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NASA NTRS2020

Forecasting Global Fire Emissions on Subseasonal to Seasonal (S2S) Time Scales

Fire emissions of gases and aerosols alter atmospheric composition and have substantial impacts on climate, ecosystem function, and human health. Warming climate and human expansion in fire‐prone landscapes exacerbate fire impacts and call for more effective management tools. Here we developed a global fire forecasting system that predicts monthly emissions using past fire data and climate variables for lead times of 1 to 6 months. Using monthly fire emissions from the Global Fire Emissions Database (GFED) as the prediction target, we fit a statistical time series model, the Autoregressive Integrated Moving Average model with eXogenous variables (ARIMAX), in over 1,300 different fire regions. Optimized parameters were then used to forecast future emissions. The forecast system took into account information about region‐specific seasonality, long‐term trends, recent fire observations, and climate drivers representing both large‐scale climate variability and local fire weather. We cross‐validated the forecast skill of the system with different combinations of predictors and forecast lead times. The reference model, which combined endogenous and exogenous predictors with a 1 month forecast lead time, explained 52% of the variability in the global fire emissions anomaly, considerably exceeding the performance of a reference model that assumed persistent emissions during the forecast period. The system also successfully resolved detailed spatial patterns of fire emissions anomalies in regions with significant fire activity. This study bridges the gap between the efforts of near‐real‐time fire forecasts and seasonal fire outlooks and represents a step toward establishing an operational global fire, smoke, and carbon cycle forecasting system.

vapor pressure↗

DOE OSTI2013

Modeling the effects of fire severity and climate warming on active layer thickness and soil carbon storage of black spruce forests across the landscape in interior Alaska

There is a substantial amount of carbon stored in the permafrost soils of boreal forest ecosystems, where it is currently protected from decomposition. The surface organic horizons insulate the deeper soil from variations in atmospheric temperature. The removal of these insulating horizons through consumption by fire increases the vulnerability of permafrost to thaw, and the carbon stored in permafrost to decomposition. In this study we ask how warming and fire regime may influence spatial and temporal changes in active layer and carbon dynamics across a boreal forest landscape in interior Alaska. To address this question, we (1) developed and tested a predictive model of the effect of fire severity on soil organic horizons that depends on landscape-level conditions and (2) used this model to evaluate the long-term consequences of warming and changes in fire regime on active layer and soil carbon dynamics of black spruce forests across interior Alaska. The predictive model of fire severity, designed from the analysis of field observations, reproduces the effect of local topography (landform category, the slope angle and aspect and flow accumulation), weather conditions (drought index, soil moisture) and fire characteristics (day of year and size of the fire) on the reduction of the organic layercaused by fire. The integration of the fire severity model into an ecosystem process-based model allowed us to document the relative importance and interactions among local topography, fire regime and climate warming on active layer and soil carbon dynamics. Lowlands were more resistant to severe fires and climate warming, showing smaller increases in active layer thickness and soil carbon loss compared to drier flat uplands and slopes. In simulations that included the effects of both warming and fire at the regional scale, fire was primarily responsible for a reduction in organic layer thickness of 0.06 m on average by 2100 that led to an increase in active layer thickness of 1.1 m on average by 2100. The combination of warming and fire led to a simulated cumulative loss of 9.6 kgC m 2 on average by 2100. Our analysis suggests that ecosystem carbon storage in boreal forests in interior Alaska is particularly vulnerable, primarily due to the combustion of organic layer thickness in fire and the related increase in active layer thickness that exposes previously protected permafrost soil carbon to decomposition.

