United States US: Urban Land Area data was reported at 802,053.592 sq km in 2010. This stayed constant from the previous number of 802,053.592 sq km for 2000. United States US: Urban Land Area data is updated yearly, averaging 802,053.592 sq km from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 802,053.592 sq km in 2010 and a record low of 802,053.592 sq km in 2010. United States US: Urban Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Land Use, Protected Areas and National Wealth. Urban land area in square kilometers, based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights. Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Sum;

This dataset represents the road density within individual, local NHDPlusV2 catchments and upstream, contributing watersheds. Attributes of the landscape layer were calculated for every local NHDPlusV2 catchment and accumulated to provide watershed-level metrics. This data set is derived from TIGER/Line Files of roads in the conterminous United States. Road density describes how many kilometers of road exist in a square kilometer. A raster was produced using the ArcGIS Line Density Tool to form the landscape layer for analysis. The (kilometer of road/square kilometer) was summarized by local catchment and by watershed to produce local catchment-level and watershed-level metrics as a continuous data type.

United States US: Rural Land Area data was reported at 8,549,545.510 sq km in 2010. This stayed constant from the previous number of 8,549,545.510 sq km for 2000. United States US: Rural Land Area data is updated yearly, averaging 8,549,545.510 sq km from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 8,549,545.510 sq km in 2010 and a record low of 8,549,545.510 sq km in 2010. United States US: Rural Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Land Use, Protected Areas and National Wealth. Rural land area in square kilometers, derived from urban extent grids which distinguish urban and rural areas based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights. Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Sum;

Two datasets provide geographic, land use and population data for US Counties within the contiguous US. Land area, water area, cropland area, farmland area, pastureland area and idle cropland area are given along with latitude and longitude of the county centroid and the county population. Variables in this dataset come from the US Dept. of Agriculture (USDA) Natural Resources Conservation Service (NRCS) and the US Census Bureau.

EOS-WEBSTER provides seven datasets which provide county-level data on agricultural management, crop production, livestock, soil properties, geography and population. These datasets were assembled during the mid-1990's to provide driving variables for an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data (except nitrogen fertilizer use) were all derived from publicly available, national databases. Each dataset has a separate DIF.

The US County data has been divided into seven datasets.

US County Data Datasets:

1) Agricultural Management 2) Crop Data (NASS Crop data) 3) Crop Summary (NASS Crop data) 4) Geography and Population 5) Land Use 6) Livestock Populations 7) Soil Properties

Dataset Card for Map It Anywhere (MIA)

The Map It Anywhere (MIA) dataset contains map-prediction-ready data curated from public datasets.

  Dataset Details





  Dataset Description

The Map It Anywhere (MIA) dataset contains 1.2 million high quality first-person-view (FPV) and bird's eye view (BEV) map pairs covering 470 squared km, thereby facilitating future map prediction research on generalizability and robustness. The dataset is curated using the MIA data engine… See the full description on the dataset page: https://huggingface.co/datasets/cherieho/mia_dataset.

