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Dryland Expansion Regulates Variability in Plant Biodiversity

Image of low-lying scrub brush with mountains in the background.

Model shows quantified impact of accelerated dryland expansion on its productivity

August 27, 2020
August 27, 2020
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The Science

Drylands, such as grasslands, savannas, and deserts, are expected to expand and become more arid at an accelerating rate over the next century. The effects of this expansion and degradation on their gross primary production (GPP) remain elusive. A recent paper in Nature Communications is the first to quantify the impact of accelerated dryland expansion on their productivity. In addition, as different subtypes of drylands expand and convert, large changes will be seen in how regional and subtypes contribute to variability in global dryland productivities.

The Impact

Drylands are the largest source of interannual variability in the global carbon sink. Any changes in dryland ecosystems under climate change would have large implications for global carbon cycle. This work improves our understanding of how accelerated dryland expansion impacts the productivity of drylands. Dryland expansion and climate-induced conversions among sub-humid, semi-arid, arid, and hyper-arid subtypes will lead to substantial changes in regional and subtype contributions to global dryland GPP variability.

Summary

Drylands, such as grasslands, savannas, and deserts, cover approximately 41% of the Earth’s land surface and support more than 38% of the global population. Global dryland ecosystems with high plant productivity account for approximately 40% of global land net primary production (NPP.) They also act as the dominate global land CO2 sink and, over recent decades, have contributed the largest amount of net CO2 flux affecting interannual variability.

To study the impact of accelerated dryland expansion and degradation on global dryland GPP, researchers from Washington State University and Pacific Northwest National Laboratory assessed MODIS GPP data from 2000-2014 and the CMIP5 aridity index (AI.) Results from the investigation shows a positive relationship between GPP and AI over dryland regions, with total dryland GPP increasing by the end of the 21st century by 12 ± 3% relative to 2000–2014 increases. However, GPP per unit dryland area will decrease with degradation of drylands. Such expansion and conversions among different subtypes of drylands will lead to large changes in regional and subtype contributions to variability in global dryland productivity.

Researchers in this study used a cubic fitting method to find the relationship between dryland GPP and CMIP5 AI data. With long-term GPP data, they analyzed the trend and interannual variability of dryland GPP into the future. To verify the accuracy of projected GPP data, the team compared projected GPP data to GPP data from 15 CMIP5 models. The results showed agreement with the modeling data in eight regions during the same period.

Dynamic Earth system models are essential to more fully understand dryland ecosystem–climate interactions.

Funding

This work is supported by the U.S. Department of Energy (DOE) Office of Science, Biological and Environmental Research (BER) program as part of BER’s Subsurface Biogeochemical Research Program (SBR) at the Pacific Northwest National Laboratory (PNNL.) We also acknowledge support by the Second Tibetan Plateau Scientific Expedition and Research Program (STEP), Grant No. 2019QZKK0602, the National Natural Science Foundation of China under grants 41521004, 41991231 and 41975075, the Foundation of Key Laboratory for Semi-Arid Climate Change of the Ministry of Education in Lanzhou University, the China 111 Project (No. B13045), the Fundamental Research Funds for the Central Universities (lzujbky-2017-it18.)

10.1038/s41467-020-15515-2

Research topics

Yao, J., Liu, H., Huang, J., Gao, Z., Wang, G., Li, D., Yu, H., Chen, X. 2020. Accelerated dryland expansion regulates future variability in dryland gross primary production. Nature Communications, (2020) 11:1665 | https://doi.org/10.1038/s41467-020-15515-2.

Secretary of Energy Advisory Board (SEAB) Report Recognizes PNNL Contributions

ML and AI

Report features how PNNL’s computing capabilities are affecting the nation’s security, science, and energy missions

August 25, 2020
August 25, 2020
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Contributions from researchers across Pacific Northwest National Laboratory (PNNL) were recognized in the preliminary findings of a Secretary of Energy Advisory Board (SEAB) report from a working group dedicated to the U.S. Department of Energy’s (DOE’s) capabilities and future in artificial intelligence (AI) and machine learning. PNNL researchers’ expertise is prominent throughout DOE’s AI efforts, particularly in the areas of data sciences and national security.

