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High-Fidelity Heavy-Duty Vehicle Modeling Using Sparse Telematics Data
Heavy-duty commercial vehicles consume a significant amount of energy due to their large size and mass, directly leading to vehicle operators prioritizing energy efficiency to reduce operational costs and comply with environmental regulations. One tool that can be used for the evaluation of energy efficiency in heavy-duty vehicles is the evaluation of energy efficiency using vehicle modeling and simulation. Simulation provides a path for energy efficiency improvement by allowing rapid experimentation of different vehicle characteristics on fuel consumption without the need for costly physical prototyping. The research presented in this paper focuses on using real-world, sparsely sampled telematics data from a large fleet of heavy-duty vehicles to create high-fidelity models for simulation. Samples in the telematics dataset are collected sporadically, resulting in sparse data with an infrequent and irregular sampling rate. Captured in the dataset was geospatial information, time series measurements, and vehicle-specific metadata from a subset of 96 vehicles from varied geographic regions across North America. A series of custom algorithms was developed to process vehicle data and derive both vehicle model input parameters and representative drive cycles. Derived models provide a basis on which to simulate real-world vehicles and iterate on vehicle aerodynamics, auxiliary power loads, transmission shift schedules, and other parameters to achieve reduced fuel consumption and increase energy efficiency. Notably, these models were developed without the use of expensive field data collection, using only data collected through fleet telematics. Processed representative drive cycles are used to validate the fuel economy of derived models. The models developed through this research allow for more representative vehicle simulations with increased flexibility regarding vehicle-to-vehicle variations.
A Review of Value of Solar Studies In Theory and In Practice
This brief summarizes a collection of state- and utility-commissioned value-of-solar (VoS) studies and related literature, with a focus on who commissioned the study, which value and cost categories were discussed and/or quantified, and the methods used. Our objective is to compile information on prior VoS studies to inform state regulators and other stakeholders that may pursue related studies or integrate findings into rate design. The brief is organized into three parts: 1) an introduction to distributed solar photovoltaic (DPV) compensation; 2) a review of theoretical research on VoS; and 3) a review of VoS studies. The vast majority of VoS studies have served an informational role of quantifying the net benefits of PV. Three studies were commissioned in states or utility service territories that subsequently implemented VoS tariffs in California, New York, and Austin, Texas. When applied as a tariff, VoS aims to compensate PV output as efficiently as possible by doing so at rates that reflect the marginal benefits and costs of PV through value and cost categories that may vary temporally and/or geographically. This could lead to higher compensation in locations and times where more PV output is more valuable and consequently drive adoption in those locations to provide more societal benefits. Value and cost factors can be broadly grouped into five categories: generation, transmission, distribution, other utility, and other social categories. Those conducting VoS studies must weigh various tradeoffs when deciding which categories to include and quantify. Tradeoffs include prioritizing values based on their magnitude of value or cost impact, as well as taking into account the feasibility of data collection and accurate quantification. Values of higher magnitude and estimation feasibility are quantified in the majority of studies, including the earliest of studies conducted in the 2000s and 2010s. Additionally, some values of higher magnitude but low feasibility in the earliest of studies have become quantifiable in recent years. There are some values with low average system-wide levels but very high magnitude in specific locations or hours. The value magnitude in some cases can be tied to DPV penetration with low value in areas with little congestion and/or low penetration and vice versa. In these cases, values that are easier to quantify are often incorporated, while those that are more difficult are often addressed via a placeholder value. The placeholder value is paired with a discussion around data needs and methods to improve future estimates, as well as a conversation about when these value categories may increase in magnitude and necessitate more rigorous quantification. This brief summarizes findings from two meta-analyses of VoS studies that took place between 2005 and 2018, as well as findings from four additional studies published from 2018 to 2023. Table ES-1 summarizes the various value and cost categories included in each respective study and whether they were quantified, discussed, or omitted. Values such as avoided energy, capacity, transmission capacity, line losses, and avoided environmental costs are quantified in every study. Some categories were deemed harder to quantify and less impactful at the time of the study, so they were discussed but not quantified (e.g., ancillary services). Other categories, including many at the distribution level, were very locationally and/or temporally specific and dependent on high DPV penetration. These were sometimes quantified and at other times discussed. Notably, when it came to utility costs, integration costs were discussed in all cases, though they were deemed to have a small impact. Other utility costs were omitted for the most part; however, the utility-commissioned study (by NorthWestern Energy in Montana) included both lost utility revenue and programmatic/administrative cost categories. While there are some similarities across studies, each had fairly unique methods that are detailed in the body of this brief.