These geotiff files represent road network statistics for each core-based statistical area (CBSA) in the conterminous U.S., within grid cells of 1km x 1km. The road network statistics are based on the National transportation dataset (USGS-NTD) v2019. These statistics include: gridcell_stats_azimuthvariety_1km_all_cbsas.tif: The number of unique road angles (azimuth / orientation) in bins of 10 degrees per 1 sqkm grid cell. gridcell_stats_deadendrate_1km_all_cbsas.tif: The proportion of dead ends (nodes of degree 1) of all nodes per 1 sqkm grid cell. gridcell_stats_kmroad_1km_all_cbsas.tif: The approximate total road network length per 1 sqkm grid cell. This is based on the road segment length appended to each road segment centroid and may be biased for very long road segments. gridcell_stats_meandegree_1km_all_cbsas.tif: The average nodal degree of all nodes per 1 sqkm grid cell. gridcell_stats_meangriddedness_1km_all_cbsas.tif: The average griddedness of all nodes per 1 sqkm grid cell. gridcell_stats_nodedensity_1km_all_cbsas.tif: The number of nodes per 1 sqkm grid cell. gridcell_stats_nodesperkmroad_1km_all_cbsas.tif: The number of nodes per km road within each 1 sqkm grid cell. gridcell_stats_firstbuiltup_1km_all_cbsas.tif: The approximate settlement age per 1 sqkm grid cell. This layer is derived from the HISDAC-US First-built-up year (FBUY) layer, which is derived from Zillow's Transaction and Assessment Dataset (ZTRAX). The FBUY data is available here: Leyk, Stefan; Uhl, Johannes H., 2018, "FBUY.tar.gz", Historical settlement composite layer for the U.S. 1810 - 2015, https://doi.org/10.7910/DVN/PKJ90M/BOA5YC, Harvard Dataverse, V2 gridcell_stats_1km_all_cbsas_arcmap10.8.mxd: ESRI ArcMap 10.8 MXD file for quick visualization of the gridded surfaces. Spatial resolution: 1x1km Spatial reference: SR-ORG:7480, USA_Contiguous_Albers_Equal_Area_Conic_USGS_version Source data: USGS-NTD, HISDAC-US. File format: GeoTIFF. Spatial coverage of the road network metrics: All CBSAs in the conterminous U.S. Spatial coverage of the "first built-up year" surface: all U.S. counties that are covered by the HISDAC-US historical settlement layers. This datasets includes around 2,700 U.S. counties. In the remaining counties, construction year coverage in the underlying ZTRAX data (Zillow Transaction and Assessment Dataset) is low. See Leyk & Uhl (2018) for details. All data created by Johannes H. Uhl, University of Colorado Boulder, USA. Code available at https://github.com/johannesuhl/USRoadNetworkEvolution. References: Burghardt, K., Uhl, J., Lerman, K., & Leyk, S. (2022). Road Network Evolution in the Urban and Rural United States Since 1900. Computers, Environment and Urban Systems. Leyk, S., & Uhl, J. H. (2018). HISDAC-US, historical settlement data compilation for the conterminous United States over 200 years. Scientific data, 5(1), 1-14. DOI: https://doi.org/10.1038/sdata.2018.175

