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.

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.

The original citation by Chaney et al. (2016) included a link to gridded soil datasets for the contiguous USA. That link, http://stream.princeton.edu/POLARIS, no longer works. In 2019 Chaney et al. published a new paper on the POLARIS dataset and put out a new data link, http://hydrology.cee.duke.edu/POLARIS/. This new data link does not include the variables awc (Available Water Capacity), CACO3 (calcium carbonate), or resdt (depth to restrictive layer). Studies have used these now unavailable layers. For continuity I have included these layers for the uppermost soil layer (0-5 cm). The data are available as geotiff files at 250-mter resolution for the contiguous 48 states of the United States of America.

Retrieving soil raster data from POLARIS using the XPolaris R-package.

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.

No description is available. Visit https://dataone.org/datasets/doi%3A10.18739%2FA2Q23R08G for complete metadata about this dataset.

No description is available. Visit https://dataone.org/datasets/doi%3A10.18739%2FA2Q23R08G for complete metadata about this dataset.

The Polaris Project II seeks to amplify the impact of Polaris I (now in its third and final year) through its extension, expansion, and enhancement. The three overarching objectives of Polaris II are to 1) train the next generation of arctic researchers, 2) advance scientific understanding of the Arctic, and 3) expand public awareness of the feedbacks between the Arctic and the global climate system. These objectives will be accomplished through a multi-faceted effort that includes a summer field course/research experience in the Siberian Arctic, a series of on-campus arctic-focused courses, and a wide range of outreach activities. While undergraduate students remain the primary focus of Polaris II, participation in the annual field course will be expanded to include a K-12 teacher, graduate student, postdoctoral researcher, and visiting faculty member each year. Outreach activities will target K-12 students and teachers, undergraduate students and faculty, and a diverse public audience. The unifying scientific theme of the Polaris Project is the transport and transformation of carbon and nutrients as they move with water from terrestrial uplands to the Arctic Ocean. Research conducted by the interdisciplinary Polaris Project team of faculty and students will make fundamental contributions to the scientific understanding of this topic, a central issue in arctic system science. While continued scientific advances are essential for arctic system understanding, prediction, and protection, tackling the climate change challenge is also a matter of education. Polaris II offers a unique experience in undergraduate research that will inspire and prepare a new generation of arctic researchers. Further, it will convey the importance of the Arctic to the public and to policy-makers, providing them with the knowledge they need to make informed decisions. The Polaris Project will achieve a broad and lasting impact by linking interdisciplinary scientific research to innovative undergraduate education and imaginative public outreach. In addition to providing a transformative experience for the participants in the annual Siberian field course, Polaris II will educate large numbers of undergraduate students who complete the Polaris-affiliated on-campus courses. The project will also engage K-12 students and teachers through direct and sustained interactions with Polaris PIs and broad dissemination of education and outreach materials. Finally, Polaris II will inform a diverse public audience about the state of the Arctic, ecosystems research, and global climate change. Approaches to project outreach include expansion of the Polaris website and associated blog (www.thepolarisproject.org) and the development of an online seminar series for undergraduates, K-12 teachers, and public participants. The production and wide distribution of multimedia videos addressing key arctic science themes will further expand the reach of the project, as will the inclusion of a writer in the 2011 field course with the objective of publishing a book about the Arctic, climate change, and the Polaris Project experience. As a resource for the scientific and education communities, Polaris data will be freely available through the project website and the Advanced Cooperative Arctic Data and Information Service (ACADIS). Please cite this dataset as: Kathryn Heard, Susan Natali, Andrew Bunn, Heather D. Alexander (2015). Northeast Siberia Plant and Soil Data: Plant Composition and Cover, Plant and Soil Carbon Pools, and Thaw Depth. UCAR/NCAR - CISL - ACADIS, Dataset. http://dx.doi.org/doi:10.5065/D6NG4NP0

The objectives for these data are to characterize seasonal and spatial patterns of drought propagation across a set of seven watersheds in the western United States. These watersheds vary in aridity, contain extensive elevation gradients, and support river systems with long-term gage data. We used a soil water balance model SOILWAT2 with soil physical structure from POLARIS and daily weather data from gridMET as well as DayMet as the inputs. Using gridMET inputs, we simulated daily timestep soil moisture and water balance for dozens to hundreds of 1/24th-degree pixels within each watershed. Using DayMet inputs, we simulated daily timestep soil moisture and water balance for hundreds of 1-km pixels within one watershed. We provide daily potential evapotranspiration, daily transpiration, daily total evaporation, daily diffuse recharge and runoff, daily available soil moisture at different soil depths, and long-term daily frozen soil conditions for each pixel.