Based largely on input from DOE sponsors, the report features how PNNL’s computing capabilities are affecting the nation’s security, science, and energy missions. Key highlights include:

  • Studying how AI affects the global landscape for securing nuclear materials, potentially using deep learning to enhance physical and digital protections against material concealment, delivery, theft, and sabotage.
  • Describing how the United States and its partners might employ deep learning to combat attack efforts for enhanced nuclear security.
  • Designing advanced deep learning models to characterize operations with buildings, using electrical signatures on power lines, enabling new designs for energy-efficient buildings in addition to enhanced security features for nuclear facilities.
  • Leading the nuclear explosive monitoring project with data scientists working to significantly lower detection thresholds of low-yield, evasive underground nuclear explosions without increasing time-to-detection or the amount of human analysis.
  • Co-design of advanced accelerator, memory and data movement concepts to support convergence of AI and machine learning methods with other forms of data analytics and traditional scientific high performance computing (HPC). 

The report highlights PNNL’s support to the National Nuclear Security Administration, featuring joint laboratory collaborations between PNNL and others, including the Y-12 National Security Complex, Sandia National Laboratories, Lawrence Livermore National Laboratory, Los Alamos National Laboratory, and Oak Ridge National Laboratory. Additionally, PNNL is working as part of DOE’s comparative advantages in AI, providing the Office of Energy Efficiency and Renewable Energy access to AI subject matter experts.

View full preliminary findings of the Secretary of Energy Advisory Board (SEAB) report.

For more information about PNNL’s research contributions, contact Aaron Luttman

Study Shows Coastal Wetlands Aid in Carbon Sequestration

data collection in marsh

PNNL scientist, Amy Borde collects data in a marsh on the Columbia River estuary.

Photo: Heida Diefenderfer

Sea-level rise impacts will likely decrease ecosystem carbon stocks

August 13, 2020
August 13, 2020
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Tidal marshes, seagrass beds, and tidal forests are exceptional at absorbing and storing carbon. They are referred to as total ecosystem carbon stocks, yet little data exists quantifying how much carbon is absorbed and stored by tidal wetlands in the Pacific Northwest (PNW). Knowing this information is valuable, particularly in the context of sea level rise and with the associated need for Earth system modeling to predict changes at the coast.

The Science

Researchers found that the average total ecosystem carbon stock in the PNW is higher than in other areas of the U.S. and other parts of the world. Marsh carbon stocks, in particular, are twice the global average. Researchers found progressive increases in total ecosystem carbon stocks along the elevation gradient of coastal wetland types common in the PNW: seagrass, low marshes, high marshes, and tidal forests. Total carbon also increased along the salinity gradient, with more carbon occurring in lower salinity areas.

Additionally, this research showed that common methods used to estimate soil carbon actually underestimate soil carbon stocks in coastal wetlands. Soil carbon storage below the depth of 100 centimeters proved to be an important carbon pool in PNW tidal wetlands.

The Impact

The results suggest that long-term sea-level rise impacts, such as tidal inundation and increased soil salinity, will likely decrease ecosystem carbon stocks. This is a concern if wetlands can’t migrate with increased sea level due to being bound by topography and human development.  

Summary

This research arose from the Pacific Northwest Blue Carbon Working Group, of which Amy Borde and Heida Diefenderfer of Pacific Northwest National Laboratory’s Coastal Sciences Division are members. The team studied 28 tidal ecosystems across the PNW coast, from Humboldt Bay, California, to Padilla Bay, Washington. They sampled common coastal wetland types that occur along broad gradients of elevation, salinity, and tidal influences, collecting the data necessary to calculate total carbon stocks in both above ground biomass and the soil profile.