U.S. Geological Survey scientists, funded by the Climate and Land Use Change Research and Development Program, developed a dataset of 2006 and 2011 land use and land cover (LULC) information for selected 100-km2 sample blocks within 29 EPA Level 3 ecoregions across the conterminous United States. The data was collected for validation of new and existing national scale LULC datasets developed from remotely sensed data sources. The data can also be used with the previously published Land Cover Trends Dataset: 1973-2000 (http:// http://pubs.usgs.gov/ds/844/), to assess land-use/land-cover change in selected ecoregions over a 37-year study period. LULC data for 2006 and 2011 was manually delineated using the same sample block classification procedures as the previous Land Cover Trends project. The methodology is based on a statistical sampling approach, manual classification of land use and land cover, and post-classification comparisons of land cover across different dates. Landsat Thematic Mapper, and Enhanced Thematic Mapper Plus imagery was interpreted using a modified Anderson Level I classification scheme. Landsat data was acquired from the National Land Cover Database (NLCD) collection of images. For the 2006 and 2011 update, ecoregion specific alterations in the sampling density were made to expedite the completion of manual block interpretations. The data collection process started with the 2000 date from the previous assessment and any needed corrections were made before interpreting the next two dates of 2006 and 2011 imagery. The 2000 land cover was copied and any changes seen in the 2006 Landsat images were digitized into a new 2006 land cover image. Similarly, the 2011 land cover image was created after completing the 2006 delineation. Results from analysis of these data include ecoregion based statistical estimates of the amount of LULC change per time period, ranking of the most common types of conversions, rates of change, and percent composition. Overall estimated amount of change per ecoregion from 2001 to 2011 ranged from a low of 370 km2 in the Northern Basin and Range Ecoregion to a high of 78,782 km2 in the Southeastern Plains Ecoregion. The Southeastern Plains Ecoregion continues to encompass the most intense forest harvesting and regrowth in the country. Forest harvesting and regrowth rates in the southeastern U.S. and Pacific Northwest continued at late 20th century levels. The land use and land cover data collected by this study is ideally suited for training, validation, and regional assessments of land use and land cover change in the U.S. because it is collected using manual interpretation techniques of Landsat data aided by high resolution photography. The 2001-2011 Land Cover Trends Dataset is provided in an Albers Conical Equal Area projection using the NAD 1983 datum. The sample blocks have a 30-meter resolution and file names follow a specific naming convention that includes the number of the ecoregion containing the block, the block number, and the Landsat image date. The data files are organized by ecoregion, and are available in the ERDAS Imagine (.img) format. U.S. Geological Survey scientists, funded by the Climate and Land Use Change Research and Development Program, developed a dataset of 2006 and 2011 land use and land cover (LULC) information for selected 100-km2 sample blocks within 29 EPA Level 3 ecoregions across the conterminous United States. The data was collected for validation of new and existing national scale LULC datasets developed from remotely sensed data sources. The data can also be used with the previously published Land Cover Trends Dataset: 1973-2000 (http:// http://pubs.usgs.gov/ds/844/), to assess land-use/land-cover change in selected ecoregions over a 37-year study period. LULC data for 2006 and 2011 was manually delineated using the same sample block classification procedures as the previous Land Cover Trends project. The methodology is based on a statistical sampling approach, manual classification of land use and land cover, and post-classification comparisons of land cover across different dates. Landsat Thematic Mapper, and Enhanced Thematic Mapper Plus imagery was interpreted using a modified Anderson Level I classification scheme. Landsat data was acquired from the National Land Cover Database (NLCD) collection of images. For the 2006 and 2011 update, ecoregion specific alterations in the sampling density were made to expedite the completion of manual block interpretations. The data collection process started with the 2000 date from the previous assessment and any needed corrections were made before interpreting the next two dates of 2006 and 2011 imagery. The 2000 land cover was copied and any changes seen in the 2006 Landsat images were digitized into a new 2006 land cover image. Similarly, the 2011 land cover image was created after completing the 2006 delineation. Results from analysis of these data include ecoregion based statistical estimates of the amount of LULC change per time period, ranking of the most common types of conversions, rates of change, and percent composition. Overall estimated amount of change per ecoregion from 2001 to 2011 ranged from a low of 370 square km in the Northern Basin and Range Ecoregion to a high of 78,782 square km in the Southeastern Plains Ecoregion. The Southeastern Plains Ecoregion continues to encompass the most intense forest harvesting and regrowth in the country. Forest harvesting and regrowth rates in the southeastern U.S. and Pacific Northwest continued at late 20th century levels. The land use and land cover data collected by this study is ideally suited for training, validation, and regional assessments of land use and land cover change in the U.S. because it’s collected using manual interpretation techniques of Landsat data aided by high resolution photography. The 2001-2011 Land Cover Trends Dataset is provided in an Albers Conical Equal Area projection using the NAD 1983 datum. The sample blocks have a 30-meter resolution and file names follow a specific naming convention that includes the number of the ecoregion containing the block, the block number, and the Landsat image date. The data files are organized by ecoregion, and are available in the ERDAS Imagine (.img) format.

This dataset of 40 square kilometer (sq. km) hexagons was created by the U.S. EPA's Environmental Monitoring and Assessment Program (EMAP) and is being released by the U.S. Geological Survey for public use. The 40 sq. km hexagons were derived from a grid consisting of a triangular array of points that cover the United States and neighboring Canada and Mexico. The base grid of points had a companion areal structure called a tessellation. The base tessellation hexagons constituted this tessellation. In other words, surrounding each grid point was a hexagon that defines the area within which all points are closer to this grid point than to any other, and the set of hexagons defined this way completely and -mutually exclusively covers the space of the grid. The grid had a base density of approximately 648 sq. km per point with a spacing of approximately 27 km between points. The original 40 sq. km hexagons (which do not form a tessellation) were centered about the randomized grid points and are exactly 1/16th the size of the tessellation hexagons (and therefore slightly more than 40 sq. km). Hexagon boundaries are distributed in geodetic coordinates based on the Clarke 1866 model of the Earth, meaning that the coordinates are latitude and longitude on the ellipsoid used by most North American geodetic coordinate systems. Distribution can also be made in GRS 80 coordinates if desired. The precision of the coordinates is to millionths of a degree (i.e., to 6 decimal places of a degree). This corresponds to about 0.1 meter on the surface of the Earth. The point grid was constructed in the plane of a special version of the Lambert azimuthal equal area projection; for subsequent use they may be projected using other map projections. When other projections are used, the geometry of the point grid will not be perfectly triangular nor will the hexagons surrounding the points be perfect, since sizes and/or shapes and/or distances will necessarily be distorted in another projection relative to the one used to construct the grid. This 40 sq. km hexagon tessellation was created by two successive enhancements of the 648 sq. km tessellation by factors of four. See White et al. 1992 in references.