Reproductive and leaf litterfall fluxes in forest ecosystem sites globally (1950-2022)
Forest allocation of net primary productivity (NPP) to reproduction is poorly quantified globally, despite its critical role in forest regeneration and a well-supported trade-off with allocation to growth. Although field measurements of total NPP are rare, our work finds that a proxy for reproductive carbon allocation constructed from leaf (L) and reproductive (R) litterfall fluxes, R/(R+L), is strongly correlated with R/NPP, facilitating analysis across a wide range of sites where biometric estimates of NPP are not available (R² = 0.85; Hanbury-Brown et al., 2022, Ward et al., in prep). To investigate relationships between ecosystem-scale reproductive allocation (RA) and climate, soil fertility, and stand age gradients, we conducted a literature search and synthesized 824 observations of annual average leaf and reproductive litterfall fluxes across forest sites globally. The zip file includes 1) a folder Data/ containing the litterfall data ("GlobalForestRA_data.csv") and metadata ("GlobalForestRA_metadata.doc") files. The data file includes geographic coordinates, long-term mean annual temperature and precipitation (1970-2000, extracted from WorldClim2.1), leaf and reproductive litterfall fluxes, sampling interval and protocols, forest characteristics (dominant leaf morphology, information pertaining to forest age and successional stage, and disturbance history) and soil properties (% sand, %silt, %clay, total phosphorus (P), nitrogen (N), cation exchange capacity (CEC) and pH) extracted from SoilGrids250 and from on-site measurements, where available. The metadata file contains information about each variable reported in the data file, including data sources, processing methods, and all references. The Data folder contains two additional files used to create Figure 1; these are described in greater detail in the README.2) R scripts GloalForestRA_analysis.r and GlobalForestRA_SI.r and a folder /Functions used to produce results, figures, and tables in the manuscript Ward et al. (in press)3) a README file describing how the data and R scripts can be used to reproduce statistical results, figures, and tables found in the manuscript. Ward et al. (in press)This repository can also be found at: https://github.com/r-ward/Global_Analysis_ForestRA.Ward, R.E., Zhang-Zheng, H. Aernethy, K., Adu-Bredu, S., Arroyo, L., Bailey, A. et al. (in press). Forest age rivals climate to explain reproductive allocation patterns in forest ecosystems globally. Ecology Letters. Hanbury-Brown, A.R., Ward, R.E. & Kueppers, L.M. (2022). Forest regeneration within Earth system models: current process representations and ways forward. New Phytol., 235, 20–40.Ward et al. (2025), Forest age rivals climate to explain reproductive allocation patterns in forest ecosystems globally, in prep.
Data-Driven Modeling and Control of Systems with Plasma-Surface Interactions (Final Technical Report)
This final technical report summarizes the activities and accomplishments in the period from February 2023 thru January 2026. The objective of the proposed research is to investigate the physical mechanisms and processes underlying the formation of structures and patterns in systems with plasma-surface interactions. In the past decades, there have been extensive studies on the interaction of glow discharges, dielectric barrier discharges, and arc discharges with confining or intervening surfaces. The advancement of the understanding of these phenomena is not only of fundamental scientific interest and relevance to the knowledge of the plasma state, but also with profound implications in various technological applications. The research will integrate theoretical, computational, and experimental work within an innovative framework of data assimilation, i.e., optimally combining model predictions with measurements. The scientific merit of this research has three aspects. Firstly, it extends the studies of plasma-surface interactions to systems with insulator surfaces and multi-layer systems, while existing studies are predominantly on electrode surfaces. Secondly, it expects to develop a novel data-driven modeling approach based on data assimilation to enhance the predictive and control capabilities, which could make transformative contributions to basic plasma research. Thirdly, it will shed new light on outstanding problems related to formation of patterns interfacing plasmas. This project also aims to launch an education and outreach initiative at Texas A&M University-Kingsville, a non-R1, minority-serving institution in South Texas. The initiative is structured as a four-tier pyramid. Tier one will be a webinar series for culture and capacity building to inform broader audience in the region about the research fields of plasma science and engineering. Tier two will be the creation and offering of an upper-level undergraduate course on introductory plasma physics, which will help with the recruitment for the upper tiers. On tier three, we will engage and mentor senior design students to conduct work toward the research goal of this project. There will also be a certificate program on general plasma science for undergrad and graduate students, part of which will be lab training at Princeton University. Tier four will be the supervision and mentoring of Ph.D. students. Therefore, this project will systematically expand the talent pipeline, broaden participation from communities historically and geographically underrepresented in DOE SC research portfolio, significantly improve the research and education capacity at the PI’s institution, and contribute to developing a diverse workforce in plasma science and engineering.
