Find research datasets worth reusing

opencc-by-4.0Dec 2021View details →

Data to support the publication "Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe", https://doi.org/10.3390/land10121362

Soil organic carbon content and water content at the different pressure points, as measured by Ioanna Panagea for  the publication "Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe",  https://doi.org/10.3390/land10121362 from the the long term experiments  belonging in some of the SoilCare project partners.

opencc-by-4.0May 2022View details →

Seafloor organic carbon flux output from the NEMO-MEDUSA model

This output was produced by a simulation using a coupled ocean physics and marine biogeochemistry model. The physical ocean submodel was the Nucleus for European Modeling of the Ocean (NEMO) physical ocean model (Madec, 2014), run here in a global 1/12-degree resolution configuration (ORCA0083). The marine biogeochemistry submodel was the Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration and Acidification (MEDUSA-2), an intermediate-complexity plankton ecosystem model (Yool et al., 2013). The horizontal resolution of this configuration of NEMO has non-uniform grid cells ranging 2 to 9 km in size (mean 7.5 km), with 75 vertical depth levels (31 levels between the surface and 200 m depth). Sea-ice is represented in the model by the Louvian‐la‐Neuve Ice Model (LIM2) (Fichefet, & Maqueda, M. a. M., 1997; Goosse & Fichefet, 1999). The configuration was forced at the air-sea interface with version 5.2 of the DRAKKAR forcing set (DFS) (Brodeau et al., 2010). DFS 5.2 is based on ERA40 reanalysis data, comprising of 6‐hourly means for wind, humidity, and atmospheric temperature, daily means for radiative fluxes (both longwave and shortwave), and monthly means for precipitation. A monthly climatology was used for river runoff, taken from the CORE2 reanalysis (Brodeau et al., 2010; Timmermann et al., 2005). The resulting model hindcast was created using this forcing set for the period 1958–2015, with marine biogeochemistry initialised in 1990. This archive includes the flux of organic carbon reaching the seafloor and the area of the grid cells for the global domain. In MEDUSA, the seafloor flux is the sum of slow- and fast-sinking detrital particles that reach the base of the water column and enter the benthic submodel of MEDUSA. In general, away from shallow water regions (< 200 m), this flux is dominated by fast-sinking material produced by ecological processes associated with the large components of MEDUSA. The specific subset of output used was drawn from the decadal period 2006-2015, and was regridded from the non-uniform ORCA0083 grid to a regular 1/12-degree grid. Output processing was undertaken by A. Yool (axy@noc.ac.uk; National Oceanography Centre, Southampton UK). In addition to the netCDF files, text file dumps of their contents are included to assist with interpretation. References: Brodeau, L., Barnier, B., Treguier, A.‐M., Penduff, T., & Gulev, S. (2010). An ERA40‐based atmospheric forcing for global ocean circulation models. Ocean Modelling, 31, 88–104. Fichefet, T., & Maqueda, M. a. M. (1997). Sensitivity of a global sea ice model to the treatment of ice thermodynamics and dynamics. Journal of Geophysical Research, Oceans, 102, 12,609–12,646. Goosse, H., & Fichefet, T. (1999). Importance of ice‐ocean interactions for the global ocean circulation: A model study. Journal of Geophysical Research, Oceans, 104, 23,337–23,355. Kelly, S., Popova, E., Aksenov, Y., Marsh, R., & Yool, A. (2018). Lagrangian modeling of Arctic Ocean circulation pathways: Impact of advection on spread of pollutants. J. Geophys. Res. Oceans, 123, 2882‐2902, doi: 10.1002/2017JC013460. Madec, G. (2014). "NEMO Ocean engine" (draft edition r5171) "NEMO Ocean engine" (draft edition r5171). Note du Pôle de modélisation, Institut Pierre‐Simon Laplace (IPSL), France, 27, 1288–1619. Timmermann, R., Goosse, H., Madec, G., Fichefet, T., Ethe, C., & Dulière, V. (2005). On the representation of high latitude processes in the ORCA‐LIM global coupled sea ice–ocean model. Ocean Modelling, 8, 175–201. Yool, A., Popova, E.E. and Anderson, T.R. (2013).  MEDUSA-2.0: an intermediate complexity biogeochemical model of the marine carbon cycle for climate change and ocean acidification studies.  Geoscientific Model Development 6, 1767-1811, doi: 10.5194/gmd-6-1767-2013.