This dataset represents the road density within individual local and accumulated upstream catchments for NHDPlusV2 Waterbodies. Catchment boundaries in LakeCat are defined in one of two ways, on-network or off-network. The on-network catchment boundaries follow the catchments provided in the NHDPlusV2 and the metrics for these lakes mirror metrics from StreamCat, but will substitute the COMID of the NHDWaterbody for that of the NHDFlowline. The off-network catchment framework uses the NHDPlusV2 flow direction rasters to define non-overlapping lake-catchment boundaries and then links them through an off-network flow table. This data set is derived from TIGER/Line Files of roads in the conterminous United States. Road density describes how many kilometers of road exist in a square kilometer. A raster was produced using the ArcGIS Line Density Tool to form the landscape layer for analysis. The (kilometer of road/square kilometer) was summarized by local catchment and by watershed to produce local catchment-level and watershed-level metrics as a continuous data type.

GLORIA data for the Gulf of Alaska Exclusive Economic Zone (EEZ) were acquired during five cruises over a four year period. The first cruise conducted in 1986 (F-1-86-GA) surveyed an area of the north-central mosaic area and covered an area of approximately 40,000 square kilometers (sq km). The second two cruises (F-8-88-AA, F-9-88-WG) were conducted in 1988. One of the 1988 cruises (F-8-88-AA) focused on a survey of the Aleutian Arc. The eastern most portion of that survey extended outside of the Aleutian Arc survey area and covered an area of approximately 52,000 square kilometers (sq km) of seafloor on the western edge of the Gulf of Alaska. The final two cruises (F-6-89-GA, F-7-89-EG) were completed in 1989. As in earlier EEZ reconnaissance surveys, the USGS utilized the GLORIA (Geological LOng-Range Inclined Asdic) sidescan-sonar system to complete the geologic mapping. The collected GLORIA data were processed and digitally mosaicked to produce continuous imagery of the seafloor. Thirty digital mosaics with a 50-meter pixel resolution were completed for the region.

This dataset represents canal density within individual, local and accumulated upstream catchments for NHDPlusV2 Waterbodies. Catchment boundaries in LakeCat are defined in one of two ways, on-network or off-network. The on-network catchment boundaries follow the catchments provided in the NHDPlusV2 and the metrics for these lakes mirror metrics from StreamCat, but will substitute the COMID of the NHDWaterbody for that of the NHDFlowline. The off-network catchment framework uses the NHDPlusV2 flow direction rasters to define non-overlapping lake-catchment boundaries and then links them through an off-network flow table. This data set is derived from NHDPlusV2 line features classified as canal, ditch, or pipeline in the conterminous United States. Canal density describes how many kilometers of canal exist in a square kilometer. A raster was produced using the ArcGIS Line Density Tool to form the landscape layer for analysis. The (kilometer of canal/square kilometer) was summarized by local catchment and by watershed to produce local catchment-level and watershed-level metrics as a continuous data type.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

United States US: Urban Land Area Where Elevation is Below 5 Meters data was reported at 17,520.222 sq km in 2010. This stayed constant from the previous number of 17,520.222 sq km for 2000. United States US: Urban Land Area Where Elevation is Below 5 Meters data is updated yearly, averaging 17,520.222 sq km from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 17,520.222 sq km in 2010 and a record low of 17,520.222 sq km in 2010. United States US: Urban Land Area Where Elevation is Below 5 Meters data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Land Use, Protected Areas and National Wealth. Urban land area below 5m is the total urban land area in square kilometers where the elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Sum;

