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15 results for “biomass potential”
Data from: Spatial distribution of the potential forest biomass availability in Europe
<p>European forests are considered a crucial resource for supplying biomass to a growing bio-economy in Europe. This study aimed to assess the potential availability of forest biomass from European forests and its spatial distribution. We tried to answer the questions (i) how is the potential forest biomass availability spatially distributed across Europe and (ii) where are hotspots of potential forest biomass availability located?</p> <p>The spatial distribution of woody biomass potentials was assessed for 2020 for stemwood, residues (branches and harvest losses) and stumps for 39 European countries. Using the European Forest Information SCENario (EFISCEN) model and international forest statistics, we estimated the theoretical amount of biomass that could be available based on the current and future development of the forest age-structure, growing stock and increment and forest management regimes. We combined these estimates with a set of environmental (site productivity, soil and water protection and biodiversity protection) and technical (recovery rate, soil bearing capacity) constraints, which reduced the amount of woody biomass that could potentially be available. We mapped the potential biomass availability at the level of administrative units and at the 10 km × 10 km grid level to gain insight into the spatial distribution of the woody biomass potentials.</p> <p>According to our results, the total availability of forest biomass ranges between 357 and 551 Tg dry matter per year. The largest potential supply of woody biomass per unit of land can be found in northern Europe (southern Finland and Sweden, Estonia and Latvia), central Europe (Austria, Czech Republic, and southern Germany), Slovenia, southwest France and Portugal. However, large parts of these potentials are already used to produce materials and energy. The distribution of biomass potentials that are currently unused only partially coincides with regions that currently have high levels of wood production.</p> <p>Our study shows how the forest biomass potentials are spatially distributed across the European continent, thereby providing insight into where policies could focus on an increase of the supply of woody biomass from forests. Future research on potential biomass availability from European forests should also consider to what extent forest owners would be willing to mobilise additional biomass from their forests and at what costs the estimated potentials could be mobilised.</p> <p>This dataset contains the data of the map presented in Figure 2A: Estimated spatial distribution of forest biomass availability according to the BASE potential (ton dry matter ha-1 land yr-1) at the grid (10x10 km) level.</p>
DBFZ Resource Database: DE-Biomass Monitor. Biomass Potentials and Utilization of Biogenic Wastes and Residues in Germany 2010-2020
<p>Excerpt from the<a href="https://datalab.dbfz.de/resdb?lang=en"> DBFZ Resource Database</a> (DE Biomass Monitor) as of 02.12.2024 (v6.3.2).</p>
Potential aboveground biomass increase in Brazilian Atlantic Forest fragments with climate change
<p>This file collection contains the estimated spatial distribution of the above-ground biomass density (AGB) by the end of the 21st century across the Brazilian Atlantic Forest domain and the respective uncertanty. To develop the models, we used the maximum entropy method with projected climate data to 2100, based on the Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathway (RCP) 4.5 from the fifth Assessment Report (AR5).</p> <p>The dataset is composed of four files in GeoTIFF format:</p> <p><strong>calibrated-AGB-distribution.tif</strong>: raster file representing the present spatial distribution of the above-ground biomass density in the Atlantic Forest from the calibrated model. Unit: Mg/ha </p> <p><strong>estimated-uncertanty-for-calibrated-agb-distribution.tif</strong>: raster file representing the estimated spatial uncertanty distribution of the calibrated above-ground biomass density. Unit: percentage.</p> <p><strong>projected-AGB-distribution-under-rcp45.tif</strong>: raster file representing the projected spatial distribution of the above-ground biomass density in the Atlantic Forest by the end of 2100 under RCP 4.5 scenario. Unit: Mg/ha </p> <p><strong>estimated-uncertanty-for-projected-agb-distribution.tif</strong>: raster file representing the estimated spatial uncertanty distribution of the projected above-ground biomass density. Unit: percentage.</p> <p><strong>Spatial resolution:</strong> 0.0083 degree (ca. 1 km)</p> <p><strong>Coordinate reference system:</strong> Geographic Coordinate System - Datum WGS84</p>
Exploring Soil Exchangeable Cations and Auditing the Potential of Phoenix dactylifera and Mangifera indica in CO2 Sequestration into Soil Biomass in a Naturally Occurring Tree Patches Using Infrared Gas Analyzer
<p><em>Soil Exchangeable Cations (EC) were audited, and the potential of economic trees in atmospheric CO2 sequestration into soil biomass was investigated. . The experiment indicated that economic trees are perfect for CO2 sequestration. </em></p>
Biomass potential via light interception of novel miscanthus hybrids from field trials
