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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Material efficiency and climate change mitigation of passenger vehicles

A transition to electric vehicles and renewable energy is currently underway but may not be rapid enough in order to reach ambitious climate change mitigation targets. Therefore, additional, preferably instantaneous, measures are needed for quick emissions reductions, which is where material efficiency (ME) could constitute a promising solution. ME strategies include but are not limited to vehicle lightweighting through material substitution, increased recycling of materials, reuse and remanufacturing of vehicle components, vehicle downsizing (switching to a smaller vehicle) and more intensive use by means of increased vehicle occupancy through sharing practices. While recent analyses have focused on a narrow subset of these, we find striking differences in the overall potential of different strategies to decrease vehicular carbon footprints. Downsizing and more intensive use offer the largest mitigation potential but strongly depend on consumer behavior and are highly sensitive to modeling assumptions. Combined, the analyzed strategies can achieve emission reductions of up to 57% over the life cycle of a single vehicle, which is comparable to up to 83% achieved through a shift to low-carbon energy supply. ME can cut carbon footprints of already efficient vehicles charging renewable electricity by half again. This makes ME both an excellent short-term solution for climate change mitigation targeting the light vehicle sector but also an important complementary strategy to the long-term transition towards electric vehicles and renewable energy supply.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

A State-of-the-Art Experimental Laboratory for Cloud and Cloud-Aerosol Interaction Research

The state of the art for predicting climate changes due to increasing greenhouse gasses in the atmosphere with high accuracy is problematic. Confidence intervals on current long-term predictions (on the order of 100 years) are so large that the ability to make informed decisions with regard to optimum strategies for mitigating both the causes of climate change and its effects is in doubt. There is ample evidence in the literature that large sources of uncertainty in current climate models are various aerosol effects. One approach to furthering discovery as well as modeling, and verification and validation (V&V) for cloud-aerosol interactions is use of a large "cloud chamber" in a complimentary role to in-situ and remote sensing measurement approaches. Reproducing all of the complex interactions is not feasible, but it is suggested that the physics of certain key processes can be established in a laboratory setting so that relevant fluid-dynamic and cloud-aerosol phenomena can be experimentally simulated and studied in a controlled environment. This report presents a high-level argument for significantly improved laboratory capability, and is meant to serve as a starting point for stimulating discussion within the climate science and other interested communities.

Fremaux, Charles M.↗
DOE OSTI2024

Global and regional hydrological impacts of global forest expansion

Abstract. Large-scale reforestation, afforestation, and forest restoration schemes have gained global support as climate change mitigation strategies due to their significant carbon dioxide removal (CDR) potential. However, there has been limited research into the unintended consequences of forestation from a biophysical perspective. In the Community Earth System Model version 2 (CESM2), we apply a global forestation scenario, within a Paris Agreement-compatible warming scenario, to investigate the land surface and hydroclimate response. Compared to a control scenario where land use is fixed to present-day levels, the forestation scenario is up to 2 °C cooler at low latitudes by 2100, driven by a 10 % increase in evaporative cooling in forested areas. However, afforested areas where grassland or shrubland are replaced lead to a doubling of plant water demand in some tropical regions, causing significant decreases in soil moisture (∼ 5 % globally, 5 %–10 % regionally) and water availability (∼ 10 % globally, 10 %–15 % regionally) in regions with increased forest cover. While there are some increases in low cloud and seasonal precipitation over the expanded tropical forests, with enhanced negative cloud radiative forcing, the impacts on large-scale precipitation and atmospheric circulation are limited. This contrasts with the precipitation response to simulated large-scale deforestation found in previous studies. The forestation scenario demonstrates local cooling benefits without major disruption to global hydrodynamics beyond those already projected to result from climate change, in addition to the cooling associated with CDR. However, the water demands of extensive forestation, especially afforestation, have implications for its viability, given the uncertainty in future precipitation changes.

