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368 results for “wildfires”
Data from: Plant uptake offsets silica release from a large Arctic tundra wildfire
Rapid climate change at high latitudes is projected to increase wildfire extent in tundra ecosystems by up to five-fold by the end of the century. Tundra wildfire could alter terrestrial silica (SiO2) cycling by restructuring surface vegetation and by deepening the seasonally-thawed active layer. These changes could influence the availability of silica in terrestrial permafrost ecosystems and alter lateral exports to downstream marine waters, where silica is often a limiting nutrient. In this context, we investigated the long-term effects of the largest Arctic tundra fire in recent times on plant and peat amorphous silica content and dissolved silica concentration in streams. Ten-years after the fire, vegetation in burned areas had 73% more silica in aboveground biomass compared to adjacent, unburned areas. This increase in plant silica was attributable to significantly higher plant silica concentration in bryophytes and increased prevalence of silica-rich gramminoids in burned areas. Tundra fire redistributed peat silica, with burned areas containing significantly higher amorphous silica concentrations in the O-layer, but 29% less silica in peat overall due to shallower peat depth post burn. Despite these dramatic differences in terrestrial silica dynamics, dissolved silica concentration in tributaries draining burned catchments did not differ from unburned catchments, potentially due to the increased uptake by terrestrial vegetation. Together, these results suggest that tundra wildfire enhances terrestrial availability of silica via permafrost degradation and associated weathering, but that changes in lateral silica export may depend on vegetation uptake during the first decade of post-wildfire succession.
Data - Sustainable Development Key to Limiting Climate Change-Driven Wildfire Damages
<p>This repository contains the data and scripts required to reproduce the results of the manuscript "Sustainable Development Key to Limiting Climate Change-Driven Wildfire Damages" submitted to the Environmental Research Climate Journal (ERCL). </p> <p><strong>Brief description of project</strong></p> <p>This project has two main goals:</p> <ol> <li>Examine the key factors influencing global economic wildfire damages </li> <li>Projecting future damages under three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP370)</li> </ol> <p><strong>Repository structure</strong></p> <ul> <li>/data directory: contains the data to reproduce the regression analyses and plot the figures presented in the manuscript <ul> <li>/data/historical: contains the historical (training) data that was used for fitting the linear regression model </li> <li>/data/ssp: contains the SSP projection data for all predictors, as well as the projected model output for future wildfire damages</li> <li>/data/source: contains all raw data used in this study</li> </ul> </li> <li>/scripts directory: contains the python scripts to run the regression model and to plot the figures presented in the manuscript <ul> <li>/scripts/linregress: contains the scripts for running the linear regression model and to conduct various model validation steps <ul> <li>run_linregress.py: script to run the linear regression model </li> <li>run_nonlinregress.py: script to run the nonlinear models (preliminary)</li> <li>run_plm.py: script to run the supplementary panel regression model (plm)</li> <li>run_gdp_linregress.py: script to run the alternative linear regression model using absolute damages as outcome variable and GDP as additional independent predictor</li> <li>inspect_model.py: script to conduct model validation</li> </ul> </li> <li>/scripts/plotting: contains the scripts to plot all figures presented in the manuscript <ul> <li>plot_map_y_X_hist.py: script to plot Figure 1 (world maps of historical wildfire damage and predictors used in this study)</li> <li>plot_residual_plots.py: script to plot Figure 2 (residual and partial residual plots of the fitted regression model)</li> <li>plot_beta_coef_model_prediction.py: script to plot Figure 3 (standardized beta coefficients of the fitted regression model and the scatterplots for reported vs. model-estimated wildfire damages)</li> <li>plot_predictor_ssp_timeseries_global.py: script to plot Figure 4 (time-series of the SSP projections of the predictors)</li> <li>plot_map_X_ssp.py: script to plot Figure 5 (world maps of predictor values for the three SSPs explored in this study)</li> <li>plot_ssp_damage_projection_by_region.py: script to plot Figure 6 (projected wildfire damages under the three SSPs and for the six IPCC AR6 regions)</li> <li>plot_ssp_damage_projection_per_predictor.py: script to plot Figure 7 (time-series of global mean projected wildfire damage with all predictors changing and only individual predictors changing)</li> <li>plot_ssp3_ssp1_difference.py: script to plot Figure 8 (time-series of mean avoided wildfire damage in SSP126 compared to