This data set represents 1990 block group population density, in people per square kilometer, in the conterminous United States. This data set represents The data set was used as an input data layer for a national model to predict nitrate concentration in ground water used for drinking. Nolan and Hitt (2006) developed two national models to predict contamination of ground water by nonpoint sources of nitrate. The nonlinear approach to national-scale Ground-WAter Vulnerability Assessment (GWAVA) uses components representing nitrogen (N) sources, transport, and attenuation. One model (GWAVA-S) predicts nitrate contamination of shallow (typically less than 5 meters deep), recently recharged ground water, which may or may not be used for drinking. The other (GWAVA-DW) predicts ambient nitrate concentration in deeper supplies used for drinking. This data set is one of 14 data sets (1 output data set and 13 input data sets) associated with the GWAVA-DW model. Full details of the model development are in Nolan and Hitt (2006). For inputs to the model, spatial attributes representing 13 nitrogen loading and transport and attenuation factors were compiled as raster data sets (1-km by 1-km grid cell size) for the conterminous United States (see table 1). >Table 1.-- Parameters of nonlinear regression model for > nitrate in ground water used for drinking (GWAVA-DW) > and corresponding input spatial data sets. > [kg, kilograms; km2, square kilometers.] > >Nitrogen Source Factors Data Set Name > 1 farm fertilizer (kg/hectare) gwava-dw_ffer > 2 confined manure (kg/hectare) gwava-dw_conf > 3 orchards/vineyards (percent) gwava-dw_orvi > 4 population density (people/km2) gwava-dw_popd > >Transport to Aquifer Factors > 5 water input (km2/cm) gwava-dw_wtin > 6 glacial till (yes/no) gwava-dw_gtil > 7 semiconsolidated sand aquifers gwava-dw_semc > (yes/no) > 8 sandstone and carbonate rocks gwava-dw_sscb > (yes/no) > 9 drainage ditch (km2) gwava-dw_ddit > 10 Hortonian overland flow gwava-dw_hor > (percent of streamflow) > >Attenuation Factors > 11 fresh surface water withdrawal gwava-dw_swus > for irrigation (megaliters/day) > 12 irrigation tailwater recovery (km2) gwava-dw_twre > 13 Dunne overland flow gwava-dw_dun > (percent of streamflow) > 14 well depth (meters) - "Farm fertilizer" is the average annual nitrogen input from commercial fertilizer applied to agricultural lands, 1992-2001, in kilograms per hectare. "Confined manure" is the average annual nitrogen input from confined animal manure, 1992 and 1997, in kilograms per hectare. "Orchards/vineyards" is the percent of orchards/vineyards land cover classification. "Population density" is 1990 block group population density, in people per square kilometer. "Water input" is the ratio of the total area of irrigated land to precipitation, in square kilometers per centimeter. "Glacial till" is the presence or absence of poorly sorted glacial till east of the Rocky Mountains. "Semiconsolidated sand aquifers" is the presence or absence of semiconsolidated sand aquifers. "Sandstone and carbonate rocks" is the presence or absence of sandstone and carbonate rock aquifers. "Drainage ditch" is the area of National Resources Inventory surface drainage, field ditch conservation practice, in square kilometers. "Hortonian overland flow" is infiltration excess overland flow estimated by TOPMODEL, in percent of streamflow. "Fresh surface water withdrawal for irrigation" is the amount of fresh surface water withdrawal for irrigation, in megaliters per day. "Irrigation tailwater recovery" is the area of National Resources Inventory irrigation system, tailwater recovery conservation practice, in square kilometers. "Dunne overland flow" is saturation overland flow estimated by TOPMODEL, in percent of streamflow. "Well depth" is the depth of the well, in meters. Well depth was not compiled as a spatial data set. Well depth equals 50 meters for the model simulation being presented. Reference cited: Nolan, B.T. and Hitt, K.J., 2006, Vulnerability of shallow ground water and drinking-water wells to nitrate in the United States: Environmental Science and Technology, vol. 40, no. 24, pages 7834-7840.

