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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
NASA NTRS1994

Human impacts on soil carbon dynamics of deep-rooted Amazonian forests

Deforestation and logging degrade more forest in eastern and southern Amazonia than in any other region of the world. This forest alteration affects regional hydrology and the global carbon cycle, but our current understanding of these effects is limited by incomplete knowledge of tropical forest ecosystems. It is widely agreed that roots are concentrated near the soil surface in moist tropical forests, but this generalization incorrectly implies that deep roots are unimportant in water and C budgets. Our results indicate that half of the closed-canopy forests of Brazilian Amazonic occur where rainfall is highly seasonal, and these forests rely on deeply penetrating roots to extract soil water. Pasture vegetation extracts less water from deep soil than the forest it replaces, thus increasing rates of drainage and decreasing rates of evapotranspiration. Deep roots are also a source of modern carbon deep in the soil. The soils of the eastern Amazon contain more carbon below 1 m depth than is present in above-ground biomass. As much as 25 percent of this deep soil C could have annual to decadal turnover times and may be lost to the atmosphere following deforestation. We compared the importance of deep roots in a mature, evergreen forest with an adjacent man-made pasture, the most common type of vegetation on deforested land in Amazonia. The study site is near the town of Paragominas, in the Brazilian state of Para, with a seasonal rainfall pattern and deeply-weathered, kaolinitic soils that are typical for large portions of Amazonia. Root distribution, soil water extraction, and soil carbon dynamics were studied using deep auger holes and shafts in each ecosystem, and the phenology and water status of the leaf canopies were measured. We estimated the geographical distribution of deeply-rooting forests using satellite imagery, rainfall data, and field measurements.

Nepstad, Daniel C.↗
DOE OSTI2022

Improving and testing machine learning methods for benchmarking soil carbon dynamics representation of land surface models

Representation of soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon climate feedbacks. The magnitude of this uncertainty can be reduced by accurate representation of environmental controllers of SOC stocks in ESMs. In this study, we used data of environmental factors, field SOC observations, ESM projections and machine learning approaches to identify dominant environmental controllers of SOC stocks and derive functional relationships between environmental factors and SOC stocks. Our derived functional relationships predicted SOC stocks with similar accuracy as the machine learning approach. We used the derived relationships to benchmark the coupled model intercomparison project phase six ESM representation of SOC stocks. We found divergent environmental control representation in ESMs in comparison to field observations. Representation of SOC in ESMs can be improved by including additional environmental factors and representing their functional relationships with SOC consistent with observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Tracing priming effects in palsa peat carbon dynamics using a stable isotope-assisted metabolomics approach

Introduction: Peatlands store up to a third of global soil carbon, and in high latitudes their litter inputs are increasing and changing in composition under climate change. Although litter significantly influences peatland carbon and nutrient dynamics by changing the overall lability of peatland organic matter, the physicochemical mechanisms of this impact—and thus its full scope—remain poorly understood. Methods: We applied multimodal metabolomics (UPLC-HRMS, 1 H NMR) paired with 13 C Stable Isotope-Assisted Metabolomics (SIAM) to track litter carbon and its potential priming effects on both existing soil organic matter and carbon gas emissions. Through this approach, we achieved molecule-specific tracking of carbon transformations at unprecedented detail. Results: Our analysis revealed several key findings about carbon dynamics in palsa peat. Microbes responded rapidly to litter addition, producing a short-term increase in CO 2 emissions, fueled nearly exclusively by transformations of litter carbon. Litter inputs significantly contributed to the organic nitrogen pool through amino acids and peptide derivatives, which served as readily accessible nutrient sources for microbial communities. We traced the fate of plant-derived polyphenols including flavonoids like rutin, finding evidence of their degradation through heterocyclic C-ring fission, while accumulation of some polyphenols suggested their role in limiting overall decomposition. The SIAM approach detected subtle molecular changes indicating minimal and transient priming activity that was undetectable through conventional gas measurements alone. This transient response was characterized by brief microbial stimulation followed by rapid return to baseline metabolism. Pre-existing peat organic matter remained relatively stable; significant priming of its consumption was not observed, nor was its structural alteration. Discussion: This suggests that while litter inputs temporarily increase CO 2 emissions, they don’t sustain long-term acceleration of stored carbon decomposition or substantially decrease peat’s carbon store capacity. Our findings demonstrate how technological advancements in analytical tools can provide a more detailed view of carbon cycling processes in complex soil systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Across‐model spread and shrinking in predicting peatland carbon dynamics under global change