This project is integrating scientific research in the Arctic with education and outreach, with a strong central focus on engaging undergraduate students and visiting faculty from groups that have had little involvement in Arctic science to date. The central element of the project is a month-long research expedition to the Yukon River Delta in Alaska. The expedition provides a deep intellectual and cultural immersion in the context of an authentic research experience that is paramount for "hooking" students and keeping them moving along the pipeline to careers as Arctic scientists. The overarching scientific issue that drives the research is the vulnerability and fate of ancient carbon stored in Arctic permafrost (permanently frozen ground). Widespread permafrost thaw is expected to occur this century, but large uncertainties remain in estimating the timing, magnitude, and form of carbon that will be released when thawed. Project participants are working in collaborative research groups to make fundamental scientific discoveries related to the vulnerability of permafrost carbon in the Yukon River Delta and the potential implications of permafrost thaw in this region for the global climate system. This data set contains pressure, Photosynthetically Active Radiation (PAR), air temperature, wind direction, wind speed, wind gust speed, rain, relative humidity, soil moisture at 15 centimeter (cm) depth, and two measurements of soil temperature at 15 cm depth from the 2018 and 2019 expeditions.

Climate change induced alterations to winter conditions may affect decomposer organisms controlling the vast carbon stores in northern soils. Soil microarthropods are abundant decomposers in Arctic ecosystems affecting soil carbon release through their activities. We studied whether increased snow depth affected microarthropods, and if effects were consistent over two consecutive winters. We sampled Collembola and soil mites from a snow accumulation experiment at Svalbard in early summer and used soil microclimatic data to explore to which aspects of winter climate change microarthropods are most sensitive. Community densities differed substantially between years and increased snow depth in winter had inconsistent effects. Increased snow depth hardly affected microarthropods in 2015, but decreased overall abundance and altered relative abundances of microarthropod groups and Collembola species after a milder winter in 2016. Although our increased snow depth treatment enhanced soil temperatures by 3.2 ⁰C in the snow cover periods, the only good predictors of microarthropod density changes were soil conditions around snowmelt. Our study underpins that extrapolation of observations of decomposer responses to altered winter climate conditions to future scenarios should be avoided when communities are only sampled on a single occasion, since effects of longer-term gradual changes in winter climate may be obscured by inter-annual weather variability. Methods The published dataset encompasses:

Soil invertebrate community (density, ind. m-2) in 'Snoeco_microarhropods_Dryad.csv'

Microarthropods were identified to ‘group level’, ‘Collembola’, ‘Oribatid mites’, ‘Predatory mites’ (Prostigmata and Mesostigmata) and ‘Mite juveniles/other’ (nymphal Oribatids and Mesostigmata and nymphal Prostigmata) and counted. Collembola were identified to species or genus level. Microarthropods were sampled as described: Three cores were taken (ø 4.5 cm, 5-9 cm deep) from each fence/ambient plot from Salix polaris-dominated patches. In increased snow depth plots, approximately 10 m west of the snow fence, in the early summer of 2015 (15th of July) and 2016 (6th of July). Cores were taken so that the samples always contained the complete organic layer (on average approx. 5 cm thick (Semenchuk et al. 2019) in which most microarthropods can be found, as well as a part of the mineral soil (>1 cm), in which microarthropod densities are generally low or absent 63. In 2015, these cores were stored at 6⁰ C and transported to Abisko, Sweden for Tullgren extraction (12 bank Tullgren funnel, Burkard Scientific, Uxbridge, UK) within four days after sampling. In 2016, the cores were stored overnight at 6⁰ C and extracted in the same type of Tullgren extractor at UNIS in Longyearbyen the day after sampling. Both Tullgren extractions lasted for seven days to ensure the cores were completely dry.

