BER Research Highlights

Search Date: December 11, 2019

6 Records match the search term(s):


August 24, 2019

New Approach for Studying How Microbes Influence Their Environment

A diverse group of scientists suggests a common framework and targeting of known microbial processes as a useful approach for understanding the role microbes play in how ecosystems function.

The Science
Co-authors of a paper in Nature Microbiology, including Pacific Northwest National Laboratory’s Emily Graham, propose a new conceptual research framework that would harness the ever-increasing wealth of information on microbiomes. This framework, offered as a new approach to formalizing inquiries into microbiome science, proposes to empirically link three distinct categories of microbiome characteristics to each other and to the broader ecosystem processes they affect. As a result, the framework would reveal how—at the ecosystem level—microorganisms influence the ecological systems they inhabit.

The Impact
The new conceptual framework, informed by decades of research on environmental microbiomes and ecosystem processes, offers a promising pathway for discovering empirical linkages between the microorganisms in an ecosystem and the processes of that ecosystem. The framework would also help focus future research on potential microbiome-ecosystem links that are most likely to be detected empirically.

Summary
Identifying relationships between microbiomes and the ecosystem-level processes they influence is an exceptionally hard research challenge. This situation exists because of the absence of a robust conceptual research framework that would help elucidate underlying causal mechanisms and an explosion in the availability of data on microbiomes in the natural environment. Current research frameworks for understanding the microbial role in ecosystem function are often limited in their applications because they do not align with mechanistic representations of microbial processes in models of ecosystem function.

Presently, causal relationships are implied yet rarely tested, and researchers mostly rely on identifying correlations between microbes and ecosystem properties. Correlative approaches limit the potential to expand the influence of a single microbiome-ecosystem relationship to additional systems, and they do not yield any information on mechanisms that can be transferred across systems. As a result, current frameworks often yield ambiguous results that fail to provide new insights into processes and blur the mechanisms by which microbiomes relate to system-level functioning.

The authors propose a new framework that targets microbial characteristics known to contribute to system-level processes of interest. The framework, intended to link measurable microbiome characteristics with ecosystem-level processes, is constructed based on three distinct categories of microbiome characteristics: microbial processes, microbial community properties, and microbial membership.

From there, the authors show how researchers can use existing methods of investigating microbial ecology to elucidate properties within each of these categories and to connect these three categories of microbial characteristics with each other.

Central to the framework is one particularly important idea: distinguishing microbial community properties that can be predicted (called community aggregated traits) and those that researchers are currently unable to be predict (called emergent properties).

Collectively, the framework introduces a new research paradigm for closing the gaps between empirical investigations and the ecosystem process models they seek to inform.

Contacts (BER PM)
Paul Bayer
U.S. Department of Energy Office of Science, Office of Biological and Environmental Research
Climate and Environmental Sciences Division (SC-23.1)
Subsurface Biogeochemical Research
paul.bayer@science.doe.gov

PI Contact
Emily B. Graham
Quantitative Ecosystem Ecologist, Pacific Northwest National Laboratory
emily.graham@pnl.gov

Funding
This work is a product of the Next Generation of Ecosystem Indicators Working Group, supported by the U.S. Geological Survey John Wesley Powell Center for Synthesis and Analysis. Preparation of this manuscript was supported by National Science Foundation DEB IOS #1456959 awarded to EKH. In addition, this work was also supported by the U.S. Department of Energy, Office of Biological and Environmental Research, as part of the Subsurface Biogeochemical Research Scientific Focus Area at Pacific Northwest National Laboratory.

Publication
Hall, E. K., E. S. Bernhard, R. L. Bier, M. A. Bradford, C. M. Boot, J. B. Cotner, P. A. del Giorgio, S. E. Evans, E. B. Graham, S. E. Jones, J. T. Lennon, K. J. Locey, D. Nemergut, B. B. Osborne, J. D. Rocca, J. S. Schimel, M. P. Waldrop, and M. W. Wallenstein. “Understanding how microbiomes influence the systems they inhabit.” Nature Microbiology 9, 977–82 (2018). [DOI: 10.1038/s41564-018-0201-z]

Topic Areas:

Division: SC-23.1 Climate and Environmental Sciences Division, BER


April 09, 2019

Improving Measurements of CO2 Fluxes from Landscapes

Understanding the links between instances of large swirling winds and CO2 fluxes is critical for closing the energy balance of the land surface and interpreting turbulent heat and carbon exchanges.