opencc-by-sa-4.0May 2022View details →

Soil organic carbon content [g/kg] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe

Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale. The short description of currently available soil properties: log organic carbon [g/kg] to back-transform use exp(x/10)-1; Soil properties were predicted at fixed depths:     Surface soil = s0..0cm,     Subsoil 1 = s30..30cm,     Subsoil 2 = s60..60cm,     Subsoil 3 = s100..100cm. To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula. Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020; To back-transform the log.oc maps use formula: exp(x/10)-1. These are examples of back-transformed values:     log.oc = 15 → 0.3% SOC;     log.oc = 20 → 0.6% SOC;     log.oc = 25 → 1.1% SOC;     log.oc = 30 → 1.9% SOC;     log.oc = 35 → 3.2% SOC;     log.oc = 40 → 5.3% SOC;     log.oc = 50 → 14.8% SOC;

opencc-by-4.0May 2022View details →

MOdern River archivEs of Particulate Organic Carbon: MOREPOC

Modern River Archives of Particulate Organic Carbon (MOREPOC) version 1.1 is a new, open-access, georeferenced, global database, featuring data on POC in suspended particulate matter (SPM) collected at 233 locations across 121 major river systems. This database includes 3,546 SPM data entries, among which 3,053 with POC content, 3,402 with stable carbon isotope (δ13C) values, 2,283 with radiocarbon activity (Δ14C) values, 1,936 with total nitrogen content, and 299 with aluminum-to-silicon mass ratios (Al/Si). The MOREPOC database aims at being used by the Earth System community to build comprehensive and quantitative models for the mobilization, alteration, and fate of terrestrial POC. The supply of particulate organic carbon (POC) associated with terrigenous solids transported to the ocean by rivers plays a significant role in the global carbon cycle. To advance our understanding of the source, transport, and fate of fluvial POC from regional to global scales, databases of riverine POC are needed, including elemental and isotope composition data from contrasted river basins in terms of geomorphology, lithology, climate, and anthropogenic pressure. MOREPOC will benefit the scientific community carrying out research on riverine POC sources, transport, and fate, furthermore, helping inform and validate Earth system models to improve the ability to model and understand the global carbon cycle. Existing environmental raster global datasets for climate, geomorphology, lithology, tectonics, hydrology, and land use, also offer promising prospects for the use of MOREPOC for identifying the controls on POC fluxes and composition, in particular using advanced statistical analysis or machine learning techniques. Moreover, MOREPOC enables a better understanding of sources, transport, and fate of fluvial POC combined with some existing ocean sediment databases. Future updates of MOREPOC should include new bulk POC parameters as well as data on molecular fractions, thermal labile fractions, or specific components such as black carbon or fossil carbon, which should, in turn, provide additional insight into the alteration of riverine POC from source to sink, an essential feature of the global carbon cycle. Data description The MOREPOC database consists of two parts: 1) the master metadata (MOREPOC_v1.1); 2) the summarization of references and methods (MOREPOC_v1.1_RM). A Readme is provided to better understand all parameters provided in the MOREPOC v1.1 database.  MOREPOC_v1.1 includes one table, avaible as Excel spreadsheet (.xslx), comma-limited table (.csv), and GIS shapefile (compiled in .rar) using WGS84 coordinate system. <ul> <li>MOREPOC_v1.1.xlsx</li> <li>MOREPOC_v1.1.csv</li> <li>MOREPOC_v1.1.rar (GIS shapefile)</li> </ul> MOREPOC_v1.1_RM only provides one table, avaible as Excel spreadsheet (.xslx), comma-limited table (.csv). <ul> <li>MOREPOC_v1.1_RM.xlsx</li> <li>MOREPOC_v1.1_RM.csv</li> </ul> The database structure of MOREPOC is listed in Table.1 to understand all provided parameters, more information can be found in the companion manuscript. <table> <caption>Table. 1 Description of the parameters of the MOREPOC v1.1 database.