Abstract Large across‐model spread in simulating land carbon (C) dynamics has been ubiquitously demonstrated in model intercomparison projects (MIPs), and became a major impediment in advancing climate change prediction. Thus, it is imperative to identify underlying sources of the spread. Here, we used a novel matrix approach to analytically pin down the sources of across‐model spread in transient peatland C dynamics in response to a factorial combination of two atmospheric CO 2 levels and five temperature levels. We developed a matrix‐based MIP by converting the C cycle module of eight land models (i.e., TEM, CENTURY4, DALEC2, TECO, FBDC, CASA, CLM4.5 and ORCHIDEE) into eight matrix models. While the model average of ecosystem C storage was comparable to the measurement, the simulation differed largely among models, mainly due to inter‐model difference in baseline C residence time. Models generally overestimated net ecosystem production (NEP), with a large spread that was mainly attributed to inter‐model difference in environmental scalar. Based on the sources of spreads identified, we sequentially standardized model parameters to shrink simulated ecosystem C storage and NEP to almost none. Models generally captured the observed negative response of NEP to warming, but differed largely in the magnitude of response, due to differences in baseline C residence time and temperature sensitivity of decomposition. While there was a lack of response of NEP to elevated CO 2 (eCO 2 ) concentrations in the measurements, simulated NEP responded positively to eCO 2 concentrations in most models, due to the positive responses of simulated net primary production. Our study used one case study in Minnesota peatland to demonstrate that the sources of across‐model spreads in simulating transient C dynamics can be precisely traced to model structures and parameters, regardless of their complexity, given the protocol that all the matrix models were driven by the same gross primary production and environmental variables.

Hou, Enqing↗
DOE OSTI2023

Lipid–enhanced Oilcane does not impact soil carbon dynamics compared with wild–type Sugarcane

The carbon neutral potential of bioenergy relies in part on the ability of feedstocks to sequester carbon (C) in the soil. Sugarcane is one of the most widely used bioenergy crops, yet there remain unknowns about how it impacts soil C dynamics. In addition, Oilcane, a genetically modified version of Sugarcane has been produced to accumulate more energy-dense oils and less soluble lignin, which enhances conversion efficiency but may also impact soil C cycling. Thus, our objectives were to examine the impact of Sugarcane litter decomposition on soil C formation and losses and determine if the genetic modifications to produce Oilcane alter these dynamics. To do this, we incubated bagasse (processed stem litter) and leaf litter from Sugarcane and Oilcane in microcosms with forest soil for 11 weeks. We used differences in natural abundance δ 13 C between C 3 forest soil and C 4 litter to trace the fate of the litter into respiratory losses as well as stable and unstable soil C pools. Our results show that genetic modifications to Oilcane did not substantially alter soil C dynamics. Sugarcane and Oilcane litter both led to net soil C gains dominated by an accumulation of the added litter as unstable, particulate organic C (POC). Oilcane litter led to small but significantly greater net soil C gains than Sugarcane litter due to greater POC formation, but the formation of stable, mineral associated organic matter (MAOC) did not differ between crop types. Sugarcane and Oilcane had opposing effects on tissue type where Sugarcane bagasse formed more MAOC, while Oilcane leaves preferentially remained as POC which may have important management implications. These results suggest that genetic modifications to Sugarcane will not significantly impact soil C dynamics; however, this may not be universal to other crops particularly if modifications lead to greater differences in litter chemistry.

