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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 595 records · Page 33
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NASA NTRS1989

Electromagnetic wave extinction within a forested canopy

A forested canopy is modeled by a collection of randomly oriented finite-length cylinders shaded by randomly oriented and distributed disk- or needle-shaped leaves. For a plane wave exciting the forested canopy, the extinction coefficient is formulated in terms of the extinction cross sections (ECSs) in the local frame of each forest component and the Eulerian angles of orientation (used to describe the orientation of each component). The ECSs in the local frame for the finite-length cylinders used to model the branches are obtained by using the forward-scattering theorem. ECSs in the local frame for the disk- and needle-shaped leaves are obtained by the summation of the absorption and scattering cross-sections. The behavior of the extinction coefficients with the incidence angle is investigated numerically for both deciduous and coniferous forest. The dependencies of the extinction coefficients on the orientation of the leaves are illustrated numerically.

Karam, M. A.↗
DOE OSTI2024

A Vertically Resolved Canopy Improves Chemical Transport Model Predictions of Ozone Deposition to North Temperate Forests

Abstract Dry deposition is the second largest tropospheric ozone (O 3 ) sink and occurs through stomatal and nonstomatal pathways. Current O 3 uptake predictions are limited by the simplistic big‐leaf schemes commonly used in chemical transport models (CTMs) to parameterize deposition. Such schemes fail to reproduce observed O 3 fluxes over terrestrial ecosystems, highlighting the need for more realistic treatment of surface‐atmosphere exchange in CTMs. We address this need by linking a resolved canopy model (1D Multi‐Layer Canopy CHemistry and Exchange Model, MLC‐CHEM) to the GEOS‐Chem CTM and use this new framework to simulate O 3 fluxes over three north temperate forests. We compare results with in situ measurements from four field studies and with standalone, observationally constrained MLC‐CHEM runs to test current knowledge of O 3 deposition and its drivers. We show that GEOS‐Chem overpredicts observed O 3 fluxes across all four studies by up to 2×, whereas the resolved‐canopy models capture observed diel profiles of O 3 deposition and in‐canopy concentrations to within 10%. Relative humidity and solar irradiance are strong O 3 flux drivers over these forests, and uncertainties in those fields provide the largest remaining source of model deposition biases. Flux partitioning analysis shows that: (a) nonstomatal loss accounts for 60% of O 3 deposition on average; (b) in‐canopy chemistry makes only a small contribution to total O 3 fluxes; and (c) the CTM big‐leaf treatment overestimates O 3 ‐driven stomatal loss and plant phytotoxicity in these temperate forests by up to 7×. Results motivate the application of fully online vertically explicit canopy schemes in CTMs for improved O 3 predictions.

Vermeuel, Michael P. [Department of Soil, Water, a↗
DOE OSTI2023

A comparison of machine learning surrogate models of street-scale flooding in Norfolk, Virginia

Low-lying coastal cities, exemplified by Norfolk, Virginia, face the challenge of street flooding caused by rainfall and tides, which strain transportation and sewer systems and can lead to personal and property damage. While high-fidelity, physics-based simulations provide accurate predictions of urban pluvial flooding, their computational complexity renders them unsuitable for real-time applications. Using data from Norfolk rainfall events between 2016 and 2018, this study compares the performance of a previous surrogate model based on a random forest algorithm with two deep learning models: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The comparison of deep learning to the random forest algorithm is motivated by the desire to utilize a machine learning architecture that allows for the future inclusion of common uncertainty quantification techniques and the effective integration of relevant, multi-modal features.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Carbon-phosphorus cycle models overestimate CO 2 enrichment response in a mature Eucalyptus forest

The importance of phosphorus (P) in regulating ecosystem responses to climate change has fostered P-cycle implementation in land surface models, but their CO 2 effects predictions have not been evaluated against measurements. Here, we perform a data-driven model evaluation where simulations of eight widely used P-enabled models were confronted with observations from a long-term free-air CO 2 enrichment experiment in a mature, P-limited Eucalyptus forest. We show that most models predicted the correct sign and magnitude of the CO 2 effect on ecosystem carbon (C) sequestration, but they generally overestimated the effects on plant C uptake and growth. We identify leaf-to-canopy scaling of photosynthesis, plant tissue stoichiometry, plant belowground C allocation, and the subsequent consequences for plant-microbial interaction as key areas in which models of ecosystem C-P interaction can be improved. Together, this data-model intercomparison reveals data-driven insights into the performance and functionality of P-enabled models and adds to the existing evidence that the global CO 2 -driven carbon sink is overestimated by models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Evaluating three evapotranspiration estimates from model of different complexity over China using the ILAMB benchmarking system