</caption> <tbody> <tr> <td>Parameter</td> <td>Description</td> <td>MOREPOC column name</td> </tr> <tr> <td>River name</td> <td>Name of the major river basin</td> <td>bas_id</td> </tr> <tr> <td>Sub river name</td> <td>Name of the sampled river/stream</td> <td>riv_id</td> </tr> <tr> <td>Country</td> <td>Name of country or places</td> <td>country</td> </tr> <tr> <td>Continent</td> <td>Name of the continent</td> <td>cont</td> </tr> <tr> <td>Sampling site/code</td> <td>Expedition sampling ID</td> <td>code</td> </tr> <tr> <td>Sampling date</td> <td>Time (month/day/year) when the SPM sample was collected</td> <td>time_m/d/y</td> </tr> <tr> <td>Latitude</td> <td>Decimal latitude using WGS 1984</td> <td>lat</td> </tr> <tr> <td>Longitude</td> <td>Decimal longitude using WGS 1984</td> <td>lon</td> </tr> <tr> <td>Sampling technique</td> <td>Method of SPM sampling</td> <td>type_spm</td> </tr> <tr> <td>Size fraction of SPM</td> <td>Reported size fractions analyzed</td> <td>fra_spm</td> </tr> <tr> <td>SPM concentration (mg/L)</td> <td>The total dry weight of SPM in mg per liter water column</td> <td>conc_spm</td> </tr> <tr> <td>POC concentration (mg/L)</td> <td>The total dry weight of POC in mg per liter water column</td> <td>conc_poc</td> </tr> <tr> <td>POC content (%)</td> <td>The total POC content of SPM in wt %</td> <td>per_poc</td> </tr> <tr> <td>POC content uncertainty (1σ)</td> <td>The analytical uncertainty for POC content (1σ)</td> <td>perc_poc_1sd</td> </tr> <tr> <td>δ13C (&permil;)</td> <td>δ13C values of POC (carbonate removed) in &permil;</td> <td>d13C_poc</td> </tr> <tr> <td>δ13C uncertainty (1σ)</td> <td>The analytical uncertainty for δ13C of POC</td> <td>d13C_1sd</td> </tr> <tr> <td>Δ14C (&permil;)</td> <td>Δ14C values of POC (carbonate removed) in &permil;</td> <td>D14C_poc</td> </tr> <tr> <td>Δ14C uncertainty (1σ)</td> <td>The analytical uncertainty for Δ14C of POC</td> <td>D14C_1sd</td> </tr> <tr> <td>Fraction modern (Fm)</td> <td>Fraction modern of POC</td> <td>F14C</td> </tr> <tr> <td>Radiocarbon ages (year)</td> <td>Radiocarbon ages before present (1950)</td> <td>age_14C</td> </tr> <tr> <td>TN content (%)</td> <td>The total nitrogen content of SPM in wt %</td> <td>perc_tn</td> </tr> <tr> <td>Corg/N mass ratio</td> <td>Mass ratio of POC to TN in SPM</td> <td>cn_ratio</td> </tr> <tr> <td>Al/Si mass ratio</td> <td>Mass ratio of Al to Si in SPM</td> <td>alsi_ratio</td> </tr> <tr> <td>Reference</td> <td>Full list of citations of the data source</td> <td>ref</td> </tr> <tr> <td>Complete reference</td> <td>Complete information for cited references</td> <td>ref_c</td> </tr> <tr> <td>Measured parameters</td> <td>Summarization of elemental and isotopic carbon parameters measured</td> <td>para_m</td> </tr> <tr> <td>Calculated parameters</td> <td>Summarization of elemental and isotopic carbon parameters calculated</td> <td>para_c</td> </tr> <tr> <td>Filter</td> <td>Filter used to obtain SPM</td> <td>filter</td> </tr> <tr> <td>Acid</td> <td>The acid type used to remove carbonate in SPM</td> <td>acid</td> </tr> <tr> <td>Carbonate removal method</td> <td>The method used to remove carbonate in SPM</td> <td>m_acid</td> </tr> <tr> <td>Acid concentration</td> <td>The concentration of adopted acid to remove carbonate in SPM</td> <td>conc_acid</td> </tr> <tr> <td>carbonate removal temperature</td> <td>The environmental temperature for acid to remove carbonate in SPM</td> <td>temp_acid</td> </tr> <tr> <td>Carbonate removal duration</td> <td>The reaction time used for acid to remove carbonate in SPM</td> <td>time_acid</td> </tr> <tr> <td>Note</td> <td>Additional information for carbonate removal process</td> <td>note</td> </tr> </tbody> </table> Contributing Data Please contact Yutian Ke at <a href="mailto:yutianke@caltech.edu">yutianke@caltech.edu</a> or  <a href="mailto:yutian.ke@universite-paris-saclay.fr">yutian.ke@universite-paris-saclay.fr</a> if you are interested in contributing your published or unpublished data to MOREPOC. Citation Ke, Y. T., Calmels, D., Bouchez, J., Cécile, Q.: MOdern River archivEs of Particulate Organic Carbon: MOREPOC, Dataset version 1.1, Zenodo [dataset], <a href="https://doi.org/10.5281/zenodo.6541925">https://doi.org/10.5281/zenodo.7055970</a>.