Genet, Helene↗

DOE OSTI2024

Life Cycle Greenhouse Gas Emissions of Coal-Biomass Co-Firing Power Plants with Carbon Capture and Storage

The United States has set a target to achieve the net-zero economy by 2050. Bioenergy with Carbon Capture and Sequestration (BECCS) is one of the promising negative-emission routes in the mitigation portfolio to help meet this goal. Coal-biomass co-firing with carbon capture and storage (CCS) is a key BECCS technology to realize the carbon mitigation at fossil-fuel power plants. The mitigation potential of co-firing option is affected by numerous critical factors, such as biomass properties, co-firing level, and carbon capture rate. The objectives of the study are to characterize and estimate the life cycle greenhouse gas (GHG) emissions and performance of coal-biomass co-firing power plants with CCS, determine the breakeven co-firing level at power plants necessary to achieve net-zero life cycle emissions, and quantify the variabilities and uncertainties in life cycle emissions. The scope of the life cycle assessment includes the fuel supply, combustion-based power generation, and CO2 transport and storage. A fuel-based life cycle module is developed and embedded in the Integrated Environmental Control Model (IECM), a fossil-fuel power plant modeling tool. This study then applies the enhanced IECM to conduct the process-based life cycle assessment for an array of biomass co-firing scenarios. Deterministic analysis indicates that reaching net-zero life cycle emissions in a biomass co-firing plant without CCS deployment is challenging. Combining biomass co-firing and CCS deployment can significantly lower the overall life cycle emissions of power plants. Net-zero life cycle emissions can be achieved with a 20 wt.% co-firing level and 90% CCS when the Powder River Basin coal is co-fired with energy crops or forestry residues. However, the breakeven co-firing level for net-zero emissions depend on the selected fuel properties. Fuel supply and plant operation are the critical stages influencing the life cycle emissions of power plants with 90% CCS. Deployment of deep CCS beyond 90% CO2 capture can remarkably reduce operational emissions and the breakeven co-firing level. With 99% CCS, the breakeven co-firing rate can be reduced to 12% on average. These findings highlight the trade-offs between technical performance and environmental impact of biomass co-firing at coal-fired power plants and emphasize the role of deep CCS in achieving a net-zero emissions future.

Wu, Wanying↗

DOE OSTI2024

Forest structural complexity and ignition pattern influence simulated prescribed fire effects

Background: Forest structural characteristics, the burning environment, and the choice of ignition pattern each influence prescribed fire behaviors and resulting fire effects; however, few studies examine the influences and interactions of these factors. Understanding how interactions among these drivers can influence prescribed fire behavior and effects is crucial for executing prescribed fires that can safely and effectively meet management objectives. To analyze the interactions between the fuels complex and ignition patterns, we used FIRETEC, a three-dimensional computational fluid dynamics fire behavior model, to simulate fire behavior and effects across a range of horizontal and vertical forest structural complexities. For each forest structure, we then simulated three different prescribed fires each with a unique ignition pattern: strip-head, dot, and alternating dot. Results: Forest structural complexity and ignition pattern affected the proportions of simulated crown scorch, consumption, and damage for prescribed fires in a dry, fire-prone ecosystem. Prescribed fires in forests with complex canopy structures resulted in increased crown consumption, scorch, and damage compared to less spatially complex forests. The choice of using a strip-head ignition pattern over either a dot or alternating-dot pattern increased the degree of crown foliage scorched and damaged, though did not affect the proportion of crown consumed. We found no evidence of an interaction between forest structural complexity and ignition pattern on canopy fuel consumption, scorch, or damage. Conclusions: We found that forest structure and ignition pattern, two powerful drivers of fire behavior that forest managers can readily account for or even manipulate, can be leveraged to influence fire behavior and the resultant fire effects of prescribed fire. These simulation findings have critical implications for how managers can plan and perform forest thinning and prescribed burn treatments to meet risk management or ecological objectives.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Parsing Weather Variability and Wildfire Effects on the Post-Fire Changes in Daily Stream Flows: A Quantile-Based Statistical Approach and Its Application