Land Use Change Alters Soil Organic Carbon: Constrained Global Patterns and Predictors
Abstract Land use change (LUC) alters the global carbon (C) stock, but our estimation of the alteration remains uncertain and is a major impediment to predicting the global C cycle. The uncertainty is partly due to the limited number and geographical bias of observations, and limited exploration of its predictors. Here we generated a comprehensive global database of 5,980 observations from 790 articles. The number of sites evaluated is at least seven times larger than in previous meta‐analyses. Our constrained estimates of different LUC's effects on soil organic C (SOC) and their variations across global climates reveal underestimation/overestimation in previous estimates. Converting forests and grasslands to croplands reduced SOC by 24.5% ± 1.53% (−11.03 ± 1.06 Mg ha −1 ) and 22.7% ± 1.22% (−8.09 ± 0.67 Mg ha −1 ), while 28.0% ± 1.56% (4.46 ± 0.42 Mg ha −1 ) and 33.5% ± 1.68% (5.8 ± 0.38 Mg ha −1 ) increases, respectively, were obtained in the reverse processes. Converting forests to grasslands decreased SOC by 2.1% ± 1.22% (−1.13 ± 0.44 Mg ha −1 ), while the reverse process increased SOC by 18.6% ± 1.73% (3.31 ± 0.51 Mg ha −1 ). Modeled relative importance of 10 drivers of LUC's impact on SOC revealed that higher initial SOC (iSOC) does not solely determine SOC loss in SOC‐negative LUC scenarios as previously proposed. Across four decades, reconverting croplands to forests and grasslands recovered only 49.5% (6.1 ± 0.51 Mg ha −1 ) and 75.3% (7.0 ± 0.38 Mg ha −1 ) of the iSOC, respectively, indicating the need for protecting C‐rich ecosystems. Our global data set advances information on LUC's effect on SOC and can be valuable to constrain Earth system models to reliably estimate global SOC stocks and plan climate change mitigation strategies.
Google Health Trends performance reflecting dengue incidence for the Brazilian states
Abstract Background Dengue fever is a mosquito-borne infection transmitted by Aedes aegypti and mainly found in tropical and subtropical regions worldwide. Since its re-introduction in 1986, Brazil has become a hotspot for dengue and has experienced yearly epidemics. As a notifiable infectious disease, Brazil uses a passive epidemiological surveillance system to collect and report cases; however, dengue burden is underestimated. Thus, Internet data streams may complement surveillance activities by providing real-time information in the face of reporting lags. Methods We analyzed 19 terms related to dengue using Google Health Trends (GHT), a free-Internet data-source, and compared it with weekly dengue incidence between 2011 to 2016. We correlated GHT data with dengue incidence at the national and state-level for Brazil while using the adjusted R squared statistic as primary outcome measure (0/1). We used survey data on Internet access and variables from the official census of 2010 to identify where GHT could be useful in tracking dengue dynamics. Finally, we used a standardized volatility index on dengue incidence and developed models with different variables with the same objective. Results From the 19 terms explored with GHT, only seven were able to consistently track dengue. From the 27 states, only 12 reported an adjusted R squared higher than 0.8; these states were distributed mainly in the Northeast, Southeast, and South of Brazil. The usefulness of GHT was explained by the logarithm of the number of Internet users in the last 3 months, the total population per state, and the standardized volatility index. Conclusions The potential contribution of GHT in complementing traditional established surveillance strategies should be analyzed in the context of geographical resolutions smaller than countries. For Brazil, GHT implementation should be analyzed in a case-by-case basis. State variables including total population, Internet usage in the last 3 months, and the standardized volatility index could serve as indicators determining when GHT could complement dengue state level surveillance in other countries.
Mobility, Energy, and Electric Vehicle Typology for New York State
Mobility patterns, technology adoption and associated energy outcomes vary tremendously across settlement types. This paper explores how a highly geographically resolved exploration of the social, economic, techno-infrastructural and environmental domains of New York State is key to understanding observed variations in transportation technology adoption and associated mobility and energy outcomes. Current socioeconomic and mobility data sets at the census block group level are integrated in a hierarchical clustering approach to show how variations in mobility and energy outcomes are shaped by these domains - which may enhance progress on investments, or effectively inform planning and other decisions for state-wide goals. The clustering produces four settlement types to predict dependent variables of electric vehicle (EV) adoption rates, commute mode, vehicle fuel economy, and vehicles per household. This typology shows EV adoption rates among the core urban population, which is wealthy and highly-educated, are high - 3 EVs/1000 vehicles versus 1 EV/1000 vehicles among the urban working class. Commuting mode is closely linked with population and employment density - more than 90% of core urbanites use transit or active modes, compared with only 22% of suburbanites and 17% of rural residents. Household vehicle ownership also varies, with 2 vehicles per household in rural areas and only 0.5 in core urban settings. Important findings on differences among the rural, suburban, urban, and urban core settlement types suggest a need to explore how best to manage and anticipate very different types of services that may be supportive in achieving energy-efficient and affordable mobility systems state-wide.
