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LBA-ECO CD-02 Carbon, Nitrogen, Oxygen Stable Isotopes in Organic Material, Brazil

This data set reports the measurement of stable carbon, nitrogen, and oxygen isotope ratios in organic material (plant, litter and soil samples) in forest canopy profiles and pasture (grasses and shrubs) as well as corresponding carbon and nitrogen tissue concentrations in a number of different sites across Brazil. The sampling design captured the temporal variation in rainfall over the course of several years. Carbon and nitrogen isotope ratios can act as a proxy for interpreting aspects of the carbon and nitrogen cycles in Amazonian rainforests. Data are in three comma-delimited ASCII files.

Stocks of Surface Soil Organic Carbon Fractions, Great Plains Region, USA, 2007-2010

This dataset provides estimates of total organic soil carbon (SOC), pyrogenic (PyC), particulate (POC), and other organic soil carbon (OOC) fractions in 473 surface layer soil samples collected from stratified-sampling locations in Colorado, Kansas, New Mexico, and Wyoming, USA. Terrain, climate, soil, fire, and land cover data used to predict and map SOC, PyC, POC, and OOC at 1 km resolution throughout the study region are also included. The estimates were derived using a best random forest regression model and cover the period 2007-05-01 to 2010-10-01.

Soil Organic Carbon Distributions in Tidal Wetlands of the Northeastern USA

This dataset provides estimates of soil organic carbon (SOC) in tidal wetlands for the northeastern United States. The data cover the period 1998-2018. Northeastern U.S. tidal wetlands and bordering areas were harmonized from government agencies [U.S. Department of Agriculture - Natural Resources Conservation Service (USDA-NRCS), National Cooperative Soil Survey (NCSS), USDA-NRCS - Rapid Carbon Assessment (RaCA), U.S. Environmental Protection Agency - National Wetland Condition and Assessment (EPA-NWCA)] and published studies. Point data for carbon stocks (in kg m-2) at four soil depths (0-5, 0-30, 0-100, and 0-200 cm) are included. SOC for the four depths was predicted for eight regional zones using regression models driven by environmental covariates. Two methods were used to estimate parameters for these models, a Random Forest (RF) Ranger method and a Quantile Regression Forest (QRF) model. The distribution of SOC was predicted for tidal wetland cover types mapped by Correll et al. (2019). Predictions and uncertainties are available at a 3 m resolution.

Soil Organic Carbon and Wetland Intrinsic Potential, Hoh River Watershed, WA, 2012-13

This dataset contains estimates of soil organic carbon stocks and wetland intrinsic potential (WIP) across the Hoh River Watershed in the Olympic Peninsula, WA, USA in 2012-2013. Estimates were derived from an equation based on wetland intrinsic potential and geology type (Stewart et al., 2023). Wetland intrinsic potential estimates the likelihood that that an area is a wetland using a random forest model built on vegetation, hydrology, and soil data (Halabisky et al., 2022). SOC estimates at 1 m and 30 cm, SOC standard deviations, and WIP are presented in Cloud-Optimized GeoTIFF (*.tif) format at 4-m resolution. Also included are 36 field observations of SOC collected from 2020-08-01 to 2022-06-29. These are contained in a comma separated (*.csv) file.

Soil Organic Carbon Estimates and Uncertainty at 1-m Depth across Mexico, 1999-2009

This dataset provides an estimate of soil organic carbon (SOC) in the top one meter of soil across Mexico at a 90-m resolution for the period 1999-2009. Carbon estimates (kg/m2) are based on a field data collection of 2852 soil profiles by the National Institute for Statistics and Geography (INEGI). The profile data were used for the development of a predictive model along with a set of environmental covariates that were harmonized in a regular grid of 90x90 m2 across all Mexican states. The base of reference was the digital elevation model (DEM) of the INEGI at 90-m spatial resolution. A model ensemble of regression trees with a recursive elimination of variables explained 54% of the total variability using a cross-validation technique of independent samples. The error associated with the predictive model estimates of SOC is provided. A summary of the total estimated SOC per state, statistical description of the modeled SOC data, and the number of pixels modeled for each state are also provided.

BOREAS TGB-10 Volatile Organic Carbon Data over the SSA

The BOREAS TGB-10 team collected several trace gas data sets in their efforts to determine the role of biogenic hydrocarbon emissions with respect to boreal forest carbon cycles. This data set contains measured VOC concentrations. These data were obtained at the SSA Old Jack Pine site from May to September 1994.

