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Data for 'In temperate Europe, fire is already here: the case of the Netherlands'
<p><strong>Dataset about recent wildfire statistics for the Netherlands, 2017-2022</strong></p> <p><strong>Stoof, C.R., Kok, E., Cardil Forradellas, A., M. van Marle<em>.</em> In temperate Europe, fire is already here: The case of The Netherlands. <em>Ambio</em> (2024). https://doi.org/10.1007/s13280-023-01960-y</strong></p> <p>This dataset contains:</p> <p>1) Data for Figure 1: Historic patterns of a) annual fire occurrence and b) annual area burned (1945-1993) and c) distribution of fire size (1978-1993) in The Netherlands. Source: IKC (1995). File names: burned_area_distribution_historic.csv, fires_1945-1993.csv</p> <p>2) Raw data for individual fire records, because of privacy reasons this file is limited to information regarding fire date, time, municipality, area burned by EU vegetation class, and detailed country-specific vegetation classification. File name: All_fires.csv. For information regarding the availability of other raw data please contact the corresponding author.</p> <p>2) KMZ files for the five largest fires since 1970 (Figure 2)</p> <p>3) The aggregated data based on which Figure 3 in the article is based:</p> <p>Temporal characteristics and other metrics of recent landscape fires in The Netherlands (2017-2022): number of fires by month and year (a), year (b), month (c), weekday (d), and time of day (e, time that fire was reported), mean fire size (f), vegetation type affected following EU classification (g), detailed vegetation type (h), and fire size distribution (i). </p> <p>4) The aggregated data based on which Figure 4 in the associated article is based:</p> <p>Presumed fire cause (a, n=3667), number of fire engines and water tenders requested (b, for n=3583 and n=2270 fires, respectively), and estimate of suppression and restoration costs (c). **Note that while the fire cause classification in (a) follows the EU classification system, fire cause was only informally assessed, hence these data consider presumed cause only.</p> <p>5) A metadata file about all csv files, explaining all column names in each file </p> <p> </p> <p> </p>
Structure of the Canadian Forest Fire Weather Index System: the model and its components
<p>This material is part of:</p> <p>de Rigo, D. 2018. <strong>The Canadian Forest Fire Weather Index System: a synopsis of computational semantics</strong>. https://doi.org/10.6084/m9.figshare.4046673<br><br><strong>Structure of the Canadian Forest Fire Weather Index system: the model and its components</strong> — The <a href="../record/10806780#preview-iframe">figure below</a> (formats: <a href="../record/10806780/files/FWI-sys_simple_diagram.png?download=1">PNG</a> or <a href="../record/10806780/files/FWI-sys_simple_diagram.pdf?download=1">PDF</a>) shows the logical subdivision of the Canadian Forest Fire Weather Index system (FWI-sys) in components.</p> <p> </p> <p>The Canadian FWI-sys (De Groot,1987; Van Wagner,1987) is an index of fire danger by weather designed to consider the effects on vegetation fuels of the sequence of weather conditions. It is designed to estimate a uniform numerical rating for the relative fire potential accounting for the local sequence of temperature, wind speed, relative humidity, and precipitation, for the day in which the rating is estimated but also modelling the dynamics of the previous days. In addition, the variable amount of possible drying due to the varying solar irradiation in different seasons is taken into account by adjusting the parameters per each month of the year.<br><br>The system is standardised to consider the behaviour of a reference typology of vegetation fuel (mature pine stand) regardless of other non-weather factors which may locally influence the fire danger, such as the specific topography or the pattern, composition, and structure of vegetation assemblages. Therefore, FWI-sys is suitable to support the harmonised comparison among variable weather conditions, either spatially (comparing different spatial regions) or temporally (comparing the same region over time).<br><br>The FWI-sys components are organised in three layers, processing at the daily frequency weather information (either from observations, reanalysis, forecast, or climate scenarios) and estimating from it a final standard aggregated numerical rating of fire intensity.