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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Leveraging Environmental Monitoring Results from Department of Energy Sites for Research on the Food-Energy-Water Nexus

The Annual Site Environmental Report (ASER) from a U.S. Department of Energy (DOE) Site summarizes environmental data; environmental management performance; compliance with applicable DOE, federal, state, and local regulations; and restoration and surveillance monitoring programs. The Savannah River Site (SRS) produces ASERs that include results of monitoring mercury levels in fish samples obtained in the vicinity of SRS. Mercury is a toxic trace metal and the methylated organic form is a neurotoxin that bioaccumulates in the food web, endangering humans and other organisms. In United States, power plants are the largest source of mercury emissions. Research Objective: To determine the patterns of mercury levels in fish samples reported in the SRS environmental monitoring reports. We developed box plot visualizations to make sense of the data as well as identify fish species, dates and location with outlier mercury content. The integration and visual representation of multi-year datasets can support decision making processes on environmental compliance and environmental sustainability conducted at U.S. DOE Sites. We are constructing datasets from textual, image and multivariable data sources in the multi-year ASERs of the U.S. DOE Savannah River Site (SRS https://www.srs.gov/general/pubs/ERsum/index.html). The Fish Surveillance for mercury presents a case study for the intersection of food, energy and water systems. The dataset of 864 records (2008 and 2009) of mercury levels includes water location of fish, fish species, collection date and mercury content of fish sample. The patterns of mercury levels in the fish species as well as locations provide knowledge for further investigations. A research area for future studies is the composition of microbial communities in fish guts of varying levels of mercury.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

DOE EV Data Collection - Vehicle Data

Vehicle data consist of electric vehicle performance data collected directly from the vehicle during standard operations. Data were collected using onboard data loggers that were either installed by the project team or preinstalled by the original equipment manufacturer. Data recorded by the data loggers were made accessible via an online web portal or an application programming interface. Different data loggers were used (HEM, ViriCiti, and Geotab), and the method for each vehicle is defined in the vehicle attributes file. Some systems collected data on a “trip-level” basis, in which each row of a table represents a single trip (the period between a key-on and key-off event), whereas other data were collected on a per-day basis, in which each row represents a single day of operation. Data were collected over a range of data collection periods, depending on the project. Data have been anonymized by removing information or decreasing information resolution as necessary so that fleets are not identifiable. Due to the wide range of vehicle types represented and variation in data collection, data parameters and frequencies differ between vehicles and fleets The **Performance Data Daily/Trip Data Dictionaries** contain definitions for each available parameter associated with a vehicle’s operations, aggregated at either a daily or trip level. The parameters available will vary from vehicle to vehicle, but every possible parameter will be defined. The **Vehicle Attributes Data Dictionary** contains definitions for each available parameter associated with a vehicle’s physical and functional attributes and fleet context. The **Vehicle Attributes** table contains specific vehicle characteristics, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. The **Vehicle Data** tables contain the data from each vehicle’s operations, aggregated at either a daily or trip level, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

CHESS 2025: Leaf Area Index (LAI) for meadow, shrub, tree, and understory vegetation

