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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production

Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth system model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a GPP (photosynthesis per unit ground area) model, the P-model, that combines the Farquhar–von Caemmerer–Berry model for C 3 photosynthesis with an optimality principle for the carbon assimilation–transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C 3 vegetation type. The model builds on the theory developed in Prentice et al. (2014) and Wang et al. (2017a) and is extended to include low temperature effects on the intrinsic quantum yield and an empirical soil moisture stress factor. The model is forced with site-level data of the fraction of absorbed photosynthetically active radiation (fAPAR) and meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs, the R 2 for predicted versus observed GPP based on the full model setup is 0.75 (8 d mean, 126 sites) – similar to comparable satellite-data-driven GPP models but without predefined vegetation-type-specific parameters. The R 2 is reduced to 0.70 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R 2 for the P-model-predicted LUE is 0.32 (means by site) and 0.48 (means by vegetation type). Applying this model for global-scale simulations yields a total global GPP of 106–122 Pg C yr –1 (mean of 2001–2011), depending on the fAPAR forcing data. The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosynthesis across a wide range of conditions. The model is available as an R package (rpmodel).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Carbon and Water Fluxes of the Boreal Evergreen Needleleaf Forest Biome Constrained by Assimilating Ecosystem Carbonyl Sulfide Flux Observations

Gross primary production (GPP) by boreal forests is highly sensitive to environmental changes. However, GPP simulated by land surface models (LSMs) remains highly uncertain due to the lack of direct photosynthesis observations at large scales. Carbonyl sulfide (COS) has emerged as a promising proxy to improve the representation of GPP in LSMs. Because COS is absorbed by vegetation following the same diffusion pathway as CO2 during photosynthesis and not emitted back to the atmosphere, incorporating a mechanistic representation of vegetation COS uptake in LSMs allows using COS observations to refine GPP representation. Here, we perform ecosystem COS flux and GPP data assimilations to constrain the COS- and GPP-related parameters in the ORCHIDEE LSM for boreal evergreen needleleaf forests (BorENF). Assimilating ecosystem COS fluxes at Hyytiälä forest increases the simulated net ecosystem COS uptake by 14%. This increase largely results from changes in the internal conductance to COS, highlighting the need to improve the representation of COS internal diffusion and consumption. Moreover, joint assimilation of ecosystem COS flux and GPP at Hyytiälä improves the simulated latent heat flux, contrary to the GPP-only data assimilation, which fails to do so. Finally, we scaled this assimilation framework up to the boreal region and find that the joint assimilation of COS at Hyytiälä and GPP fluxes at 10 BorENF sites increases the modeled vegetation COS uptake up to 18%, but not GPP. Therefore, this study encourages the use of COS flux observations to inform GPP and latent heat flux representations in LSMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Near-Surface Hydrology and Soil Properties Drive Heterogeneity in Permafrost Distribution, Vegetation Dynamics, and Carbon Cycling in a Sub-Arctic Watershed: Modeling Archive

This Modeling Archive is in support of a NGEE-Arctic publication: Shirley et al. (2022) “Near-Surface Hydrology and Soil Properties Drive Heterogeneity in Permafrost Distribution, Vegetation Dynamics, and Carbon Cycling in a Sub-Arctic Watershed". [DOI].The dataset contains outputs from the global sensitivity analysis (GSA) of the “ecosys” model as reported in Shirley et al. (2022). The study showed that discontinuous permafrost environments are characterized by complex feedback loops and strong spatial heterogeneity which is created by variability in near-surface hydrology and soil properties. Additionally, the study demonstrated that missing representation of sub-grid heterogeneity in terrestrial ecosystem models can lead to biased estimates of the high-latitude carbon budget. Included in this dataset are the factor values for each run in the GSA and the model outputs used in this study. Included are two *.csv data files and one *.pdf.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Influence of sun zenith angle on canopy clumping and the resulting impacts on photosynthesis

