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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Machine learning models inaccurately predict current and future high-latitude C balances

The high-latitude carbon (C) cycle is a key feedback to the global climate system, yet because of system complexity and data limitations, there is currently disagreement over whether the region is a source or sink of C. Recent advances in big data analytics and computing power have popularized the use of machine learning (ML) algorithms to upscale site measurements of ecosystem processes, and in some cases forecast the response of these processes to climate change. Due to data limitations, however, ML model predictions of these processes are almost never validated with independent datasets. To better understand and characterize the limitations of these methods, we develop an approach to independently evaluate ML upscaling and forecasting. We mimic data-driven upscaling and forecasting efforts by applying ML algorithms to different subsets of regional process-model simulation gridcells, and then test ML performance using the remaining gridcells. In this study, we simulate C fluxes and environmental data across Alaska using ecosys, a process-rich terrestrial ecosystem model, and then apply boosted regression tree ML algorithms to training data configurations that mirror and expand upon existing AmeriFLUX eddy-covariance data availability. We first show that a ML model trained using ecosys outputs from currently-available Alaska AmeriFLUX sites incorrectly predicts that Alaska is presently a modeled net C source. Increased spatial coverage of the training dataset improves ML predictions, halving the bias when 240 modeled sites are used instead of 15. However, even this more accurate ML model incorrectly predicts Alaska C fluxes under 21st century climate change because of changes in atmospheric CO 2 , litter inputs, and vegetation composition that have impacts on C fluxes which cannot be inferred from the training data. Our results provide key insights to future C flux upscaling efforts and expose the potential for inaccurate ML upscaling and forecasting of high-latitude C cycle dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Soil carbon stocks across tropical forests of Panama regulated by base cation effects on fine roots

We report that tropical forests are the most carbon (C)- rich ecosystems on Earth, containing 25–40% of global terrestrial C stocks. While large-scale quantifi- cation of aboveground biomass in tropical forests has improved recently, soil C dynamics remain one of the largest sources of uncertainty in Earth system models, which inhibits our ability to predict future climate. Globally, soil texture and climate predict B 30% of the variation in soil C stocks, so ecosystem models often predict soil C using measures of aboveground plant growth. However, this approach can underestimate tropical soil C stocks, and has proven inaccurate when compared with data for soil C in data-rich northern ecosystems. By quantifying soil organic C stocks to 1 m depth for 48 humid tropical forest plots across gradients of rainfall and soil fertility in Panama, we show that soil C does not correlate with common predictors used in models, such as plant biomass or litter production. Instead, a structural equation model including base cations, soil clay content, and rainfall as exogenous factors and root biomass as an endogenous factor predicted nearly 50% of the variation in tropical soil C stocks, indicating a strong indirect effect of base cation availability on tropical soil C storage. Including soil base cations in C cycle models, and thus emphasizing mechanistic links among nutrients, root biomass, and soil C stocks, will improve prediction of climate-soil feedbacks in tropical forests.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A multi-objective optimization model for cropland design considering profit, biodiversity, and ecosystem services

More sustainable agricultural methods are needed to alleviate the decreases in biodiversity and ecosystem services that have occurred because of industrial agriculture. One such method is the inclusion of alternative crops into croplands that can support biodiversity, reduce erosion and chemical runoff, and sequester carbon in the soil. However, the question of where such crops should be planted to balance competing economic and environmental objectives remains open. To this end, we develop a mixed-integer quadratically constrained program to optimize the layout of a cropland considering economic, biodiversity, greenhouse gas emissions, and water quality objectives. We include spatially varying fertilization as a decision variable in addition to crop establishment location. We further include the effect of core area and edges between different crops on biodiversity. To demonstrate the applicability of the model, we apply it to an example field, showing how the optimal cropland design changes as a decision-maker prioritizes different objectives and as edges have different impacts on biodiversity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Compound fire-drought regimes promote ecosystem transitions in Mediterranean ecosystems