<br><br>The required input variables are</p> <ul> <li>Temperature T (nominally, FWI-sys requires T at noon)</li> <li>Wind speed W (nominally, FWI-sys requires T at noon)</li> <li>Relative humidity</li> <li>Precipitation (24-hour rainfall)</li> <li>Month of the year</li> </ul> <p>The FWI-sys was originally designed to fit the Candian conditions. Following its success, adaptations of the system were studied for different areas of the globe. This implies that the parameters used inside the FWI-sys globally also depend on the latitude (Alexander, 2008).</p> <p>The first layer of components (the <em>fuel moisture codes</em>: Fine Fuel Moisture Content, FFMC; Duff Moisture Code, DMC; Drought Code, DC) is composed by dynamic variables. This means that the value of each component for a given day depends also on the value of the same component the day before. The dynamic components with longer memory of their past history also approximate the seasonal changes in solar radiation, by considering the month of the year (see Figure, bottom left).</p> <ul> <li><strong>Fine Fuel Moisture Code (FFMC)</strong> : provides a numerical rating of the moisture content of the top litter and other cured fine fuels, indicating the relative ease of ignition and flammability of fine fuel.</li> <li><strong>Duff Moisture Code (DMC)</strong> : models a standard moisture content of loosely-compacted organic layers of moderate depth (duff layers and medium-sized woody material). This component of the FWI-sys represents wooden fuels of intermediate thickness.</li> <li><strong>Drought Code (DC)</strong> : models a standard moisture content of deeper, compact, organic layers. This component of the FWI-sys is able to track seasonal drought effects on coarse wooden fuels.</li> </ul> <p> </p> <p>The second layer of components (the<em> fire behaviour indices</em>: Initial Spread Index, ISI; Buildup Index, BUI; Fire Weather Index, FWI) mathematically is composed by stateless D-TM components. This means that these components do not have an internal memory of the past conditions, while instead they rely on the combined information offered by the different temporal inertia of the fuel moisture codes, which they process as input information.</p> <ul> <li><strong>Initial Spread Index (ISI)</strong> : represents the expected rate of fire spread. It considers the combined effects of wind and the FFMC on the rate of spread. However, it excludes the influence of fuel moisture and availabity for the coarser wooden fuels.</li> <li><strong>Buildup Index (BUI)</strong> : combines DMC and DC to model the total amount of fuel available for combustion to the spreading fire.</li> <li><strong>Fire Weather Index (FWI)</strong> : offers a standard aggregated numerical rating of fire intensity which combines ISI and BUI.</li> </ul> <p><br>Given its structure, the model can also be interpreted as a recurrent neural network (RNN) where the input variables are transformed into the final aggregated numerical rating (FWI) by means of two hidden layers: the <em>fuel moisture codes</em> (three nodes/neurons); and the <em>fire behaviour indices</em> (two nodes/neurons).</p> <p>Note that this structure is not a simple feedforward network, as the first hidden layer is made by dynamic components (FFMC, DMC, DC, see highlighted feedack loops in <a href="../record/10806780/files/FWI-sys_simple_diagram_recurrent.png?download=1">PNG</a> format). The activation functions are complex, and the D-TM components (either dynamic or stateless) generally mix physically-based and empirical aspects. A consequence of the complexity of the FWI-sys activation functions is that a neural network with standard (e.g. sigmoidal) activation functions would need to exploit disproportionally many more additional neurons for the same FWI-sys D-TM complexity to be reasonably approximated.</p> <p> </p> <p>An additional FWI-sys component is a simple transfromation of the aggregated FWI values to better account for the nonlinear increase of fire control effort with increasing FWI values (Van Wagner, 1987):</p> <ul> <li><strong>Daily Severity Rating (DSR)</strong>: this transformation of FWI is meant to provide a measure of control difficulty:<br> DSR = 0.0272 ⋅ FWI <sup>1.77</sup><br>which easily invertible:<br> FWI = ( DSR / 0.0272 ) <sup>1 / 1.77</sup></li> </ul> <p><br><br>To cite the Figure, please refer to:<br><br>de Rigo, 2016. <strong>Structure of the Canadian Forest Fire Weather Index System: the model and its components</strong>. https://doi.org/10.5281/zenodo.6558576</p> <p>which is part of</p> <p>de Rigo, D. 2018. <strong>The Canadian Forest Fire Weather Index System: a synopsis of computational semantics</strong>. https://doi.org/10.6084/m9.figshare.4046673<br><br> </p> <p> </p> <p><strong>References</strong></p> <p>De Groot, W.J., 