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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Global patterns of forest autotrophic carbon fluxes

Carbon (C) fixation, allocation, and metabolism by trees set the basis for energy and material flows in forest ecosystems and define their interactions with Earth’s changing climate. However, we lack a cohesive synthesis on how forest carbon fluxes vary globally with respect to climate and one another. Here, we draw upon 1,319 records from the Global Forest Carbon Database (ForC), representing all major forest types and the nine most significant autotrophic carbon fluxes, to comprehensively explore how C cycling in mature, undisturbed forests varies with latitude and climate on a global scale. We show that, across all flux variables analyzed, C cycling decreases continuously with absolute latitude – a finding that confirms multiple previous studies but contradicts the idea that net primary productivity of temperate forests rivals that of tropical forests. C flux variables generally displayed similar trends across latitude and multiple climate variables, with no differences in allocation detected at this global scale. Temperature variables in general, and mean annual temperature and temperature seasonality in particular, were the best univariate predictors of C flux, explaining 19 - 71% of variation in the C fluxes analyzed. The effects of temperature were modified by moisture availability, with C flux reduced under hot and dry conditions and sometimes under very high precipitation. C fluxes increased with growing season length, but this was never the best univariate predictor. Within the growing season, the influence of climate on C cycling was small but significant for a number of flux variables. These findings clarify how forest C flux varies with latitude and climate on a global scale. In a period of accelerating climatic change, this improved understanding of the fundamental climatic controls on forest C cycling sets a foundation for understanding patterns of change.

Banbury Morgan, Rebecca↗
DOE OSTI2022

AmeriFlux FLUXNET-1F US-GLE GLEES

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-GLE GLEES. This is the FLUXNET version of the carbon flux data for the site US-GLE GLEES produced by applying the standard ONEFlux (1F) software. Site Description - The Glacier Lakes Ecosystem Experiments Site (GLEES) site is located on land owned by the U.S. government and managed by US Forest Service as part of the Medicine Bow National Forest. Many of the trees in the immediate vicinity of the site are older than 400 years, inter-dispersed among trees much younger in age. This widespread age distribution is most likely a derivation of one of two scenarios: 1) A widespread stand replacement about 400 years ago followed by a slow replacement; 2) Intermittent random disturbances over the past 400 years (Bradford et al. 2008). A decade long spruce beetle outbreak that peaked in 2008 resulted in the mortality of 85% of the forested basal area. There are a few private land holdings in the area, with scattered uncorked mining claims. Following the establishment of the National Forest, mining was banned and grazing was closed in the early 1990's in the upper portion of the GLEES site. Recreation in the winter, when snow can remain in patches into the summer months, snow mobiling and cross country skiing are popular. During the limited summer, hiking, camping and fishing are common activities. The site is accessible by vehicle only during the summer on Forest Road FDR 317, and in the winter, the tower is only reachable via snowmobile.

Frank, John↗
DOE OSTI2021

Hydraulically‐vulnerable trees survive on deep‐water access during droughts in a tropical forest

Summary Deep‐water access is arguably the most effective, but under‐studied, mechanism that plants employ to survive during drought. Vulnerability to embolism and hydraulic safety margins can predict mortality risk at given levels of dehydration, but deep‐water access may delay plant dehydration. Here, we tested the role of deep‐water access in enabling survival within a diverse tropical forest community in Panama using a novel data‐model approach. We inversely estimated the effective rooting depth (ERD, as the average depth of water extraction), for 29 canopy species by linking diameter growth dynamics (1990–2015) to vapor pressure deficit, water potentials in the whole‐soil column, and leaf hydraulic vulnerability curves. We validated ERD estimates against existing isotopic data of potential water‐access depths. Across species, deeper ERD was associated with higher maximum stem hydraulic conductivity, greater vulnerability to xylem embolism, narrower safety margins, and lower mortality rates during extreme droughts over 35 years (1981–2015) among evergreen species. Species exposure to water stress declined with deeper ERD indicating that trees compensate for water stress‐related mortality risk through deep‐water access. The role of deep‐water access in mitigating mortality of hydraulically‐vulnerable trees has important implications for our predictive understanding of forest dynamics under current and future climates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Coastal groundwater salinization impairs tree carbon–water balance

How coastal forest productivity varies with local nutrient availability and water supply remains a knowledge gap under climate change. In a two-decade field experiment manipulating fertilization and density in coastal pine forests, we show that a decade of growth enhancement by simulated sedimentary nutrient inputs has resulted in a striking reversal in growth and increased mortality risk as drought and sea level rise progressed. Recent groundwater salinization has further triggered a shift from nutrient to water limitation, causing severe stomatal closure and decoupling of tree carbon–water balance, which induces a negative intrinsic water use efficiency (iWUE)–growth relationship. Higher tree iWUE predicted sharper tree growth declines, and both nutrient enrichment and high stand density amplified this feedback, increasing the risk of hydraulic failure and mortality. These results suggest that a transient nutrient-stimulatory effect could drive further coastal forest degradation due to heightened belowground saltwater stress under sea-level rise.