The Science
Turbulent vertical fluxes of heat, water vapor, and carbon dioxide (CO2) occur constantly between land surfaces and the atmosphere. For decades, measuring such fluxes has relied on eddy covariance (EC), a complex statistical technique. However, most studies using EC are unable to close the surface-energy balance between sensible and latent heat fluxes. This widely reported gap, known as the nonclosure problem of the surface energy balance, is commonly attributed to the influence of large-scale eddies of swirling wind on both kinds of heat fluxes.

A new paper by scientists at Washington State University and Pacific Northwest National Laboratory provides new insights into EC by investigating two understudied issues: (1) how CO2 fluxes are influenced by large eddies and (2) the mechanistic links between CO2 fluxes and energy balance nonclosure.

The results demonstrate, in part, that reductions in the magnitude of CO2 fluxes associated with large turbulent eddies are mechanistically linked to nonclosure of the surface energy budget.

The Impact
The research findings improve understanding of how nonclosure of the surface-energy balance impacts measurements of CO2 fluxes. They also provide direct evidence that further studies are needed to investigate how landscape heterogeneity—in this case, sagebrush terrain—influences CO2 fluxes.

Summary
The new study relies on a dataset collected by an EC flux system in a semi-arid sagebrush ecosystem in the Hanford Area of rural southeastern Washington. The research shows a link between nonclosure and reduced CO2 fluxes associated with large turbulent eddies. It attributes that link to the simultaneous influence of low-frequency motions on sensible and latent heat fluxes and on CO2 fluxes.

The researchers used a recently developed approach, ensemble empirical mode decomposition, to extract large eddies from the turbulence time series. Then they analyzed the impacts of amplitude and phase differences on flux contribution.

One challenge in this work was identifying occasional spectral gaps, especially under unstable atmospheric conditions when convective motions tend to overlap the scales between large eddies and small eddies. Based on previous work by these scientists, the authors defined large eddies as the sum of a certain number of oscillatory components that are largely responsible for the run-to-run variations in fluxes. There was no surprise at the nonclosure of the surface energy balance and therefore biases in CO2 fluxes. However, the researchers found that the energy balance closure ratio decreased as atmospheric instability increased. The underlying causes of that remain unclear. Work on finding those causes is underway.

The research team, which also includes researchers from Lanzhou University in China, collected the high-quality data from three eddy covariance flux sites within the Hanford Area.

Contacts (BER PM)
Paul Bayer
U.S. Department of Energy Office of Science, Office of Biological and Environmental Research
Climate and Environmental Sciences Division (SC-23.1)
Subsurface Biogeochemical Research
paul.bayer@science.doe.gov

(PI Contact)
Heping Liu
Washington State University
heping.liu@wsu.edu

Funding
This work was supported by the U.S. Department of Energy Office of Biological and Environmental Research (BER) as part of BER’s Subsurface Biogeochemical Research program (SBR) at Pacific Northwest National Laboratory.  

Publications
Gao, Z., H. Liu, J. E. C. Missik, J. Yao, M. Huang, X. Chen, E. Arntzen, and D. P. McFarland. “Mechanistic links between underestimated CO2 fluxes and non-closure of the surface energy balance in a semi-arid sagebrush ecosystem.” Environmental Research Letters 14(4), 044016 (2019). [DOI: 10.1088/1748-9326/ab082d]

Related Links

Topic Areas:

Division: SC-23.1 Climate and Environmental Sciences Division, BER


April 01, 2019

Multiomics Data Are Key to Advancing Reactive Transport Models

An overview of recent advances in reactive transport models identifies multiomics data as the “current frontier” for understanding system-scale microbial behavior and dynamics.