Soil Organic Carbon Content estimations over the Lithuanian pilot area (2022)

In the context of the EU-funded project DIONE (No. 870378), Soil Organic Carbon Content (SOC) estimations have been released as outputs of novel machine learning algorithms which combined the point measurements (i.e. soil properties detected by the Soil Scanning Systems) with temporal EO multispectral imagery and other ancillary variables, enabling end-users, and for the DIONE case, the national paying agency of Lithuania (National Paying Agency - NPA) to mine meaningful information about overall soil health and the effects applied agricultural practices. The dataset is delivered in a single-banded GeoTIFF file (DIONE_SOC_estimations_LT_2022.tif- EPSG:4326) containing the SOC content (SOC %) labelled as Band 1.

Soil Organic Carbon Content estimations over the Cypriot pilot area (2022)

In the context of the EU-funded project DIONE (No. 870378), Soil Organic Carbon Content (SOC) estimations have been released as outputs of novel machine learning algorithms which combined the point measurements (i.e. soil properties detected by the Soil Scanning Systems) with temporal EO multispectral imagery and other ancillary variables, enabling end-users, and for the DIONE case, the national paying agency of Cyprus (Cyprus Agricultural Payments Organisation - CAPO) to mine meaningful information about overall soil health and the effects applied agricultural practices. The dataset is delivered in a single-banded GeoTIFF file (DIONE_SOC_estimations_CY_2022.tif- EPSG:4326) containing the SOC content (SOC %) labelled as Band 1.

Soil Organic Carbon Content estimations over the Cypriot pilot area (2021)

In the context of the EU-funded project DIONE (No. 870378), Soil Organic Carbon Content (SOC) estimations have been released as outputs of novel machine learning algorithms which combined the point measurements (i.e. soil properties detected by the Soil Scanning Systems) with temporal EO multispectral imagery and other ancillary variables, enabling end-users, and for the DIONE case, the national paying agency of Cyprus (Cyprus Agricultural Payments Organisation - CAPO) to mine meaningful information about overall soil health and the effects applied agricultural practices at a parcel level. The dataset is delivered in a shapefile format (DIONE_SOC_estimations_CY_2021.shp - EPSG: 4326) containing the SOC content (SOC %) labeled as SOC.

