The U.S. Geological Survey (USGS) Integrated Water Availability Assessments (IWAAs) Program is designed to deliver nationally consistent assessments of water supplies for human and ecological needs, and to identify factors that influence water availability. In support of these studies, a National-Extent Hydrogeologic Framework (NEHF) is under development. The NEHF is a three-dimensional digital representation of the subsurface of the United States. Three depth zones are of particular interest: a shallow zone within which groundwater interacts with streams (meters to tens of meters); an intermediate zone comprised of potable water (tens to hundreds of meters); and deep, saline groundwater (hundreds of meters to kilometers). Laterally, the NEHF will be developed at a 1-kilometer (km) resolution across the continental United States (CONUS). Vertically, the NEHF will extend from the land surface to a depth of several kilometers. The vertical resolution of the NEHF will vary, with relatively fine resolution at shallow depth and relatively coarse resolution at depth. Soils are a part of the shallow groundwater system, and soil properties can be used to develop predictive models for characteristics of the deeper subsurface. The NEHF is utilizing the Polaris soil properties data set (Chaney et al, 2019) because it harmonizes the previously published Soil Survey Geographic Database (SSURGO, USDA NRCS, 2023) and the National Cooperative Soil Survey Soil Characterization (USDA, 2023) databases. The Polaris database includes soil properties such as soil texture, which is the percentage of sand, silt, or clay present in a soil as well as saturated hydraulic conductivity (Ksat), which indicates the ease with which water can move through the soil. Soil hydraulic conductivity can vary spatially, and representative values of a heterogeneous distribution can be obtained in one of several ways, including the geometric mean. The geometric mean provides an estimate of the mean value of a log-normal distribution; soil hydraulic conductivity is often log-normally distributed. Raster data are provided at a 30-meter (m) resolution across the contiguous United States for six depth zones: 0-5 centimeters (cm), 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm. The rasters in this Data Release provide a weighted average value over the six depth intervals rescaled to a resolution of 1-km and 100-m. These rasters can be used in the development of the NEHF and for other purposes. A total of 11 rasters are included in the data release. They include the following: Soil Texture (SoilTextureRasters_100m.7z; SoilTextureRasters_1km.7z): Values range from 1 - 99% (values may not add up to 100% as they represent a weighted mean, as well as a change in resolution from the source files). The specific ranges for each property can be found in the metadata.xml files for each raster. Mean Percent Sand at 1-km and 100-m resolution (2 rasters: sand_1km.tif and sand_100m.tif). Mean Percent Clay at 1-km and 100-m resolution (2 rasters: clay_1km.tif and clay_100m.tif) Mean Percent Silt at 1-km and 100-m resolution (2 rasters: silt_1km.tif and silt_100m.tif) Classified Soil Texture at 1km (1 raster: texture_3class.tif): The above three soil texture rasters were classified into three categories based on the percentages of each soil property within a 1-km cell. Saturated Hydraulic Conductivity (SaturatedHydraulicConductivity_100m.7z; SaturatedHydraulicConductivity_1km.7z): Values range from -2.4 to 2.1 in the logarithmic scale, and 0-126.3 for the arithmetic mean. The specific ranges for each property can be found in the metadata.xml files for each raster. Logarithmic Saturated Hydraulic Soil Conductivity (KSat) at 1-km and 100-m resolution (2 rasters: KSat_Log_100m.tif; KSat_Log_100m.tif): KSat rasters in the Polaris Soils database were provided as logarithmic values. Arithmetic Saturated Hydraulic Soil Conductivity (KSat) at 1-km and 100-m resolution (2 rasters: KSat_Arithmetic_100m.tif; KSat_Arithmetic_100m.tif): The logarithmic values were transformed into the arithmetic values to determine a geometric mean value.

This App provides 30 m resolution map of soil textures based on USDA soil classification for contiguous United States. US soil texture classifications (defined by the USDA) derived from 30-m POLARIS Soil dataset Over the Contiguous United States.

