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12 results for “biomass, fuel”
Measurement of Fuel load (organic biomass) Quantities of Organic Soils, Understory Plants and Trees at Sites within the Bonanza Creek LTER Regional Site Network in Interior Alaska, 2019
This dataset contains fuel load measurements for a subset of the BNZ LTER's RSN sites (n = 28). The data for each site separted by 16 fuel types (or plant functional types) including organic soil layers (fibric, mesic), vascular understory (evergreen shrub, short deciduous shrub, graminoid, forb, tall deciduous shrub, tree seedling/sapling, dead-downed wood), nonvascular understory (feather moss, Sphagnum moss, colonizer moss, lichen) and trees (evergreen tree, deciduous tree).
Fine fuel biomass in grasslands and shrublands of the Intermountain West
<p>The Great Basin Coordination Center partners with Bureau of Land Management field offices in the Intermountain West to monitor fine fuel loading to make firefighting resource allocation decisions in the early spring. Fine fuel loads, a measurement of the small, herbaceous, and flammable biomass in a system can vary greatly inter-annually in grass and shrub dominated systems. These data are a compilation of those measurements from 11 district offices spanning from 1996-2020 years over 164 sites and the methods used to collect those measurements, which varies between field offices.</p>
Livestock and kangaroo grazing have little effect on biomass and fuel hazard in semi-arid woodlands
<ol> <li>Using livestock grazing as a tool to manage biomass and reduce fuel hazard has gained widespread popularity, but examples from across the globe demonstrate that it often yields mixed, context-dependent results. Grazing has potential to deliver practical solutions in systems where grazing reduces not only biomass but also reduces fuel hazard by altering vegetation connectivity or composition.</li> <li>We assessed the extent to which recent rainfall, rabbit and kangaroo grazing and recent and historic livestock grazing alters and accounts for variation in above-ground biomass, biomass composition and fuel hazard ratings across three broad communities in eastern Australia. We used nested linear models to assess biomass in three vertical vegetation strata, that matched the strata assessed in the Overall Fuel Hazard Assessment guide (i.e. litter/surface fuel; groundstorey vegetation/near surface fuel; and midstorey vegetation/elevated fuel) and Ordinal Logistic Regression to assess categorical fuel hazard ratings.</li> <li>Only recent kangaroo grazing reduced groundstorey biomass across all communities. Kangaroo grazing altered litter mass and significantly reduced surface fuel hazard in one community. Recent livestock grazing did not reduce fuel hazard, and despite significantly reducing half of our measures of biomass, these were not practical reductions. For instance, livestock grazing significantly reduced litter mass, however our model predicts that doubling our assessment of livestock grazing intensity only reduces total litter mass by 0.8 %, or 8 kg per hectare in landscapes where average litter loads ranged from 3,600 to 12,600 kg per hectare. Furthermore, long-term livestock grazing increased shrub biomass and in one community this increased elevated fuel hazard. There were few effects of rabbits. The effects of rainfall on biomass were up to an order of magnitude greater than any effects due to grazing.</li> <li> <i>Synthesis and applications:</i> Our data suggest that management practices that seek to use livestock grazing to reduce biomass in these systems will not achieve practical reductions in biomass and or fuel hazard.</li> </ol>
Measurement report: quantifying source contribution of fossil fuels and biomass-burning black carbon aerosol in the southeastern margin of the Tibetan Plateau
<p>Anthropogenic emissions of Black carbon (BC) aerosol are transported from Southeast Asia to the southwestern Tibetan Plateau (TP) during the pre-monsoon; however, the quantities of BC from different anthropogenic sources and the transport mechanisms are still not well constrained because there have been no high-time-resolution BC source apportionments. Intensive measurements were taken in a transport channel for pollutants from Southeast Asia to the southeastern margin of TP during the pre-monsoon to investigate the influences of fossil fuels and biomass burning on BC. A receptor model coupled multi-wavelength absorption with aerosol species concentrations was used to retrieve site-specific Ångström exponents (AAE) and mass absorption cross-sections (MAC) for BC. An ‘aethalometer model’ that used those values showed that biomass burning