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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 OSTI2022

SolSmart Technical Assistance Provider (Final Technical Report)

The SolSmart program was established in late 2015 to “establish a prominent national recognition and technical assistance program for local governments that will signal to installers and the public that a community is receptive to solar businesses and has established a supportive solar market environment. This, in turn, will reduce market barriers and lower soft costs, thus contributing to SunShot goals. The program will also assist communities who are just beginning to improve their solar markets.” By March 31, 2022 (the grant end date), the SolSmart Team designated 460 communities, more than 50% more than the initial program objective of 300 communities. The designated communities represent 42 states, the District of Columbia, the U.S. Virgin Islands, and Puerto Rico. The SolSmart team developed and revised criteria representing best practices for local governments; recruited, trained, evaluated, and mentored communities to incorporate these best practices; and disseminated resources about solar and solar-related technologies as well as the newest best practices on a regular basis. 98% of local government officials who participated in the program said that SolSmart increased their knowledge of solar energy. The SolSmart program had both real and perceived tangible impacts on barriers to solar adoption in designated communities. Significantly, the time to permit a solar installation decreased by 7.5 days on average. This reduced the soft costs for both the solar installer (and therefore the customer) and the local government. The result is that solar installations in SolSmart communities increased by 17% per month compared with similar non-SolSmart communities. Larger and wealthier communities were more likely to pursue SolSmart designation and earn more points within SolSmart. As a result, IREC/TSF developed strategies to reach underserved communities and created new resources to help them more easily achieve designation, as described below. In the last year of the Award, 44% of designated communities were underserved, compared with 31% in the earlier years of the program. DOE issued two awards for SolSmart: the SolSmart Technical Assistance Provider (TAP), led by The Solar Foundation (TSF) which has since merged with the Interstate Renewable Energy Council (IREC), and the Designation Program Administrator (DPA), led by the International City/County Management Association (ICMA). IREC was lead for the TAP project and, until the time of TSF/IREC merger, was a subrecipient on the DPA project. These two projects were interdependent; one could not exist without the other. While this report will focus on the work performed by the TAP, it will be impossible to speak of this without talking about the successes, impacts, and benefits of the overall SolSmart program.

14 SOLAR ENERGY↗
DOE OSTI2024

Biosphere Futures: a database of social-ecological scenarios

T. Biosphere Futures (https://biospherefutures.net/) is a new online database to collect and discover scenario studies from across the world, with a specific focus on scenarios that explicitly incorporate interdependencies between humans and their supporting ecosystems. It provides access to a globally diverse collection of case studies that includes most ecosystems and regions, enabling exploration of the multifaceted ways in which the future might unfold. Together, the case studies illuminate the diversity and plurality of people’s expectations and aspirations for the future. The objective of Biosphere Futures is to promote the use of scenarios for sustainable development of the biosphere and to foster a community of practice around social-ecological scenarios. We do so by facilitating the assessment, synthesis, and comparative analysis of scenario case studies, pointing to relevant resources, and by helping practitioners and researchers to disseminate and showcase their own work. This article begins by outlining the rationale behind the creation of the database, followed by an introduction to its functionality and the criteria employed for selecting case studies. Subsequently, we present a synthesis of the first 100 case studies included in the scenarios database, highlighting emerging patterns and identifying potential avenues for further research. Finally, given that broader utilization and contributions to the database will enhance the achievement of Biosphere Futures’ objectives, we invite the creators of social-ecological scenarios to contribute additional case studies. By expanding the database’s breadth and depth, we can collectively foster a more nuanced understanding of the possible trajectories of our biosphere and enable better decision making for sustainable development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Quantum/AI Topology-Aware Latency-Adaptive HPC Workflow Scheduling Optimization