SSP370)</li> <li>SI_plot_ssp_damage_projection_lin_vs_nonlin.py: script to plot Figure S1 (comparison of time-series of mean projected wildfire damage with the linear and nonlinear models)</li> <li>SI_plot_ssp_damage_projection_xterm.py: script to plot Figure S2 (comparison of time-series of mean projected wildfire damages using models with and without interaction terms)</li> <li>SI_plot_beta_coef_pop_wui.py: script to plot Figure S3 (same as Figure 3 but for the model using pop_wui instead of PDforest)</li> <li>SI_plot_ssp_population.py: script to plot Figure S4 (population projection under the three SSP scenarios)</li> <li>SI_plot_ssp_map_pop_wui.py: script to plot Figure S5 (world maps of the pop_wui predictor under three SSP scenarios)</li> <li>SI_plot_ssp_map_damage.py: script to plot Figure S6 (world maps of projected wildfire damages under the three SSP scenarios and for the years 2030, 2050 and 2070)</li> <li>SI_plot_ssp_damage_projection_pop_wui.py: script to plot Figure S7 (comparison of the time-series of projected wildfire damage using pop_wui vs PDforest as predictor)</li> <li>SI_plot_predictor_ssp_trend_by_dev_region.py: script to plot Figure S8 (time-series of the SSP projections of the predictors by developmental regions)</li> </ul> </li> </ul> </li> </ul>
WildfireSpreadTS: A dataset of multi-modal time series for wildfire spread prediction
<p>We present a <strong>multi-temporal</strong>, <strong>multi-modal</strong> remote-sensing dataset for predicting <strong>how active wildfires will spread</strong> at a resolution of 24 hours. The dataset consists of <strong>13.607 images</strong> across 607 fire events in the United States from January 2018 to October 2021. For each fire event, the dataset contains a <strong>full time series of daily observations</strong>, containing detected active fires and variables related to <strong>fuel, topography and weather conditions</strong>.</p><h2>Documentation</h2><p><i><strong>WildfireSpreadTS_Documentation.pdf</strong></i> includes further details about the dataset, following Gebru et al.'s <strong>"Datasheets for Datasets"</strong> framework. This documentation is similar to the supplementary material of the associated NeurIPS paper, excluding only information about experimental setup and results. For full details, please refer to the associated paper. </p><h2>Code: Getting started</h2><p>Get started working with the dataset at <a href="https://github.com/SebastianGer/WildfireSpreadTS">https://github.com/SebastianGer/WildfireSpreadTS</a>. </p><p>The code includes a <strong>PyTorch Dataset</strong> and <strong>Lightning DataModule </strong>to allow for easy access. We recommend converting the GeoTIFF files provided here to HDF5 files (bigger files, but much faster). The necessary code is also available in the repository.</p><p> </p><p>This work is funded by Digital Futures in the project EO-AI4GlobalChange. The computations were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) at C3SE partially funded by the Swedish Research Council through grant agreement no. 2022-06725.</p>
FLAME Deliverable 8 - Extreme Wildfires Dataset (D3.1)
<p>This dataset comprises extreme wildfire growth events that occurred in Greece during the 2002 - 2020 period. The data are provided in GeoPackage (.gpkg) format. The accompanying report documents information concerning the methods used for deriving the dataset. </p>
Data for the submitted paper by Yasunari et al., "Comprehensive Impact of Changing Siberian Wildfire Severities on Air Quality, Climate, and Economy: MIROC5 Global Climate Model's Sensitivity Assessments"
<p>The dataset contains some of the outputs from the global climate model experiments by MIROC5 on changing Siberian wildfire severities, the other data used in the paper (see READ_ME files on the data sources), the analyzed data, and the scripts for analyses, which were used in the following submitted paper. Note that this dataset also includes unused data for the paper:</p> <p><br>Yasunari, T. J., D. Narita, T. Takemura, S. Wakabayashi, and A. Takeshima, Comprehensive Impact of Changing Siberian Wildfire Severities on Air Quality, Climate, and Economy: MIROC5 Global Climate Model's Sensitivity Assessments, submitted.</p> <p>Please read the READ_ME files for detailed information in each directory (especially see the "about_figures_and_tables/" directory first). Because of their large sizes, the data were separated into three zipped files.</p>
Wildfire-related publications (1980-2022)