This dataset contains Leaf Area Index (LAI) measurements made as part of the Colorado Headwaters Ecological Spectroscopy Study (CHESS) during June and July of 2025. Data were collected in the Upper Gunnison Basin, Colorado, across three study domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). Field observations of LAI were collected within 72 hours of airborne data collection by the National Ecological Observatory Network’s Aerial Observation Platform (NEON AOP). The NEON AOP collected waveform LiDAR (Light Detection and Ranging) and imaging spectrometer data in 426 spectral bands from the visible to shortwave infrared. LAI measurements were collected using the LICOR LAI-2200C Plant Canopy Analyzer following protocols outlined in the instrument manual (LI-COR 2019). Sampling targeted four distinct vegetation types: meadows, shrubs, trees, and aspen forest understory. We have archived data separately by site type because different field methods were used for each. At meadow sites, measurements were made at the four corners of 1m x 1m plots, with the instrument moving inward toward the center of the plot. At shrub sites, we measured the canopies of individual shrubs. At tree sites, we made measurements within a 10m x 10m subplot centered around a focal tree, with 30 observations taken on a regular grid. At aspen understory sites, we measured overstory trees following the tree protocol and understory herbaceous vegetation following the meadow protocol. All measurements included above-canopy (A) and below-canopy (B) readings, with specific protocols for scattering correction measurements in direct-sun conditions. Data were processed using the R package `rlai` (Worsham 2025). This package includes functions to calculate LAI, gap fraction, apparent clumping factor (Ω), scattering correction, and other canopy metrics. Package contents: Full file descriptions appear in ‘flmd.csv’. Files named according to the convention ‘lai_*_summary_data_cleaned.csv’ contain summary values of LAI, apparent clumping factor (Ωapp), and scattering correction factors for each site. These are the analysis-ready products that most data users will work with. Files named ‘lai_*_metadata_cleaned.csv’ contain additional site-level observations made during field collection. We have also archived intermediate and supplementary data for users who wish to check our processing approach or apply alternative methods. ‘raw_lai_2200C.zip’ contains the raw files as read from the LI-COR instrument, with no processing applied, in TXT format. The zip archive contains subdirectories by site type, which are further subdivided by sampling area. Filenames correspond to the sampling site number. ‘intermediate_results.zip’ contains detailed output from the processing routines, in JSON format. The zip archive contains subdirectories by site type; filenames correspond to the sampling site number. ‘scattering_correction_logs.zip’ contains logfiles from the implementation of Kobayashi et al.'s (2013) scattering correction algorithm. The logfiles report values of several parameters at each iteration of the algorithm, as the model converges toward a stable solution. They are intended for users who want to verify scattering correction performance. The zip archive contains subdirectories by site type; filenames correspond to the sampling site number. ‘spot_checks.csv’ reports LAI and other values for a small number of files processed with LI-COR FV2200 software (LI-COR 2013) using the same control parameters as in our R-based approach. Additional metadata are provided in a data dictionary describing column names and definitions (dd.csv), and in a file-level metadata file (flmd.csv). All zip files can be expanded with common archive utilities. TXT, CSV, and JSON files can be ingested into R or Python computing environments or read in common text editor utilities. Geospatial information: Geospatial data for mapping measurement site locations are in the files CHESS_polygons_lai_UTM.geojson, CHESS_polygons_shrub_UTM.geojson, and CHESS_polygons_meadow_UTM.geojson in the companion geospatial package for the 2025 CHESS campaign, ‘CHESS 2025: Location data for field observations and sampling’ (Henderson et al., 2026). 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. * Todorov and Worsham are co–first authors.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2020

Success Using A Granular-Activated Carbon Filtration Swab to Rehabilitate Westbay{sup R} Multi-level Groundwater Monitoring Wells at the INL - 20544

In November 2015, tetrachloroethylene (PCE) was discovered in groundwater samples collected from a Westbay{sup R} multi-level groundwater monitoring well at the Idaho National Laboratory (INL).a Subsequent investigation determined that PCE was not present in the groundwater. The detected PCE was instead due to inadvertent contamination of the internal tubing fluid in the Westbay well-monitoring system, which is isolated from the surrounding groundwater. Further investigation showed that three of the 11 Westbay wells at the INL contained PCE-contaminated tubing fluid at concentrations above the U.S. Environmental Protection Agency's maximum contaminant level for drinking water (5 μg/L). Loose sediment at the bottom of some Westbay wells was also found to contain PCE; the three wells were then identified for rehabilitation as part of the INL well maintenance program. The unique construction of the Westbay wells - i.e., the small diameter (5.72 cm [2.25 in.]) and the depths of the tubing fluid ranging from 144.8 to 419 m (475 to 1,376 ft) below ground surface - posed unique challenges to the rehabilitation. Because the tubing fluid provides structural integrity, the fluid cannot be removed and then replaced without damaging the integrity of the Westbay well. Additionally, the small diameter of the systems and the associated depths limit the availability of equipment that can be used for the rehabilitation. After an evaluation of potential methods, it was determined that the use of an in-well, custom-fabricated, granular-activated-carbon filtration swab deployed via a wire line would provide the safest and most cost-effective method for rehabilitation. Westbay well rehabilitation started at well MIDDLE-2051 in late June 2018 and paused in December 2018 for the season. The PCE concentration in the tubing fluid at MIDDLE-2051 was initially reduced to a maximum concentration of 3.81 μg/L from a maximum of 642 μg/L, while the concentration of PCE in the sediment had been reduced from 5,360 to 2,270 μg/kg. Sampling was performed at MIDDLE-2051 again in August 2019, and the tubing fluid increased to a maximum concentration of 45.4 μg/L, while the concentration in the sediment decreased further to 1,890 μg/kg due to desorption of PCE from the sediment to the tubing fluid. Rehabilitation resumed at well MIDDLE-2050A in late August 2019, and the PCE concentration in the tubing fluid had been reduced to a maximum concentration of 19.0 μg/L. Although the method has been shown to be time-consuming, it has proven to be effective and safe for personnel performing the work and to the integrity of the well. Future sample data results at all wells will help determine an estimated completion of the rehabilitation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Nuclear Data Sheets for A=154