The app is available at: https://water-delineation.users.earthengine.app/view/soil-texture-for-contiguous-united-states

and the home page url: https://water-delineation.users.earthengine.app/

Per- and polyfluoroalkyl substances (PFAS) chemicals are known to strongly sorb onto soils when being transported downward through the vadose zone. The degree to which this sorption occurs depends on the length of the specific PFAS molecular chain and the properties of the soil. The properties with greatest influence on the soils PFAS sorption potential are percent silt and clay, and organic matter content (Fabregat-Palau and others, 2021), which have small size fractions that provide more sorption sites. In addition to sorption, the estimated long-term mean-annual vertical transport velocity of any chemical in a soil zone can be calculated given the recharge rate and volumetric water content. The latter can be calculated given the recharge rate, percent clay, and saturated hydraulic conductivity (Clapp and Hornberger, 1978). Also, the retardation factor can be calculated if the bulk density and water content are known. Given these requirements, raster data of these soil properties, in addition to several others, were downloaded from the Polaris Soils database made available in 2019, and used in preliminary analyses to assess the vulnerability of shallow groundwater to perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) contamination at a national scale. The POLARIS data that were chosen in this study were percent silt, percent clay, percent sand, percent organic matter, saturated water content, saturated hydraulic conductivity, and bulk density. Rasters of these soil properties for each of the six depth layers included in the database were created for the contiguous United States (see compressed files percentclay.7z, percentsilt.7z, percentsand.7z, bulkdensity_bd.7z, saturatedhydraulicconductivity_ksat.7z, soilwatercontent_theta_s.7z, and soilorganicmatter_om.7z in Child Item section). The resulting rasters were used in analyses to create rasters of PFOS and PFOA sorption distribution coefficients (Kd values) as well as a classified soil raster based on the classic ternary diagram of the U.S. Department of Agriculture (Davis and Bennett, 1927). A 250-m resolution was chosen to be coincident with the 1-kilometer resolution grid of the USGS national hydrogeologic framework (Brassington and Younger, 2010). The POLARIS data are well represented at this, and even finer, resolutions (Chaney and others 2016). Future analyses to be conducted include combining these files with other existing rasters of mean-annual recharge and depth to the water table (Zell and Sanford, 2020) to develop a raster representing the vulnerability of shallow groundwater to PFOA and PFOS contamination for the contiguous United States.

Per- and polyfluoroalkyl substances (PFAS) chemicals are known to strongly sorb onto soils when being transported downward through the vadose zone. The degree to which this sorption occurs depends on the length of the specific PFAS molecular chain and the properties of the soil. The properties with greatest influence on the soils PFAS sorption potential are percent silt and clay, and organic matter content (Fabregat-Palau and others, 2021), which have small size fractions that provide more sorption sites. In addition to sorption, the estimated long-term mean-annual vertical transport velocity of any chemical in a soil zone can be calculated given the recharge rate and volumetric water content. The latter can be calculated given the recharge rate, percent clay, and saturated hydraulic conductivity (Clapp and Hornberger, 1978). Also, the retardation factor can be calculated if the bulk density and water content are known. Given these requirements, raster data of these soil properties, in addition to several others, were downloaded from the Polaris Soils database made available in 2019, and used in preliminary analyses to assess the vulnerability of shallow groundwater to perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) contamination at a national scale. The POLARIS data that were chosen in this study were percent silt, percent clay, percent sand, percent organic matter, saturated water content, saturated hydraulic conductivity, and bulk density. Rasters of these soil properties for each of the six depth layers included in the database were created for the contiguous United States (see compressed files percentclay.7z, percentsilt.7z, percentsand.7z, bulkdensity_bd.7z, saturatedhydraulicconductivity_ksat.7z, soilwatercontent_theta_s.7z, and soilorganicmatter_om.7z in Child Item section). The resulting rasters were used in analyses to create rasters of PFOS and PFOA sorption distribution coefficients (Kd values) as well as a classified soil raster based on the classic ternary diagram of the U.S. Department of Agriculture (Davis and Bennett, 1927). A 250-m resolution was chosen to be coincident with the 1-kilometer resolution grid of the USGS national hydrogeologic framework (Brassington and Younger, 2010). The POLARIS data are well represented at this, and even finer, resolutions (Chaney and others 2016). Future analyses to be conducted include combining these files with other existing rasters of mean-annual recharge and depth to the water table (Zell and Sanford, 2020) to develop a raster representing the vulnerability of shallow groundwater to PFOA and PFOS contamination for the contiguous United States.