Addressing the impact of vegetation architecture on shortwave radiation transfer in land surface models is important for accurate weather forecasting, carbon budget estimates, and climate predictions. This paper investigates to what extent it is possible to retrieve structural parameters of two different parameterization schemes from direct transmittance derived from digital hemispherical photography and 3D radiative transfer modeling for two study sites with different vegetation canopy architectures. Neglecting the representation of 3D canopy structure in radiative transfer schemes leads to significant errors in shortwave radiation partitioning (up to 3.5 times more direct transmittance in the 3D model). Structural parameters, referred to as whole canopy ‘clumping indices’, were obtained in order to evaluate the impact of angular variation in clumping on shortwave radiation transfer. Impacts on photosynthesis were evaluated at site level with the UKESM land surface model, JULES. A comparison between flux tower derived and modeled photosynthesis indicates that considering zenith angular variations of structural parameters in the radiative transfer scheme of the UKESM land surface model significantly improves photosynthesis prediction in light limited ecosystems (from RMSE = 2.91 μmol CO 2 .m -2 .s -1 to RMSE = 1.51 μmol CO 2 .m -2 .s -1 , 48% smaller), typically with enhanced photosynthesis from bottom layers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Applying double cropping and interactive irrigation in the North China Plain using WRF4.5

Abstract. Irrigated cultivation exerts a significant influence on the local climate and the hydrological cycle. The North China Plain (NCP) is known for its intricate agricultural system, marked by expansive cropland, high productivity, compact rotation, a semi-arid climate, and intensive irrigation practices. As a result, there has been considerable attention on the potential impact of this intensive irrigated agriculture on the local climate. However, studying the irrigation impact in this region has been challenging due to the lack of an accurate simulation of crop phenology and irrigation practices within the climate model. By incorporating double cropping with interactive irrigation, our study extends the capabilities of the Weather Research Forecast (WRF) model, which has previously demonstrated commendable performance in simulating single-cropping scenarios. This allows for two-way feedback between irrigated crops and climate, further enabling the inclusion of irrigation feedback from both ground and vegetation perspectives. The improved crop modeling system shows significant enhancement in capturing vegetation and irrigation patterns, which is evidenced by its ability to identify crop stages, estimate field biomass, predict crop yield, and project monthly leaf area index. The improved simulation of large-scale irrigated crops in the NCP can further enhance our understanding of the intricate relationship between agricultural development and climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Modeling Spatial Heterogeneity in Surface Turbulent Heat Flux in the U.S. Southern Great Plains

Advances in numerical modeling of cloud dynamics are driving a need for improved land model prediction at convective storm scales. Here, satellite and ground-based vegetation remote sensing data were combined with land model experiments to more accurately characterize land surface spatial heterogeneity in the Community Land Model (CLM4.0). The new subgrid classification of plant functional types (PFT) and leaf area index (LAI) enables consistent comparison between models and ground-based flux measurements in the U.S. southern Great Plains. Errors in vegetation data sets (inferred from comparison between 250 m satellite and ground-based LAI), while large, had less impact on the simulated characteristics of spatial heterogeneity compared to errors in model representation of surface energy partitioning (between latent and sensible heat flux) and its relationship to LAI. Predicted spatial heterogeneity in surface energy partitioning was enhanced after replacing soil and stomatal resistance parameters with a new set that better predicts the observed relationship to LAI. These modifications increase the number of smaller (mesoscale) dry land patches having higher sensible heat flux. The parameter experiments suggest that vegetation state and processes (transpiration) act to broaden the size spectrum of surface heat flux heterogeneity, which can influence clouds and convective initiation. Moreover, improvements in vegetation input data and model parameters had partially compensating effects on surface flux heterogeneity, indicating the importance of evaluating input data and parameterizations together to improve prediction at higher spatial resolutions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Sorghum bicolor Core Metabolism Model

Upregulation of triacylglycerols (TAGs) in vegetative plant tissues such as leaves has the potential to drastically increase the energy density and biomass yield of bioenergy crops. In this context, constraint-based analysis has the promise to improve metabolic engineering strategies. Here we present a core metabolism model for the C4 biomass crop Sorghum bicolor (iTJC1414) along with a minimal model for photosynthetic CO2 assimilation, sucrose and TAG biosynthesis in C3 plants. Extending iTJC1414 to a four-cell diel model we simulate C4 photosynthesis in mature leaves with the principal photo-assimilatory product being replaced by TAG produced at different levels. Independent of specific pathways and per unit carbon assimilated, energy content and biosynthetic demands in reducing equivalents are about 1.3 to 1.4 times higher for TAG than for sucrose. For plant generic pathways, ATP- and NADPH-demands per CO2 assimilated are higher by 1.3- and 1.5-fold, respectively. If the photosynthetic supply in ATP and NADPH in iTJC1414 is adjusted to be balanced for sucrose as the sole photo-assimilatory product, overproduction of TAG is predicted to cause a substantial surplus in photosynthetic ATP. This means that if TAG synthesis was the sole photo-assimilatory process, there could be an energy imbalance that might impede the process. Adjusting iTJC1414 to a photo-assimilatory rate that approximates field conditions, we predict possible daily rates of TAG accumulation, dependent on varying ratios of carbon partitioning between exported assimilates and accumulated oil droplets (TAG, oleosin) and in dependence of activation of futile cycles of TAG synthesis and degradation. We find that, based on the capacity of leaves for photosynthetic synthesis of exported assimilates, mature leaves should be able to reach a 20% level of TAG per dry weight within one month if only 5% of the photosynthetic net assimilation can be allocated into oil droplets. From this we conclude that high TAG levels should be achievable if TAG synthesis is induced only during a final phase of the plant life cycle.