1987. <strong>Interpreting the Canadian Forest Fire Weather Index (FWI) System</strong>. In: <em>Fourth Central Regional Fire Weather Committee Scientific and Technical Seminar, Proceedings</em>. Winnipeg, Manitoba, Canada, pp. 3-14. <a href="https://purl.org/INRMM-MiD/c-14176512">https://purl.org/INRMM-MiD/c-14176512</a> </p> <p>Van Wagner, C.E., 1987. <strong>Development and structure of the Canadian Forest Fire Weather Index System</strong>. <em>Forestry Technical Report</em>. Canadian Forestry Service, Ottawa, Canada. <a href="https://purl.org/INRMM-MiD/c-14168337">https://purl.org/INRMM-MiD/c-14168337</a> </p> <p>Alexander, M.E., 2008. <strong>Latitude considerations in adapting the Canadian Forest Fire Weather Index System for use in other countries</strong>. In: Lawson, B.D., Armitage, O.B. (Eds.), <em>Weather Guide for the Canadian Forest Fire Danger Rating System</em>. Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, Edmonton, Alberta, Canada, pp. 67–73. ISBN:978-1-100-11565-8 <a href="https://purl.org/INRMM-MiD/z-MBDA6A6I">https://purl.org/INRMM-MiD/z-MBDA6A6I</a></p> <p> </p>
Impacts of Post-fire Debris Flows on Fluvial Morphology and Sediment Transport in a California Central Coast Stream
<p>Structure from Motion orthoimagery, lidar differencing products, and grain size data to be published with the submission of "Impacts of Post-fire Debris Flows on Fluvial Morphology and Sediment Transport in a California Central Coast Stream" to <em>Journal of Geophysical Research: Earth Surface.</em> </p> <p> </p> <p>2016, 2021, and 2022 orthoimagery for Upper Big Creek:</p> <p>J_2016.tif, J_2021.tif, J_2022.tif, K_2016.tif, K_2021.tif, K_2022.tif, L_2016.tif, L_2021.tif, L_2022.tif</p> <p>Files titled K_[year].tif encompass our upstream study reach; files titled J_[year].tif encompass our middle study reach; files titled L_[year].tif encompass our downstream study reach.</p> <p> </p> <p>2016, 2021, and 2022 orthoimagery for Devil's Creek:</p> <p>G_2016.tif, G_2021.tif, G_2022.tif, _2016.tif, H_2021.tif, H_2022.tif, I_2016.tif, I_2021.tif, I_2022.tif</p> <p>Files labeled G_[year].tif encompass our upstream study reach; files labeled H_[year].tif encompass our middle study reach; files labeled I_[year].tif encompass our downstream study reach.</p> <p> </p> <p>2016, 2021, and 2022 grain size data for Upper Big Creek with units in meters:</p> <p>BC_2016.csv, BC_2021.csv, BC_2022.csv</p> <p> </p> <p>2016, 2021, and 2022 grain size data for Devil's Creek with units in meters:</p> <p>DC_2016.csv, DC_2021.csv, DC_2022.csv</p> <p> </p> <p>Differenced lidar digital terrain models for Big Creek and Devil's Creek with units in meters:</p> <p>DoD_11_22.tif (difference between 2011 and 2022 lidar DTMs), DoD_11_15.tif (difference between 2011 and 2022 lidar DTMs)</p> <p> </p> <p>This work was funded by the Geological Society of America, the National Center for Airborne Laser Mapping, the Washington Section of the American Water Resources Association, the Western Washington University Research and Sponsored Programs Office, and the Western Washington University Geology Department.</p>
Supporting data for managing fire-prone forests in a time of decreasing carbon carrying capacity
<p>These data and code include surface fuels and prescribed fire emissions data from the Teakettle Experimental Forest in the Sierra Nevada, California, USA. These data include transect data of surface fuels and the emissions from a 2017 prescribed burn. Emissions from the prescribed burn were calculated using a stock change approach by subtracting pre-burn surface fuels from post-burn surface fuels. We used these data and a Monte Carlo simulation approach to estimate the frequency of prescribed burning required to reduce surface fuels following a widespread overstory tree mortality event.</p>
Abiotic factors modify ponderosa pine regeneration outcomes after high-severity fire
<p>Large high-severity burn patches are increasingly common in southwestern US dry conifer forests. Seed-obligate conifers often fail to quickly regenerate large patches because their seeds rarely travel the distances required to reach the core patch area. Abiotic factors may further alter the distance seeds can travel to regenerate a patch, which would change expected post-fire regeneration patterns. We used the presence and density of ponderosa pine regeneration as a proxy for seed dispersal to quantify the effect of abiotic factors on seed dispersal into high-severity patches. We established 45 transects in burn patches across the Gila National Forest, NM, USA to measure regeneration density in areas that varied by aspect, slope, and