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21 results for “fuel load”
Fuel Loading in Simulated Hurricane Experiment at Harvard Forest since 1993
Wind disturbance profoundly shapes temperate forests but few studies have evaluated patterns and mechanisms of long-term forest dynamics following major windthrows. In 1990, we initiated a large hurricane simulation experiment in a 0.8 ha manipulation (pulldown) and 0.6 ha control area of a maturing Quercus rubra-Acer rubrum forest in New England. We toppled 276 trees in the pulldown, using a winch and cable, in the northwesterly direction of natural treefall from major hurricanes. Eighty percent of canopy trees and two-thirds of all trees greater than 5 cm dbh suffered direct and indirect damage. An enormous input of dead wood was one result of the manipulation. Many perceive an increased risk of wildfire after trees are blown down, but this depends on the amount, size, and persistence of the dead wood inputs.
Pre- and post-fire vegetation and fuel loading data from mixed conifer plots in Arizona and New Mexico: 2010-2023
A permanent plot network was installed in mixed conifer stands across the U.S. Southwest (Arizona and New Mexico) between 2010-2013, primarily to monitor the spread and severity of white pine blister rust (WPBR), a disease caused by the fungal pathogen Cronartium ribicola on southwestern white pine (Pinus strobiformis). Study sites were mid-to-high elevation mixed conifer stands composed of southwestern white pine, Douglas-fir (Pseudotsuga menziesii), white fir (Abies concolor), ponderosa pine (Pinus ponderosa), quaking aspen (Populus tremuloides), Gambel oak (Quercus gambelii), blue spruce (Picea pungens), Engelmann spruce (Picea engelmannii), corkbark fir (Abies latifolia var. arizonica), Rocky Mountain bristlecone pine (Pinus aristata), and New Mexico locust (Robinia neomexicana). After plot installation, 6 fires occurred in the study area, burning an estimated total of 489,390 acres and 30 plots. We remeasured plots at 1-, 5- and 10-year intervals post-fire, quantifying burn severity via a composite burn index (CBI) at the first year post-fire. We also assessed regeneration, overstory mortality, and fuel loading. Overstory variables collected included tree species, status, diameter at breast height (DBH), and mortality, as well as height, height to live crown base, strata, and crown class on a subset of trees. Understory trees were tallied by species. Fuel load was measured via transect and calculated in megagrams per hectare categorically based on fuel type. Other variables such as basal area and trees per hectare were derived and calculated. This dataset was utilized in the manuscript "Climate, fire, and the future of mixed conifer ecosystems in the U.S. Southwest" (currently in review), and R code used for analyses is included in the dataset.
Fuel loads monitoring dataset of Campos Amazônicos Fire Experiment (Amazonas, Brazil)
<p>This dataset presents fuel loads monitoring data related to the Campos Amazônicos Fire Experiment (CAFE) (Amazonas, Brazil) project. Located within a protected area within the largest enclave of tropical savanna in the Southern Amazon, CAFE comprises a careful and systematic experimental design that was conceived from the start to evaluate satellite observations and their potential and limitations in studying spatial and temporal fire dynamics in tropical savannas.</p> <p>More information about the experimental design can be found in the following publication:</p> <p>Alves, D. B.; Fidelis, A.; Pérez-Cabello, F.; Alvarado, S. T.; Conciani, D. E.; Cambraia, B. C.; Silveira, A. L. P.; Silva, T. S. F. Impact of Image Acquisition Lag-Time on Monitoring Short-Term Postfire Spectral Dynamics in Tropical Savannas: the Campos Amazônicos Fire Experiment. Journal of Applied Remote Sensing v. 16, n. 3 (2022) - <a href="https://doi.org/10.1117/1.JRS.16.034507">https://doi.org/10.1117/1.JRS.16.034507</a></p> <p>_____________________________________________________________________________</p> <p>August 08, 2022 - Version 1.0 includes data from 30 monitored experimental plots of 1 hectare each (100×100 m). Three experimental treatments were then established: 12 plots were burned in May (Early-Dry Season – EDS), further 12 plots were burned in August (Mid-Dry Season – MDS), and 6 plots were kept as control by ensuring fire exclusion throughout the duration of the experiment. Controlled burning occurred during two separate field campaigns in 2019, the first between May 19th and 25th, and the second between August 22nd and 26th.