Determining wildland fire impacts on streamflow can be problematic as the hydrology in burned watersheds is influenced by post-fire weather conditions. Here, this study presents a quantile-based analytical framework for assessing fire impacts on low and peak daily flow magnitudes, while accounting for post-fire weather influences. This framework entails (a) the bootstrap method to compute the relative change in the post-fire annual flow and weather statistics, (b) double mass analysis to detect if post-fire baseflow and quick-flow yield ratios are significantly altered, and (c) a quantile regression method to parse fire effects on flow at a specific quantile. We illustrate the applicability of this analytical framework using 44 western US streams with at least 5% of their watershed area burned. Results indicate that large, high-severity burns in upland watersheds can raise the streamflow magnitude at the 0. 05 th and 0. 95 th quantiles for at least the five post-fire years. Quantile regression results show that the median fire-related increase in flow for the five post-fire years can be up to 5,000% (Standard Error; S.E. < 2%) at the 0. 05 th E quantile and 161% (S.E. < 10%) at the 0. 95 th quantile. The fire-related increase in flow was often pronounced at the 0. 05 th quantile for streams in the Pacific Northwest and California regions. The difference in fire effects on flow (at both quantiles) across streams was related to post-fire weather, pyrology, physiography, and land cover. The proposed analytical framework can be useful for detecting and quantifying fire effects on the low and peak stream flows in burned watersheds without overlapping disturbances.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2016

Improving Nocturnal Fire Detection with the VIIRS Day-Night Band

Building on existing techniques for satellite remote sensing of fires, this paper takes advantage of the day-night band (DNB) aboard the Visible Infrared Imaging Radiometer Suite (VIIRS) to develop the Firelight Detection Algorithm (FILDA), which characterizes fire pixels based on both visible-light and infrared (IR) signatures at night. By adjusting fire pixel selection criteria to include visible-light signatures, FILDA allows for significantly improved detection of pixels with smaller and/or cooler subpixel hotspots than the operational Interface Data Processing System (IDPS) algorithm. VIIRS scenes with near-coincident Advanced Spaceborne Thermal Emission and Reflection (ASTER) overpasses are examined after applying the operational VIIRS fire product algorithm and including a modified "candidate fire pixel selection" approach from FILDA that lowers the 4-μm brightness temperature (BT) threshold but includes a minimum DNB radiance. FILDA is shown to be effective in detecting gas flares and characterizing fire lines during large forest fires (such as the Rim Fire in California and High Park fire in Colorado). Compared with the operational VIIRS fire algorithm for the study period, FILDA shows a large increase (up to 90%) in the number of detected fire pixels that can be verified with the finer resolution ASTER data (90 m). Part (30%) of this increase is likely due to a combined use of DNB and lower 4-μm BT thresholds for fire detection in FILDA. Although further studies are needed, quantitative use of the DNB to improve fire detection could lead to reduced response times to wildfires and better estimate of fire characteristics (smoldering and flaming) at night.

Day-night band↗

NASA NTRS2021

COVID-19 Lockdowns Drive Decline in Active Fires in Southeastern United States

Fire is a common ecosystem process in forests and grasslands worldwide. Increasingly, ignitions are controlled by human activities either through suppression of wildfires or intentional ignition of prescribed fires. The Southeastern United States leads the nation in prescribed fire, burning ca. 80% of the countries extent annually. The COVID-19 pandemic radically changed human behavior as workplaces implemented social-distancing guidelines and provided an opportunity to evaluate relationships between humans and fire as fire management plans were postponed or cancelled. Using active fire data from satellite-based observations, we found that in the Southeastern United States, COVID-19 led to a 21% reduction in fire activity compared to the 2003-2019 average. The reduction was more pronounced for federally managed lands, up to 41% below average compared to the past 20-years (38% below average compared to the past decade). Declines in fire activity were partly affected by an unusually wet February before the COVID-19 shutdown began in mid-March 2020. Despite the wet spring, the predicted number of active fire detections was still lower than expected, confirming a COVID-19 signal on ignitions. In addition, prescribed fire management statistics, reported by US federal agencies, confirmed the satellite observations, and showed that following the wet February and before the mid-March COVID-19 shutdown, cumulative burned area was approaching record highs across the region. With fire return intervals in the Southeastern United States as frequent as 1-2 years, COVID-19 fire impacts will contribute to an increasing backlog in necessary fire management activities, affecting biodiversity and future fire danger.