Preliminary Workforce Development and Environmental and Co-use Management Plans for a Floating Offshore Wind Platform - CRADA 609 (Final Report)
Pacific Northwest National Laboratory (PNNL) provided technical assistance to Glosten, Inc. and its affiliate, PelaStar, LLC to advance the development of their floating offshore wind (FOSW) platform. PNNL provided guidance and assessment in two areas that are important to address in the development of FOSW platforms: (1) workforce development and (2) environmental impacts and ocean co-use considerations. This work was funded by the U.S. Department of Energy’s (DOE) Wind Energy Technologies Office (WETO) through Phase 2 of the FLoating Offshore Wind ReadINess (FLOWIN) Prize. It should be noted that the Plans presented in this report are specific to the PelaStar tension-leg platform (TLP) and may not be applicable to all FOSW platforms. Workforce development and environmental/co-use impacts are highly dependent on the geographical region in which activities take place. At the request of PelaStar, PNNL focused on two regions where development may take place: the Gulf of Maine and Northern California. PNNL generated a preliminary Workforce Development Plan for PelaStar, which includes estimated job numbers and skillsets required to establish a workforce to manufacture, install, and operate their platform as part of FOSW projects. The Plan offers methods to increase diversity, equity, and inclusion practices when developing a new workforce and includes colleges and training centers for potential recruitment. Both positive and negative impacts to communities are evaluated, with potential mitigation strategies for reducing negative impacts. The structure of Community Benefit Agreements and Project Labor Agreements are discussed, noting the limitations of the role of a platform manufacturer versus the offshore wind developer. PNNL also drafted a preliminary Environmental and Co-Use Management Plan that serves as a guide to preparing an environmental assessment related to the installation and operation of PelaStar’s unique TLP design, including its potential ecological, socioeconomic, and emissions impacts. The Plan summarizes information on relevant regulatory requirements, potential impact producing factors, monitoring and mitigation measures, and physical and biological resources in the Gulf of Maine and Northern California. One of the primary perceived benefits of the PelaStar TLP is its reduced footprint due to its tensioned tendons versus catenary or taut moorings, but more research must be done as there are no studies on PelaStar’s TLP system to-date. The section also highlights ocean co-use considerations for PelaStar’s TLP system, specifically for fisheries, including existing perspectives, methods, examples, and limitations. The PNNL team established through this preliminary work and review of available literature and resources that there is not yet much research or planning around FOSW. With FOSW being a new industry, many of the findings and planning are adapted from fixed bottom offshore wind, which itself is only just taking off in the United States. More research is needed to establish best practices for workforce development and to assess environmental and ocean co-use impacts and mitigation approaches.
CHESS 2025: Waveform LiDAR data from NEON AOP surveys
This dataset provides Level 1 (L1) full-waveform light detection and ranging (LiDAR) data collected for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). These data were acquired to enable characterization of vegetation structure and other three-dimensional features of the land surface, and to evaluate structural changes that may have occurred between a prior LiDAR acquisition in 2018 and the 2025 overflight. Waveform LiDAR data can provide more detailed information about objects on the ground than discrete point clouds typically do, and they are often used for granular target segmentation and characterization of subcanopy vegetation. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. LiDAR data were acquired using the Optech Galaxy Prime Airborne LiDAR Terrain Mapper onboard the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP). These are the primary waveform LiDAR data delivered by NEON and are provided per flightline in compressed Pulsewaves format, an open-source binary file standard. A Pulsewaves object comprises a two files: a pulse (.pls) file, which stores the geographic origin, outgoing vector, and metadata for every laser pulse emitted by the scanner, and a wave file (.wvs), which stores the sequential amplitude samples of the outgoing pulse and the returning signals. The files are published here in their compressed forms (.plz, .wvz). All waveform data were processed following the theoretical workflow described in the NEON L0-to-L1 Waveform LiDAR Algorithm Theoretical Basis Document (Krause and Goulden 2022a); however, the Pulsewaves output format differs from a legacy format described in that document. Waveform amplitude samples are recorded at 1 nanosecond intervals. All coordinates are provided in meters. Horizontal coordinates are referenced in Universal Transverse Mercator (UTM) zone 13N and the World Geodetic System (WGS) 1984 ensemble datum. Elevations are referenced to Geoid12A. Waveform data for the UPTA survey area were collected without incident and the published records are complete. However, both the ALMO and CRBU collections experienced issues that resulted in incomplete data for those areas. On collection day 2018-06-16 a hardware failure caused the waveform digitizer to lose data from the eastern edge of the ALMO site (Figure 22). The waveform data for flightlines 2–20 could not be extracted from the digitizer, and the data proved unrecoverable. As a result, a portion of the site does not have coverage with waveform data. Although no hardware failure was observed during collection over the CRBU area, final waveform files generated by vendor software contained only ~25% of the expected number of return pulses. After discovery, NEON initiated troubleshooting with the vendor. The root cause of the data ablation had not been identified at the time of publication. Additional data will be published in an update to this package if further recovery proves successful. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.