Streams provide water for human activities and consumption in much of the world. Streamflow is largely controlled by climate forces, therefore it is likely sensitive to climate changes. We analyzed daily air temperature (AT), precipitation (P), and stream discharge (Q) metrics for 124 watersheds in Maryland, Virginia, and North Carolina, United States, from 1981 through 2020. Spatial-raster datasets of daily P in mm were downloaded from Parameter-elevation Regressions on Independent Slopes Model (PRISM; http://prism.oregonstate.edu) on March 30, 2021, and datasets of daily AT in degrees Celsius (°C) were downloaded June 22, 2021, both at a 4-square kilometer (km2) resolution for the contiguous U.S. The final quarter of approved 2020 data for both datasets was downloaded between January 6 and 8, 2022. Daily mean-Q data were downloaded from the USGS’s National Water Information System on January 5, 2022, checked for completeness, and converted to cubic meters per second. The input data sets used to derive trends and PCA results are presented here in the zip file found below. Due to Excel’s maximum row constraints, the files have been subset by decade. Please refer to the linked manuscript below for more information.

This dataset represents the canal density within individual, local NHDPlusV2 catchments and upstream, contributing watersheds. Attributes of the landscape layer were calculated for every local NHDPlusV2 catchment and accumulated to provide watershed-level metrics. This data set is derived from NHDPlusV2 line features classified as canal, ditch, or pipeline in the conterminous United States. Canal density describes how many kilometers of canal exist in a square kilometer. A raster was produced using the ArcGIS Line Density Tool to form the landscape layer for analysis. The (kilometer of canal/square kilometer) was summarized by local catchment and by watershed to produce local catchment-level and watershed-level metrics as a continuous data type.

United States US: Rural Land Area Where Elevation is Below 5 Meters data was reported at 91,676.458 sq km in 2010. This stayed constant from the previous number of 91,676.458 sq km for 2000. United States US: Rural Land Area Where Elevation is Below 5 Meters data is updated yearly, averaging 91,676.458 sq km from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 91,676.458 sq km in 2010 and a record low of 91,676.458 sq km in 2010. United States US: Rural Land Area Where Elevation is Below 5 Meters data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Land Use, Protected Areas and National Wealth. Rural land area below 5m is the total rural land area in square kilometers where the elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Sum;

