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When a pinch is problematic: Detecting pertechnetate in groundwater

pertechnetate

A PNNL researcher holds a redox sensor in the project’s lab in the Radiochemical Processing Laboratory.  Andrea Starr | PNNL

PNNL develops an effective tool for measuring a tricky contaminant

March 30, 2020
March 30, 2020
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Imagine trying to detect and measure a pinch of salt in an Olympic-size swimming pool. Now pretend the tools you are using don’t work well. Some can detect the salt but can’t tell you how much is in there, and others confuse salt with chlorine.

Now swap the swimming pool for a source of groundwater and the salt for a radioactive contaminant called pertechnetate.

ACS Journal Pertechnetate
The future of groundwater contamination measurement? The large thiol claws of PNNL’s subsurface probe with custom gold tips detect and measure pertechnetate in aqueous environments. Cover illustration by Rose Perry, PNNL

Pertechnetate is a byproduct of nuclear waste. If it ends up where it is not supposed to be—like, in groundwater—it could impact human health, which is why researchers and regulators keep a close lookout for it. The environmental safety limits for pertechnetate are roughly equivalent to a pinch of salt in an Olympic pool. And there are only a few technologies to measure it, each with limitations.

PNNL research tackles this challenge with new technology to detect and accurately measure pertechnetate at super low levels in groundwater. This research, “Redox-Based Electrochemical Affinity Sensor for Detection of Aqueous Pertechnetate Anion,” was the cover article for the March 2020 edition of ACS Sensors (DOI: 10.1021/acssensors.9b01531). 

Why it matters: The Environmental Protection Agency drinking water standard for pertechnetate is 0.000000052 grams per liter (that’s roughly 1/6000th the weight of a single poppy seed). While techniques exist for detection of pertechnetate in the environment, many have their drawbacks. PNNL’s technology can accurately measure low levels of pertechnetate in groundwater. Additionally, this proof of concept has the potential to be applied to other target contaminants simultaneously, increasing efficiency for environmental sensing.

Summary: The new technology acts like a coin counter, but at a microscopic level. It sorts one type of chemical from another, providing the total amount of a target chemical at the end. The tool uses custom probes with a gold electrode that only allows the target groundwater contaminants to stick while the other chemicals bounce off.

Sulfur likes to bind to gold and it also tends to react with pertechnetate, making sulfur-containing compounds an ideal intermediate in tool development. The sulfur sticks to the gold probe, then reacts with the pertechnetate, which forms a precipitate. The precipitate inhibits an electric current pulsing through the probe, providing an inverse measurement of pertechnetate concentration.

What’s Next: While this work was specifically focused on pertechnetate, there is potential to expand the technology to simultaneous multiple targets with the goal of increasing the efficiency of environmental measurements.

Sponsors: This research was funded by the Laboratory Directed Research and Development program at PNNL and by the Deep Vadose Zone program under the U.S. Department of Energy’s (DOE’s) Office of Environmental Management. Part of this research was performed at the Environmental Molecular Sciences Laboratory, a national user facility at PNNL managed by the DOE Office of Biological and Environmental Research.

PNNL Research Team: Sayandev Chatterjee, Meghan S. Fujimoto, Yingge Du, Gabriel B. Hall, Nabajit Lahiri, Eric D. Walter, Libor Kovarik. ACS Sensors cover illustration by Rose Perry, PNNL.

 

March 27, 2020

Tracking the Behavior of a Uranium Plume

View of Columbia River

Field research coupled with three-dimensional modeling are used to predict how groundwater and river exchange influence a contaminant plume.

March 16, 2020
March 16, 2020
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The Science
A recent paper published in Water Resources Research found that the spatial variability of subsurface sediments, and seasonal fluctuations in a river’s water level, influences the behavior of a uranium contaminant plume, particularly in rivers influenced by dams.

The research team created a complex numerical model and ran a 5-year simulation of the exchange between surface water and groundwater in a portion of the Columbia River at the Hanford Site. Model accuracy was evaluated using detailed water sampling data from the site, distributed in space and time. Both the model and data from site samples showed that the uranium can travel via multiple paths of river water and groundwater exchange.