This dataset includes aboveground biomass (AGB) growth trajectories for the first 300 years of forest succession over the Regional Greenhouse Gas Initiative (RGGI) domain, which includes the states of Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont. These data were derived from a process-based ecosystem model called the Ecosystem Demography (ED) model (Hurtt et al 1998; Moorcroft et al. 2001; Ma et al. 2022a). Here, ED was run at a spatial resolution of 1 km with forcings including meteorology from Daymet (Thornton et al 2016) and MERRA2 (Gelaro et al. 2017) and soil hydraulic properties from POLARIS (Chaney et al 2016) and CONUS-SOIL (Miller and White 1998). This dataset is spatially interpolated from its native resolution of 1 km to 30 m to support small scale data analysis. The unit is kg C/m2.

This dataset can support multiple applications relevant to reforestation and afforestation planning. Utilizing this stack of annualized and spatially explicit forest growth trajectories, data users can estimate how much carbon could be stored via natural regeneration in any particular geographic location by any point over the next 300 years under current environmental conditions (air temperature, precipitation, CO2, etc). These data are currently being utilized by the State of Maryland to support climate-smart afforestation and serve as the basis for several carbon sequestration calculations in the University of Maryland Peer-Reviewed Offset Protocol for Maryland Reforestation/Afforestation Projects.

This dataset is the underlying input to a high-resolution forest carbon modeling system developed for the RGGI region. This modeling system combines modeled AGB growth with forest canopy height from airborne lidar data and tree cover fraction to estimate contemporary AGB, carbon sequestration potential, carbon sequestration potential gap and time to reach carbon sequestration potential. More details about the modeling system and ED simulation can be found Ma et al. 2021 and related data products can be found in Ma et al. 2022b.

For questions and support please contact lma6@umd.edu, rachlamb@umd.edu and gchurtt@umd.edu.

References

Chaney N W, Wood E F, McBratney A B, Hempel J W, Nauman T W, Brungard C W and Odgers N P 2016 POLARIS: a 30-meter probabilistic soil series map of the contiguous United States Geoderma 274 54–67. https://doi.org/10.1016/j.geoderma.2016.03.025

Gelaro R et al 2017 The modern-era retrospective analysis for research and applications, version 2 (MERRA-2) J. Clim. 30 5419–54. https://doi.org/10.1175/JCLI-D-16-0758.1 Hurtt, G.C., P.R. Moorcroft, S.W. Pacala, and S.A. Levin. 1998 Terrestrial models and global change: challenges for the future. Global Change Biology 4:581-590. https://doi.org/10.1046/j.1365-2486.1998.t01-1-00203.x

Ma, L., G. Hurtt, H. Tang, R. Lamb, E. Campbell, R. Dubayah, M. Guy, W. Huang, A. Lister, J. Lu, J. O’Neil-Dunne, A. Rudee, Q. Shen, and C. Silva. 2021. High-resolution forest carbon modelling for climate mitigation planning over the RGGI region, USA. Environmental Research Letters 16:045014. https://doi.org/10.1088/1748-9326/abe4f4

Ma, L., G. Hurtt, L. Ott, R. Sahajpal, J. Fisk, R. Lamb, H. Tang, S. Flanagan, L. Chini, A. Chatterjee, and J. Sullivan. 2022a. Global evaluation of the Ecosystem Demography model (ED v3.0). Geoscientific Model Development 15:1971–1994. https://doi.org/10.5194/gmd-15-1971-2022

Ma, L., G.C. Hurtt, H. Tang, R. Lamb, E. Campbell, R.O. Dubayah, M. Guy, W. Huang, J. Lu, A. Rudee, Q. Shen, C.E. Silva, and A.J. Lister. 2022b. Forest Aboveground Biomass and Carbon Sequestration Potential, Northeastern USA. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1922

Miller D A and White R A 1998 A conterminous United States multilayer soil characteristics dataset for regional climate and hydrology modeling Earth Interact. 2 1–26

Moorcroft, P. R., G.C. Hurtt. and S.W. Pacala, 2001 A method for scaling vegetation dynamics: the ecosystem demography model (ED) Ecol. Monogr. 71 557–86. https://doi.org/10.1890/0012-9615(2001)071[0557:AMFSVD]2.0.CO;2

Thornton, M.M., Thornton P E, Wei Y, Mayer B W, Cook R B and Vose R S 2016 Daymet: monthly climate summaries on a 1-km grid for North America, version 3 (available at: https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1345)