Soil Organic Carbon Content estimations over the Lithuanian pilot area (2021)

In the context of the EU-funded project DIONE (No. 870378), Soil Organic Carbon Content (SOC) estimations have been released as outputs of novel machine learning algorithms which combined the point measurements (i.e. soil properties detected by the Soil Scanning Systems) with temporal EO multispectral imagery and other ancillary variables, enabling end-users, and for the DIONE case, the national paying agency of Lithuania (National Paying Agency - NPA) to mine meaningful information about overall soil health and the effects applied agricultural practices at a parcel level. The dataset is delivered in a shapefile format (DIONE_SOC_estimations_LT_2021.shp - EPSG:3346) containing the SOC content (SOC %) labeled as SOC.

opencc-by-sa-4.0Oct 2018View details →

Soil organic carbon content in x 5 g / kg at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution

Soil organic carbon content in × 5 g / kg (to convert to % divide by 2) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. The maps are provided using Byte type to significantly reduce file size. Predicted from a global compilation of soil points. Also available for download: soil organic stock maps in in kg / m2 (<a href="https://doi.org/10.5281/zenodo.1475453">https://doi.org/10.5281/zenodo.1475453</a>) and bulk density maps in kg / m3 (<a href="https://doi.org/10.5281/zenodo.1475970">https://doi.org/10.5281/zenodo.1475970</a>). Processing steps are described in detail <a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a>. Antarctica is not included. To access and visualize maps use:  <a href="http://www.openlandmap.org/">OpenLandMap.org</a> If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: <ul> <li>sol = theme: soil,</li> <li>organic.carbon = variable: soil organic carbon content in x 5 g / kg,</li> <li>usda.6a1c = determination method: laboratory method code,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950–2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>

opencc-by-sa-4.0Oct 2018View details →

Soil organic carbon stock (0–30 cm) in kg/m2 time-series 2001–2015 based on the land cover changes

Estimated SOC loss based on the European Space Agency (ESA) Climate Change Initiative (ESACCI-LC) land cover maps 2001–2015. This only shows estimated SOC loss (in kg/m2) as a result of change in land use / land cover (assuming standard change factors based on the literature and IPCC reports). Methodology produced for the purpose of the Land Degradation Neutrality (UNCCD) project. Processing steps are described in detail <a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil/LDN">here</a>. Antartica is not included. To access and visualize maps use:  <a href="http://www.openlandmap.org/">OpenLandMap.org</a> If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: <ul> <li>sol = theme: soil,</li> <li>organic.carbon.stock = variable: soil organic carbon stock in kg/m2,</li> <li>msa.kgm2 = determination method: derived from carbon content, bulk density and coarse fragments,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..30cm = vertical reference: standard layer 0-30 cm below surface,</li> <li>2014 = time reference: year 2014,</li> <li>v0.2 = version number: 0.2,</li> </ul>

opencc-by-sa-4.0Dec 2018View details →

Soil organic carbon stock in kg/m2 for 5 standard depth intervals (0–10, 10–30, 30–60, 60–100 and 100–200 cm) at 250 m resolution

