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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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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Experimental Results on SS 304H and 2.25Cr-1Mo in Support of Developing Inelastic Constitutive Models

To address critical data gaps necessary for updating viscoplastic constitutive material models for Class A materials used in inelastic design analysis under the American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code, Section III, Division 5, High Temperature Reactors, Oak Ridge National Laboratory (ORNL) conducted experimental studies on stainless steel 304H (SS 304H) and 2¼Cr-1Mo steel (F22), to probe their temperature dependent deformation behaviors. These studies covered a range of temperatures up to the maximum limits specified in Division 5 and are intended to support the development of material models by collaborators at Argonne National Laboratory (ANL).

36 MATERIALS SCIENCE↗
DOE OSTI2025

Circularity Futures Workshop Series: Summary Report

The aim of this report is to synthesize key feedback received from the three-part Circularity Futures workshop series held in Spring 2024. The workshop series was conducted by the National Renewable Energy Laboratory (NREL) on behalf of U.S. Department of Energy, Office Energy Efficiency and Renewable Energy (EERE), and was broken into three workshops: Workshop 1 - Circularity Analysis Needs and Priorities; Workshop 2 - Circularity Metrics and Indicators; and Workshop 3 - Circularity Data. Together, the workshops focused on identifying the existing priorities and gaps in the circularity modeling space, understanding different stakeholders' use and interpretation of circularity metrics and indicators, identifying common data gaps and data quality challenges, and assessing the robustness of available solutions. The workshop series brought a diverse group of stakeholders - including representatives from U.S. government offices, national labs, nonprofit organizations, industry, and academia - to collect first-hand feedback on needs, priorities, challenges and opportunities in the circularity modeling and analysis space. The workshop discussions highlighted numerous common needs, priorities and challenges among the interviewed groups. Several topics were frequently discussed, including: 1) Circularity as a pathway for sustainable economic growth: While circularity is generally defined in terms of resource conservation and reducing wasteful disposal of materials, participants agreed that circular strategies should serve broader economic, environmental, and social goals. It is therefore crucial for circularity analysis to look beyond waste reduction and instead evaluate a variety of impact metrics such as cost savings, job creation, air quality, and pollutant emissions. Mutli-criteria decision-making frameworks may be useful for making sense of disparate metrics and evaluating tradeoffs between impact categories.; 2) Economic and social factors are not well understood: Underdevelopment of existing end-of-life (EOL) management infrastructure, inconsistent standardization codes and policy space in reusing recycled content, and suboptimal collection and sorting strategies collectively contribute to uncertainty about the economic potential of circular pathways. The latter observation is consistent among all technologies but more emphasized for renewable energy systems. Social impacts of circularity practices are less understood and less researched than other sustainability aspects.; 3) Inconsistent methods for assessing emerging technologies: LCA and TEA results vary widely depending on the assumptions made with regards to market adoption of new technologies. Emerging technologies suffer limited availability of data needed to conduct a robust circularity analysis. Yet, understanding projected impacts of proposed nascent technology is a key need for different stakeholder groups.; and 4) Lack of temporally and geospatially explicit data: There is a need for open data that represents variations in circularity technologies over time and location. The lack thereof leads to aggregated and potentially misrepresented results in circularity analysis. Sensitivity analyses should be included to verify whether options perceived as more sustainable align with real-world practices.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Friction Experiment Data Analysis

The Spent Fuel Waste Disposition (SFWD) program under the U.S. Department of Energy (DOE) is planning a seismic shake table test of full-scale dry storage systems of spent nuclear fuel (SNF) to close the gap related to the seismic loads on the fuel assemblies in dry storage systems. This test will allow for quantifying the strains and accelerations on surrogate fuel assembly during representative earthquakes. A concrete layer will be installed on the shake table before the test to simulate conditions representative of an ISFSI pad. In the shake table tests with the vertical cask, the cask will be free-standing because this is representative of all, except two, ISFSIs in the U.S. with vertical dry storage casks. The static and dynamic friction coefficients between the steel bottom of the cask and the concrete layer on the shake table are important parameters that will affect cask behavior during the test. These parameters must be known for the pre- and post-test modelling, data analysis, and model validation. The friction experiment was performed at the Engineering Department of the University of New Mexico (UNM) to determine the friction coefficients between a steel plate with the same finish as the bottom of the vertical cask manufactured for the test and different concrete surfaces. In this experiment the steel plate was fixed and the concrete sample was pulled over the plate with a constant displacement rate using an MTS machine. This allowed for collecting continuous horizontal force data over the length of the steel plate. Four displacement rates and three vertical loads were used. The tests were performed with four concrete blocks with different degrees of the surface roughness - light sandblast, light to medium sandblast, medium bush hammer, and heavy sandblast. The total number of tests was 48. The data were used to calculate static and dynamic friction coefficients.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