The experimental results published before Aug 2022 from the various reaction and decay studies leading to nuclides of Z=56 to Z=72, 154 Ba, 154 La, 154 Ce, 154 Pr, 154 Nd, 154 Pm, 154 Sm, 154 Eu, 154 Gd, 154 Tb, 154 Dy, 154 Ho, 154 Er, 154 Tm, 154 Yb, 154 Lu, 154 Hf, in the A=154 mass chain have been reviewed. These data are collected and presented in decay or reaction datasets, together with Adopted Levels and gammas datasets that are the most extensive collections of nuclear structure data for each nuclide. Furthermore this work is intended to supersede the previous evaluation of the A=154 nuclides by C.W. Reich (2009Re14), which was published in Nuclear Data Sheets 110, 2257 (2009).

Nica, N. [Texas A&M University, College Station, T↗
DOE OSTI2023

Evaluation of sterol-o-acyl transferase 1 and cholesterol ester levels in plasma, peritoneal fluid and tumor tissue of patients with endometrial cancer: A pilot study

Endometrial cancer (EC) is the most prevalent gynecological malignancy. Abnormal accumulation of sterol-O-acyl transferase 1 (SOAT1) and SOAT1-mediated cholesterol ester (CE) contributes to cancer progression in various malignancies, including ovarian cancer. Therefore, it was hypothesized that similar molecular changes may occur in EC. The present study aimed to evaluate the diagnostic and/or prognostic potential of SOAT1 and CE in EC by: i) Determining SOAT1 and CE levels in plasma, peritoneal fluid and endometrial tissue from patients with EC and control subjects; ii) performing receiver operating characteristic curve analysis to determine diagnostic performance; iii) comparing SOAT1 and CE expression to that of the tumor proliferation marker Ki67; and iv) assessing the association between SOAT1 expression and survival. Enzyme-linked immunosorbent assay was used to determine the levels of SOAT1 protein in tissue, plasma and peritoneal fluid. The mRNA and protein expression levels of SOAT1 and Ki67 in tissues were detected by reverse transcription-quantitative polymerase chain reaction and immunohistochemistry, respectively. CE levels were determined colorimetrically in plasma and peritoneal fluid. SOAT1-associated survival data from the cBioPortal cancer genomics database were used to assess prognostic relevance. The results revealed that SOAT1 and CE levels were significantly elevated in tumor tissue and peritoneal fluid samples collected from the EC group. By contrast, the plasma levels of SOAT1 and CE in the EC and control groups were similar. Significant positive associations between CE and SOAT1, SOAT1/CE and Ki67, and SOAT1/CE and poor overall survival in patients with EC suggested that SOAT1/CE may be associated with malignancy, aggressiveness and poor prognosis. In conclusion, SOAT1 and CE may serve as potential biomarkers for prognosis and target-specific treatment of EC.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Reanalysis of Water Withdrawal for Irrigation, Electric Power, and Public Supply Sectors in the Conterminous United States, 1950–2016

