Data release for "The drying regimes of non-perennial rivers"

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 GA...

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Bibliographic Details
Main Authors: Adam N Price, Margaret Zimmer, Nathan Jones, John Hammond, Samuel Zipper
Format: Dataset
Language:unknown
Published: Hydroshare 2021
Subjects:
Online Access:https://search.dataone.org/view/sha256:582d62f1718f01a43b1e4df2aae7c8fc689d6d52b3b6f6993a3f044d4f1c6a81
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author Adam N Price
Margaret Zimmer
Nathan Jones
John Hammond
Samuel Zipper
author_facet Adam N Price
Margaret Zimmer
Nathan Jones
John Hammond
Samuel Zipper
author_sort Adam N Price
collection Hydroshare (via DataONE)
description 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.
format Dataset
genre National Snow and Ice Data Center
genre_facet National Snow and Ice Data Center
geographic Bouchard
Gage
Gleeson
Gonzalez
geographic_facet Bouchard
Gage
Gleeson
Gonzalez
id dataone:sha256:582d62f1718f01a43b1e4df2aae7c8fc689d6d52b3b6f6993a3f044d4f1c6a81
institution Open Polar
language unknown
long_lat ENVELOPE(-57.333,-57.333,-64.200,-64.200)
ENVELOPE(-118.503,-118.503,56.133,56.133)
ENVELOPE(66.093,66.093,-71.238,-71.238)
ENVELOPE(-58.250,-58.250,-63.917,-63.917)
ENVELOPE(-126.234,-66.82,48.9107,24.8302)
op_collection_id dataone:urn:node:HYDROSHARE
op_coverage ENVELOPE(-126.234,-66.82,48.9107,24.8302)
BEGINDATE: 1979-01-01T00:00:00Z ENDDATE: 2019-09-27T00:00:00Z
publishDate 2021
publisher Hydroshare
record_format openpolar
spelling dataone:sha256:582d62f1718f01a43b1e4df2aae7c8fc689d6d52b3b6f6993a3f044d4f1c6a81 2025-06-03T18:49:52+00:00 Data release for "The drying regimes of non-perennial rivers" Adam N Price Margaret Zimmer Nathan Jones John Hammond Samuel Zipper ENVELOPE(-126.234,-66.82,48.9107,24.8302) BEGINDATE: 1979-01-01T00:00:00Z ENDDATE: 2019-09-27T00:00:00Z 2021-06-21T20:21:04.688Z https://search.dataone.org/view/sha256:582d62f1718f01a43b1e4df2aae7c8fc689d6d52b3b6f6993a3f044d4f1c6a81 unknown Hydroshare drying regime United States intermittent USGS CONUS Non-perennial flow regime streamflow Dataset 2021 dataone:urn:node:HYDROSHARE 2025-06-03T18:18:36Z 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. Dataset National Snow and Ice Data Center Hydroshare (via DataONE) Bouchard ENVELOPE(-57.333,-57.333,-64.200,-64.200) Gage ENVELOPE(-118.503,-118.503,56.133,56.133) Gleeson ENVELOPE(66.093,66.093,-71.238,-71.238) Gonzalez ENVELOPE(-58.250,-58.250,-63.917,-63.917) ENVELOPE(-126.234,-66.82,48.9107,24.8302)
spellingShingle drying regime
United States
intermittent
USGS
CONUS
Non-perennial
flow regime
streamflow
Adam N Price
Margaret Zimmer
Nathan Jones
John Hammond
Samuel Zipper
Data release for "The drying regimes of non-perennial rivers"
title Data release for "The drying regimes of non-perennial rivers"
title_full Data release for "The drying regimes of non-perennial rivers"
title_fullStr Data release for "The drying regimes of non-perennial rivers"
title_full_unstemmed Data release for "The drying regimes of non-perennial rivers"
title_short Data release for "The drying regimes of non-perennial rivers"
title_sort data release for "the drying regimes of non-perennial rivers"
topic drying regime
United States
intermittent
USGS
CONUS
Non-perennial
flow regime
streamflow
topic_facet drying regime
United States
intermittent
USGS
CONUS
Non-perennial
flow regime
streamflow
url https://search.dataone.org/view/sha256:582d62f1718f01a43b1e4df2aae7c8fc689d6d52b3b6f6993a3f044d4f1c6a81