09 BIOMASS FUELS↗
DOE OSTI2025

On a simplified solution of climate-carbon dynamics in idealized flat10MIP simulations

Abstract. Idealized experiments with coupled climate-carbon Earth system models (ESMs) provide a basis for understanding the response of the carbon cycle to external forcing and for quantifying climate-carbon feedbacks. Here, we analyze globally-averaged results from idealized esm-flat10 experiments and show that most models exhibit a quasi-linear relationship between cumulative carbon uptake on land and in the ocean during a period of constant fossil fuel emissions of 10 Pg C yr−1. We hypothesize that this relationship does not depend on emission pathways. Further, as a simplification, we quantify the relationship between cumulative ocean carbon uptake and changes in ocean heat content using a linear approximation. In this way, changes in oceanic heat content and atmospheric CO2 concentration become interdependent variables, reducing the coupled temperature-CO2 system to just one differential equation. The equation can be solved analytically or numerically for the atmospheric CO2 concentration as a function of fossil fuel emissions. This approach leads to a simplified description of global carbon and climate dynamics, which could be used for applications beyond existing analytical frameworks.

Brovkin, Victor↗
DOE OSTI2025

Experimental Soil Warming Impacts Soil Moisture and Plant Water Stress and Thereby Ecosystem Carbon Dynamics

Experimental soil heating experiments have found a consistent increase in soil-surface CO 2 emissions ( F s ), but inconsistent soil organic carbon (SOC) responses. Interpretation of heating effects is complicated by spatial heterogeneity and soil moisture, nitrogen availability, and microbial and plant responses. Here we applied a mechanistic ecosystem model to interpret heating impacts on a California forest subjected to 1 m deep, 4°C heating. The model accurately simulated control-plot CO 2 fluxes, SOC stocks, fine root biomass, soil moisture, and soil temperature, and the observed increases in F s and decreases in fine root biomass. We show that a complex suite of interactions can lead to a consistent increase in F s (~17%) over the 5-year study period, with very small changes in SOC stocks (<1%). Modeled increases in leaf water stress from soil drying reduced GPP and NPP. The resulting reduction in leaf and fine root allocation increased fine root litter inputs to the soil and reduced root exudation. Soil heating led to about a 50% larger increase in root autotrophic respiration than in heterotrophic respiration, with the heating effect on both these fluxes decreasing over the simulation period. Increased heterotrophic respiration led to increased soil N availability and plant N uptake. These heating responses are mechanistically linked, of magnitudes that can affect ecosystem dynamics, and long-term observations of them are rarely made. Therefore, we conclude that a coupled observational and mechanistic modeling framework is needed to interpret manipulation experiments, and to improve projections of climate change impacts on terrestrial ecosystem carbon dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Diverse organic carbon dynamics captured by radiocarbon analysis of distinct compound classes in a grassland soil

Soil organic carbon (SOC) is a large, dynamic reservoir composed of a complex mixture of plant- and microbe-derived compounds with a wide distribution of cycling timescales and mechanisms. The distinct residence times of individual carbon components within this reservoir depend on a combination of factors, including compound reactivity, mineral association, and climate conditions. To better constrain SOC dynamics, bulk radiocarbon measurements are commonly used to trace biosphere inputs into soils and to estimate timescales of SOC cycling. However, understanding the mechanisms driving the persistence of organic compounds in bulk soil requires analyses of SOC pools that can be linked to plant sources and microbial transformation processes. Here, we adapt approaches, previously developed for marine sediments, to isolate organic compound classes from soils for radiocarbon ( 14 C) analysis. We apply these methods to a soil profile from an annual grassland in Hopland, California (USA), to assess changes in SOC persistence with depth (down to 1 m). We measured the radiocarbon values of water-extractable organic carbon (WEOC), total lipid extracts (TLEs), total hydrolyzable amino acids (AAs), and an acid-insoluble (AI) fraction from bulk and physically separated size fractions (< 2 mm, 2 mm–63 µm, and < 63 µm). Our results show that Δ 14 C values of bulk soil, size fractions, and extracted compound classes became more depleted with depth, and individual SOC components have distinct age–depth distributions that suggest distinguishable cycling rates. We found that AAs and TLEs cycle faster than the bulk soils and the AI fraction. The AI was the most 14 C-depleted fraction, indicating that it is the most chemically inert in this soil. Our approach enables the isolation and measurement of SOC fractions that separate functionally distinct SOC pools that can cycle relatively quickly (e.g., plant and microbial residues) from more passive or inert SOC pools (associated with minerals or petrogenic) from bulk soils and soil physical fractions. With the effort to move beyond SOC bulk analysis, we find that compound class 14 C analysis can improve our understanding of SOC cycling and disentangle the physical and chemical factors driving OC cycling rates and persistence.