Simulation-based characterization of the variability of earthquake risk to buildings in the near-field
Recent advancements in high performance computing platforms and computational workflow for regional-scale simulations are enabling unprecedented modeling of fault-to-structure earthquake processes. Regional simulations resolving ground motions at frequencies relevant to engineered systems are becoming computationally viable and provide a new capability to improve understanding of the geographical distribution and intensity of risk to buildings and critical infrastructure. As computational capabilities advance, it is essential to move beyond illustrative single rupture realizations for scenario earthquake events towards the development of a full suite of rupture realizations that appropriately characterize the range of risk to building systems. The work described in this article investigates the application of a suite of fault rupture realizations with the objective of assessing near-fault, site-specific seismic demand variability for building structures. A representative high-performance regional-scale computational model is utilized to execute ground motion and building response simulations based on 18 kinematic rupture realizations of an M7 strike-slip scenario earthquake. The fault rupture models for the scenario earthquake are created by systematically perturbing the hypocenter location and stochastically generating rupture parameters (slip, rise time, rake angle) to represent a breadth of ground motion intensities resulting from the spatial and temporal variabilities of an earthquake rupture process. The resulting seismic demand variability for three-story (short period) and forty-story (long period) steel moment-resisting frame buildings is characterized in terms of the median and distribution of peak inter-story drift ratio for a range of near-fault sites. The full suite of 18 fault rupture realizations and approximately 280,000 nonlinear dynamic building simulations indicate that the three-story building undergoes higher median seismic demand and significantly greater variability of demand at a given site than the forty-story building, which has important implications for the level of certainty in predicting building performance during an earthquake. The simulations performed provide deeper insight into the relationship between fault rupture parameterization and building response, which is essential information for developing a representative suite of rupture realizations for specific earthquake scenarios.
Overcoming persistent challenges in putting environmental flow policy into practice: a systematic review and bibliometric analysis
The implementation of environmental flows (e-flows) aims to reduce the negative impacts of hydrological alteration on freshwater ecosystems. Despite the growing attention to the importance of e-flows since the 1970s, actual implementation has lagged. Therefore, we explore the limitations in e-flows implementation, their systemic reasons, and solutions. We conducted a systematic review and a bibliometric analysis to identify peer-reviewed articles published on the topic of e-flows implementation research in the last two decades, resulting in 68 research and review papers. Co-occurrence of terms, and geographic and temporal trends were analyzed to identify the gaps in environmental water management and propose recommendations to address limitations on e-flows implementation. We identify the underlying causes and potential solutions to such challenges in environmental water management. The limitations to e-flow implementation identified were categorized into 21 classes. The most recognized limitation was the competing priorities of human uses of water (n = 29). Many secondary limitations, generally co-occurring in co-causation, were identified as limiting factors, especially for implementing more nuanced and sophisticated e-flows. The lack of adequate hydrological data (n = 24) and ecological data (n = 28) were among the most mentioned, and ultimately lead to difficulties in starting or continuing monitoring/adaptive management (n = 28) efforts. The lack of resource/capacity (n = 21), experimentation (n = 19), regulatory enforcement (n = 17), and differing authorities involved (n = 18) were also recurrent problems, driven by the deficiencies in the relative importance given to e-flows when facing other human priorities. In order to provide a clearer path for successful e-flow implementation, system mapping can be used as a starting point and general-purpose resource for understanding the sociohydrological problems, interactions, and inherited complexity of river systems. Secondly, we recommend a system analysis approach to address competing demands, especially with the use of coupled water-energy modeling tools to support decision-making when hydropower generation is involved. Such approaches can better assess the complex interactions among the hydrologic, ecological, socioeconomic, and engineering dimensions of water resource systems and their effective management. Lastly, given the complexities in environmental water allocation, implementation requires both scientific rigor and proven utility. Consequently, and where possible, we recommend a move from simplistic flow allocations to a more holistic approach informed by hydroecological principles. To ease conflicts between competing water demands, water managers can realize more 'pop per drop' by supporting key components of a flow regime that include functional attributes and processes that enhance biogeochemical cycling, structural habitat formation, and ecosystem maintenance.
Deeply Rooted: Evaluating Plant Rooting Depth as a Means for Enhanced Soil Carbon Sequestration (Full Technical Report)
Soils store three times as much carbon (C) as the atmosphere, but are not at capacity, and enhanced soil C storage is considered an essential strategy to mitigate rising atmospheric CO 2 levels. Agricultural soils have experienced substantial C loss in the past century due to poor agricultural practices and erosion. A shift towards deep-rooting crops and low-impact soil management could potentially increase long-term sequestration of C fixed by plants and stored in their root tissues, particularly for crops that have naturally deep root systems (>1 meter). A substantial amount of the CO 2 taken up by plants is allocated to their root systems, and because C deposited in deep soil layers has a longer residence time (up to millennia, in contrast to C deposited in topsoils), C increases at depth may have better long-term C sequestration potential than topsoils. However, the accrual, turnover, and stabilization of C in subsoils is a critical knowledge gap. We investigated a deeply rooted plant, switchgrass (Panicum virgatum), as a means of increasing carbon stocks in marginal and agricultural soils. We hypothesized that deep (>30 cm) SOC stocks would be greater under bioenergy crops relative to stocks under shallow-rooted conventional crop cover. To test this hypothesis, we compared soil depth profiles beneath deeply rooted switchgrass (cultivated for 4-30 years) and paired shallowrooted annual controls. We studied 12 field sites, 3 that were collected in 2018 before the start of the project as part a Department of Energy (DOE) Sustainable Biofuels study, and 9 that we collected in 2019 on a national field sampling campaign across the eastern US. In our publication from the 2018 study, which was written in collaboration with the LLNL Soil Microbiome Scientific Focus Area (SFA), we found that C stocks increased under switchgrass, but that the increases were dependent on soil texture. In the 2019 study, we found that carbon accrual tended to occur most consistently in low C soil in the southern US, which could indicate that perennial grasses may be a viable strategy to increase SOC in marginals soils in this region. We have published two studies from the 2019 sampling campaign thus far, we measured microbial growth parameters that will aid in modeling subsoil carbon cycling, and we found that switchgrass appears to move water upward in the soil profile and could promote drought tolerance, which a phenomenon commonly performed by trees known as ‘hydraulic redistribution;’ our study is the first to show this can occur in deep-rooted grasses. Finally, we published a modeling paper in collaboration with the LLNL Soil Microbiome SFA, where we found that poorly crystalline minerals are abundant and strongly correlated with organic C in geographically limited zones with enhanced weathering rates. Our results will inform technological development in the agricultural carbon sequestration sector as well as future negative emissions policies.
