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NASA NTRSDate not supplied

Curating AI-Ready Datasets for Equity and Environmental Justice: A Data-Centric AI Case Study

An equitable and environmentally just community is essentialin order to avoid disproportionate burden borne by vulnerablecommunities. This need becomes pressing in the aftermathof an extreme event such as disaster or hazard when it is diffi-cult for the governing bodies to implement resource allocationas per the need. Artificial Intelligence (AI) algorithms canhelp surface Equity and Environmental Justice (EEJ) issueswhen trained on EEJ datasets. However, curating AI-readyEEJ training datasets is challenging due to differences in fac-tors such as heterogeneity, resolution, modality, and level ofexpertise in labeling. Additionally, EEJ issues involve sensi-tive information where uncertainties and errors could degradethe performance of AI algorithms. For eg. Error in seasonalcrop yield information can highly affect the prediction of an-nual crop yield. To address these challenges, Data-centricAI (DCAI) methods are employed, which enhance AI algo-rithm performance even with limited training samples. DCAIprioritizes data quality, thereby reducing the adverse effectsof uncertainties and errors during the model training process.This research proposes a novel dataset and benchmark for an-alyzing the effect of the Maui Wildfire of 2023 for Equityand Environmental Justice (EEJ) issues. The proposed datasetaligns with the concepts of DCAI such as annotation quality,data preprocessing, privacy, feature engineering, governanceand provenance. We firmly believe that the proposed datasetwould lay a foundation to implement robust and reliable mod-ern AI algorithms for addressing EEJ issues.

Paridhi Parajuli↗

DOE OSTI2022

Forest fire threatens global carbon sinks and population centres under rising atmospheric water demand

Levels of fire activity and severity that are unprecedented in the instrumental record have recently been observed in forested regions around the world. Using a large sample of daily fire events and hourly climate data, here we show that fire activity in all global forest biomes responds strongly and predictably to exceedance of thresholds in atmospheric water demand, as measured by maximum daily vapour pressure deficit. The climatology of vapour pressure deficit can therefore be reliably used to predict forest fire risk under projected future climates. We find that climate change is projected to lead to widespread increases in risk, with at least 30 additional days above critical thresholds for fire activity in forest biomes on every continent by 2100 under rising emissions scenarios. Escalating forest fire risk threatens catastrophic carbon losses in the Amazon and major population health impacts from wildfire smoke in south Asia and east Africa.

54 ENVIRONMENTAL SCIENCES↗

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.↗

DOE OSTI2023

Potential bioavailability of representative pyrogenic organic matter compounds in comparison to natural dissolved organic matter pools

Abstract. Pyrogenic organic matter (PyOM) from wildfires impacts river corridors globally and is widely regarded as resistant to biological degradation. Though recent work suggests PyOM may be more bioavailable than historically perceived, estimating bioavailability across its chemical spectrum remains elusive. To address this knowledge gap, we assessed potential bioavailability of representative PyOM compounds relative to ubiquitous dissolved organic matter (DOM) with a substrate-explicit model. The range of potential bioavailability of PyOM was greater than natural DOM; however, the predicted thermodynamics, metabolic rates, and carbon use efficiencies (CUEs) overlapped significantly between all OM pools. Compound type (e.g., natural versus PyOM) had approximately 6-fold less impact on predicted respiration rates than simulated carbon and oxygen limitations. Within PyOM, the metabolism of specific chemistries differed strongly between unlimited and oxygen-limited conditions – degradations of anhydrosugars, phenols, and polycyclic aromatic hydrocarbons (PAHs) were more favorable under oxygen limitation than other molecules. Notably, amino sugar-like, protein-like, and lignin-like PyOM had lower carbon use efficiencies relative to natural DOM of the same classes, indicating potential impacts in process-based model representations. Overall, our work illustrates how similar PyOM bioavailability may be to that of natural DOM in the river corridor, furthering our understanding of how PyOM may influence riverine biogeochemical cycling.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Fire Predictions and Impacts