The growing demand for more powerful high-performance computing (HPC) systems has led to a steady rise in energy consumption by supercomputing worldwide. This study is focused on comparing our Application-Topology Mapper (ATMapper) to the popular Simple Linux Utility for Resource Management (SLURM) for the purpose of exploring methods that can further optimize job-scheduling within HPC systems. ATMapper is an Artificial-Intelligence based approach to job-scheduling that is currently being enhanced with quantum annealing (QA) to generate optimal schedules faster. We are applying QA to speedup our ATMapper process to achieve higher computing efficiency, thereby reducing HPC energy consumption. Here, we examine how four job-scheduling approaches perform in processor node assignment when using an example network architecture of 4 interconnected nodes. Using a specialized script, we are assessing the schedule of a computation flow with 11 interdependent tasks. The data movements among nodes were tracked to count for the number of interactions (network hops) between nodes needed to complete the tasks. The total number of hops and the job completion time were then used to quantify the efficiency of the different mapping approaches. In addition to SLURM, we also compare our ATMapper to the QA-enabled LBNL TIGER and the D-Wave Distributed Computing processor assignment approaches. The preliminary results showed that our topology-aware, latency-adaptive ATMapper is significantly more efficient when compared to the other scheduling approaches due to its load-imbalance network allocation. The scheduler displayed a computing efficiency of 53% by performing significantly fewer network hops than its alternatives. By reducing the number of hops, ATMapper was able to perform all 11 tasks by using only 3 nodes out of given 4. This research indicates the potential to use QA/AI for HPC job-scheduling. Later, we will test a SLURM simulator program to draw further comparisons on the effectiveness of ATMapper's scheduling approach. The results of this comparison will serve as a baseline for later improving SLURM's performance using a QA-enhanced ATMapper approach.

Caraveo, Braulio [University of Huston - Clear Lak↗
NASA NTRS2012

Investigation, Modeling, and Analysis of Integrated Metroplex Arrival and Departure Coordination Concepts

This work involves the development of a concept that enhances integrated metroplex arrival and departure coordination, determines the temporal (the use of time separation for aircraft sharing the same airspace resources) and spatial (the use of different routes or vertical profiles for aircraft streams at any given time) impact of metroplex traffic coordination within the National Airspace System (NAS), and quantifies the benefits of the most desirable metroplex traffic coordination concept. Researching and developing metroplex concepts is addressed in this work that broadly applies across the range of airspace and airport demand characteristics envisioned for NextGen metroplex operations. The objective of this work is to investigate, formulate, develop models, and analyze an operational concept that mitigates issues specific to the metroplex or that takes advantage of unique characteristics of metroplex airports to improve efficiencies. The concept is an innovative approach allowing the NAS to mitigate metroplex interdependencies between airports, optimize metroplex arrival and departure coordination among airports, maximize metroplex airport throughput, minimize delay due to airport runway configuration changes, increase resiliency to disruptions, and increase the tolerance of the system to degrade gracefully under adverse conditions such as weather, traffic management initiatives, and delays in general.

Clarke, John-Paul B.↗
DOE OSTI2020

Evaluating cross-sectoral impacts of climate change and adaptations on the energy-water nexus: a framework and California case study

Abstract Electricity and water systems are inextricably linked through water demands for energy generation, and through energy demands for using, moving, and treating water and wastewater. Climate change may stress these interdependencies, together referred to as the energy-water nexus, by reducing water availability for hydropower generation and by increasing irrigation and electricity demand for groundwater pumping, among other feedbacks. Further, many climate adaptation measures to augment water supplies—such as water recycling and desalination—are energy-intensive. However, water and electricity system climate vulnerabilities and adaptations are often studied in isolation, without considering how multiple interactive risks may compound. This paper reviews the fragmented literature and develops a generalized framework for understanding these implications of climate change on the energy-water nexus. We apply this framework in a case study to quantify end-century direct climate impacts on California’s water and electricity resources and estimate the magnitude of the indirect cross-sectoral feedback of electricity demand from various water adaptation strategies. Our results show that increased space cooling demand and decreased hydropower generation are the most significant direct climate change impacts on California’s electricity sector by end-century. In California’s water sector, climate change impacts directly on surface water availability exceed demand changes, but have considerable uncertainty, both in direction and magnitude. Additionally, we find that the energy demands of water sector climate adaptations could significantly affect California’s future electricity system needs. If the worst-case water shortage occurs under climate change, water-conserving adaptation measures can provide large energy savings co-benefits, but other energy-intensive water adaptations may double the direct impacts of climate change on the state’s electricity resource requirement. These results highlight the value of coordinated adaptation planning between the energy and water sectors to achieve mutually beneficial solutions for climate resilience.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

CRITICAL FUNCTION ASSURANCE: Understanding Critical Function and Critical Function Delivery is Foundational for Meaningful ICS Security Improvement and Policy Efforts