In three years of study, the researchers found that most carbon is in the wetland soils not aboveground, and much of it is deeper than one meter—a typical lower limit of sampling. Total ecosystem carbon stocks progressively increased along the terrestrial-aquatic gradient of coastal wetland ecosystems common in the temperate zone including seagrass, low marshes, high marshes, and tidal forests. The findings were reported in “Total Ecosystem Carbon Stocks at the Marine-Terrestrial Interface: Blue Carbon of the Pacific Northwest Coast, USA,” published in the August 2020 online edition of Global Change Biology (DOI: 10.1111/gcb.15248).

Research Team: PNNL’s Amy Borde and Heida Diefenderfer, along with J. Boone Kauffman, Leila Giovanonni, James Kelly, Nicholas Dunstan, and Christopher Janousek (Oregon State University); Craig Cornu and Laura Brophy (Institute for Applied Ecology/Estuary Technical Group); and Jude Apple (Padilla Bay National Estuarine Research Reserve).

Funding

The grant award was administered by the Institute of Applied Ecology, and other partners included Oregon State University and the Padilla Bay National Estuarine Research Reserve. This research was supported by the National Oceanic and Atmospheric Administration, through a cooperative agreement with the University of Michigan. 

10.1111/gcb.15248

Kauffman, J Boone, Leila Giovanonni, James Kelly, Nicholas Dunstan, Amy Borde, Heida Diefenderfer, Craig Cornu, Christopher Janousek, Jude Apple, and Laura Brophy. “Total Ecosystem Carbon Stocks at the Marine‐terrestrial Interface: Blue Carbon of the Pacific Northwest Coast, United States.” Global change biology, no. 0 (August 11, 2020). DOI: 10.1111/GCB.15248

August 11, 2020

Deconstructing the Soil Microbiome

shovel in soil with tiny green plants around

Microbes in the soil play a major role in nutrient cycling and plant health, but the inherent complexity of the soil microbiome makes it challenging to effectively analyze microbial functions and relationships. 

Image courtesy of Lukas from Pexels

Deconstruction of soil microbial communities into discrete functional groups enables piecing together the functional potential of the complex soil microbiome

July 23, 2020
July 23, 2020
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The Science                                

The soil microbiome plays a major role in nutrient cycling and plant health. However, its inherent complexity, with a vast array of microbes that metabolize many different molecules, makes it challenging to effectively analyze ecosystem functions performed by interacting members of soil microbial communities. Researchers dissected the complex microbial community of a native Washington soil into reproducible, low-complexity communities called 'functional modules.' Because these subcommunities are easier to study than a bulk community, researchers could analyze microbial species and functions present in the soil in more depth than before.

The Impact

By studying discrete functional components of the soil microbiome at high resolution, the researchers obtained a more complete picture of soil diversity compared to analysis of the entire soil community. They identified specific evolutionary relationships and biochemical characteristics of the soil microbiome that otherwise would have been hidden in previous community-scale genomic analyses. Improved understanding of the functions of the soil microbiome could help scientists harness beneficial aspects of the soil microbiome to increase soil health or crop productivity.  

Summary

One gram of soil contains microbes from thousands of different evolutionary groups. These microbes also have a wide variety of metabolic functions that help them survive in different soil microenvironments. Analyzing the complete functional and taxonomic diversity of a soil microbiome requires a large amount of computing power, and it may fail to capture large populations of quiet or rare microbes.

To simplify the analysis of a soil microbial community, researchers incubated a parent soil microbiome under several different conditions to create different subcommunities of microbes with specific functions, or functional modules. The functional modules included: usage of simple and complex carbon substrates, antibiotic resistance, anaerobic growth with different redox acceptors, and stress resistance. For each functional module, the researchers performed 16S rRNA gene amplicon sequencing to determine the community composition and RNA sequencing to identify expressed functions. Approximately 27% of unique taxa present in the parent soil were found in the functional modules, in addition to 341 taxa not detected in the parent community. The functional modules had unique gene expression patterns that were also enriched for transcripts associated with functional characteristic of each module. By dissecting the soil microbiome into discrete components, the researchers obtained a more comprehensive and highly detailed view of a soil microbiome and its biochemical potential than through analysis of a soil microbiome as a whole.

