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USGS Table AHG Parameters And Supplementary Data MannN
<p>For more information please send me an email to either: <strong>sha17hab.afshari@gmail.com</strong> or <strong>safshar00@citymail.cuny.edu</strong></p> <p>Simplified hydraulic geometry relationships representing the average conditions over longer reaches could reduce the need for detailed field surveys and minimize the computational burden while studying or carrying out numerical analyses and of the flow dynamics.</p> <p> Natural streams are characterized by changes in cross-section geometry, slope, and geophysical properties (bed-roughness, channel slope, etc.) along with their reaches. Variations in the shape and size of the channel bed geometry result from several interacting features of the river system including the effect of different flow regimes, slope, sediment load, etc. Simplifying the river bed geometries could reduce the burden of assembling the required data, so implementing less detailed routing procedures could lower the computational burden. “At-A-Station” Hydraulic Geometry (or AHG) relations are power-law functions which relate river key the hydraulics (i.e., velocity, depth, width, and flow area) to discharge at a river monitoring station (Dingman 2007; Dingman and Afshari 2018).</p> <p> The AHG relations have been introduced and discussed among researchers, engineers, and geomorphologist since the '50s based upon a limited number of observations made over a few flow monitoring stations across the United States. Afshari et. al., 2017 introduced a data filtering procedure which was trained and tested over both synthetic and realistic data followed by being applied over ~4000 U.S. Geological Survey’s river monitoring stations to compute AHG parameters based upon robust hydraulic vs. discharge measures. Given “refined” dataset, estimated AHG parameters are combined with basic statistics (mean, minimum, maximum, and standard deviation) of key morphological and geophysical features at all USGS river monitoring sites, e.g. stream (Stahler) order, channel pattern (channel sinuosity), channel bed-slope, and channel lateral [or overbank] slope. The fundamental hydraulics, geographical, and geophysical data sources (websites) applied for making the "USGS Table AHG Parameters And Supplementary Data" table are</p> <ul> <li>USGS National Water Information System (<a href="https://waterdata.usgs.gov/nwis/sw">USGS-NWIS</a>)</li> <li>USGS Staged Product Directory (<a href="https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/1/ArcGrid/">The National Map</a>)</li> <li>National Hydrography Dataset Plus V2 (<a href="http://www.horizon-systems.com/NHDPlus/NHDPlusV2_home.php">Horizon System Corporation</a>)</li> </ul> <p> Doing so, potential interrelation among independent and dependent variables will be highlighted. Accordingly, given some assumptions, it is verified how well channel morphology and hydraulic components are intertwined and combined with AHG parameters and how categorizing river monitoring stations according to these characteristics will be practical and useful for further studies.</p> <p><strong>References:</strong></p> <ol> <li>Afshari, S. 2019. USGS Table AHG Parameters And Supplementary Data (Version v1.1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.2563440</li> <li>Afshari, S., B.M. Fekete, S.L. Dingman, N. Devineni, D.M. Bjerklie, and R.M. Khanbilvardi. 2017. "Statistical filtering of river survey and streamflow data for improving At-A-Station hydraulic geometry relations." J. Hydrol. 547: 443–454. doi:10.1016/j.jhydrol.2017.01.038 </li> <li>Dingman, S.L., and S. Afshari. 2018. "Field verification of analytical at-a-station hydraulic- geometry relations." J. Hydrol. 564: 859-872. doi:10.1016/j.jhydrol.2018.07.020</li> <li>Dingman, S.L. 2007. "Analytical derivation of at-a-station hydraulic geometry relations." J. Hydrol. 334: 17–27</li> </ol>
F-IDF values at USGS stream gage sites