prevailing wind direction relative to intact forest. We modeled the effect of abiotic features on regeneration presence and density, comparing density estimates against a distance-only model to assess differences in model performance and expected regeneration density. We found the highest regeneration densities on north-facing aspects that were near, downwind, and downslope of intact forest, which decreased in density and likelihood as conditions for seed dispersal became less favorable. Accounting for abiotic factors improved model performance and increased regeneration density estimates compared to the distance-only model. Our findings indicate that regeneration presence and density vary as a function of the interaction between abiotic factors and distance to the primary seed source, which is determined by patch characteristics. Therefore, abiotic factors will have a smaller effect on regeneration outcomes in large, simple patches, which have more area further from the patch edge.</p>
Supplementary Information for Chemical Properties of the Southeast Asian Haze from Indonesian Peatland Fires
<p>This repository contains supplementary information (SI-1 and SI-2) related to the article entitled "Chemical Properties of the Southeast Asian Haze from Indonesian Peatland Fires" published in Global Environmental Research (GER, Volume 27, No.1, Pages 37–48, Year 2023, <a href="https://doi.org/10.57466/ger.27.1_37" target="_blank" rel="noopener">https://doi.org/10.57466/ger.27.1_37</a>). SI-1 contains the newly created dataset used in GER and SI-2 contains supplementary documents for Sections 4 and 6 as well as tables and figures referred to but not included in the main article of GER.</p>
Fire promotes functional plant diversity and modifies soil carbon dynamics in tropical savanna
<p>The dataset associated with the manuscript "Fire promotes functional plant diversity and modifies soil carbon dynamics in tropical savanna" (Teixeira et al.) includes 6 different datasets, for which we provided one metadata.<br> </p> <p><strong>Version 2</strong> includes an update of the biomass data set, including the correct transformation to g/m2 on fine roots biomass data.<br><br><strong>Version 3 </strong>includes an update of the belowground traits data set based on correcting formatting errors in the belowground traits data. <br><br><strong>Version 4 </strong>Sorry for the inconvenience. This version includes the correct updated belowground traits data file based on the correct formatting errors in the belowground trait data. <br><br>fluxes: it includes data related to net ecosystem C and water exchange. NEE and ET from each plot were measured using the LiCOR 7500 infrared gas analyzer (Li-Cor Inc.). See the method section in the manuscript for full details.</p> <p>soil_carbon: it includes carbon soil data.<br><br>biomass_v2: it includes data related to aboveground and belowground biomass. Aboveground data were collected in 0.5m2 subplot and belowground at 0.25m2 at 20cm depth both within 1m2 sampling plot. See the method section in the manuscript for full details.</p> <p>aboveground_traits: all aboveground functional traits from plant species. See the method section in the manuscript for full details.</p> <p>belowground_traitsv3: all roots functional traits from plant species. See the method section in the manuscript for full details.</p> <p>species_composition: plant community composition. See the method section in the manuscript for full details.</p> <p><br><strong>Abstract</strong><br>Fire is an evolutionary environmental filter in tropical savanna ecosystems altering functional diversity and associated C pools in the biosphere and fluxes between the atmosphere and biosphere. Therefore, alterations in fire regimes (e.g. fire exclusion) will strongly influence ecosystem processes and associated dynamics. In those ecosystems, C dynamics and functions are underestimated by the fire-induced offset between C output and input. To determine how fire shapes ecosystem C pools and fluxes in an open savanna across recently burned and fire excluded areas, we measured the following metrics: (I) plant diversity including taxonomic (i.e. richness, evenness) and plant functional diversity (i.e. functional diversity, functional richness, functional dispersion and community weighted means); (II) structure (i.e. above- and below-ground biomass, litter accumulation); and (III) functions related to C balance (i.e. net ecosystem carbon dioxide (CO<sub>2</sub>)<sub> </sub>exchange (NEE), ecosystem transpiration (ET), soil respiration (soil CO<sub>2</sub> efflux), ecosystem water use