Tidal Wetlands Soil Organic Carbon and Estuarine Characteristics, USA, 1972-2015

This dataset provides a synthesis of soil organic carbon (SOC) estimates and a variety of other environmental information from tidal wetlands within estuaries in the conterminous United States for the period 1972-2015. The data were compiled from several existing data resources and include the following: soil organic carbon stock estimates, the proportion of the catchment area containing the wetlands that is barren, tidal wetland area, nontidal wetland land, open water, saltwater zone, mixed zone, agricultural, urban, forest, and wetland areas, land elevation, ocean salinity, sea surface temperature, ocean dissolved inorganic phosphorus, estuary latitude, longitude, depth, perimeter, salinity, and estuary volume, river flow, carbon, nitrogen, and phosphorus river flux, sediment organic carbon content, windspeed, mean temperature, daily and mean precipitation, frost days, and the population within each catchment. Estuaries were also classified to one of six typological categories. Coastal locations were determined by natural environmental and political divisions within the US. The data were used to investigate how tidal wetland soil organic carbon density is distributed across the continental US among various coastal locations, estuarine typologies, vegetation types, water regimes, and management regimes, and to identify whether SOC density is correlated with different environmental variables. The analytical results are not included with this dataset.

BOREAS TGB-05 Dissolved Organic Carbon Data from NSA Beaver Ponds

The BOREAS TGB-05 team collected several data sets related to carbon and trace gas fluxes and concentrations in the NSA. This data set contains concentrations of dissolved organic and inorganic carbon species from water samples collected at various NSA sites. In particular, this set covers the NSA Tower Beaver Pond Site and the NSA Gillam Road Beaver Pond Site, including data from all visits to open water sampling locations during the BOREAS field campaigns from April to September 1994.

FLEXPART organic carbon aerosol L4 global daily 1 x 1 degrees V1 (OCFLEXPART)

This is a global simulation of organic carbon (OC) aerosol concentrations and daily deposition (wet+dry) from the FLEX-ible PARTicle (FLEXPART) Lagrangian particle dispersion model version 10.4. The FLEXPART model code are open source and freely available.

LBA Regional Organic Soil Carbon and Nitrogen Data (Zinke et al.)

The data set contains a subset of a global organic soil carbon and nitrogen data set (Zinke et al. 1986). The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10 N to 25 S, 30 to 85 W). The point data are available in three formats: a comma-delimited ASCII file (*.csv), an ESRI shapefile, and an ESRI export file (*.e00).The data for the global data set (Zinke et al. 1986) were obtained from soil surveys conducted by Zinke in 1965-1984 and from soil survey literature. The main samples for laboratory analyses were collected at uniform soil increments and included bulk density determinations. Many samples reported in the literature did not have uniform soil increments or bulk density determinations. Only soil profiles that had been sampled either to a meter in depth or to actual depth were included in this database from soil survey literature. When carbon content was known but bulk densities were absent from soil samples reported in the literature, densities were estimated by regression analysis on the basis of the relationship between organic carbon content and measured bulk density in 1800 soil profiles for which bulk densities were known.Further information can be found at ftp://daac.ornl.gov/data/lba/carbon_dynamics/Zinke_soil/comp/zinke_readme.pdf.LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.

Soil Organic Carbon Estimates for 30-cm Depth, Mexico and Conterminous USA, 1991-2011

This dataset provides two sets of gridded estimates of estimated soil organic carbon (SOC) and associated uncertainties for 0-30 cm topsoil layer in kg SOC/m2 at 250-m resolution across Mexico and the conterminous USA (CONUS). The first set of gridded SOC estimates, for the period 1991-2010, were derived using multi-source SOC field data and multiple environmental variables representative of the soil forming environment coupled with a machine learning approach (i.e., simulated annealing) and regression tree ensemble modeling for optimized SOC prediction. Predictions of gridded SOC and uncertainty based on multiple bulk density (BD) pedotransfer functions (PFTs) are also included. The second set of gridded SOC estimates, for the period 2009-2011, were derived from two fully independent validation field datasets from across both countries. Note that the same environmental variables and modeling approach used for the first set of estimates were applied to the second set to assess the models' sensitivity to multiple SOC data sources. The SOC field data for the first set of estimates are provided in this dataset and the other data sources, including the two independent validation field datasets, are referenced.

SAFARI 2000 Organic Soil Carbon and Nitrogen Data (Zinke et al.)

The data set contains a subset of the Worldwide Organic Soil Carbon and Nitrogen (Zinke et al. 1986) data set for southern Africa. The data were obtained from soil surveys by Zinke and soil survey literature. The main samples for laboratory analyses were collected at uniform soil increments and included bulk density determinations. Many samples reported in the literature did not have uniform soil increments or bulk density determinations. Only soil profiles that had been sampled either to a meter in depth or to actual depth were included in this data base from soil survey literature. In literature where bulk densities were absent, densities were estimated by regressions based on organic carbon content of the soil samples associated with the profile using 1800 soil profiles for which bulk densities were known. More information can be found at: ftp://daac.ornl.gov/data/safari2k/soils/Zinke_soil/comp/zinke_readme.pdf.

opencc-by-4.0Mar 2020View details →

Iron and Organic Carbon Colloids in Boreal Rivers and their Fate at high Salinity: Meta data

Meta data to the article Iron and Organic Carbon Colloids in Boreal Rivers and their Fate at high Salinity in the Journal of Geophysical Research - Biogeosciences. Both raw data and processed data used for the figures are attched.

openAug 2023View details →

Organic carbon mineralization and its impact on metal behavior in sediments of the Yellow Sea and East China Sea

