ABSTRACT:

Snow persistence (SP) or the snow cover index (SCI), is the fraction of time that snow is present on the ground for a defined period. No data index (NDI) is the fraction of time that there is no data, cloud, or sensor saturation for the same period. SP and NDI were calculated on a pixel by pixel basis using MODIS/Terra Snow Cover 8-Day L3 Global 500m Grid, Collection 5 obtained from the National Snow and Ice Data Center (NSIDC). We computed the 1 January – 3 July SP for each year as the fraction of 8-day MODIS images with snow present. The selected period brackets the temporal extent of peak snow accumulation to complete snow ablation in most parts of the western United States. Spatial coverage is for MODIS tiles h08v04, h08v05, h09v04, h09v05, and h10v04. The 3 July date is used because the 8-day MODIS image does not fall on the first of the month in this case. Files are provided in the "USA Contiguous Albers Equal Area Conic USGS" projection. File nomenclature follows the following structure: "MOD10A2_(SCI or NDI)_(Water year the values correspond to, ex. 2001)_eq_alb.tif." Funding provided by NSF grant EAR-1446870.

ABSTRACT:

Here I provide water year discharge (Q) and precipitation (P) in units of mm as well as the water year runoff ratio (Q/P) for all reference USGS watersheds as defined by the GAGES-II project (Falcone, 2011) for water years 1981 to 2016. Precipitation values were extracted from PRISM monthly totals for the "Recent years" 4 km gridded dataset, and discharge values come from summations of USGS daily mean streamflow values. The dataset contains Q, P, and Q/P data by watershed for 1,594 reference USGS watersheds.

ABSTRACT:

Snow season length (SS), reported in days, is the length of time that snow is present on the ground on an annual basis. It is determined by finding the first and last snow occurrence for any given year, and then finding the difference between these two dates. SS was calculated on a pixel by pixel basis using MODIS/Terra Snow Cover 8-Day L3 Global 500m Grid, Collection 6 obtained from the National Snow and Ice Data Center (NSIDC). Spatial coverage is for MODIS tiles h08v04, h08v05, h09v04, h09v05, and h10v04 for water years 2001 - 2015. Files are provided in the "USA Contiguous Albers Equal Area Conic USGS" projection. Funding provided by NSF grant EAR-1446870.

ABSTRACT:

This resource is useful for characterizing the intermittence of snow, or how continuously it covers an area (as opposed to snow persistence which quantifies the fraction of time that snow is present for any location for a defined time period: https://www.hydroshare.org/resource/1c62269aa802467688d25540caf2467e/, or the snow season, which provides the first day of snow occurrence, last day of snow occurrence, and the length of time between the first and last day of snow occurrence per water year: https://www.hydroshare.org/resource/197adcdc76b34591bd78a811bf1dfbfe/).Snow intermittence (snow to no snow counts) for the western U.S. for water years 2001 - 2015 contains annual and mean annual raster datasets with the number of snow to no snow events. The events consist of any time that there was snow that was present and then followed by bare ground within 10 days of the original snow fall. Both MOD10A1 and MOD10A2 binary snow products were used resulting in annual and mean annual rasters at the daily (MOD10A1) and 8-day (MOD10A2) temporal resolutions. This product is primarily intended for areas with sparse vegetation, as dense vegetation obscures the binary snow classification used for the MOD10A1 and MOD10A2 V5 products. These grids are available at the original 500 m MODIS resolution.

ABSTRACT:

Snow season length (SS), reported in days, is the length of time that snow is present on the ground on an annual basis. It is determined by finding the first and last snow occurrence for any given water year (Oct 1 - Sep 30 NH, Jan 1 - Dec 31 SH), and then finding the difference between these two dates. SS was calculated on a pixel by pixel basis using MODIS/Terra Snow Cover 8-Day L3 Global 500m Grid, Collection 6 obtained from the National Snow and Ice Data Center (NSIDC) for global calculation and MOD10A1 for US calculation. Spatial coverage is for MODIS tiles h08v04, h08v05, h09v04, h09v05, and h10v04 for water years 2001 - 2015. Files are provided in the "USA Contiguous Albers Equal Area Conic USGS" projection. Funding provided by NSF grant EAR-1446870.