<p>The CSV file contains all 60,488 publications that are related with wildfire studies. </p> <p>54 columns are contained and can be summarized into 4 categories:</p> <ul> <li>Columns (authors, ISBN, abstract, doi, type, title, journal, dates, affiliations) are accessed directly from the Web of Science (WOS) bibliographic database.</li> <li>Columns that start with "token_" are the consumed time of LLM responses (in this paper, gpt-3.5-turbo that was updated on 13th Feb 2023)</li> <li>Columns (study_area, study_period) are the interpreted or geoparsed results by LLM.</li> <li>Columns that start with "main_" are the selected topics in Fig. 1. Those start with "sub_" are the sub-topics. The results are marked by 1 (true) or 0 (false).</li> <li>Columns that start with "ignition_type_" are the interpreted results pertaining to different sources of ignitions. The results are marked by 1 (true) or 0 (false).</li> <li>Columns that start with "paradigms_" are the interpreted results pertaining to different aspects of wildfire study paradigms. The results are marked by 1 (true) or 0 (false).</li> </ul> <p>The code is written in Python and shows the process of interactions with gpt models.</p> <p><strong>The data for the main text figures are uploaded to this repository: <a href="../records/10859331">Wildfire-related publications (1980-2022) associated main figure data (zenodo.org)</a></strong></p>
Extreme wildfire events analysis for dry pyrocloud hypothesis
<p>Dataset for EWE to test the dry pyrocloud hypothesis. The files are Excel files from 182 extreme wildfires (EWE_globla.xlsx). From those fires, we extract extreme fire spread events to use in the research when we can reconstruct the vertical profile from the ERA5 ECMWF reanalysis data (fires_verticalprofile.xlsx) and the accurate rate of spread from observations (fire_spread_events.xlsx).</p> <p>The dataset contains two videos ilustratingthe concept of dry pyrocloud</p> <p>The dataset contains a variable explanatory document ('Table of variables on the dataset. docx')</p> <p>The dataset contains a README file to guide the use of code contained in the Demo ZIP</p> <p>The demo ZIP contains the phyton codes to obtain the fire-spread-events variables and a DEMO fire to test the codes. The fire is the Santa Coloma wildfire from 24 and 25 of July 2021 in Catalonia, SPAIN.</p> <p> </p>
High-Resolution Canopy Fuel Maps Based on GEDI: A Foundation for Wildfire Modeling in Germany
<p>Open access publication under review.</p> <p>Visit <a href="https://ee-forestfuels-ger.projects.earthengine.app/view/gedi-fuels"><strong>this Earth Engine app</strong></a> to explore the data interactively.</p> <p> </p> <p>Abstract:</p> <p>Forest fuels are essential for wildfire behavior modeling and risk assessments but difficult to quantify accurately. An increase in fire frequency in recent years, particularly in regions traditionally not prone to fire, such as central Europe, has increased demands for large-scale remote sensing fuel information. This study develops a methodology for mapping canopy fuels over large areas (Germany) at high spatial resolution, exclusively relying on open remote sensing data.</p> <p><br>We propose a two-step approach where we first use measurements from NASA’s GEDI instrument to estimate canopy fuel variables at the footprint level, before predicting high-resolution raster maps. Instead of using field measurements, we generate (GEDI-) footprint-level estimates for Canopy (Base) Height (CH, CBH),<br>Cover (CC), Bulk Density (CBD), and Fuel Load (CFL) by segmenting airborne LiDAR point clouds and processing tree-level metrics with allometric crown biomass<br>models. To predict footprint-level canopy fuels we fit and tune Random Forest models, which are cross-validated using k-fold Nearest Neighbor Distance Matching.<br>Predictions at >1.6 M GEDI footprints and biophysical raster covariates are combined with a Universal Kriging method to produce countrywide maps at 20-meter resolution.</p> <p><br>Agreement (RMSE/R²) with validation data (from the same population) was strong for footprint-level predictions and moderate for map predictions. A validation<br>with estimates based on National Forest Inventory data revealed low to modest agreement. Better accuracy was achieved for variables related to height (CH, CBH)<br>rather than to cover or biomass (CBD, CFL). Error analysis pointed towards a mixture of biases in model predictions and validation data, as well as underestimation of<br>model prediction standard errors. Contributing factors may be simplification through allometric equations and spatial and temporal mismatch of data inputs.<br>The proposed workflow has the potential to support regions where wildfire is an emerging issue, and fuel and field information is scarce or unavailable.</p> <p> </p> <p>Data:</p> <p>This repository contains modeling data, model objects (R), and predicted maps. The TIFF-files each have six bands, which includes (1) the final Universal Kriging result, (2) the linear model prediction (3) the prediction of residual Kriging, (4) the Kriging variance, (5) the linear model prediction standard error, and (6) Universal Kriging standard error.</p> <p> </p> <p>Disclaimer:<br>Maps in this repository are predicted using canopy fuel estimates from GEDI measurements. These are limited the region between 51.6° North and South. Map predictions exceeding this range should be considered an extrapolation of the model to an unknown biophysical domain. Error maps (6) can aid in utilizing our canopy fuel maps.</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>
Canadian Wildfire Evacuation Data