Climate-change ecology↗
DOE OSTI2022

Challenges and Capabilities in Estimating Snow Mass Intercepted in Conifer Canopies With Tree Sway Monitoring

Snowpack accumulation in forested watersheds depends on the amount of snow intercepted in the canopy and its partitioning into sublimation, unloading, and melt. A lack of canopy snow measurements limits our ability to evaluate models that simulate canopy processes and predict snowpack. We tested whether monitoring changes in wind-induced tree sway is a viable technique for detecting snow interception and quantifying canopy snow water equivalent (SWE). Over a 6 year period in Colorado, we monitored hourly sway of two conifers, each instrumented with an accelerometer sampling at 12 Hz. We developed an approach to distinguish changes in sway frequency due to thermal effects on tree rigidity versus intercepted snow mass. Over 60% of days with canopy snow had a sway signal that could not be distinguished from thermal effects. However, larger changes in tree sway could not generally be attributed to thermal effects, and canopy snow was present 93%–95% of the time, as confirmed with classified PhenoCam imagery. Using sway tests, we converted changes in sway to canopy SWE, which were correlated with total snowstorm amounts from a nearby SNOTEL site (Spearman r = 0.72 to 0.80, p < 0.001). Greater canopy SWE was associated with storm temperatures between -7°C and 0°C and wind speeds less than 4 m s -1 . Lower canopy SWE prevailed in storms with lower temperatures and higher wind speeds. Monitoring tree sway is a viable approach for quantifying canopy SWE, but challenges remain in converting changes in sway to mass and distinguishing thermal and snow mass effects on tree sway.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

ORNL FACE Tree Height, with data on height of a subset of trees measured every year, 1997-2009. Free-Air CO2 Enrichment (FACE) Experiment Synthesis Activities.

Height of 20 sweetgum trees in three rows of each of the plots in the Oak Ridge National Laboratory (ORNL) free-air CO2 enrichment (FACE) experiment was measured each year of the experiment (1997-2009). The trees were exposed to ambient or elevated CO2. Tree height data contribute to the overall project objectives to determine the integrated response of a deciduous forest to elevated CO2. Average tree height per plot was used in allometric equations to calculate tree dry mass each year and to test whether there were effects of elevated CO2 on canopy development. Height was derived from trigonometric calculation from measurements of horizontal distance of observer from the bole and the angles from observer’s eye to top and base of tree, using a Haglof sonic hypsometer. The data are presented in a csv file, with plot number in the first column, tree number in the second column, and height recorded to 0.1 m in 1997 to 2009 in remaining columns. Plots 1 and 2 had elevated CO2, and plots 3-5 were in ambient CO2. Missing data occur after the tree died. The experiment and the height measurement are described in: Norby, R. J., D. E. Todd, J. Fults, and D. W. Johnson. 2001. Allometric determination of tree growth in a CO2-enriched sweetgum stand. New Phytologist 150: 477-487. The ORNL FACE experiment is described at: https://richnorby.org/home/face-home/.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

An ecologically-constrained deep learning model for tropical leaf phenology monitoring using PlanetScope satellites