58 GEOSCIENCES↗
DOE OSTI2023

Microbial Models for Simulating Soil Carbon Dynamics: A Review

Abstract Soils store the largest amount of carbon (C) in the biosphere, and the C pool in soil is critical to the global C balance. Numerous microbial models have been developed over the last few decades to represent microbial processes that regulate the responses of soil organic carbon (SOC) to climate change. However, the representation of microbial processes varies, and how microbial processes are incorporated into SOC models has not been well explored. Here, we reviewed 71 microbial models to characterize the microbial processes incorporated into SOC models and analyzed variations in mechanistic complexity. We revealed that (a) four processes (microbial‐mediated decomposition, mineral interaction, microbial necromass recycling, and active and dormant microbial dynamics) are commonly incorporated in microbial models, (b) ∼48% of models simulate only one microbial process (i.e., microbial‐mediated decomposition) and 35% of models simulate two microbial processes: for example, microbial‐mediated decomposition and mineral interaction, (c) more than 80% microbial models use nonlinear equations, such as forward Michaelis‐Menten kinetics, to represent SOC decomposition, (d) the concept of persistence of SOC due to its intrinsic properties has been replaced by organo‐mineral interaction (∼39% of microbial models) that protects SOC from decomposition, and (e) various temperature and moisture modifiers and pH effects have been used to explain the environmental effect on microbial processes. In the future, to realistically incorporate microbial processes into Earth System Models, it is imperative to identify experimental evidence on rate limitation processes and firmly ground model structure on the field and laboratory data.

Environmental Sciences & Ecology↗
DOE OSTI2023

Investigating permafrost carbon dynamics in Alaska with artificial intelligence

Abstract Positive feedbacks between permafrost degradation and the release of soil carbon into the atmosphere impact land–atmosphere interactions, disrupt the global carbon cycle, and accelerate climate change. The widespread distribution of thawing permafrost is causing a cascade of geophysical and biochemical disturbances with global impacts. Currently, few earth system models account for permafrost carbon feedback (PCF) mechanisms. This research study integrates artificial intelligence (AI) tools and information derived from field-scale surveys across the tundra and boreal landscapes in Alaska. We identify and interpret the permafrost carbon cycling links and feedback sensitivities with GeoCryoAI, a hybridized multimodal deep learning (DL) architecture of stacked convolutionally layered, memory-encoded recurrent neural networks (NN). This framework integratesin-situmeasurements and flux tower observations for teacher forcing and model training. Preliminary experiments to quantify, validate, and forecast permafrost degradation and carbon efflux across Alaska demonstrate the fidelity of this data-driven architecture. More specifically, GeoCryoAI logs the ecological memory and effectively learns covariate dynamics while demonstrating an aptitude to simulate and forecast PCF dynamics—active layer thickness (ALT), carbon dioxide flux (CO 2 ), and methane flux (CH 4 )—with high precision and minimal loss (i.e. ALT RMSE : 1.327 cm [1969–2022]; CO 2 RMSE : 0.697µmolCO 2 m −2 s −1 [2003–2021]; CH 4 RMSE : 0.715 nmolCH 4 m −2 s −1 [2011–2022]). ALT variability is a sensitive harbinger of change, a unique signal characterizing the PCF, and our model is the first characterization of these dynamics across space and time.

Environmental Sciences & Ecology↗
DOE OSTI2025

Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics

Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long-term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C-cycle processes. Bayesian calibration was conducted using quality-controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass-shrub mixture, and grass-tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R 2 > 0.6, RMSE <390 g C m -2 ) relative to net ecosystem exchange of CO 2 (NEE) (R 2 > 0.4, RMSE <180 g C m -2 ). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with R 2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long-term network-based monitoring of vegetation biomass, C fluxes, and SOC stocks.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2005

