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| Type: | Resource | |
| Storage: | The size of this resource is 8.3 MB | |
| Created: | Feb 11, 2021 at 7:24 p.m. | |
| Last updated: | Jun 21, 2021 at 8:21 p.m. (Metadata update) | |
| Published date: | Jun 21, 2021 at 8:21 p.m. | |
| DOI: | 10.4211/hs.5f974604766a4c03a2e24b9d1ba720d4 | |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
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Abstract
This resource contains the data supporting the paper "The drying regimes of non-perennial rivers" currently in preparation. The data provided with this release contains streamflow drying characteristics for over 25,000 discrete drying events at 894 non-perennial U.S. Geological Survey GAGES-II (Falcone, 2011) gaging stations for years 1979 to 2019.
The columns of the dataset associated with stream drying are described below:
gage = USGS station ID (STAID)
event_id = unique drying event identifier
dec_lat_va = Latitude in decimal degrees of streamgage location
dec_long_va = Longitude in decimal degrees of streamgage location
peak_date = Day of year that peak occurred marking the beginning of drying event
peak_value = Discharge value in cubic feet per second of peak marking the beginning of drying event
peak_quantile = Discharge quantile value of peak marking the beginning of drying event
peak2zero = Number of days from peak_date to dry_date_start
drying_rate = The streamflow recession rate defined as the slope in log-log space of −d(discharge)/d(time) plotted against discharge
p_value = P-value reported from the fit of a linear model for discharge and time in log-log space
calendar_year = The calendar year in which the first no flow of the drying event occurred
season = The season in which the first no flow of the drying event occurred (April, May, June = spring; July, August, September = summer; October, November, December = fall; January, February, March = winter)
meteorologic_year = The meteorologic year in which the first no flow of the drying event occurred. Meteorologic years begin April 1 and conclude Mach 30.
dry_date_start = Julian day of the first no flow occurrence associated with the drying event
dry_date_mean = Julian day at the center of continuous no flow associated with the drying event
dry_dur = Duration (in days) of continuous no flow associated with the drying event
For information on the additional columns of data supplied that were used to run random forest models please see the section below "Additional Metadata."
References:
- Abatzoglou, J. T. (2013), Development of gridded surface meteorological data for ecological applications and modelling. Int. J. Climatol., 33: 121–131.
- Broxton, P., X. Zeng, and N. Dawson. 2019. Daily 4 km Gridded SWE and Snow Depth from Assimilated In-Situ and Modeled Data over the Conterminous US, Version 1. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/0GGPB220EX6A.
- Falcone, J. A. (2011). GAGES-II: Geospatial attributes of gages for evaluating streamflow (Digit. Spat. Data set). Reston, VA: U.S. Geological Survey.
- Gleeson, T., Moosdorf, N., Hartmann, J., & Van Beek, L. P. H. (2014). A glimpse beneath earth's surface: GLobal HYdrogeology MaPS (GLHYMPS) of permeability and porosity. Geophysical Research Letters, 41(11), 3891-3898.
- Hammond, J. C., Zimmer, M., Shanafield, M., Kaiser, K., Godsey, S. E., Mims, M. C., ... & Allen, D. C. Spatial patterns and drivers of non‐perennial flow regimes in the contiguous US. Geophysical Research Letters, 2020GL090794.
- Hengl, T., Mendes de Jesus, J., Heuvelink, G. B., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., ... & Kempen, B. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS one, 12(2), e0169748.
- Homer, C. H., Fry, J. A., & Barnes, C. A. (2012). The national land cover database. US Geological Survey Fact Sheet, 3020(4), 1-4.
- Sohl, T.L., Reker, Ryan, Bouchard, Michelle, Sayler, Kristi, Dornbierer, Jordan, Wika, Steve, Quenzer, Rob, and Friesz, Aaron, 2018a, Modeled historical land use and land cover for the conterminous United States: 1938-1992: U.S. Geological Survey data release, https://doi.org/10.5066/F7KK99RR.
- Sohl, T.L., Sayler, K.L., Bouchard, M.A., Reker, R.R., Freisz, A.M., Bennett, S.L., Sleeter, B.M., Sleeter, R.R., Wilson, T., Soulard, C., Knuppe, M., and Van Hofwegen, T., 2018b, Conterminous United States Land Cover Projections - 1992 to 2100: U.S. Geological Survey data release, https://doi.org/10.5066/P95AK9HP.