The Impact
Predicting and modeling the evolution of a contaminant plume in a river corridor subject to rising and falling water levels from upstream dam operations is challenging. Factors such as seasonal dam discharge variation, the permeability of surface and subsurface materials, and changes in water chemistry, make river corridors a complex environment to study. This study is one of a few that have used a highly detailed three-dimensional modeling approach to simulate the migration of contaminants as influenced by the hydrologic exchanges between surface and subsurface waters at a large spatial scale. High-resolution monitoring, to ground truth the model’s accuracy, provided researchers a robust evaluation of the model’s predictive capabilities. Ultimately, the methods and findings in this study provide a foundation for designing future modeling and monitoring research to assist in environmental management and decision making.

Heatmap image of plume
Study site showing a) the uranium plume within Hanford’s 300 area, along the Columbia River, and b) the groundwater-surface water study site and water topography. Wells sampled in this study are marked with red circles.

Summary
A team of scientists led by John Zachara of Pacific Northwest National Laboratory examined the impact of river water and groundwater exchange in relation to a uranium contaminant plume migration at Hanford, Washington. The goal was to develop and improve the predictive understanding of hydrologic exchange flows and their role in changing river corridor biogeochemistry.

The team used an innovative combination of field sampling and three-dimensional mathematical models to investigate how river stage variation over seasons and subsurface hydrogeology interact to influence subsurface contaminant migration. In this work, the authors note that there are very few studies that model solute transport or plume behavior during dynamic hydrologic exchange in large river corridors, and that the model they developed can be applied to other, similar riverine systems.

According to both their model and data, river water exchange with groundwater in large, gravel-bed river corridors may create a wide interaction zone, which is different from most headwater systems. Water level variations in dam-regulated river corridors lead to changing flow directions, velocities, and sediment compositions, that influence contaminant plume behavior. The residence time and transport distance of intruded river water is controlled by both river stage and subsurface hydrogeologic features.

Funded by the Department of Energy’s Biological and Environmental Research (BER) program, this work addresses DOE’s mission to improve the predictive understanding of how watershed systems respond to environmental perturbations caused by changes in water availability/quality, land use/vegetation cover, and inorganic element/contaminant loading. Researchers used the DOE-funded NERSC, National Energy Research Scientific Computing Center, user facility in their model development.

Contact
John Zachara, Pacific Northwest National Laboratory, john.zachara@pnnl.gov

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

J.M. Zachara, C. Xingyuan, S. Xuehang, P. Shuai, C. Murray, C. T. Resch. “Kilometer‐scale hydrologic exchange flows in a gravel‐bed river corridor and their implications to solute migration.” Water Resources Research, e02851-18 (2020). DOI: 10.1029/2019WR025258

Microbial diversity influences nitrogen cycling in rivers

Image of streambed

Seasonal changes affect microbiome communities, genes, and subsurface biogeochemical pathways differently

March 4, 2020
March 4, 2020
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The Science
DOE researchers investigated the role of microbial genetic diversity in two major subsurface biogeochemical processes: nitrification and denitrification. Results show that across different seasons only a few microbe species, namely Nitrosoarchaeum, carry out nitrification functions—demonstrating high resistance to environmental change. However, denitrification genes, which are more broadly distributed in the community, displayed a variety of diversity patterns and abundance dynamics—demonstrating greater microbial interactions as conditions change.

Nitrogen cycling in hyporheic zone
Figure shows nitrogen transformations in the hyporheic zone, where a vast microbiome community influences nutrient cycling. Upper layers, closer to the riverbed contain more oxygen and organic matter. Under these conditions nitrification (orange arrows) occurs. Microbes transform the nitrogen from organic matter through a variety of steps and ultimately deplete the oxygen. As oxygen depletes, denitrification (blue arrows) further transforms nitrogen, resulting in an electron acceptor for catabolism of organic matter.

The Impact
There is little research connecting microbiomes at the genetic level to hydrobiogeochemical modeling. This research helps broaden collective knowledge for a better understanding of the pathways affected by environmental changes. For example, under extreme environmental conditions an entire biochemical pathway could be altered or eliminated if a single step has low genetic diversity such that its loss could not be replaced.

Summary
The Pacific Northwest National Laboratory research team, led by Bill Nelson, found that major environmental processes—specifically nitrification and denitrification—are maintained through a variety of diversity strategies. Historically, the bulk of biogeochemical research has focused on microbial communities at the organismal level. But this research focused on the importance of genetic distribution and diversity.