Biological Sciences↗

DOE OSTI2026

IAM-FIRE: a Climate Emulator–Based Framework to Project Wildfire Impacts and Risks for Integrated Assessment Models

Most Integrated Assessment Models (IAMs) underrepresent dynamic feedbacks from climate-driven disturbances such as wildfires, potentially overestimating the permanence of land-based carbon sinks. In particular, representing the impacts of forest fires is becoming increasingly important, as these are expected to intensify in the coming years. We introduce IAM-FIRE (Integrated Assessment Model – Fire Impacts & Risks Emulator), a novel framework that enables the projection of wildfire burned area (BA) and carbon emissions (CE) directly from IAM outputs. IAM-FIRE combines a spatial climate emulator, land-use downscaling, vegetation productivity modelling, and an empirical fire model to generate global annual wildfire impacts for arbitrary socioeconomic and emissions scenarios at 0.5° resolution for the period 2020–2100. Calibrated against GFEDv5 observations and using inputs from the Global Change Analysis Model (GCAM), we report projections BA and CE derived from IAM-FIRE for four scenarios: SSP1-2.6, SSP2-4.5, SSP3-6.6 and SSP5-7.6. The model reproduces historical global trends for total BA, including the observed global decline since the early 2000s, and for forest BA. Projected fire trajectories differ strongly among scenarios: total BA range from declines under SSP1-2.6 (-3.36 Mha yr-1) to increases under SSP3-6.6 (+1.6 Mha yr-1). Corresponding total CE show a similar divergence ranging from -15 to +10.6 TgC yr-1. Socioeconomic development exerts a dominant suppressing effect on wildfire impacts while climate change and CO2-driven increases in vegetation productivity amplify fire risk, particularly under high-emissions pathways. Compared with CMIP6 fire-enabled Earth System Models, IAM-FIRE exhibits greater sensitivity to radiative forcing and a stronger role for human-driven fire suppression, highlighting substantial structural uncertainties in future fire projections. By providing a computationally efficient and internally consistent approach to represent wildfire impacts within IAMs, IAM-FIRE enables systematic exploration of fire–climate–land feedbacks and supports improved assessments of mitigation permanence and climate risks in future integrated scenarios.

Rouhette, Theo↗

DOE OSTI2023

Characterizing post-fire delayed tree mortality with remote sensing: sizing up the elephant in the room

Abstract Background Despite recent advances in understanding the drivers of tree-level delayed mortality, we lack a method for mapping delayed mortality at landscape and regional scales. Consequently, the extent, magnitude, and effects of delayed mortality on post-fire landscape patterns of burn severity are unknown. We introduce a remote sensing approach for mapping delayed mortality based on post-fire decline in the normalized burn ratio (NBR). NBR decline is defined as the change in NBR between the first post-fire measurement and the minimum NBR value up to 5 years post-fire for each pixel. We validate the method with high-resolution aerial photography from six wildfires in California, Oregon, and Washington, USA, and then compare the extent, magnitude, and effects of delayed mortality on landscape patterns of burn severity among fires and forest types. Results NBR decline was significantly correlated with post-fire canopy mortality (r 2 = 0.50) and predicted the presence of delayed mortality with 83% accuracy based on a threshold of 105 NBR decline. Plots with NBR decline greater than 105 were 23 times more likely to experience delayed mortality than those below the threshold (p < 0.001). Delayed mortality occurred across 6–38% of fire perimeters not affected by stand-replacing fire, generally affecting more areas in cold (22–41%) and wet (30%) forest types than in dry (1.7–19%) types. The total area initially mapped as unburned/very low-severity declined an average of 38.1% and generally persisted in smaller, more fragmented patches when considering delayed mortality. The total area initially mapped as high-severity increased an average of 16.2% and shifted towards larger, more contiguous patches. Conclusions Differences between 1- and 5-year post-fire burn severity maps depict dynamic post-fire mosaics resulting from delayed mortality, with variability among fires reflecting a range of potential drivers. We demonstrate that tree-level delayed mortality scales up to alter higher-level landscape patterns of burn severity with important implications for forest resilience and a range of fire-driven ecological outcomes. Our method can complement existing tree-level studies on drivers of delayed mortality, refine mapping of fire refugia, inform estimates of habitat and carbon losses, and provide a more comprehensive assessment of landscape and regional scale fire effects and trends.

