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SEPTEMBER 17, 2020
Web Feature

Not Your Average Refinery

In a new review, PNNL researchers outline how to convert stranded biomass to sustainable fuel using electrochemical reduction reactions in mini-refineries powered by renewable energy.
SEPTEMBER 14, 2020
Web Feature

VOLTTRON™ Goes to School

The PNNL-developed VOLTTRON™ software platform’s advancement has benefited from a community-driven approach. The technology has been used in buildings nationwide, including most recently on a university campus.
Web Feature

When Nano Meets Bio

Pacific Northwest National Laboratory (PNNL) is part of a continuing National Science Foundation (NSF) team investigating the environmental impact of nanoparticles at the molecular level.

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

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.


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.


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.)


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 |

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

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

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.  


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).


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. 


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

Integrative Network Modeling Reveals Key Drought-Associated Genes Key in the Soil Microbiome

Network Model

A new network analysis of microbial molecular responses to environmental stresses reveals connections between bacterial genes that respond to drought conditions (large red circles) and other genes and processes (smaller grey circles). Image from R.S. McClure, et al., “Integrated network modeling approach defines key metabolic responses of soil microbiomes to perturbations.” Scientific Reports 10, 10882 (2020). [DOI: 10.1038/s41598-020-67878-7]

Molecular and gene networks were combined to better understand how soil microbial communities respond to changes in water levels and nutrient sources

July 24, 2020
July 24, 2020

The Science

Harnessing the soil microbiome to enhance ecosystem services, like plant productivity or bioenergy production, requires understanding how soil microbiomes respond to environmental stresses, such as flood, drought, or changing nutrient levels. In this study, researchers examined how the soil microbiome responds at genetic and metabolic levels to changes in water content and nutrient sources. This integrated network analysis identified unique sets of genes and metabolic reactions that are expressed only under wet, dry, or high nutrient conditions. When focusing on these unique genes and pathways, the analysis showed that genes associated with dry soil conditions are central to the soil microbiome's response to environmental shifts.

The Impact

The soil microbiome promotes plant health and affects the cycling of carbon through the ecosystem. Here, researchers examined how many different genes and molecules expressed by soil microbes are related to each other and to certain environmental conditions. They were also able to identify how individual genes occupied key positions in a functional network. The researchers found that the soil microbiome particularly responded to dry conditions. This knowledge will help in future efforts to harness the soil microbiome for optimizing plant productivity under drought conditions. 


It is challenging to untangle the complex response of the soil microbial community to environmental change, partly due to the absence of modeling frameworks that can predict how environmental changes in soil can lead to changes in the microbial community’s function and role in promoting soil health. To fill this gap, researchers performed a combined analysis of metabolic and gene co-expression networks to explore how the soil microbiome responds to changes in moisture and nutrient conditions. The integrated modeling approach revealed previously unknown, but critically important, aspects of the soil microbiomes’ response to environmental perturbations, including soil desiccation. Incorporation of metabolomic and transcriptomic data into metabolic reaction networks identified condition-specific signature genes that are uniquely associated with dry, wet, and glycine-amended treatments. A subsequent gene co-expression network analysis revealed that dry-associated genes, in particular, are central to the network; this means they are especially critical to the soil microbial community’s response to changing conditions. These results indicate the occurrence of a system-wide microbiome metabolic coordination when soil microbiomes cope with moisture or nutrient perturbations. Importantly, this approach to analyzing large-scale multi-omics data from a natural soil environment is applicable to other microbiome systems for which genomic and metabolite data are available.

PNNL Contacts

Janet K. Jansson, Pacific Northwest National Laboratory,

Kirsten Hofmockel, Pacific Northwest National Laboratory,


This research was supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research Program, as part of the Genomic Science Program, and is a contribution of the Pacific Northwest National Laboratory Soil Microbiome Scientific Focus Area "Phenotypic Response of the Soil Microbiome to Environmental Perturbations."

R.S. McClure, et al., “Integrated network modeling approach defines key metabolic responses of soil microbiomes to perturbations.” Scientific Reports 10, 10882 (2020). [DOI: 10.1038/s41598-020-67878-7]