Subject Keywords
Coverage
Spatial
Temporal
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Content
Additional Metadata
| Name | Value |
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| Explanation of dataset columns and sources of data | The columns of the dataset are as follows: gage = USGS station ID (STAID) event_id = unique drying event identifier dec_lat_va = Latitude in decimal degrees of streamgage location dec_long_va = Longitude in decimal degrees of streamgage location peak_date = Day of year that peak occurred marking the beginning of drying event peak_value = Discharge value in cubic feet per second of peak marking the beginning of drying event peak_quantile = Discharge quantile value of peak marking the beginning of drying event peak2zero = Number of days from peak_date to dry_date_start drying_rate = The streamflow recession rate defined as the slope in log-log space of −d(discharge)/d(time) plotted against discharge p_value = P-value reported from the fit of a linear model for discharge and time in log-log space calendar_year = The calendar year in which the first no flow of the drying event occurred season = The season in which the first no flow of the drying event occurred (April, May, June = spring; July, August, September = summer; October, November, December = fall; January, February, March = winter) meteorologic_year = The meteorologic year in which the first no flow of the drying event occurred. Meteorologic years begin April 1 and conclude Mach 30. dry_date_start = Julian day of the first no flow occurrence associated with the drying event dry_date_mean = Julian day at the center of continuous no flow associated with the drying event dry_dur = Duration (in days) of continuous no flow associated with the drying event AggEcoregion = Aggregated EPA Level 1 Ecoregion as used in Hammond et al., 2020 TOPWET = Watershed average topographic wetness index included in GAGES-II dataset (Falcone, 2011) TrueCluster = Cluster the drying event was assigned to using kmeans clustering approach lulc_water_prc = Percent watershed area covered by surface water* lulc_dev_prc = Percent watershed area covered by development* lulc_forest_prc = Percent watershed area covered by forest* lulc_barren_prc = Percent watershed area covered by barren surfaces* lulc_grass_prc = Percent watershed area covered by grass* lulc_ag_prc = Percent watershed area covered by cultivation* lulc_wetland_prc = Percent watershed area covered by wetlands* DRAIN_SQKM = Watershed drainage area in square kilometers included in GAGES-II dataset (Falcone, 2011) SNOW_PCT_PRECIP = Watershed average mean annual fraction of precipitation falling as snow included in GAGES-II dataset (Falcone, 2011) GEOL_REEDBUSH_DOM = Dominant (highest percent of area) geology, derived from a simplified version of Reed & Bush (2001) - Generalized Geologic Map of the Conterminous United States included in GAGES-II dataset (Falcone, 2011) FRESHW_WITHDRAWAL = Watershed total freshwater withdrawal in megaliters per year per sqkm included in GAGES-II dataset (Falcone, 2011) AWCAVE = Watershed average available water capacity included in GAGES-II dataset (Falcone, 2011) PERMAVE = Watershed average permeability (in/hr) included in GAGES-II dataset (Falcone, 2011) CLAYAVE = Watershed average clay content % included in GAGES-II dataset (Falcone, 2011) SILTAVE = Watershed average silt content % included in GAGES-II dataset (Falcone, 2011) SANDAVE = Watershed average sand content % included in GAGES-II dataset (Falcone, 2011) ELEV_MEAN_M_BASIN = Watershed average elevation included in GAGES-II dataset (Falcone, 2011) porosity = Watershed average porosity from Gleeson et al., 2014 storage_m = Watershed average soil storage from Hengel et al., 2017 P_mm = Watershed average precipitation in millimeters from gridMET (Abatzoglou, 2013) for the day of the first no flow associated with the drying event PET_mm = Watershed average potential evapotranspiration (PET) in millimeters from gridMET (Abatzoglou, 2013) for the day of the first no flow associated with the drying event SWE_mm = Watershed averaged snow water equivalent (SWE) in millimeters from Broxton et al., 2019 for the day of the first no flow associated with the drying event melt_mm = Watershed average snowmelt calculated from the daily difference in watershed averaged SWE for the day of the first no flow associated with the drying event Tmax_C = Watershed