Environmental Sciences & Ecology↗

DOE OSTI2016

Impacts of global open-fire aerosols on direct radiative, cloud and surface-albedo effects simulated with CAM5

Abstract. Aerosols from open-land fires could significantly perturb the global radiation balance and induce climate change. In this study, Community Atmosphere Model version 5 (CAM5) with prescribed daily fire aerosol emissions is used to investigate the spatial and seasonal characteristics of radiative effects (REs, relative to the case of no fires) of open-fire aerosols including black carbon (BC) and particulate organic matter (POM) from 2003 to 2011. The global annual mean RE from aerosol–radiation interactions (REari) of all fire aerosols is 0.16 ± 0.01 W m−2 (1σ uncertainty), mainly due to the absorption of fire BC (0.25 ± 0.01 W m−2), while fire POM induces a small effect (−0.05 and 0.04 ± 0.01 W m−2 based on two different methods). Strong positive REari is found in the Arctic and in the oceanic regions west of southern Africa and South America as a result of amplified absorption of fire BC above low-level clouds, in general agreement with satellite observations. The global annual mean RE due to aerosol–cloud interactions (REaci) of all fire aerosols is −0.70 ± 0.05 W m−2, resulting mainly from the fire POM effect (−0.59 ± 0.03 W m−2). REari (0.43 ± 0.03 W m−2) and REaci (−1.38 ± 0.23 W m−2) in the Arctic are stronger than in the tropics (0.17 ± 0.02 and −0.82 ± 0.09 W m−2 for REari and REaci), although the fire aerosol burden is higher in the tropics. The large cloud liquid water path over land areas and low solar zenith angle of the Arctic favor the strong fire aerosol REaci (up to −15 W m−2) during the Arctic summer. Significant surface cooling, precipitation reduction and increasing amounts of low-level cloud are also found in the Arctic summer as a result of the fire aerosol REaci based on the atmosphere-only simulations. The global annual mean RE due to surface-albedo changes (REsac) over land areas (0.03 ± 0.10 W m−2) is small and statistically insignificant and is mainly due to the fire BC-in-snow effect (0.02 W m−2) with the maximum albedo effect occurring in spring (0.12 W m−2) when snow starts to melt.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Interpreting machine learning prediction of fire emissions and comparison with FireMIP process-based models

Annual burned areas in the United States have increased 2-fold during the past decades. With more large fires resulting in more emissions of fine particulate matter, an accurate prediction of fire emissions is critical for quantifying the impacts of fires on air quality, human health, and climate. This study aims to construct a machine learning (ML) model with game-theory interpretation to predict monthly fire emissions over the contiguous US (CONUS) and to understand the controlling factors of fire emissions. The optimized ML model is used to diagnose the process-based models in the Fire Modeling Intercomparison Project (FireMIP) to inform future development. Results show promising performance for the ML model, Community Land Model (CLM), and Joint UK Land Environment Simulator-Interactive Fire And Emission Algorithm For Natural Environments (JULES-INFERNO) in reproducing the spatial distributions, seasonality, and interannual variability of fire emissions over the CONUS. Regional analysis shows that only the ML model and CLM simulate the realistic interannual variability of fire emissions for most of the subregions (r >0.95 for ML and r =0.14~0.70 for CLM), except for Mediterranean California, where all the models perform poorly (r =0.74 for ML and r <0.30 for the FireMIP models). Regarding seasonality, most models capture the peak emission in July over the western US. However, all models except for the ML model fail to reproduce the bimodal peaks in July and October over Mediterranean California, which may be explained by the smaller wind speeds of the atmospheric forcing data during Santa Ana wind events and limitations in model parameterizations for capturing the effects of Santa Ana winds on fire activity. Furthermore, most models struggle to capture the spring peak in emissions in the southeastern US, probably due to underrepresentation of human effects and the influences of winter dryness on fires in the models. As for extreme events, both the ML model and CLM successfully reproduce the frequency map of extreme emission occurrence but overestimate the number of months with extremely large fire emissions. Comparing the fire PM 2.5 emissions from the ML model with process-based fire models highlights their strengths and uncertainties for regional analysis and prediction and provides useful insights into future directions for model improvements.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1999