Ectomycorrhizal effects on decomposition are highly dependent on fungal traits, climate, and litter properties: A model-based assessment. Dataset.
To simulate the effects of mycorrhizal fungi on soil organic matter cycling, we incorporated mycorrhizal processes into the Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment (CORPSE) model to develop a new soil model Myco-CORPSE. The new model was calibrated and evaluated against soil measurements taken at temperate forests in New Hampshire (NH) and Georgia (GA). A series of scenario analysis were also conducted to explore the conditions under which ectomycorrhizal (ECM) N acquisition processes can induce different soil C accumulation in ECM systems compared to arbuscular (AM) systems.In this data package, we included:-The Python codes of the standard Myco-CORPSE model we developed: "Standard Myco_CORPSE python codes.zip". The main program is the "gradient_sim.py" which calculates the bulk soil microbes and CN content along a user defined gradient of clay, soil temperature, soil moisture and mycorrhizal dominance, and relies on two subprograms "CORPSE_deriv.py" and "CORPSE_integrate.py". "CORPSE_deriv.py" calculated the changes in all simulated soil stock within every time step and "CORPSE_integrate.py" integrate the changes in all simulated soil stock within simulated time period. The program "Plot.py" is used to plot the major outputs produced by the main program "gradient_sim.py".-The modified Python codes of Myco-CORPSE models with site-level environmental inputs (in NH and GA) used to conduct simulations in NH and GA sites: "NH_GA model simulations.zip". -The Python codes used to evaluate the Myco-CORPSE simulation outputs in NH and GA sites against site-level measurements: "Plot NH_GA simulation against measurements.zip". It includes both the evaluation Python code, the model outputs on NH and GA sites, and the measured soil properties in both sites.-The modified Python codes of Myco-CORPSE models "Scenario analysis_model simulations.zip" that is used to conduct scenario analysis of how different litter properties, mycorrhizal fungal traits, climate, and seasonal variation in temperature and vegetation phenology impact the mycorrhizal effects on soil CN properties. The sub file folder "Scenario analysis_litter traits" contains the codes for scenario analysis of different litter properties; The sub file folder "Scenario analysis_ECM types" contains the codes for scenario analysis of different ECM fungal traits; The sub file folder "Scenario analysis_climate&seasonality" contains the codes for scenario analysis of different climate and seasonalities;-"Scenario analysis_model results and plotting codes.zip" contains all the output files from the the scenario analysis of Myco-CORPSE model as described above and the plotting codes used to the generate the heatmaps and scatterplots shown in the manuscript "Ectomycorrhizal effects on decomposition are highly dependent on fungal traits, climate, and litter properties: A model-based assessment"The majority of the model outputs did not have specific geographic information or temporal coverage because the analysis we conducted are mainly hypothetical model simulations. We only provided geographic description, coordinates and temporal coverage for those soil measurements which we used for model evaluations (included in the "Plot NH_GA simulation against measurements.zip").
