nasa-gesdisc/es-publications-researchareas huggingface dataset:
Cloud‐to‐Ground Lightning and Near‐Surface Fire Weather Control Wildfire Occurrence in Arctic Tundra (2022)

Wildfire is a dominant disturbance agent in pan-Arctic tundra and can significantly impact terrestrial carbon balance and ecosystem functioning. Interactions between fire and climate change can enhance their impacts on the Arctic. However, the driving mechanisms of tundra fire occurrence remain poorly understood. This study focuses on identifying key environmental factors controlling fire occurrence in Arctic tundra of Alaska. Our random forest models, considering ignition source, fuel, fire weather, and topography, have shown a strong predictive capability with an overall accuracy above 91%. We found cloud-to-ground (CG) lightning to be the dominant driver controlling tundra fire occurrence. Near-surface weather warmer and drier than normal was required to support burning, while fuel composition and topography have modest correlations with fire occurrence. Our results highlight the critical role of CG lightning in driving tundra fires and that incorporating lightning in modeling is essential for fire monitoring, forecasting, and management in the Arctic.

Research areas: Wildfires
Satellite-Observed Impacts of Wildfires on Regional Atmosphere (2018)

Emissions of aerosols and trace gases from wildfires and their direct shortwave radiative forcing (DSRF) at the top of atmosphere were studied using satellite observations from Moderate-Resolution Imaging Spectroradiometer, Atmospheric Infrared Sounder, Clouds and Earth Radiant Energy System on Aqua, and Ozone Monitoring Instrument on Aura. The dominant fuel types of the selected fire cases in the northeast of China (NEC), Siberia (Russia), and California (USA) are cropland, mixed forest, and needle-leaf forest, respectively. For the cropland fire case in NEC, the fire radiative power-based emission coefficients (Ce) of aerosol is 20.51 ± 2.55 g/MJ, half that of the forest fire cases in Siberia (40.01 ± 9.21 g/MJ) and California (45.23 ± 8.81 g/MJ), and the carbon monoxide (CO) Ce (23.94 ± 11.83 g/MJ) was about one third and half that of the forest fire cases in Siberia and California, respectively. However, the NO<SUB>x</SUB> (NO2 + NO) Ce (2.76 ± 0.25g MJ<SUP>-1</SUP>) of the cropland fire in NEC was nearly 3 times that of those forest fire cases. Ratios of NO<SUB>x</SUB> to aerosol, HCHO, and CO in the cropland case in NEC show much higher values than those in the forest fire cases. Despite the differences of the Ce and the composition ratios, the DSRF efficiency of smoke aerosol at the top of atmosphere showed similar values among those fire cases. Our results highlight the large variability of emission rate and relative chemical composition but similar DSRF efficiencies among wildfires, which would provide valuable information for understanding the impact of fire on air quality and climate.

Research areas: Wildfires
Projections of fire probability and ecosystem vulnerability under 21st century climate across a trans-Andean productivity gradient in Patagonia (2022)

Warming trends are altering fire regimes globally, potentially impacting on the long-term persistence of some ecosystems. However, we still lack clear understanding of how climatic stressors will alter fire regimes along productivity gradients. We trained a Random Forests model of fire probabilities across a 5lat  2 long trans-Andean rainfall gradient in northern Patagonia using a 23-year long fire record and biophysical, vegetation, human activity and seasonal fire weather predictors. The final model was projected onto mid- and late 21st century fire weather conditions predicted by an ensemble of GCMs using 4 emission scenarios. We finally assessed the vulnerability of different forest ecosystems by matching predicted fire return intervals with critical forest persistence fire return thresholds developed with landscape simulations. Modern fire activity showed the typical hump-shaped relationship with productivity and a negative distance relationship with human settlements. However, fire probabilities were far more sensitive to current season fire weather than to any other predictor. Sharp responsiveness of fire to the accelerating drier/warmer fire seasons predicted for the remainder of the 21st century in the region led to 2 to 3-fold (RCPs 4.5 and 8.5) and 3 to 8-fold increases in fire probabilities for the mid- and late 21st century, respectively. Contrary to current generalizations of larger impacts of warming on fire activity in fuel-rich ecosystems, our modeling results showed first an increase in predicted fire activity in less productive ecosystems (shrublands and steppes) and a later evenly amplified fire activity-productivity relationship with it shape resembling (at higher fire probabilities) the modern hump-shaped relationship. Despite this apparent homogeneous effect of warming on fire activity, vulnerability to predicted late 21st century shorter fire intervals were higher in most productive ecosystems (subalpine deciduous and evergreen Nothofagus-dominated rainforests) due to a general lack of fire-adapted traits in the dominant trees that compose these forests.