<p>This nc file contains F-IDF values at streamflow gage sites over the CONUS.</p> <p>The header of this netcdf file is shown below:</p> <p>----------------------------------------------------------------------------------------------------------</p> <p>netcdf flashiness_dataset {<br> dimensions:<br> stnid = 6987 ;<br> duration = 6 ;<br> frequency = 6 ;<br> variables:<br> double flashiness(stnid, duration, frequency) ;<br> flashiness:description = "It is calculated with the slope of a given time window (cfs/15min*len(window)), divided by the drainage area (sqkm) and convert to a standardized unit" ;<br> flashiness:units = "mm/h^2" ;<br> flashiness:_FillValue = NaN ; area (sqkm)" ;<br> string stnid(stnid) ;<br> int64 frequency(frequency) ;<br> int64 duration(duration) ;<br> double lon(stnid) ;<br> lon:_FillValue = NaN ;<br> double lat(stnid) ;<br> lat:_FillValue = NaN ;<br> double area(stnid) ;<br> area:_FillValue = NaN ;<br> area:units = "sqkm" ;<br> double data_length(stnid) ;<br> data_length:_FillValue = NaN ;<br> data_length:units = "years" ;<br> data_length:description = "years of available data from USGS 15-min observation" ;<br> double dor_pc_pva(stnid) ;<br> dor_pc_pva:_FillValue = NaN ;<br> dor_pc_pva:units = "percent" ;<br> dor_pc_pva:long_name = "degree of regulation" ;<br> dor_pc_pva:description = "degree of regulation retrieved from hydrobasin V10 level 12" ;<br> double slp_dg_uav(stnid) ;<br> slp_dg_uav:_FillValue = NaN ;<br> slp_dg_uav:units = "degree" ;<br> slp_dg_uav:long_name = "Terrain slope" ;<br> slp_dg_uav:description = "Terrain slope of total watershed upstream of a pour point" ;<br> double sgr_dk_sav(stnid) ;<br> sgr_dk_sav:_FillValue = NaN ;<br> sgr_dk_sav:units = "degree" ;<br> sgr_dk_sav:long_name = "Stream gradient" ;<br> sgr_dk_sav:description = "the stream gradient was calculated as the ratio between the elevation drop within the river reach (i.e. the difference between min. and max. elevation along the reach) and the length of the reach." ;<br> double tmp_dc_uyr(stnid) ;<br> tmp_dc_uyr:_FillValue = NaN ;<br> tmp_dc_uyr:units = "degree celsius" ;<br> tmp_dc_uyr:long_name = "Annual mean air temperature" ;<br> tmp_dc_uyr:description = "Annual mean air temperature retrieved from WorldClim, station-based monitoring network" ;<br> double pre_mm_uyr(stnid) ;<br> pre_mm_uyr:_FillValue = NaN ;<br> pre_mm_uyr:units = "mm" ;<br> pre_mm_uyr:long_name = "Annual mean precipitation" ;<br> pre_mm_uyr:description = "Annual mean precipitation retrieved from WorldClim, station-based monitoring network and interpolated by the thin-plate smoothing spline algorithm" ;<br> double pet_mm_uyr(stnid) ;<br> pet_mm_uyr:_FillValue = NaN ;<br> pet_mm_uyr:units = "mm" ;<br> pet_mm_uyr:long_name = "Annual mean potential evaporation" ;<br> pet_mm_uyr:description = "Annual mean PET based on termperature inputs from WorldClim and a simple temperature-based transfer model" ;<br> double aet_mm_uyr(stnid) ;<br> aet_mm_uyr:_FillValue = NaN ;<br> aet_mm_uyr:units = "mm" ;<br> aet_mm_uyr:long_name = "Annual mean actural evaporation" ;<br> aet_mm_uyr:description = "Annual mean AET based on the Global High-Resolution Soil-Water Balance dataset which contains gridded estimates of actual evapotranspiration and soil water deficit" ;<br> double ari_ix_uav(stnid) ;<br> ari_ix_uav:_FillValue = NaN ;<br> ari_ix_uav:units = "" ;<br> ari_ix_uav:long_name = "Global aridity index" ;<br> ari_ix_uav:description = "The Global Aridity Index (Global-Aridity) is modeled using data from WorldClim as input parameters" ;<br> double cmi_ix_uyr(stnid) ;<br> cmi_ix_uyr:_FillValue = NaN ;<br> cmi_ix_uyr:units = "" ;<br> cmi_ix_uyr:long_name = "Global climate moisture index" ;<br> cmi_ix_uyr:description = "The Climate Moisture Index (CMI) was derived from the annual precipitation (P) and potential evapotranspiration (PET) datasets as provided by the WorldClim v1.4" ;<br> double snw_pc_uyr(stnid) ;<br> snw_pc_uyr:_FillValue = NaN ;<br> snw_pc_uyr:units = "%" ;<br> snw_pc_uyr:long_name = "snow cover extent" ;<br> snw_pc_uyr:description = "data obtained from The MODIS/Aqua Snow Cover Daily L3 Global 500m Grid (MYD10A1)" ;<br> double cly_pc_uav(stnid) ;<br> cly_pc_uav:_FillValue = NaN ;<br> cly_pc_uav:units = "%" ;<br> cly_pc_uav:long_name = "clay fraction in soils" ;<br> cly_pc_uav:description = "Data obtained from SoilGrids1km" ;<br> double slt_pc_uav(stnid) ;<br> slt_pc_uav:_FillValue = NaN ;<br> slt_pc_uav:units = "%" ;<br> slt_pc_uav:long_name = "silt fraction in soils" ;<br> slt_pc_uav:description = "Data obtained from SoilGrids1km" ;<br> double snd_pc_uav(stnid) ;<br> snd_pc_uav:_FillValue = NaN ;<br> snd_pc_uav:units = "%" ;<br> snd_pc_uav:long_name = "sand fraction in soils" ;<br> snd_pc_uav:description = "Data obtained from SoilGrids1km" ;<br> double swc_pc_uyr(stnid) ;<br> swc_pc_uyr:_FillValue = NaN ;<br> swc_pc_uyr:units = "%" ;<br> swc_pc_uyr:long_name = "Soil water content" ;<br> swc_pc_uyr:description = "Soil water content is provided as part of the Global High-Resolution Soil-Water Balance dataset which contains gridded estimates of actual evapotranspiration and soil water deficit" ;<br> double kar_pc_use(stnid) ;<br> kar_pc_use:_FillValue = NaN ;<br> kar_pc_use:units = "%" ;<br> kar_pc_use:long_name = "Karst area extent" ;<br> kar_pc_use:description = "The World Map of Carbonate Rock Outcrops represents an upper limit of the area of exposed karst terrain." ;<br> double ero_kh_uav(stnid) ;<br> ero_kh_uav:_FillValue = NaN ;<br> ero_kh_uav:units = "kg/hectare per year" ;<br> ero_kh_uav:long_name = "Soil erosion" ;<br> string ero_kh_uav:description = "GloSEM erosion estimates were produced with a high resolution (250 × 250 m) global potential soil erosion model, using a combination of remote sensing, GIS modelling and census data" ;<br> double pop_ct_usu(stnid) ;<br> pop_ct_usu:_FillValue = NaN ;<br> pop_ct_usu:units = "count" ;<br> pop_ct_usu:long_name = "Population count" ;<br> pop_ct_usu:description = "The Gridded Population of the World (GPW) database." ;<br> double urb_pc_use(stnid) ;<br> urb_pc_use:_FillValue = NaN ;<br> urb_pc_use:units = "%" ;<br> urb_pc_use:long_name = "Urban extent" ;<br> urb_pc_use:description = "The Global Human Settlement (GHS) framework produces global spatial information about the human presence on the planet over time" ;<br> double rdd_mk_uav(stnid) ;<br> rdd_mk_uav:_FillValue = NaN ;<br> rdd_mk_uav:units = "meters per km^2" ;<br> rdd_mk_uav:long_name = "Road density" ;<br> rdd_mk_uav:description = "The Global Roads Inventory Project (GRIP) dataset" ;<br> double dis_m3_pyr(stnid) ;<br> dis_m3_pyr:_FillValue = NaN ;<br> dis_m3_pyr:units = "cms" ;<br> dis_m3_pyr:long_name = "Natural discharge" ;<br> dis_m3_pyr:description = "Simulated discharge by WaterGAP" ;<br> double run_mm_syr(stnid) ;<br> run_mm_syr:_FillValue = NaN ;<br> run_mm_syr:units = "mm" ;<br> run_mm_syr:long_name = "Land surface runoff" ;<br> run_mm_syr:description = "Simulated land surface runoff by WaterGAP" ;<br> double inu_pc_umx(stnid) ;<br> inu_pc_umx:_FillValue = NaN ;<br> inu_pc_umx:units = "%" ;<br> inu_pc_umx:long_name = "Annual maximum inundation extent " ;<br> inu_pc_umx:description = "GIEMS-D15 is a high-resolution global inundation map at a pixel size of 15 arc-seconds" ;<br> double ria_ha_usu(stnid) ;<br> ria_ha_usu:_FillValue = NaN ;<br> ria_ha_usu:units = "hectares" ;<br> ria_ha_usu:long_name = "River area" ;<br> ria_ha_usu:description = "River area was calculated using the the HydroSHEDS database at 15 arc-second resolution. It is based on a rating curve" ;<br> double riv_tc_usu(stnid) ;<br> riv_tc_usu:_FillValue = NaN ;<br> riv_tc_usu:units = "1000 m^3" ;<br> riv_tc_usu:long_name = "River volume" ;<br> riv_tc_usu:description = "River volume was calculated using the the HydroSHEDS database at 15 arc-second resolution." ;<br> double gwt_cm_sav(stnid) ;<br> gwt_cm_sav:_FillValue = NaN ;<br> gwt_cm_sav:units = "cm" ;<br> gwt_cm_sav:long_name = "Groundwater table depth" ;<br> gwt_cm_sav:description = "Fan et al. (2013) compiled global observations of water table depth from government archives and literature" ;<br> double ele_mt_uav(stnid) ;<br> ele_mt_uav:_FillValue = NaN ;<br> ele_mt_uav:units = "m" ;<br> ele_mt_uav:long_name = "Elevation" ;<br> ele_mt_uav:description = "Elevation above mean sea level based on EarthEnv-DEM90" ;<br> double glc_pc_u01(stnid) ;<br> glc_pc_u01:_FillValue = NaN ;<br> glc_pc_u01:units = "%" ;<br> glc_pc_u01:long_name = "Land cover area extent" ;<br> glc_pc_u01:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u02(stnid) ;<br> glc_pc_u02:_FillValue = NaN ;<br> glc_pc_u02:units = "%" ;<br> glc_pc_u02:long_name = "Land cover area extent" ;<br> glc_pc_u02:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u03(stnid) ;<br> glc_pc_u03:_FillValue = NaN ;<br> glc_pc_u03:units = "%" ;<br> glc_pc_u03:long_name = "Land cover area extent" ;<br> glc_pc_u03:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u04(stnid) ;<br> glc_pc_u04:_FillValue = NaN ;<br> glc_pc_u04:units = "%" ;<br> glc_pc_u04:long_name = "Land cover area extent" ;<br> glc_pc_u04:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u05(stnid) ;<br> glc_pc_u05:_FillValue = NaN ;<br> glc_pc_u05:units = "%" ;<br> glc_pc_u05:long_name = "Land cover area extent" ;<br> glc_pc_u05:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u06(stnid) ;<br> glc_pc_u06:_FillValue = NaN ;<br> glc_pc_u06:units = "%" ;<br> glc_pc_u06:long_name = "Land cover area extent" ;<br> glc_pc_u06:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u07(stnid) ;<br> glc_pc_u07:_FillValue = NaN ;<br> glc_pc_u07:units = "%" ;<br> glc_pc_u07:long_name = "Land cover area extent" ;<br> glc_pc_u07:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u08(stnid) ;<br> glc_pc_u08:_FillValue = NaN ;<br> glc_pc_u08:units = "%" ;<br> glc_pc_u08:long_name = "Land cover area extent" ;<br> glc_pc_u08:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u09(stnid) ;<br> glc_pc_u09:_FillValue = NaN ;<br> glc_pc_u09:units = "%" ;<br> glc_pc_u09:long_name = "Land cover area extent" ;<br> glc_pc_u09:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u10(stnid) ;<br> glc_pc_u10:_FillValue = NaN ;<br> glc_pc_u10:units = "%" ;<br> glc_pc_u10:long_name = "Land cover area extent" ;<br> glc_pc_u10:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u11(stnid) ;<br> glc_pc_u11:_FillValue = NaN ;<br> glc_pc_u11:units = "%" ;<br> glc_pc_u11:long_name = "Land cover area extent" ;<br> glc_pc_u11:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u12(stnid) ;<br> glc_pc_u12:_FillValue = NaN ;<br> glc_pc_u12:units = "%" ;<br> glc_pc_u12:long_name = "Land cover area extent" ;<br> glc_pc_u12:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u13(stnid) ;<br> glc_pc_u13:_FillValue = NaN ;<br> glc_pc_u13:units = "%" ;<br> glc_pc_u13:long_name = "Land cover area extent" ;<br> glc_pc_u13:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u14(stnid) ;<br> glc_pc_u14:_FillValue = NaN ;<br> glc_pc_u14:units = "%" ;<br> glc_pc_u14:long_name = "Land cover area extent" ;<br> glc_pc_u14:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u15(stnid) ;<br> glc_pc_u15:_FillValue = NaN ;<br> glc_pc_u15:units = "%" ;<br> glc_pc_u15:long_name = "Land cover area extent" ;<br> glc_pc_u15:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u16(stnid) ;<br> glc_pc_u16:_FillValue = NaN ;<br> glc_pc_u16:units = "%" ;<br> glc_pc_u16:long_name = "Land cover area extent" ;<br> glc_pc_u16:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u17(stnid) ;<br> glc_pc_u17:_FillValue = NaN ;<br> glc_pc_u17:units = "%" ;<br> glc_pc_u17:long_name = "Land cover area extent" ;<br> glc_pc_u17:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u18(stnid) ;<br> glc_pc_u18:_FillValue = NaN ;<br> glc_pc_u18:units = "%" ;<br> glc_pc_u18:long_name = "Land cover area extent" ;<br> glc_pc_u18:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u19(stnid) ;<br> glc_pc_u19:_FillValue = NaN ;<br> glc_pc_u19:units = "%" ;<br> glc_pc_u19:long_name = "Land cover area extent" ;<br> glc_pc_u19:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u20(stnid) ;<br> glc_pc_u20:_FillValue = NaN ;<br> glc_pc_u20:units = "%" ;<br> glc_pc_u20:long_name = "Land cover area extent" ;<br> glc_pc_u20:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u21(stnid) ;<br> glc_pc_u21:_FillValue = NaN ;<br> glc_pc_u21:units = "%" ;<br> glc_pc_u21:long_name = "Land cover area extent" ;<br> glc_pc_u21:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u22(stnid) ;<br> glc_pc_u22:_FillValue = NaN ;<br> glc_pc_u22:units = "%" ;<br> glc_pc_u22:long_name = "Land cover area extent" ;<br> glc_pc_u22:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double pnv_pc_u01(stnid) ;<br> pnv_pc_u01:_FillValue = NaN ;<br> pnv_pc_u01:units = "%" ;<br> pnv_pc_u01:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u01:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u02(stnid) ;<br> pnv_pc_u02:_FillValue = NaN ;<br> pnv_pc_u02:units = "%" ;<br> pnv_pc_u02:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u02:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u03(stnid) ;<br> pnv_pc_u03:_FillValue = NaN ;<br> pnv_pc_u03:units = "%" ;<br> pnv_pc_u03:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u03:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u04(stnid) ;<br> pnv_pc_u04:_FillValue = NaN ;<br> pnv_pc_u04:units = "%" ;<br> pnv_pc_u04:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u04:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u05(stnid) ;<br> pnv_pc_u05:_FillValue = NaN ;<br> pnv_pc_u05:units = "%" ;<br> pnv_pc_u05:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u05:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u06(stnid) ;<br> pnv_pc_u06:_FillValue = NaN ;<br> pnv_pc_u06:units = "%" ;<br> pnv_pc_u06:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u06:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u07(stnid) ;<br> pnv_pc_u07:_FillValue = NaN ;<br> pnv_pc_u07:units = "%" ;<br> pnv_pc_u07:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u07:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u08(stnid) ;<br> pnv_pc_u08:_FillValue = NaN ;<br> pnv_pc_u08:units = "%" ;<br> pnv_pc_u08:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u08:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u09(stnid) ;<br> pnv_pc_u09:_FillValue = NaN ;<br> pnv_pc_u09:units = "%" ;<br> pnv_pc_u09:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u09:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u10(stnid) ;<br> pnv_pc_u10:_FillValue = NaN ;<br> pnv_pc_u10:units = "%" ;<br> pnv_pc_u10:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u10:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u11(stnid) ;<br> pnv_pc_u11:_FillValue = NaN ;<br> pnv_pc_u11:units = "%" ;<br> pnv_pc_u11:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u11:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u12(stnid) ;<br> pnv_pc_u12:_FillValue = NaN ;<br> pnv_pc_u12:units = "%" ;<br> pnv_pc_u12:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u12:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u13(stnid) ;<br> pnv_pc_u13:_FillValue = NaN ;<br> pnv_pc_u13:units = "%" ;<br> pnv_pc_u13:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u13:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u14(stnid) ;<br> pnv_pc_u14:_FillValue = NaN ;<br> pnv_pc_u14:units = "%" ;<br> pnv_pc_u14:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u14:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double wet_pc_u01(stnid) ;<br> wet_pc_u01:_FillValue = NaN ;<br> wet_pc_u01:units = "%" ;<br> wet_pc_u01:long_name = "Wet land area extent" ;<br> wet_pc_u01:description = "Wetland extent for the drainage system. Data from EarthStat" ;<br> double wet_pc_u02(stnid) ;<br> wet_pc_u02:_FillValue = NaN ;<br> wet_pc_u02:units = "%" ;<br> wet_pc_u02:long_name = "Wet land area extent" ;<br> wet_pc_u02:description = "Wetland extent for the drainage system. Data from EarthStat" ;<br> double wet_pc_u03(stnid) ;<br> wet_pc_u03:_FillValue = NaN ;<br> wet_pc_u03:units = "%" ;<br> wet_pc_u03:long_name = "Wet land area extent" ;<br> wet_pc_u03:description = "Wetland extent for the drainage system. Data from EarthStat" ;<br> double wet_pc_u04(stnid) ;<br> wet_pc_u04:_FillValue = NaN ;<br> wet_pc_u04:units = "%" ;<br> wet_pc_u04:long_name = "Wet land area extent" ;<br> wet_pc_u04:description = "Wetland extent for the drainage system. Data from EarthStat" ;<br> double wet_pc_u05(stnid) ;<br> wet_pc_u05:_FillValue = NaN ;<br> wet_pc_u05:units = "%" ;<br> wet_pc_u05:long_name = "Wet land area extent" ;<br> wet_pc_u05:description = "Wetland extent for the drainage system. Data from EarthStat" ;<br> double wet_pc_u06(stnid) ;<br> wet_pc_u06:_FillValue = NaN ;<br> wet_pc_u06:units = "%" ;<br> wet_pc_u06:long_name = "Wet land area extent" ;<br> wet_pc_u06:description = "Wetland extent for the drainage system. Data from EarthStat" ;<br> double wet_pc_u07(stnid) ;<br> wet_pc_u07:_FillValue = NaN ;<br> wet_pc_u07:units = "%" ;<br> wet_pc_u07:long_name = "Wet land area extent" ;<br> wet_pc_u07:description = "Wetland extent for the drainage system. Data from EarthStat" ;<br> double wet_pc_u08(stnid) ;<br> wet_pc_u08:_FillValue = NaN ;<br> wet_pc_u08:units = "%" ;<br> wet_pc_u08:long_name = "Wet land area extent" ;<br> wet_pc_u08:description = "Wetland extent for the drainage system. Data from EarthStat" ;</p> <p>// global attributes:<br> :description = "Created by Zhi Li (li1995@ou.edu)\nFitted empirical F-IDF values for USGS gauges." ;<br> }<br> </p>
Water quality measurements, stream order, channel slope and hydraulic equations of conterminous USGS sites: 1919-2009.