An Improved Analysis of Forest Carbon Dynamics using Data Assimilation

There are two broad approaches to quantifying landscape C dynamics - by measuring changes in C stocks over time, or by measuring fluxes of C directly. However, these data may be patchy, and have gaps or biases. An alternative approach to generating C budgets has been to use process-based models, constructed to simulate the key processes involved in C exchange. However, the process of model building is arguably subjective, and parameters may be poorly defined. This paper demonstrates why data assimilation (DA) techniques - which combine stock and flux observations with a dynamic model - improve estimates of, and provide insights into, ecosystem carbon (C) exchanges. We use an ensemble Kalman filter (EnKF) to link a series of measurements with a simple box model of C transformations. Measurements were collected at a young ponderosa pine stand in central Oregon over a 3-year period, and include eddy flux and soil C02 efflux data, litterfall collections, stem surveys, root and soil cores, and leaf area index data. The simple C model is a mass balance model with nine unknown parameters, tracking changes in C storage among five pools; foliar, wood and fine root pools in vegetation, and also fresh litter and soil organic matter (SOM) plus coarse woody debris pools. We nested the EnKF within an optimization routine to generate estimates from the data of the unknown parameters and the five initial conditions for the pools. The efficacy of the DA process can be judged by comparing the probability distributions of estimates produced with the EnKF analysis vs. those produced with reduced data or model alone. Using the model alone, estimated net ecosystem exchange of C (NEE)= -251 f 197g Cm-2 over the 3 years, compared with an estimate of -419 f 29gCm-2 when all observations were assimilated into the model. The uncertainty on daily measurements of NEE via eddy fluxes was estimated at 0.5gCm-2 day-1, but the uncertainty on assimilated estimates averaged 0.47 g Cm-2 day-1, and only exceeded 0.5gC m-2 day-1 on days where neither eddy flux nor soil efflux data were available. In generating C budgets, the assimilation process reduced the uncertainties associated with using data or model alone and the forecasts of NEE were statistically unbiased estimates. The results of the analysis emphasize the importance of time series as constraints. Occasional, rare measurements of stocks have limited use in constraining the estimates of other components of the C cycle. Long time series are particularly crucial for improving the analysis of pools with long time constants, such as SOM, woody biomass, and woody debris. Long-running forest stem surveys, and tree ring data, offer a rich resource that could be assimilated to provide an important constraint on C cycling of slow pools. For extending estimates of NEE across regions, DA can play a further important role, by assimilating remote-sensing data into the analysis of C cycles. We show, via sensitivity analysis, how assimilating an estimate of photosynthesis - which might be provided indirectly by remotely sensed data - improves the analysis of NEE.

Williams, Mathew↗
NASA NTRS2019

Modeling Phenological Controls on Carbon Dynamics in Dryland Sagebrush Ecosystems

Dryland ecosystems play an important role in determining how precipitation anomalies affect terrestrial carbon fluxes at regional to global scales. Thus, to understand how climate change may affect the global carbon cycle, we must also be able to understand and model its effects on dryland vegetation. Dynamic Global Vegetation Models (DGVMs) are an important tool for modeling ecosystem dynamics, but they often struggle to reproduce seasonal patterns of plant productivity. Because the phenological niche of many plant species is linked to both total productivity and competitive interactions with other plants, errors in how process-based models represent phenology hinder our ability to predict climate change impacts. This may be particularly problematic in dryland ecosystems where many species have developed a complex phenology in response to seasonal variability in both moisture and temperature. Here, we examine how uncertainty in key parameters as well as the structure of existing phenology routines affect the ability of a DGVM to match seasonal patterns of leaf area index (LAI) and gross primary productivity (GPP) across a temperature and precipitation gradient. First, we optimized model parameters using a combination of site-level eddy covariance data and remotely-sensed LAI data. Second, we modified the model to include a semi-deciduous phenology type and added flexibility to the representation of grass phenology. While optimizing parameters reduced model bias, the largest gains in model performance were associated with the development of our new representation of phenology. This modified model was able to better capture seasonal patterns of both leaf area index (R(exp 2) = 0.75) and gross primary productivity (R(exp 2) = 0.84), though its ability to estimate total annual GPP depended on using eddy covariance data for optimization. The new model also resulted in a more realistic outcome of modeled competition between grass and shrubs. These findings demonstrate the importance of improving how DGVMs represent phenology in order to accurately forecast climate change impacts in dryland ecosystems.

