic, seasonal, and flow-duration statistics; records for 390 streamgage periods of record were used to compute n-day statistics and statistics that can be used for regional regression. Methods used to compute the summary statistics contained in this data release are described in an accompanying report (Armstrong and others, 2025).