Remote Sensing of Forest Fires from Space

Forest fires, and fires used for deforestation and agriculture are sporadic. Some may last an hour others several days. It is difficult to find the fires or to estimate their effect on atmospheric pollution without an "eye in the sky" a satellite or an array of satellites that monitors them routinely from space. Since fires have a significant effect on the quality of air that we breath, on the surface vegetation, on clouds and precipitation and even on climate, NASA and other space agencies try to develop fire monitoring capability from space. Presently satellites were not designed to monitor fires. But the AVHRR and GOES satellites were used for fire monitoring. AVHRR is an orbiter that passes over the same area twice a day with detailed observations of fires from a distance of 800 km, GOES is a stationary satellite located above the equator, and observes the larger fires from a distance of 20,000 km. Field experiments, such as the "SCAR-B" experiment in Brazil conducted in 1995 by INPE, NASA and Universities of Sao Paulo, Washington and Wisconsin, were used to determine the ability of satellites to observe fires and the emitted pollution. They are the basis of a new system of satellites designed by NASA to observe fires and pollution, the Earth Observing System AM1 and PM1. NASA plans to use the information for four observations a day of the fires and the emitted smoke. The information can be used to determine the location of the fires, to distinguish between small and large fires and monitor their development. The satellites will measure the emitted smoke and with trajectory models can be used to predict the density and spread of the smoke.

Kaufman, Y.↗

NASA NTRS2003

Use of MODIS-Derived Fire Radiative Energy to Estimate Smoke Aerosol Emissions over Different Ecosystems

Biomass burning is the main source of smoke aerosols and certain trace gases in the atmosphere. However, estimates of the rates of biomass consumption and emission of aerosols and trace gases from fires have not attained adequate reliability thus far. Traditional methods for deriving emission rates employ the use of emission factors e(sub x), (in g of species x per kg of biomass burned), which are difficult to measure from satellites. In this era of environmental monitoring from space, fire characterization was not a major consideration in the design of the early satellite-borne remote sensing instruments, such as AVHRR. Therefore, although they are able to provide fire location information, they were not adequately sensitive to variations in fire strength or size, because their thermal bands used for fire detection saturated at the lower end of fire radiative temperature range. As such, hitherto, satellite-based emission estimates employ proxy techniques using satellite derived fire pixel counts (which do not express the fire strength or rate of biomass consumption) or burned areas (which can only be obtained after the fire is over). The MODIS sensor, recently launched into orbit aboard EOS Terra (1999) and Aqua (2002) satellites, have a much higher saturation level and can, not only detect the fire locations 4 times daily, but also measures the at-satellite fire radiative energy (which is a measure of the fire strength) based on its 4 micron channel temperature. Also, MODIS measures the optical thickness of smoke and other aerosols. Preliminary analysis shows appreciable correlation between the MODIS-derived rates of emission of fire radiative energy and smoke over different regions across the globe. These relationships hold great promise for deriving emission coefficients, which can be used for estimating smoke aerosol emissions from MODIS active fire products. This procedure has the potential to provide more accurate emission estimates in near real-time, providing opportunities for various disaster management applications such as alerts, evacuation and, smoke dispersion forecasting.

