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4,775 results for “carbon”
Seafloor organic carbon flux output from the NEMO-MEDUSA model
<p>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.</p> <p>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.</p> <p>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).</p> <p>In addition to the netCDF files, text file dumps of their contents are included to assist with interpretation.</p> <p>References:</p> <p>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.</p> <p>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.</p> <p>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.</p> <p>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.</p> <p>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.</p> <p>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.</p> <p>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.</p>
Modelling pan-Arctic peatland carbon dynamics under alternative warming scenarios
<p>The purpose of this study is to simulate peatland carbon dynamics in the future climate conditions for four major future warming scenarios. The study examines whether less pronounced warming could further enhance the peatland carbon sink capacity and buffer the effects of climate change. It will also determine which trajectory peatland carbon balance will follow, what the main drivers are and which one will dominate in the future.</p> <p>In this study, LPJGUESS Peatland has been employed across the pan-Arctic and we carried out four sets of simulations. The data files contain the information about carbon accumulation, NEE, NPP and ice fraction.</p>
Soil organic carbon content [g/kg] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>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.</p> <p>The short description of currently available soil properties: log organic carbon [g/kg] to back-transform use exp(x/10)-1;</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p> <p>To back-transform the log.oc maps use formula: exp(x/10)-1. These are examples of back-transformed values:</p> <p> log.oc = 15 → 0.3% SOC;<br> log.oc = 20 → 0.6% SOC;<br> log.oc = 25 → 1.1% SOC;<br> log.oc = 30 → 1.9% SOC;<br> log.oc = 35 → 3.2% SOC;<br> log.oc = 40 → 5.3% SOC;<br> log.oc = 50 → 14.8% SOC;</p>
Data for "Temperate Regenerative Agriculture practices increase soil carbon but not crop yield – a meta-analysis"
<p>Supplementary Files for systematic review and meta-analysis: Temperate Regenerative Agriculture practices increase soil carbon but not crop yield – a meta-analysis</p> <p> </p>
Simultaneously Enhanced Tenacity, Rupture Work, and Thermal Conductivity of Carbon Nanotubes Fibers by Raising Effective Tube Portion
<p>Although individual carbon nanotubes (CNTs) are superior as constituents to polymer chains, the mechanical and thermal properties of CNT fibers (CNTFs) remain inferior to synthetic fibers due to the failure of embedding CNTs effectively in superstructures. Conventional techniques resulted in a mild improvement of target properties while achieving parity at best on others. Here, a Double-Drawing technique is developed to rearrange the constituent CNTs in both mesoscale and nanoscale morphology. Consequently, the mechanical and thermal properties of the resulting CNTFs can simultaneously reach their highest performances with specific strength ~3.30 N/tex, work of rupture ~70 J/g, and thermal conductivity ~354 W/m/K, despite starting from low-crystallinity materials (<em>I</em><sub>G</sub>:<em>I</em><sub>D</sub>~5). The processed CNTFs are more versatile than comparable carbon fiber, Zylon and Dyneema. Based on evidence of load transfer efficiency on individual CNTs measured with In-Situ-Stretching-Raman, we find the main contributors to property enhancements are the increasing of the effective tube contribution, in addition to the known optimization on CNTs alignment and stacking.</p>
MOdern River archivEs of Particulate Organic Carbon: MOREPOC
<p>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 (δ<sup>13</sup>C) values, 2,283 with radiocarbon activity (Δ<sup>14</sup>C) 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.</p> <p>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.</p> <p><strong>Data description</strong></p> <p>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. </p> <p>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.</p> <ul> <li>MOREPOC_v1.1.xlsx</li> <li>MOREPOC_v1.1.csv</li> <li>MOREPOC_v1.1.rar (GIS shapefile)</li> </ul> <p>MOREPOC_v1.1_RM only provides one table, avaible as Excel spreadsheet (.xslx), comma-limited table (.csv).</p> <ul> <li>MOREPOC_v1.1_RM.xlsx</li> <li>MOREPOC_v1.1_RM.csv</li> </ul> <p>The database structure of MOREPOC is listed in Table.1 to understand all provided parameters, more information can be found in the companion manuscript.</p> <table> <caption><strong>Table. 1 Description of the parameters of the MOREPOC v1.1 database.</strong></caption> <tbody> <tr> <td><strong>Parameter</strong></td> <td><strong>Description</strong></td> <td><strong>MOREPOC column name</strong></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>δ<sup>13</sup>C (‰)</td> <td>δ<sup>13</sup>C values of POC (carbonate removed) in ‰</td> <td>d13C_poc</td> </tr> <tr> <td>δ<sup>13</sup>C uncertainty (1σ)</td> <td>The analytical uncertainty for δ<sup>13</sup>C of POC</td> <td>d13C_1sd</td> </tr> <tr> <td>Δ<sup>14</sup>C (‰)</td> <td>Δ<sup>14</sup>C values of POC (carbonate removed) in ‰</td> <td>D14C_poc</td> </tr> <tr> <td>Δ<sup>14</sup>C uncertainty (1σ)</td> <td>The analytical uncertainty for Δ<sup>14</sup>C 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>C<sub>org</sub>/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> <p><strong>Contributing Data</strong></p> <p>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.</p> <p><strong>Citation</strong></p> <p>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>.</p>