In their recent PLoS ONE paper, the researchers discuss the roles microbes play in ecological functions; the novelty of the genetic makeup of these microbes; and future research opportunities to determine which organisms are genetically expressing nitrogen cycling functions.

The novelty of this study comes from examining the temporal dynamics of diversity at the gene level. To evaluate all genes in the nitrification and denitrification pathways, novel Hidden Markov Models (HMMs) were developed that can recognize the broad diversity found in environmental samples. They found that while different environmental conditions impair microbiome growth and the gene expression of some populations, at the same time, it can stimulate others. High biodiversity at the organism or genetic level creates more resiliency, and the microbiome community can respond more rapidly to environmental changes.

Contact
Bill Nelson, Pacific Northwest National Laboratory, William.Nelson@pnnl.gov

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

W.C. Nelson, E.B. Graham, A.R. Crump, S.J. Fansler, E.V. Arntzen, D.W. Kennedy, J.C. Stegen, “Distinct temporal diversity profiles for nitrogen cycling genes in a hyporheic microbiome”. PLoS ONE 15(1) e0228165 (2020). [DOI: 10.1371/ journal.pone.0228165]

Creating better models to predict subsurface water flow and transport

river soil

New framework improves the predictions of subsurface sediment permeability

February 19, 2020
February 17, 2020
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The Science
Co-authors of a paper in Water Resources Research led by PNNL researchers developed a new iterative data assimilation framework to more accurately describe the permeability of subsurface sediments in numerical models when using facies, a system that classifies dissimilar sediments into distinct geological units that share important features of interest to modelers. The iterative framework applies data from field observations and experiments to inform the delineation of facies at the start of each model run. Further refinements are achieved at each iteration through the application of statistical constraints that maintain geologic continuity among adjacent locations.

Distribution of Facies
Spatial distribution of three facies (red, yellow and blue colors) in a 2D vertical cross section of a 3D case. Figures show the new method provides a more accurate and continuous estimation of facies distribution compared to the conventional method. White colors in the figures are bore samples and black dots are the conditioning points selected by the new method.

The Impact
Spatial distribution of three facies (red, yellow and blue colors) in a 2D vertical cross section of a 3D case. Figures show the new method provides a more accurate and continuous estimation of facies distribution compared to the conventional method. White colors in the figures are bore samples and black dots are the conditioning points selected by the new method.

More realistic numerical representations of the permeability of subsurface sediments lead to improved predictions of groundwater flow and the concentration of constituents that are transported with the flow. The data assimilation framework can also be applied to estimate other subsurface properties from field measurements, or from data from other systems such as watersheds, as long as they can be categorized into a few discrete representative units.

Summary
Observational data on subsurface permeability is limited for most watersheds because of the impracticality of digging enough boreholes or wells to capture the heterogeneous nature of the subsurface environment. To solve for this limitation, researchers have widely adopted approaches that estimate permeability from field experiments such as a) measuring how water levels at a cluster of wells change when water is pumped at a nearby well, or b) monitoring how quickly a tracer released at one well reaches other wells in the aquifer. The U.S. Department of Energy’s Hanford 300 Area Integrated Field Research Challenge site, for example, is well characterized from data assimilation methods that were used to understand the long-term persistence of nuclear fuel fabrication wastes disposal from 1943 to 1975.

The use of a facies approach to segment the subsurface reduces complexity in numerical models by grouping heterogeneous sediments into distinct homogenous units defined by hydraulic, physical and or chemical properties. A major difficulty with existing facies-based approaches in numerical models is that each facies is treated as its own, independent unit. Therefore, these models fail to capture the spatial continuity of subsurface sediments. The authors of this paper developed a framework that maintains continuity between neighboring facies in numerical models and thus better reflects true subsurface geology, and thereby groundwater movement. The improvements come from an iterative data assimilation approach that incorporates direct and indirect data about subsurface permeability gathered from field observations and experiments at the start of each model run as well as the application of statistical constraints about subsurface geology. The data assimilation and statistical constraint steps are re-imposed for each iteration, leading to refined facies delineation. This framework reduces uncertainty about the spatial distribution of sediment types in the subsurface, which results in more accurate predictions of groundwater flow and constituent transport.