<p>New biomass crop hybrids for bioeconomic expansion require yield projections to determine their potential for strategic land use planning in the face of global challenges. Our biomass growth simulation incorporates radiation interception and conversion efficiency. Models often use leaf area to predict interception which is demanding to determine accurately, so instead, we use low-cost rapid light interception measurements using a simple lab-made line ceptometer and relate the dynamics of canopy closure to thermal time, and to measurements of biomass. We apply the model to project the European biomass potentials of new market-ready hybrids for 2020–30. Field measurements are easier to collect, the calibration is seasonally dynamic and reduces influence of weather variation between field sites. The model obtained is conservative, being calibrated by crops of varying establishment and varying maturity on less productive (marginal) land. This results in conservative projections of miscanthus hybrids for 2020-30 based on 10% land use conversion of the least (productive) grassland and arable for farm diversification, which show a European potential of 80.7–89.7 Mt y<sup>-1</sup> biomass, with potential for 1.2-1.3 EJ y<sup>-1</sup> energy and 36.3–40.3 Mt y<sup>-1</sup> carbon capture, with seeded <em>M. sacchariflorus</em> x <em>sinensis</em> displaying highest yield potential. Simulated biomass projections must be viewed in light of the field measurements on less productive land with high soil water deficits. We are attempting to model results from an ambitious and novel project combining new hybrids across Europe with agronomy which has not been perfected on less productive sites. Nevertheless, at a time of energy sourcing issues, seed-propagated miscanthus hybrids for the upscaled provision of bioenergy offer an alternative source of renewable energy. If European countries provide incentives for growers to invest, seeded hybrids can improve product availability and biomass yields over the current commercial miscanthus variety.</p>
Data from: Empirical evidence for the potential climate benefits of decarbonizing light vehicle transport in the U.S. with bioenergy from purpose-grown biomass with and without BECCS
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Biomass potential via light interception of novel miscanthus hybrids from field trials
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Mirror of "ENSPRESO - an open data, EU-28 wide, transparent and coherent database of wind, solar and biomass energy potentials"
<h2>Mirrored from Joint Research Centre Data Catalogue</h2><p><a href="https://data.jrc.ec.europa.eu/collection/id-00138#datasets">https://data.jrc.ec.europa.eu/collection/id-00138#datasets</a></p><blockquote><p>This collection contains datasets from ENSPRESO, an EU-28 wide, open dataset for energy models on renewable energy potentials, at national (NUTS0) and regional levels (NUTS2) for the 2010-2050 period. Within ENSPRESO, ENergy Systems Potential Renewable Energy SOurces, technical potentials are provided for wind, solar and biomass, based on coherent GIS-based land-restriction scenarios. For wind, resource evaluation also considers setback distances as well as high resolution geo-spatial wind speed data. For solar, potentials are derived from irradiation data and available area for solar applications. For biomass, agriculture, forestry and waste sectors are considered. The temporal resolution for wind and solar is both annual and year fractions (timeslices as used by JRC-EU-TIMES). ENSPRESO complements the EMHIRES collection, that provides meteorologically derived power time series at high temporal and spatial resolution. ENSPRESO can impact the results of any energy model by improving its analyses of the competition and complementarity of energy technologies.</p></blockquote><p><a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:RUIZ%20CASTELLO%20Pablo">RUIZ CASTELLO Pablo</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:NIJS%20Wouter">NIJS Wouter</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:TARVYDAS%20Dalius">TARVYDAS Dalius</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:SGOBBI%20Alessandra">SGOBBI Alessandra</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:ZUCKER%20Andreas">ZUCKER Andreas</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:PILLI%20Roberto">PILLI Roberto</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:CAMIA%20Andrea">CAMIA Andrea</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:THIEL%20Christian">THIEL Christian</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:HOYER-KLICK%20Carsten">HOYER-KLICK Carsten</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:DALLA%20LONGA%20Francesco">DALLA LONGA Francesco</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:KOBER%20Tom">KOBER Tom</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:BADGER%20Jake">BADGER Jake</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:VOLKER%20Patrick">VOLKER Patrick</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:ELBERSEN%20Berien">ELBERSEN Berien</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:BROSOWSKI%20Andre">BROSOWSKI Andre</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:THR%C3%84N%20Daniela">THRÄN Daniela</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:JONSSON%20Klas">JONSSON Klas</a></p><h3>How to cite</h3><p>Ruiz Castello, P., Nijs, W., Tarvydas, D., Sgobbi, A., Zucker, A., Pilli, R., Camia, A., Thiel, C., Hoyer-Klick, C., Dalla Longa, F., Kober, T., Badger, J., Volker, P., Elbersen, B., Brosowski, A., Thrän, D. and Jonsson, K., ENSPRESO - an open data, EU-28 wide, transparent and coherent database of wind, solar and biomass energy potentials, European Commission, 2019, JRC116900.</p><p>European Commission</p><p>JRC116900</p><h3>Remarks</h3><p>The originator of this mirror requires stable and reliable URLs due to an integration of the dataset into an automated workflow. The data catalogue has frequent outages.</p>