HPDR: High-Performance Portable Scientific Data Reduction Framework

The rapid growth in scientific data generation is outpacing advancements in computing systems necessary for efficient storage, transfer, and analysis, particularly in the context of exascale computing. With the deployment of first-generation exascale computing systems and next-generation experimental facilities, this gap is widening and necessitates effective data reduction techniques to manage enormous data volumes. Over the past decade, various data reduction methods, including lossless compression, error-controlled lossy compression, and data refactoring, have been developed to accelerate I/O in scientific workflows. Despite significant reductions in data volume, these methods introduce considerable computational overhead, which can become the new bottleneck in data processing. To mitigate this, GPU-accelerated data reduction algorithms have been introduced. However, challenges remain in their integration into exascale workflows, including limited portability across different GPU architectures, substantial memory transfer overhead, and reduced scalability on dense multi-GPU systems. To address these challenges, we propose HPDR, a high-performance and portable data reduction framework. HPDR is designed to enable the execution of state-of-the-art reduction algorithms across diverse processor architectures while reducing memory transfer overhead to 2.3 % of the original, resulting in up to 3.5× faster throughput compared to existing solutions. It also achieves up to 96% of the theoretical speedup in multi-GPU settings. In addition, evaluations on accelerating I/O operations at scale up to 1,024 nodes of the Frontier supercomputer demonstrate that HPDR can achieve up to 103 TB/s reduction throughput, providing up to 4× acceleration in parallel I/O performance compared to existing data reduction routines. This work highlights the potential of HPDR to significantly enhance data reduction efficiency in exascale computing environments.

Chen, Jieyang [University of Oregon]↗
DOE OSTI2022

Gap Analysis on the Impacts of Hydrogen Addition to the North American Natural Gas Infrastructure Polyethylene Pipelines

The existing natural gas pipeline system is being considered as a means to distribute blends of clean hydrogen and natural gas as one component of clean hydrogen’s role in enabling a future low-carbon energy system. With nearly three million miles of transmission and distribution lines in the United States, this national asset is uniquely situated as a transition strategy to assist in the decarbonization of the US economy. 1 This report reviews existing data on the effects of hydrogen on polyethylene (PE) natural gas pipeline materials and identifies gaps where additional investigation is required to fully assess deleterious impacts to the polyethylene pipes from exposure to hydrogen. This gap analysis will assist in determining the feasibility of blending hydrogen gas into existing natural gas pipelines.

08 HYDROGEN↗
DOE OSTI2023

Energy Inputs for LCA

This presentation was given at the ASTM [Virtual] Workshop on Decarbonization: A Gap Analysis of LCA Standards for Industry on October 10, 2023. It discusses concerns about available life cycle data due to a lack of standards and shows how NETL has approached data development for two key areas that satisfy these concerns: electricity and natural gas.

Jamieson, Matthew↗
DOE OSTI2021

Learning to identify electrons

In this report we investigate whether state-of-the-art classification features commonly used to distinguish electrons from jet backgrounds in collider experiments are overlooking valuable information. A deep convolutional neural network analysis of electromagnetic and hadronic calorimeter deposits is compared to the performance of typical features, revealing a ≈ 5% gap which indicates that these lower-level data do contain untapped classification power. To reveal the nature of this unused information, we use a recently developed technique to map the deep network into a space of physically interpretable observables. We identify two simple calorimeter observables which are not typically used for electron identification, but which mimic the decisions of the convolutional network and nearly close the performance gap.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Fine Structures in the Main Sequence Revealed by Gaia Data Release 2