Contact

Ryan McClure, Pacific Northwest National Laboratory, ryan.mcclure@pnnl.gov

Funding

This research was supported by the U.S. Department of Energy’s Office of Science, Biological and Environmental Research Program and is a contribution of the Scientific Focus Area “Phenotypic response of the soil microbiome to environmental perturbations.”

D. Naylor, et al., “Deconstructing the Soil Microbiome into Reduced-Complexity Functional Modules.” mBio 11, e01349-20 (2020). [DOI: 10.1128/mBio.01349-20]

NWRTC Notes From the Field (June 2020)

Interviews with public health professionals who are helping to keep us safe

July 20, 2020
July 20, 2020
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PNNL's Northwest Regional Technology Center interviews Assistant Chief of Resource Management for Seattle Fire Department Willie Barrington about how his team faced the unknown when the COVID-19 pandemic hit Seattle, Washington.

Research topics

June 25, 2020

Digging into the Details of Phosphorus Availability

Photo of plant with roots under ground

Courtesy of Shutterstock

New root blotting technique visualizes relationship between root growth, microbial activity, and soil nutrients.

July 7, 2020
July 7, 2020
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The Science

Phosphorous is an important nutrient for plants. However, the mechanisms used by plants to extract phosphorus from soil and incorporate it into their biomass are not well understood. Now, researchers developed a new technique to visualize the activity and distribution of enzymes that mobilize phosphate around plant roots. Tracking the location of these enzymes can help researchers better understand the chemical dynamics between roots, microbes, and soil that influence how plants get nutrients. The approach could also be applied to other nutrient-cycling enzymes.

Diagram showing rhizosphere blotting nondestructive process
A new root blotting technique produces an imprint of plant roots growing in flat slabs. The paper imprints can then be probed with different fluorescent indicators to visualize both the distribution and activity of phosphate-mobilizing enzymes surrounding the roots.

The Impact

Phosphorus is an essential nutrient for plants and therefore, global demand for phosphorus fertilizers is expected to grow to accommodate the world’s growing population. However, most of these fertilizers are made from rock phosphorus, a non-renewable resource. This research provides new insights into the complex dynamics of phosphorous exchange between soil, microbes, and plant roots. Knowledge from this newly developed approach will help scientists identify strategies to improve phosphorus use efficiency for bioenergy crop production in marginal environments, as well as for agriculture in general.

Summary

Soil bacteria, fungi, and plants produce enzymes called phosphatases, which convert organic sources of phosphorus into a form that plants can absorb. Researchers have studied the microbial activity in bulk soil samples, providing information about the overall functional potential of the environment. But to better understand the dynamics between soil, plants, and microbes, more detail is needed. To accomplish that, a team of researchers developed a new technique based on root blotting to reveal phosphatase activity and distribution around plant roots. They grew switchgrass in flat pots or “rhizoboxes” containing soil with pellets of root matter as sources of organic phosphorus. Then, they applied a nitrocellulose membrane to capture proteins around the roots. Finally, the researchers stained the membrane with fluorescent indicators for phosphatase activity and protein concentration. This revealed the spatial distribution of phosphatase around the roots of plants, and highlighted regions of increased phosphatase activity.

This approach could be used to study phosphatase activity over time, as well as other nutrient-cycling enzymes. The combination of membrane extraction, with rapid analysis via fluorescent probes to reveal localization of phosphatase activity in the rhizosphere, offers a new technique for environmental applications. Expanding this approach could enable simultaneous visualization of multiple enzyme types in soil systems.

Funding

Development of this method was funded by DOE’s Office of Science, Biological and Environmental Research Program by the Early Career Research Award program (PI: Jim Moran).

10.1016/j.soilbio.2020.107820

V.S. Lin, et al. “Non-destructive spatial analysis of phosphatase activity and total protein distribution in the rhizosphere using a root blotting method.” Soil Biology and Biochemistry, 146 (2020). DOI: 10.1016/j.soilbio.2020.107820

Predicting Soil CO2 Emissions from Air Temperature

graph with multicolored dots

The mean annual air temperature and precipitation coverage of soil respiration samples used in this study, by ecosystem type. The gray dotes represent worldwide data points.