Accurately measuring water use by the economy is essential for developing reliable models of water resource availability. Indeed, these models rely on retrospective analyses that provide insights into shifting human population demands and adaptions to water shortages. However, accurate, methodologically consistent, empirically authentic, and spatiotemporally comprehensive historical datasets for water withdrawals are scarce. Herein, we present a reanalysis of annual resolution (1950–2016) historical data set on irrigation, electric power, and public supply water withdrawal within the conterminous United States (US) at the county-level, and, for power plants, at the site-level. To estimate electric power water use, we synthesized a historically comprehensive list of generators and historic patterns in generation across fuels, prime movers, and cooling technologies. Irrigation water use estimation required building a crop-demand model that utilized historical information on irrigated acreage for crops and golf courses, stage-specific crop water demand, and climate information. To estimate public water supply use, we developed a random forest model constructed from information on population, infrastructure, climate, and land cover. These estimates generally agree with total county and state water use information provided by the US Geological Survey (USGS) water use circular and estimates generated from independent studies for specific years. However, we also observed discrepancies between our estimates and USGS data that appear to be caused by inconsistencies in the methods used by the USGS's primary data sources at the state level over decades of data collection, highlighting the importance of reanalysis to yield spatiotemporally consistent and intercomparable estimates of water use.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Physics informed neural network can retrieve rate and state friction parameters from acoustic monitoring of laboratory stick-slip experiments

Various machine learning (ML) and deep learning (DL) techniques have been recently applied to the forecasting of laboratory earthquakes from friction experiments. The magnitude and timing of shear failures in stick-slip cycles are predicted using features extracted from the recorded ultrasonic or acoustic emission (AE) signals. In addition, the Rate and State Friction (RSF) constitutive laws are extensively used to model the frictional behavior of faults. In this work, we use data from shear experiments coupled with passive acoustic (variance, kurtosis, and AE rate) interleaved with active source ultrasonic monitoring (transmitted wave amplitude) to develop physics-informed neural network (PINN) models incorporating the RSF law and AE rate generation equation with wave amplitude serving as a proxy for friction state variable. This PINN framework allows learning RSF parameters from stick-slip experiments rather than measuring them through a series of velocity step experiments. We observe that when the stick-slip cycles are irregular, the PINN models outperform the data-driven DL models. Transfer learning (TL) PINN models are also developed by pre-training on data collected at one normal stress level followed by forecasting shear failures and retrieving RSF parameters at other stress levels (i.e., with different recurrence intervals) after retraining on a limited amount of new data. Our findings suggest that TL models perform better compared to standalone models. Both standalone and TL PINN-estimated RSF parameters and their ground truth values show excellent agreements thus demonstrating that RSF parameters can be retrieved from laboratory stick-slip experiments using the corresponding acoustic data and that the transmitted wave amplitude provides a good representation of the evolving frictional state during stick-slips.

58 GEOSCIENCES↗
DOE OSTI2021

ESS-DIVE Reporting Format for Comma-separated Values (CSV) File Structure

The ESS-DIVE reporting format for Comma-separated Values (CSV) file structure is based on a combination of existing guidelines and recommendations including some found within the Earth Science Community with valuable input from the Environmental Systems Science (ESS) Community. The CSV reporting format is designed to promote interoperability and machine-readability of CSV data files while also facilitating the collection of some file-level metadata content. Tabular data in the form of rows and columns should be archived in its simplest form, and we recommend submitting these tabular data following the ESS-DIVE reporting format for generic comma-separated values (CSV) text format files. In general, the CSV file format is more likely accessible by future systems when compared to a proprietary format and CSV files are preferred because this format is easier to exchange between different programs increasing the interoperability of a data file. By defining the reporting format and providing guidelines for how to structure CSV files and some field content within, this can increase the machine-readability of the data file for extracting, compiling, and comparing the data across files and systems.Data package files are in .csv, .png, and .md. Open the .csv with e.g. Microsoft Excel, LibreOffice, or Google Sheets. Open the .md files by downloading and using a text editor (e.g., notepad or TextEdit). Open the .png in e.g. a web browser, photo viewer/editor, or Google Drive.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Extreme Scale Infrasound Inversion and Prediction for Weather Characterization and Acute Event Detection