In tropical forests, leaf phenology signals leaf-on/off status and exhibits considerable variability across scales from a single tree-crown to the entire forest ecosystem. Such phenology signals importantly regulate large-scale biogeochemical cycles and regional climate. PlanetScope CubeSats data with a 3-m resolution and near-daily global coverage provide an unprecedented opportunity to monitor both fine- and ecosystem-scale phenology variability along large environmental gradients. However, a scalable method that accurately characterizes leaf phenology from PlanetScope with biophysically meaningful metrics remains lacking. Here we developed an index-guided, ecologically constrained autoencoder (IG-ECAE) method to automatically derive a deciduousness metric (i.e. percentage of tree canopies with leaf-off status within an image pixel) from PlanetScope. The IG-ECAE first estimated the reflectance spectra of leafy/leafless canopies based on their spectral indices characteristics, then used the derived reflectance spectra to guide an autoencoder deep learning method with additional ecological constraints to refine the reflectance spectra, and finally used linear spectral unmixing to estimate the relative abundance of leafless canopies (or deciduousness) per PlanetScope image pixel. We tested the IG-ECAE method at 16 tropical forest sites spanning multiple continents and a large precipitation gradient (1470-2819 mm year -1 ). Among these sites, we evaluated the PlanetScope-derived deciduousness with corresponding measures derived from WorldView-2 (n=9 sites) and local phenocams (n=9 sites). Our results show that PlanetScope-derived deciduousness agrees: 1) with that derived from WorldView-2 at the patch level (90m×90m) with r 2 =0.89 across all sites; and 2) with that derived from phenocams to quantify ecosystem-scale seasonality with r 2 ranging from 0.62-0.96. These results demonstrate the effectiveness and scalability of IG-ECAE in characterizing the wide variability in deciduousness across scales from pixels to forest ecosystems, and from a single date to the full annual cycle, indicating the potential for using high-resolution satellites to track the large-scale phenological patterns and response of tropical forests to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A Comparison of Infectious Disease Forecasting Methods across Locations, Diseases, and Time

Accurate infectious disease forecasting can inform efforts to prevent outbreaks and mitigate adverse impacts. This study compares the performance of statistical, machine learning (ML), and deep learning (DL) approaches in forecasting infectious disease incidences across different countries and time intervals. We forecasted three diverse diseases: campylobacteriosis, typhoid, and Q-fever, using a wide variety of features (n = 46) from public datasets, e.g., landscape, climate, and socioeconomic factors. We compared autoregressive statistical models to two tree-based ML models (extreme gradient boosted trees [XGB] and random forest [RF]) and two DL models (multi-layer perceptron and encoder–decoder model). The disease models were trained on data from seven different countries at the region-level between 2009–2017. Forecasting performance of all models was assessed using mean absolute error, root mean square error, and Poisson deviance across Australia, Israel, and the United States for the months of January through August of 2018. The overall model results were compared across diseases as well as various data splits, including country, regions with highest and lowest cases, and the forecasted months out (i.e., nowcasting, short-term, and long-term forecasting). Overall, the XGB models performed the best for all diseases and, in general, tree-based ML models performed the best when looking at data splits. There were a few instances where the statistical or DL models had minutely smaller error metrics for specific subsets of typhoid, which is a disease with very low case counts. Feature importance per disease was measured by using four tree-based ML models (i.e., XGB and RF with and without region name as a feature). The most important feature groups included previous case counts, region name, population counts and density, mortality causes of neonatal to under 5 years of age, sanitation factors, and elevation. This study demonstrates the power of ML approaches to incorporate a wide range of factors to forecast various diseases, regardless of location, more accurately than traditional statistical approaches.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Tree-level carbon stock estimations across diverse species using multi-source remote sensing integration

Forests are critical carbon sinks, and remote sensing has been increasingly widely used for forest monitoring and biomass estimations. However, species-specific tree-level studies remain limited. In this study, we demonstrated the feasibility of integrating UAV-based LiDAR with high-resolution optical satellite imagery (0.5 m) to estimate biomass for individual trees across different species. The proposed method accurately estimated biomass for 53 trees (R² = 0.82, rRMSE = 0.44), with species-specific datasets, showing an average 25.2% increase in R² and a 14.8% reduction in rRMSE. A novel vegetation index combining forest structure parameters with vegetation indices (VIs) was developed using high-resolution multispectral satellite data (3 m) to explore its relationship with individual tree biomass. Combining forest structural parameters with VIs further improved estimation accuracy, achieving an R²of 0.89 and an rRMSE of 0.34. Species-specific datasets show an 11.6% increase in R²compared to methods without VIs, and a 22.2% improvement over methods using only VIs. SHapley Additive exPlanations (SHAP) analysis shows that the volume feature played a key role in model performance and remained stable throughout the training process. Altogether, the proposed approach enhances individual tree biomass and carbon sink estimations, showing great potential for large-scale precise forest carbon monitoring using multi-source remote sensing data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Characterizing Canopy Openness in Open Forests: Spherical Densiometer and Canopy Photography Are Equivalent but Less Sensitive than Direct Measurements of Solar Radiation