</p> <p>Measurements of fuel load were obtained from eight subplots of 0.5 × 0.5 m randomly distributed within each plot. Samples included graminoids, leaves and branches near the ground. Biomass was dried at 70°C for 48 hours, and weighed to determine total fuel load (kg. m<sup>-2</sup>) for each sample. Samples were taken before fire for all control, EDS and MDS burn plots during both field campaigns, and repeated sampling was carried out after fire for the burned plots. In 2020, all 30 monitored plots were sampled again in May and August.</p> <p>The files available include: i) a table (Fuel_load_measures.csv) that contains the measures of fuel loads for each plot; ii) a text file (List_of_variables.rtf), which details each variable available in the table.</p> <p>_____________________________________________________________________________</p> <p>We thank the management team of the Campos Amazônicos National Park, and in particular to its Fire Brigade (squad leaders José Furtado Neto, Genaldo Júnior, Ademilton Carvalho, Simei Limoeiro, José Alexandre Medeiros, Antonio Machado and Leandro Lacerda, and on behalf of them to all other members of the brigade), who ensure safe burning of all fire experiments (SISBIO license number 67210-5). This work was supported by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, grant numbers 2019/07357-8; 2015/06743-0); the Conselho Nacional de Pesquisa e Desenvolvimento (CNPq, grant numbers 154660/2018-3; 441968/2018-0; 303988/2018-5).</p>
DATASET : Thresholds of fire response to moisture and fuel load differ between tropical savannas and grasslands across continents
<p><strong>Abstract </strong></p> <p><strong>Aim:</strong> An emerging framework for tropical ecosystems states that fire activity is either ‘<em>fuel build-up limited</em>’ or ‘<em>fuel moisture limited</em>’ i.e. as you move up along rainfall gradients, the major control on fire occurrence switches from being the amount of fuel, to the moisture content of the fuel. Here we used remotely sensed datasets to assess whether interannual variability of burned area is better explained by annual rainfall totals driving fuel build-up, or by dry season rainfall driving fuel moisture.</p> <p><strong>Location:</strong> Pantropical savannas and grasslands</p> <p><strong>Time period:</strong> 2002-2016</p> <p><strong>Methods:</strong> We explored the response of annual burned area to interannual variability in rainfall. We compared several linear models to understand how <em>fuel moisture </em>and <em>fuel build-up effect </em>(accumulated rainfall during 6 and 24 months prior to the end of the burning season respectively) determine the interannual variability of burned area and explore if tree cover, dry season duration and human activity modified these relationships.</p> <p><strong>Results:</strong> Fuel and moisture controls on fire occurrence in tropical savannas varied across continents. Only 24% of South American savannas were <em>fuel build-up limited</em> against 61% of Australian savannas and 47% of African savannas. On average, South America switched from fuel limited to moisture limited at 500 mm yr<sup>-1</sup>, Africa at 800 mm yr<sup>-1</sup> and Australia at 1000 mm yr<sup>-1 </sup>of mean annual rainfall.</p> <p><strong>Main conclusions:</strong> In 42% of tropical savannas (accounting for 41% of current area burned) increased drought and higher temperatures will not increase fire, but there are savannas, particularly in South America, that are likely to become more flammable with increasing temperatures. These findings highlight that we cannot transfer knowledge of fire responses to global change across ecosystems/regions – local solutions to local fire management issues are required, and different tropical savanna regions may show contrasting responses to the same drivers of global change.</p>
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).