Ecosystem model↗
DOE OSTI2026

Accounting for linkages between wildfire-driven shifts in plant-microbial interactions and soil carbon dynamics in Arctic tundra

Increasing wildfire regimes in the rapidly changing Arctic tundra are altering the soil carbon budget through increased permafrost degradation, shrubs expansion, and shifts in microbial activities. Whether future arctic wildfires will result in net C losses or gains in the future will depend on complex biotic and abiotic interactions that regulate belowground C biogeochemical processes, including linkages among biotic communities. One important linkage is plant-microbe interactions. While these interactions are likely shaped or altered by wildfires, they remain little explored in the context of successional trajectories. Yet, incorporating plant-microbe interactions in frameworks for defining and understanding post-fire soil C trajectories is critical to predict belowground C responses to future tundra wildfires. Here, we provide examples of and discuss how fire-mediated changes in plant-soil-microbe (PSM) interactions can impact soil C dynamics in the Arctic tundra. We consider different impacts of wildfires on PSM interactions and their implications to soil C dynamics, as well as the nuances associated with particular wildfire regimes (severity and intensity) and successional timescales. We suggest that accounting for plant-microbial linkages in future wildfire-succession interactions frameworks can inform future experimental designs and reduce uncertainties in our ability to predict the net effect of Arctic wildfires on ecosystem C.

fungi, bacteria↗
NASA NTRS2005

Carbon Dynamics in Vegetation and Soils

The overall goals of CD-08 team in Phase I were to quantify the contributions of different components of the carbon cycle to overall ecosystem carbon balance in Amazonian tropical forests and to undertake process studies at a number of sites along the eastern LBA transect to understand how and why these fluxes vary with site, season, and year. We divided this work into a number of specific tasks: (1) determining the average rate (and variability) of tree growth over the past 3 decades; (2) determining age demographics of tree populations, using radiocarbon to determine tree age; (3) assessing the rate of production and decomposition of dead wood debris; (4) determining turnover rates for organic matter in soils and the mean age of C respired from soil using radiocarbon measurements; and (5) comparing our results with models and constructing models to predict the potential of tropical forests to function as sources or sinks of C. This report summarizes the considerable progress made towards our original goals, which have led to increased understanding of the potential for central Amazon forests to act as sources or sinks of carbon with altered productivity. The overall picture of tropical forest C dynamics emerging from our Phase I studies suggests that the fraction of gross primary production allocated to growth in these forests is only 25-30%, as opposed to the 50% assumed by many ecosystem models. Consequent slow tree growth rates mean greater mean tree age for a given diameter, as reflected in our measurements and models of tree age. Radiocarbon measurements in leaf and root litter suggest that carbon stays in living tree biomass for several years up to a decade before being added to soils, where decomposition is rapid. The time lags predicted from 14C, when coupled with climate variation on similar time scales, can lead to significant interannual variation in net ecosystem C exchange.

Trumbore, Susan↗
DOE OSTI2026

Contrasting carbon dynamics in grazed and flood-prone grasslands on mineral and degraded peat soils

Ecosystem-scale methane (CH₄) flux measurements from grazed grasslands remain scarce, despite their importance for understanding grassland contributions to the global carbon budget. In this study, we present full annual budgets of both carbon dioxide (CO₂) and CH₄ derived from eddy covariance measurements at two contrasting grazed grasslands: a floodplain grassland at Marchegg, Austria, and an intensively grazed pasture at Sherman Barn, California. By combining continuous, year-round observations of CO₂ and CH₄, this study provides a rare, comparative assessment of greenhouse gas dynamics across distinct climatic, hydrological, and management regimes. Our results highlight how environmental conditions, grazing intensity, and hydrology jointly regulate CO₂ exchange and CH₄ emissions, underscoring the importance of including methane alongside net ecosystem exchange when evaluating grassland carbon balances. At Marchegg, characterized by seasonal flooding and moderate horse grazing on mineral soils, the ecosystem acted as a weak carbon sink in 2024, with a GWP 100 of 42.2 ± 113.4 g CO₂ eq m⁻². Net CO₂ uptake (–27.3 ± 30.2 g C m⁻² yr⁻¹) was partly offset by CH₄ emissions (1.6 ± 0.04 g C m⁻² yr⁻¹), which were strongly linked to soil moisture and inundation events. In contrast, Sherman Barn, a cattle pasture on degraded peat soils, was a consistent carbon source in 2019, with a GWP 100 of 567.6 ± 120.5 g CO₂ eq m⁻², associated with high ecosystem respiration during summer. The site released 125.3 ± 32.2 g C m⁻² yr⁻¹ of CO₂ and 3 ± 0.06 g C m⁻² yr⁻¹ of CH₄. Across both sites, net ecosystem exchange was primarily linked to photosynthetically active radiation and vegetation greenness, while CH₄ fluxes were related to soil moisture rather than grazing intensity. Flooding at Marchegg reduced CO₂ uptake but enhanced CH₄ emissions, highlighting the critical role of flood timing within the growing season. Moreover, inundation appeared to suppress the spread of invasive plant species, emphasizing the ecological value of dynamic hydrological regimes. Together, these findings reveal the complexity of grassland carbon budgets and the need for site-specific, year-round CO₂ and CH₄ monitoring to inform climate-adaptive management strategies.