Soil Organic Carbon Content estimations over the Lithuanian pilot area (2022)
<p>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.<br> 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.</p>
Soil Organic Carbon Content estimations over the Cypriot pilot area (2022)
<p>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.<br> 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.<br> </p>
Zeolite Templated Carbon Materials - DFTB Structural Database
<p>Zeolite-templated carbon (ZTC) is a unique porous carbonaceous material in that its structure is ordered at the nanometre scale, enabling a representative periodic description at the atomistic level. A structural library for ZTC of varying compositions was created using density functional tight binding (DFTB) potentials parameterized for materials science applications (matsci-0-3). We provide here quantum chemical-refined structures of models with CH, CHO, CHON, CHOB, and CHOBN compositions with various degrees of heteroatom substitution. The "initial ZTC structure" files correspond to the initial model used in our work that was developed using molecular mechanics, empirical force fields. These structural models comprise the characteristic morphological features of highly porous carbon materials, such as open-blade surfaces, edges, saddles, and closed-strut formations, spanning a range of curvatures and characteristic sizes. The optimized structures in CIF and native DFTB file formats are organized in the "stationary structure" file based on the optimization pathways that lead to the stationary structures.</p> <p>Secondly, we carried out alternating compression and expansion of the CHO model unit cell to determine the lowest energy structure as well as to obtain the bulk modulus. The file "bulk modulus" contains two data sets that describe the deformational energy landscape of pure faujasite zeolite, Na-substituted zeolite, and the ZTC model structure.</p> <p>The file "analysis tools" is a representative compilation of utilities for file format conversion, fractional vs. Cartesian crystal coordinates, and structural analysis spreadsheets.</p> <p>The agreement between experimental measurements and the computational model is remarkable that demonstrates the power of approximate density functional theory as a cost-effective computational tool with chemical accuracy for the investigation of structure/property relationships in real-world carbon-based solids.</p>
Soil Organic Carbon Content estimations over the Cypriot pilot area (2021)
<p>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.</p> <p>The dataset is delivered in a shapefile format (DIONE_SOC_estimations_CY_2021.shp - EPSG: 4326) containing the SOC content (SOC %) labeled as SOC.</p>
Soil Organic Carbon Content estimations over the Lithuanian pilot area (2021)
<p>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.</p> <p>The dataset is delivered in a shapefile format (DIONE_SOC_estimations_LT_2021.shp - EPSG:3346) containing the SOC content (SOC %) labeled as SOC.</p>
Commodity-driven deforestation, associated carbon emissions and trade 2001-2022
<p><span>This dataset contains estimates of commodity-driven deforestation and associated carbon emissions for the period 2001-2022, estimated by the Deforestation Driver and Carbon Emission (DeDuCE) model (Singh & Persson 2024), which combines remote sensing data on forest loss and land-use with agricultural statistics to identify and attribute deforestation across the world to expansion of cropland, pastures and forest plantation, and the commodities produced on this land. This also contains data on deforestation embodied in the production, exports, imports, and consumption of agricultural and forestry commodities by country, year, and commodity for the time period 2005-2022 derived using physical and monetary trade models. The data is an update of the results presented in Pendrill et al. (2022) and the differences between the two datasets are detailed in the explainer available here.</span></p>
UK Low Carbon Technology Database (UKLCTD)