Rohingya Refugee Camp Fuel Load Dataset (Camp 4Ex, 5, KRC)
<p>A comprehensive survey was conducted on July 2, 2022, to prepare the fuel inventory map featuring dwelling structure, fuel load density, placement of fuel etc. In the 5-day long survey, three camps having contrasting fuel inventory have been examined. They are- Camp 4 Extension (Ex), Camp 5 and Kutupalong Registered Camp (KRC). These camps were selected based on the following features :</p> <ul> <li>Being the youngest of all the camp in Kutupalong, Camp 4 Ex followed a standard design and an average separation distance of 7m.</li> <li>On the other hand, Camp 5 is densely populated, having no visible space between two dwellings. It was one of the largest camps with a population of almost 25 thousand. The Rohingya community there are as equally impoverished as those living in Camp 4 Ext. The shelters of both of these camps are made of cellulose based structure material like wood, bamboo, timber etc., but the dwellings of camp 5 don’t follow any standard design.</li> <li>With a sharp contrast, dwellings in KRC follow a shaded structure consisting of six dwellings under one roof but separated by brick wall or corrugated steel sheet. KRC began informally in 1991, making it the oldest of all camps and its dwellers somewhat self-sufficient. A lot more permanent structure e.g., wooden furnitures, bed, wardrobe, showcase has been found here, increasing its fuel load larger than that of Camp 4 Ex and 5.</li> </ul>
Dataset for the paper "High Loading of Single Atomic Iron Sites in Pyrolysed Fe-NC Oxygen Reduction Catalysts for Proton Exchange Membrane Fuel Cells", DOI:10.1038/s41929-022-00772-9
<p>The data in this spreadsheet was used to produce the figures in the paper </p> <p>Authors: Asad Mehmood, Mengjun Gong, Frédéric Jaouen, Aaron Roy, Andrea Zitolo, Anastassiya Khan, Moulay-Tahar Sougrati, Mathias Primbs, Alex Martinez Bonastre, Dash Fongalland, Goran Drazic, Peter Strasser, Anthony Kucernak</p> <p>Title: High Loading of Single Atomic Iron Sites in Pyrolysed Fe-NC Oxygen Reduction Catalysts for Proton Exchange Membrane Fuel Cells</p> <p>Journal: Nature Materials</p> <p>DOI: 10.1038/s41929-022-00772-9</p> <p>Please cite the above reference if you wish to use this data </p> <p> </p> <p>DOI of data: 10.5281/zenodo.6411262</p>
Fig. 2 in Fuel Load And Flight Range Estimation Of Migrating Passerines In The Western Part Of The Carpathian Basin During The Autumn Migration
Fig. 2. Potential flight ranges (+ SE) of passerine species stopping over in Tömörd during
Fig. 1 in Fuel Load And Flight Range Estimation Of Migrating Passerines In The Western Part Of The Carpathian Basin During The Autumn Migration
Fig. 1. Potential Mediterranean flyway in southern Europe (arrows) and the location of Tömörd
FIRST PRINCIPLE DESIGN LOAD ESTIMATION FOR LH2 FUEL TANKS BY MEANS OF 0D APPROACH - OMAE Data Set
<p>Supplementary data set for the OMAE publication "FIRST PRINCIPLE DESIGN LOAD ESTIMATION FOR LH2 FUEL TANKS BY MEANS OF 0D APPROACH". Contains the complete set of data for the presented cases while only selected data series are shown in the paper.</p>
Evaluating short-term effects of fuel treatments on fuel loading in western US dry conifer forests: a meta-analysis
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Data from: Small-scale variation in fuel loads differentially affects two co-dominant bunchgrasses in a species-rich pine savanna
Ecological disturbances frequently control the occurrence and patterning of dominant plants in high-diversity communities like C4 grasslands and savannas. In such ecosystems disturbance-related processes can have important implications for species, and for whole communities when those species are dominant, yet mechanistic understanding of such processes remains fragmentary. Multiple bunchgrass species commonly co-dominate disturbance-dependent and species-rich pine savannas, where small-scale fuel heterogeneity may influence bunchgrass survival and growth following fires. We quantified how fire in locally varying fuel loads influenced dynamics of dominant C4 bunchgrasses in a species-rich pine savanna in southeastern Louisiana, USA. We focused on two congeneric, co-dominant species (Schizachyrium scoparium and S. tenerum) with similar growth forms, functional traits and reproductive strategies to highlight effects of fuel heterogeneity during fires. In experimental plots with either reduced or increased fuels versus controls with unmanipulated fuels, we compared: 1) bunchgrass damage and 2) mortality from fires; 3) subsequent growth and 4) flowering. Compared to controls, fire with increased fuels caused greater damage, mortality and subsequent flowering, but did not affect post-fire growth. Fire with reduced fuels had no effect on any of the four measures. The two species responded differently to fire with increased fuels – S. scoparium incurred measurably more damage and mortality than S. tenerum. Logistic regression indicated that the larger average size of S. tenerum tussocks made them resistant to more severe burning where fuels were increased. We speculate that locally increased fuel loading may be important in pine savannas for creating colonization sites because where fuels are light or moderate, dominant bunchgrasses persist through fires. Small-scale heterogeneity in fires, and differences in how species tolerate fire may together promote shared local dominance by different bunchgrasses.