Understanding ecosystem responses to compound disturbance regimes and the influence of specific sequences of events in determining ecosystem shifts remains a challenge. We use a modelling framework for Mediterranean-type ecosystems to assess the effects of fire–drought interactions on long-term vegetation dynamics and to identify disturbance-driven changes in trait composition (tree seeder vs. tree resprouter prevalence) and ecosystem state (forest vs. non-forest). Changes in tree seeder and the tree resprouter dominance show nonlinear, threshold-type trends over gradients of increasing compound disturbance frequency. Vegetation composition thresholds mostly occur in a narrow range of the compound fire–drought disturbance space. Additionally, trait compositional switches and the likelihood of sudden changes in ecosystem state are promoted by fire-drought interactions. Distinct sequences of disturbance events cause vegetation transitions, disrupting ecosystem resilience, even under moderate recurrence of individual disturbances. An extreme drought year followed by one or two large fire events promotes shifts from resprouter- to seeder dominance. Contrastingly, a large crown fire followed by an extreme drought promotes changes from seeder to resprouter dominance. This disturbance sequence is also a mechanism strong enough to trigger sudden shifts in ecosystem state (from forest to non-forest). Synthesis. Thresholds of change in vegetation composition occur over a narrow range of the modelled gradients of compound fire-drought recurrence, and the loss of ecosystem resilience is contingent on particular sequences of disturbance events. Overall, our findings highlight that disturbance interactions define the relative location of tipping points in ecosystem state, and that effects and feedbacks of compound disturbance regimes increase the long-term likelihood of sudden ecosystem shifts and, therefore, uncertainty in predicting vegetation state.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A Diffusion‐Based Uncertainty Quantification Method to Advance E3SM Land Model Calibration

Abstract Calibrating land surface models and accurately quantifying their uncertainty are crucial for improving the reliability of simulations of complex environmental processes. This, in turn, advances our predictive understanding of ecosystems and supports climate‐resilient decision‐making. Traditional calibration methods, however, face challenges of high computational costs and difficulties in accurately quantifying parameter uncertainties. To address these issues, we develop a diffusion‐based uncertainty quantification (DBUQ) method. Unlike conventional generative diffusion methods, which are computationally expensive and memory‐intensive, DBUQ innovates by formulating a parameterized generative model and approximates this model through supervised learning, which enables quick generation of parameter posterior samples to quantify its uncertainty. DBUQ is effective, efficient, and general‐purpose, making it suitable for site‐specific ecosystem model calibration and broadly applicable for parameter uncertainty quantification across various earth system models. In this study, we applied DBUQ to calibrate the Energy Exascale Earth System Model land model at the Missouri Ozark AmeriFlux forest site. Results indicated that DBUQ produced accurate parameter posterior distributions similar to those from Markov Chain Monte Carlo sampling but with 30 times less computing time. This significant improvement in efficiency suggests that DBUQ can enable rapid, site‐level model calibration at a global scale, enhancing our predictive understanding of climate impacts on terrestrial ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Implementation and evaluation of the unified stomatal optimization approach in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES)