Environmental Sciences & Ecology↗
DOE OSTI2022

Feasibility of enhancing carbon sequestration and stock capacity in temperate and boreal European forests via changes to management regimes

Forest management practices might act as nature-based methods to remove CO 2 from the atmosphere and slow anthropogenic climate change and thus support an EU forest-based climate change mitigation strategy. However, the extent to which diversified management actions could lead to quantitatively important changes in carbon sequestration and stocking capacity at the tree level remains to be thoroughly assessed. To that end, we used a state-of-the-science bio-geochemically based forest growth model to simulate effects of multiple forest management scenarios on net primary productivity (NPP) and potential carbon woody stocks (pCWS) under twenty scenarios of climate change in a suite of observed and virtual forest stands in temperate and boreal European forests. Previous modelling experiments indicated that the capacity of forests to assimilate and store atmospheric CO 2 in woody biomass is already being attained under business-as-usual forest management practices across a range of climate change scenarios. Nevertheless, we find that on the long-term, with increasing atmospheric CO 2 concentration and warming, managed forests show both higher productivity capacity and a larger potential pool size of stored carbon than unmanaged forests as long as thinning and tree harvesting are of moderate intensity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Thermodynamics of Minerals Stable near Earth's Surface

Project “Thermodynamics of Minerals Stable Near the Earth’s Surface” uses specialized calorimetric techniques to obtain new thermochemical data for solid phases of interest in the Earth’s “critical zone”, the near surface regions of the planet most directly affecting and affected by human activity. Major activity was in three areas: manganese oxide materials, aluminum clusters which are precursors to aluminum oxyhydroxide precipitates, and zeolites. All these classes of minerals are potential carriers of heavy metals and radionuclides in the environment, both within their crystal structures and as adsorbed layers or colloids. Their thermodynamic stability, as well as the sequence of stability of competing phases, governs the aqueous solubility of contaminants (and nutrients) in soil, groundwater, and sediments. The thermodynamics of these phases is strongly affected by particle size and crystallinity, since they often occur as very fine particles which do not coarsen in the near-surface environment. Many of these materials exhibit extensive polymorphism, multicomponent solid solution, and variable hydration states. Thus, one must define and systematize important thermodynamic variables, since one cannot make measurements on each and every possible phase, particle size, and composition. A major finding of the work on manganese oxides was labile water-induced oxidation-reduction reactions. These will be studied further by a combination of thermodynamic and synchrotron-based spectroscopic methods, the latter in collaboration with Peggy O’Day at UC Merced. The adsorption of other species, including simple organics, on such redox-active surfaces will also be investigated by a combination of thermochemical and spectroscopic studies. Studies of the energetics of water adsorption on mineral surfaces will be expanded to emphasize the adsorption of CO2 and small organic molecules, relevant to soil, groundwater, and the subsurface environment. Gas adsorption calorimetry, immersion calorimetry, and solution calorimetry will be combined with appropriate analytical and spectroscopic techniques to obtain a comprehensive picture of the interactions and their energetics. The evolution of aqueous aluminum clusters to form aluminum oxyhydroxide precipitates will be studied in detail by a combination of calorimetric and structural methods applied to aqueous samples aged at room temperature. The energetics of new classes of clusters will be explored. The project addresses several common themes relevant to the broad mission of the Department of Energy. Thermochemical data for minerals stable near the Earth’s surface, though still incomplete, are essential for understanding both natural and anthropogenic processes that affect our very existence on this planet. Such data contribute to the fundamental underpinning for mitigation and management strategies for the extraction and use of energy. Fundamental thermodynamic and mechanistic understanding is required to model processes ranging from heavy metal pollution to nuclear waste disposal to climate change.

58 GEOSCIENCES↗
DOE OSTI2023

Adaptation Strategies Strongly Reduce the Future Impacts of Climate Change on Simulated Crop Yields

Abstract Simulations of crop yield due to climate change vary widely between models, locations, species, management strategies, and Representative Concentration Pathways (RCPs). To understand how climate and adaptation affects yield change, we developed a meta‐model based on 8703 site‐level process‐model simulations of yield with different future adaptation strategies and climate scenarios for maize, rice, wheat and soybean. We tested 10 statistical models, including some machine learning models, to predict the percentage change in projected future yield relative to the baseline period (2000–2010) as a function of explanatory variables related to adaptation strategy and climate change. We used the best model to produce global maps of yield change for the RCP4.5 scenario and identify the most influential variables affecting yield change using Shapley additive explanations. For most locations, adaptation was the most influential factor determining the projected yield change for maize, rice and wheat. Without adaptation under RCP4.5, all crops are expected to experience average global yield losses of 6%–21%. Adaptation alleviates this average projected loss by 1–13 percentage points. Maize was most responsive to adaptive practices with a projected mean yield loss of −21% [range across locations: −63%, +3.7%] without adaptation and −7.5% [range: −46%, +13%] with adaptation. For maize and rice, irrigation method and cultivar choice were the adaptation types predicted to most prevent large yield losses, respectively. When adaptation practices are applied, some areas are predicted to experience yield gains, especially at northern high latitudes. These results reveal the critical importance of implementing adequate adaptation strategies to mitigate the impact of climate change on crop yields.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Comprehensive Analysis of Model Parameter Uncertainty Influence on Evaluation of HVAC Operation to Mitigate Indoor Virus: A Case Study for an Office Building in a Cold and Dry Climate