This data set represents the area of National Resources Inventory surface drainage, field ditch conservation practice, in square kilometers, in the conterminous United States. The data set was used as an input data layer for a national model to predict nitrate concentration in shallow ground water. Nolan and Hitt (2006) developed two national models to predict contamination of ground water by nonpoint sources of nitrate. The nonlinear approach to national-scale Ground-WAter Vulnerability Assessment (GWAVA) uses components representing nitrogen (N) sources, transport, and attenuation. One model (GWAVA-S) predicts nitrate contamination of shallow (typically less than 5 meters deep), recently recharged ground water, which may or may not be used for drinking. The other (GWAVA-DW) predicts ambient nitrate concentration in deeper supplies used for drinking. This data set is one of 17 data sets (1 output data set and 16 input data sets) associated with the GWAVA-S model. Full details of the model development are in Nolan and Hitt (2006). For inputs to the model, spatial attributes representing 16 nitrogen loading and transport and attenuation factors were compiled as raster data sets (1-km by 1-km grid cell size) for the conterminous United States (see table 1). >Table 1.-- Parameters of nonlinear regression model for nitrate in shallow > ground water (GWAVA-S) and corresponding input spatial data sets. > [kg, kilograms; km2, square kilometers.] > >Nitrogen Source Factors Data Set Name > 1 farm fertilizer (kg/hectare) gwava-s_ffer > 2 confined manure (kg/hectare) gwava-s_conf > 3 orchards/vineyards (percent) gwava-s_orvi > 4 population density (people/km2) gwava-s_popd > 5 cropland/pasture/fallow (percent) gwava-s_crpa > >Transport to Aquifer Factors > 6 water input (km2/cm) gwava-s_wtin > 7 carbonate rocks (yes/no) gwava-s_crox > 8 basalt and volcanic rocks (yes/no) gwava-s_vrox > 9 drainage ditch (km2) gwava-s_ddit > 10 slope (percent x 1000) gwava-s_slop > 11 glacial till (yes/no) gwava-s_gtil > 12 clay sediment (percent x 1000) gwava-s_clay > >Attenuation Factors > 13 fresh surface water withdrawal gwava-s_swus > for irrigation (megaliters/day) > 14 irrigation tailwater recovery (km2) gwava-s_twre > 15 histosol soil type (percent) gwava-s_hist > 16 wetlands (percent) gwava-s_wetl "Farm fertilizer" is the average annual nitrogen input from commercial fertilizer applied to agricultural lands, 1992-2001, in kilograms per hectare. "Confined manure" is the average annual nitrogen input from confined animal manure, 1992 and 1997, in kilograms per hectare. "Orchards/vineyards" is the percent of orchards/vineyards land cover classification. "Population density" is 1990 block group population density, in people per square kilometer. "Cropland/pasture/fallow" is the percent of cropland/pasture/fallow land cover classifications. "Water input" is the ratio of the total area of irrigated land to precipitation, in square kilometers per centimeter. "Carbonate rocks" is the presence or absence of Valley and Ridge carbonate rocks. "Basalt and volcanic rocks" is the presence or absence of basalt and volcanic rocks. "Drainage ditch" is the area of National Resources Inventory surface drainage, field ditch conservation practice, in square kilometers. "Slope" is the soil surface slope, in percent times 1000. "Glacial till" is the presence or absence of poorly sorted glacial till east of the Rocky Mountains. "Clay sediment" is the amount of clay sediment in the soil, in percent times 1000. "Fresh surface water withdrawal for irrigation" is the amount of fresh surface water withdrawal for irrigation, in megaliters per day. "Irrigation tailwater recovery" is the area of National Resources Inventory irrigation system, tailwater recovery conservation practice, in square kilometers. "Histosol soil type" is the amount of histosols soil taxonomic order, in percent. "Wetlands" is the percent of woody wetlands and emergent herbaceous wetlands land cover classifications. Reference cited: Nolan, B.T. and Hitt, K.J., 2006, Vulnerability of shallow ground water and drinking-water wells to nitrate in the United States: Environmental Science and Technology, vol. 40, no. 24, pages 7834-7840.

