NWCA 2011 Soil Chemistry Data. This dataset is associated with the following publication: Nahlik, A., and M.S. Fennessy. Carbon storage in US wetlands. Nature Communications. Nature Publishing Group, London, UK, 7: 1-9, (2016).

NWCA 2011 Soil Chemistry Data. This dataset is associated with the following publication: Nahlik, A., and M.S. Fennessy. Carbon storage in US wetlands. Nature Communications. Nature Publishing Group, London, UK, 7: 1-9, (2016).

These data were compiled to improve our understanding of how water, carbon (C), nitrogen (N), and phosphorus (P) interact to regulate below ground carbon cycling. Objective(s) of our study were to evaluate how soil heterotrophic carbon cycling responded to inputs of water, C, N, and P individually and interactively on the Colorado Plateau. These data represent soil microbial and CO2 respiration responses to amendments of carbon, nitrogen, phosphorous, and water. Soils were collected at a study site located in Arches National Park in southeastern Utah on 14 August 2017 and again on 17 July 2018 from the upper 10 cm of the soil profile in open spaces among plant canopies after the biological soil crust layer (< 1 cm depth) was removed. These data were processed by the U.S. Geological Survey (USGS) and Utah State University in a lab at the USGS-Southwest Biological Science Center, Moab, Utah. These data can be used to assess some carbon pools, fluxes, and responses to resource additions for a dryland ecosystem.

This dataset provides estimates of carbon pools, fluxes, and associated uncertainties across the contiguous USA (CONUS) at 0.5-degree resolution for all terrestrial land cover types. Carbon pools include labile carbon, foliar carbon, fine root, woody carbon, litter carbon, and soil organic carbon. Carbon fluxes include gross primary production (GPP), net primary production (NPP), net biome exchange, autotrophic respiration, and heterotrophic respiration. The modeled estimates are provided as monthly averages over the 16-year period, 2001 through 2016. The data were derived from the CARbon DAta MOdel fraMework (CARDAMOM) that included climate data, and above and below ground biomass maps of CONUS for the years 2005, 2010, 2015 and 2016 as input data sources to this model-data fusion framework. The input data were integrated into the CARDAMOM model to constrain on the terrestrial carbon and to specifically attribute changes of forest carbon stocks and spatial distributions of carbon emissions and removals across forested lands. United States Forest Service's Forest Inventory and Analysis (FIA) plot data were used to train models for the prediction of forest above-ground biomass (AGB).

These data quantify components of the carbon cycle within Big Cypress National Preserve in Florida. Continuous data time-series include net ecosystem exchange of carbon dioxide, methanogenesis, and soil bulk density.

This data set provides a bottom-up CO2 emissions inventory for the mid-continent region of the United States for the year 2007. The study was undertaken as part of the North American Carbon Program (NACP) Mid-Continent Intensive (MCI) campaign. Emissions for the MCI region were compiled from these resources into nine inventory sources (Table 1):(1) forest biomass and soil carbon, harvested woody products carbon, and agricultural soil carbon from the U.S. Greenhouse Gas (GHG) Inventory (EPA, 2010; Heath et al., 2011);(2) high resolution data on fossil and biofuel CO2 emissions from Vulcan (Gurney et al,. 2009); (3) CO2 uptake by agricultural crops, lateral transport in crop biomass harvest, and livestock CO2 emissions using USDA statistics (West et al., 2011); (4) agricultural residue burning (McCarty et al., 2011);(5) CO2 emissions from landfills (EPA, 2012);(6) and CO2 losses from human respiration using U.S. Census data (West et al., 2009). The CO2 inventory in the MCI region was dominated by fossil fuel combustion, carbon uptake during crop production, carbon export in biomass (commodities) from the region, and to a lesser extent, carbon sinks in forest growth and incorporation of carbon into timber products.

These datasets represent a revised national scale estimate of wetland soil carbon stock assessments by improving representation of soil organic carbon densities. This assessment is based on a three-step approach to harmonize survey and point-based data for predicting soil organic carbon density from percent organic carbon alone (or percent organic matter, with conversion), when reliable dry bulk density information is not available. Given issues with survey-level extrapolation of soil pedons into discontinuous hydric soils, quantile, segmented data analysis provides a more accurate spatially explicit soil organic carbon density product. These modeled data leverage spatial and statistical distributions of soil organic carbon percent data of the conterminous United States (CONUS) for two national-scale soil datasets: a wetland-specific field campaign, the EPA National Wetland Condition Assessment, and the USDA NRCS SSURGO survey. See https://doi.org/10.3389/fsoil.2021.706701 for details.

These datasets represent a revised national scale estimate of wetland soil carbon stock assessments by improving representation of soil organic carbon densities. This assessment is based on a three-step approach to harmonize survey and point-based data for predicting soil organic carbon density from percent organic carbon alone (or percent organic matter, with conversion), when reliable dry bulk density information is not available. Given issues with survey-level extrapolation of soil pedons into discontinuous hydric soils, quantile, segmented data analysis provides a more accurate spatially explicit soil organic carbon density product. These modeled data leverage spatial and statistical distributions of soil organic carbon percent data of the conterminous United States (CONUS) for two national-scale soil datasets: a wetland-specific field campaign, the EPA National Wetland Condition Assessment, and the USDA NRCS SSURGO survey. See https://doi.org/10.3389/fsoil.2021.706701 for details.

Pollen data from sediment core DS-77 were generated in support of research on long-term patterns of vegetation, fire, and climate in Great Dismal Swamp National Wildlife Refuge (Willard et al., in review: IP-143520). Raw counts of pollen data are provided. These data represent new counts from a core collected by Donald R. Whitehead in 1961 and previously presented in Whitehead, 1972.

Pollen and geochronological data from sediment core DS-49 were generated in support of research on long-term patterns of vegetation, fire, and climate in Great Dismal Swamp National Wildlife Refuge (Willard et al., in review: IP-143520). Raw counts of pollen data are provided. These data represent new counts from a core collected by Donald R. Whitehead in 1961 and previously presented in Whitehead, 1972. Calibrated and uncalibrated radiocarbon dates were used to develop age models for analyses by Willard et al, in review).