This dataset is created for manuscript entitle of Carbon mineralization and its impact on metal behavior in sediments of the Yellow Sea and East China Sea

opencc-by-4.0Nov 2023View details →

Long time-series (1980-2020) high-resolution (1km) and multi-depth soil organic carbon dataset in China

unit: kg C m-2 (soil oganic carbon density)0100: denote 0-100 cm020: denote 0-20 cmExample 1980: 1980-1984 (five years mean soc)

opencc-by-4.0Dec 2020View details →

Model output data of the paper: "Management induced changes of soil organic carbon on global croplands"

# Model output data of the paper: "Management induced changes of soil organic carbon on global croplands" This data was prodused using the the MadRat framework and the mrsoil R-library by the R-script SOCBudget.R, which is stored together with the data. mrsoil is based on the R-libraries mrcommons, mrmagpie and mrvalidation. ### REFERENCES Dietrich J, Baumstark L, Wirth S, Giannousakis A, Rodrigues R, Bodirsky B, Kreidenweis U, Klein D (2020). _madrat: May All Data be Reproducible and Transparent (MADRaT)_. doi: 10.5281/zenodo.1115490 (URL: https://doi.org/10.5281/zenodo.1115490), R package version 1.86.0, <URL: https://github.com/pik-piam/madrat>. rstens K, Dietrich J (2020). _mrsoil: MadRat Soil Organic Carbon Budget Library_. doi: 10.5281/zenodo.4317933 (URL: https://doi.org/10.5281/zenodo.4317933), R package version 1.1.0, <URL: https://github.com/pik-piam/mrsoil>. Bodirsky B, Karstens K, Baumstark L, Weindl I, Wang X, Mishra A, Wirth S, Stevanovic M, Steinmetz N, Kreidenweis U, Rodrigues R, Popov R, Humpenoeder F, Giannousakis A, Levesque A, Klein D, Araujo E, Beier F, Oeser J, Pehl M, Leip D, Molina Bacca E, Martinelli E, Schreyer F, Dietrich J (2020). _mrcommons: MadRat commons Input Data Library_. doi: 10.5281/zenodo.3822009 (URL: https://doi.org/10.5281/zenodo.3822009), R package version 0.11.10, <URL: https://github.com/pik-piam/mrcommons>. Karstens K, Dietrich J, Chen D, Windisch M, Alves M, Beier F, v. Jeetze P, Mishra A, Humpenoeder F (2020). mrmagpie: madrat based MAgPIE Input Data Library. doi: 10.5281/zenodo.4319612 (URL: https://doi.org/10.5281/zenodo.4319612), R package version 0.31.0, <URL: https://github.com/pik-piam/mrmagpie>. Bodirsky B, Wirth S, Karstens K, Humpenoeder F, Stevanovic M, Mishra A, Biewald A, Weindl I, Chen D, Molina Bacca E, Kreidenweis U, W. Yalew A, Humpenoeder F, Wang X, Dietrich J (2020). _mrvalidation: madrat data preparation for validation purposes_. doi: 10.5281/zenodo.4317826 (URL: https://doi.org/10.5281/zenodo.4317826), R package version 2.5.0, <URL: https://github.com/pik-piam/mrvalidation>. ## LICENSE This data is open-source: you can redistribute it and/or modify it under the terms of the **CC Attribution 4.0 International** as published by the Creative Commons Corporation at https://creativecommons.org/licenses/by/4.0/legalcode. ## CONTACT karstens@pik-potsdam.de

opencc-by-4.0Dec 2019View details →

Fig. 1 in Seasonal Carbon Emissions And Sequestration In Agroecosystems Of Organic Crops In Central Lithuania

Fig. 1. Soil respiration (Rs) in organic crops, 2014-2016 (mean±SE).

restrictedcc-by-sa-4.0Oct 2024View details →

Organic carbon content and estimated organic carbon stocks for Northern Irish Marine Sediments

Northern Ireland is a constituent region of the United Kingdom, consisting of 14,330 square kilometers of land in the north east of the island of Ireland. Northern Ireland's devolved administration has repsonsibility for 6000 square kilometers of shelf seas, encompassing the north western portion of the Irish Sea, the North Channel and part of the Malin Sea. There is growing interest in the capacity of marine sediments to sequester and store carbon, as a potential natural feedback against climate change. Between 2021 and 2024, the Northern Ireland Department of Agriculture, the Environment and Rural Affairs provided funding to AFBI NI to support the initial quantification and mapping of sediment carbon content in Northern Ireland's Marine Sediments. Here we present a consolidated dataset of sediment organic carbon content and estimates of areal seabed carbon stocks (for the upper 10cm of sediment) across the Northern Irish portion of the UK's territorial seas. The dataset includes sediment carbon data from recent oceanographic surveys, alongside historical datasets from AFBI's internal data archive. The data are supported by metadata including location (Latitude and Longitude), bathymetric depth, seabed type (percentage mud, sand and gravel), year of collection, sampling metier and information on the data source.