ABSTRACT:

This compilation of data serves as the data repository for Hammond et al., 2019. Included are four spreadsheets of data containing HYDRUS 1-D model simulation outputs at event and annual time scales. Simulations were run for historical periods, historical periods where all snow was converted to rainfall, multiple different soil profiles depth and texture alterations, and for artificial concentrated and intermittent input scenarios. For more, see the citation below:

Hammond, J. C., Harpold, A. A., Weiss, S., and Kampf, S. K.: Partitioning snowmelt and rainfall in the critical zone: effects of climate type and soil properties, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-98, 2019.

ABSTRACT:

This data release provides the underlying data for Kampf et al., in review: "Rethinking the role of the water balance in hydrologic research." Mean annual climatic variables based on the Northern Hemisphere water year (October 1 to September 30) and several watershed properties are provided for 121 USGS reference watersheds smaller than 1,000 square kilometers. For each climatic variable, mean annual values were derived from watershed average annual values.

The columns of the dataset are as follows:

SP- watershed averaged January 1 to July 1 snow persistence as in Hammond et al., 2018
P_mm - watershed averaged total water year precipitation from PRISM, Daly, 2013
Q_mm - total water year water yield from USGS NWIS
QdivP - runoff ratio, total water year water yield divided by total water year precipitation
PET - watershed averaged total water year potential evapotranspiration from gridMET - Abatzoglou, 2013
PdivPET - the ratio of total water year precipitation to total water year potential evapotranspiration from the sources above.
Elev_mean_m - GAGES-II, Falcone, 2011
Area_km2 - GAGES-II, Falcone, 2011

Abatzoglou, J. T. (2013). Development of gridded surface meteorological data for ecological applications and modelling. International Journal of Climatology, 33(1), 121–131.

Daly, C. (2013). Descriptions of PRISM spatial climate datasets for the conterminous United States (PRISM Doc., 14 p.). Corvallis, OR: PRISM Climate Group, Oregon State University.

Falcone, J. A. (2011). GAGES-II: Geospatial attributes of gages for evaluating streamflow (Digit. Spat. Data set). Reston, VA: U.S. Geological
Survey.

Hammond, J. C., Saavedra, F. A., & Kampf, S. K. (2018). How does snow persistence relate to annual streamflow in mountain watersheds of the Western U.S. with wet maritime and dry continental climates? Water Resources Research, 54, 2605–2623. https://doi.org/10.1002/ 2017WR021899

ABSTRACT:

This data release provides mean annual flow and climate variables based on the Northern Hemisphere water year (October 1 to September 30) and several watershed properties are provided for A) 161 USGS reference watersheds smaller than 500 square kilometers that are also part of the Catchment attributes for large-sample studies (CAMELS) dataset (Addor et al., 2017) and B) 924 USGS non-reference watersheds smaller than 500 square kilometers, C) mean annual flow and climate variables for 12 Colorado Division of Water Resources (CDWR) gages. For each climatic variable, mean annual values were derived from watershed average annual values.

The columns of the datasets are as follows:

A) "USGS_CAMELS_ref.csv"

GAGE_ID = USGS gage station number
Q_mm = total water year water yield from USGS NWIS
P_PRISM_mm = watershed averaged total water year precipitation from PRISM- Daly, 2013
PET_gridMET_mm = watershed averaged total water year potential evapotranspiration from gridMET - Abatzoglou, 2013
drain_SQKM = USGS watershed drainage area - Falcone, 2011
elevation_m = USGS watershed mean elevation - Falcone, 2011
SP = watershed averaged January 1 to July 1 snow persistence - Hammond et al., 2017
p_camels_mm = watershed averaged total water year precipitation from CAMELS- Addor et al., 2017
pet_camels_mm = watershed averaged total water year potential evapotranspiration from CAMELS- Addor et al., 2017
q_camels_mm = total water year water yield from from CAMELS- Addor et al., 2017
PETdivP_PRISM_gridMET = ratio of PET_gridMET_mm to P_PRISM_mm
PETdivP_camels = ratio of pet_camels_mm to p_camels_mm
PETdivP_camels_PRISM = ratio of pet_camels_mm to P_PRISM_mm
PETdivP_gridMET_camels = ratio of PET_gridMET_mm to p_camels_mm
AET_PRISM = P_PRISM_mm minus Q_mm
AET_camels = p_camels_mm minus q_camels_mm
AETdivP_PRISM = ratio of AET_PRISM to P_PRISM_mm
AETdivP_camels = ratio of AET_camels to p_camels_mm
PETdif = (PET_gridMET_mm-PET_camels_mm)/PET_gridMET_mm
DEVNLCD06 = Watershed percent "developed" (urban), 2006 era. Sum of classes 21, 22, 23, and 24 - Falcone, 2011
FORESTNLCD06 = Watershed percent "forest", 2006 era. Sum of classes 41, 42, and 43 - Falcone, 2011
PLANTNLCD06 = Watershed percent "planted/cultivated", 2006 era. Sum of classes 81 and 82 - Falcone, 2011
SHRUBNLCD06 = Watershed percent Shrubland (class 52) - Falcone, 2011
GRASSNLCD06 = Watershed percent Herbaceous (class 71) - Falcone, 2011