Megafires are increasing in the Western US due to sustained drought and prolonged fire exclusion. LANL uniquely positioned to lead in both modeling (fine scales, coupled fire-atmosphere, 3D fuels) and observations (CAFÉ).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Development of an Extended Reality (XR) Tool for Earth Science Visualization

In this presentation, we will discuss our work in adapting the NASA open source XR software, the Mixed Reality Exploration Toolkit (MRET), to an earth science domain. MRET is a NASA open source XR software for rapidly building extended reality (XR) environments for NASA domain problems, e.g., pulling in CAD models of thermal vac chamber and Roman Space Telescope to do fit checks. Primarily used for hardware integration & test, we have been adapting and extending MRET for science problems. Traditionally, scientists view and analyze the result of calculated or measured observables with static 1-D, 2-D or 3-D plots. It can be difficult to identify, track and understand the evolution of key features due to poor viewing angles and the nature of flat computer screens. Additionally, numerical models, such as the NASA GEOS climate model, are almost exclusively formulated and analyzed on Eulerian grids with points fixed in space and time. However, atmospheric phenomena such as convective clouds, hurricanes and wildfire smoke plumes move with the 3-D flow field, and it is often difficult and unnatural to understand these phenomena in an Eulerian reference frame as opposed to the Lagrangian reference frame in which nature operates. As part of an Earth Science Technology Office (ESTO) proposal, we have been adapting MRET to be a scientific exploration and analysis XR tool with integrated Lagrangian Dynamics (LD) for the Goddard Earth Observing System (GEOS) numerical weather prediction model. We believe this will help scientists identify, track, and understand the evolution of Earth Science phenomena. This presentation will discuss current results in our work in developing this XR tool for Earth Science.

Thomas G Grubb↗

NASA NTRSDate not supplied

Development of an Extended Reality (XR) Tool for Earth Science Visualization

Thomas Grubb↗

DOE OSTI2022

Resilience in soil bacterial communities of the boreal forest from one to five years after wildfire across a severity gradient

Wildfires can represent a major disturbance to ecosystems, including soil microbial communities belowground. Furthermore, fire regimes are changing in many parts of the world, altering and often increasing fire severity, frequency, and size. The boreal forest and taiga plains ecoregions of northern Canada are characterized by naturally-occurring stand-replacing wildfires on a 40–350 year basis. We previously studied the effects of wildfire on soil microbial communities one year post-fire across 40 sites, spanning a range of burn severity. Here, we return to the same sites five years post-fire to test a series of hypotheses about the effects of fire on bacterial community composition. We ask questions on two themes: which factors control bacterial community composition during post-fire recovery, and how does the importance of different fire-responsive traits change during post-fire recovery? We find the following: Five years post-fire, vegetation community, moisture regime, pH, total carbon, texture, and burned/unburned all remained significant predictors of bacterial community composition with similar predictive value (R 2 ). Bacterial communities became more similar to unburned sites five years post-fire, across the range of severity, suggesting resilience, while general structure of co-occurrence networks remained similar one and five years post-fire. Fast growth potential, as estimated using predicted 16S rRNA copy numbers, was no longer significantly correlated with burn severity five years post-fire, indicating the importance of this trait for structuring bacterial community composition may be limited to relatively short timescales. Many taxa that were enriched in burned sites one year post-fire remained enriched five years post-fire, although the degree to which they were enriched generally decreased. Specific taxa of interest from the genera Massilia, Blastococcus, and Arthrobacter all remained significantly enriched, suggesting that they may have traits that allow them to continue to flourish in the post-fire environment, such as tolerance to increased pH or ability to degrade pyrogenic organic matter. This hypothesis-based work expands our understanding of the post-fire recovery of soil bacterial communities and raises new hypotheses to test in future studies.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRSDate not supplied