Streams and rivers emit petagrams of CO2 yet there is little known about how discharge (Q) variability impacts stream CO2 at broad scales. Herein, we compiled historical water quality (including pH, alkalinity and temperature) measurements for conterminous USGS sites and coupled them with daily Q for this analysis (the water_quality.csv dataset, 10,822 sites). Based on this dataset, NHDplus channel slopes (NHDplus_slopeSO.csv, 24,764 sites) and hydraulic geometry equations (lm_vQ.csv, 12,854 sites), we calculated partial pressure of dissolved CO2 (pCO2), gas transfer velocity (k) and CO2 effluxes (F) for a total of 813 USGS sites across conterminous US. We derived hydrologic responses (log-linear regressions) for pCO2, k and F versus Q at each site and explored how these responses varied across stream order and different regions. Ancillary datasets provided coordinates (coor_sites.xls), hydrologic unit code (HUC.csv), and watershed area of conterminous USGS sites (watersheds_area.csv).
Annual summaries of daily observations from the USGS Streamflow Gauging Station on the Altamaha River near Doctortown, Georgia, for 1932 to 2004
Daily averaged river discharge data were obtained from the United States Geological Survey for streamflow gauging station USGS 02226000 on the Altamaha River near Doctortown, Georgia. Processed tabular data were downloaded from the USGS real-time web server (http://waterdata.usgs.gov/nwis/) by the Georgia Coastal Ecosystems LTER project, documented, and standardized to metric units. Missing values of mean discharge were estimated by cubic spline interpolation to fill in data gaps of five or fewer consecutive days. Annual summary statistics were then calculated from daily values aggregated by year.
ASD01 Stream discharge for Kings Creek measured at USGS gaging station
The 1060 hectacre Kings Creek watershed (STATION NUMBER 06879650) is a U.S. Geological survey hydrologic benchmark. (cf. Cobb, e.D. and J.E. Beisecker. 1971. USGS circular 460-d) flow is measured continuously with a bubble gage. The period of record started in March, 1979. The hydrologic regime is driven by the mid-continent climate characterized by summer thunderstorms and drought. The stream, at the gaging station, is fourth order in gallery forest. The riparian vegetation in the headwaters (1st & 2nd order) is tallgrass, or true, prairie. Flow occurred in spring and early summer in all years. Late summer, fall and winter records also include periods of no flow, the longest of these was ca. nine months. The time from base flow to peak flow for typical storm flow hydrographs is an hour or so and amplitude of ca. three meters above base flow has been recorded (Data comes from USGS office in Lawrence, Kansas; Contact person:Butch Lawcock, (913) 842-9709). Organization: U.S. Geological Survey For more metadata description and data, please see:https://waterdata.usgs.gov/nwis/uv/?site_no=06879650
Fig. 9. Dinoflagellates from USGS Paleobotan. loc. R6234 in Cephalopods From The Cretaceous/Tertiary Boundary Interval On The Atlantic Coastal Plain, With A Description Of The Highest Ammonite Zones In North America. Part 2. Northeastern Monmouth County, New Jersey
Fig. 9. Dinoflagellates from USGS Paleobotan. loc. R6234, lower part of the Hornerstown Formation, Parkers Creek, 0.3 km east of Rt. 35, northwest of Eatontown, northeastern Monmouth County,
Fig. 8. Dinoflagellates from USGS Paleobotan. loc. R6234 in Cephalopods From The Cretaceous/Tertiary Boundary Interval On The Atlantic Coastal Plain, With A Description Of The Highest Ammonite Zones In North America. Part 2. Northeastern Monmouth County, New Jersey
Fig. 8. Dinoflagellates from USGS Paleobotan. loc. R6234, upper part of the New Egypt Formation and lower part of the Hornerstown Formation, Parkers Creek, 0.3 km east of Rt. 35, northwest of
Fig. 7. Dinoflagellates from northeastern Monmouth County, New Jersey. A, B, H, I. USGS Paleobotan. loc. R6234E in Cephalopods From The Cretaceous/Tertiary Boundary Interval On The Atlantic Coastal Plain, With A Description Of The Highest Ammonite Zones In North America. Part 2. Northeastern Monmouth County, New Jersey
Fig. 7. Dinoflagellates from northeastern Monmouth County, New Jersey. A, B, H, I. USGS Paleobotan. loc. R6234E, New Egypt Formation, approximately 1.5–2.0 m below the base of the Hornerstown Formation, Parkers Creek, 0.1 km east of Rt. 35, northwest of Eatontown. C, F, G. USGS Paleobotan.