efficiency (eWUE) and total soil organic C (SOC). We found that fire promoted aboveground live and belowground biomass, including belowground organs, and coarse and fine root biomass, and contributed to higher biomass allocation belowground. Fire also increased both functional diversity and dispersion. NEE and total SOC were higher in burned plots compared to fire-excluded plots whereas soil respiration recorded lower values in burned areas. Both ET and eWUE were not affected by fire. Fire strongly favored functional diversity, fine root, and belowground organ biomass in piecewise SEM models but the role of both functional diversity and ecosystem structure to mediate the effect of fire on ecosystem functions remain unclear. Fire regime will impact C balance, and fire exclusion may lead to lower C input in open savanna ecosystems.</p>
Post-fire flood hazard model (PF2HazMo) version 1.0.0: Model scripts and parameterization and validation data
<p>Human development at the foot of the mountains faces sediment-laden flood hazards characterized by high-velocity, erosive flows carrying mud and debris, and when flood control infrastructure that protects communities fills with sediment, it loses capacity. The estimation and management of sediment-laden floods have proven challenging because cycles of wildfire, precipitation, and infrastructure sedimentation are still poorly understood. Efforts to model compound hazards such as post-fire floods are relatively new, and existing models do not consider the role of flood control infrastructure, such as debris retention basins and flood channels, in the development of post-fire floods. Here we present data sources and calibration methods to estimate sediment-laden flood hazards downstream of infrastructure on a catchment-by-catchment basis using the Post-Fire Flood Hazard Model (PF2HazMo), a stochastic modeling approach that utilizes continuous simulation to resolve the effects of antecedent conditions and system memory. Data sources provide parameter ranges needed for stochastic modeling, and several performance measures are considered for model calibration. With application to three catchments in Southern California, we show that PF2HazMo predicts the median of the simulated distribution of peak bulked flows within the 95% confidence interval of observed flows, with an order of magnitude range in bulked flow estimates depending on the performance measure used for calibration. Using infrastructure overtopping data from a post-fire wet season, we show that PF2HazMo accurately predicts the number of flood channel exceedances. Model applications to individual watersheds reveal whether existing infrastructure is undersized to contain present-day and future overtopping hazards based on current design standards.</p>
Managing multiple threats: Evaluating the efficacy of broad-scale introduced predator management in improving native mammal resilience to fire
<p>Preventing further biodiversity loss requires understanding which processes threaten biodiversity and the effectiveness of management actions in mitigating them. Threatening processes can interact in complex and unexpected ways, but different threats are often managed independently. Here, we develop a conceptual model to identify the conditions needed for management of a single threat to achieve a net conservation benefit in systems with multiple interacting threats, and demonstrate its relevance in a replicated case-study experiment. In Australia, introduced red foxes (<em>Vulpes vulpes</em>) and feral cats (<em>Felis catus</em>) may hunt vulnerable native mammals more effectively after fire, due to loss of understory vegetation. However, the efficacy of broad-scale control of introduced predators in improving native mammal resilience to fire has not been quantified. Moreover, many studies assessing the impacts of prescribed fire on species rely on a much smaller number of independent replicates. Using a natural before-after control-impact experiment with 14 prescribed fires, each > 200 ha, we tested whether existing landscape-scale fox baiting programs influenced the immediate effects of prescribed fire on these two introduced predators and five medium-sized native mammals, including the threatened long-nosed potoroo (<em>Potorous tridactylus</em>) and southern brown bandicoot (<em>Isoodon obesulus</em>). Fox occupancy increased across both treatments post-fire, but baiting reduced the magnitude of increase. In contrast, mean feral cat occupancy remained constant in unbaited areas post-fire, but nearly doubled in fox-controlled areas, possibly due to a mesopredator release. Existing landscape-scale fox control programs did not clearly improve the short-term resilience of native mammals to prescribed fire (at least under the current fire and fox management regimes in our study landscapes). These results likely emphasise the need to integrate fire and predator management strategies for threatened faunal conservation iIn the presence of acute disturbances such as fire, threatened native mammals may require more intensive and integrated management of fire and introduced predators, such as (e.g., through more intensive targeted predator controlbaiting around fire events, or intensive protection usingthrough natural or artificial refuges).</p>