A cheaper, more efficient way to estimate soil respiration and carbon flux

June 2, 2020
June 2, 2020
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The Science

Soil respiration—the flow of CO2 from the soil surface to the atmosphere—is one of the largest carbon fluxes in the terrestrial biosphere. In recent DOE-funded study, researchers created a model that predicted annual soil respiration in different parts of the world based on average air temperature for each region.

The Impact

Monitoring greenhouse gas exchange between the soil and the atmosphere is important in tracking worldwide CO2 emissions. Despite this, many regions are either inaccessible or do not have the resources to undertake rigorous research to monitor soil respiration. In this study, researchers found that soil respiration measured at annual mean temperature can be used to predict annual soil respiration. The findings could be used to reduce soil respiration measurement frequency and greatly decrease cost-- enabling easier measurements in low income and inaccessible regions worldwide.

Summary

Led by Jinshi Jian of Pacific Northwest National Laboratory, this internationally diverse research collaboration used data from more than 800 site-year observations worldwide. The team developed a predictive model to test the relationship between annual soil respiration and instant soil respiration rate at mean annual temperature among diverse ecosystems and climates throughout the world. Air temperature data is more common than soil temperature data, making it a more achievable measurement to gauge carbon emissions in lower income countries. Their results were recently published in Agricultural and Forest Meteorology.

PNNL Contact

Jinshi Jian, Pacific Northwest National Laboratory, jinshi.jian@pnnl.gov

Funding

This research was supported by the DOE Office of Biological and Environmental Research (BER), as part of BER’s Terrestrial Ecosystem Science Program [number: DE-AC05-76RL01830].

 

Jian, J., Bahn, M., Wang, C., Bailey, V. L., Bond-Lamberty, B. Prediction of annual soil respiration from its flux at mean annual temperature. Agricultural and Forest Meteorology. Volume 287. DOI: 10.1016/j.agrformet.2020.107961

APRIL 28, 2020
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PNNL quantum algorithm theorist and developer Nathan Wiebe is applying ideas from data science and gaming hacks to quantum computing

Machine Learning Produces Unprecedented High-Resolution Map of Global Soil Respiration

forest floor and tree trunks

Soil respiration is one of the largest fluxes in the global carbon cycle, providing critical insights into biological activity in the underlying soil. Photo by Carl Newton on Unsplash

Research provides a new understanding of the magnitude and uncertainties surrounding this major global flow of carbon to the atmosphere.

April 27, 2020
April 27, 2020
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The Science

Scientists at the U.S. Department of Energy’s Pacific Northwest National Laboratory have developed and continue to maintain a global database of measurements made of soil-to-atmosphere CO2 flows, termed soil respiration. A research team at the University of Delaware has leveraged these observations in a machine-learning approach to create a new high-resolution global map of soil respiration and its uncertainties.

The Impact

Soil respiration is one of the largest fluxes in the global carbon cycle, providing critical insights into biological activity in the underlying soil. This new global map of soil respiration and its uncertainties provides modelers and experimentalists with a “gold standard” benchmark dataset identifying areas with the highest uncertainties to target in the future.

Summary

Soils emit large amounts of carbon dioxide to the atmosphere every year via the process of soil respiration. Rates of soil respiration are highly variable in space, however, limiting scientists’ ability to balance global carbon budgets and forecast climate change. This study used a novel machine learning approach to predict soil respiration rates at high resolution (1 km2) globally, based on how observations of soil respiration were related to climate (annual temperature, annual and seasonal precipitation) and vegetation. It also examined the spatial patterns of the associated uncertainty of these predictions. Predicted annual soil respiration and prediction uncertainty varied across ecosystem types and regions, with evergreen tropical forests dominating global annual soil respiration emissions. Dryland, wetland, and cold ecosystems had the highest associated prediction uncertainties, suggesting that future soil respiration measurements would be especially useful in these areas. The high spatial resolution of these predictions will help researchers studying the carbon cycle at local to global scales and provide a high-quality benchmark dataset for Earth System Models.