Fig. 6. Dinoflagellates from USGS Paleobotan. loc. R6234H in Cephalopods From The Cretaceous/Tertiary Boundary Interval On The Atlantic Coastal Plain, With A Description Of The Highest Ammonite Zones In North America. Part 2. Northeastern Monmouth County, New Jersey
Fig. 6. Dinoflagellates from USGS Paleobotan. loc. R6234H, Tinton Formation, Hochhockson Brook, 0.2 km north of the intersection of Water Street and Tinton Avenue, northeastern Monmouth County, New Jersey. A–E. Isabelidinium cooksoniae (Alberti, 1959) Lentin & Williams, 1977 sensu lato.
Summary Table of AHG Parameters, Hydraulics, Morphological and geophysical variables, and Suspending Sediment Concentration Derived at 1246 USGS River Monitoring Stations
<p>This table contains information about:</p> <p>1) At-A-Station Hydraulic Geometry Parameters,</p> <p>2) Hydraulics Variables,</p> <p>3) Morphological and Geophysical Variables, and</p> <p>4) Suspending Sediment Concentration and Fraction of Sand, Silt, and Clay,</p> <p>derived at 1246 USGS river monitoring stations across the conterminous United States. </p>
Text-fig. Fig. 9. Cucurbitaceae 1–4. Cucurbitaciphyllum lobatum (KNOWLTON) comb. nov. Specimens from Shirley Canal, Montana, USGS loc. 8519. 1. Trilobate leaf, with rounded lobal sinues, and entire to serrated margin. USNM 313167. 2. Leaf with pronounced secondary lobes and cordate base, USNM 313179. 3. Detail of central lobe from fig. 1. 4. Higher magnification, showing abundant trichome impressions. Scales = 3 cm in 1–3; 3 mm in 4. in Revisions To Roland Brown'S North American Paleocene Flora
Text-fig. Fig. 9. Cucurbitaceae 1–4. Cucurbitaciphyllum lobatum (KNOWLTON) comb. nov. Specimens from Shirley Canal, Montana, USGS loc. 8519. 1. Trilobate leaf, with rounded lobal sinues, and entire to serrated margin. USNM 313167. 2. Leaf with pronounced secondary lobes and cordate base, USNM 313179. 3. Detail of central lobe from fig. 1. 4. Higher magnification, showing abundant trichome impressions. Scales = 3 cm in 1–3; 3 mm in 4.
Linked collectors and determiners for: USGS NRP - Submerged Aquatic Plants - 1966-2009.
Natural history specimen data linked to collectors and determiners held within, "USGS NRP - Submerged Aquatic Plants - 1966-2009". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/fda694ce-fb23-4c47-b765-4cd7d8b4d43d">https://bionomia.net/dataset/fda694ce-fb23-4c47-b765-4cd7d8b4d43d</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/fda694ce-fb23-4c47-b765-4cd7d8b4d43d">https://gbif.org/dataset/fda694ce-fb23-4c47-b765-4cd7d8b4d43d</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: USGS PWRC - Native Bee Inventory and Monitoring Lab (BIML).
Natural history specimen data linked to collectors and determiners held within, "USGS PWRC - Native Bee Inventory and Monitoring Lab (BIML)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/f519367d-6b9d-411c-b319-99424741e7de">https://bionomia.net/dataset/f519367d-6b9d-411c-b319-99424741e7de</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/f519367d-6b9d-411c-b319-99424741e7de">https://gbif.org/dataset/f519367d-6b9d-411c-b319-99424741e7de</a>. Formatted as a Frictionless Data package.