Research areas: Wildfires
Health Risk Implications of Volatile Organic Compounds in Wildfire Smoke During the 2019 FIREXAQ Campaign and Beyond (2022)

Fire Influence on Regional to Global Environments and Air Quality was a NOAA/NASA collaborative campaign conducted during the summer of 2019. The objectives included identifying and quantifying wildfire composition, smoke evolution, and climate and health impacts of wildfires and agricultural fires in the United States. Ground based mobile sampling via sorbent tubes occurred at the Nethker and Williams Flats fires (2019) and Chief Timothy and Whitetail Loop fires (2020) in Idaho and Washington. Air samples were analyzed through thermal desorption-gas chromatography-mass spectrometry for a variety of volatile organic compounds to elucidate both composition and health impacts. Benzene, toluene, ethylbenzene, xylenes, butenes, phenol, isoprene and pinenes were observed in the wildfire smoke, with benzene ranging from 0.04 to 25 ppbv. Health risk was assessed for each fire by determining sub-chronic (wildfire event) and projected chronic inhalation risk exposure from benzene, a carcinogen, as well as other non-carcinogenic compounds including toluene, ethylbenzene, xylenes, and hexane. The cancer risk of benzene from sub-chronic exposure was 1 extra cancer per million people and ranged from 1 to 19 extra cancers per million people for the projected chronic scenarios, compared to a background level of 1 extra cancer per million people. The hazard index of non-carcinogenic compounds was less than one for all scenarios and wildfires sampled, which was considered low risk for non-cancer health events.

Research areas: Wildfires
Smoke with induced rotation and lofting (SWIRL) in the stratosphere (2020)

Abstract The Australian bushfires of 2019/20 produced an unusually large number of pyrocumulonimbus (pyroCb) that injected huge amounts of smoke into the lower stratosphere. The pyroCbs from 29 December 2019 to 4 January 2020 were particularly intense, producing hemispheric-wide aerosol that persisted for months. One plume from this so-called Australian New Year (ANY) event evolved into a stratospheric aerosol mass ~1000 km across and several kilometers thick. This plume initially moved eastward toward South America in January, then reversed course and moved westward passing south of Australia in February and eventually reached South Africa in early March. The peculiar motion was related to the steady rise in plume potential temperature of ~8 K day1 in January and ~6 K day1 in February, due to local heating by smoke absorption of solar radiation. This heating resulted in a vertical temperature anomaly dipole, a positive potential vorticity (PV) anomaly, and anticyclonic circulation. We call this dynamical component of the smoke plume smoke with induced rotation and lofting (SWIRL). This study uses Navy Global Environmental Model (NAVGEM) analyses to detail the SWIRL structure over 2 months. The main diagnostic tool is an anticyclone edge calculation based on the scalar Q diagnostic. This provides the framework for calculating the time evolution of various SWIRL properties: PV anomaly, streamfunction, horizontal size, vertical thickness, flow speed, and tilt. In addition, we examine the temperature anomaly dipole, the SWIRL interaction with the large-scale wind shear, and the ozone anomaly associated with lofting of air from the lower to the middle stratosphere.