Carbon sequestration↗
DOE OSTI2024

Responses of Drying-rewetting (Transient Soil Moisture) and Steady State Soil Moisture Incubation on Soil Organic Carbon Dynamics in Three US Soils, 2017

This data set contains measurements of soil characteristics (aggregate size distribution and mean size, total aggregate associated carbon, extractable organic C, and microbial biomass C), microbial respiration, and soil metabolite concentrations from a transient and steady soil moisture incubation experiment using soils of different textures (sandy, loamy, and clayey). The study investigated mechanisms driving the Birch effect (increased carbon mineralization pulses with wetting following a drying period) in differing soil textures. Three different soils of distinctly different textures were collected from 0-15cm depth in Georgia (sandy, 2017-05-01), Missouri (loamy, 2017-06-14), and Texas (clayey, December 2017). Soils were incubated for 140 days with destructive harvests done on days 1, 29, 33, 56, 112, 116, and 140 in transient soil moisture incubation and on days 1, 33, 116, and 140 in steady state soil moisture incubation. This dataset contains six data files in comma separate (.csv) format. Additional metadata are provided: six data dictionaries and a file-level metadata file in comma separate (*.csv) format and a user guide in PDF (*.pdf) format.

1-Methyladenosine concentration↗
NASA NTRS2019

Modeling Phenological Controls on Carbon Dynamics in Dryland Sagebrush Ecosystems

Dryland ecosystems play an important role in determining how precipitation anomalies affect terrestrial carbonfluxes at regional to global scales. Thus, to understand how climate change may affect the global carbon cycle,we must also be able to understand and model its effects on dryland vegetation. Dynamic Global VegetationModels (DGVMs) are an important tool for modeling ecosystem dynamics, but they often struggle to reproduceseasonal patterns of plant productivity. Because the phenological niche of many plant species is linked to bothtotal productivity and competitive interactions with other plants, errors in how process-based models representphenology hinder our ability to predict climate change impacts. This may be particularly problematic in drylandecosystems where many species have developed a complex phenology in response to seasonal variability in bothmoisture and temperature. Here, we examine how uncertainty in key parameters as well as the structure ofexisting phenology routines affect the ability of a DGVM to match seasonal patterns of leaf area index (LAI) andgross primary productivity (GPP) across a temperature and precipitation gradient. First, we optimized modelparameters using a combination of site-level eddy covariance data and remotely-sensed LAI data. Second, wemodified the model to include a semi-deciduous phenology type and added flexibility to the representation ofgrass phenology. While optimizing parameters reduced model bias, the largest gains in model performance wereassociated with the development of our new representation of phenology. This modified model was able to bettercapture seasonal patterns of both leaf area index (R2=0.75) and gross primary productivity (R2=0.84), thoughits ability to estimate total annual GPP depended on using eddy covariance data for optimization. The new modelalso resulted in a more realistic outcome of modeled competition between grass and shrubs. These findingsdemonstrate the importance of improving how DGVMs represent phenology in order to accurately forecastclimate change impacts in dryland ecosystems.

Ecosystem model Phenology Parameter optimization E↗