Simulation-based studies of HVAC operation to mitigate indoor virus have been conducted to understand tradeoffs between indoor air quality (IAQ) and energy consumption. However, the influence of model parameter uncertainty in these studies has not been systematically quantified, which is critical when providing guidance to building operators. To address this gap, we identify 20 model parameters for a typical medium office building system in a cold and dry climate that can influence IAQ and energy consumption for indoor virus scenarios. Next, the distributions of the parameter values are estimated from literature and a set of simulation samples for three representative days is generated that simultaneously sample all of the parameters from their distributions. Two HVAC virus mitigation strategies are studied: increased filtration using MERV 13 filters and increased ventilation, via supply of 100% outdoor air into buildings. The model parameter uncertainty leads to significant variability in the results, particularly for the IAQ because of the highly uncertain virus generation rate. Use of 100% outdoor air can be beneficial for some uncertain scenarios on the hot day, but shows less IAQ improvement and/or significant energy increases on the other days. The virus removal efficiency and pressure drop of the HVAC filter, fan efficiency, and internal heat gain are the most important parameters to determining the tradeoffs of the two strategies. Our results demonstrate how this model parameter uncertainty analysis methodology can provide practical guidance to building operators.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Response patterns of simulated corn yield and soil nitrous oxide emission to precipitation change

Abstract Background Precipitation plays an important role in crop production and soil greenhouse gas emissions. However, how crop yield and soil nitrous oxide (N 2 O) emission respond to precipitation change, particularly with different background precipitations (dry, normal, and wet years), has not been well investigated. In this study, we examined the impacts of precipitation changes on corn yield and soil N 2 O emission using a long-term (1981–2020, 40 years) climate dataset as well as seven manipulated precipitation treatments with different background precipitations using the DeNitrification-DeComposition (DNDC) model. Results Results showed large variations of corn yield and precipitation but small variation of soil N 2 O emission among 40 years. Both corn yield and soil N 2 O emission showed near linear relationships with precipitation based on the long-term precipitation data, but with different response patters of corn yield and soil N 2 O emission to precipitation manipulations. Corn yield showed a positive linear response to precipitation manipulations in the dry year, but no response to increases in precipitation in the normal year, and a trend of decrease in the wet year. The extreme drought treatments reduced corn yield sharply in both normal and wet years. In contrast, soil N 2 O emission mostly responded linearly to precipitation manipulations. Decreases in precipitation in the dry year reduced more soil N 2 O emission than those in the normal and wet years, while increases in precipitation increased more soil N 2 O emission in the normal and wet years than in the dry year. Conclusions This study revealed different response patterns of corn yield and soil N 2 O emission to precipitation and highlights that mitigation strategy for soil N 2 O emission reduction should consider different background climate conditions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Overview of Submesoscale-Climate Connections

In this talk, I will present a brief overview of some recent results and current research priorities regarding the significant participation of upper ocean / submesoscale processes in regional to global Earth system dynamics in a warming climate, such as ocean overturning, biogeochemistry, and heat and carbon uptake as well as atmospheric, coastal, and cryospheric dynamics. In addition to reviewing the evidence for these links by topic, I will highlight how advances in both understanding and prediction are derived from the integrated triad of process studies, modeling, and regional and global observing systems. Finally, I will discuss how triadic investments focused on these upper ocean processes might lead to better-informed decision-making for climate change adaptation and mitigation strategies.