The authors evaluated the performance of the new framework on a two-dimensional, two-facies model and a three-dimensional, three-facies model of DOE’s well-characterized Hanford 300 Area that were conceptualized from borehole and field tracer experiments. The results of the research shows that the framework can identify facies spatial patterns and reproduce tracer breakthrough curves with much improved accuracy over facies-based approaches that lack spatial continuity constraints. With additional data, the authors say that the framework can also be used to categorize biogeochemical reactive units in an aquifer.

Contact
Xingyuan Chen, Earth Scientist, Xingyuan.Chen@pnnl.gov

Funding
Funding for this research came from DOE Office of Science BER, PNNL Subsurface Biogeochemical Research SFA.

Song, X., Chen, X., Ye, M., Dai, Z., Hammond, G., And Zachara, J.M. (2019). Delineating facies spatial distribution by integrating ensemble data assimilation and Indicator Geostatistics with level-set transformation. Water Resources Research, 55. https://doi.org/10.1029/2018WR023262

March 9, 2019
JANUARY 21, 2020
Web Feature

Forensic Proteomics: Beyond DNA Profiling

A new book by PNNL biochemist Erick Merkley details forensic proteomics, a technique that directly analyzes proteins in unknown samples, in pursuit of making proteomics a widespread forensic method when DNA is missing or ambiguous.

Fire Increases Ecosystem Vulnerability to Future Disturbance Events

Image of forest fire

Burned landscapes are hit harder by extreme rain than unscathed ones.

October 4, 2019
October 4, 2019
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The Science         
New work from a team including researchers Vanessa Garayburu-Caruso, Swatantar Kumar, and corresponding author Emily Graham at Pacific Northwest National Laboratory (PNNL), and Joseph Knelman at the University of Colorado, examines how back-to-back extreme events can affect a forest landscape. They find that a forest fire leaves marks far deeper than the destruction visible on the surface, making the soil more vulnerable to damage from subsequent flooding.

The Impact
This study is one of a few in an emerging field of investigation that is able to capture ecosystem effects of multiple disturbances in natural settings. It bridges scientific disciplines by linking changes in soil chemistry, microbiome structure, and biogeochemical function using methods from ecological theory.

Summary
Extreme natural events are often thought to be in isolation from each other—a big wildfire in one season, heavy rains in another. But as climate change makes such disturbances more frequent and intense, ecosystems are likely to face chains of disturbance events in relatively quick succession, with one instance affecting the ability to recover from the next.  The compounding effects of multiple disturbances on ecosystem health remain poorly understood, since the unpredictability of natural events makes them challenging to study.

To better understand the issue, the researchers repeatedly collected soil samples in Boulder, Colorado’s Four Mile Canyon for over three years after a major wildfire. At the 37-month mark, an extreme precipitation event dropped more than 400 millimeters of rain within a week. Samples were collected from an undisturbed forest landscape and an adjacent fire-disturbed landscape, allowing the researchers to investigate the combined effects of multiple disturbances in comparison to a landscape experiencing flooding only. Researchers assessed the samples’ soil edaphic properties (moisture, pH, percent nitrogen, and percent carbon); bacterial community composition and assembly; and soil enzyme activities. They found that previous fire exposure caused forests to be more strongly affected by a subsequent flooding event than unburned forests. This was driven by increases in pH, shifts in microbiome structure, and increased microbial investment in nitrogen versus carbon cycling.

The team is also investigating compounding disturbances using the Columbia River as a model system. River stage variation in the Columbia causes frequent wetting and drying of the shoreline that provides a natural laboratory for investigating compounding disturbances. Results are expected in the fall of fiscal year 2020.

Contact
Emily B. Graham, Quantitative Ecosystem Ecologist, emily.graham@pnnl.gov

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

J.E. Knelman, S.K. Schmidt, V. Garayburu-Caruso, S. Kumar, E.B. Graham, “Multiple, Compounding Disturbances in a Forest Ecosystem: Fire Increases Susceptibility of Soil Edaphic Properties, Bacterial Community Structure, and Function to Change with Extreme Precipitation Event.” Soil Systems (2019) 3(2):40.

Effects of Water Flow Variation in Large Rivers Exacerbated by Drought

Large river flowing

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.

September 9, 2019
September 6, 2019
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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.

Figure shows the location of monitoring sites as well as a cross section of the Columbia River Hanford Reach’s hyporheic zone.
Figure shows the location of monitoring sites as well as a cross section of the Columbia River Hanford Reach’s hyporheic zone.