The Science
Reactive transport models (RTMs) are used to describe and predict the distribution of chemicals in time and space, in both marine and terrestrial (surface and near-surface) environments where microbially mediated processes govern biogeochemical patterns. Yet, challenges exist in modeling microbially driven systems, as well as in integrating data across the vast range of scales relevant to models of biogeochemical cycling.

In the April 2019 topical issue of the journal Elements on reactive transport modeling, Tim Scheibe of Pacific Northwest National Laboratory (PNNL) and coauthor Chistof Meile of the University of Georgia discuss common approaches that have been used to incorporate microbial community interactions and their influence on geochemical processes in RTMs, as well as future opportunities to leverage new instrument and data capabilities—including multiomics—to create new and more realistic modeling approaches.

In particular, the authors argue that RTMs with multiomics will help advance understanding of how complex microbial communities respond to environmental changes. These new models will also help identify microbial impacts on local and global elemental cycling, the fate of contaminants, redox transformations, and other processes mediated by microorganisms.

The Impact
Integrating multiomics data into RTMs will improve predictive understanding of critical watershed processes such as carbon and nitrogen cycling within those watersheds and more broadly. Modeling informed by multiomics will also reveal how critical microbial processes change in response to environmental perturbations—an urgent imperative for watersheds subject to increasingly frequent or sustained perturbations.

Summary
Representation of microbial processes in RTMs has advanced significantly over the past few decades, accounting for dynamic changes in biomass, functional regulation in response to environmental changes, and thermodynamic constraints. Current RTMs represent microbial functions with greater process fidelity and reduced empiricism.

The authors say that incorporating multiomics data is a current frontier in RTMs, and offers great potential for improving scientific understanding of microbial processes and predictive modeling. To that end, they are engaged in research to integrate complex metagenomics, metabolomics, and other omics data into reaction network models. In turn, these can be linked with state-of-the-art RTMs to simulate system-scale behavior.

In the article, the authors introduce relevant case studies and discuss ways to integrate multiomics data to inform and validate RTMs. Their results advance and enhance those modeling capabilities by identifying and promoting how to integrate multiomics data into microbial models.

The result, the authors say, will be an improved predictive understanding of critical watershed processes such as carbon and nitrogen cycling within specific watersheds and more broadly. Modeling informed by multiomics will also reveal how critical microbial processes change in response to environmental perturbations.

Funded by the Department of Energy’s (DOE) Biological and Environmental Research (BER) program, this article addresses BER’s mission to advance predictive understanding of how hydro-biogeochemically complex watersheds function by promoting a vision of microbial process modeling informed by omics data. The article also promotes the use of DOE-funded capabilities such as DOE’s Systems Biology Knowledgebase (KBase), and DOE user facilities such as the Environmental Molecular Sciences Laboratory (EMSL) and Joint Genome Institute.

With support from DOE’s Subsurface Biogeochemical Research (SBR) program, Scheibe and fellow scientists recently organized a  workshop to build a community of researchers around these ideas and to promote new advancements.

Contacts (BER PM)
Paul Bayer
U.S. Department of Energy Office of Science, Office of Biological and Environmental Research
Climate and Environmental Sciences Division (SC-23.1)
Subsurface Biogeochemical Research and DOE Environmental Molecular Sciences Laboratory
paul.bayer@science.doe.gov

PI Contact
Timothy D. Scheibe
Pacific Northwest National Laboratory
Tim.Scheibe@pnnl.gov

Funding
U.S. Department of Energy, Office of Science, Biological and Environmental Research (BER), Subsurface Biogeochemical Research (SBR) program.

Publication
Meile, C., and T. D. Scheibe. “Reactive transport modeling of microbial dynamics.” Elements 15(2), 111–16 (2019). [DOI: 10.2138/gselements.15.2.111]

Related Links

Topic Areas:

Division: SC-23.1 Climate and Environmental Sciences Division, BER


March 15, 2019

Assessing Sources of Uncertainty in Predictions from a Reactive Transport Model

Researchers used Bayesian networks to develop a new method for measuring and ranking which components contribute the most uncertainty to outputs from a reactive transport model.