had a larger contribution to BC mass than fossil fuels (BCbiomass = 57% versus BCfossil = 43%). The potential source contribution function indicated that BCbiomass was transported to the site from northeastern India and northern Burma, The Weather Research and Forecasting model coupled with chemistry (WRF-Chem) model indicated that 40% of BCbiomass originated from Southeast Asia, while the high BCfossil was transported from the southwest of sampling site. A radiative transfer model indicated that the average atmospheric direct radiative effects (DRE) of BC was +4.6 ± 2.4 W m<sup>-2</sup> with +2.5 ± 1.8 W m<sup>-2</sup> from BCbiomass and +2.1 ± 0.9 W m<sup>-2</sup> from BCfossil. The DRE of BCbiomass and BCfossil produced heating rates of 0.07 ± 0.05 and 0.06 ± 0.02 K day<sup>-1</sup>, respectively. This study provides insights into sources of BC over a transport channel to the southeastern TP and the influence of the cross-border transportation of biomass burning emissions from Southeast Asia during the pre-monsoon.</p>
Contrasting Activation Characteristics of Biomass Burning and Fossil Fuel Combustion Aerosols in Fogs and Clouds: Implications for Regional Air Quality and Climate
<p>The key 'jul' in data use 2021-01-01 as the referece day, for example, 2021-01-02 12:00:00 corresponding to jul of 2.5. </p>
Field data synthesis accompanying "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>Synthesis of fuel load and fuel consumption field measurements accompanying the publication:</p><p>"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"</p><p>Dave van Wees1, Guido R. van der Werf1, James T. Randerson2, Brendan M. Rogers3, Yang Chen2, Sander Veraverbeke1, Louis Giglio4, and Douglas C. Morton5</p><p>1Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br>2Department of Earth System Science, University of California, Irvine, CA 92697, USA<br>3Woodwell Climate Research Center, Falmouth, MA 02540, USA<br>4Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br>5Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</p><p>DOI: https://doi.org/10.5194/gmd-15-8411-2022</p><p> </p><p>Units are g C / m2</p>
Model data for "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>500 m fire carbon emissions and burned area as part of the publication:</p> <p>"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"</p> <p>Dave van Wees<sup>1</sup>, Guido R. van der Werf<sup>1</sup>, James T. Randerson<sup>2</sup>, Brendan M. Rogers<sup>3</sup>, Yang Chen<sup>2</sup>, Sander Veraverbeke<sup>1</sup>, Louis Giglio<sup>4</sup>, and Douglas C. Morton<sup>5</sup></p> <p><sup>1</sup>Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br><sup>2</sup>Department of Earth System Science, University of California, Irvine, CA 92697, USA<br><sup>3</sup>Woodwell Climate Research Center, Falmouth, MA 02540, USA<br><sup>4</sup>Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br><sup>5</sup>Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</p> <p>DOI: https://doi.org/10.5194/gmd-15-8411-2022</p> <p> </p> <p><strong>UPDATE OF DATASET TO 2023:</strong></p> <p>This dataset has now been extended to 2023. Since the first release of this dataset, multiple updates to the model input data have been made:</p> <p>- Update from MODIS C6 to MODIS C6.1 for all MODIS input data, including MCD12Q1 land cover types, MCD14ML active fires, MCD15A2H fPAR, MOD44B VCF, MOD44W land-water mask, and MCD64A1 burned area.<br>- Update of Hansen forest loss data from v1.9 to v1.11.<br>- Update of GLEAM evaporative stress data from v3.6b to v3.7b.<br>- Extension of ERA5-land data to 2023.<br>- Addition of land cover type layers to the 500-m resolution data files.</p> <p> </p> <p>Files contain 500-m (per MODIS tile) and 0.25 degree aggregated (global grid) carbon emissions and burned area from biomass burning for 2002-2022, as part of the paper "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)" published in Geoscientific Model Development (https://doi.org/10.5194/gmd-15-8411-2022). 500-m resolution files include land cover type grids. 0.25 degree global grid files also include biome partitioning and accompanying biome fractional cover grids.</p> <p>Zip archives with filenames "500m_YYYY.zip" contain annual files named "Model500m_2002-2023yr_h##v##_YYYY.nc", which are the 500-meter resolution model results per MODIS tile using the MODIS sinusoidal projection. Carbon emission data layers are:</p> <p>- Total biomass burning carbon emissions from aboveground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_AG_TOT)</p> <p>- Total biomass burning carbon emissions from belowground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_BG_TOT)</p> <p>- Fire-related forest loss carbon emissions from aboveground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_AG_FL)</p> <p>- Fire-related forest loss carbon emissions from belowground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_BG_FL)</p> <p>Total emissions are calculated as: C_AG_TOT + C_BG_TOT. Total fire-related forest loss emissions are calculated as: C_AG_FL + C_BG_FL.