REFIT.AC v22 fire emissions for Forkel et al. "Burning of woody debris dominates fire emissions in the Amazon and Cerrado"
<p>Emissions for Amazon and Cerrado biome areas generated by the Remote sensing based Emission model by FIre types - Amazon and Cerrado (REFIT.AC) for the publication Forkel et al. "Burning of woody debris dominates fire emissions in the Amazon and Cerrado".</p> <p>REFIT.AC is a bookkeeping-based fire model using ESA CCI biomass, LULCC data and MODIS based fire occurrence information (burned area and active fires). </p> <p>Emission information is provided in 0.1 degree spatial resolution for dry matter (DM), CO2, CO, CH4, NOx and PM2.5, in units of g/m2/month.</p>
GOES-Observed Fire Event Representation (GOFER) product for 28 California wildfires from 2019-2021
<p>The GOES-Observed Fire Event Representation (GOFER) algorithm uses geostationary satellite observations of active fires from GOES-East and GOES-West to map the hourly progression of large wildfires (over 50,000 acres or 202 sq. km). GOES observes North and South America with a spatial resolution of 2 km at the equator and at a frequency of 10-15 minutes for the full disk view. Along with the fire perimeter, we derive the active fire lines and fire spread rates. We tested the GOFER algorithm on a set of 28 wildfires in California from 2019-2021 and produced three versions of the product: GOFER-Combined, GOFER-East, and GOFER-West. GOFER-Combined uses both GOES-East and GOES-West observations, while GOFER-East and GOFER-West use only GOES-East and only GOES-West observations, respectively. We find that GOFER performs reasonably well compared to final perimeters from California's Fire and Resource Assessment Program (FRAP) and 12-hourly perimeters from the Fire Event Data Suite (FEDS), derived from 375-m active fire observations. See our <a href="https://globalfires.earthengine.app/view/gofer">GOFER Visualization</a> app on Earth Engine Apps for an overview of the dataset, alongside other datasets, such as FEDS and FRAP perimeters and 30-m burn severity from Monitoring Trends in Burn Severity (MTBS). Please refer to the corresponding <a href="https://github.com/tianjialiu/GOFER">GitHub repository</a> for the code, detailed dataset description, and version history.</p>
Dataset for Temperature Development in Ultra-High-Performance Concrete-Filled Double-Skin Tubes Exposed to Fire
<p>This dataset presents extensive experimental data available in the literature for concrete-filled tubes (CFT) and ductile concrete-filled double-skin tubes (CFDST) exposed to fire. Different concrete grades, including normal strength (NS), high strength (HS), and ultra-high-strength (UHS) concrete, were tested, and the temperature developments within various cross-sections are provided in the supplementary data. The density, specific heat, and conductivity that are temperature-dependent, are documented by different researchers and current specifications. The preferred models are provided for carbon-steel, stainless-steel, and different grades of concrete. Numerical studies such as those conducted by the authors in a companion paper would find value in this data for validation and verification purposes</p>
Data from: A burning issue: Savanna fire management can generate enough carbon revenue to help restore Africa's rangelands and fill Protected Area funding gaps