Research areas: Wildfires
Trends in wildfire burn severity across Canada, 1985 to 2015 (2021)

 Burn severity is an important component of the fire regime that has not yet been fully characterized for the forests of Canada. The objectives of this study were to (i) create a Canada-wide geospatial database of burn severity for wildland fires across forested regions of Canada from 1985 to 2015, and (ii) use this database to evaluate seasonal and annual trends in burn severity across Canada and regionally using two different regional units (ecozones and Homogeneous Fire Regime zones). We developed the 30 m resolution geospatial Canadian Landsat Burn Severity (CanLaBS) product from Landsat imagery, using values of pre-fire to post-fire differences in normalized burn ratios (dNBRs) for nearly 60 Mha of area burned across Canadas forests from 1985 to 2015, complemented with estimates of pre-fire forest composition, crown closure, and biomass. Our results suggest that burn severity is generally lower in spring fires than in summer ones nationally and in almost every regional unit. We found no consistent relationship between burn severity and annual area burned across ecozones. Finally, we observed a small but significant decrease in burn severity from 1985 to 2015 across Canada, although this is regionally variable. The CanLaBS database is publicly available at https://doi.org/10.23687/b1f61b7e-4ba6-4244-bc79-c1174f2f92cd . 

Research areas: Wildfires
Reassessment of carbon emissions from fires and a new estimate of net carbon uptake in Russian forests in 2001–2021 (2022)

Russia has the largest forest area on earth. Its boreal forests officially store about 97 Pg C, which significantly affect the global carbon cycle. In recent years, forest fires have been intensifying on the planet, leading to increased carbon emissions. Here we review how differences in fire control management of Russian forests affect fire related emissions. Carbon emissions due to fire were estimated using satellite data and compared to official reports for 20012021. We found that the relative areas affected by fire did differ between different fire protection zones, and 89 % of the area burnt was in forests controlled by fire-fighting aircraft or areas without protection. As a result, 417.7 Mha of poor or unprotected Russian forests (42 % of total) account about a half of total carbon emissions. According to our estimates, the average area of burnt forests in Russia was about 8.3 Mha per year between 2016 and 2021, resulting in annual carbon emission of 193 million metric tons (Mt) C emissions, and 53 % of them were from unprotected forest. These estimated carbon emissions are significantly higher than official national reports (79 Mt C yr1). We estimated that net carbon uptake for Russia for 20152021 was about 333  37 Mt C, which is roughly double the official estimates. Our results highlight large spatial differences in fire protection and prevention strategies in fire related emissions. The so-called control zone which stretches across large parts of Eastern Russia has no fire control and is the region of major recent fires. Our study shows that to estimate the Russian forest carbon balance it is critical to include this area. Implementation of some forest management in the remote areas (i.e., control zone) would help to decrease forest loss and resulting carbon emissions.

Research areas: Wildfires
Impacts of the Western Pacific and Indian Ocean warm pools on wildfires in Yunnan, Southwest China: Spatial patterns with inter‐and intra‐annual variations (2022)

The Indo-Pacific warm pool affects the regional climatic conditions in Southeast Asia mediated by the Western Pacific (WP) and Indian Ocean (IO) monsoons, which are recognized as critical regulators of wildfires in this region. Our study explored the spatiotemporal effects of the warm pools on wildfire records in Yunnan, Southwest China. We showed the interannual records had a negative relationship with the simultaneous WP warm pool area index. The Ailao Mountains in the central Yunnan could be a geographical barrier that blocked the influences of the WP warm pool from the east side. For the intraannual changes, the wildfire number variations had a stronger connection to the IO warm pool dynamics than the WP. Our study provided the novel evidence on the teleconnection between the Indo-Pacific warm pool dynamics and regional environmental processes in Yunnan, also indicating this area as a part of the transitional zone between the two warm pools.