This data set represents "water input," the ratio of the total area of irrigated land to precipitation, in square kilometers per centimeter, in the conterminous United States. The data set was used as an input data layer for a national model to predict nitrate concentration in shallow ground water. Nolan and Hitt (2006) developed two national models to predict contamination of ground water by nonpoint sources of nitrate. The nonlinear approach to national-scale Ground-WAter Vulnerability Assessment (GWAVA) uses components representing nitrogen (N) sources, transport, and attenuation. One model (GWAVA-S) predicts nitrate contamination of shallow (typically less than 5 meters deep), recently recharged ground water, which may or may not be used for drinking. The other (GWAVA-DW) predicts ambient nitrate concentration in deeper supplies used for drinking. This data set is one of 17 data sets (1 output data set and 16 input data sets) associated with the GWAVA-S model. Full details of the model development are in Nolan and Hitt (2006). For inputs to the model, spatial attributes representing 16 nitrogen loading and transport and attenuation factors were compiled as raster data sets (1-km by 1-km grid cell size) for the conterminous United States (see table 1). >Table 1.-- Parameters of nonlinear regression model for nitrate in shallow > ground water (GWAVA-S) and corresponding input spatial data sets. > [kg, kilograms; km2, square kilometers.] > >Nitrogen Source Factors Data Set Name > 1 farm fertilizer (kg/hectare) gwava-s_ffer > 2 confined manure (kg/hectare) gwava-s_conf > 3 orchards/vineyards (percent) gwava-s_orvi > 4 population density (people/km2) gwava-s_popd > 5 cropland/pasture/fallow (percent) gwava-s_crpa > >Transport to Aquifer Factors > 6 water input (km2/cm) gwava-s_wtin > 7 carbonate rocks (yes/no) gwava-s_crox > 8 basalt and volcanic rocks (yes/no) gwava-s_vrox > 9 drainage ditch (km2) gwava-s_ddit > 10 slope (percent x 1000) gwava-s_slop > 11 glacial till (yes/no) gwava-s_gtil > 12 clay sediment (percent x 1000) gwava-s_clay > >Attenuation Factors > 13 fresh surface water withdrawal gwava-s_swus > for irrigation (megaliters/day) > 14 irrigation tailwater recovery (km2) gwava-s_twre > 15 histosol soil type (percent) gwava-s_hist > 16 wetlands (percent) gwava-s_wetl "Farm fertilizer" is the average annual nitrogen input from commercial fertilizer applied to agricultural lands, 1992-2001, in kilograms per hectare. "Confined manure" is the average annual nitrogen input from confined animal manure, 1992 and 1997, in kilograms per hectare. "Orchards/vineyards" is the percent of orchards/vineyards land cover classification. "Population density" is 1990 block group population density, in people per square kilometer. "Cropland/pasture/fallow" is the percent of cropland/pasture/fallow land cover classifications. "Water input" is the ratio of the total area of irrigated land to precipitation, in square kilometers per centimeter. "Carbonate rocks" is the presence or absence of Valley and Ridge carbonate rocks. "Basalt and volcanic rocks" is the presence or absence of basalt and volcanic rocks. "Drainage ditch" is the area of National Resources Inventory surface drainage, field ditch conservation practice, in square kilometers. "Slope" is the soil surface slope, in percent times 1000. "Glacial till" is the presence or absence of poorly sorted glacial till east of the Rocky Mountains. "Clay sediment" is the amount of clay sediment in the soil, in percent times 1000. "Fresh surface water withdrawal for irrigation" is the amount of fresh surface water withdrawal for irrigation, in megaliters per day. "Irrigation tailwater recovery" is the area of National Resources Inventory irrigation system, tailwater recovery conservation practice, in square kilometers. "Histosol soil type" is the amount of histosols soil taxonomic order, in percent. "Wetlands" is the percent of woody wetlands and emergent herbaceous wetlands land cover classifications. Reference cited: Nolan, B.T. and Hitt, K.J., 2006, Vulnerability of shallow ground water and drinking-water wells to nitrate in the United States: Environmental Science and Technology, vol. 40, no. 24, pages 7834-7840.

Coastal Louisiana wetlands are one of the most critically threatened environments in the United States. These wetlands are in peril because Louisiana currently experiences greater coastal wetland loss than all other States in the contiguous United States combined. The datasets presented here were utilized in a larger effort to quantify landscape changes from 1932 to 2016. Analyses show that coastal Louisiana has experienced a net change in land area of approximately -4,833 square kilometers (modeled estimate: -5,197 +/- 443 square kilometers) from 1932 to 2016. This net change in land area amounts to a decrease of approximately 25 percent of the 1932 land area. Previous studies have presented linear rates of change over multidecadal time periods which unintentionally suggest that wetland change occurs at a constant rate, although in many cases, wetland change rates vary with time. A penalized regression spline technique was used to determine the model that best fit the data, rather than fitting the data with linear trends. Trend analyses from model fits indicate that coastwide rates of wetland change have varied from -83.5 +/- 11.8 square kilometers per year to -28.01 +/- 16.37 square kilometers per year. To put these numbers into perspective, this equates to long-term average loss rates of approximately an American football field’s worth of coastal wetlands within 34 minutes when losses are rapid to within 100 minutes at more recent, slower rates. Of note is the slowing of the rate of wetland change since its peak in the mid- 1970s. Not only have rates of wetland loss been decreasing since that time, a further rate reduction has been observed since 2010. Possible reasons for this reduction include recovery from lows affected by the hurricanes of 2005 and 2008, the lack of major storms in the past 8 years, a possible slowing of subsidence rates, the reduction in and relocation of oil and gas extraction and infrastructure since the peak of such activities in the late 1960s, and restoration activities. In addition, many wetlands in more exposed positions in the landscape have already been lost. Most notable of the factors listed above is the lack of major storms over the past 8 years. The observed coastwide net “stability” in land area observed over the past 6–8 years does not imply that loss has ceased. Future disturbance events such as a major hurricane impact could change the trajectory of the loss rates. Sea-level rise is projected to accelerate over time which might also inflate the rate of wetland loss above current or recent trends and conditions.