The Science
A multi-institutional team of scientists developed a new sensitivity analysis framework using Bayesian networks to quantify which parameters and processes in complex multiphysics models are least understood. The method can guide continued development and refinement of predictive models of environmental systems by highlighting which components of complex systems require enhanced characterization data to reduce uncertainty.

The Impact
Sensitivity analysis is a numerical tool used to identify important parameters and processes that contribute to the overall uncertainty in model outputs. This new research applies a Bayesian network approach to sensitivity analysis frameworks. This approach increases the flexibility and power of the sensitivity analysis by quantifying the uncertainty contribution from a variety of controlling factors and ranking them; the results can better inform decisions on where to focus resources to improve the predictive capability of various multiphysics models.

Summary
Numerical modeling is an important tool for predicting the future behavior of complex systems that impact the environment and for managing natural resources. For example, Pacific Northwest National Laboratory researchers are developing numerical models to study the factors that control the exchange of river and groundwater in the Hanford Reach, the last free-flowing stretch of the Columbia River that defines the north and east boundaries of the Department of Energy’s Hanford Site.

Predictive uncertainty is inevitable in numerical models of systems such as the Hanford Reach because of the complex hydrological and biogeochemical properties of the natural system and limited site characterization data. To effectively and efficiently reduce predictive uncertainty with limited resources, researchers perform sensitivity analysis to rank the importance of different uncertainty sources that contribute to overall uncertainty in model predictions.

Current state-of-the-art sensitivity analysis frameworks are unable to describe the entire range of uncertainty sources involved in predictive models of complex systems. The integration of Bayesian network-based methods into these frameworks allows the full representation of uncertainty sources and the relationships between them, opening the door to performing sensitivity analysis on complex systems. For example, the networks allow researchers to computationally and graphically understand how uncertainty in one node of the network, or group of nodes, propagates through a network and impacts a model’s overall predictive uncertainty.

The authors implemented their Bayesian network–based method on a real-world biogeochemical model of the groundwater–surface water interface within the Hanford Site’s 300 Area. They used the framework to run model simulations to predict how factors such as variation in river stage under future climate scenarios and the release or damming of water in upstream hydroelectric dams would contribute to variations in groundwater–surface water exchange and impact biogeochemical processes that affect the rate of organic carbon consumption.

The team found that groundwater flow and reactive transport processes contribute most significantly to the predictive uncertainty in carbon consumption rate, and that future states of the climate, which defines the system’s driving forces, were less significant. Further analysis of the uncertainty contributed by groundwater flow processes revealed that the geological structural information, such as the thickness of the confining layer between the river and groundwater, was more important than the within-formation permeability field in controlling the flow processes.

The Bayesian network–based methodology in this research was implemented on a complex biogeochemical model of the Hanford Site 300 area, but it is mathematically rigorous and generally applicable for reducing uncertainty in a wide range of Earth system models.

BER PM Contact
Paul Bayer
U.S. Department of Energy, Office of Biological and Environmental Research Paul.Bayer@science.doe.gov
SC-23.1

PI Contact
Xingyuan Chen
Pacific Northwest National Laboratory
Xingyuan.Chen@pnnl.gov

Funding
Funding for this research came from the Department of Energy’s Office of Science, Office of Biological and Environmental Research, Pacific Northwest National Laboratory Subsurface Biogeochemical Research Science Focus Area.

Publication
Dai, H., X. Chen, M. Ye, X. Song, G. Hammond, B. Hu, and J. M. Zachara. “Using Bayesian networks for sensitivity analysis of complex biogeochemical models.” Water Resources Research 55(4), 3541–55 (2019). [DOI: 10.1029/2018WR023589]

Topic Areas:

Division: SC-23.1 Climate and Environmental Sciences Division, BER


February 13, 2019

New Model Shows Hydrologic Exchange Is Primarily Controlled by the Thickness of Permeable Riverbank Sediments 

A novel three-dimensional groundwater model reveals the roles of dynamic flow conditions, river morphology, and subsurface hydrogeology in controlling hydrologic exchange flows along a large dam-regulated river corridor.