Modern life is enabled by a complex and interdependent web of critical functions, including energy, communications, transportation, food, and water. Automation has significantly reduced or replaced human interactions in the delivery of these functions, resulting in a web of goods and services that are made available 24/7 only through unique and intentional deployments of microprocessors, software, and firmware technologies. The prospect of cyber-enabled sabotage of these processes disrupts traditional risk determination models. Critical Function Assurance (CFA) is a foundational approach to identifying, prioritizing, and mitigating the risk that is inherent in the delivery of critical functions that depend on digital technology. It provides rapid focus to what matters most and illuminates elements and areas of risk that otherwise are often overlooked. This focus enables effective application of available security resources and optimizes security strategy and policy efforts. This paper introduces CFA to decision makers and risk executives (including CEOs, COOs, CFOs, and CISOs) whose organizations support and deliver the critical functions that underpin national defense, societal health and safety, and a vibrant economy.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Addressing Consequence within Operational Risk (O.T. Gagnon III) 9-18-2024

Addressing Consequence within Operational Risk: Why threats and security are just not that important! When dealing with cyber or physical risk within any critical infrastructure (CI) environment, don’t concern yourself with vulnerabilities and threats, at least not at first! Also, don’t be overly fixated on “securing the systems” within the organization. The endeavor of tackling operational risk focused on consequences in any critical infrastructure environment to include the complex Aviation ecosystem is challenging even for the most resourced entity but can be advanced though a simplified approach: identifying, binning, and prioritizing the infrastructure environment. While no two entities within a single element of the 16 critical infrastructure sectors are exactly alike when it comes to risk, there is a basic process to move toward a greater understanding of operational risk through becoming more informed about the infrastructure environment in which the entity exists. The process starts with bringing internal and external stakeholders and subject matter experts together to analyze key areas such as Information Technology (IT) and Operational Technology (OT) components and points of convergence, analyzing internal and external cyber and physical dependencies, accounting for explosive growth in devices and wireless technology, and leveraging the contributions of people inside and outside the operational environment. Attaining a common understanding of the infrastructure environment as part of addressing consequences within operational risk is not easy to do or resource light, but the process outlined provides the framework to further any entity’s efforts in this space. When it comes to cyber risks, before an organization can consider vulnerabilities within and threats to its operations, it must first have a solid understanding of the consequences existing inside its infrastructure environment. Idaho National Lab’s Consequence-Driven, Cyber-Informed Engineering is offered as an example of this approach to effective and efficient cyber risk mitigation.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2012

Development of Human System Integration at NASA

Human Systems Integration seeks to design systems around the capabilities and limitations of the humans which use and interact with the system, ensuring greater efficiency of use, reduced error rates, and less rework in the design, manufacturing and operational deployment of hardware and software. One of the primary goals of HSI is to get the human factors practitioner involved early in the design process. In doing so, the aim is to reduce future budget costs and resources in redesign and training. By the preliminary design phase of a project nearly 80% of the total cost of the project is locked in. Potential design changes recommended by evaluations past this point will have little effect due to lack of funding or a huge cost in terms of resources to make changes. Three key concepts define an effective HSI program. First, systems are comprised of hardware, software, and the human, all of which operate within an environment. Too often, engineers and developers fail to consider the human capacity or requirements as part of the system. This leads to poor task allocation within the system. To promote ideal task allocation, it is critical that the human element be considered early in system development. Poor design, or designs that do not adequately consider the human component, could negatively affect physical or mental performance, as well as, social behavior. Second, successful HSI depends upon integration and collaboration of all the domains that represent acquisition efforts. Too often, these domains exist as independent disciplines due to the location of expertise within the service structure. Proper implementation of HSI through participation would help to integrate these domains and disciplines to leverage and apply their interdependencies to attain an optimal design. Via this process domain interests can be integrated to perform effective HSI through trade-offs and collaboration. This provides a common basis upon which to make knowledgeable decisions. Finally, HSI must be considered early in the requirements development phase of system design and acquisition. This will provide the best opportunity to maximize return on investment (ROI) and system performance. HSI requirements must be developed in conjunction with capability ]based requirements generation through functional. HSI requirements will drive HSI metrics and embed HSI issues within the system design. After a system is designed, implementation of HSI oversights can be very expensive. An HSI program should be included as an integral part of a total system approach to vehicle and habitat development. This would include, but not limited to, workstation design, D&C development, volumetric analysis, training, operations, and human -robotic interaction. HSI is a necessary process for Human Space Flight programs to meet the Agency Human ]System standards and thus mitigate human risks to acceptable levels. NASA has been involved in HSI planning, procedures development, process, and implementation for many years, and has been building several internal and publicly accessible products to facilitate HSI fs inclusion in the NASA Systems Engineering Lifecycle. Some of these products include: NASA STD 3001 Volumes 1 and 2, Human Integration Design Handbook, NASA HSI Implementation Plan, NASA HSI Implementation Plan Templates, NASA HSI Implementation Handbook, and a 2 ]hour short course on HSI delivered as part of the NASA Space and Life Sciences Directorate Academy. These products have been created leveraging industry best practices and lessons learned from other Federal Government agencies.