Data from: Effect of fuel deposition rate on departure fuel load of migratory songbirds during spring stopover along the northern coast of the Gulf of Mexico
Migrants are generally assumed to minimize their overall migration time by adjusting their departure fuel loads (DFL) in relation to anticipated and experienced fuel deposition rates (FDRs). We utilized a 21-yr long migration banding station dataset to examine the relationship between FDR and DFL during spring migration in six Nearctic-Neotropical migratory songbird species during stopover along the northern coast of the Gulf of Mexico (GOM) following trans-gulf flight. Estimates of fuel stores, stopover durations, and FDRs from our long term migration data set were combined to determine DFL. We expected and found that migrants across all six species adjust their DFL to the rate at which they deposit fuel reserves. This robust finding suggests that songbird migrants are sensitive to time constraints during spring passage presumably to fine-tune their stopover schedule in relation to experienced and anticipated habitat quality. Two of the species studied showed an effect of age on the FDR–DFL relationship: one was consistent with the expectation that older birds would be less sensitive to changes in FDR, while the second was contrary to our expectations and likely suggesting an age-dependent response to habitat quality. We found sex-dependent differences consistent with male DFL being more sensitive to FDR in only two of six species studied, and argue that both males and females are time constrained during spring passage in relation to arrival at breeding destinations. The positive relationship between FDR and DFL among all species and for age and sex groups in some species reflects a migration strategy sensitive to time.
Dataset for the paper "Nanostructured Catalyst Layer Allowing Production of Ultralow Loading Electrodes for Polymer Electrolyte Membrane Fuel Cells with Superior Performance" published in ACS Appl. Energy Mater.
<p>The data in this spreadsheet was used to produce the figures in the paper</p><p>Authors:</p><p>Colleen Jackson, Michalis Metaxas, Jack Dawson, Anthony Kucernak</p><p>Title:</p><p>Nanostructured Catalyst Layer Allowing Production of Ultralow Loading Electrodes for Polymer Electrolyte Membrane Fuel Cells with Superior Performance</p><p>Journal:</p><p>ACS Appl. Energy Mater. </p><p>DOI:</p><p>10.1021/acsaem.3c01987</p><p>Please cite the above reference if you wish to use this data</p><p>DOI of data:</p><p>10.5281/zenodo.10256698</p>
Data from: Landscape variation in tree regeneration and snag fall drive fuel loads in 25-yr old post-fire lodgepole pine forests
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Data from: Effect of fuel deposition rate on departure fuel load of migratory songbirds during spring stopover along the northern coast of the Gulf of Mexico
Open the record for dataset details and reuse information.
Data from: Small-scale variation in fuel loads differentially affects two co-dominant bunchgrasses in a species-rich pine savanna
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Fire Fuel Load: Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
Data from: Invasive grass fuel loads suppress native species by increasing fire intensity and soil heating
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SAFARI 2000 Modeled Fuel Load in Southern Africa, 1999-2000
This data set contains global, spatially explicit (1 km2 grid cells) and temporally explicit (semi-monthly) modeled output of fuel loads over southern Africa. The fuel types considered in the data set are litter (dead tree leaves), dead grass, green grass, and small-diameter twigs. The Production Efficiency Model (PEM) was used to produce the estimated fuel loads for southern Africa for the 1999-2000 growing seasons. In order to accommodate the southern African growing season, which spans October to May, the PEM was run on twenty-four 15-day intervals from September 1, 1999 to August 31, 2000. The fuel load model was calibrated from measurements recorded in 1996 along the Kalahari Transect, and has been verified from independent site measurements of fuel loads recorded during the SAFARI 2000 dry season field campaign in August and September of 2000 (Alleaume et al., 2003, Hely et al., 2003a) as well as other past campaigns.This data set consists of gridded binary image files. The single-byte images have a 1 km resolution and the grid is 3900 columns by 4800 rows in size. There are 24 files each for litter, dead grass, and green grass fuel loads, and a single file for the twig fuel loads. For the latter, small twigs (less than 1cm in diameter) are calculated from the tree cover percentage (UMD Tree Cover from Hansen et al., 2000) using an empirical relationship (Alleaume et al., 2003). Therefore, this fuel type is a constant throughout the year.
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