The Hertzsprung–Russell diagram (HRD) is fully examined using the Fourier analysis. This work shows more stars are above the gap in the lower main sequence than below it, and this implies that stars spend more time above the gap while they undergo variability associated with the {sup 3}He instability. The enhanced HRD also shows the width of the gap is not linear and depends on the G{sub BP}−G{sub RP} color up until G{sub BP}−G{sub RP}=2.7. Beyond this color limit, the gap is hardly seen. Besides, a new low density region is revealed for the first time centered at M {sub G} ≈ 10.7 and G {sub BP} − G {sub RP} ≈ 2.8, which is below the lower right corner of the gap. This work also shows that the main sequence appears to have fine stripes where stellar densities are relatively low or high compared to their adjacent regions on the main sequence. These stripes can be seen throughout the main sequence of stars redder than G {sub BP} − G {sub RP} = 0.8 and are not limited to any specific color or spectral type. Slopes of these features are different from the main sequence, but are pretty consistent throughout the main sequence, with a few exceptions. We are perplexed by these new features, but the complexities of stellar atmospheric features and opacities of dwarfs may have caused these patterns.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Domain Adaptation for Measurements of Strong Gravitational Lenses

Upcoming surveys are predicted to discover galaxy-scale strong lenses on the magnitude of 105, making deep learning methods necessary in lensing data analysis. Currently, there is insufficient real lensing data to train deep learning algorithms, but training only on simulated data results in poor performance on real data. Domain adaptation can bridge the gap between simulated and real datasets. We adopt domain adaptation on the estimation of Einstein radius in simulated galaxy-scale gravitational lensing images. We evaluate two domain adaptation techniques - domain adversarial neural networks (DANN) and maximum mean discrepancy (MMD). We train on a source domain of simulated lenses and apply it to a target domain with emulation of DES survey conditions. We show that both domain adaptation techniques can significantly improve the model performance on the more complex target domain datasets. Our results show the potential of using domain adaptation to perform analysis on future survey data with a deep neural network trained on simulated data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Supply Chain Sustainability Analysis of Renewable Hydrocarbon Fuels via Indirect Liquefaction, Ex Situ Catalytic Fast Pyrolysis, Hydrothermal Liquefaction, Combined Algal Processing, and Biochemical Conversion: Update of the 2020 State-of-Technology Cases

The Department of Energy’s (DOE) Bioenergy Technologies Office (BETO) aims to develop and deploy technologies to transform renewable biomass resources into commercially viable, high-performance biofuels, bioproducts, and biopower through public and private partnerships (U.S. Department of Energy, 2016). BETO and its national laboratory teams conduct in-depth techno-economic assessments (TEA) of biomass feedstock supply and logistics and conversion technologies to produce biofuels. There are two general types of TEAs: A design case outlines a target case (future projection) for a particular biofuel pathway. It enables identification of data gaps and research and development needs and provides goals and benchmarks against which technology progress is assessed. A state of technology (SOT) analysis assesses progress within and across relevant technology areas based on actual results at current experimental scales relative to technical targets and cost goals from design cases, and includes technical, economic, and environmental criteria as available. In addition to developing a TEA for a pathway of interest, BETO also performs a supply chain sustainability analysis (SCSA). The SCSA takes the life-cycle analysis approach that BETO has been supporting for about 20 years. It enables BETO to identify energy consumption, environmental, and sustainability issues that may be associated with biofuel production. Approaches to mitigate these issues can then be developed. Additionally, the SCSA allows for comparison of energy and environmental impacts across biofuel pathways in BETO’s research and development portfolio. This technical report describes the SCSAs for the production of renewable hydrocarbon transportation fuels via a range of conversion technologies in the 2020 SOTs: (1) renewable high octane gasoline (HOG) via indirect liquefaction (IDL) of woody lignocellulosic biomass (note that the IDL pathway in this SCSA represents the syngas conversion design [Harris et al. 2021]); (2) renewable gasoline (RG) and diesel (RD) blendstocks via ex situ catalytic fast pyrolysis of woody lignocellulosic biomass [Abhijit et al. 2021]; (3) RD via hydrothermal liquefaction (HTL) of wet sludge from a wastewater treatment plant; (4) renewable hydrocarbon fuels via biochemical conversion of herbaceous lignocellulosic biomass (Davis et al. 2021; Lin et al. 2021); (5) renewable diesel via HTL of a blend of algae (Davis and Klein, 2021) and woody biomass (Hartley et al. 2020); and (6) renewable diesel via combined algae processing (CAP) (Wiatrowski and Davis, 2021). This technical report focuses on the environmental performance of these six biofuel production pathways in their 2020 SOT cases. The results of these renewable hydrocarbon fuel pathways in these SCSA analyses update those for the respective 2019 SOT cases (Cai et al. 2020). They also provide an opportunity to examine the impact of technology improvements in both biomass feedstock production and biofuel production that have been achieved in 2020 SOTs on the sustainability performance of these renewable transportation fuels. The SCSA results also reflect updates to Argonne National Laboratory’s Greenhouse gases, Regulated Emissions, and Energy use in Technologies (GREET®) model, which was released in October 2020 (Wang et al. 2020). These GREET updates include the production of natural gas, electricity, and petroleum-based fuels that can influence biofuels’ supply chain greenhouse gas (GHG) (CO 2 , CH 4 , and N 2 O) emissions, water consumption, and air pollutant emissions. GHG emissions, water consumption, and nitrogen oxides (NO x ) emissions are the main sustainability metrics assessed in this analysis. In this analysis, we define water consumption as the amount of water withdrawn from a freshwater source that is not returned (or returnable) to a freshwater source at the same level of quality. Life-cycle fossil energy consumption and net energy balance, which is the life-cycle fossil energy consumption deducted from the renewable biofuel energy produced, are also assessed.