Feedstock Production↗
DOE OSTI2025

Better practices for inferring ecosystem water use strategy from eddy covariance data

Eddy covariance data are critical for inferring ecosystem water use strategies. Yet, such inferences are sensitive to a range of assumptions applied across studies, hindering our understanding of water use strategies within and across eddy covariance sites. A recent analysis across 151 FLUXNET2015 and AmeriFlux-FLUXNET datasets found that poor model performance was the key driver of non-robust inferences of ecosystem water use strategies. Here, we leverage this previous analysis to (i) identify the specific assumptions that improve inference model performance across most sites, (ii) explain the mechanisms behind the performance improvements, and (iii) check whether better performance improves water use inference. We find that the common practice of fitting a model to canopy conductance (G c ) derived from the evapotranspiration (ET) observations, rather than to observed ET itself, artificially amplifies data errors and degrades the model performance. Next, accounting for vegetation dynamics by applying a growing season filter or incorporating satellite LAI data improves performance, but the former practice may remove soil water stress periods. Lastly, using the leaf-to-air vapor pressure deficit (VPD l ) derived from ET observations as a model input may artificially inflate performance. Based on these results, we recommend selecting observed ET (rather than derived G c ) as the response variable, carefully accounting for vegetation dynamics, and avoiding derived VPD l as a model input; these best practices improve model performance by c. 20% and robustness by c. 80% across all eddy covariance sites. Nevertheless, the performance improvements do not always correspond to more robust inference of water use strategies, as model parameter selection and surface energy budget closure corrections still strongly influence the ecosystem water use parameter estimation in a site-specific manner.

AmeriFlux↗
DOE OSTI2022

Future Hurricanes Will Increase Palm Abundance and Decrease Aboveground Biomass in a Tropical Forest

Hurricanes are expected to intensify throughout the 21st century, yet the impact of frequent major hurricanes on tropical ecosystems remains unknown. To investigate tropical forest damage and recovery under different hurricane regimes, we generate a suite of scenarios based on Coupled Model Intercomparison Project Phase 6 climate projections and increased hurricane recurrence and intensity for the Luquillo Experimental Forest, Puerto Rico. We then use the Ecosystem Demography model to predict changes in carbon stocks, forest structure and composition. In this work, our results indicate that frequent hurricane disturbances in the future would decrease the overall aboveground biomass, decrease the dominance of late-successional species, but increase the dominance of palm species. Warmer climates with increased CO 2 would have little effect on the functional-type composition but increase the aboveground biomass. However, the predicted climate and CO 2 fertilization effects would not compensate for the biomass loss due to more frequent severe-hurricane disturbances.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Transmission risk of Oropouche fever across the Americas

Abstract Background Vector-borne diseases (VBDs) are important contributors to the global burden of infectious diseases due to their epidemic potential, which can result in significant population and economic impacts. Oropouche fever, caused by Oropouche virus (OROV), is an understudied zoonotic VBD febrile illness reported in Central and South America. The epidemic potential and areas of likely OROV spread remain unexplored, limiting capacities to improve epidemiological surveillance. Methods To better understand the capacity for spread of OROV, we developed spatial epidemiology models using human outbreaks as OROV transmission-locality data, coupled with high-resolution satellite-derived vegetation phenology. Data were integrated using hypervolume modeling to infer likely areas of OROV transmission and emergence across the Americas. Results Models based on one-support vector machine hypervolumes consistently predicted risk areas for OROV transmission across the tropics of Latin America despite the inclusion of different parameters such as different study areas and environmental predictors. Models estimate that up to 5 million people are at risk of exposure to OROV. Nevertheless, the limited epidemiological data available generates uncertainty in projections. For example, some outbreaks have occurred under climatic conditions outside those where most transmission events occur. The distribution models also revealed that landscape variation, expressed as vegetation loss, is linked to OROV outbreaks. Conclusions Hotspots of OROV transmission risk were detected along the tropics of South America. Vegetation loss might be a driver of Oropouche fever emergence. Modeling based on hypervolumes in spatial epidemiology might be considered an exploratory tool for analyzing data-limited emerging infectious diseases for which little understanding exists on their sylvatic cycles. OROV transmission risk maps can be used to improve surveillance, investigate OROV ecology and epidemiology, and inform early detection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Assessing Simulations of Forest Hurricane Disturbance and Recovery in Puerto Rico by ELM-FATES Using Field Measurements