The Science
Hydrologic exchange flows (HEFs) between groundwater and river water increase the contact between river water and subsurface sediments, and, thereby, play a critical role in biogeochemical and ecological functions along river corridors. In a recent study led by Pin Shuai and Xingyuan Chen at Pacific Northwest National Laboratory (PNNL), a multi-institutional team of researchers found that the dominant factors controlling the hydro-geochemical signatures of HEFs along a dam-regulated river reach are river channel morphology and a river channel’s subsurface hydrogeology. These features were found to control the locations of high exchange flow rates—that is, likely “hot spots” of biogeochemical activity. They also found that the magnitude and timing of river stage fluctuations, caused by dam operations, controlled hydrological “hot moments,” when biogeochemical activity was likely to be high.

The Impact
This research improves scientific understanding of hydro-geomorphic controls on HEFs at river-reach scale under high-frequency flow variations, an important issue in an era of increased worldwide interest in building hydropower dams. The paper also demonstrates the influences of river water intrusion on the migration of groundwater contaminant plumes—particularly for contaminant sources located within the preferential flow path shaped by ancient, deep river remnants called paleochannels. Importantly, the paper’s modeling approach and main findings are transferrable to other river corridor systems that experience regular, periodic fluctuations.

Summary
HEFs across the interface of a river and its aquifer have important implications for biogeochemical processes and for contaminant plume migration in river corridors, including those that are increasingly regulated by dams across the world. Yet little is known about the hydro-geomorphic factors that control the dynamics of HEFs under dynamic flow conditions.

To help close that knowledge gap, this follow-up study to Song et al. (2018) expands the model domain from a two-dimensional (2D) transect to a simulated 3D river corridor. In this new paper, the modeling domain now covers the entire Hanford Reach of the Columbia River. The results demonstrate large spatial and temporal variability in exchange flow magnitude and direction in response to dynamic river flow conditions. The study also highlights the role of upstream dam operations in enhancing the exchange between river water and groundwater. In turn, that enhanced exchange posits a strong potential influence on associated biogeochemical processes and on the fate and transport of groundwater contaminant plumes in river corridors.

This is the first study to mechanistically simulate, at relatively fine resolution, reach-scale hydrologic exchange as it is influenced by dynamic river-stage variations, channel morphology, and subsurface hydrogeology. Because of complex geologic and dynamic flow boundary conditions, the authors faced a great challenge in running their large numerical model (60 x 60 km) using relatively fine model resolution.

However, they were able to develop a large groundwater model using PFLOTRAN, developed by the U.S. Department of Energy (DOE), a next-generation, massively parallel, reactive flow and transport simulator. This scheme, typically employed to simulate the migration of contaminants in groundwater, enabled researchers to use reasonably fine grids (100 m horizontally and 2 m vertically), while at the same time simulating the complexity of a large field setting. To perform their simulations, the researchers employed resources from DOE’s National Energy Research Scientific Computing Center (NERSC).

In all, the PNNL-led research aligns with DOE’s mission to provide next-generation science-based models of watershed systems. The next step, already underway, is to study the effect of dam operations on river corridor thermal regimes and the resulting implications for river ecology.

Contacts (BER PM)
Paul Bayer
U.S. Department of Energy Office of Science, Office of Biological and Environmental Research
Climate and Environmental Sciences Division (SC-23.1)
Subsurface Biogeochemical Research
paul.bayer@science.doe.gov

PI Contact
Xingyuan Chen
Pacific Northwest National Laboratory
Xingyuan.Chen@pnnl.gov

Funding
This research was supported by the U.S. Department of Energy (DOE), Office of Science, Office of Biological and Environmental Research (BER), as part of the Subsurface Biogeochemical Research Scientific Focus Area (SFA) at Pacific Northwest National Laboratory (PNNL). 