The land surface models range in complexity of terrestrial evapotranspiration, yet it is unknown how model complexity translates to accuracy of modeled evapotranspiration estimates. Here, we use the International Land Model Benchmarking system to assess ET estimates from three models of varying complexity driven by the same forcing datasets: an earth system model, a terrestrial biosphere model, and a stand-alone ET model. The performance assessment includes both temporal and spatial evaluation, and different plant functional types across China. Our results indicate that the most complex model, an earth system model, performed best against the benchmarking datasets and metrics. Terrestrial biosphere model performed best in simulating inter-annual variability of ET, while earth system model performed best in simulating the seasonal cycle. The more complex models (earth system model and terrestrial biosphere model) perform better in forest, shrub and crop ecosystems, while the simpler model (stand-alone ET model) perform better in grass ecosystems. Our study demonstrates the impact of model complexity on ET estimates and highlights directions for future ET model improvements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Plant roots stimulate the decomposition of complex, but not simple, soil carbon

Roots release carbon into soil and can alleviate energy limitation of microbial organic matter decomposition. We know little about the effects of roots on microbial decomposition of different organic matter substrates, despite the importance for soil carbon stocks and turnover. Through implementing root-microbe interactions, the Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment (CORPSE) model was previously shown to represent dynamics of total soil carbon in temperate forest field experiments. However, the model permits alternative hypotheses concerning microbial-substrate affinity. We investigated how root inputs affect decomposition of soil organic carbon (SOC) with variable decomposability. We simulated SOC stocks in CORPSE and compared microbial degradation of two substrates types with varying root-microbe interactions under two alternative hypotheses that varied in microbial-substrate affinity. We compared our modeled hypotheses to a forest field experiment where we quantified decomposition of isotopically-labeled starch and leaf tissues in soils with manipulated root access to microbes. Here, we tested the hypothesis that decomposition of leaves would be more sensitive to root inputs than decomposition of starch, corresponding to the alternative model hypothesis. In the field study, leaf decomposition increased with root density while starch decomposition was unchanged by root density. Microbial biomass and enzyme activity consistently increased with root inputs in CORPSE and the field study. Our field experiment supported the CORPSE simulations with high microbial-substrate affinity. Roots stimulated microbial growth and enzyme production, which increased degradation of more complex substrates such as leaf tissues. Substrates that were easily decomposed, such as starch, may already be degrading at a maximum rate in the absence of rhizosphere influence because their decomposition rate was unchanged by root inputs. We found that the degree to which roots stimulate microbial decomposition depends on the substrate being decomposed, and that root-microbe interactions influenced SOC stocks in both our model and field experiment. Environmental changes that alter root-microbe interactions could, therefore, alter soil C stocks and biogeochemical cycling, and models of these interactions should incorporate differential influence of rhizosphere inputs on different substrates.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2000

Satellite Observation of El Nino Effects on Amazon Forest Phenology and Productivity

Climate variability may affect the functioning of Amazon moist tropical forests, and recent modeling analyses suggest that the carbon dynamics of the region vary interannually in response to precipitation and temperature anomalies. However, due to persistent orbital and atmospheric artifacts in the satellite record, remote sensing observations have not provided quantitative evidence that climate variation affects Amazon forest phenology or productivity, We developed a method to minimize and quantify non-biological artifacts in NOAA AVHRR satellite data, providing a record of estimated forest phenological variation from 1982-1993. The seasonal Normalized Difference Vegetation Index (NDVI) amplitude (a proxy for phenology) increased throughout much of the basin during El Nino periods when rainfall was anomalously low. Wetter La Nina episodes brought consistently smaller NDVI amplitudes. Using radiative transfer and terrestrial biogeochemical models driven by these satellite data, we estimate that canopy-energy absorption and net primary production of Amazon forests varied interannually by as much as 21% and 18%, respectively. These results provide large-scale observational evidence for interannual sensitivity to El Nino of plant phenology and carbon flux in Amazon forests.