In the past three decades, Puerto Rico (PR) experienced five hurricanes that met or exceeded category three, and they caused severe forest structural damage and elevated tree mortality. To improve our mechanistic understanding of hurricane impacts on tropical forests and assess hurricane-affected forest dynamics in Earth system models, we use in situ forest measurements at the Bisley Experimental Watersheds in Northeast PR to evaluate the Functionally Assembled Terrestrial Ecosystem Simulator coupled with the Energy Exascale Earth System Model Land Model (ELM-FATES). The observations show that before Hurricane Hugo, 77.3% of the aboveground biomass (AGB) is from the shade-tolerant plant function type (PFT). The Hugo-induced mortality rates are over ~50%, and they induce a ~39% AGB reduction, which recovers to a level like the pre-Hugo condition in 2014, following a second, lower intensity hurricane, Georges. We perform numerical experiments that simulate damage from Hugo and Georges on the forests, including defoliation, sapwood and structural biomass damage, and hurricane-induced mortality. ELM-FATES can reasonably represent coexistence between the two PFTs–light-demanding and shade-tolerant–for both the pre-Hugo and post-Hugo conditions. The model represents a reasonable size distribution of mid-and large-sized trees although it underestimates AGB, likely due to the overestimated nonhurricane mortality. ELM-FATES temporarily stimulated leaf biomass and diameter increment after Georges, an effect that should be tested with observations of future hurricane defoliation events. This research indicates that addressing model-data mismatches in tree mortality and understory dynamics are essential to simulation of more extreme hurricane effects under climate change.

58 GEOSCIENCES↗
DOE OSTI2022

Using soil library hyperspectral reflectance and machine learning to predict soil organic carbon: Assessing potential of airborne and spaceborne optical soil sensing

Soil organic carbon (SOC) is a key variable to determine soil functioning, ecosystem services, and global carbon cycles. Spectroscopy, particularly optical hyperspectral reflectance coupled with machine learning, can provide rapid, efficient, and cost-effective quantification of SOC. However, how to exploit soil hyperspectral reflectance to predict SOC concentration, and the potential performance of airborne and satellite data for predicting surface SOC at large scales remain relatively underknown. Here, this study utilized a continental-scale soil laboratory spectral library (37,540 full-pedon 350–2500 nm reflectance spectra with SOC concentration of 0–780 g·kg –1 across the US) to thoroughly evaluate seven machine learning algorithms including Partial-Least Squares Regression (PLSR), Random Forest (RF), K-Nearest Neighbors (KNN), Ridge, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) along with four preprocessed spectra, i.e. original, vector normalization, continuum removal, and first-order derivative, to quantify SOC concentration. Furthermore, by using the coupled soil-vegetation-atmosphere radiative transfer model, we simulated twelve airborne and spaceborne hyper/multi-spectral remote sensing data from surface bare soil laboratory spectra to evaluate their potential for estimating SOC concentration of surface bare soils. Results show that LSTM achieved best predictive performance of quantifying SOC concentration for the whole data sets (R 2 = 0.96, RMSE = 30.81 g·kg –1 ), mineral soils (SOC ≤ 120 g·kg –1 , R 2 = 0.71, RMSE = 10.60 g·kg –1 ), and organic soils (SOC > 120 g·kg –1 , R 2 = 0.78, RMSE = 62.31 g·kg –1 ). Spectral data preprocessing, particularly the first-order derivative, improved the performance of PLSR, RF, Ridge, KNN, and ANN, but not LSTM or CNN. We found that the SOC models of mineral and organic soils should be distinguished given their distinct spectral signatures. Finally, we identified that the shortwave infrared is vital for airborne and spaceborne hyperspectral sensors to monitor surface SOC. This study highlights the high accuracy of LSTM with hyperspectral/multispectral data to mitigate a certain level of noise (soil moisture <0.4 m 3 ·m –3 , green leaf area < 0.3 m 2 ·m –2 , plant residue <0.4 m 2 ·m –2 ) for quantifying surface SOC concentration. Forthcoming satellite hyperspectral missions like Surface Biology and Geology (SBG) have a high potential for future global soil carbon monitoring, while high-resolution satellite multispectral fusion data can be an alternative.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Continental-scale niche differentiation of dominant topsoil archaea in drylands