<p>Many savanna-dependent species in Africa including large herbivores and apex predators are at increasing risk of extinction. Achieving effective management of protected areas (PAs) in Africa where lions live will cost an estimated USD >$1-2 B/year in new funding. We explored the potential for fire management-based carbon-financing programs to fill this funding gap and benefit degrading savanna ecosystems. We demonstrated how introducing early dry season fire management programs could produce potential carbon revenues (PCR) from either a single carbon-financing method (avoided emissions) or from multiple sequestration methods ranging from USD $59.6-$655.9 M/year (at USD $5/ton) or USD $155.0 M–$1.7 B/year (at USD $13/ton). We highlighted variable but significant PCR for savanna PAs from USD $1.5–$44.4 M/year per PA. We suggest investing in fire management programs to jump-start the United Nations Decade of Ecological Restoration to help restore degraded African savannas and conserve imperiled keystone herbivores and apex predators. <br> <br> Open Access article: <a href="https://doi.org/10.1016/j.oneear.2021.11.013">https://doi.org/10.1016/j.oneear.2021.11.013</a></p>
Data Storage for Baylis and Boomhower (2022): Fire Characteristics, Expenditures, and Other Miscellaneous Datasets
<pre># Description Zenodo data storage for large, non-proprietary data used in "The Economic Incidence of Wildfire Suppression in the United States", by Patrick Baylis and Judson Boomhower. Main OpenICPSR repository (contains code and main README.txt): https://www.openicpsr.org/openicpsr/workspace?goToPath=/openicpsr/144601 # Contents This storage mirrors the following offline directories used in the code. Each .tar file contains a directory of the same name. To replicate the existing code, users should decompress each directory into raw/, following the structure used in the code. (Note: as described in the main README, running most of the code requires access to proprietary data which is not included in this storage). ## Resulting directory structure To be consistent with the original source code, included the .tar files should be decompressed into the following directory structure within the directory designated by the RAW global in 01_Code/globals.R in the main reposistory. raw/calfire/ raw/census/county-tract/ raw/census/income/ raw/census/populated_places raw/gacc/ raw/geo/ raw/gpw/ raw/hpi/ raw/incidents/CalFire/ raw/incidents/FAMWEB/ raw/incidents/InteriorDepartment/ raw/incidents/FEMA/ raw/incidents/KCFAST/ raw/MTBS/ raw/nifc/ raw/preparedness-spending/doi/ raw/preparedness-spending/usfs/ raw/roads/ raw/USFS/ raw/whp/ raw/wui/</pre>
Vegetation structure and fuel dynamics in fire-prone, Mediterranean-type Banksia woodlands
<p>Increasing extreme wildfire occurrence globally is boosting demand to understand the fuel dynamics and fire risk of fire-prone areas. This is particularly pressing in fire-prone, Mediterranean climate-type vegetation, such as the Banksia woodlands surrounding metropolitan Perth, southwestern Australia. Despite an extensive wildland-urban interface and frequent fire occurrence, fuel accumulation and the spatial variation in fuel risk is not well quantified across the broad extent of this ecosystem. Using a space for time sampling approach to generate a chronosequence of time since fire, we selected sites that spanned across two distinct sandy soil types (Spearwood and Bassendean sands) and a rainfall gradient (550 to 750 mm north–south). We examined 82 sites in Banksia woodlands, southwestern Australia. Of the 82 sites, 44 burnt during the measurement period (2016 to 2021), which provided the opportunity for fuel measurements following fire (resulting in total N = 126). We wanted to answer two key questions: 1) How do measures of fuel load (mass) and arrangement (structure and continuity) vary across space and time, particularly with respect to time since the last fire? 2) How do biophysical drivers, such as soil type and rainfall, influence fuel accumulation and arrangement, and do these covariates improve litter fuel modelling beyond traditional asymptotic models? We found that fine surface fuel loads (litter and small twigs) differed between sand types, accumulating faster and reaching a higher peak on Spearwood sands (7–9 Mg ha−1) compared to Bassendean sands (6–7 Mg ha−1). Shrub layer fuel loads also accumulated faster on Spearwood sands than on Bassendean sands. While shrub layer fuels on Spearwood sands peaked at 14 years and declined thereafter, those on Bassendean sand did not decline over time but have lower overall connectivity. Total fine fuels (fine surface plus fine shrub layer fuels) had no significant decline over the same time period, on either sand type. Total fine fuel loads reached a peak of 9–10 Mg ha−1 between 13- and 20-years following fire, depending on the underlying sand type. Our quantitative fuel accumulation models confirmed the strength of time since fire as a predictor of hazard, but nonetheless included up to 40% unexplained variance. Importantly, while components fluctuated over time, the combined total of fine fuels did not decline with the long absence of fire, suggesting fire risk does not necessarily decrease in long unburned vegetation.</p>