Asner, Gregory P.↗
DOE OSTI2014

TU-C-12A-12: Differentiating Bone Lesions and Degenerative Joint Disease in NaF PET/CT Scans Using Machine Learning

Purpose: [F-18]NaF PET can be used to image bone metastases; however, tracer uptake in degenerative joint disease (DJD) often appears similar to metastases. This study aims to develop and compare different machine learning algorithms to automatically identify regions of [F-18]NaF scans that correspond to DJD. Methods: 10 metastatic prostate cancer patients received whole body [F-18]NaF PET/CT scans prior to treatment. Image segmentation resulted in 852 ROIs, 69 of which were identified by a nuclear medicine physician as DJD. For all ROIs, various PET and CT textural features were computed. ROIs were divided into training and testing sets used to train eight different machine learning classifiers. Classifiers were evaluated based on receiver operating characteristics area under the curve (AUC), sensitivity, specificity, and positive predictive value (PPV). We also assessed the added value of including CT features in addition to PET features for training classifiers. Results: The training set consisted of 37 DJD ROIs with 475 non-DJD ROIs, and the testing set consisted of 32 DJD ROIs with 308 non-DJD ROIs. Of all classifiers, generalized linear models (GLM), decision forests (DF), and support vector machines (SVM) had the best performance. AUCs of GLM (0.929), DF (0.921), and SVM (0.889) were significantly higher than the other models (p<0.001). GLM and DF, overall, had the best sensitivity, specificity, and PPV, and gave a significantly better performance (p<0.01) than all other models. PET/CT GLM classifiers had higher AUC than just PET or just CT. GLMs built using PET/CT information had superior or comparable sensitivities, specificities and PPVs to just PET or just CT. Conclusion: Machine learning algorithms trained with PET/CT features were able to identify some cases of DJD. GLM outperformed the other classification algorithms. Using PET and CT information together was shown to be superior to using PET or CT features alone. Research supported by the Prostate Cancer Foundation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Resource Occurrence and Productivity in Existing and Proposed Wind Energy Lease Areas on the Northeast US Shelf

States in the Northeast United States have the ambitious goal of producing more than 22 GW of offshore wind energy in the coming decades. The infrastructure associated with offshore wind energy development is expected to modify marine habitats and potentially alter the ecosystem services. Species distribution models were constructed for a group of fish and macroinvertebrate taxa resident in the Northeast US Continental Shelf marine ecosystem. These models were analyzed to provide baseline context for impact assessment of lease areas in the Middle Atlantic Bight designated for renewable wind energy installations. Using random forest machine learning, models based on occurrence and biomass were constructed for 93 species providing seasonal depictions of their habitat distributions. We developed a scoring index to characterize lease area habitat use for each species. Subsequently, groups of species were identified that reflect varying levels of lease area habitat use ranging across high, moderate, low, and no reliance on the lease area habitats. Among the species with high to moderate reliance were black sea bass ( Centropristis striata ), summer flounder ( Paralichthys dentatus ), and Atlantic menhaden ( Brevoortia tyrannus ), which are important fisheries species in the region. Potential for impact was characterized by the number of species with habitat dependencies associated with lease areas and these varied with a number of continuous gradients. Habitats that support high biomass were distributed more to the northeast, while high occupancy habitats appeared to be further from the coast. There was no obvious effect of the size of the lease area on the importance of associated habitats. Model results indicated that physical drivers and lower trophic level indicators might strongly control the habitat distribution of ecologically and commercially important species in the wind lease areas. Therefore, physical and biological oceanography on the continental shelf proximate to wind energy infrastructure development should be monitored for changes in water column structure and the productivity of phytoplankton and zooplankton and the effects of these changes on the trophic system.

17 WIND ENERGY↗
NASA NTRS2021

Developing a Set of Indicators to Identify, Monitor, and Track Impacts and Change in Forests of the United States

United States forestland is an important ecosystem type, land cover, land use, and economic resource that is facing several drivers of change including climatic. Because of its significance, forestland was identified through the National Climate Assessment(NCA) as a key sector and system of concern to be included in a system of climate indicators as part of a sustained assessment effort. Here, we describe 11 informative core indicators of forests and climate change impacts with metrics available or nearly available for use in the NCA efforts. The recommended indicators are based on a comprehensive conceptual model which recognizes forests as a land use, an ecosystem, and an economic sector. The indicators cover major forest attributes such as extent, structural components such as biomass, functions such as growth and productivity, and ecosystem services such as biodiversity and outdoor recreation. Interactions between humans and forests are represented through indicators focused on the wildland-urban interface, cost to mitigate wildfire risk, and energy produced from forest-based biomass. Selected indicators also include drought and disturbance from both wildfires and biotic agents. The forest indicators presented are an initial set that will need further refinement in coordination with other NCA indicator teams. Our effort ideally will initiate the collection of critical measurements and observations and lead to additional research on forest-climate indicators.

