This data release contains rainfall data from the 2020 Archie Creek, Holiday Farm, and Riverside Fire’s. These are gages identified in the parent OR_field_observations.csv release and used to calculate peak rainfall intensity-durations. The csv files here are organized by the station name and followed by the year of data collection. The locations of the stations, dates of deployment, interval, and unit of rainfall measurement are available in gage_locations.csv in the parent data release. All rainfall data are reported as a cumulative total. The Archie1, Archie2, Archie3, Holiday1, Holiday2, Holiday3, Holiday4, and Oregon Rain 4 rain gages are non-telemetered. These gages were deployed following the fires within the first few months of the 2020 water year. These rainfall data files are the raw output of the HOBO data logger file that have been converted to a csv using HOBO software version 3.7.25. These are tipping bucket gages where each bucket tip represents 0.2 mm of rainfall. The column headers for the non-telemetered gages are: #: Number of data logs recorded. Date Time, GMT-07:00: Time stamp of when data event was recorded [m/d/yyyy H:M:S]. Event, units (Sensor IDs): Bucket tip. OregonRain4 additionally includes a temperature recording column Temp, °C (LGR S/N: 10741450, SEN S/N: 10741450, LBL: temp), which describes the temperature recorded for the timestamp in degrees C. The D7564, E6414, F0379, F9895, HGNO3, LNEO3, RWXO3, TCFO3, and WPKO3 gages are telemetered, and rainfall data were downloaded from MESOWEST (https://mesowest.utah.edu/). MESOWEST only allows for rainfall data to be downloaded at a maximum of 365 days at a time, and rainfall data associated with these telemetered gages span multiple years. The multiple years of data for each gage were combined and adjusted so that our cumulative rainfall data starts at a value of 0 at the start of our downloaded data. These data are reported in inches. The column headers for these telemetered gages are: date: Time stamp of when data event was recorded [m/d/yyyy H:M]. precip: Cumulative total of rainfall in inches.

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.

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.

In the southwestern US, the meteorological phenomenon known as atmospheric rivers (ARs) has gained increasing attention due to its strong connections to floods, snowpacks and water supplies in the West Coast states. Relatively less is known about the ecological implications of ARs, particularly in the interior Southwest, where AR storms are less common. To address this gap, we compared a chronology of AR landfalls on the west coast between 1989-2011 and between 25-42.5ºN, to annual metrics of the Normalized Difference Vegetation Index (NDVI; an indicator of vegetation productivity) and daily-resolution precipitation data to assess influences of AR-fed winter precipitation on vegetation productivity across the southwestern US. We mapped correlations between winter AR precipitation during landfalling ARs and 1) annual maximum NDVI and 2) area burned by large wildfires summarized by ecoregion during the same year as the landfalls and during the following year. The data produced by this study include four sets of eight raster grids (total = 32 grids) representing Spearman Rank correlation coefficients for four types of comparisons across eight different latitudinal bands. Each dataset is named according to the comparison type and latitude of AR landfall. The four types of comparisons (with corresponding filenames indicated in parentheses) include: 1) annual winter atmospheric river precipitation vs. total annual winter precipitation (AR_WinterPrecip), 2) annual winter atmospheric river precipitation vs. annual maximum NDVI (AR_NDVI), 3) spatially-averaged annual winter atmospheric river precipitation vs. area burned by wildfire during the same year by Level IV ecoregion (AR_Fire_SameYear), and 4) spatially-averaged annual winter atmospheric river precipitation vs. area burned by wildfire with a 1-year lag by Level IV ecoregion (AR_Fire_OneYearLag). The eight landfall latitudes are indicated in filenames as follows: 25N, 27_5N, 30N, 32_5N, 35N, 37_5_N, 40N, 42_5N.