Research areas: Wildfires
Building a machine learning surrogate model for wildfire activities within a global Earth system model (2022)

Abstract. Wildfire is an important ecosystem process, influencing land biogeophysical and biogeochemical dynamics and atmospheric composition. Fire-driven loss of vegetation cover, for example, directly modifies the surface energy budget as a consequence of changing albedo, surface roughness, and partitioning of sensible and latent heat fluxes. Carbon dioxide and methane emitted by fires contribute to a positive atmospheric forcing, whereas emissions of carbonaceous aerosols may contribute to surface cooling. Process-based modeling of wildfires in Earth system land models is challenging due to limited understanding of human, climate, and ecosystem controls on fire counts, fire size, and burned area. Integration of mechanistic wildfire models within Earth system models requires careful parameter calibration, which is computationally expensive and subject to equifinality. To explore alternative approaches, we present a deep neural network (DNN) scheme that surrogates the process-based wildfire model with the Energy Exascale Earth System Model (E3SM) interface. The DNN wildfire model accurately simulates observed burned area with over 90 % higher accuracy with a large reduction in parameterization time compared with the current process-based wildfire model. The surrogate wildfire model successfully captured the observed monthly regional burned area during validation period 2011 to 2015 (coefficient of determination, R2=0.93). Since the DNN wildfire model has the same input and output requirements as the E3SM process-based wildfire model, our results demonstrate the applicability of machine learning for high accuracy and efficient large-scale land model development and predictions.

Research areas: Wildfires
Geospatial characteristics of fire occurrences in southern hemispheric Africa and Madagascar during 20012020 (2022)

For this study of long-term spatial patterns and trends of active fires in southern hemispheric Africa and on Madagascar from 2001 to 2020, active fire data from the MODIS FIRMS global fire data products were analyzed. The annual center of fire concentration tended to migrate toward the preserved rainforests and nature conservation areas in the Congo Basin and the mountain forests on the northeastern coast of Madagascar. Fire frequency varied seasonally at both study areas. We used geostatistical analysis techniques, such as measures of dispersion and emerging hot spot analysis, to reveal long-term trends in spatial patterns of fire events. In southern hemispheric Africa, the observed active fires tended to drift northward toward the Zambia-DRC border in the Congo basin. This northward migration progressed toward humid rainforests, which were better suited to sustaining repeated fire events. On Madagascar, the observed active fires tended to migrate toward the east coast in protected mountain forests. The spatial patterns of long-term trends showed a concentration of fires in the tropical regions of southern hemispheric Africa. Moreover, smaller clusters of new hot spots were located over eastern South Africa, overlapping with undifferentiated woodlands. On Madagascar, both hot and cold spots were identified and were separated by the highland region in the center of the island. Most of the eastern island was characterized by cold spots that received less precipitation than did the rest of the island. The presence of increasing hots spots in the densely vegetated areas highlights the urgent need for fire prevention and management in this region.

Research areas: Wildfires
Linking fire-induced evapotranspiration shifts to streamflow magnitude and timing in the western United States (2022)

The impact of wildfire on water availability is a critical issue in the western United States. Because actual evapotranspiration (ETa) constitutes the largest loss in the terrestrial water budget, it has been suggested that fire-induced ETa reduction is a primary driver of elevated post-fire discharge. Ten gaged watersheds with burns exceeding 5% of their total contributing drainage area were selected from California, Oregon, Montana, Utah, New Mexico, and Colorado. Continuous daily stream gage data were compiled, and 30-meter ETa estimates were calculated with the Operational Simplified Surface Energy Balance (SSEBop) model. Fire-induced ETa shifts were quantified with statistical tests that compared pre and post-fire monthly ETa in burned and unburned pixels; the dampening effect of scale was also evaluated by repeating tests on all pixels from the entire basin. As streamflow data are point measurements that aggregate a large spatial area, additional statistical methods were required to isolate the effect of fire from climate on baseflow and runoff. Key findings include a) significant fire-induced ETa reductions were only distinguishable in basin-scale monthly datasets when at least 73% of the basin burned, b) the effect of wildfire disturbance on streamflow magnitude was seasonably variable, c) streamflow was modified in basins with as little as 6% burned drainage area; however, shifts only persisted beyond the fifth post-fire year where more than three-quarters of the basin was fire-impacted, and d) surplus water from ETa reduction was sufficient to account for boosted fire-induced streamflow. Where fire-induced streamflow increases were not significantly correlated with ETa anomaly, other fire-impacted landscape processes may have contributed to modified runoff generation and routing. Where fire reduced ETa but streamflow shifts were not detected, compensatory ETa pathways may have consumed the excess water before it reached the gage. Findings suggest that water providers with small source-water collection areas have higher relative risk for fire-induced hydromodification than providers with larger or more diversified supply portfolios. Results also illustrate the tendency of overarching climate signals to mask or artificially boost the apparent effect of landscape disturbance on streamflow at the basin outlet.