Overview↗
DOE OSTI2026

Comparative Performance of Gaussian Plume and Backward Lagrangian Stochastic Models for Near-Field Methane Emission Estimation Using a Single Controlled Release Experiment

Methane (CH 4 ) is a major component of natural gas and a potent greenhouse gas. Increasing atmospheric methane concentrations are attributed to emissive anthropogenic activities by an average of 13 ppb per yr since 2020 and are linked to a changing global climate. Mitigating CH 4 emissions from oil and gas production sites has recently become a target to reduce overall greenhouse gas emissions; however, monitoring the efficacy of mitigation strategies depends on accurate quantification of CH 4 emissions at the facility-level. Near-field quantification of methane (CH 4 ) emissions from oil and gas (O&G) facilities remains challenging due to the effects of atmospheric variability and sensor configuration on atmospheric dispersion models. This study evaluates the performance of two atmospheric dispersion models, the Gaussian plume (GP) and backward Lagrangian stochastic (bLS), by comparing calculated CH 4 emissions to controlled single-point emissions between 0.4 and 5.2 kg CH 4 h −1 . Emissions were calculated by both models using 121 individual sets of measurements comprising five-minute averaged downwind methane mixing ratios and matching meteorological data. The comparison shows that the bLS approach achieved a higher proportion of emission estimates within a factor of two (FAC2) of the known emission rates compared to the GP approach. The emissions calculated by the bLS model also had a lower multiplicative error and reduced bias relative to GP. Other error-based metrics further confirmed the bLS model performed better, as it yielded lower RMSE and MAE than GP. Statistical analysis of the emission data shows that the lateral and vertical alignment of the source and the sensor plays a critical role in emission estimations, as measurements made closer to the plume centerline and at a distance between 40 and 80 m downwind yielded the best FAC2 agreement. High wind meander degraded the ability of both approaches to generate representative emissions, particularly with the GP approach, as it violates the modeling approach’s assumption of steady-state emissions. Data suggest emissions calculated by the bLS model are comprehensively in better agreement, but the computational demands of the modeling approach and integration into fenceline systems limit real-time applicability. While these results provide insight into model performance under controlled near-field conditions, their applicability to more complex or heterogeneous oil and gas production environments (e.g., the regions Marcellus or Unita Basins) remains limited and uncertain.

gaussian plume↗
DOE OSTI2025

CMIP7 data request: impacts and adaptation priorities and opportunities

The Coupled Model Intercomparison Project Phase 7 (CMIP7) undertook an extensive process to gather community input and refine data requests related to impacts and adaptation applications of Earth System Model (ESM) outputs. The Impacts and Adaptation (I&A) Data Request Team worked with CMIP7 leadership to distribute an open solicitation across many communities that use climate model outputs requesting inputs for new and existing variables, the most applicable temporal characteristics, and groupings of variables that together allow for specific application opportunities. This input was then collated and translated into CMIP7 standard templates for inclusion in the broader data request, leading to 13 I&A data request opportunities, 60 variable groups and 539 unique variables sought by vulnerability, impacts, adaptation, and climate services user communities. Here, we describe these opportunities and variable groups, as well as new insights into how ESM groups can prioritize outputs that set off a chain of further analyses, ultimately informing decisions impacting society and natural systems. These include an emphasis on high-resolution outputs to allow further modeling of climate impacts at regional and local scales, improved representation of extreme weather events, enhanced accuracy of downscaling and bias-adjustment techniques, and support for more detailed assessments for decision-making in adaptation and mitigation strategies. There is also broad interest in more extensive provisioning of two-dimensional variables at the Earth's surface, prioritizing experiments that enhance our understanding of both the recent past and future scenarios, and providing outputs that allow further downscaling and bias adjustment. We emphasize that variable groups are the fundamental level at which to engage with the I&A data request, matching the scale of input and the way output provision enables specific I&A applications. Given resource constraints, we applaud CMIP7 efforts to foster strong engagement and communication between ESM groups and the I&A team to build consensus around prudent compromises in priority variables, temporal resolutions, simulation experiments, time subsets, and ensemble members.