Abstract. Stomata play a central role in regulating the exchange of carbon dioxide and water vapor between ecosystems and the atmosphere. Their function is represented in land surface models (LSMs) by conductance models. The Functionally Assembled Terrestrial Ecosystem Simulator (FATES) is a dynamic vegetation demography model that can simulate both detailed plant demographic and physiological dynamics. To evaluate the effect of stomatal conductance model formulation on forest water and carbon fluxes in FATES, we implemented an optimality-based stomatal conductance model – the Medlyn (MED) model – that simulates the relationship between photosynthesis (A) and stomatal conductance to water vapor (gsw) as an alternative to the FATES default Ball–Woodrow–Berry (BWB) model. To evaluate how the behavior of FATES is affected by stomatal model choice, we conducted a model sensitivity analysis to explore the response of gsw to climate forcing, including atmospheric CO2 concentration, air temperature, radiation, and vapor pressure deficit in the air (VPDa). We found that modeled gsw values varied greatly between the BWB and MED formulations due to the different default stomatal slope parameters (g1). After harmonizing g1 and holding the stomatal intercept parameter (g0) constant for both model formulations, we found that the divergence in modeled gsw was limited to conditions when the VPDa exceeded 1.5 kPa. We then evaluated model simulation results against measurements from a wet evergreen forest in Panama. Results showed that both the MED and BWB model formulations were able to capture the magnitude and diurnal changes of measured gsw and A but underestimated both by about 30 % when the soil was predicted to be very dry. Comparison of modeled soil water content from FATES to a reanalysis product showed that FATES captured soil drying well, but translation of drying soil to modeled physiology reduced the models' ability to match observations. Our study suggests that the parameterization of stomatal conductance models and current model response to drought are the critical areas for improving model simulation of CO2 and water fluxes in tropical forests.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Simulating Dynamic Roots in the Energy Exascale Earth System Land Model

Abstract Roots are important contributors to plant development, functioning to provide nutrients and water for plant growth. However, roots and their functions are often simplified in Earth system models, which limit the feedback of root foraging strategy on plant productivity, and their impacts on the carbon cycle. The goal of this study is to introduce a new method to resolve the vertical structure of roots over time. The method allows plasticity of rooting depth distribution under nonuniform profiles of water and nitrogen, which influences aboveground dynamics. The dynamic root model optimizes root distribution for both water and nitrogen uptake but gives priority to plant water demands. I implement this new method in the Energy Exascale Earth System model. The resulting root distribution maintains agreement with observations in most ecosystems and marginally improves the gross primary productivity estimated by the model, compared to satellite observations. Increases in gross primary productivity are simulated in desert and boreal ecosystems. However, the model does not capture deep roots in the dry tropics, and therefore, productivity losses are observed in parts of the Amazon and the African savannah. I discuss details of the model algorithm, along with some sensitivity studies that shed light on the model behavior in water‐limited ecosystems. The study shows that additional model processes, such as climate dependent root depth, root hydraulics, root form and function, and better nitrogen uptake, should be considered to improve the root water uptake in the Energy Exascale Earth System Land Model (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2018

The role of environmental driving factors in historical and projected carbon dynamics of wetland ecosystems in Alaska

Abstract Wetlands are critical terrestrial ecosystems in Alaska, covering ~177,000 km 2 , an area greater than all the wetlands in the remainder of the United States. To assess the relative influence of changing climate, atmospheric carbon dioxide (CO 2 ) concentration, and fire regime on carbon balance in wetland ecosystems of Alaska, a modeling framework that incorporates a fire disturbance model and two biogeochemical models was used. Spatially explicit simulations were conducted at 1‐km resolution for the historical period (1950–2009) and future projection period (2010–2099). Simulations estimated that wetland ecosystems of Alaska lost 175 Tg carbon (C) in the historical period. Ecosystem C storage in 2009 was 5,556 Tg, with 89% of the C stored in soils. The estimated loss of C as CO 2 and biogenic methane (CH 4 ) emissions resulted in wetlands of Alaska increasing the greenhouse gas forcing of climate warming. Simulations for the projection period were conducted for six climate change scenarios constructed from two climate models forced under three CO 2 emission scenarios. Ecosystem C storage averaged among climate scenarios increased 3.94 Tg C/yr by 2099, with variability among the simulations ranging from 2.02 to 4.42 Tg C/yr. These increases were driven primarily by increases in net primary production (NPP) that were greater than losses from increased decomposition and fire. The NPP increase was driven by CO 2 fertilization (~5% per 100 parts per million by volume increase) and by increases in air temperature (~1% per °C increase). Increases in air temperature were estimated to be the primary cause for a projected 47.7% mean increase in biogenic CH 4 emissions among the simulations (~15% per °C increase). Ecosystem CO 2 sequestration offset the increase in CH 4 emissions during the 21st century to decrease the greenhouse gas forcing of climate warming. However, beyond 2100, we expect that this forcing will ultimately increase as wetland ecosystems transition from being a sink to a source of atmospheric CO 2 because of (1) decreasing sensitivity of NPP to increasing atmospheric CO 2 , (2) increasing availability of soil C for decomposition as permafrost thaws, and (3) continued positive sensitivity of biogenic CH 4 emissions to increases in soil temperature.