Model Evaluation and Intercomparison Using Data Collected by the Langley Mobile Ozone Lidar in Hampton, Virginia

Throughout the year 2022, the Langley Mobile Ozone Lidar (LMOL) has been frequently collecting ozone and aerosol measurements in the lower troposphere. The lidar is located in the parking lot behind the Atmospheric Sciences building at NASA Langley in Hampton, Virginia, approximately 37.095 N, -76.389 W. The data was collected during a range of several distinct atmospheric conditions, including stratospheric intrusions, surface frontal passages, and long-range transported wildfire smoke plumes. We use this data to evaluate and intercompare the forecast accuracy of two atmospheric chemistry models: the GEOS Composition Forecasting model (GEOS-CF) and the Weather Research and Forecasting model with Chemistry (WRF-Chem). Both models make daily three-dimensional forecasts of many trace gases and species for the contiguous United States. Over a range of ten distinct collection events, each spanning 36 hours to nearly seven days, the forecast models predict tropospheric ozone at NASA Langley with reasonable accuracy. The models best predict the timing and shape of the observed stratospheric intrusions in the middle troposphere but often vary in the magnitude of the ozone mixing ratios. For other atmospheric conditions, there is more variability in the model accuracy. Here, we summarize the accuracy of the models for all events and investigate reasons for differences between the models and the lidar.

Daniel B. Phoenix↗

DOE OSTI2025

Investigating Particle Phase State Dependencies during a Historic Pacific Northwest Wildfire Event

The function and lifetime of an atmospheric particle are greatly dependent upon its phase state. Studying particle phase states from specific ambient source types is often challenging due to uncontrolled environmental conditions and multiple source contributions. However, a historic Pacific Northwest wildfire event occurred in September 2020, providing a rare opportunity to study a pseudo-single particle type ambient sample (i.e., wildfire) over time. Sampling was performed in Richland, Washington, capturing aged particles from shifting wildfire sources. In addition to source variability, temporal variation in wildfire particle phase state was observed from single particle analyses. Despite this, single particle elemental compositions were homogenous over time (=87% carbonaceous) with no significant temporal variation in organic functional group abundances within individual particles either (e.g., 4% relative standard deviation in alkene contributions). While particle composition is indeed known to affect phase state, water uptake appeared to be the most significant contributor to phase state variability amongst single particles here, as particle aspect ratios (related to phase state) linearly correlated with ambient relative humidity during sampling (R2 = 0.65). Therefore, while limited to a case study, meteorological considerations are found to convey greater comparative importance than particle composition for aged particle phase state predictions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2011

Applying Geospatial Technologies for International Development and Public Health: The USAID/NASA SERVIR Program

Background: SERVIR -- the Regional Visualization and Monitoring System -- helps people use Earth observations and predictive models based on data from orbiting satellites to make timely decisions that benefit society. SERVIR operates through a network of regional hubs in Mesoamerica, East Africa, and the Hindu Kush-Himalayas. USAID and NASA support SERVIR, with the long-term goal of transferring SERVIR capabilities to the host countries. Objective/Purpose: The purpose of this presentation is to describe how the SERVIR system helps the SERVIR regions cope with eight areas of societal benefit identified by the Group on Earth Observations (GEO): health, disasters, ecosystems, biodiversity, weather, water, climate, and agriculture. This presentation will describe environmental health applications of data in the SERVIR system, as well as ongoing and future efforts to incorporate additional health applications into the SERVIR system. Methods: This presentation will discuss how the SERVIR Program makes environmental data available for use in environmental health applications. SERVIR accomplishes its mission by providing member nations with access to geospatial data and predictive models, information visualization, training and capacity building, and partnership development. SERVIR conducts needs assessments in partner regions, develops custom applications of Earth observation data, and makes NASA and partner data available through an online geospatial data portal at SERVIRglobal.net. Results: Decision makers use SERVIR to improve their ability to monitor air quality, extreme weather, biodiversity, and changes in land cover. In past several years, the system has been used over 50 times to respond to environmental threats such as wildfires, floods, landslides, and harmful algal blooms. Given that the SERVIR regions are experiencing increased stress under larger climate variability than historic observations, SERVIR provides information to support the development of adaptation strategies for nations affected by climate change. Conclusions: SERVIR is a platform for collaboration and cross-agency coordination, international partnerships, and delivery of web-based geospatial information services and applications. SERVIR makes a variety of geospatial data available for use in studies of environmental health outcomes.