Ichoku, Charles↗

NASA NTRS2004

Modeling Forest Understory Fires in an Eastern Amazonian Landscape

Forest understory fires are an increasingly important cause of forest impoverishment in Ammonia, but little is known of the landscape characteristics and climatic phenomena that determine their occurrence. We developed empirical functions relating the occurrence of understory fires to landscape features near Paragominas, a 35- yr-old ranching and logging center in eastern Ammonia. An historical sequence of maps of forest understory fire was created based on field interviews With local farmers and Landsat TM images. Several landscape features that might explain spatial variations in the occurrence of understory fires were also mapped and co-registered for each of the sample dates, including: forest fragment size and shape, forest impoverishment through logging and understory fires, source of ignition (settlements and charcoal pits), roads, forest edges, and others. The spatial relationship between forest understory fire and each landscape characteristic was tested by regression analyses. Fire probability models were then developed for various combinations of landscape characteristics. The analyses were conducted separately for years of the El Nino Southern Oscillation (ENSO), which are associated with severe drought in eastern Amazonia, and non-ENS0 years. Most (91 %) of the forest area that burned during the 10-yr sequence caught fire during ENSO years, when severe drought may have increased both forest flammability and the escape of agricultural management fires. Forest understory fires were associated with forest edges, as reported in previous studies from Ammonia. But the strongest predictor of forest fire was the percentage of the forest fragment that had been previously logged or burned. Forest fragment size, distance to charcoal pits, distance to agricultural settlement, proximity to forest edge, and distance to roads were also correlated with forest understory fire. Logistic regression models using information on fragment degradation and distance to ignition sources accurately predicted the location of lss than 80% of the forest fires observed during the ENSO event of 1997- 1998. In this Amazon landscape, forest understory fire is a complex function of several variables that influence both the flammability and ignition exposure of the forest.

Alencar, A. A. C.↗

NASA NTRS2011

Developing Standards to Qualify a Fine Water Mist Fire Extinguisher for Human Spaceflight

NASA is developing a Fine Water Mist Portable Fire Extinguisher for use on the International Space Station. The International Space Station presently uses two different types of fire extinguishers: a water foam extinguisher in the Russian Segment, and a carbon dioxide extinguisher in the US Segment and Columbus and Kibo pressurized elements. Changes in emergency breathing equipment make Fine Water Mist operationally preferable. Supplied oxygen breathing systems allow for safe discharge of a carbon dioxide fire extinguisher, without concerns of the crew inhaling unsafe levels of carbon dioxide. But the Portable Breathing Apparatus offers no more than 15 minutes of capability, and continued use of hose based supplied oxygen systems increases the oxygen content in a fire situation. NASA has developed a filtering respirator cartridge for use in a fire environment. It is qualified to provide up to 90 minutes of capability, and because it is a filtering respirator it does not add oxygen to the environment. The fire response respirator cartridge does not filter carbon dioxide, so a crew member discharging a CO2 fire extinguisher while wearing this filtering respirator would be at risk of inhaling unsafe levels of CO2. Fine Water Mist extinguishes a fire without creating a large volume of air with reduced oxygen and elevated CO2. Compared to the carbon dioxide based Portable Fire Extinguisher, the flight qualification of Fine Water Mist systems requires special care. Qualification of the CO2 based Portable Fire Extinguisher began with the assumption that any fire on ISS would be extinguished if the air in the fire environment reached a critical concentration of CO2. Qualification of a CO2 based system requires the developers to make assertions and assumptions about vehicle geometry and the ability of the extinguisher to deliver CO2 in different geometric configurations, but the developers did not need to make assertions or assumptions about the size of the fire, the temperature, or the heat generation rate. Fine Water Mist systems extinguish a fire predominantly by removing heat -- so qualification standards must evaluate geometry, but also temperature, heat transfer, and heat generation rate. This paper outlines and describes the methods used to develop standards used to qualify Fine Water Mist systems for a human spaceflight environment.

Graf, John↗

NASA NTRS2011

Daily and 3-hourly Variability in Global Fire Emissions and Consequences for Atmospheric Model Predictions of Carbon Monoxide