Code and model outputs for article "Assessing the potential of backscattering as a proxy for phytoplankton carbon biomass"
<p>Scripts from the scientific article "Assessing the potential of backscattering as a proxy for phytoplankton carbon biomass"<br> Submitted to Global Biogeochemical Cycles<br> Camila Serra-Pompei, Anna Hickman, Gregory L. Britten, Stephanie Dutkiewicz</p> <p>This repository contains all the files needed to run the MITgcm Biogeochemical and Optical model (MITgcmBgc, a.k.a. The Darwin model) and generate the figures in the paper.<br> Model outputs are also provided, so there is no need to run the entire model to generate the figures.</p> <p>The main folder "codes_phyto_bbp_paper.zip" contains all the files to process model output and reproduce the figures from the paper. The main files are:<br> - "Script_main.m" loads and processes model outputs and generates figures 2 to 9.<br> - "Script_new_optical_params" generates the optical parameters that are used as inputs in the MITgcmBgc model. It also generates figure 1.<br> - "Script_sensitivity_analysis" Performs offline sensitivity analysis and figure 10.</p> <p>The "mitgcm_darwin_3.zip" folder contain the code of the MITgcm with the Biogeochemical and Optical modules (Darwin package), with the configurations used in this study. Explanations of the model can be found in https://darwin3.readthedocs.io/en/latest/phys_pkgs/darwin.html </p> <p>These folders also contain the BGC-Argo floats data and climatological bbp and Chl NASA MODIS-Aqua GIOP data used in this study.</p> <p>The BGC-Argo data used in this article (download date: 27/10/2021) is available in "Other_extra_files/Argo_matrix.mat". The BGC-Argo data were collected and made freely available by the International Argo Program and the national programs that contribute to it (https://argo.ucsd.edu, https://www.ocean-ops.org, https://doi.org/10.17882/42182). The Argo Program is part of the Global Ocean Observing System. BGC-Argo float data was extracted using the BGC-Argo-Mat Matlab toolbox (Frenzel et al. 2021).</p> <p>The MODIS-Aqua satellite remote sensing data used in this article (download date: 23/12/2022) is available here in the folder "nasa_data". Satellite remote sensing data was extracted from NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group; (2014): MODIS-Aqua Ocean Color Data; NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. http://dx.doi.org/10.5067/AQUA/MODIS_OC.2014.0</p> <p> </p>
Data from: The potential for spatial distribution indices to signal thresholds in marine fish biomass
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Data from: Potentially peat-forming biomass of fen sedges increases with increasing nutrient levels
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Forest Aboveground Biomass and Carbon Sequestration Potential, Northeastern USA
This dataset provides 90 m estimates of forest aboveground biomass (Mg/ha) for nominal 2011 and projections of carbon sequestration potential for 11 states in the Regional Greenhouse Gas Initiative (RGGI) domain. The RGGI is a cooperative, market-based effort among States in the eastern United States. Estimated biomass and sequestration potential were computed using the Ecosystem Demography (ED) model. The ED Model integrates several key data including climate variables from Daymet and MERRA2 products; physical soil and hydraulic properties from Probabilistic Remapping of SSURGO (POLARIS) and CONUS-SOIL; land cover characteristics from airborne lidar, the National Agriculture Imagery Program (NAIP), and the National Land Cover Database (NLCD); and vegetation parameters from the Forest Inventory and Analysis (FIA) Program.
Forest Aboveground Biomass and Carbon Sequestration Potential for Maryland, USA.
This dataset provides 90-m resolution maps of estimated forest aboveground biomass (Mg/ha) for nominal year 2011 and projections of carbon sequestration potential for the state of Maryland. Estimated biomass and sequestration potential were computed using the Ecosystem Demography (ED) model, which integrates data from multiple sources, including: climate variables from the North American Regional Reanalysis (NARR) Product, soil variables from the Soil Survey Geographic Database (SSURGO), land cover variables from airborne lidar, the National Agriculture Imagery Program (NAIP) and the National Land Cover Database (NLCD), and vegetation parameters from the Forest Inventory and Analysis (FIA) Program.
Dataset Characterization of the invasive macroalgae Rugulopteryx Okamurae for potential biomass valorisation
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Potential aboveground biomass carbon density
<p>This dataset is the potential above-ground biomass carbon density in the eight provinces of southern China from 2002 to 2017 at the resolution of 500m x 500m, with the urban and water areas, cropland, and the southeast margin of the Tibet Plateau masked. The difference between observed carbon density and the carbon carrying capacity is the potential carbon density. </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.