Hemmings, Sarah↗

DOE OSTI2024

Projecting Large Fires in the Western US With an Interpretable and Accurate Hybrid Machine Learning Method

More frequent and widespread large fires are occurring in the western United States (US), yet reliable methods for predicting these fires, particularly with extended lead times and a high spatial resolution, remain challenging. In this study, we proposed an interpretable and accurate hybrid machine learning (ML) model, that explicitly represented the controls of fuel flammability, fuel availability, and human suppression effects on fires. The model demonstrated notable accuracy with a F 1 -score of 0.846 ± 0.012, surpassing process-driven fire danger indices and four commonly used ML models by up to 40% and 9%, respectively. More importantly, the ML model showed remarkably higher interpretability relative to other ML models. Specifically, by demystifying the “black box” of each ML model using the explainable AI techniques, we identified substantial structural differences across ML fire models, even among those with similar accuracy. The relationships between fires and their drivers, identified by our model, were aligned closer with established fire physical principles. The ML structural discrepancy led to diverse fire predictions and our model predictions exhibited greater consistency with actual fire occurrence. With the highly interpretable and accurate model, we revealed the strong compound effects from multiple climate variables related to evaporative demand, energy release component, temperature, and wind speed, on the dynamics of large fires and megafires in the western US. Our findings highlight the importance of assessing the structural integrity of models in addition to their accuracy. They also underscore the critical need to address the rise in compound climate extremes linked to large wildfires.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Phenolic compounds and black carbon feedback controls on peat decomposition and carbon accumulation in southeastern peatlands under regimes of seasonal drought, drainage and frequent fire

Peatlands store one-third of soil C in terrestrial ecosystems and have persisted through changing climate over millennia from the arctic to the tropics. Approximately one-third of peat stores are found in subtropical and tropical peatlands (STPs) formed from high-lignin woody biomass. In this project, our questions are: 1) why do these non-sphagnum peatlands (STPs) accumulate C under warmer-drier climates and 2) how might insights coming from studying control mechanisms in STPs improve the management and conservation of the vast C stores in boreal peatlands subject to increasing climate forcing. We hypothesized that a dual control or “latch mechanism” reduces decomposition in shrub/tree communities in STPs due to both (1) higher production of polyphenol and aromatic compounds in STPs than found in northern Sphagnum/Carex communities and (2) the buildup of recalcitrant organic matter produced by light fire-drought-warming-adapted communities, together leading to a reduction in the microbial decay rate of peat. After three-years of intensive biological and chemical analysis in a series of field and microcosm experiments along our north to south bog gradient from Minnesota to Peru, we show how previously unrecognized biotic factors, particularly dynamic interlinked above- and belowground attributes control C sequestration in peatlands. Our key findings include (1) phenolics-bridged plant-microbe symbioses, principally slow-growing microbes dominated in higher phenolic wooded STPs, preserving C in peatlands under climate change, 2) phenolics are the overarching factor controlling the relative abundance of slow-and fast-growing microbes, the slow-growing microbes in STPs metabolize C slowly and are inherently resistant to disturbance, 3) global data analysis shows that soil respiration does not increase exponentially from boreal to tropical peatlands, suggesting that slow-growing microbes may have become dominant in most non-boreal peatlands, 4) peat chemistry analysis from over 2000 samples show that across peatlands both from the arctic to tropics and from high to low elevation peatlands recalcitrance increases as aromatic content increases, and 5) peat affected by low-severity wildfires displays a similar pattern of higher aromatic content. Our findings all demonstrate links between peat recalcitrance and increased content of phenolics and other aromatic compounds in plants. Thus, linked plant–microbe symbiotic traits are a key to understanding ecological resilience and resistance developed in peatlands under disturbance. New trait-based approaches that can better link above-and below ground processes are needed to advance both the accuracy and precision of current abiotic-factor-based Earth system models in predicting future soil C responses to climate-change feedbacks.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