Accurate and timely weather predictions are critical to many aspects of society with a profound impact on our economy, general well-being, and national security. In particular, our ability to forecast severe weather systems is necessary to avoid injuries and fatalities, but also important to minimize infrastructure damage and maximize mitigation strategies. The weather community has developed a range of sophisticated numerical models that are executed at various spatial and temporal scales in an attempt to issue global, regional, and local forecasts in pseudo real time. The accuracy however depends on the time period of the forecast, the nonlinearities of the dynamics, and the target spatial resolution. Significant uncertainties plague these predictions including errors in initial conditions, material properties, data, and model approximations. To address these shortcomings, a continuous data collection occurs at an effort level that is even larger than the modeling process. It has been demonstrated that the accuracy of the predictions depends on the quality of the data and is independent to a certain extent on the sophistication of the numerical models. Data assimilation has become one of the more critical steps in the overall weather prediction business and consequently substantial improvements in the quality of the data would have transformational benefits. This paper describes the use of infrasound inversion technology, enabled through exascale computing, that could potentially achieve orders of magnitude improvement in data quality and therefore transform weather predictions with significant impact on many aspects of our society.

58 GEOSCIENCES↗
DOE OSTI2022

Microgrid Conceptual Design Guidebook | 2022

This guide is meant to assist communities – from residents to energy experts to decision makers – in developing a conceptual microgrid design that meets site-specific energy resilience goals. Using the framework described in this guidebook, stakeholders can come together and start to quantify site-specific vulnerabilities, identify the most significant risks to delivery of electricity, and establish electric outage tolerances across the community. In addition to establishing minimum service needs, this framework encourages communities to consider broader sustainability goals and policy constraints and begin to estimate up-front costs associated with the installation of alternative microgrid solutions. The framework guides a community through data collection and a high-level assessment of its needs, constraints, and priorities, prior to engaging engineers, vendors, and contractors. The first sections of this guidebook provide a high-level primer on electric systems. The latter sections include guidance for step-by-step data gathering and analysis of site conditions. The ultimate product resulting from the stepwise approach is a conceptual microgrid design. A conceptual design is defined as an initial design (10%-20% complete) that considers the specific threats, needs, limitations, and investment options for a given location. Going through this exercise and developing the conceptual microgrid design as a community ensures the same community members who will ultimately live with the solution are the developers of its foundational design. Often, these are also the very same people who understand system tolerances and needs the best and are therefore the ideal candidates for establishing these criteria. Especially when it comes to evaluating critical infrastructure, it is the community that best understands the most critical services. The framework is intended to facilitate a systematic approach to planning for resilience and provide a deeper understanding of how to use a framework to make decisions around microgrid solutions. Like many processes where tradeoffs need to be considered, this is often an iterative process. If this guide serves to help educate and empower communities who are beginning the process of deploying a microgrid, it has met the goal of its authors.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Assessment of BQ-9000 Biodiesel Properties for 2023

This is the seventh in a series of reports documenting the quality of biodiesel from U.S.- and Canadian-based producers that participate in the BQ-9000 program, the biodiesel industry’s voluntary quality assurance program. Participants provided monthly data on critical quality parameters for calendar year 2023 with quality data provided to a team of experts, who removed any identifying company information and provided anonymized data to the National Renewable Energy Laboratory (NREL) for statistical analysis. New for 2023, data on kinematic viscosity, sulfated ash, distillation temperature, carbon residue, and cetane were collected, as well as individual levels of sodium, potassium, calcium, and magnesium. Critical quality parameters analyzed are listed in Table ES-1 with descriptive statistics.

09 BIOMASS FUELS↗
DOE OSTI2023

Soil Sampling Results for Delineating BH-056 for Partial Closure of Solid Waste Management Unit #16

The U.S. Department of Energy/National Nuclear Security Administration (DOE/NNSA) and National Technology & Engineering Solutions of Sandia, LLC (NTESS), the management and operating contractor for Sandia National Laboratories/California (SNL/CA), has prepared this addendum to Soil Sampling Results for Closure of a Portion of Solid Waste Management Unit #16 to report the results of additional soil sampling relating to the closure of a portion of Solid Waste Management Unit (SWMU) #16. This additional sampling was in response to a request by the San Francisco Bay Regional Water Quality Control Board (SFRWQCB) in their letters dated February 16 and August 18, 2022 relating to the detection of the benzidine above the defined project action level in a soil sample collected adjacent to the sanitary sewer line in borehole BH-056 (SFRWQCB, 2022A; 2022b).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Assessment of BQ-9000 Biodiesel Properties for 2024

This is the eighth in a series of reports documenting the quality of biodiesel from U.S.- and Canadian-based producers that participate in the BQ-9000 program, the biodiesel industry's voluntary quality assurance program. Participants provided monthly data on critical quality parameters for calendar year 2024 with quality data provided to a team of experts, who removed any identifying company information and provided anonymized data to the National Renewable Energy Laboratory (NREL) for statistical analysis. Similar to 2023, data on kinematic viscosity, sulfated ash, distillation temperature, carbon residue, and cetane were collected, as well as individual levels of sodium, potassium, calcium, and magnesium.