09 BIOMASS FUELS↗
DOE OSTI2024

Estimating geographic origins of corn and soybean biomass for biofuel production: A detailed dataset

Sustainable fuel initiatives in the United States such as the Environmental Protection Agency’s Renewable Fuel Stan- dard and the Department of Energy’s Sustainable Aviation Fuel Grand Challenge have increased the production of corn ethanol and soybean biodiesel. However, the lack of precise information regarding biomass sourcing at a localized level has hindered accurate understanding of both biofuel costs and environmental impact of these production pathways. By harnessing the power of geospatial analysis and leveraging United States Department of Agriculture (USDA) crop cen- sus data, this dataset fills this critical knowledge gap. This dataset offers a novel estimation of geospatial biomass sourc- ing for biofuel production in the United States by synthe- sizing 2017 USDA crop census data, biorefinery data from the United States Energy Information Administration, and publicly available information about biomass sourcing for biofuel production. This dataset provides a detailed under- standing of biomass use for first generation biofuel pro- duction, enabling stakeholders to make informed decisions about resource allocation, investment strategies, and infras- tructure development. Furthermore, the county-level gran- ularity of the dataset allows for increased fidelity in the techno-economic assessments and life-cycle analyses of first- generation biofuels in the United States.

09 BIOMASS FUELS↗
DOE OSTI2026

Investigating the opioid epidemic across the United States: Associations between county-level characteristics and overdose mortality

The opioid crisis remains a critical public health challenge in the United States. Despite national efforts that reduced opioid prescribing by nearly 44% between 2011 and 2021, opioid overdose deaths more than tripled during the same period. This alarming trend reflects a major shift in the crisis, with illegal opioids now driving the majority of overdose deaths instead of prescription opioids. Although supply-side factors fueling this transition have been widely studied, the structural and community-level conditions that shape overdose mortality are less well understood. To help address this gap, this study has three primary objectives: (1) overcome structural gaps in national data to construct a complete nationwide county-level dataset from 2010 to 2022; (2) using data analysis, identify and investigate spatiotemporal anomalies in overdose mortality; and (3) using two machine-learning models, quantify the importance of thirteen social vulnerability variables in predicting overdose mortality. Our results identify unemployment and limited vehicle access as key county-level predictors of overdose mortality. Higher levels of these vulnerabilities are associated with elevated mortality, whereas lower levels are associated with reduced mortality. These findings highlight factors that may be relevant for public health planning and policy prioritization within the context of the opioid crisis.