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 100C. 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.

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

Funding
Funding for this research came from the DOE Office of Science BER, PNNL Subsurface Biogeochemical Research SFA.

X. Song, 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 (2018).

Gaps Identified to Improve Accuracy of CO2 Fluxes Measured By Eddy Covariance Systems

Eddy covariance diagram

Understanding the links between the energy balance non-closure caused by large eddies and CO2 fluxes is critical for interpreting turbulent heat and carbon exchanges.

September 9, 2019
September 6, 2019
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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 fail to balance the available energy of both sensible and latent heat fluxes. This widely-reported gap, known as the non-closure problem of the surface energy balance, is commonly attributed to the influence of large-scale eddies 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 under-studied issues: how CO2 fluxes are influenced by large eddies, and the mechanistic links between CO2 fluxes and energy balance non-closure.

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

The Impact
The paper improves the understanding of how non-closure of the surface-energy balance impacts measurements of CO2 fluxes. It also provides direct evidence that further studies are needed to investigate how landscape heterogeneity—sagebrush terrain, in the case of this paper—influences CO2 fluxes.

Summary
The new study relies on a dataset collected by an EC eddy covariance flux system in a semi-arid sagebrush ecosystem in the Hanford Area of rural southeastern Washington. The research shows a link between non-closure 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 a previous study of theirs, 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 non-closure 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, the authors say, is underway.

The authors, who also include researchers from Lanzhou University in China, collected their high-quality data from three eddy covariance flux sites within the Hanford area.

PI Contact
Heping Liu, Washington State University, heping.liu@wsu.edu
Zhongming Gao, Washington State University, heping.liu@wsu.edu
Maoyi Huang, Pacific Northwest National Laboratory, Maoyi.Huang@pnnl.gov

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

Gao, Z., Liu, H. Missik, J. E. C. Missik, J. Yao, M. Huang, X. Chen, E. Arntzen, and D.P. McFarland. (2019) “Mechanistic links between underestimated CO2 fluxes and non-closure of the surface energy balance in a semi-arid sagebrush ecosystem.” Environmental Research Letters, 14 044016, open access.

Key to Understanding How Watersheds Function Lies in the Sediment

Photo of river's edge

Researchers categorized and mapped riverbed sediments along the Columbia River to understand sediment impacts on water exchange and chemical activity

September 9, 2019
September 6, 2019
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The Science
Co-authors of a paper in Hydrological Processes led by PNNL researchers Zhangshuan Hou, Timothy Scheibe, and Christopher Murray, produced a map that identifies different classes of sediments which compose the riverbed along the Hanford Reach of the Columbia River. These sediment classes, called facies, have distinct textures that play important roles in surface water/groundwater exchanges and biogeochemical activity.

Figure shows a map of riverine facies based on simulated shear stress for a 7 kilometer stretch in the Hanford Reach of the Columbia River.
Figure shows a map of riverine facies based on simulated shear stress for a 7 kilometer stretch in the Hanford Reach of the Columbia River.

The Impact
The riverbed sediments along the Hanford Reach of the Columbia River are strongly heterogeneous, making it challenging to incorporate their complexity in predictive models. This research categorized the sediments into classes facies to reduce the complexity of the heterogeneous system to classes with distinct sediment texture that correspond to variations in hydrologic properties. The riverine facies enable more accurate modeling of hydrologic exchange flows and biogeochemical processes.

Summary
In the Hanford Reach of the Columbia River, the texture of sediments on the riverbed have a strong influence on the exchange of groundwater and surface water and is associated with elevated levels of biogeochemical activity. This layer of sediments is strongly heterogeneous, making it a challenge to model, for example, the environmental impact of increased river flows.

To overcome this type of challenge in aquifers, researchers often turn to facies, a sediment classification scheme that groups complex geologic materials into a set of discrete classes according to distinguishing features and can be used to assign heterogenous material properties to grid cells of numerical models.

The usefulness of the facies approach, however, hinges on the ability to relate facies to quantitative properties needed for flow and reactive transport modeling. Previous research has shown that the grain size distribution of sediments in the riverbed is associated with properties of interest to the exchange of groundwater and surface water and related biogeochemical activity. Direct observational data on grain size distribution in the Hanford Reach of the Columbia River, however, is limited to selected locations with inadequate spatial coverage and resolution.