average maximum daily temperature in degrees celsius from gridMET (Abatzoglou, 2013) for the day of the first no flow associated with the drying event P_90 = Watershed average total precipitation for the 90 days leading to the first no flow associated with the drying event in millimeters PET_90 = Watershed average total potential evapotranspiration for the 90 days leading to the first no flow associated with the drying event in millimeters Tmax_90 = Watershed average maximum temperature in degrees celsius for the 90 days leading to the first no flow associated with the drying event melt_90 = Watershed average total snowmelt for the 90 days leading to the first no flow associated with the drying event P.PET = Watershed averaged precipitation (P) divided by potential evapotranspiration (PET) from gridMET (Abatzoglou, 2013) for the day of the first no flow associated with the drying event P.PET90 = P/PET for the 90 days leading to the first no flow associated with the drying event PredCluster = The predicted cluster assignment resulting from the global random forest model *Available annually 1980-1991 (Sohl et al., 2018a), 1992-2005 (Sohl et al., 2018b), then 2006, 2011 and 2016 (Homer et al., 2012). Annual time series generated by linear interpolation between years with data, with 2016 value used for 2016-2017. References: - Abatzoglou, J. T. (2013), Development of gridded surface meteorological data for ecological applications and modelling. Int. J. Climatol., 33: 121–131. - Broxton, P., X. Zeng, and N. Dawson. 2019. Daily 4 km Gridded SWE and Snow Depth from Assimilated In-Situ and Modeled Data over the Conterminous US, Version 1. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/0GGPB220EX6A. - Falcone, J. A. (2011). GAGES-II: Geospatial attributes of gages for evaluating streamflow (Digit. Spat. Data set). Reston, VA: U.S. Geological Survey. - Gleeson, T., Moosdorf, N., Hartmann, J., & Van Beek, L. P. H. (2014). A glimpse beneath earth's surface: GLobal HYdrogeology MaPS (GLHYMPS) of permeability and porosity. Geophysical Research Letters, 41(11), 3891-3898. - Hammond, J. C., Zimmer, M., Shanafield, M., Kaiser, K., Godsey, S. E., Mims, M. C., ... & Allen, D. C. Spatial patterns and drivers of non‐perennial flow regimes in the contiguous US. Geophysical Research Letters, 2020GL090794. - Hengl, T., Mendes de Jesus, J., Heuvelink, G. B., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., ... & Kempen, B. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS one, 12(2), e0169748. - Homer, C. H., Fry, J. A., & Barnes, C. A. (2012). The national land cover database. US Geological Survey Fact Sheet, 3020(4), 1-4. - Sohl, T.L., Reker, Ryan, Bouchard, Michelle, Sayler, Kristi, Dornbierer, Jordan, Wika, Steve, Quenzer, Rob, and Friesz, Aaron, 2018a, Modeled historical land use and land cover for the conterminous United States: 1938-1992: U.S. Geological Survey data release, https://doi.org/10.5066/F7KK99RR. - Sohl, T.L., Sayler, K.L., Bouchard, M.A., Reker, R.R., Freisz, A.M., Bennett, S.L., Sleeter, B.M., Sleeter, R.R., Wilson, T., Soulard, C., Knuppe, M., and Van Hofwegen, T., 2018b, Conterminous United States Land Cover Projections - 1992 to 2100: U.S. Geological Survey data release, https://doi.org/10.5066/P95AK9HP. |
Related Resources
| The content of this resource is derived from | U.S. Geological Survery National Water Information System - https://waterdata.usgs.gov/nwis |
| The content of this resource is derived from | Abatzoglou, J. T. (2013), Development of gridded surface meteorological data for ecological applications and modelling. Int. J. Climatol., 33: 121–131. |
| The content of this resource is derived from | Broxton, P., X. Zeng, and N. Dawson. 2019. Daily 4 km Gridded SWE and Snow Depth from Assimilated In-Situ and Modeled Data over the Conterminous US, Version 1. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/0GGPB220EX6A. |
| The content of this resource is derived from | Falcone, J. A. (2011). GAGES-II: Geospatial attributes of gages for evaluating streamflow (Digit. Spat. Data set). Reston, VA: U.S. Geological Survey. |
Credits
Contributors
People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.
| Name | Organization | Address | Phone | Author Identifiers |
|---|---|---|---|---|
| Dry River Research Coordination Network |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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