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.