Research areas: Wildfires
In situ measurements of water uptake by black carbon‐containing aerosol in wildfire plumes (2017)

Water uptake by black carbon (BC)-containing aerosol was quantified in North American wildfire plumes of varying age (1 to 40 h old) sampled during the SEAC<SUP>4</SUP>RS mission (2013). A Humidified Dual SP2 (HD-SP2) is used to optically size BC-containing particles under dry and humid conditions from which we extract the hygroscopicity parameter, κ, of materials internally mixed with BC. Instrumental variability and the uncertainty of the technique are briefly discussed. An ensemble average κ of 0.04 is found for the set of plumes sampled, consistent with previous estimates of bulk aerosol hygroscopicity from biomass burning sources. The temporal evolution of κ in the Yosemite Rim Fire plume is explored to constrain the rate of conversion of BC-containing aerosol from hydrophobic to more hydrophilic modes in these emissions. A BC-specific κ increase of 0.06 over 40 h is found, fit well with an exponential curve corresponding to a transition from a κ of 0 to a κ of 0.09 with an e-folding time of 29 h. Although only a few percent of wildfire particles contain BC, a similar κ increase is estimated for bulk aerosol and the measured aerosol composition is used to infer that the observed κ change is driven by a combination of incorporation of ammonium sulfate and oxidation of existing organic materials. Finally, a substantial fraction of wildfire-generated BC-containing aerosol is calculated to be active as cloud condensation nuclei shortly after emission likely indicating efficient wet removal. These results can constrain model treatment of BC from wildfire sources.

Research areas: Wildfires
Nationwide summer peaks of OC/EC ratios in the contiguous United States (2011)

The ratios of observed organic carbon (OC) to elemental carbon (EC) from the rural sites of the IMPROVE network are analyzed for the 5-year period from 2000 to 2004. Among these years, nationwide OC/EC peaks are observed most consistently in the summer of 2002. Several potential factors are analyzed, including biomass burning, secondary organic aerosols (SOA) formation from biogenic sources and in-cloud processing, long-range transport from East Asia, and meteorological conditions over the U.S. We find that biomass burning and SOA formation make the most significant contributions using the global GEOS-Chem model simulations. The effect of model estimated in-cloud SOA formation is significant compared to the estimate of (non-cloud) biogenic SOA. The impacts of Canadian and western U.S. fires are larger than fires in Russia or Mexico in summer. The dry meteorological condition of the summer of 2002 tends to promote higher OC/EC ratios by inducing larger fire emissions, SOA formation, and a longer OC lifetime.

Research areas: Air Quality
Next Day Wildfire Spread: A Machine Learning Dataset to Predict Wildfire (2022)

Predicting wildfire spread is critical for land management and disaster preparedness. To this end, we present `Next Day Wildfire Spread,' a curated, large-scale, multivariate data set of historical wildfires aggregating nearly a decade of remote-sensing data across the United States. In contrast to existing fire data sets based on Earth observation satellites, our data set combines 2D fire data with multiple explanatory variables (e.g., topography, vegetation, weather, drought index, population density) aligned over 2D regions, providing a feature-rich data set for machine learning. To demonstrate the usefulness of this data set, we implement a neural network that takes advantage of the spatial information of this data to predict wildfire spread. We compare the performance of the neural network with other machine learning models: logistic regression and random forest. This data set can be used as a benchmark for developing wildfire propagation models based on remote sensing data for a lead time of one day.

Research areas: Wildfires