Soil moisture (cm3 water per cm3 soil) in 'Snoeco_microarhropods_Dryad.csv'

Data has been collected according to description: Soil moisture at time of sampling (in both sampled years) was determined gravimetrically from each plot using three replicate (ø 4.5 cm, 5-9 cm deep) cores from Salix polaris-dominated patches that were also used for microarthropod extraction. These cores were weighed upon sampling, subsequently dried at ~ 35⁰C for seven days during microarthropod extraction, and finally dried in an oven at 70 ⁰ C for 48 hours. Water weight was assumed to correspond to volume, and soil volumes were obtained by measuring the sampled soil depth to determine soil moisture content (water volume/ volume).

CWM: Commuity weighted mean for Collembola body size in 'Snoeco_microarhropods_Dryad.csv'

Data has been obtained using the following calculations: Average body size/length per Collembola species was determined for 30 randomly chosen individuals per species (or as many individuals as available, a minimum 10 individuals) by measuring Collembola length from the head to tip of the abdomen by a calibrated microscope (Leica, 40x magnification). The body lengths obtained were used to calculate community weighted mean (CWM) body size of the community. We calculate the CWM for a whole community as:

where nj is the number of species sampled in community j, Ak,j is the relative abundance of species k in community j and FTk,j is the functional trait of interest of species k in community j.

Soil temperature (C) daily average in 'Snoeco_Soil_temp_Dryad.csv'

Soil temperature data as used for analyses in this manuscript. Soil temperature data was obtained as described: Each plot (F and C) had a temperature logger (Tinytag data loggers, model TGP‐402 (Gemini)) placed just below the soil surface (in the increased snow depth treatment where snow depth reaches ~150 cm), but data from these loggers was not available for all plots in both years. Analyses have been performed only for plots in which data was available for both treatments (control and fence (deep snow area)) and years (n=6) Presented data in file are average daily temperature means (inferred from hourly logged data) for those plots for which a full dataset was avaialble for both years. A complete description of methods and description of the experimental setup can be found in the published manuscript

This dataset provides 90 m estimates of forest aboveground biomass (Mg/ha) for nominal 2011 and projections of carbon sequestration potential for 11 states in the Regional Greenhouse Gas Initiative (RGGI) domain. The RGGI is a cooperative, market-based effort among States in the eastern United States. Estimated biomass and sequestration potential were computed using the Ecosystem Demography (ED) model. The ED Model integrates several key data including climate variables from Daymet and MERRA2 products; physical soil and hydraulic properties from Probabilistic Remapping of SSURGO (POLARIS) and CONUS-SOIL; land cover characteristics from airborne lidar, the National Agriculture Imagery Program (NAIP), and the National Land Cover Database (NLCD); and vegetation parameters from the Forest Inventory and Analysis (FIA) Program.

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)

We provide labeled hyperspectral reflectance datasets for Arctic vegetation. The datasets consist of hyperspectral reflectance spectra for dominant plant species and environment classes (e.g., soil and bareground) in Adventdalen, on the Spitsbergen Island of the Svalbard archipelago. These datasets were constructed by extracting pixel data from hyperspectral images captured using a terrestrial hyperspectral camera (Specim IQ, Specim Ltd., Oulu, Finland). The wavelength range of the data spans from visible to near-infrared (VNIR, 400 nm - 1000 nm), with a total of 204 spectral bands. The pixels associated with the classes were identified and labeled using visual inspection and field surveys. Ten classes were investigated, including Dryas octopetala (Leaf), Dryas octopetala (Flower), Eriophorum scheuchzeri (Leaf), Eriophorum scheuchzeri (Leaf), Cassiope tetragona (Flower), Cassiope tetragona (Dead), Equisetum arvense (Leaf), Salix polaris (Leaf), Soil (Mixed), and Bareground. The spectral library was developed using the average spectra of each class.

Teleseismic travel-time and hypocentral data detected at Syowa Station, East Antarctica. All the data are available from network library system (POLARIS) in NIPR.