Anomaly analysis↗
DOE OSTI2024

Beam energy dependence of net-hyperon yield and its implication on baryon transport mechanism

In the constituent quark model, each quark inside a baryon carries 1/3 unit of the baryon number. An alternative picture exists where the center of a Y-shaped topology of gluon fields, called the baryon junction, carries a unit baryon number. Studying baryon transport over a large rapidity gap (δy) in nuclear collisions provides a possible tool to distinguish these two pictures. A recent analysis of global data on net-proton yield at mid-rapidity in Au+Au collisions showed an exponential dependence on δy and the exponential slope does not vary with event centrality, favoring the baryon junction picture. Since junctions are flavor blind, hyperons – baryons containing valence strange quarks – are expected to exhibit a similar behavior as the proton. This study aims to test this prediction by analyzing hyperon yields in Au+Au collisions at various energies. We observe that net-hyperon yields, after correcting for the strangeness production suppression, adhere to the expected exponential form. The extracted slope parameters for net-Λ, net-$Ξ$ and net-Ω are consistent with each other and with those of net-proton within uncertainties, and exhibit no centrality dependence. Various implementations of the PYTHIA event generator, primarily based on valence quarks for baryon transport, are unable to simultaneously describe the slope parameters for all baryons.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

A fine pore-preserved deep neural network for porosity analytics of a high burnup U-10Zr metallic fuel

Abstract U-10 wt.% Zr (U-10Zr) metallic fuel is the leading candidate for next-generation sodium-cooled fast reactors. Porosity is one of the most important factors that impacts the performance of U-10Zr metallic fuel. The pores generated by the fission gas accumulation can lead to changes in thermal conductivity, fuel swelling, Fuel-Cladding Chemical Interaction (FCCI) and Fuel-Cladding Mechanical Interaction (FCMI). Therefore, it is crucial to accurately segment and analyze porosity to understand the U-10Zr fuel system to design future fast reactors. To address the above issues, we introduce a workflow to process and analyze multi-source Scanning Electron Microscope (SEM) image data. Moreover, an encoder-decoder-based, deep fully convolutional network is proposed to segment pores accurately by integrating the residual unit and the densely-connected units. Two SEM 250 × field of view image datasets with different formats are utilized to evaluate the new proposed model’s performance. Sufficient comparison results demonstrate that our method quantitatively outperforms two popular deep fully convolutional networks. Furthermore, we conducted experiments on the third SEM 2500 × field of view image dataset, and the transfer learning results show the potential capability to transfer the knowledge from low-magnification images to high-magnification images. Finally, we use a pre-trained network to predict the pores of SEM images in the whole cross-sectional image and obtain quantitative porosity analysis. Our findings will guide the SEM microscopy data collection efficiently, provide a mechanistic understanding of the U-10Zr fuel system and bridge the gap between advanced characterization to fuel system design.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Greater aperture counteracts effects of reduced stomatal density on WUE: a case study on sugarcane and meta-analysis

Stomata regulate CO 2 and water vapor exchange between leaves and the atmosphere. Stomata are a target for engineering to improve crop intrinsic water use efficiency (iWUE). One example is by expressing genes that lower stomatal density (SD) and reduce stomatal conductance (g sw ). However, the quantitative relationship between reduced SD, g sw , and the mechanisms underlying it is poorly understood. We addressed this knowledge gap using low-SD sugarcane (Saccharum spp. hybrid) as a case study alongside a meta-analysis of data from 10 species. Transgenic expression of EPIDERMAL PATTERNING FACTOR 2 from Sorghum bicolor (SbEFP2) in sugarcane reduced SD by 26-38% but did not affect gsw compared to wildtype. Further, no changes occurred in stomatal complex size or proxies for photosynthetic capacity. Measurements of gas exchange at low CO 2 concentrations that promote complete stomatal opening to normalize aperture size between genotypes were combined with modeling of maximum gsw from anatomical data. These data suggest that increased stomatal aperture is the only possible explanation for maintaining gsw when SD is reduced. Meta-analysis across C 3 dicots, C 3 monocots, and C 4 monocots revealed engineered reductions in SD are strongly correlated with lower gsw (r 2 =0.60-0.98), but this response is damped relative to the change in anatomy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Landscaper v1