Therefore, to map facies in the Hanford Reach of the Columbia River, the authors integrated high-resolution observations such as the river geomorphology, depth, slope and signs of erosion with numerical simulations of historical river flows such as floods that are known to shape sediment texture by washing rocks and pebbles downstream. The team used machine learning models to determine which factors have the best correspondence with distinct distributions of sediment texture, creating a facies map with four classes of sediment textures that correspond to variations in hydrologic properties.

The authors say that the identification and mapping of facies in the Hanford Reach of the Columbia River will enable more accurate modeling of the system behavior, leading to more robust predictions, for example, of the fate and migration of groundwater contaminant plumes from nuclear waste materials produced and stored at the Hanford Site during the Cold War era as well as recent agricultural practices.

Contact
Zhangshuan Hou, Pacific Northwest National Laboratory, Zhangshuan.Hou@pnnl.gov

Funding
Funding for this research came from DOE Office of Science BER, PNNL Subsurface Biogeochemical Research SFA.

Hou, Z., Scheibe, TD, Murray, CJ, et al. Identification and mapping of riverbed sediment facies in the Columbia River through integration of field observations and numerical simulations. Hydrological Processes. 2019; 33: 1245–1259.

New Approach to Assess Uncertainty in Predictions from Reactive Transport Models

Image of computer model code

Researchers used Bayesian networks to develop a new method to measure and rank which components of numerical models contribute the most uncertainty to model outputs.

September 9, 2019
September 6, 2019
Highlight

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 multi-physics 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.

Bayesian network process flow
Figure shows a Bayesian network developed for groundwater biogeochemical reactive transport modeling. Rectangles are nodes grouped into the driving force (climate) and three physical processes. Ovals represent the deterministic nodes linked to their parents through physical laws.

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 contribution of uncertainty from a variety of controlling factors and ranking them, which can better inform decisions on where to focus resources in order to improve the predictive capability of a variety of multi-physics 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, PNNL 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 DOE Hanford Site.

Predictive uncertainty is inevitable in numerical models of systems such as the Hanford Reach because of the complexity of the hydrologic and biogeochemical properties of the natural system and the limited site characterization data available. 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 perform 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 the overall predictive uncertainty of a model.

The authors implemented their method based on Bayesian networks 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 the future states of the climate, which defines the driving forces of the system, 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.

While the Bayesian-network based methodology in this research was implemented on a complex biogeochemical model of the Hanford Site 300 area, the authors say it is mathematically rigorous and generally applicable to reduce uncertainty in a wide range of Earth system models.

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

Funding
Funding for this research came from DOE Office of Science BER, PNNL Subsurface Biogeochemical Research SFA.

Dai, H., Chen, X., Ye, M., Song, X., Hammond, G., Hu, B., & Zachara, J.M. (2019). Using Bayesian networks for sensitivity analysis of complex biogeochemical models. Water Resources Research, 55.

Multi-omics Data are Key to Advancing Reactive Transport Models

Microscope image of microbes

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

September 9, 2019
September 6, 2019
Highlight

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 inintegrating 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, and future opportunities to leverage new instrument and data capabilities—including multi-omics—to create new and more realistic modeling approaches.

Reactive transport model
Workflow of microbial reactive transport modeling that includes three key elements of omics-data integration (1) model formulation, (2) parameterization, and (3) validation. Metagenomes and metabolites inform what microbial reactions must be considered. These workflows are typically iterative, with model results guiding additional data collection and model updates.

In particular, the authors argue that omics-informed microbial models 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 omics data into RTMs will improve predictive understanding of critical watershed processes such as carbon and nitrogen cycling within those watersheds and more broadly. Omics-informed modeling 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 multi-omics 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 in order to simulate system-scale behavior.

In the article, the authors introduce relevant case studies and discuss ways to integrate multi-omics data to inform and validate RTMs. Their results advance and enhance those modeling capabilities by identifying and promoting how to integrate multi-omics 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. Omics-informed modeling will also reveal how critical microbial processes change in response to environmental perturbations.

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.

Meile, C. and Scheibe, T.D. 2019. Reactive Transport Modeling of Microbial Dynamics, Elements, 15(2): 111-116, doi: 10.2138/gselements.15.2.111

New Model Shows Hydrologic Exchange Flow in Large Rivers Mostly Determined by Subsurface Hydrogeology

Columbia River Hanford Reach

A novel 3-D 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.

