This data release comprises the data files and code necessary to perform all analyses presented in the associated publication. The *.csv data files are aggregations of water extent on the basis of the European Commission's Joint Research Centre (JRC) Monthly Water History database (v1.0) and the Dynamic Surface Water Extent (DSWE) algorithm. The shapefile dataset contains the study area 8-digit hydrologic unit code (HUC) regions used as the basis for analysis. Html files provide an overview of the study workflow and integrated R notebooks (in .Rmd format) for recreating all project results and plots. The R notebook ingest the necessary data files from their online locations. These data support the following publication: Walker JJ, Soulard CE, Petrakis RE. In press. Integrating stream gage data and Landsat imagery to complete time-series of surface water extents in Central Valley, California. International Journal of Applied Earth Observation and Geoinformation, http://dx.doi.org/xx.xxxxx/

This data release comprises the data files and code necessary to perform all analyses presented in the associated publication. The *.csv data files are aggregations of water extent on the basis of the European Commission's Joint Research Centre (JRC) Monthly Water History database (v1.0) and the Dynamic Surface Water Extent (DSWE) algorithm. The shapefile dataset contains the study area 8-digit hydrologic unit code (HUC) regions used as the basis for analysis. Html files provide an overview of the study workflow and integrated R notebooks (in .Rmd format) for recreating all project results and plots. The R notebook ingest the necessary data files from their online locations. These data support the following publication: Walker JJ, Soulard CE, Petrakis RE. In press. Integrating stream gage data and Landsat imagery to complete time-series of surface water extents in Central Valley, California. International Journal of Applied Earth Observation and Geoinformation, http://dx.doi.org/xx.xxxxx/

This dataset provides compiled and computed data from 1900 through 2017 associated with Streamflow statistics used to perform regional analyses for the Brazos, Colorado, Big Cypress, Guadalupe, Neches, Sulphur, and Trinity river basins. These seven river basins are mostly within Texas, but parts of some of the basins extend into New Mexico and Louisiana. Because changes in precipitation, temperature and groundwater levels can appreciably affect streamflow, understanding changes in streamflow requires taking these forcing variables into account. Long-term streamflow statistics for these seven river basins were derived by analyzing streamflow data and other observed climatological variables. Data include tables of accumulated surface-water storage data modified from the National Inventory of Dams (NID), (Table 1), delineation of State counties or parishes by study basin (Table 2), National Oceanic and Atmospheric Administration (NOAA) precipitation stations by study basin (Table 3), and daily mean precipitation data (Table 4). In addition to data collected in 188 counties in Texas, this data release includes data collected in 4 counties in New Mexico, and 1 parish in Louisiana. Data not included in this dataset include temperature and groundwater-level elevation data, which are referenced in the associated larger work citation.

These tabular data sets represent the average daily soil moisture water content (kg/m^2) for four different soil layers processed from North American Land Data Assimilation System (NLDAS-2) data (Xia and others, 2012) for the period of record 1980 through 2020 and compiled for three spatial components: 1) select United States Geological Survey stream gage basins (Staub and Wieczorek, 2023), 2) individual reach flowline catchments of the Upper Colorado (ucol) portion of the Geospatial Fabric for the National Hydrologic Model, version 1.1 (nhgfv11, Bock and others, 2020 ), and 3) the upstream watersheds of each individual nhgfv11 flowline catchments. Flowline reach catchment information characterizes data at the local scale using the python tool set called gdptools (McDonald, 2021). Upstream watershed values for each reach catchment were computed using the published python software package Xstrm (Wieferich and others). The following mean daily soil moisture water content layers were processed: 0-10 centimeters, 10-40 centimeters, and 40-100 centimeters.

These tabular data sets represent daily climate metrics processed from 4 kilometer GridMET data (Abatzoglou, 2013) for the period of record 1980 through 2020 and compiled for three spatial components: select United States Geological Survey stream gage basins (Staub and Wieczorek, 2023), 2) individual reach flowline catchments of the Upper and Lower Colorado (ucol) portions of the Geospatial Fabric for the National Hydrologic Model, version 1.1 (nhgfv11, Bock and others, 2020 ), and 3) the upstream watersheds of each individual nhgfv11 flowline catchments. Flowline reach catchment information characterizes data at the local scale using the python tool set called gdptools (McDonald, 2021). Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions. Network-accumulated values were computed using the published python software package Xstrm (Wieferich and others). The following daily climate metrics were processed: minimum and maximum temperature (Celsius), precipitation (millimeters), potential evapotranspiration (millimeters), reference evapotranspiration (millimeters), and 5 day standardized precipitation evapotranspiration index (unitless).

These tabular data sets represent the average daily soil moisture water content (kg/m^2) for four different soil layers processed from North American Land Data Assimilation System (NLDAS-2) data (Xia and others, 2012) for the period of record 1980 through 2020 and compiled for three spatial components: 1) select United States Geological Survey stream gage basins (Staub and Wieczorek, 2023), 2) individual reach flowline catchments of the Upper Colorado (ucol) portion of the Geospatial Fabric for the National Hydrologic Model, version 1.1 (nhgfv11, Bock and others, 2020 ), and 3) the upstream watersheds of each individual nhgfv11 flowline catchments. Flowline reach catchment information characterizes data at the local scale using the python tool set called gdptools (McDonald, 2021). Upstream watershed values for each reach catchment were computed using the published python software package Xstrm (Wieferich and others). The following mean daily soil moisture water content layers were processed: 0-10 centimeters, 10-40 centimeters, and 40-100 centimeters.