In forest ecosystems, canopy openness affects understory light availability, plant growth, and tree species recruitment, thus shaping future forest composition, structure, and functional diversity. Foresters must correctly and quickly measure canopy openness to meet their management objectives. To help guide the selection of an appropriate method for measuring canopy openness, we compared three common techniques that vary in cost, complexity, and time required for measurements and data processing: smartphone-based hemispherical photography, spherical densiometer measurements, and direct measurements of solar radiation (using AccuPAR ceptometer). We measured canopy openness using these three methods on 28 permanent forest health monitoring plots in pine-oak forests of the Central Pine Barrens of Long Island in New York State. By analysis of variance and regression analyses, we found the three methods (particularly densiometer and hemispherical photographs) yielded broadly equivalent and strongly positively correlated descriptions of canopy openness. The direct measurements of solar radiation seemed to have a greater potential to detect subtle variation in forest understory light. Forest managers may sufficiently characterize canopy openness using quick and cheap methods (e.g., spherical densiometers) and avoid larger costs of devices for direct light measurements (e.g., ceptometers) and the larger data processing times of hemispherical photography.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A comparative study of machine learning models for predicting the state of reactive mixing

Mixing phenomena are important mechanisms controlling flow, species transport, and reaction processes in fluids and porous media. Accurate predictions of reactive mixing are critical for many Earth and environmental science problems such as contaminant fate and remediation, macroalgae growth, and plankton biomass evolution. Here, to investigate the evolution of mixing dynamics under different scenarios (e.g., anisotropy, fluctuating velocity fields), a finite-element-based numerical model was built to solve the fast, irreversible bimolecular reaction-diffusion equations to simulate a range of reactive-mixing scenarios. A total of 2,315 simulations were performed using different sets of model input parameters comprising various spatial scales of vortex structures in the velocity field, time-scales associated with velocity oscillations, the perturbation parameter for the vortex-based velocity, anisotropic dispersion contrast (i.e., ratio of longitudinal-to-transverse dispersion), and molecular diffusion. The outputs comprised concentration profiles of reactants and products. The inputs to and outputs from these simulations were concatenated into feature and label matrices, respectively, to train 20 different machine learning (ML) models intended to emulate system behavior. These 20 ML emulators, based on linear methods, Bayesian methods, ensemble learning methods, and multilayer perceptrons (MLPs), were trained to classify the state of mixing and predict three quantities of interest (QoIs) characterizing species production, decay (i.e., average concentration, square of average concentration), and degree of mixing (i.e., variances of species concentration). Unsurprisingly, linear classifiers and regressors failed to reproduce the QoIs; however, ensemble methods (classifiers and regressors) and the MLP model accurately classified the state of reactive mixing and the QoIs. Among ensemble methods, random forest and decision-tree-based AdaBoost faithfully predicted the QoIs. At run time, trained ML emulators produced results times faster than the finite-element simulations. Due to their low computational expense and high accuracy, ensemble and MLP models are excellent emulators for these numerical simulations and great utilities in uncertainty quantification exercises, which can require 1,000s of forward model runs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Carbon stored in live ponderosa pines in the Sierra Nevada will not return to pre-drought (2012) levels during the 21st century due to bark beetle outbreaks

Outbreaks of several bark beetle species can develop rapidly in response to drought and may result in large transfers of carbon (C) stored in live trees to C stored in dead trees (10s of Tg C yr -1 in the western U.S. alone), which over time will be released back to the atmosphere. The western pine beetle (WPB) outbreak incited by the 2012–2015 mega-drought in the Sierra Nevada, California, U.S., could portend more frequent and/or severe bark beetle outbreaks as the temperature warms and drought frequency and intensity increase in the future. However, changes in the frequency and/or severity (resultant levels of host tree mortality) of beetle outbreaks are difficult to predict as outbreaks are complex with non-linear and eruptive processes primarily driven by interactions among beetle populations, the demography of hosts and other tree species, and climate and weather. Using an insect phenology and tree defense model, we projected the future likelihood of WPB outbreaks in the Sierra Nevada with climate drivers from different Earth System Models. Our goal was to understand how host (ponderosa pine, PIPO) recovery and future warming and drought affect the frequency and severity of WPB outbreaks and their C consequences. Our projections suggested that by 2100 the C stored in live PIPO (mean: 1.98 kg C m -2 , 95% CI: 1.74–2.21 kg C m -2 ) will not return to levels that occurred before the 2012–2015 drought (2012: ~2.30 kg C m –2 ) due to future WPB outbreaks. However, differences in climate models indicate a wide range of possible WPB outbreak frequencies and severities. Our results suggest that total plot basal area is the most significant factor in the mortality rate of PIPO by WPB in any given year, followed by drought severity and temperature. High levels of host basal area, higher temperature, and extreme drought all contribute to the frequency and severity of future WPB outbreaks. While PIPO basal area may decline under increased drought and warming, limiting high-stand basal area (>60 m 2 ha -1 ) may reduce the severity of future WPB outbreaks in the Sierra Nevada.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