Understanding the inner workings of machine learning models through their loss landscapes offers crucial insights into model properties, optimization dynamics, and generalizability. However, accessing these insights has traditionally required specialized mathematical expertise, limiting broader adoption. Landscaper is an open-source Python package designed to bridge this gap. Landscaper seamlessly integrates a suite of multi-dimensional loss landscape analyses with cutting-edge topological data analysis (TDA) methods. This powerful combination makes both fundamental loss landscape analysis and advanced TDA techniques accessible to the broader scientific ML community, without requiring deep pre-existing mathematical knowledge. Landscaper offers three key functionalities: * Construction: Builds detailed loss landscape representations through versatile low and high-dimensional sampling techniques. * Quantification: Applies advanced metrics, including a novel topological data analysis (TDA) based smoothness metric, enabling new perspectives on model behavior. * Visualization: Offers intuitive tools to visualize and interpret loss landscapes, providing actionable insights beyond traditional performance metrics.

Weber, Gunther [Lawrence Berkeley National Laborat↗
DOE OSTI2021

Quantitative analysis of Raman spectral parameters for carbon fibers: practical considerations and connection to mechanical properties

Although the literature on the Raman spectra of carbon fibers is vast, no consistent, robust predictive relationship between mechanical properties of carbon fibers and spectral parameters exists. This shortcoming is due to the use of numerous fitting functions to evaluate Raman spectra of carbon fibers and the inconsistencies in establishing the best fitting models in a statistically robust fashion. To address this gap, we present a comprehensive work on the Raman spectra of carbon fibers that combines a vast library of experimental data with a robust numerical analysis and a statistical evaluation of a wide range of suggested fitting models. This manuscript begins with a brief review of the commonly applied fitting models. Then, the Raman spectra of 32 commercially available polyacrylonitrile-based carbon fibers collected at excitation wavelengths 532, 633, and 785 nm are presented and the best fit for all fibers is evaluated based on several statistical criteria in conjunction with numerical calculations and physical arguments. The results suggest that high-performance fibers must be fit with at least five peaks, whereas high-tensile modulus fibers are best fit with at least six distinct peaks. Finally, we employ simultaneous fitting of the Raman spectra of specific fibers and wavelengths and demonstrate that strong correlations exist between mechanical properties and the D1 peak position and shape across the range of evaluated mechanical properties. Finally, we suggest straightforward improvements in fitting analysis procedures that can be implemented to increase coherency in the understanding of the underlying carbon fiber microstructure intuited from Raman spectroscopy.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Critical needs to close monitoring gaps in pan-tropical wetland CH 4 emissions

Global wetlands are the largest and most uncertain natural source of atmospheric methane (CH 4 ). The FLUXNET-CH 4 synthesis initiative has established a global network of flux tower infrastructure, offering valuable data products and fostering a dedicated community for the measurement and analysis of methane flux data. Existing studies using the FLUXNET-CH 4 Community Product v1.0 have provided invaluable insights into the drivers of ecosystem-to-regional spatial patterns and daily-to-decadal temporal dynamics in temperate, boreal, and Arctic climate regions. However, as the wetland CH 4 monitoring network grows, there is a critical knowledge gap about where new monitoring infrastructure ought to be located to improve understanding of the global wetland CH 4 budget. Here we address this gap with a spatial representativeness analysis at existing and hypothetical observation sites, using 16 process-based wetland biogeochemistry models and machine learning. We find that, in addition to eddy covariance monitoring sites, existing chamber sites are important complements, especially over high latitudes and the tropics. Furthermore, expanding the current monitoring network for wetland CH 4 emissions should prioritize, first, tropical and second, sub-tropical semi-arid wetland regions. Considering those new hypothetical wetland sites from tropical and semi-arid climate zones could significantly improve global estimates of wetland CH 4 emissions and reduce bias by 79% (from 76 to 16 TgCH 4 y -1 ), compared with using solely existing monitoring networks. Our study thus demonstrates an approach for long-term strategic expansion of flux observations.

54 ENVIRONMENTAL SCIENCES↗