,The Southern Great Plains 1997 (SGP97) Hydrology Experiment originated from an interdisciplinary investigation, "Soil Moisture Mapping at Satellite Temporal and Spatial Scales" (PI: Thomas J. Jackson, USDA Agricultural Research Service, Beltsville, MD) selected under the NASA Research Announcement 95-MTPE-03. The core of the 1997 experiment involves the deployment of the L-band Electronically Scanned Thinned Array Radiometer (ESTAR) for daily mapping of surface soil moisture. The region selected for investigation is the best instrumented site for surface soil moisture, hydrology and meteorology in the world. This includes the USDA/ARS Little Washita Watershed, the USDA/ARS facility at El Reno, Oklahoma, the ARM/CART central facility, as well as the Oklahoma Mesonet. The temporal coverage for this dataset is as follows: Begin datetime: 1995-10-01 00:00:00, End datetime: 2001-03-31 23:59:59. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Soil Water Retention Data Set is one of the various sub-surface data sets developed for the ARM/GCIP (Global Energy and Water Cycle Experiment (GEWEX) Continental-scale International Project) 1996 Near-Surface Observation (NESOB-96) Data Set. This data set contains a table for each of the ARM SWATS (Soil Water and Temperature System) sites at the SGP site containing the observed soil water retention data as obtained from laboratory tests using pressure plates and hanging columns. The soil characterizations were perfomed by Oklahoma State University.,

This metadata record describes a series of tabular datasets containing metrics used to characterize drought for select United States Geological Survey (USGS) streamgages in and surrounding the Colorado River Basin for the climate years (April 1 – March 31) 1981 to 2020. These streamgages are a subset of those used in Geospatial Attributes of Gages for Evaluating Streamflow, version 2 (GAGES-II, Falcone, 2011) with some additional USGS streamgages not in the GAGES-II dataset added. The metrics include streamflow percentiles, identified drought events, annual low streamflow, and statistics for each drought event.

This metadata record describes a series of tabular datasets containing metrics used to characterize drought for select United States Geological Survey (USGS) streamgages in and surrounding the Colorado River Basin for the climate years (April 1 – March 31) 1981 to 2020. These streamgages are a subset of those used in Geospatial Attributes of Gages for Evaluating Streamflow, version 2 (GAGES-II, Falcone, 2011) with some additional USGS streamgages not in the GAGES-II dataset added. The metrics include streamflow percentiles, identified drought events, annual low streamflow, and statistics for each drought event.

This USGS data release consists of two geospatial raster datasets and three geospatial vector data sets of water-level data. The data sets include a raster (A1) representing water-level change from predevelopment (about 1950) to 2015; the primary vector dataset (A2) of water-level-change data of static or near-static water levels in wells measured in predevelopment and 2015 (for wells in Colorado, Kansas, Nebraska, Oklahoma, South Dakota, and Texas) and in wells measured in predevelopment and the latest available static or near-static water level from 2011 to 2015 (for wells in New Mexico and Wyoming), a supplemental vector dataset (A3) of water-level data used to manually substantiate the raster of water-level change from predevelopment (about 1950) to 2015, a raster (B1) representing water-level change from 2013 to 2015; and the vector dataset (B2) of water-level-change data for wells measured in 2013 and 2015. The supplemental vector data sets of water-level-change data used to manually substantiate the raster of water-level change from predevelopment (about 1950) to 2015 are composed of (1) water-level-change data from wells measured before June 15, 1978, but not during or before the predevelopment period for the area, and in 2015, (2) for wells not measured in predevelopment or before June 15, 1978 but measured in 1980 and in 2015, calculated water-level-change data derived from the sum of the water-level-change value from 1980 to 2015 and the beginning water-level-change value from the contours of water-level change, predevelopment to 1980 (Luckey and others, 1981; Cederstrand and Becker, 1999), (3) water-level-change data for wells located in Colorado, Kansas, Nebraska, Oklahoma, South Dakota, and Texas and measured in predevelopment and 2014 and not measured in 2015, (4) water-level-change data for wells located in Colorado, Kansas, Nebraska, Oklahoma, South Dakota, and Texas and measured in measured in predevelopment and 2013 and not measured in 2014 or in 2015, (5) the water-level-change data for wells located in Colorado, Kansas, Nebraska, Oklahoma, South Dakota, and Texas and measured in measured in predevelopment and 2012 and not measured in 2013, 2014, or 2015, (6) the water-level-change data for wells located in Colorado, Kansas, Nebraska, Oklahoma, South Dakota, and Texas and measured in measured in predevelopment and 2011 and not measured in 2012, 2013, 2014, or 2015. The raster and vector data support USGS Scientific Investigations Report 2017-5040, Water-Level Changes and Change in Recoverable Water in Storage in the High Plains Aquifer, Predevelopment to 2015 and 2013-15.