<p><strong>UK Low Carbon Technology Database (UKLCTD)</strong></p> <p>Version used for revised paper submitted to Nature Energy: Sheridan Few, Predrag Djapic, Gpran Strbac, Jenny Nelson, Chiara Candelise, "A geographically disaggregated approach to integrate low-carbon technologies across local electricity networks"</p> <p><strong>Overview</strong></p> <p><br>This repository contains:</p> <p>(1) The United Kingdom Low Carbon Technology Database (UKLCTD), a collection of real geographically disaggregated data on current deployment of small scale photovoltaics (PV), heat pumps (HPs), electric vehicles (EVs), network inrastructure, domestic and nondomestic meter density, electricity demand, and rurality at an LSOA / Scottish Data Zone level. (UKLCTD.csv)</p> <p>(2) Scenarios for future deployment of PV, HPs, EVs, and battery storage upto 2050 at an LSOA level based upon current data, National Grid's Future Energy Scenarios (FES) and UKPN, NPG, and WPD's Distribution Future Energy Scenarios (DFES). (UKLCTD_Scenarios_DFES_base_[date].csv, 2050 file has PV deployment capped at two per meter)</p> <p>(3) Raw data from which each of the above are generated, and R scripts used to generate the above databases from raw data. Links to sources of raw data are included in scripts to facilitate upadates to this framework as new data becomes available. (UKLCTD.zip)</p> <p><br><strong>Usage</strong></p> <p>R scripts in the zip file have a short comment at the start describing their function. Before running, 'root_path' variable will need to be updated in each script to reflect the path these files are kept in on your local repository.</p> <p>The data may be explored using the following script:</p> <p>- Import_UKLCTD.R</p> <p>To generate the UKLCTD and scenarios from scratch, scripts are intended to be run in this order (names mostly self explanatory)</p> <p>- Generate_UKLCTD.R<br>- Add_substations_to_UKLCTD.R<br>- Add_Scottish_rurality_to_UKLCTD.R<br>- Generate_NG_scenarios.R<br>- Add_DFES_scenarios_w_plot.R<br>- Cap_Deployment.R</p> <p><br>Each of these scripts generates data used by subsequent scripts. These are broken down into stages and commented as far as possible.</p> <p><strong>Data Structure</strong></p> <p>Data: All raw data is in "Input_Data". This data can be updated as new information becomes available (input data files, sheets, and cells referred to in the above scripts will likely need to be updated accordingly). Data produced by these scripts in "Intermediate Data" and "Output Data" folders depending on whether it is used by subsequent scripts. Plots are generated in the "Plots" folder</p> <p><strong>Attribution</strong></p> <p>If this framework has been useful, please cite the following papers outlining our methodology:</p> <p>Few, S., Djapic, P., Strbac, G., Nelson J., Candelise C. A geographically disaggregated approach to integrate low-carbon technologies across local electricity networks. <em>Nat Energy</em> (2024). <a href="https://doi.org/10.1038/s41560-024-01542-6" target="_blank" rel="noopener">https://doi.org/10.1038/s41560-024-01542-6</a></p> <p>Few, S., Djapic, P., Strbac, G., Nelson J., Candelise C. Assessing Local Costs and Impacts of Distributed Solar PV Using High Resolution Data from across Great Britain. <em>Renewable Energy</em> 162 (2020) 1140–50. <a href="https://doi.org/10.1016/j.renene.2020.08.025" target="_blank" rel="noopener">https://doi.org/10.1016/j.renene.2020.08.025</a></p> <p> </p>
Knowledge gaps on trade-offs of soil carbon sequestration related to soil management strategies
<p>The database contains 87 unique literature items (29 reviews, 42 meta-analyses, 16 original papers) describing the effect of a soil management strategy (tillage management, cropping systems, water management, cover crops, crop residues, livestock manure, slurry, compost, biochar, liming) on the trade-offs between soil carbon sequestration or SOC change and N2O emission, CH4 emission and nitrogen leaching. Since some literature items describe effects of several SMS categories, the database_summary tab comprises a total of 112 unique inputs. For each input it is indicated in the Database_summary tab if it was used as input for the "Soil management effect assessment" in Maenhout et al. (2024) [Maenhout, P., Di Bene, C., Cayuela, M. L., Diaz-Pines, E., Govednik, A., Keuper, F., Mavsar, S., Mihelic, R., O'Toole, A., Schwarzmann, A., Suhadolc, M., Syp, A., & Valkama, E. (2024). Trade-offs and synergies of soil carbon sequestration: Addressing knowledge gaps related to soil management strategies. European Journal of Soil Science, 75(3), e13515. https://doi.org/10.1111/ejss.13515] and/or to define knowledge gaps ("Knowledge gap in tab"-column). Knowledge gaps and research recommendations are gouped per soil management strategy in different tabs in this database. Per soil management strategy, knowledge gaps are clustered per theme in groups. These themes include: the specific soil management strategy, pedoclimatic conditions, establishment of experiments, other soil management strategies, meta-analysis, modelling and other</p>
Data from: "Damage deflection and subsequent damage diffusion in carbon-boron fibre hybrid composites under longitudinal compression"