Hydrogen Storage for Flexible Fossil Fuel Power Generation: Integration of Underground Hydrogen Storage with Gas Turbine (Final Report)
As the nation continues to encourage, through market structures and financial incentives, the proliferation of intermittent renewable electricity, how to optimize the ever-changing electric grid and identify means to retain and improve resilience, while ensuring continued reductions in GHG emissions, will be critical. According to Bloomberg, wind & solar generated 10.5% of US electricity in 2020 and that percentage continues to grow. In support of expanding renewable energy use, and to address its intermittent nature, this project will develop the Hydrogen Storage for Flexible Fossil Fuel Power Generation platform that is dispatchable, reliable, repeatable and have the ability to produce zero or negative carbon power while interfacing with geology capable of CO2 and hydrogen storage. GTI Energy (GTIE) and team members Illinois State Geological Survey (ISGS), Mitsubishi Heavy Industries America (MHIA), Ameren Illinois, Hexagon Purus, and the Low Carbon Resources Initiative (LCRI) completed a Phase I Conceptual Study under contract DE-FE0032012 for Hydrogen Storage for Flexible Fossil Fuel Power Generation: Integration of Underground Hydrogen Storage with Gas Turbine. The Hydrogen Storage for Flexible Fossil Fuel Power Generation platform addresses the intermittent nature of the expanding use of Variable Renewable Energy (VRE) generation. The low cost of the electricity (COE) generated results in greater dispatch and more operation at higher power levels (higher efficiency), fewer short intervals, and fewer start/stop cycles. The reliable, resilient system can produce zero carbon power and store hydrogen. It will demonstrate hydrogen storage in geologic formations like those used in natural gas underground storage thus enabling large scale storage of hydrogen in sedimentary strata across the United States rather than in geographically restricted salt caverns. The Phase I study confirmed the system is feasible and generates power at lower cost than other low carbon approaches. The demonstration defines the pathway for broad commercial application and will accelerate the development of larger systems suitable for centralized utility scale electricity production. The study advanced the maturity of the H 2 storage-based system with flexible power generation by completing a Pre-FEED study (Phase II). The Pre-FEED focused on the selected Energy Farm on the University of Illinois Urbana-Champaign (UIUC) site that includes above ground and underground hydrogen storage, low-carbon hydrogen production (GTI’s Compact Hydrogen Generator, CHG) with underground CO2 sequestration, and a 40-MW class gas turbine. The Pre-FEED addressed the entire system and its interconnection to the natural gas and electric grid and mitigation of key risks, such as storage behavior, load-following, and system operation. During Phase 1 of the project, the team completed key tasks, which moved the entire demonstration project, specific components and approaches closer to commercialization. These Phase I Accomplishments include: Completing System Requirements Review; Completing System Layout and Modeling - Heat & Mass Balance and Process Flow Diagram; Completing modelling of 9 turbine performance cases; Evaluating rock strata for underground storage of hydrogen and sequestration of carbon dioxide; Completing initial modelling of underground storage of hydrogen and withdrawal with evaluation of loss and water production; Identifying roadable storage for above ground hydrogen storage; Identifying existing electrical infrastructure for receiving/delivering electricity; Identifying existing gas supply infrastructure for receiving natural gas; Document concept design/development plans in required reports. Conclusions: The 12-month Feasibility study in Phase I study was completed and confirmed the system is feasible and generates power at lower cost than other low carbon approaches and even lower cost than the reference NGCC plant without carbon capture when taking advantage of 45Q carbon credits. The study enabled the fidelity of the concept to be improved and allowed identification of the requirements for the system. Defining the individual system and component requirements was performed via the system requirements review with the whole team. These requirements were then incorporated into and iterated with our Heat & Mass Balance process model and process flow diagrams were generated to reflect the overall system. This information was then used to complete the TEA and show economic feasibility. Large scale non-salt geologic storage of hydrogen is an enabling technology for a hydrogen-fired turbine that can be retrofitted into large-scale electric generating units (EGU). Our demonstration will include 428 MWh or ~4 hours full load of hydrogen storage (above and underground). Carbon capture inherent to the CHG process can capture 90% CO 2 (with upgrades to >98%). This system provides a COE of 23% savings relative to an NGCC with a post combustion amine system. Our proposed storage system decouples carbon capture and hydrogen production from power production; therefore, we expect our proposed system’s efficiency and variable COE to be superior resulting in overall higher dispatch and reduced deep cycling. Our demonstration will be full to multi-day hydrogen storage and has the potential for longer (seasonal) duration commercially. The demonstration defines the pathway for broad commercial application and will accelerate the development of larger systems suitable for centralized utility scale electricity production.
The Pan-Arctic Vegetation Cover (PAVC) database v1.1
The Pan-Arctic Vegetation Cover (PAVC) database contains synthesized field-data observations of vegetation cover from 978 Arctic Alaska plots with observations from 2010 to 2021. The cover datasets contain plot data at both the plant functional type (PFT) and species-level resolution, with standardized PFT definitions and species names. We synthesized publicly available point-intercept and visual estimate plots from the Arctic Vegetation Archive of Alaska, the Alaska Vegetation Plots Database, the North Slope Science Catalog, and the National Ecological Observatory Network; as well as previously unpublished data from the Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic).Users will find four synthesized datasets, 4 associated data descriptor (dd) files, and 1 metadata file in the PAVC database:synthesized_species_fcover.csv contains fractional cover (fcover) for unique accepted species names, where names include vegetation identified at the family, genus, species, subspecies, and variety levels, as well as general functional types across all 5 data sources. The synthesized_species_fcover_dd.csv accompanies this dataset with header information.synthesized_pft_fcover.csv contains fcover for