Lyu, Zhou↗
NASA NTRS2016

2016 International Land Model Benchmarking (ILAMB) Workshop Report

As earth system models (ESMs) become increasingly complex, there is a growing need for comprehensive and multi-faceted evaluation of model projections. To advance understanding of terrestrial biogeochemical processes and their interactions with hydrology and climate under conditions of increasing atmospheric carbon dioxide, new analysis methods are required that use observations to constrain model predictions, inform model development, and identify needed measurements and field experiments. Better representations of biogeochemistryclimate feedbacks and ecosystem processes in these models are essential for reducing the acknowledged substantial uncertainties in 21st century climate change projections.

earth system models (ESMs)↗
DOE OSTI2023

Coupled Long-Term Experiment and Model Investigation of the Differential Response of Plants and Soil Microbes in a Changing Permafrost Tundra Ecosystem

The major research goal of this project was to understand and quantify the fate of carbon stored in permafrost ecosystems using a combination of field and laboratory experiments to measure and model isotope ratios and carbon fluxes in a tundra ecosystem exposed to experimental warming. Field measurements centered on a two-factor experimental warming to increase air and soil temperatures alone, and in combination, at a tundra field site in Alaska. A second manipulation of water table was embedded within the warming treatment such that both major environmental factors (temperature, moisture) controlling ecosystem carbon dynamics were experimentally altered. The experiment was interfaced with modeling activities using both data assimilation and forward modeling approaches. Models were used to make forecasts of ecosystem carbon dynamics beyond the time frame and environmental space of the experiment itself. This project was a follow-on to previous work that made use of the same field manipulation; here we continued the experimental manipulation in order to test new hypotheses regarding long-term effects of warming and permafrost thaw and subsequent changes in moisture availability on ecosystem carbon dynamics. These results were linked with ongoing synthesis and model intercomparison activities through the Permafrost Carbon Network, which led to additional synthesis publications as a result of collaboration and data sharing from this project. As key outcomes, this project has produced new papers published in the peer-reviewed literature, archived datasets used for model-data intercomparisons, trained graduate students and postdoctoral researchers, and has raised awareness about the vulnerability of permafrost carbon to a wider audience.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

Linkages between terrestrial ecosystems and the atmosphere

The primary research issue in understanding the role of terrestrial ecosystems in global change is analyzing the coupling between processes with vastly differing rates of change, from photosynthesis to community change. Representing this coupling in models is the central challenge to modeling the terrestrial biosphere as part of the earth system. Terrestrial ecosystems participate in climate and in the biogeochemical cycles on several temporal scales. Some of the carbon fixed by photosynthesis is incorporated into plant tissue and is delayed from returning to the atmosphere until it is oxidized by decomposition or fire. This slower (i.e., days to months) carbon loop through the terrestrial component of the carbon cycle, which is matched by cycles of nutrients required by plants and decomposers, affects the increasing trend in atmospheric CO2 concentration and imposes a seasonal cycle on that trend. Moreover, this cycle includes key controls over biogenic trace gas production. The structure of terrestrial ecosystems, which responds on even longer time scales (annual to century), is the integrated response to the biogeochemical and environmental constraints that develop over the intermediate time scale. The loop is closed back to the climate system since it is the structure of ecosystems, including species composition, that sets the terrestrial boundary condition in the climate system through modification of surface roughness, albedo, and, to a great extent, latent heat exchange. These separate temporal scales contain explicit feedback loops which may modify ecosystem dynamics and linkages between ecosystems and the atmosphere. The long-term change in climate, resulting from increased atmospheric concentrations of greenhouse gases (e.g., CO2, CH4, and nitrous oxide (N2O)) will further modify the global environment and potentially induce further ecosystem change. Modeling these interactions requires coupling successional models to biogeochemical models to physiological models that describe the exchange of water, energy, and biogenic trace gases between the vegetation and the atmosphere at fine time scales. There does not appear to be any obvious way to allow direct reciprocal coupling of atmospheric general circulation models (GCM's), which inherently run with fine time steps, to ecosystem or successional models, which have coarse temporal resolution, without the interposition of physiological canopy models. This is equally true for biogeochemical models of the exchange of carbon dioxide and trace gases. This coupling across time scales is nontrivial and sets the focus for the modeling strategy.