Ruane, Alex C. [NASA Goddard Inst. for Space Studi↗
DOE OSTI2025

ClimGen: Learning the Forcing-Response Relationship in Climate System

Solar Radiation Management (SRM) is emerging as a potential geoengineering strategy to address the anthropogenic impact on climate, but its effective implementation requires an iterative and large ensemble of highly accurate and efficient climate projections. Traditional climate projections rely on executing computationally demanding and time-consuming numerical climate models. Recent advances in machine learning (ML) aim to enhance these approaches by emulating traditional methods. In this work, we propose a novel framework for directly learning the relationship between solar radiation flux at the top of the atmosphere and the corresponding surface temperature response. To evaluate the feasibility of this direct ML-based projection, we developed a dataset using an intermediate complexity model, incorporating a comprehensive suite of different forcing patterns and evaluation metrics to rigorously assess the ML model’s performance. We introduce a Conditional Denoising Diffusion Probabilistic Model (cDDPM) for this task, which demonstrates encouraging skill in representing climate statistics under previously unseen forcing patterns. This approach provides a promising pathway for direct climate projections by accurately learning the forcing-response relationship, with a wide range of applications in impact mitigation, emissions policy design, and SRM strategies.

Chen, Tse-Chun [BATTELLE (PACIFIC NW LAB)] (ORCID:↗
DOE OSTI2022

Pervasive alterations to snow-dominated ecosystem functions under climate change

Climate change projections consistently demonstrate that warming temperatures and dwindling seasonal snowpack will elicit cascading effects on ecosystem function and water resource availability. Despite this consensus, little is known about potential changes in the variability of ecohydrological conditions, which is also required to inform climate change adaptation and mitigation strategies. Considering potential changes in ecohydrological variability is critical to evaluating the emergence of trends, assessing the likelihood of extreme events such as floods and droughts, and identifying when tipping points may be reached that fundamentally alter ecohydrological function. Using a single-model Large Ensemble with sophisticated terrestrial ecosystem representation, we characterize projected changes in the mean state and variability of ecohydrological processes in historically snow-dominated regions of the Northern Hemisphere. Widespread snowpack reductions, earlier snowmelt timing, longer growing seasons, drier soils, and increased fire risk are projected for this century under a high-emissions scenario. In addition to these changes in the mean state, increased variability in winter snowmelt will increase growing-season water deficits and increase the stochasticity of runoff. Thus, with warming, declining snowpack loses its dependable buffering capacity so that runoff quantity and timing more closely reflect the episodic characteristics of precipitation. This results in a declining predictability of annual runoff from maximum snow water equivalent, which has critical implications for ecosystem stress and water resource management. So our results suggest that there is a strong likelihood of pervasive alterations to ecohydrological function that may be expected with climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A net-zero emissions strategy for China’s power sector using carbon-capture utilization and storage

Decarbonized power systems are critical to mitigate climate change, yet methods to achieve a reliable and resilient near-zero power system are still under exploration. This study develops an hourly power system simulation model considering high-resolution geological constraints for carbon-capture-utilization-and-storage to explore the optimal solution for a reliable and resilient near-zero power system. This is applied to 31 provinces in China by simulating 10,450 scenarios combining different electricity storage durations and interprovincial transmission capacities, with various shares of abated fossil power with carbon-capture-utilization-and-storage. Here, we show that allowing up to 20% abated fossil fuel power generation in the power system could reduce the national total power shortage rate by up to 9.0 percentages in 2050 compared with a zero fossil fuel system. A lowest-cost scenario with 16% abated fossil fuel power generation in the system even causes 2.5% lower investment costs in the network (or $16.8 billion), and also increases system resilience by reducing power shortage during extreme climatic events.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2020

Land management and climate change determine second‐generation bioenergy potential of the US Northern Great Plains