<p>Database of wildfire evacuations across the forested regions of Canada from 1980 to 2019. The database provides the data evaluated in the publication titled, “Wildfire evacuation patterns and syndromes across Canada’s forested regions,” which was submitted to Ecological Applications in November, 2021. The data file provides information on each documented wildfire evacuation and the characteristics of the fire that most likely led to the evacuation. The information on wildfire evacuations includes the location, the estimated population, whether it is a First Nations reserve, the estimated number of evacuess in five broad categories, the date that the evacuation order was issued, the date the order ended, and the reason why the order was issued. This information was compiled from more than 2,000 news reports, which were found using keyword searches in the Canadian Reference Centre, Canadian Newsstand, Canadian Research Index, Canadian Business and Current Affairs, and Proquest. Additional data were acquired by contacting provincial and territorial emergency management agencies and the Royal Canadian Mounted Police, as described in Beverly and Bothwell (2011; Wildfire evacuations in Canada 1980-2007; Natural Hazards 59:571-596). The fire characteristics include the fire size, ignition source, day of year on which it was first reported, and the database from which these values were derived. The databases for the original fire data include the National Burned Area Composite (NBAC), the National Fire Database (NFDB) fire polygon data, and the NFDB fire point data. The fire databases are available to the public at the CWFIS datamart (https://cwfis.cfs.nrcan.gc.ca/datamart).</p>
Fig. 2 in Changes In The Structure Of Assemblages Of Three Liolaemus Lizards (Iguania, Liolaemidae) In A Protected Area Of South-Central Chile Affected By A Mixed-Severity Wildfire
Fig. 2. Species of Liolaemus lizards recorded in the study area. A — L. tenuis (© G. Zúñiga); B — L. pictus (© A. H. Zúñiga); C — L. lemniscatus (© A. H. Zúñiga).
Fig. 3 in Changes In The Structure Of Assemblages Of Three Liolaemus Lizards (Iguania, Liolaemidae) In A Protected Area Of South-Central Chile Affected By A Mixed-Severity Wildfire
Fig. 3. Percentages of microhabitat use by lizards in study area according to severity of damage caused by fire.
Fig. 1 in Changes In The Structure Of Assemblages Of Three Liolaemus Lizards (Iguania, Liolaemidae) In A Protected Area Of South-Central Chile Affected By A Mixed-Severity Wildfire
Fig. 1. Study area: A — Geographical context; B — Mosaic of areas of different degrees of severity (modified from CONAF, 2014, 2015).
Training data for submitted paper "Wildfire Danger Prediction and Understanding with Deep Learning"
<p>Training data for submitted paper "Wildfire Danger Prediction and Understanding with Deep Learning". To run the code in https://github.com/Orion-AI-Lab/wildfire_forecasting</p>
Northwestern Europe Wildfire Perimeter Polygons [2012-2022]
<p>This dataset contains KML files for wildfires detected from VIIRS across northwestern Europe from 2012 to 2022. Each file contains the fire progression at each satellite overpass. These files were used to detect the rate of spread for each wildfire event to characterize the fire behavior in temperate northwest Europe, a region with limited fire history. </p>
A wildfire risk index and its components and subcomponents - NUTS2 Centro, Portugal
<p>The dataset, presented as a MS Excel file and an ArcGIS Shapefile, includes a wildfire risk index and its components and subcomponents, as well as the results of a clustering process based on the main components of the wildfire risk index. The analysis units are the civil parishes comprised within the NUTS2 Centro territorial unit in central Portugal, identified by name in the column <em>ParishName </em>of the Excel file.</p> <p>All variables are identified in the Excel file Data, and all are included as attributes to the parish polygons in the shapefile.</p>
Text-fig. 4. Charred grass from diatomite of Saint-Bauzile. a: Overview of diatomite slab with one larger specimen of charred grass (left) and several smaller, lath-shaped charcoal fragments; SM.B 22260; scale bar = 1 cm. b: Detail of vein exhibited on split grass blade, with stomata oriented parallel to vein. c: Stomata oriented in rows and bands parallel to veins exposed on split grass blade. d: Surface of grass leaf with rectangular, elongated cells with strongly undulating margins in an intercostal area. in Evidence For Wildfires During Deposition Of The Late Miocene Diatomites Of The Konservat-Lagerstätte Lake Saint-Bauzile (Ardèche, France) - Preliminary Results
Text-fig. 4. Charred grass from diatomite of Saint-Bauzile. a: Overview of diatomite slab with one larger specimen of charred grass (left) and several smaller, lath-shaped charcoal fragments; SM.B 22260; scale bar = 1 cm. b: Detail of vein exhibited on split grass blade, with stomata oriented parallel to vein. c: Stomata oriented in rows and bands parallel to veins exposed on split grass blade. d: Surface of grass leaf with rectangular, elongated cells with strongly undulating margins in an intercostal area.