Soil organic carbon stock in kg/m2 for 5 standard depth intervals (0–10, 10–30, 30–60, 60–100 and 100–200 cm) at 250 m resolution. To convert to t/ha multiply by 10. Derived using soil organic carbon content (<a href="https://doi.org/10.5281/zenodo.1475457">https://doi.org/10.5281/zenodo.1475457</a>), bulk density (<a href="https://doi.org/10.5281/zenodo.1475970">https://doi.org/10.5281/zenodo.1475970</a>) and coarse fragments (<a href="https://doi.org/10.5281/zenodo.2525681">https://doi.org/10.5281/zenodo.2525681</a>), predicted from point data at 6 standard depths. Depth to bed rock has been ignored, hence total stocks might be about 10–15% lower then reported. Processing steps are described in detail <a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a>. Antarctica is not included. To access and visualize maps use: <a href="https://openlandmap.org">https://openlandmap.org</a> If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: <ul> <li>sol = theme: soil,</li> <li>organic.carbon.stock = variable: soil organic carbon stock in kg/m2,</li> <li>msa.kgm2 = determination method: derived from organic carbon content, bulk density and coarse fragments,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..10cm = vertical reference: 0-10 cm layer below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>

opencc-by-4.0Feb 2024View details →

Data from: Shift of bacterial and fungal communities upon soil amelioration is driven by carbon degradability of organic amendments

Microbial communities of bacteria and fungi have been analyzed in soil. Agricultural soil was amended with different organic amendments including straw, compost, biogas residues, and biochar, and incubated in the lab. After 6 months, DNA extracted from soil samples was analyzed via Illumia MiSeq DNA sequencing (16S V3V4 for bacteria, ITS1 for fungi) to evaluate changes to the microbial community structure. For details, please see the respective publication (DOI: 10.1007/s44378-024-00012-5).

opencc-by-4.0Oct 2024View details →

Map of soil organic carbon loss of mineral soils in Estonia

The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales. The map was generated to evaluate soil organic carbon (SOC) loss in Estonian agricultural soils. It is directly related to SERENA project WP3, T3.2, D3.3 with the aim of applying cookbooks to assess soil threats or ecosystem services. This map is the outcome of applying a cookbook developed by ISRIC (Genova, G., Poggio, L., Kempen, B., & Colman, B. DSM Workflow Seedling. ISRIC - World Soil Information. https://doi.org/10.17027/ISRIC-FSX2-2691). The generated map of SOC loss expressed as absolute sequestration rate (t C ha-1 a-1) between 2015 and 2021 is in GEOTIFF format at the resolution of 100m. The input data for the cookbook was from the PANDA database, which contains regular soil monitoring and voluntary soil sampling data by farmers in Estonia. To achieve the aim for accounting SOC loss in agricultural soils temporal pairs were selected resulting in 1037 paired points where the interval between second sampling was more than 5 years. SOC stocks were calculated for the depth of 20 cm using the equation by Adams (1973) to calculate soil bulk density. The calculated SOC stock for time0 and time2 (> 5 years resampled locations) were used as input points for digital soil mapping, that is the ISRIC cookbook.

opencc-by-4.0Oct 2024View details →

EJPSOIL_SERENA: Maps of Soil Organic Carbon Loss Scenarios in Elva Parish, Estonia

The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.         The study examined the effects of winter cropping systems on long-term soil fertility and their potential to mitigate SOC (Soil Organic Carbon) loss compared to bare soil during the winter months. It analyzed changes in SOC stocks (0–30 cm) at the field level in Elva Parish over the period 2020–2040, under different land-use scenarios. The modeling was based on a SOC stock map layer for Estonian mineral arable soils, developed by the Centre of Estonian Rural Research and Knowledge, which represented the baseline conditions in 2020. SOC stock projections were made using the RothC model, which simulates soil carbon turnover.  In the first scenario (Scenario 1), the average SOC stock in Elva Parish by 2040 was estimated assuming the land would remain bare, without vegetation, during the winter months from October to April. In the second scenario (Scenario 2), the SOC stock projection accounted for the presence of winter vegetation, which means the soil is covered with vegetation year-round. The dataset includes four files: a projected SOC stock map for Elva Parish in 2040 and the stock changes from 2020–2040 under Scenario 1, along with a projected SOC stock map for 2040 and the stock changes from 2020–2040 under Scenario 2.