Bioenergy with carbon capture and storage (BECCS) has been proposed as a potential climate mitigation strategy raising concerns over trade‐offs with existing ecosystem services. We evaluate the feasibility of BECCS in the Upper Missouri River Basin (UMRB), a landscape with diverse land use, ownership, and bioenergy potential. We develop land‐use change scenarios and a switchgrass (Panicum virgatum L.) crop functional type to use in a land‐surface model to simulate second‐generation bioenergy production. By the end of this century, average annual switchgrass production over the UMRB ranges from 60 to 210 Tg dry mass/year and is dependent on the Representative Concentration Pathway for greenhouse gas emissions and on land‐use change assumptions. Under our simple phase‐in assumptions this results in a cumulative total production of 2,000–6,000 Tg C over the study period with the upper estimates only possible in the absence of climate change. Switchgrass yields decreased as average CO2 concentrations and temperatures increased, suggesting the effect of elevated atmospheric CO2 was small because of its C4 photosynthetic pathway. By the end of the 21st century, the potential energy stored annually in harvested switchgrass averaged between 1 and 4 EJ/year assuming perfect conversion efficiency, or an annual electrical generation capacity of 7,000–28,000 MW assuming current bioenergy efficiency rates. Trade‐offs between bioenergy and ecosystem services were identified, including cumulative direct losses of 1,000–2,600 Tg C stored in natural ecosystems from land‐use change by 2090. Total cumulative losses of ecosystem carbon stocks were higher than the potential ~300 Tg C in fossil fuel emissions from the single largest power plant in the region over the same time period, and equivalent to potential carbon removal from the atmosphere from using biofuels grown in the same region. Numerous trade‐offs from BECCS expansion in the UMRB must be balanced against the potential benefits of a carbon‐negative energy system.

climate change↗
DOE OSTI2023

Environmental microbiome engineering for the mitigation of climate change

Abstract Environmental microbiome engineering is emerging as a potential avenue for climate change mitigation. In this process, microbial inocula are introduced to natural microbial communities to tune activities that regulate the long‐term stabilization of carbon in ecosystems. In this review, we outline the process of environmental engineering and synthesize key considerations about ecosystem functions to target, means of sourcing microorganisms, strategies for designing microbial inocula, methods to deliver inocula, and the factors that enable inocula to establish within a resident community and modify an ecosystem function target. Recent work, enabled by high‐throughput technologies and modeling approaches, indicate that microbial inocula designed from the top‐down, particularly through directed evolution, may generally have a higher chance of establishing within existing microbial communities than other historical approaches to microbiome engineering. We address outstanding questions about the determinants of inocula establishment and provide suggestions for further research about the possibilities and challenges of environmental microbiome engineering as a tool to combat climate change.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2014

Electric Vehicle Charging Stations as a Climate Change Mitigation Strategy

In order to facilitate the use of electric vehicles at NASA Langley Research Center (LaRC), charging stations should be made available to LaRC employees. The implementation of charging stations would decrease the need for gasoline thus decreasing CO2 emissions improving local air quality and providing a cost savings for LaRC employees. A charging station pilot program is described that would install stations as the need increased and also presents a business model that pays for the electricity used and installation at no cost to the government.

Cave, Bridget↗
NASA NTRSDate not supplied

Assessing Urban Heat in the Cincinnati and Covington Area to Inform Local Decision-Makers and Environmental Justice Initiatives

The Urban Heat Island effect is a phenomenon characterized by urban areas experiencing temperatures that are, on average, warmer than surrounding suburban and rural regions. Urban Heat Islands are fueled by expansive impervious surfaces, vehicle emissions, and insufficient urban green space. Densely populated urban centers like the Cincinnati, Ohio and Covington, Kentucky area can experience negative health impacts due to the Urban Heat Island. NASA DEVELOP partnered with Groundwork USA and Groundwork Ohio River Valley to combine environmental education and outreach with technical capacity building in NASA Earth observations. The DEVELOP team used Landsat 5 TM and ISS ECOSTRESS to calculate daytime and nighttime land surface temperature anomalies. The team found that the Cincinnati and Covington area was 8.32°F warmer during the day and 4.97°F warmer at night compared to non-urban areas. The team also ran the Natural Capital Project InVEST Urban Cooling Model to map a heat mitigation index for the study area. The heat mitigation index models the cooling capacity of each pixel and the effect of green spaces on the pixel’s ability to mitigate heat. Results show which communities are most vulnerable to impacts of increased urban heat. The team also assessed alternative tree canopy scenarios with the InVEST model to better understand the effectiveness of potential heat mitigation strategies. Increasing tree canopy cover by 25% in urbanized land cover types was found to reduce mean temperature across the study area by 0.87°F. Combining these results with work from Groundwork’s Climate Safe Neighborhoods program highlights the relationship between extreme heat and historical-race based housing practices in the region. Results provide partners at Groundwork with refined methodologies to support future education and outreach, as well as increase future capacity to NASA Earth observations.

Celeste Gambino↗