Contact

Ben Bond-Lamberty, Pacific Northwest National Lab, bondlamberty@pnnl.gov  

Funding

Rodrigo Vargas acknowledges support from NASA’s Carbon Monitoring Systems (80NSSC18K0173). Ben Bond-Lamberty was supported by the US Department of Energy, Office of Science, Biological and Environmental Research as part of the Terrestrial Ecosystem Sciences Program.

Research topics

D. Warner, B. Bond-Lamberty, J. Jian, E. Stell, and R. Vargas “Spatial patterns of global soil respiration at 1 km resolution.” Global Biogeochemical Cycles 33, 1733-1745 (2019). [DOI: 10.1029/2019GB006264]

January 10, 2020
APRIL 21, 2020
Web Feature

Beneath It All

At PNNL, subsurface science inhabits two separate but interlocking worlds. One looks at basic science, the other at applied science and engineering. Both are funded by the U.S. Department of Energy (DOE).

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

Quantifying Decision Uncertainty in Water Management via a Coupled Agent-Based Model

Water resource management with crop irrigation

Agent-based models of water resource management can include the interaction between human-engineered systems and natural processes.

Philip Swinburn on Unsplash

Considering risk perception can improve the representation of human decision-making processes in agent-based models.

July 1, 2019
March 26, 2020
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The Science
Modeling water resource management is a challenge because of the interactions between human decisions, the natural hydrologic cycle, and the impact of risk perception on human decision-making.  A study by scientists at Lehigh University, Sandia National Laboratories, and the National Renewable Energy Laboratory (NREL) showed that risk perception can be addressed via the Theory of Planned Behavior, thus improving model representations of how people make water management decisions.

The Impact
This approach improves on rule-based risk decision making by considering how previous experiences and new information play a role in the decision-making process. Analysis of how farmers manage annual irrigation acreage demonstrates the dynamic nature of decision making, something that is essential to represent in future research regarding evolving natural factors. The approach also allows for a more flexible representation of real-world decision making that can be further expanded to various spatial scales in the future. Results showed that farm location upstream or downstream of a reservoir will affect farmers’ risk perception regarding water availability and influence their behavior about expanding irrigation areas.

Summary
Researchers “two-way” coupled an agent-based model (ABM) with a river-routing and reservoir management model (RiverWare) to address the interaction between human-engineered systems and natural processes while quantifying the influence of incomplete/ambiguous information on decision-making processes. The ABM combines Bayesian Inference mapping with a Cost-Loss model to simulate farmers’ psychological risk-based decision processes under evolving socioeconomic conditions. The San Juan River Basin in New Mexico, USA is used to demonstrate the utility of this method. The calibrated model captures the annual variations of historical irrigated areas. The results suggest that the new approach provides an improved representation of human decision-making processes and outperforms the conventional rule-based ABMs that do not consider risk perception. Future studies will focus on modifying the Bayesian Inference mapping to consider farmer interactions, the up-front costs of farmer decisions, and upscaling this method to the regional scale.

PI Contacts

Ian Kraucunas, Ph.D., Pacific Northwest National Laboratory, ian.kraucunas@pnnl.gov
Jennie Rice, Pacific Northwest National Laboratory, jennie.rice@pnnl.gov

 Funding
This research was supported by the U.S. Department of Energy Office of Science, Biological and Environmental Research through the MultiSector Dynamics, Earth and Environmental System Modeling Program. 

J.Y. Hyun, S.Y. Huang, Y.C.E. Yang, V. Tidwell, and J. Macknick, “Using a coupled agent-based modeling approach to analyze the role of risk perception in water management decisions.” Hydrology and Earth System Science 23, 2261-2278 (2019). [DOI:10.5194/hess-23-2261-2019].

May 10, 2019
May 10, 2019
MARCH 16, 2020
Web Feature

Carving Out Quantum Space

The race toward the first practical quantum computer is in full stride. Scientists at PNNL are bridging the gap between today’s fastest computers and tomorrow’s even faster quantum computers.