A Field Campaign to Study Lightning that Ignites the Bush

The impact of recent wildland fires in the United States and Australia have received much attention in the past several years. As a result, NASA has developed a new Earth Science program to better understand, predict, and manage this phenomenon, as well as a future suborbital mission to studying pyro-cumulonimbus clouds and their effects on the Earth system. In Australia, government and philanthropic stakeholders are supporting a series of field campaigns over the next few years to inform science requirements and advance technology for a future satellite mission to monitor Bushfires. Lightning flashes are a major source of wildland fires, but uncertainties remain about the physical characteristics of lightning and their parent thunderstorms responsible for igniting wildfires. It was long believed that most lightning-ignited wildfires are largely caused by positive flashes to the ground (CGs) and multi-stroke CGs, but a recent study looking at 26-years of NLDN data indicate otherwise. Perhaps this finding is due to statistical chance—there are more negative CGs that occur globally—or perhaps there is some unknown electrical property of the thundercloud from which the igniting flashes emanate. Regardless, new observing strategies are needed. Additionally, long continuing current CGs, which are more likely to ignite a fire, can elude detection by operational ground-based lightning location systems, making it difficult to efficiently identify potential wildland fires and manage them before they have adverse impacts. To address these science and operational gaps, NASA’s Lightning Mapping Array along with electric and magnetic field change meters will be deployed for the Australian Bushfire campaign to document the electrical structure and properties of thunderstorms and lightning that occur in a wildland fire susceptible region. Another aspect of the campaign will be the use of NASA’s airborne lightning observatory, which includes a spectrometer and high-speed imager, to document the radiometric attributes of these flashes coincident with the ground-based RF observations. These observations will be used to better understand the properties of lightning that ignite wildland fires as well as inform design of a lightning detection system for the future Bushfire Monitoring satellite mission.

lightning↗

DOE OSTI2014

DOE SBIR Phase II Final Technical Report - Assessing Climate Change Effects on Wind Energy

Specialized Vertum Partners software tools were prototyped, tested and commercialized to allow wind energy stakeholders to assess the uncertainties of climate change on wind power production and distribution. This project resulted in three commercially proven products and a marketing tool. The first was a Weather Research and Forecasting Model (WRF) based resource evaluation system. The second was a web-based service providing global 10m wind data from multiple sources to wind industry subscription customers. The third product addressed the needs of our utility clients looking at climate change effects on electricity distribution. For this we collaborated on the Santa Ana Wildfire Threat Index (SAWTi), which was released publicly last quarter. Finally to promote these products and educate potential users we released “Gust or Bust”, a graphic-novel styled marketing publication.

17 WIND ENERGY↗

NASA NTRSDate not supplied

Assessing Alaskan boreal forest landcover affected by climate-wildfire interactions from ground truth surveys and NASA airborne remote sensing