September 9, 2019
September 6, 2019
Highlight

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

Model of geologic layers
(a) Model domain showing numerous geologic layers. (b) Cross-section showing complexity of model

The Impact
This research improves scientific understanding of hydrogeomorphic controls on HEFs at river-reach scale under high-frequency flow variations, an important issue in an era of energetic dam-building worldwide.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 hydrogeomorphic 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 2-D transect to a simulated 3-D river corridor. In this new paper, the 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 (100m horizontally and 2m vertically), while at the same time simulating the complexity of a large field setting. To perform their simulations, the researchers also employed resources from the National Energy Research Scientific Computing Center.

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.

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 Biological and Environmental Research (BER), as part of the Subsurface Biogeochemical Research Scientific Focus Area (SFA) at Pacific Northwest National Laboratory (PNNL).

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.” 2019. Water Resources Research. doi: 10.1029/2018WR024193

New Model to Predict Hydrologic Exchange Fluxes at River Reach Scale

Image of river

Fluid dynamics modeling along a 7-kilometer river reach reveals factors controlling large-scale hydrologic exchange fluxes.

September 9, 2019
September 6, 2019
Highlight

The Science
Hydrologic exchange fluxes (HEFs) between rivers and surrounding subsurface environments strongly influence water temperatures and biogeochemical processes. Yet, quantitative measures of their effects on the strength and direction of such exchanges in large rivers are lacking. A study reported in Hydrological Processes, led by scientists at Pacific Northwest National Laboratory (PNNL), demonstrates the efficacy of a new coupled surface and subsurface fluid dynamics model in quantifying HEFs at kilometer scales.

Computer model layers
This figure was created by the model to show the interaction of velocity and pressure measurements in the surface flow region, the river bed surface, and in the subsurface flow region.

The Impact
In a world where dam-regulated river corridors are increasingly common, quantifying HEFs and their effects at river-reach scales is vitally important in protecting water quality and ecosystem health. Through 3D application of computational fluid dynamics (CFD) modeling, combined with uncertainty quantification tools, the new model can quantify HEFs in a large-scale river channel extending 1-km wide and 7-km long. This a dramatic improvement over traditional simulations, which (at most) model just a few hundred meters of river corridor.

Summary
HEFs are critical to shaping hydrological and biogeochemical processes along river corridors. Yet, in current research, numerical modeling studies to quantify riverine HEFs are typically confined to local-scale simulations in which the river is a few meters wide and up to a just few hundred meters long. Even then, such studies are challenging because of high computational demands and the complexity of riverine geomorphology and subsurface geology. In addition, there are limitations in field accessibility, and the physical demands of labor-intensive data collection along river shorelines.

A new model, developed by a multi-institutional team, addresses these challenges. Their recently published paperin Hydrological Processes demonstrates a new coupled surface and subsurface water flow model that can be applied at large scales.

The new model was validated against field-scale observations—including velocity measurements from an acoustic Doppler current profiler, a set of temperature profilers installed across the riverbed to measure vertical HEFs, and simulations from PFLOTRAN, a reactive transport model. Then, along a 7-km segment of the Columbia River which experiences high dam-regulated flow variations, the model was used to systematically investigate how HEFs could be influenced by surface water fluid dynamics, subsurface structures, and hydrogeological properties.

The simulations demonstrated that reach-scale HEFs are dominated by the thickness of the riverbed alluvium layer, followed by alluvium permeability, the depth of the underlying impermeable layer, and the pressure boundary condition.

These results are being used to guide the design and placement of new field sensor systems that will further enhance scientific understanding of HEFs in large dam-regulated rivers.

Contact
Jie Bao, Pacific Northwest National Laboratory, Jie.Bao@Pnnl.gov

Funding
Funding was provided by the U.S. Department of Energy (DOE) Office of Biological and Environmental Research (BER) as part of the BER’s Subsurface Biogeochemistry Research (SBR) program. This research is part of the SBR Scientific Focus Area project at PNNL.

Bao, J., T. Zhou, M. Huang, Z. Hou, W. Perkins, et al, “Modulating factors of hydrologic exchanges in a large-scale river reach: Insights from three-dimensional computational fluid dynamics simulations.” Hydrological Processes 32: 3446-3463 (2018). DOI: 10.1002/hyp.13266