opencc-by-4.0Nov 2022View details →

Particulate organic carbon (POC) concentration in meltwater runoff of Leverett Glacier, Russell Glacier, and Isunnguata Sermia, southwest Greenland (2009-2018)

This dataset describes particulate organic carbon (POC) and particulate carbon (PC) concentrations of suspended sediments in the proglacial rivers of 3 land-terminating glaciers in the Kangerlussuaq area, Southwest Greenland: Leverett Glacier (LG), Leverett River; Russell Glacier (RG), Akuliarusiarsuup Kuua; and Isunnguata Sermia (IS), Isortoq River. Both the Leverett River and Akuliarusiarsuup Kuua are tributaries of the Qinnguata Kuussua (also known as Watson River). The data have already been part of 3 different publications (Lawson et al. 2014, Kohler et al. 2017, and Vrbická et al. 2022) but are archived here for the first time. POC data was collected for LG during the 2009 and 2010 melt seasons (Lawson et al. 2014) as well as 2015 (Kohler et al. 2017). For the 2018 melt season, only total carbon concentrations of suspended sediments (PC) is archived as opposed to POC (see Vrbická et al. 2022).

opencc-by-sa-4.0Oct 2018View details →

Predicted soil organic carbon stock at 30 m in t/ha for 0-100 cm depth global / update of the map of mangrove forest soil carbon

This is the 2nd update of maps produced by <a href="https://doi.org/10.1088/1748-9326/aabe1c">Sanderman et al (2018)</a>. The improvements to the <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-prediction-of-soil-organic-carbon.html">3D spatial predictions</a> include: <ul> <li> new updated global mangrove coverage map (contact Thomas Worthington), </li> <li> spatiotemporal predictions to account for differences in spectral reflectance at the time of field work, </li> <li> additional SOC points <a href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41558-018-0162-5/MediaObjects/41558_2018_162_MOESM2_ESM.xlsx">published in Rovai et al. (2018)</a> used in model training (see gpkg file). </li> </ul> To open map in QGIS or similar, drag and drop the *.tif files. You can than add also the gpkg file contain the training points. Production steps (ensemble predictions using SuperLearner) are explained in detail at:  <ul> <li>R code: <a href="https://github.com/whrc/Mangrove-Soil-Carbon/">https://github.com/whrc/Mangrove-Soil-Carbon/</a> (see "R_code/GMW_mangroves_SOC_30m.R")</li> <li>Tutorial: <a href="https://envirometrix.github.io/PredictiveSoilMapping/soilmapping-using-mla.html#ensemble-predictions-using-superlearner-package">"Predictive Soil Mapping with R"</a></li> </ul> Produced for the purpose of Mangrove Restoration Potential Map funded by The Nature Conservancy and IUCN. Contact TNC: Emily Landis <<a href="mailto:elandis@TNC.ORG">elandis@TNC.ORG</a>>. Contact IUCN / University of Cambridge: Thomas Worthington <<a href="mailto:taw52@cam.ac.uk">taw52@cam.ac.uk</a>>. <ul> <li>The mangrove restoration potential map is available at: <a href="https://www.researchgate.net/deref/http%3A%2F%2Fmaps.oceanwealth.org%2Fmangrove-restoration%2F">http://maps.oceanwealth.org/mangrove-restoration/</a></li> </ul>

openCC (other)Nov 2023View details →

Dissolved oxygen, temperature, chlorophyll-a, total phosphorus, total nitrogen, and dissolved organic carbon at multiple depths in 822 lakes from 1921-2022