09 BIOMASS FUELS↗
DOE OSTI2020

Estimation of Large-Scale Wind Field Characteristics Using Supervisory Control and Data Acquisition Measurements

As the wind energy industry continues to push for increased power production and lower cost of energy, the focus of research has expanded from individual turbines to entire wind farms. Among a host of interesting problems to be solved when considering the wind farm as a whole, we consider the challenge of scalar field estimation, based on information already collected at the individual turbine level. We aim to estimate the large-scale, low-frequency characteristics of the wind field, such as the mean wind direction and the overall decrease in wind speed across the farm, and employ a Kalman filter that models the wind field using a polynomial function. We compare the proposed method’s performance to both a simple averaging technique and filtering of individual turbine measurements. The method presented is not limited to wind turbines and is applicable in other situations where multiple remote agents are used to estimate a scalar field.

estimation↗
DOE OSTI2025

Mortality correlates with tree functional traits across a wood density gradient in the Central Amazon

Introduction: Understanding the mechanisms of tree mortality in tropical ecosystems remains challenging, in part due to the high diversity of tree species and the inherently stochastic nature of mortality. Plant functional traits offer a mechanistic link between plant physiology and performance, yet their ability to predict growth and mortality remains poorly understood. Given recent increases in tree mortality rates in the Amazon forest following extreme drought and wind events, we tested if lower wood density and acquisitive plant functional traits were associated with increased growth and mortality for common co-occurring trees in the Central Amazon. Methods: Seventeen trees of different species with similar sizes but a range in wood density (WD) and wood traits were felled, then assessed for 27 different individual functional parameters, including whole tree architecture, stem xylem anatomical and hydraulic traits and leaf traits. Traits of the individual trees were related to stand-level growth and mortality rates collected periodically over 30 years from nearby permanent inventory plots. Results: Higher wood density was associated with smaller leaf size, lower foliar base cations, lower stem water content and sapwood fraction, in agreement with the fast-slow plant economics spectrum. Lower wood density was associated with more acquisitive characteristics with greater hydraulic capacity and foliar nutrient concentrations, correlating with greater growth and mortality rates. Discussion: Our results show that lower wood density is part of a coordinated suite of traits linked to high resource acquisition, fast growth, and increased mortality risk, providing a functional framework for predicting species performance and forest vulnerability under future climate stress.

demographics↗
DOE OSTI2020

AI-Science for Performance Optimization and Diagnosis of Science Instrument Federations

Next generation of science workflows are expected to be executed over complex federations composed of supercomputers, science instruments, storage systems and networks, with new additions of the edge and cloud systems and services. The sheer complexity of these multi-domain federations makes it hard to manage them and optimize their performance, as small impedance mismatches (that can dynamically develop between systems) could drastically degrade the entire federation performance. Recent proliferation of Software Defined Everything (SDX) technologies combined with containerization frameworks provide custom instruments that can monitor and collect critical measurements at various levels to support diagnoses and performance optimization; but their data too enormous for human operators and analysts to process and generate decisions. Machine Learning (ML) methods that extract critical parameters, relationships and trends from the data offer general solutions. Artificial Intelligence (AI) and ML methods must be custom-developed for these problems based on solid, rigorous foundations, since black-box approaches are often ineffective and unsound.We propose to develop comprehensive AI-Science for the performance of science federations to (i) monitor and control storage, networks, experiments, and computing systems across multiple domains via softwarization layers, at speeds and scales orders of magnitude superior to current practice, (ii) optimally realize and orchestrate complex workflows with high performance by using dynamic state and performance estimation methods, and (iii) aggregate measurements across sites and time to develop infrastructure-level profiles, optimizations and diagnoses using AI-Science based on foundational principles from ML, game theory, and information fusion areas.

Rao, Nageswara↗