<p>The datset contains raw data used for the work presented in the journal paper "Damage deflection and subsequent damage diffusion in carbon-boron fibre hybrid composites under longitudinal compression".<br>Specifically, it contains machine recorded data and video recordings (either SEM or with optical microscope) of the compression tests on small scale single edge notched specimens made of IM7/8552 (carbon/epoxy) and HyBor 52 FPI (carbon-boron fibre hybrid composite). It also contains specimens pictures taken during and after the tests (including SEM and optical micrographs).</p> <p>For more details, please refer to the full paper.</p>
Dataset of Norwegian forest albedo carbon offset potential
<p>This dataset contains the following six files in geotiff format with additional user detail provided as a README.txt file: 1) Forest albedo effect in C-equivalent units for the present day climate 2) Forest albedo effect in C-equivalent units for the transient 21st century RCP4.5 climate 3) Forest albedo's carbon offset potential for the present day climate 4) Forest albedo's carbon offset potential for the transient 21st century RCP4.5 climate 5) Site Index (productivity class) of dominant tree species 6) Dominant tree species 7) README</p> <p>The dataset contains results and input data related the following publication: <br>Bright, R. M., Cataneo, N., Antón-Fernández, C., Eisner, S., Astrup, R., "Relevance of surface albedo to forestry policy in high latitude and altitude regions may be overvalued". <em>Environmental Research Letters, <span><a href="https://doi.org/10.1088/1748-9326/ad657e">https://doi.org/10.1088/1748-9326/ad657e</a></span></em> </p>
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
<p>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 / m<sup>2</sup> (<a href="https://doi.org/10.5281/zenodo.1475453">https://doi.org/10.5281/zenodo.1475453</a>) and bulk density maps in kg / m<sup>3</sup> (<a href="https://doi.org/10.5281/zenodo.1475970">https://doi.org/10.5281/zenodo.1475970</a>). Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <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> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <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>
Soil organic carbon stock (0–30 cm) in kg/m2 time-series 2001–2015 based on the land cover changes
<p>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 <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil/LDN">here</a></strong>. Antartica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <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> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <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>
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
<p>Soil organic carbon stock in kg/m<sup>2</sup> 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 <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="https://openlandmap.org"><strong>https://openlandmap.org</strong></a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <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> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <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>
Microbial biomass and water-extractable carbon on Mt. Kilimanjaro
<p>This dataset presents the value of microbial biomass carbon (MBC) and water-extractable carbon (WOC) at study plots under KiLi project.</p> <p>Microbial biomass carbon (MBC) and water-extractable organic carbon (WOC) – as sensitive and important parameters for soil fertility and C turnover – are strongly affected by land-use changes all over the world. These effects are particularly distinct upon conversion of natural to agricultural ecosystems due to very fast carbon (C) and nutrient cycles and high vulnerability, especially in the tropics. The objective of this study was to use the unique advantage of Mt. Kilimanjaro – altitudinal gradient leading to different tropical ecosystems but developed all on the same soil parent material – to investigate the effects of land-use change and elevation on MBC and WOC contents during a transition phase from dry to wet season. Down to a soil depth of 50 cm, we compared MBC and WOC contents of 2 natural (<em>Ocotea</em> and <em>Podocarpus</em> forest), 3 seminatural (lower montane forest, grassland, savannah), 1 sustainably used (homegarden) and 2 intensively used (maize field, coffee plantation) ecosystems on an elevation gradient from 950 to 2850 m a.s.l.</p> <p>The KiLi project (2010-2018) is a German Science Foundation (DFG) funded research unit (DFG research unit FOR1246) that focuses on biodiversity and ecosystem processes along altitudinal and disturbance gradients on Mt. Kilimanjaro (Tanzania, Africa), capitalizing on its world-wide unique range of climatic and vegetation zones. The research unit comprises 2 central projects and 7 subprojects from various disciplines. On a total of 60 study sites in both natural and human-disturbed ecosystems biodiversity (e.g. plants, soil arthropods, ants, bees, frogs, lizards, bats, birds), related ecosystem processes (decomposition, seed dispersal, pollination, herbivory, predation), and biogeochemical processes and properties of ecosystems (climate, soil properties and nutrient status, regulation of water and carbon fluxes, trace gas emissions, primary productivity, functional diversity) are analyzed.</p>
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