Alaska’s boreal forest is facing unprecedented challenges under rapid climate warming (increasingly severe fires, droughts, pest/disease outbreaks) that may destabilize its function as a global carbon sink. Forests near Fairbanks may be especially vulnerable, impacting air quality and ecosystem services. We combined GT (ground truthing) with Airborne Visible InfraRed Imaging Spectrometer (AVIRIS-NG) images collected by the NASA Arctic-Boreal Vulnerability Experiment (ABoVE) program (2017-2019) to assess landcover change at five recently burned sites (2001-2019) of different fire severities and moisture regimes within 30 miles of Fairbanks. GT included tree seedling counts, understory % cover and >50% leaf canopy color assessment. 36 circular plots (1/30 ha radius) including 6 moderate to severely burned plots were selected across sites. 31 additional sites including 12 burned sites were geotagged in photos. AVIRIS images were processed from 29 spectral bands selected to identify changes in chlorophyll and water content. Images were segmented into natural boundaries (polygons) using ENVI 5.5 software. A spectral library of 8 AVIRIS bands with high between-class/low within-class variation was used in two random forest models to predict vegetation classes (model 1: 12 classes, model 2: 14 classes) in each AVIRIS scene, using 20% of the data as training data. Model 2 classified 20% more polygons overall, but only 42% of GT/geotagged polygons were correctly classified by both models. More forest sites were correctly classified (63%) than open vegetation (32%) or post-fire sites (46%). 50% of aspen forest and post-fire polygons were misclassified as shrubland. GT revealed that post-fire plots supported 134,000 (± 48,000) tree seedlings and saplings ha-1 (0.2 - 4 m height, 64% deciduous) versus 2500 (± 2100) shrubs ha-1 (1-6 m height). > 50% canopy browning was observed in conifer forest (8 plots) with no signs of insect infestation. Canopy herbivory > 50% (leaf miner, leaf beetle) and moose herbivory of tree bark was seen across aspen sites. Our study suggests: 1) low canopy vegetation presents challenges for improved landcover classification, and 2) aspen forest should be differentiated in vegetation maps which would aid in tracking herbivory.

Alaska↗

NASA NTRS2021

NASA Academy Summer 2021 Equipment Development Team Report

Wildfires have become increasingly frequent, widespread, intense, and destructive. The 2021 NASA Academy at Langley Research Center was given the task of applying NASA technology to the challenges faced by wildland firefighting professionals. The Equipment Development Team focused on developing tools that could be deployed to make the work of wildland firefighters safer and more effective. The team developed both a conceptual design and a prototype of a lightweight respirator with air cooling capabilities. The team also researched body-worn biometric and environmental sensors, identifying a suite of sensors that could predict and alert firefighters to dangerous physical and environmental conditions. This report outlines the results of these activities, and analyzes each component of the systems, identifying challenges, possible technological solutions, and their feasibility.

Kilarendha Sundling↗

NASA NTRS2023

Southeast U.S. Agriculture: Evaluating the Spatial and Temporal Distribution of Flash Droughts within Agricultural Areas and Regional Crop Calendars in the Southeast using Earth Observations

A flash drought refers to the rapid onset or intensification of drought-like conditions. Within the Southeastern United States, flash droughts are made worse by the presence of consumptive vegetation and poor water-holding soils. According to the United States Drought Monitor, a 2016 flash drought occurred with roughly 92% of Georgia, Alabama, Mississippi, and Tennessee experiencing “severe drought” or worse in the fall of that year. The event resulted in widespread wildfires, reservoir shortages that led to interstate disputes over water rights, significant deterioration of pasture and rangeland, and crop sale losses. The occurrence of a flash drought similar to that listed above can cost the Southeast hundreds of millions of dollars, with effects lasting for several years. For this reason, the development of flash drought monitoring, forecasting, and communication tools is needed to alert both states and their practitioners of possible flash drought events, offsetting the potential loss caused by a flash drought event. This research employed NASA's Short-Term Prediction and Transition Center-Land Information System, Landsat 7 Enhanced Thematic Mapper Plus, and Landsat 8 Operational Land Imager and other datasets to determine climatological trends during peak growing season from 2000-2022. These products can help partners at the Alabama Office of the State Climatologist, National Integrated Drought Information System, and National Coordinated Soil Moisture Monitoring Network better manage agricultural lands inside drought-prone zones and monitor crop sensitivity during different growing seasons.

Kindrea Gibbons↗