the following PFTs: non-vascular plants with lichen and bryophyte subcategories, trees with deciduous and evergreen subcategories, shrubs with deciduous and evergreen subcategories, graminoids (grasses), and forbs (herbaceous flowering plants) measured as total cover. Litter and “other” cover are also included as total cover. Additional “types” include water and bare ground, which were measured as top cover. The synthesized_pft_fcover_dd.csv accompanies this dataset with header information.species_pft_checklist.csv is a lookup table containing the translation from a dataset species name to an accepted species name and to a PFT. This table can be used to clarify our species to PFT adjudications, and to aid users in assigning their own PFTs. Any issues found in this checklist should be reported in the Issues tab of our github.survey_unit_information.csv contains auxiliary information about the plots synthesized in this database. It contains useful information for filtering plots of interest based on temporal, geospatial, and contextual information about the plot surveys.flmd.csv contains metadata information about each file in the database.This research was performed as a part of the NGEE Arctic project. The NGEE Arctic project was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
FedEFsz: Fair Cross-Silo Federated Learning System With Error-Bounded Lossy Compression
Cross-Silo federated learning systems have been identified as an efficient approach to scaling DNN training across geographically-distributed data silos to preserve the privacy of the training data. Communication efficiency and fairness are two major issues that need to be both satisfied when federated learning systems are deployed in practice. Simultaneously guaranteeing both of them, however, is exceptionally difficult because simply combining communication reduction and fairness optimization approaches often causes non-converged training or drastic accuracy degradation. Here, to bridge this gap, we propose FedEFsz. On the one hand, it integrates the state-of-the-art error-bounded lossy compressor SZ3 into cross-silo federated learning systems to significantly reduce communication traffic during the training. On the other hand, it achieves a high fairness (i.e., rather consistent model accuracy and performance across different clients) through a carefully designed heuristic algorithm that can tune the error-bound of SZ3 for different clients during the training. Extensive experimental results based on a GPU cluster with 65 GPU cards show that FedEFsz improves the fairness across different benchmarks by up to 60.88% and meanwhile reduces the communication traffic by up to 315×.
Attack-resilient algorithms and testbed federation for wide-area protection and control in smart grid
Today’s energy infrastructure is undergoing a massive transformation across all generation, transmission, and distribution systems to provide reliability, efficiency, and sustainability to the power system network. During the past 10 years, there has been a remarkable enhancement in developing close-loop wide-area protection and control (WAPAC) applications, such as automatic generation control (AGC), wide-area protection scheme (WAPS), synchrophasor-based wide-area voltage control system (WAVCS), etc., in the energy management system (EMS) to provide an adequate situational awareness to control center operators during physical disturbances and natural events. The rapid digitalization, unencrypted communication, and numerous data-sharing devices have increased digital access points that have exposed the grid network to countless cyberattacks pertinent to the WAPAC cybersecurity. Given the amount of complex grid network coupled with legacy infrastructures, it is untrustworthy to completely rely on information technology (IT)-based conventional security measures to defend against evolving cyberattacks. Hence, there is a compelling need to go beyond the traditional paradigm of ‘security by obscurity’ and ‘bolt-on’ security measures, and develop a suite of innovative security solutions at multiple layers to transform the current “fault-resilient” grid into “fault and attack-resilient” grid of the future. This dissertation specifically focuses on developing application-specific attack-resilient solutions for critical WAPAC applications in the smart grid. The first part of the study, Attack-Resilient Wide-Area Protection Scheme, presents a cyber-physical attack-resilient system (CPARS) for WAPS, where two anomaly detection methods are proposed to detect multi-level cyberattacks followed by the rules-based intrusion mitigation system (RIMS) to initiate effective mitigation strategies, tailored to these attacks, to restore the normal grid operation after disturbances. The first anomaly detection component, Machine learning-based Anomaly Detection for Centralized WAPS, discusses a novel architecture and methodology for developing a machine-learning-based anomaly detection system (ADS) for the centralized wide-area protection system (CWAPS) by applying the variational mode decomposition (VMD) technique and decision tree (DT) algorithms using cyber logs and synchrophasor measurements. The second component, Multi-Agent-based Anomaly Detection for Decentralized WAPS, presents a two-level hierarchical multi-agent-based decentralized WAPS against the system-aware stealthy cyber-attacks. Finally, for the RIMS, the state transition diagram-based mitigation measures are defined based on the grid operation states and experimental testing and evaluation are presented in a cyber-physical testbed environment. The second part of the study, Attack-Resilient Wide-Area Voltage Control System, addresses the existing vulnerabilities in the wide-area voltage control system (WAVCS), presents the methodology for performing an impact analysis during data integrity attacks, and proposes a data-driven attackresilient system (DARS) by incorporating machine learning-based ADS and rules-based anomaly mitigation system (RAMS) to detect these attacks and provide necessary mitigation actions to maintain the transient voltage stability after disturbances. Finally, the third part, Networked Federation Testbed (NEFTSec) for Smart Grid Cybersecurity, focuses on developing an interconnected federated testbed through a common network like the internet, describes its conceptual architectures, and also presents its design components for experimental testing, validation, and evaluation. Further, it presents the co-simulation interface algorithm (CIA) to facilitate the geographically-dispersed real-time distributed simulation (GD-RTDS) during the cyber-physical federation. In general, this dissertation presents innovative attack-resilient solutions for WAPAC applications that are experimentally tested and evaluated using the PowerCyber testbed and NEFTSec platform to accelerate the transition of state-of-the-art research works to the real-field deployments.