Region-based Annotation Data of Fire Images for Intelligent Surveillance System
<p>This data presents fire segmentation annotation data on 12 commonly used and publicly available “VisiFire Dataset” videos from <a href="http://signal.ee.bilkent.edu.tr/VisiFire/">http://signal.ee.bilkent.edu.tr/VisiFire/</a>. This annotations dataset was obtained by per-frame, manual hand annotation over the fire region with 2,684 total annotated frames. Since this annotation provides per-frame segmentation data, it offers a new and unique fire motion feature to the existing video, unlike other fire segmentation data that are collected from different still images. The annotations dataset also provides ground truth for segmentation task on videos. With segmentation task, it offers better insight on how well a machine learning model understood, not only detecting whether a fire is present, but also its exact location by calculating metrics such as Intersection over Union (IoU) with this annotations data. This annotations data is a tremendously useful addition to train, develop, and create a much better smart surveillance system for early detection in high-risk fire hotspots area. </p>
Burnt forest area and CO2 emissions from fires in Russian forests by fire protection zones in 2010-2020
<p>The dataset is Supplementary Materials for the article ''<em>Reassessment of carbon emissions from fires and a new estimate of net carbon uptake in Russian forests in 2010-2020</em>'' in the Carbon Balance and Management journal. It contains files with burnt forest area and carbon dioxide emissions from fires data in Russia 2010-2020.<br> Article Supplementary materials are stored in the file Supplementary Tables and contains:</p> <p>- Table 1. Burnt forest area from NIR and MODIS (MCD64A1) in 2010-2020;</p> <p>- Table 2. Burnt forest area in the ground, aviation and the control (no fire protection) zones in 2010-2020 using MCD64A1;</p> <p>- Table 3. Carbon emissions from forest fires from National Inventory Report (NIR) and Copernicus Atmosphere Monitoring Service (CAMS) in 2010-2020</p> <p>Also, there is Supplementary Figure 1 with the Federal Districts of Russia schematic map.</p> <p>There are 22 files in GeoTIFF format for every year: </p> <p>1. Burnt forest area obtained using MODIS product MCD64A1 (250 m pixel, ESRI:102025). Coverage: -4064059.5401764437556267,1967242.6686790268868208 : 3658440.4598235562443733, 6012242.6686790268868208</p> <p>2. CO2 emissions using Copernicus Atmosphere Monitoring System (CAMS) (0.1 degrees, VGS 84). Coverage: 27.9493818283081055,42.9493612670349520 : 190.0498617200859712,78.0494651794433594<br> <br> In addition, we share Shapefiles:</p> <p>1. Russian borders (necessary to cut Russia from CO2 GeoTIFFs), EPSG:4326. Coverage: -180.0000000000000000,41.1888656599999976 : 180.00000000000000000,81.8562469499999992;</p> <p>2. Forest Fire Protection zoning in 2019: ground zone, aviation zone, the so-called control zone (no fire protection), EPSG:4326. Coverage: 27.4019779002987676,41.3483353426717599 : 173.8255532772949721,72.6575707670955353.</p>
Fires_ERA5_Reanalysis_Data
<p>ERA5 reanalysis data obtained for each fire, hourly and at different pressure levels (37) from the Copernicus Climate Change Service (C3S) Climate Data Store (CDS). The files are in netCDF format, and the variables requested: temperature, relative humidity, U-component of wind, and V-component of wind.</p> <p>Source: Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2018): ERA5 hourly data on pressure levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS).</p>
Fig. 1. The potential distribution map for B in Long-Term Bioclimatic Modelling The Distribution Of The Fire-Bellied Toad, Bombina Bombina (Anura, Bombinatoridae), Under The Influence Of Global Climate Change
Fig. 1. The potential distribution map for B. bombina under contemporary climatic conditions. The colour gradient represents high (red) to low (green) habitat suitability for the species.
Fig. 4. The potential distribution map for B. bombina under projected 2050 in Long-Term Bioclimatic Modelling The Distribution Of The Fire-Bellied Toad, Bombina Bombina (Anura, Bombinatoridae), Under The Influence Of Global Climate Change
Fig. 4. The potential distribution map for B. bombina under projected 2050 climatic conditions. The colour gradient represents high (red) to low (green) habitat suitability for the species.
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Annotated Behaviour and Observability Dataset (ABODe)
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