Fig. 50. A, B. Exiteloceras jenneyi camacki, n. subsp. Paratype, USNM 482495, USGS Mesozoic locality D13525 in Late Campanian (Cretaceous) Heteromorph Ammonites From The Western Interior Of The United States
Fig. 50. A, B. Exiteloceras jenneyi camacki, n. subsp. Paratype, USNM 482495, USGS Mesozoic locality D13525 (fig. 2, loc. 22). Figures are ×1.
Hubbard Brook Experimental Forest USGS 40ft Contours: GIS Shapefile
USGS 7.5' Topographic Quadrangles; Mount Kineo, NH - 1973, Woodstock, NH 1980. Contours at 40ft intervals were manually digitized. Data distributed as shapefile in Coordinate system EPSG:26919 - NAD83 / UTM zone 19N
Figure 4. - ATruncatoflabellummacroeschara, paratype, USNM 96661, Onslow Island, Western Australia B Truncatoflabellumveroni, paratype, USNM 96655, Soela 54A, Western Australia C Truncatoflabellumgambierense, USNM 1295473, USGS 10809, Balcombe's Bay, Victoria (Balcombian = Middle Miocene) D Truncatoflabellumirregulare, USNM 87713, Japan. Scale bars: all 10 mm.
Figure 4. - ATruncatoflabellummacroeschara, paratype, USNM 96661, Onslow Island, Western Australia B Truncatoflabellumveroni, paratype, USNM 96655, Soela 54A, Western Australia C Truncatoflabellumgambierense, USNM 1295473, USGS 10809, Balcombe's Bay, Victoria (Balcombian = Middle Miocene) D Truncatoflabellumirregulare, USNM 87713, Japan. Scale bars: all 10 mm.
Flickr: USGS Bee Inventory and Monitoring Lab
<p>https://www.flickr.com/photos/usgsbiml/ The USGS Native Bee Inventory and Monitoring Program designs and develops large and small scale surveys for native bees. As part of that program we also develop identification tools and keys for native bee species. One aspect of creating those tools is creating accurate and detailed pictures of native bees and the plants and insects they interact with. This site is designed to provide easy access to our photographs so that they may be freely used. There is no need to ask for our permission for any use of these photographs. You can download the original version directly from Flickr at your convenience. </p><p></p>Please contact Sam Droege for further information at sdroege@usgs.gov or or 301-497-5840 or visit our website at: www.pwrc.usgs.gov/nativebees/ Photos were taken with a system developed by Dr. Anthony G Gutierrez (Tony.Gutierrez@us.army.mil) and taken by Brooke Alexander, Sue Boo, Heagan Ahmed and Sierra Williams. A how to manual for taking similar pictures is available a (note the url is very long and you have to use the shift and arrow key to highlight the whole thing): ftp://ftpext.usgs.gov/pub/er/md/laurel/Droege/How to Take MacroPhotographs of Insects BIML Lab2.pdf Posts do not = endorsement: on.doi.gov/pgwu0Y <p></p>Follow us on instagram: @usgsbiml <p></p>https://eol-jira.bibalex.org/browse/DATA-1855<p></p>
The seismic data of the 2020 Nima earthquake obatined by USGS, CENC and GCMT
<p>The three files contain the seismic data of the 2020 Nima earthquake obtaind by United States Geological Survey, China Earthquake Data Center and Global Centroid-Moment-Tensor (CMT) Project.</p> <p>If you have any questions about this data, please contact me via <a href="mailto:gaohuastudent@163.com">gaohuastudent@163.com</a>.</p>
USGS National Elevation Dataset (NED) DEM reprojected into Collection 2 Landsat analysis ready data (ARD) tiles
<p>This dataset is used to test the Classifying the raw irregular time series (CRIT) codes and model for CONUS land cover classification with a deep learning model that can directly classify Landsat irrigular time series. See the code here https://github.com/hankui/CRIT</p>
USGS EdMap 2022 Swales Mountain Map
<p>The dataset attached includes all the field notes taken while mapping the Swales Mountain area for a 2021-2022 USGS EdMap funded project. This dataset is compatible with an updated geologic map of Swales Mountain, NV (7.5’ quadrangle at 1:5,000 scale). Swales Mountain hosts a shallow to mid-crustal Eocene magmatic system. The goal of this project was to remap the various subvolcanic and plutonic igneous units in greater detail. The seven different units which range from rhyolite to andesite. The newly presented information from the Swales Mountain geologic map can aid in better understanding the evolution of modern and ancient arc magmatic systems alike.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.