Archaea represent a diverse group of microorganisms often associated with extreme environments. However, an integrated understanding of biogeographical patterns of the specialist Haloarchaea and the potential generalist ammonia-oxidizing archaea (AOA) across large-scale environmental gradients remains limited. Here, we hypothesize that niche differentiation determines their distinct distributions along environmental gradients. To test the hypothesis, we use a continental-scale research network including 173 dryland sites across northern China. Our results demonstrate that Haloarchaea and AOA dominate topsoil archaeal communities. As hypothesized, Haloarchaea and AOA show strong niche differentiation associated with two ecosystem types mainly found in China's drylands (i.e. deserts vs. grasslands), and they differ in the degree of habitat specialization. The relative abundance and richness of Haloarchaea are higher in deserts due to specialization to relatively high soil salinity and extreme climates, while those of AOA are greater in grassland soils. Our results further indicate a divergence in ecological processes underlying the segregated distributions of Haloarchaea and AOA. Haloarchaea are governed primarily by environmental-based processes while the more generalist AOA are assembled mostly via spatial-based processes. Our findings add to existing knowledge of large-scale biogeography of topsoil archaea, advancing our predictive understanding on changes in topsoil archaeal communities in a drier world.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Yield From Iowa's First Commercial Miscanthus Fields: Implications of Spatial Variability for Productivity and Sustainability Beyond Research Plots

The cultivation of sterile giant miscanthus (Miscanthus × giganteus, M × g) for bioenergy and bioproducts has expanded into grain-cropped land in the United States (US) as local markets developed for this high-yielding perennial grass (10–30 Mg DM ha −1 ). However, the magnitude of spatial and temporal variability in yield within US Corn Belt fields, along with impacts on economic return and sustainable land management, is poorly understood. This study established a diagnostic model relating remote sensing-derived vegetation indices to ground truth data from 105 hand-harvested stem biomass samples, which were strategically selected to represent the full range of vegetation index observations. The high-resolution satellite-sensed vegetation indices captured > 90% of the yield variation measured within fields. This model was then used to predict yield variability and assess economic performance across four of the first commercial M × g fields in the Corn Belt state of Iowa, US. Significant spatial variability in biomass dry matter (DM) yields (9.3–18.1 Mg DM ha −1 ) and net profits ($\$$83 to $\$$1211.5 ha −1 ) was observed. All fields were profitable in all site-years. When low profit occurred, it was explained by limited management experience of the crop in Iowa. The breakeven yield at a selling price of $\$$130 Mg −1 varied from 9.0–12.1 Mg ha −1 at 15% moisture content (7.6–10.3 Mg DM ha −1 ). Breakeven prices ranged from $\$$73 to $\$$122.4 Mg −1 , matching ranges used in the Department of Energy Billion Ton Report (US Department of Energy, 2023). Notably, M × g yield and profits were commensurate with grain crops particularly with favorable precipitation. This study provides insight on the M × g management “learning curve”, performance on marginal land and in drought conditions, and demonstrates that addressing yield gaps, reducing costs, and implementing precision agriculture strategies can enhance profitability. These findings emphasize the value of remote sensing technologies in guiding sustainable and competitive commercial-scale M × g production.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Vegetation Type and Decomposition Priming Mediate Brackish Marsh Carbon Accumulation Under Interacting Facets of Global Change

