Data was collected to characterize the conditions under which sagebrush occurs after seeding and wildfire in the Great Basin, and used to parameterize models used to explore adaptive seeding approaches. Data includes plot level field data on sagebrush occurrence, density, weather, and soil moisture conditions in the year that seeding after wildfire occurred. Weather data includes both average annual summaries and average weather at 5-day intervals from day 1-250 of the year of seeding. Also included are summaries of annual temperature and soil moisture conditions from 1979 to 2016 and model predictions of the probability of sagebrush establishment in each of these years.

These tabular data sets represent daily climate metrics processed from 4 kilometer snow water equivalent (SWE) raster data in millimeters (Broxton and others, 2019) for the period of record 10-01-1981 through 09-30-2020 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).

These tabular data sets represent daily climate metrics processed from 4 kilometer snow water equivalent (SWE) raster data in millimeters (Broxton and others, 2019) for the period of record 10-01-1981 through 09-30-2020 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).

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 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 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 datasets represent retrospective forecasts of average minimum temperature (degrees Celsius), maximum temperature (degrees Celsius), and total precipitation (millimeters) within three-hour forecasting periods derived from the Global Ensemble Forecast System (GEFS) reforecast dataset (Hamill and others, 2013). Data are averaged across 7 day forecast horizons for each day within the period of record spanning 2000 through 2019. The data were compiled for two spatial components: 1) select United States Geological Survey streamgage basins (Staub and others, 2023), 2) individual reach flowline catchments of the Upper Colorado (ucol) portion of the Geospatial Fabric for the National Hydrologic Model, version 1.1 (Bock and others, 2020). Flowline reach catchment information characterizes data at the local scale using the python tool set called gdptools (McDonald, 2021).

These tabular data sets represent monthly meteorological metrics processed from North American Multi-Model Ensemble (NMME) for the hindcast (1982-2011) and forecast (2011-2023) periods of record and compiled for the spatial component of select United States Geological Survey stream gage basins (Staub and others, 2023). Flowline reach catchment information characterizes data at the local scale using the python tool set called gdptools (McDonald, 2021). The following monthly meteorological metrics were processed: reference temperature (degree Celsius), and total precipitation (millimeters) for forecast periods of 15, 45, 75, and 105 days (0.5 to 3.5 months).

This data release contains monthly 270-meter resolution Basin Characterization Model (BCMv8) climate and hydrologic variables for Localized Constructed Analog (LOCA; Pierce et al., 2014)-downscaled HadGEM2-CC Global Climate Model (GCM) for Representative Concentration Pathway (RCP) 4.5 (medium-low emissions) and 8.5 (high emissions) for hydrologic California. The LOCA climate scenarios span water years 1950 to 2099 with greenhouse-gas forcings beginning in 2006. The LOCA downscaling method has been shown to produce better estimates of extreme events and reduces the common downscaling problem of too many low-precipitation days (Pierce et al., 2014). Ten GCMs were selected from the full ensemble of models from the fifth Coupled Model Intercomparison Project from the World Climate Research Programme (CMIP5) based on GCM historical performance to address specific needs for California water-resource planning (California Department of Water Resources Climate Change Technical Advisory Group, 2015). The 10 GCMs with RCP 4.5 and 8.5 each were statistically downscaled using the LOCA method (Pierce et al., 2014) from 2-degree (approximately 222-kilometer; km) quadrangles to 6-km resolution. Next, the scenarios were spatially downscaled from 6 km to 270 meters (Flint and Flint, 2012) and run through the BCMv8 using the same model parameters and input files as the historical BCM model (BCMv8; Flint et al., 2021). Downscaled gridded climate variables include precipitation (ppt), minimum temperature (tmn), maximum temperature (tmx), and potential evapotranspiration (pet). Gridded hydrologic variables include: actual evapotranspiration (aet), climatic water deficit (cwd), snowpack (pck), recharge (rch), runoff (run), and soil storage (str). The units for temperature variables are degrees Celsius, and all other variables are in millimeters per month. Monthly variables from water years 1951 to 2099 are summarized into water year files (for example, water year 1951 includes October 1950 - September 1951) and 30-year average summaries from 1951 to 2099. Raster grids are in the NAD83 California Teale Albers, (meters) projection in an open format ascii text file (*.asc). This data release includes a child item for each RCP (4.5 & 8.5) for the HadGEM2-CC GCM. Each RCP child item contains 4 child items: 1. 30-year summaries (Water year files averaged for selected 30-year periods, zipped by variable) 2. Monthly BCM hydrology variables (monthly BCM hydrology variables zipped by decade) 3. Monthly climate variables (monthly climate variables zipped by decade) 4. Water year summaries (monthly files summed (aet, cwd, pck, rch, run, str, pet, and ppt) or averaged (tmn and tmx) by water year, zipped by variable) References cited: California Department of Water Resources Climate Change Technical Advisory Group, 2015, Perspectives and guidance for climate change analysis: Sacramento, Calif., California Department of Water Resources Technical Information Record, 142 p. Flint, L.E., Flint, A.L., and Stern, M.A., 2021, The Basin Characterization Model - A monthly regional water balance software package (BCMv8) data release and model archive for hydrologic California (ver. 3.0, June 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P9PT36UI. Flint, L.E., and Flint, A.L., 2012, Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis: Ecological Processes, v. 1, no. 2, 15 p., https://doi.org/10.1186/2192-1709-1-2. Pierce, D.W., Cayan, D.R. and Thrasher, B.L., 2014. Statistical downscaling using localized constructed analogs (LOCA). Journal of hydrometeorology, 15(6), pp.2558-2585.