In the southwestern US, the meteorological phenomenon known as atmospheric rivers (ARs) has gained increasing attention due to its strong connections to floods, snowpacks and water supplies in the West Coast states. Relatively less is known about the ecological implications of ARs, particularly in the interior Southwest, where AR storms are less common. To address this gap, we compared a chronology of AR landfalls on the west coast between 1989-2011 and between 25-42.5ºN, to annual metrics of the Normalized Difference Vegetation Index (NDVI; an indicator of vegetation productivity) and daily-resolution precipitation data to assess influences of AR-fed winter precipitation on vegetation productivity across the southwestern US. We mapped correlations between winter AR precipitation during landfalling ARs and 1) annual maximum NDVI and 2) area burned by large wildfires summarized by ecoregion during the same year as the landfalls and during the following year. The data produced by this study include four sets of eight raster grids (total = 32 grids) representing Spearman Rank correlation coefficients for four types of comparisons across eight different latitudinal bands. Each dataset is named according to the comparison type and latitude of AR landfall. The four types of comparisons (with corresponding filenames indicated in parentheses) include: 1) annual winter atmospheric river precipitation vs. total annual winter precipitation (AR_WinterPrecip), 2) annual winter atmospheric river precipitation vs. annual maximum NDVI (AR_NDVI), 3) spatially-averaged annual winter atmospheric river precipitation vs. area burned by wildfire during the same year by Level IV ecoregion (AR_Fire_SameYear), and 4) spatially-averaged annual winter atmospheric river precipitation vs. area burned by wildfire with a 1-year lag by Level IV ecoregion (AR_Fire_OneYearLag). The eight landfall latitudes are indicated in filenames as follows: 25N, 27_5N, 30N, 32_5N, 35N, 37_5_N, 40N, 42_5N.

This data release contains all of the available raw rainfall data from the Grizzly Creek Fire perimeter from September 2020 through September 2022. The csv files here are organized by the station name and followed by the year of the data. The locations of the stations are available in the file named (4_Gauge_Location.csv) in the parent data release. The rain gauge data were obtained using two different methods. The gauges named: ‘USGS_’ are non-telemetered gauges and each timestamp represents a bucket tip. The columns in each csv for these gauges includes an Index, Date Time, Name, Serial Number, and Tipping Bucket depth (in units of millmeters). Gauges GCCC2, GCDC2, GCEC2, GCFC2, GCIC2, GCNC2, GCTC2 were operated by the U.S.Geological Survey Colorado Water Science Center (USGS COWSC), and provisional data from these gages were obtained remotely via telemetry. Gauge TT394 was operated by the Bureau of Land Management (BLM), and provisional data from this gage was obtained remotely via telemetry. The USGS COWSC and BLM data have three columns: Date_Time, precip_intervals_set_1d (the depth in millimeters at a timestamp), and precip_accumulated_set_1d (the total accumulated rainfall depth in millimeters). Note that in some cases the data are discontinuous. One rain gauge, Bair Ranch, was operated by the Colorado Department of Transportation in 2021. These data are broken up into different files due to long data discontinuities. The naming format is: Bair_Ranch_start_YYYYMMDD_endYYYYMMDD, indicating the starting and ending year (YYYY), month (MM), and day (DD). These data have two columns Date/Time and 6hr Accum (inches of precipitation in 6 hours). Acknowledgements: We gratefully acknowledge the rainfall data obtained from the U.S.Geological Survey Colorado Water Science Center, Colorado Department of Transportation, and the Bureau of Land Management.

This data release contains all of the available raw rainfall data from the Grizzly Creek Fire perimeter from September 2020 through September 2022. The csv files here are organized by the station name and followed by the year of the data. The locations of the stations are available in the file named (4_Gauge_Location.csv) in the parent data release. The rain gauge data were obtained using two different methods. The gauges named: ‘USGS_’ are non-telemetered gauges and each timestamp represents a bucket tip. The columns in each csv for these gauges includes an Index, Date Time, Name, Serial Number, and Tipping Bucket depth (in units of millmeters). Gauges GCCC2, GCDC2, GCEC2, GCFC2, GCIC2, GCNC2, GCTC2 were operated by the U.S.Geological Survey Colorado Water Science Center (USGS COWSC), and provisional data from these gages were obtained remotely via telemetry. Gauge TT394 was operated by the Bureau of Land Management (BLM), and provisional data from this gage was obtained remotely via telemetry. The USGS COWSC and BLM data have three columns: Date_Time, precip_intervals_set_1d (the depth in millimeters at a timestamp), and precip_accumulated_set_1d (the total accumulated rainfall depth in millimeters). Note that in some cases the data are discontinuous. One rain gauge, Bair Ranch, was operated by the Colorado Department of Transportation in 2021. These data are broken up into different files due to long data discontinuities. The naming format is: Bair_Ranch_start_YYYYMMDD_endYYYYMMDD, indicating the starting and ending year (YYYY), month (MM), and day (DD). These data have two columns Date/Time and 6hr Accum (inches of precipitation in 6 hours). Acknowledgements: We gratefully acknowledge the rainfall data obtained from the U.S.Geological Survey Colorado Water Science Center, Colorado Department of Transportation, and the Bureau of Land Management.