Coastal wetland carbon pools are globally important, but their response to interacting facets of global change remain unclear. Numerical models neglect species-specific vegetation responses to sea level rise (SLR) and elevated CO 2 (eCO 2 ) that are observed in field experiments, while field experiments cannot address the long-term feedbacks between flooding and soil growth that models show are important. Here, we present a novel numerical model of marsh carbon accumulation parameterized with empirical observations from a long-running eCO 2 experiment in an organic rich, brackish marsh. Model results indicate that eCO 2 and SLR interact synergistically to increase soil carbon burial, driven by shifts in plant community composition and soil volume expansion. Furthermore, newly parameterized interactions between plant biomass and decomposition (i.e. soil priming) reduce the impact of eCO 2 on marsh survival, and by inference, the impact of eCO 2 on soil carbon accumulation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy

A sustainable bioeconomy would require growing high-yielding bioenergy crops on marginal agricultural areas with minimal inputs. To determine the cost competitiveness and environmental sustainability of such production systems, reliably estimating biomass yield is critical. However, because marginal areas are often small and spread across the landscape, yield estimation using traditional approaches is costly and time-consuming. This paper demonstrates the (1) initial investigation of optical remote sensing for predicting perennial bioenergy grass yields at harvest using a linear regression model with the green normalized difference vegetation index (GNDVI) derived from Sentinel-2 imagery and (2) evaluation of the model’s performance using data from five U.S. Midwest field sites. The linear regression model using midsummer GNDVI predicted yields at harvest with R2 as high as 0.879 and a mean absolute error and root mean squared error as low as 0.539 Mg/ha and 0.616 Mg/ha, respectively, except for the establishment year. Perennial bioenergy grass yields may be predicted 152 days before the harvest date on average, except for the establishment year. The green spectral band showed a greater contribution for predicting yields than the red band, which is indicative of increased chlorophyll content during the early growing season. Although additional testing is warranted, this study showed a great promise for a remote sensing approach for forecasting perennial bioenergy grass yields to support critical economic and logistical decisions of bioeconomy stakeholders.

09 BIOMASS FUELS↗
DOE OSTI2025

Rising Water Levels and Vegetation Shifts Drive Substantial Reductions in Methane Emissions and Carbon Dioxide Uptake in a Great Lakes Coastal Freshwater Wetland

ABSTRACT Coastal freshwater wetlands are critical ecosystems for both local and global carbon cycles, sequestering substantial carbon while also emitting methane (CH 4 ) due to anoxic conditions. Estuarine freshwater wetlands face unique challenges from fluctuating water levels, which influence water quality, vegetation, and carbon cycling. However, the response of CH 4 fluxes and their drivers to altered hydrology and vegetation remains unclear, hindering mechanistic modeling. To address these knowledge gaps, we studied an estuarine freshwater wetland in the Great Lakes region, where rising water levels led to a vegetation shift from emergent Typha dominance in 2015–2016 to floating‐leaved species in 2020–2022. Using eddy covariance flux measurements during the peak growing season (June–September) of both periods, we observed a 60% decrease in CH 4 emissions, from 81 ± 4 g C m −2 in 2015–2016 to 31 ± 3 g C m −2 in 2020–2022. This decline was driven by two main factors: (1) higher water levels, which suppressed ebullitive fluxes via increased hydrostatic pressure and extended CH 4 residence time, enhancing oxidation potential in the water column; and (2) reduced CH 4 conductance through plants. Net carbon dioxide (CO 2 ) uptake decreased by 90%, from −267 ± 26 g C m −2 in 2015–2016 to −27 ± 49 g C m −2 in 2020–2022. Additionally, diel CH 4 flux patterns shifted, with a distinct morning peak observed in 2015–2016 but absent in 2020–2022, suggesting changes in plant‐mediated transport and a potential decoupling from photosynthesis. The dominant factors influencing CH 4 fluxes shifted from water temperature and gross primary productivity in 2015–2016 to atmospheric pressure in 2020–2022, suggesting an increased role of ebullition as a primary transport pathway. Our results demonstrate that changes in water levels and vegetation can substantially alter CH 4 and CO 2 fluxes in coastal freshwater wetlands, underscoring the critical role of hydrological shifts in driving carbon dynamics in these ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Canonical Vegetation Simulations

LANL employed the FIRETEC model to produce a suite of simulations of flow around trees and forests. The purpose of this suite of simulations is to provide detailed simulations of the effects of various types of trees and groups of trees including the mean velocity and turbulence fields. This suite includes various tree densities ranging from individual trees, wind breaks (i.e., lateral rows of trees), orchards, and forests of various densities. In addition to the simulations that we have already run, we have developed tools to facilitate more simulations in the future.

58 GEOSCIENCES↗