</p> <p>Burned area data layers are:</p> <p>- Total burned area; fraction of 500-m grid cell per month (/MOD_Grid/burned_area/BA_TOT)</p> <p>- Burned area from fire-related forest loss; fraction of 500-m grid cell per month (/MOD_Grid/burned_area/BA_FL)</p> <p>The Zip archive with filename "025d_2002_2023.zip" contains annual files named "Model500m_2002-2023yr_025d_YYYY.nc", which are the 500-m model results aggregated to a 0.25 degree global lat-lon grid. These files contain the same variables as the 500-m files, but aggregated to 0.25 degree resolution (MOD_CMG025). Furthermore, these files include biome partitioning of emissions and burned area (MOD_CMG025BIOME) and provide accompanying biome fractional cover grids for all 20 biomes (variable 'biomes'). Biomes are listed in detail in Table S1 of the van Wees et al. (2022) paper. The biomes 'water', 'snow/ice' and 'barren' were excluded from Table S1 because of their negligible share, but are included in the files provided here for completeness.</p>
Model code for "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>500 m fire carbon emissions model code as part of the publication:</p> <p>"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"</p> <p>Dave van Wees<sup>1</sup>, Guido R. van der Werf<sup>1</sup>, James T. Randerson<sup>2</sup>, Brendan M. Rogers<sup>3</sup>, Yang Chen<sup>2</sup>, Sander Veraverbeke<sup>1</sup>, Louis Giglio<sup>4</sup>, and Douglas C. Morton<sup>5</sup></p> <p><sup>1</sup>Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br> <sup>2</sup>Department of Earth System Science, University of California, Irvine, CA 92697, USA<br> <sup>3</sup>Woodwell Climate Research Center, Falmouth, MA 02540, USA<br> <sup>4</sup>Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br> <sup>5</sup>Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</p> <p>DOI: https://doi.org/10.5194/gmd-15-8411-2022</p> <p> </p> <p>Developed in Python version 2.7.16. Please note, this code is meant to give a general overview of the model structure and not to fully reproduce the model results with the push of one button. The full model code is much more complex to account for various simulation scenarios and relies on numerous large input datasets that all require extensive preprocessing. By omitting these complexities, we tried to make this script as understandable as possible. In case your goal is to reproduce the model in detail, please contact the first author to discuss the possibilities.</p>
Livestock and kangaroo grazing have little effect on biomass and fuel hazard in semi-arid woodlands
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Multiphysics Modeling of Ultrasonic-assisted biomass torrefaction for fuel pellets production
<p>When an ultrasound wave propagates through a volume of biomass medium, the majority of the energy in the acoustic field is absorbed locally by the biomass, resulting in the generation of heat. This torrefaction effect results in a temperature increase of the biomass, converting biomass into a coal-like intermediate with upgraded fuel properties over the original biomass. However, few analyses can be found in the literature explaining the mechanism of ultrasound-assisted biomass torrefaction. This research aims to model an ultrasound-assisted biomass torrefaction system. The developed multiphysics model depicts the piezoelectric effect of a transducer, the vibration amplitude at the output end of the ultrasonic horn, and the acoustic intensity and temperature distributions in the biomass medium. The vibration amplitude and frequency of the ultrasonic horn were measured by a non-contact capacitive sensor, and it is verified the model can accurately simulate the ultrasonic vibration of the experimental system. The temperature at the center of the biomass was measured to validate the model’s temperature prediction. Both simulation and experiment showed that ultrasound-assisted biomass torrefaction can create torrefied fuel pellet within 60 seconds.</p>
Aviation Fuel Production Pathways from Lignocellulosic Biomass via Alcohol Intermediates – A Technical Analysis - Supplementary Material
<p>Data for the associated publication </p>
Seasonal Harvesting Impact on Biomass Fuel Properties and Pyrolysis-Derived Bio-oil Organic Phase Composition
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