The U.S. Geological Survey (USGS) installed and operated several flood and debris flow warning gages within or downstream from the Spring Creek burn scar, Colorado, U.S.A. The warning gages were operated during several years post fire (2019-21) in cooperation with the Colorado Department of Transportation (CDOT). The USGS warning gages were part of a larger post-wildfire hydrometeorological observatory, comprised of both remote-sensing and in-situ instrumentation. In-situ measurements of precipitation, river surface velocity, and river stage measurements collected at USGS warning gages during select storms in 2019 and 2021 are presented in this data release. These data were used to validate estimates of rainfall accumulation from the National Severe Storms Laboratory’s mobile, X-band weather radar (NOXP) and to evaluate lag times between high intensity precipitation and peak flooding. Gages were designed to provide advanced warning of hydrologic hazards at key points that could affect CDOT infrastructure (particularly where roads crossed over rivers). USGS warning gages also provided advanced warning of hydrologic hazards to the Pueblo Weather Forecast Office, local Emergency Managers (Huerfano County, CO), and residents in the immediate area.

The U.S. Geological Survey (USGS) installed and operated several flood and debris flow warning gages within or downstream from the Spring Creek burn scar, Colorado, U.S.A. The warning gages were operated during several years post fire (2019-21) in cooperation with the Colorado Department of Transportation (CDOT). The USGS warning gages were part of a larger post-wildfire hydrometeorological observatory, comprised of both remote-sensing and in-situ instrumentation. In-situ measurements of precipitation, river surface velocity, and river stage measurements collected at USGS warning gages during select storms in 2019 and 2021 are presented in this data release. These data were used to validate estimates of rainfall accumulation from the National Severe Storms Laboratory’s mobile, X-band weather radar (NOXP) and to evaluate lag times between high intensity precipitation and peak flooding. Gages were designed to provide advanced warning of hydrologic hazards at key points that could affect CDOT infrastructure (particularly where roads crossed over rivers). USGS warning gages also provided advanced warning of hydrologic hazards to the Pueblo Weather Forecast Office, local Emergency Managers (Huerfano County, CO), and residents in the immediate area.

This data release contains spatial stream network (SSN) objects and R scripts to run SSN models to predict mean monthly stream temperature between May and August and water presence on May 15, June 15, July 15, and August 15 in the Willow/Whitehorse watershed during 2015, 2016, and 2017 and the Willow/Rock watershed during 2016 and 2017. Functions referenced within the script may accessed in the SSN package version 1.1.13. (Ver Hoef et al., 2014). All functions and packages were compiled under R version 3.5.3 (R Development Core Team, 2014). The R script can be executed from any R console (R version 3.5.3) or from the RStudio GUI (version 1.1.463; https://www.rstudio.com/). R scripts to fit water temperature and flow presence SSN models are contained within the file "WW_WR_SSN_Model_R_Scripts.zip". SSN objects referenced by these scripts are contained within the file "WW_WR_SSN_Objects.zip". R Development Core Team, 2014, R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Ver Hoef, J. M., Peterson, E. E., Clifford, D., & Shah, R.,2014, SSN: An R package for spatial statistical modeling on stream networks. Journal of Statistical Software, 56(3), 1–45. https://doi.org/10.18637/jss.v056.i03