Publication
Shuai, P., X. Chen, X. Song, G. E. Hammond, J. Zachara, P. Royer, H. Ren, W. A. Perkins, M. C. Richmond, and M. Huang. “Dam operations and subsurface hydrogeology control dynamics of hydrologic exchange flows in a regulated river reach.”  Water Resources Research 55(4), 2593–612 (2019). [DOI: 10.1029/2018WR024193]

Related Links

https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018WR024193

https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2018WR022586

Topic Areas:

Division: SC-23.1 Climate and Environmental Sciences Division, BER


January 07, 2019

Effects of Water Flow Variation in Large Rivers Exacerbated by Drought

Model shows frequent fluctuation in river flows, caused by dam operations, lead to greater changes in water temperature and biogeochemical reaction rates in river sediments.

The Science
Biogeochemical activity in the hyporheic zone (HZ), sediments where the flowing waters of a river mix with shallow groundwater, supports many of the biological processes that occur within a watershed. Through the creation of a cross sectional (2-D) model of the Columbia River Hanford Reach’s HZ, PNNL researchers, led by Xuehang Song and Xingyuan Chen, found that low flow conditions contribute to warmer waters in the HZ. This, in turn, increases the rate of biogeochemical activity in the sediments. Long-term analysis shows this effect is exacerbated during times of drought.

The Impact
Thermal and biogeochemical dynamics in the HZ are important to fluvial ecology, such as thermal refugia for fish spawning and growth, benthic food production, and nitrate removal. These results can enable natural resource managers to more accurately assess the ecological consequences of long-term frequent water flow variation in riverine systems. In turn, this information will inform dam operations in the context of river and watershed management planning.

Summary
Studies of thermal changes in HZs have largely focused on short-term analysis of steady state flow conditions in smaller streams. This study is among the first to model and conduct field analyses in a large river system with high frequency in flow variation. Large fluctuations in water flow levels are a common phenomenon in most river systems with hydroelectric dam operations. To assess the long-term impact of these fluctuations, PNNL researchers created a cross sectional (2-D) thermal-hydro-biogeochemical model of the Columbia River Hanford Reach’s HZ with data supported by field monitoring.

Researchers assessed multiple years’ worth of flow level fluctuation data seeking the most powerful variations, signals unique to dam operations. Inland ground water monitoring data was also used to track the hydraulic gradients driving flow in and out of the HZ. By comparing natural variations against dam-induced differences in flow level, the researchers tracked, over time, the change in temperature, carbon consumption, and other biogeochemical-relevant variables.

Through numerical simulation the model shows a long-term persistent cold-water zone in the riverbed after winter, verified by observational data from a multi-depth thermistor array. Frequent stage fluctuations when the mean flow level is low-particularly under drought conditions during summer and early fall-enhanced heat exchange between the river and the HZ, reaching a maximum temperature difference between 5° to 10°C. All biogeochemical reactions in the HZ were enhanced by increasing nutrient supply and creating more oxygenated conditions. Total carbon consumption, a primary indicator of biogeochemical activities in the HZ, increased by almost 20%. In addition, the model demonstrated that the variable properties of riverbed sediment, such as permeability, influence water residence times and nutrient supplies by controlling flow paths. These variables also determine the spatial distribution of biogeochemical reaction hot spots in the HZ.

Already working towards further improvements to this model, PNNL researchers are expanding the scope of their work from one 2-D cross sectional analysis to a 3-D analysis of the entire Columbia River Hanford Reach.

BER PM Contact
Paul Bayer, SC-23.1
David Lesmes, SC-23.1

PI Contact
Xingyuan Chen
Pacific Northwest National Laboratory
Xingyuan.Chen@pnnl.gov

Funding
Funding for this research came from the DOE Office of Science Office of Biological and Environmental Research’s  Subsurface Biogeochemical Research program for the PNNL Subsurface Biogeochemical Research SFA.

Publications
Song, X., X. Chen, J. Stegen, G. Hammond, H-S Seob, H. Dai, E. Graham, and J. Zachara. “Drought Conditions Maximize the Impact of High-Frequency Flow Variations on Thermal Regimes and Biogeochemical Function in the Hyporheic Zone.” Water Resources Research 54(10), 7361-7382 (2018). [DOI: 10.1029/2018WR022586]

Topic Areas:

Division: SC-23.1 Climate and Environmental Sciences Division, BER