Bretherton, Francis↗
DOE OSTI2022

Microbial contribution to post-fire tundra ecosystem recovery over the 21st century

Abstract Tundra ecosystems have experienced an increased frequency of fire, and this trend is predicted to continue throughout the 21 st Century. Post-fire recovery is underpinned by complex interactions between microbial functional groups that drive nutrient cycling. Here we use a mechanistic model to demonstrate an acceleration of the nitrogen cycle post-fire driven by changes in niche space and microbial competitive dynamics. We show that over the first 5-years post-fire, fast-growing bacterial heterotrophs colonize regions of the soil previously occupied by slower-growing saprotrophic fungi. The bacterial heterotrophs mineralize organic matter, releasing nutrients into the soil. This pathway outweighs new sources of nitrogen and facilitates the recovery of plant productivity. We broadly show here that while consideration of distinct microbial metabolisms related to carbon and nutrient cycling remains rare in terrestrial ecosystem models, they are important when considering the rate of ecosystem recovery post-disturbance and the feedback to soil nutrient cycles on centennial timescales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Scaling Global Warming Impacts on Ocean Ecosystems: Lessons From a Suite of Earth System Models

An important technique used by climate modelers to isolate the impacts of increasing greenhouse gasses on Earth System processes is to simulate the impact of an abrupt increase in carbon dioxide. The spatial pattern of change provides a “fingerprint” that is generally much larger than natural variability. Insofar as the response to radiative forcing is linear (the impact of quadrupling CO 2 is twice the impact of doubling CO 2 ) this fingerprint can then be used to estimate the impact of historical greenhouse gas forcing. However, the degree to which biogeochemical cycles respond linearly to radiative forcing has rarely been tested. In this paper, we evaluate which ocean biogeochemical fields are likely to respond linearly to changing radiative forcing, which ones do not, and where linearity breaks down. We also demonstrate that the representation of lateral mixing by mesoscale eddies, which varies significantly across climate models, plays an important role in modulating the breakdown of linearity. Globally integrated surface rates of biogeochemical cycling (primary productivity, particulate export) respond in a relatively linear fashion and are only moderately sensitive to mixing. By contrast, the habitability of the interior ocean (as determined by hypoxia and calcite supersaturation) behaves non-linearly and is very sensitive to mixing. This is because the deep ocean, as well as certain regions in the surface ocean, are very sensitive to the magnitude of deep wintertime convection. The cessation of convection under global warming is strongly modulated by the representation of eddy mixing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

A Comparison of the Diel Cycle of Modeled and Measured Latent Heat Flux During the Warm Season in a Colorado Subalpine Forest