Rapid changes in climate and land use are having substantial and interacting impacts on lake water quality around the world. Here, we synthesized time-series data for dissolved oxygen, temperature, chlorophyll-a, total phosphorus, total nitrogen, and dissolved organic carbon at multiple depths in 822 lakes to facilitate analyses of these changes. The dataset extends from 1921–2022, with a median data duration of 29 years (range 5-102) and a median of 5 unique sampling dates per year at each lake. Lakes in the dataset have a median depth of 12.5 m (range 1.5–480 m), median surface area of 85.4 ha (range: 0.5–237000 ha) and median elevation of 264 m (range: -215–2804). The lakes are located in 18 countries across 5 continents, with latitudes ranging from -42.6 to 68.3. To facilitate interoperability with other large-scale datasets, each lake is linked to a unique hydroLAKES lake ID when possible (n = 683).

openCC (other)Aug 2024View details →

Soil organic carbon and associated uncertainty at 90 m resolution for peninsular Spain

Soil organic carbon (SOC) must be quantified and monitored to assess soil management practices, adapt policies, and evaluate environmental impacts. However, due to SOC spatial variability, soil surveys become a very challenging task because of the high costs of acquiring data, operational complexity, and updating. Digital soil mapping based on machine learning approaches in combination with remote sensing techniques have enabled soil carbon spatial distribution to be significantly improved, even with limited soil samples. A legacy soil database of 8,361 georeferenced profiles and a selection of environmental data-driven covariates intimately related to soil-forming factors (e.g., biota, climate, parent material) were used to generate SOC maps. Modeling of data was based on three supervised learning approaches: quantile regression forest, ensemble machine learning and auto-machine learning. For the final SOC spatial distribution maps, each pixel was assigned the prediction from the most accurate model, i.e., lowest uncertainty. We applied this modeling technique to generate cost-effective, high-resolution maps (90 m pixel resolution) of SOC distribution, and its associated spatially explicit uncertainty, in peninsular Spain. These maps showed 15.7 g.kg-1 mean SOC concentration at 0-30 cm and 3.6 g.kg-1 at 30-100 cm depth. The total SOC stock at its effective depth was 3.8 Pg C, storing the 74% in the upper 30 cm (2.82 Pg C). The correlation between SOC observed and predictions final values showed R2=0.68 for SOCc and R2=0.54 for SOCs at the upper 30cm. The methodology proposed in this study aims to improve benchmark SOC estimates in support of the National GHG Emissions Inventory Report

openCC0Nov 2024View details →

AquaMatch Dissolved Organic Carbon Data from Water Quality Portal: ~1970-2024

This dataset, “AquaMatch Dissolved Organic Carbon Data from Water Quality Portal ~1970-2024”, is a component of a forthcoming update to AquaSat (Ross et al., 2019), AquaSat version 2 (“V2”). The overarching purpose of AquaSat V2 is to emphasize the individual parts of the AquaSat pipeline that make-up the matchups between satellite and in-situ measurements. As such, we have greatly expanded and improved upon the AquaSat dissolved organic carbon dataset in two ways: First, we have incorporated additional recent in situ data beyond what was available at the publication of AquaSat. Second, we have created a data quality tiering system to provide end-users with more guidance on data usage. In this schema we have three tiers: restrictive data that are verifiably self-similar across organizations and time-periods and can be considered highly reliable; narrowed data that we have good reason to believe are self-similar, but for which we cannot verify full compatibility across data providers; and inclusive data, which are assumed to be reliable and are harmonized to our best ability given the information available from the data provider. We have also added flag columns to help users understand complexities of the available depth and field sampling data. This dataset is a derived data product created using records downloaded from the Water Quality Portal (WQP) spanning January 5, 1970, to June 27, 2024. The WQP is a data warehouse for water-related data measured or observed within the United States and US territories managed by the Environmental Protection Agency, United States Geological Survey, and the National Water Quality Monitoring Council. The dataset does not contain remote sensing matchups but can be paired with Landsat surface reflectances using the pipeline presented in Ross et al. (2019). Ross, M. R. V., Topp, S. N., Appling, A. P., Yang, X., Kuhn, C., Butman, D. et al. (2019). AquaSat: A data set to enable remote sensing of water quality for inland waters. Water