Text-fig. 2. Charred fern from diatomite of Saint-Bauzile. a: Overview of diatomite slab with large fragment of charred fern; SM.B 22258; scale bar = 1 cm. b: SEM overview image of charred fern pinnule. c: Enlargement of (b) showing details of undulating anticlinal walls of the epidermis. in Evidence For Wildfires During Deposition Of The Late Miocene Diatomites Of The Konservat-Lagerstätte Lake Saint-Bauzile (Ardèche, France) - Preliminary Results
Text-fig. 2. Charred fern from diatomite of Saint-Bauzile. a: Overview of diatomite slab with large fragment of charred fern; SM.B 22258; scale bar = 1 cm. b: SEM overview image of charred fern pinnule. c: Enlargement of (b) showing details of undulating anticlinal walls of the epidermis.
Text-fig. 1. a: Map of France showing geographic position of Saint-Bauzile (source: http://d-maps.com/m/europa/france/france/ france09.gif). b: Overview of the active diatomite quarry at the Montagne d'Andance, photograph taken in 2017. c: SEM image of a frustule of pennate diatom (cf. Navicula sp.) from Saint-Bauzile. d) SEM image of frustules forming a colony of centric diatoms (cf. Diatoma sp.) from Saint-Bauzile. in Evidence For Wildfires During Deposition Of The Late Miocene Diatomites Of The Konservat-Lagerstätte Lake Saint-Bauzile (Ardèche, France) - Preliminary Results
Text-fig. 1. a: Map of France showing geographic position of Saint-Bauzile (source: http://d-maps.com/m/europa/france/france/ france09.gif). b: Overview of the active diatomite quarry at the Montagne d'Andance, photograph taken in 2017. c: SEM image of a frustule of pennate diatom (cf. Navicula sp.) from Saint-Bauzile. d) SEM image of frustules forming a colony of centric diatoms (cf. Diatoma sp.) from Saint-Bauzile.
Climate is more influential to vegetation green-up than factors that contribute to erosion following high-severity wildfire
<p>Background</p> <p>In the southwestern United States, post-fire vegetation recovery is increasingly variable in forest burned at high-severity. Many factors, including temperature, drought, and erosion, can reduce post-fire vegetation recovery rates. Here, we examined how post-fire precipitation variability, topography, and soils influenced post-fire vegetation recovery in the southwestern United States as measured by greenness. We modeled relationships between post-fire vegetation and these predictors using Random Forest and examined changes in post-fire normalized burn ratio across fires in Arizona and New Mexico. We incorporated growing season climate to determine if year-of-fire effects were persistent during the subsequent five years or if temperature, water deficit, and precipitation in the years following fire were more influential for vegetation greenness.</p> <p>Results</p> <p>We found reductions in post-fire greenness in areas burned at high-severity when heavy and intense precipitation fell on more erodible soils immediately post-fire. In <a>highly erodible</a> scenarios, when accounting for growing season climate, coefficient of variation for year-of-fire precipitation, total precipitation, and soil erodibility decreased greenness in the fifth year. While the effects of year-of-fire factors related to erosion were significant, they were small, and the variability explained by growing season vapor pressure deficit and growing season precipitation were significantly greater.</p> <p>Conclusions</p> <p>Our results suggest that while the factors that contribute to post-fire erosion and its effects on vegetation recovery are important, at a regional scale, the majority of the variability in post-fire greenness in high-severity burned areas in southwestern forests is due to climatic drivers such as growing season precipitation and vapor pressure deficit. Given the scale of area burned at high-severity, the likelihood that high-severity burned area will continue to increase, and the potential for more post-fire erosion that can result in different vegetation trajectories, quantifying how these factors alter the trajectory of greenness and what that means in terms of ecosystem development is central to understanding how different ecosystem types will be distributed across these landscapes with additional climate change.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.