AmeriFlux FLUXNET-1F CA-NS1 UCI-1850 burn site

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site CA-NS1 UCI-1850 burn site. This is the FLUXNET version of the carbon flux data for the site CA-NS1 UCI-1850 burn site produced by applying the standard ONEFlux (1F) software. Site Description - The UCI-1850 site is located in a continental boreal forest, dominated by black spruce trees, within the BOREAS northern study area in central Manitoba, Canada. The site is a member of a chronological series of sites that are representative secondary succession growth stages after large stand replacement fires. Black spruce trees undergo a slow growth process enabling the accurate determination of the chronosequence of stand age disturbance. Additionally, boreal forests make up approximately 25% of forest ecosystems on earth. With both of these in mind, the UCI sites provide an excellent location to study the CO2 exchange between the atmosphere and boreal forest ecosystems as a function of sequential wildfires.

Goulden, Mike↗
DOE OSTI2022

AmeriFlux FLUXNET-1F CA-NS2 UCI-1930 burn site

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site CA-NS2 UCI-1930 burn site. This is the FLUXNET version of the carbon flux data for the site CA-NS2 UCI-1930 burn site produced by applying the standard ONEFlux (1F) software. Site Description - The UCI-1930 site is located in a continental boreal forest, dominated by black spruce trees, within the BOREAS northern study area in central Manitoba, Canada. The site is a member of a chronological series of sites that are representative secondary succession growth stages after large stand replacement fires. Black spruce trees undergo a slow growth process enabling the accurate determination of the chronosequence of stand age disturbance. Additionally, boreal forests make up approximately 25% of forest ecosystems on earth. With both of these in mind, the UCI sites provide an excellent location to study the CO2 exchange between the atmosphere and boreal forest ecosystems as a function of sequential wildfires.

Goulden, Mike↗
DOE OSTI2022

AmeriFlux FLUXNET-1F CA-NS3 UCI-1964 burn site

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site CA-NS3 UCI-1964 burn site. This is the FLUXNET version of the carbon flux data for the site CA-NS3 UCI-1964 burn site produced by applying the standard ONEFlux (1F) software. Site Description - The UCI-1964 site is located in a continental boreal forest, dominated by black spruce trees, within the BOREAS northern study area in central Manitoba, Canada. The site is a member of a chronological series of sites that are representative secondary succession growth stages after large stand replacement fires. Black spruce trees undergo a slow growth process enabling the accurate determination of the chronosequence of stand age disturbance. Additionally, boreal forests make up approximately 25% of forest ecosystems on earth. With both of these in mind, the UCI sites provide an excellent location to study the CO2 exchange between the atmosphere and boreal forest ecosystems as a function of sequential wildfires.

Goulden, Mike↗
DOE OSTI2022

AmeriFlux FLUXNET-1F CA-NS4 UCI-1964 burn site wet

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site CA-NS4 UCI-1964 burn site wet. This is the FLUXNET version of the carbon flux data for the site CA-NS4 UCI-1964 burn site wet produced by applying the standard ONEFlux (1F) software. Site Description - The UCI-1964 wet site is located in a continental boreal forest, dominated by black spruce trees, within the BOREAS northern study area in central Manitoba, Canada. The site is a member of a chronological series of sites that are representative secondary succession growth stages after large stand replacement fires. Black spruce trees undergo a slow growth process enabling the accurate determination of the chronosequence of stand age disturbance. Additionally, boreal forests make up approximately 25% of forest ecosystems on earth. With both of these in mind, the UCI sites provide an excellent location to study the CO2 exchange between the atmosphere and boreal forest ecosystems as a function of sequential wildfires.

Goulden, Mike↗
DOE OSTI2022

AmeriFlux FLUXNET-1F CA-NS5 UCI-1981 burn site

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site CA-NS5 UCI-1981 burn site. This is the FLUXNET version of the carbon flux data for the site CA-NS5 UCI-1981 burn site produced by applying the standard ONEFlux (1F) software. Site Description - The UCI-1981 site is located in a continental boreal forest, dominated by black spruce trees, within the BOREAS northern study area in central Manitoba, Canada. The site is a member of a chronological series of sites that are representative secondary succession growth stages after large stand replacement fires. Black spruce trees undergo a slow growth process enabling the accurate determination of the chronosequence of stand age disturbance. Additionally, boreal forests make up approximately 25% of forest ecosystems on earth. With both of these in mind, the UCI sites provide an excellent location to study the CO2 exchange between the atmosphere and boreal forest ecosystems as a function of sequential wildfires.

Goulden, Mike↗