Whitmore, Mihriban↗
NASA NTRS2005

Applications of Principled Search Methods in Climate Influences and Mechanisms

Forest and grass fires cause economic losses in the billions of dollars in the U.S. alone. In addition, boreal forests constitute a large carbon store; it has been estimated that, were no burning to occur, an additional 7 gigatons of carbon would be sequestered in boreal soils each century. Effective wildfire suppression requires anticipation of locales and times for which wildfire is most probable, preferably with a two to four week forecast, so that limited resources can be efficiently deployed. The United States Forest Service (USFS), and other experts and agencies have developed several measures of fire risk combining physical principles and expert judgment, and have used them in automated procedures for forecasting fire risk. Forecasting accuracies for some fire risk indices in combination with climate and other variables have been estimated for specific locations, with the value of fire risk index variables assessed by their statistical significance in regressions. In other cases, the MAPSS forecasts [23, 241 for example, forecasting accuracy has been estimated only by simulated data. We describe alternative forecasting methods that predict fire probability by locale and time using statistical or machine learning procedures trained on historical data, and we give comparative assessments of their forecasting accuracy for one fire season year, April- October, 2003, for all U.S. Forest Service lands. Aside from providing an accuracy baseline for other forecasting methods, the results illustrate the interdependence between the statistical significance of prediction variables and the forecasting method used.

Glymour, Clark↗
DOE OSTI2026

PopGNN: Graph Neural Network-Based Flexible Future Population Forecasting Model

Accurate population forecasts is important to plan critical infrastructure and services, from housing and education to healthcare and transport. However, traditional population prediction studies have only employed traditional machine learning models limited to capture complex spatial interdependencies and patterns. Althogh recently computer vision-based framework was introduced with with promising accuracy, it has critical limitations for real-world planning applications: it function only at fixed spatial resolutions, restricting their use in diverse boundaries such as census tracts, neighborhoods, or administrative zones. Therefore, this study suggests a Graph Neural Network (GNN)-based population prediction framework, called PopGNN. This model recorded remarkable performance compared with state-of-the-art models and traditional baseline models in the grid and administrative boundaries. Furthermore, our framework achieved comparable predictive accuracy to a computer vision-based model in both the South Korea and Tennessee case studies. Consequently, this study is valuable in that a single model can provide accurate population forecasts that address diverse planning demands, ranging from granular grid-level estimates for precise service allocation and facility location planning to aggregate administrative-level forecasts for macro-scale regional policy and resource distribution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A terminology for scientific workflow systems

The term “scientific workflow” has evolved over the last two decades to encompass a broad range of compositions of interdependent compute tasks and data movements. It has also become an umbrella term for processing in modern scientific applications. Today, many scientific applications can be considered as workflows made of multiple dependent steps, and hundreds of workflow systems have been developed to manage and run these scientific workflows. However, no turnkey solution has emerged from the field to address the diversity of scientific processes and the infrastructure on which they are supposed to be implemented. Instead, new research problems requiring the execution of scientific workflows with some novel feature often lead to the development of an entirely new workflow system. A direct consequence of this situation is that many existing workflow management systems (WMSs) share some salient features, offer similar functionalities, and can manage the same categories of workflows but at the same time also have some distinct capabilities that can be important for specific applications. This situation makes researchers who develop workflows face the complex question of selecting a WMS. This selection can be driven by technical considerations, to find the system that is the most appropriate for their application and for the computing and storage resources available to them, or other factors such as reputation, adoption, strong community support, or long-term sustainability. To address this problem, a group of WMS developers and practitioners joined their efforts to produce a community-based terminology of WMSs. This paper summarizes their findings and introduces this new terminology to characterize WMSs. Furthermore, this terminology is composed of fives axes: workflow structure and characteristics, composition, orchestration, data management, and metadata capture. Each axis comprises several concepts that capture the prominent features of WMSs. Based on this terminology, this paper also presents a classification of 23 existing WMSs according to the proposed axes and terms.