B) "USGS_nonref.csv"

GAGE_ID = USGS gage station number
Q_mm = total water year water yield from USGS NWIS
P_PRISM_mm = watershed averaged total water year precipitation - PRISM, Daly, 2013
PET_gridMET_mm = watershed averaged total water year potential evapotranspiration - gridMET - Abatzoglou, 2013
drain_SQKM = USGS watershed drainage area - Falcone, 2011
elevation_m = USGS watershed mean elevation - Falcone, 2011
SP = watershed averaged January 1 to July 1 snow persistence -Hammond et al., 2017
PETdivP = ratio of PET_gridMET_mm to P_PRISM_mm
AET = P_PRISM_mm minus Q_mm
AETdivP = ratio of AET to P_PRISM_mm
DEVNLCD06 = Watershed percent "developed", 2006 era. Sum of classes 21, 22, 23, and 24 - Falcone, 2011
FORESTNLCD06 = Watershed percent "forest", 2006 era. Sum of classes 41, 42, and 43 - Falcone, 2011
PLANTNLCD06 = Watershed percent "planted/cultivated", 2006 era. Sum of classes 81 and 82 - Falcone, 2011
SHRUBNLCD06 = Watershed percent Shrubland (class 52) - Falcone, 2011
GRASSNLCD06 = Watershed percent Herbaceous (class 71) - Falcone, 2011

C) "CDWR_Sangres.csv"

GAGE_ID = CDWR gage ID
Name = CDWR gaging station name
Area_km2 = CDWR watershed drainage area - Colorado Information Marketplace (https://data.colorado.gov/Water/Current-Surface-Water-Conditions-in-Colorado/)
P_PRISM_mm = watershed averaged total water year precipitation - PRISM, Daly, 2013
SP = watershed averaged January 1 to July 1 snow persistence - Hammond et al., 2017
PET_gridMET_mm = watershed averaged total water year potential evapotranspiration - gridMET - Abatzoglou, 2013
Q_mm = total water year water yield - Colorado Information Marketplace

-Abatzoglou, J. T. (2013). Development of gridded surface meteorological data for ecological applications and modelling. International Journal of Climatology, 33(1), 121–131.
-N. Addor, A. Newman, M. Mizukami, and M. P. Clark, 2017. Catchment attributes for large-sample studies. Boulder, CO: UCAR/NCAR.
-Daly, C. (2013). Descriptions of PRISM spatial climate datasets for the conterminous United States (PRISM Doc., 14 p.).
-Falcone, J. A. (2011). GAGES-II: Geospatial attributes of gages for evaluating streamflow. Reston, VA: U.S. Geological Survey.
-Hammond, J. C., F. A. Saavedra, S. K. Kampf (2017). MODIS MOD10A2 derived snow persistence and no data index for the western U.S., HydroShare.

ABSTRACT:

Snow persistence (SP) or the snow cover index (SCI), is the fraction of time that snow is present on the ground for a defined period. SP was calculated on a pixel by pixel basis using MODIS/Terra Snow Cover 8-Day L3 Global 500m Grid, Collection 6 obtained from the National Snow and Ice Data Center (NSIDC). We computed the 1 January – 3 July SP for each year as the fraction of 8-day MODIS images with snow present.

For more information on MODIS snow persistence please see:

-Hammond, J. C., Saavedra, F. A., & Kampf, S. K. (2018). Global snow zone maps and trends in snow persistence 2001–2016. International Journal of Climatology, 38(12), 4369-4383.
-Hammond, J. C., Saavedra, F. A., & Kampf, S. K. (2018). How does snow persistence relate to annual streamflow in mountain watersheds of the Western US with wet maritime and dry continental climates?. Water Resources Research, 54(4), 2605-2623.
-Hammond, J. C., F. A. Saavedra, S. K. Kampf (2017). MODIS MOD10A2 derived snow persistence and no data index for the western U.S., HydroShare, https://doi.org/10.4211/hs.1c62269aa802467688d25540caf2467e

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.