Attribution of the causes of atmospheric trace gas and aerosol variability often requires the use of high resolution time series of anthropogenic and natural emissions inventories. Here we developed an approach for representing synoptic- and diurnal-scale temporal variability in fire emissions for the Global Fire Emissions Database version 3 (GFED3). We disaggregated monthly GFED3 emissions during 2003.2009 to a daily time step using Moderate Resolution Imaging Spectroradiometer (MODIS) ]derived measurements of active fires from Terra and Aqua satellites. In parallel, mean diurnal cycles were constructed from Geostationary Operational Environmental Satellite (GOES) Wildfire Automated Biomass Burning Algorithm (WF_ABBA) active fire observations. Daily variability in fires varied considerably across different biomes, with short but intense periods of daily emissions in boreal ecosystems and lower intensity (but more continuous) periods of burning in savannas. These patterns were consistent with earlier field and modeling work characterizing fire behavior dynamics in different ecosystems. On diurnal timescales, our analysis of the GOES WF_ABBA active fires indicated that fires in savannas, grasslands, and croplands occurred earlier in the day as compared to fires in nearby forests. Comparison with Total Carbon Column Observing Network (TCCON) and Measurements of Pollution in the Troposphere (MOPITT) column CO observations provided evidence that including daily variability in emissions moderately improved atmospheric model simulations, particularly during the fire season and near regions with high levels of biomass burning. The high temporal resolution estimates of fire emissions developed here may ultimately reduce uncertainties related to fire contributions to atmospheric trace gases and aerosols. Important future directions include reconciling top ]down and bottom up estimates of fire radiative power and integrating burned area and active fire time series from multiple satellite sensors to improve daily emissions estimates.

Mu, M.↗

NASA NTRS2022

Tracking and Classifying Amazon Fire Events in Near-Real Time

Exceptional fire activity in 2019 sparked concern about Amazon forest conservation. However, the inability to rapidly separate satellite fire detections by fire type hampered fire suppression and assessment of ecosystem and air quality impacts. Here, we describe the development of a near–real-time approach for tracking contributions from deforestation, forest, agricultural, and savanna fires to burned area and emissions and apply the approach to the 2019 fire season in South America. Across the southern Amazon, 19,700 deforestation fire events accounted for 39% of all satellite active fire detections and the majority of fire carbon emissions (63%; 69 Tg C). Multiday fires accounted for 81% of burned area and 92% of carbon emissions from the Amazon, with many forest fires burning uncontrolled for weeks. Most fire detections from deforestation fires were correctly identified within 2 days (67%), highlighting the potential to improve situational awareness and management outcomes during fire emergencies.

Niels Andela↗

NASA NTRSDate not supplied

Overview of the NASA Earth Action Strategies Wildland Fire Initiative

As part of NASA’s new Earth Action strategy, the Wildland Fire initiative was established, which includes both the NASA Wildland Fire Program (WFP) and the FireSense project. NASA has over 50 years of experience generating data and technology to enhance fire science and operational management. The WFP’s mission is threefold: 1) assemble communities of practice through collaborative efforts with government, academia, and the private sector; 2) co-develop knowledge and applications with relevant partners and stakeholders in the wildfire community; and 3) improve wildland fire management through the transitioning of NASA data, technology, tools, and science to stakeholder organizations. The WFP is focusing on supporting proactive fire management, including situational awareness, preparedness, and risk mitigation. This will be accomplished through selected projects that identify management challenges, relevant to partners and end users, and the NASA data that will be utilized to deliver innovative solutions to enhance the management of wildland fires. Examples include: i) investigation of evaporative stress from OpenET to help predict the risk of wildfire occurrence in watersheds; ii) incorporation of space based LiDAR for the generation of 3-dimensional forest fuel metrics, used to improve wildfire risk and behavior models; iii) integration of global, multi-platform geostationary active-fire data in near-real-time into NASA’s Fire Information for Resource Management System (FIRMS); and iv) identification of post-fire ecohydrological conditions using thermal, multispectral, synthetic aperture radar (SAR), and hyperspectral remotely-sensed data to improve flood hazard forecast models. The FireSense project is a US-focused 5-year project that will focus on delivering NASA’s unique Earth science and technological capabilities to operational agencies, striving towards enhancing fire fighting and air quality management. The project will include airborne campaigns and new technology that will likely have global implications. Initial stakeholder engagement led FireSense to focus on four use-cases focused on the characterization and measurement of: (i) pre-fire fuels conditions, (ii) active fire-dynamics; (iii) post-fire impact and threats; and iv) air quality impacts and forecasting, each-developed with identified stakeholders.

Wildland Fire program↗