Community-based terminology↗
DOE OSTI2022

Multiscale simulation of integrated energy system and electricity market interactions

Accelerating the deep decarbonization of the world's electric grids requires the coordination of complex energy systems and infrastructures across timescales from seconds to decades. Here, we present a new multiscale simulation framework that integrates process- and grid-centric modeling paradigms to better design, operate, and control integrated energy systems (IESs), which combine multiple technologies, in wholesale energy markets. Traditionally, IESs are analyzed with a process-centric paradigm such as levelized cost of electricity (LCOE) or annualized net revenue, ignoring important interactions with electricity markets. This framework explicitly models the complex interactions between an IES's bidding, scheduling, and control decisions and the energy market's clearing and settlement processes, while incorporating operational uncertainties. Through two case studies, we show the importance of understanding and quantifying complex resource-grid interactions. In case study 1, we demonstrate that optimized bidding from one resource shifts the profit distribution for all energy systems in the market. This result suggests new and more flexible IES technologies can disrupt the economics of all market participants, possibly leading to accelerated retirements of less flexible resources. Interestingly, the optimized bidding has little impact on grid-level aggregate statistics, such as total generation costs and renewable penetration rate. While aggregate modeling strategies may remain valid under some IES adoption scenarios for analysis focused on regional outcomes, direct comparisons of IES technologies at specific locations without considering these interactions may lead to misleading or incorrect conclusions. In case study 2, we consider the design and flexible operation of IESs that hybridize conventional generators with energy storage. Through a sensitivity analysis, we find that as the size of the storage system increases, the total number of start-ups for coal- and natural gas-based IESs reduced by 25% and 33.6%, and the total thermal generator ramping (i.e., mileage) reduced by 86.5% and 62.5%, respectively. This shows the primary benefit of storage may not be reduced operational costs (which do not change significantly) but fewer start-ups and less ramping, which may greatly simplify the design, operation, and control of carbon capture systems. The new modeling and optimization capabilities from this work enable the coupling of rigorous, dynamic process models with grid-level production cost models to quantitatively identify the nuanced interdependencies across these vast timescales that must be addressed to realize clean, safe, and secure energy production. Moreover, the proposed general multiscale simulation framework is applicable to all IES technologies and can be easily extended to consider other energy carriers (e.g., hydrogen, ammonia) and energy infrastructures (e.g., natural gas pipelines).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Financial Analysis of the High Flow Experiment conducted at the Glen Canyon Dam during Water Year 2023

The Glen Canyon Dam (GCD) is a Colorado River Storage Project (CRSP) power resource that is a component of the Salt Lake City Area Integrated Projects (SLCA/IP). The 2016 record of decision (ROD) for the GCD long-term experimental and management plan (LTEMP) final Environmental Impact Statement (EIS) specified criteria for GCD monthly water releases, daily and hourly operating limits, and experimental releases. This report examines the financial implications of the high flow experiment (HFE) conducted at GCD during the spring of Water Year (WY) 2023 as required by the LTEMP HFE Protocol. This report is part of a series of reports that describe the financial costs of LTEMP experimental releases since the 2016 ROD was adopted in January 2017. Previous reports analyzed the impact of several past HFEs and Bug Flow Experiments. This report focuses on the HFE conducted in April 2023. For this experimental release, financial costs of approximately $1.33 million were incurred because the HFE required sustained water releases exceeding the power plant’s maximum turbine flow rate. In addition, during the experiment, operators were not allowed to shape GCD power production, either to follow Firm Electric Service (FES) customer day-ahead energy deliveries or to respond to market prices. This study identifies the main factors contributing to the HFE costs and examines the interdependencies among these factors. It applies an integrated set of tools to estimate Western Area Power Administration (WAPA) financial impacts by simulating GCD under two types of cases; namely, (1) a “With Experiment” case that mimics the operations that actually occurred and (2) a “Without Experiment” case that simulates operations under the assumption that the HFE did not occur. The “With Experiment” case mimics operations during the HFE and the entire month the HFE occurred. It complies with LTEMP hourly and daily operating criteria. The “Without Experiment” case assumes that the HFE did not occur. The monthly water release volume is assumed to be identical under both cases. The Colorado River Storage Project Python-based model (CRiSPPy) model was the main modeling tool used to simulate the dispatch of the GCD hydropower plant and associated water releases from Lake Powell. In the modeling process, the research team used extensive data sets and historical information on SLCA/IP power plant characteristics, hydrologic conditions, and WAPA’s power purchases and sales prices. In addition to estimating the financial impact of the HFE, the team used the CRiSPPy model to gain insights into the interplay among ROD operating criteria, exceptions made to criteria to accommodate the HFE, and WAPA operating practices.