Abstract Precipitation changes the physiological characteristics of an ecosystem. Because land‐surface models are often used to project changes in the hydrological cycle, modeling the effect of precipitation on the latent heat flux λE is an important aspect of land‐surface models. Here we contrast conditionally sampled diel composites of the eddy‐covariance fluxes from the Niwot Ridge Subalpine Forest AmeriFlux tower with the Community Land Model (CLM, version 4.5). With respect to measured λE during the warm season: for the day following above‐average precipitation, λE was enhanced at midday by ≈40 W m −2 (relative to dry conditions), and nocturnal λE increased from ≈10 W m −2 in dry conditions to over 20 W m −2 in wet conditions. With default settings, CLM4.5 did not successfully model these changes. By increasing the amount of time that rainwater was retained by the canopy/needles, CLM was able to match the observed midday increase in λE on a dry day following a wet day. Stable nighttime conditions were problematic for CLM4.5. Nocturnal CLM λE had only a small (≈3 W m −2 ) increase during wet conditions, CLM nocturnal friction velocity u ∗ was smaller than observed u ∗ , and CLM canopy air temperature was 2°C less than those measured at the site. Using observed u ∗ as input to CLM increased λE ; however, this caused CLM λE to be increased during both wet and dry periods. We suggest that sloped topography and the ever‐present drainage flow enhanced nocturnal u ∗ and λE . Such phenomena would not be properly captured by topographically blind land‐surface models, such as CLM.

Burns, Sean P.↗
DOE OSTI2017

Enhanced water use efficiency in global terrestrial ecosystems under increasing aerosol loadings

Aerosols play a crucial role in the climate system, affecting incoming radiation and cloud formation. Based on a modelling framework that couples ecosystem processes with the atmospheric transfer of radiation, we analyze the effect of aerosols on surface incoming radiation, gross primary productivity (GPP), water losses from ecosystems through evapotranspiration (ET) and ecosystem water use efficiency (WUE, defined as GPP/ET) for 2003–2010 and validate them at global FLUXNET sites. The total diffuse radiation increases under relatively low or intermediate aerosol loadings, but decreases under more polluted conditions. We find that aerosol-induced changes in GPP depend on leaf area index, aerosol loading and cloudiness. Specifically, low and moderate aerosol loadings cause increases in GPP for all plant types, while heavy aerosol loadings result in enhancement (decrease) in GPP for dense (sparse) vegetation. On the other hand, ET is mainly negatively affected by aerosol loadings due to the reduction in total incoming radiation. Finally, WUE shows a consistent rise in all plant types under increasing aerosol loadings. Overall, the simulated daily WUE compares well with observations at 43 eddy-covariance tower sites (R 2 =0.84 and RMSE=0.01gC (kg H 2 O) -1 ) with better performance at forest sites. In addition to the increasing portions of diffuse light, the rise in WUE is also favored by the reduction in radiation- and heat-stress caused by the aerosols, especially for wet and hot climates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Genomes to ecosystem function: Targeting critical knowledge gaps in methanogenesis and translation to updated global biogeochemical models

Natural freshwater temperate wetland systems currently represent the largest natural source of atmospheric methane, but are relatively understudied using systems biology tools (e.g. meta-omics) compared to other high producing methane systems (e.g. peat, tropical, or reconstructed wetlands). Using field investigations at the NOAA operated sentinel site on Lake Erie, methane producing activities and responses to geochemical conditions will be determined along seasonal and spatial gradients (Objective 1). Here, a combination of high-throughput activity and gas measurements, combined with high-resolution systems biology and analytical methods, will provide in depth knowledge of the microbiological, chemical, and physical constraints on methane production in wetlands. Using laboratory microcosms, the formation of anoxic microsites and their capacity to facilitate methane production in wetland soils will be simulated (Objective 2). This objective will validate the findings from the field investigations, offering a more controlled environment for teasing out the role of different yet interrelated variables. Lastly, these field and laboratory data will be used for multi-scale, process-level evaluation of an ecosystem biogeochemical model that accommodates these newly identified processes and parameterizes representation of these processes along relevant environmental gradients (Objective 3).This research will identify multiple interacting geochemical, ecological, and metabolic constraints that are poorly understood, oversimplified, or missing in global biogeochemical methane models. This proposal targets the role of oxygen limitation on methane processes in soil domains, to improve reactive transport models of microbial carbon cycling across terrestrial-aquatic soils and generate data on nutrient cycling activities in Great Lake wetlands. This information could provide new insights into the microbial controllers of Lake Erie eutrophication.

59 BASIC BIOLOGICAL SCIENCES↗