13 HYDRO ENERGY↗
NASA NTRSDate not supplied

Kinetic Model Development of the Carbon Vapor Deposition Reactor to Predict Performance Versus Design

To enable complete closure of a Sabatier-based atmosphere revitalization system where oxygen recovery is predicated by carbon dioxide reduction to methane and water, there must be a means of recovering the hydrogen from the byproduct methane. Honeywell’s carbon vapor deposition (CVD) reactor is a potential technology that provides a thermochemical means by which methane decomposes at high temperatures (> 1,000 °C) into hydrogen, solid carbon, and a small percentage of hydrocarbons. This advanced methane pyrolysis reactor utilizes a carbon fiber substrate to capture and immobilize the solid pyrolytic carbon rather than allowing continuation toward undesirable soot. The CVD reactor is a highly dynamic system whose characteristic geometry changes while operating. The reactor model was developed to provide predictive performance assessment capabilities at different operating conditions, geometries, and configurations to aid in technology development. Modeling of the CVD reactor considered the multitude of simultaneous and interdependent physicochemical processes that are entailed, including: (1) the homogeneous gas-phase pyrolysis reactions, (2) the heterogeneous deposition reactions, and (3) the transport of gaseous species in the reactor domains. A 1-D CVD reactor model was developed based on a general packed-bed, axial-dispersion plug flow reactor model with a solid substrate balance to account for carbon deposition. The model includes 59 species and 243 reactions constituting the methane pyrolysis reaction network, carbon deposition, and interconversions of oxygenate species. The reactor model was compared to brassboard test data, where it was shown to be able to capture the pertinent physicochemical phenomena as evidenced by its agreement with short-duration run data (i.e., looking at different temperature and flow rate conditions), as well as trend agreement with the brassboard reactor’s long-duration test data. Recommendations are provided on further model development steps as well as its use cases.

Air Revitalization↗
DOE OSTI2025

Temperature and Water Levels Collectively Regulate Methane Emissions From Subtropical Freshwater Wetlands

Abstract Wetlands are the largest and most climate‐sensitive natural sources of methane. Accurately estimating wetland methane emissions involves reconciling inversion (“top‐down”) and process‐based (“bottom‐up”) models within the global methane budget. However, estimates from these two model types are inherently interdependent and often reveal substantial discrepancies. To enhance the reliability of both approaches, we need a comprehensive understanding of wetland methane emissions and an independent high‐resolution long‐term flux data set. Here, we employed a data‐driven random forest approach to identify key variables influencing methane emissions from subtropical freshwater wetlands in the Southeastern United States. The model‐estimated monthly mean methane fluxes fit well with measured methane fluxes ( R 2 = 0.67) at four representative FLUXNET‐CH4 wetland sites across the region. Variable importance analysis highlighted the sensitivity of subtropical freshwater wetland methane emissions to variations in both temperature and water levels. High temperatures facilitate methanogenesis by enhancing microbial activities, while elevated water levels maintain anaerobic conditions necessary for methane production. Notably, the response of methane emissions to water level fluctuations is contingent on temperature conditions, and vice versa. Moreover, we constructed the first high‐spatial‐resolution (∼1 km × 1 km) and long‐term (1982–2010) gridded regional wetland methane flux product for the Southeastern United States, estimating annual methane emissions from subtropical freshwater wetlands in the region at 4.93 ± 0.11 Tg CH 4 yr −1 for 1982–2010. This new benchmark product holds promise for validating and parameterizing uncertain wetland methane emission processes in bottom‐up models and provides improved prior information for top‐down models.

He, Keqi [Earth and Climate Sciences Nicholas Scho↗