U.S. forests↗
DOE OSTI2015

Final report for DOE Award # DE- SC0010039*: Carbon dynamics of forest recovery under a changing climate: Forcings, feedbacks, and implications for earth system modeling

To advance understanding of C dynamics of forests globally, we compiled a new database, the Forest C database (ForC-db), which contains data on ground-based measurements of ecosystem-level C stocks and annual fluxes along with disturbance history. This database currently contains 18,791 records from 2009 sites, making it the largest and most comprehensive database of C stocks and flows in forest ecosystems globally. The tropical component of the database will be published in conjunction with a manuscript that is currently under review (Anderson-Teixeira et al., in review). Database development continues, and we hope to maintain a dynamic instance of the entire (global) database.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Photometric redshift estimation of BASS DR3 quasars by machine learning

ABSTRACT Correlating Beijing–Arizona Sky Survey (BASS) data release 3 (DR3) catalogue with the ALLWISE data base, the data from optical and infrared information are obtained. The quasars from Sloan Digital Sky Survey are taken as training and test samples while those from LAMOST are considered as external test sample. We propose two schemes to construct the redshift estimation models with XGBoost, CatBoost, and Random Forest. One scheme (namely one-step model) is to predict photometric redshifts directly based on the optimal models created by these three algorithms; the other scheme (namely two-step model) is to first classify the data into low- and high-redshift data sets, and then predict photometric redshifts of these two data sets separately. For one-step model, the performance of these three algorithms on photometric redshift estimation is compared with different training samples, and CatBoost is superior to XGBoost and Random Forest. For two-step model, the performances of these three algorithms on the classification of low and high redshift subsamples are compared, and CatBoost still shows the best performance. Therefore, CatBoost is regarded as the core algorithm of classification and regression in two-step model. In contrast to one-step model, two-step model is optimal when predicting photometric redshift of quasars, especially for high-redshift quasars. Finally, the two models are applied to predict photometric redshifts of all quasar candidates of BASS DR3. The number of high-redshift quasar candidates is 3938 (redshift ≥3.5) and 121 (redshift ≥4.5) by two-step model. The predicted result will be helpful for quasar research and follow-up observation of high-redshift quasars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Extrapolating carbon dynamics of seasonally dry tropical forests across geographic scales and into future climates: improving simulation models with empirical observations. Final report

This research focused on seasonally dry tropical forests (SDTFs), a biome that exhibits strong variation in ecological, hydrological and physical characteristics. Climate change is expected to strongly impact the atmospheric environment of SDTFs, and long-term trends in precipitation seasonality have already been detected. The extent to which such changes in seasonal moisture availability will impact SDTF carbon dynamics is highly uncertain, and much of this uncertainty hinges on poorly-understood belowground processes. Thus, the central objectives of this research were to qualitatively and quantitatively describe how belowground processes mediate the response of SDTF carbon dynamics to environmental change. We accomplished these aims through field campaigns in four tropical countries to characterize soil biogeochemistry and plant hydraulic traits, ecosystem-scale field experiments that manipulated both nutrients and rainfall, and simulation modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A novel improved model for building energy consumption prediction based on model integration

Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into “meta-features” to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%–31.6% for Case A and 16.2%–49.4% for Case B), generalization (improvement of 6.7%–29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%–34.1% for Case A and 1.8%–19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Carbon budget of the Harvard Forest Long-Term Ecological Research site: pattern, process, and response to global change

How, where, and why carbon (C) moves into and out of an ecosystem through time are long-standing questions in biogeochemistry. Here, we bring together hundreds of thousands of C-cycle observations at the Harvard Forest in central Massachusetts, USA, a mid-latitude landscape dominated by 80–120-yr-old closed-canopy forests. These data answered four questions: (1) where and how much C is presently stored in dominant forest types; (2) what are current rates of C accrual and loss; (3) what biotic and abiotic factors contribute to variability in these rates; and (4) how has climate change affected the forest’s C cycle? Harvard Forest is an active C sink resulting from forest regrowth following land abandonment. Soil and tree biomass comprise nearly equal portions of existing C stocks. Net primary production (NPP) averaged 680–750 g C·m –2 ·yr –1 ; belowground NPP contributed 38–47% of the total, but with large uncertainty. Mineral soil C measured in the same inventory plots in 1992 and 2013 was too heterogeneous to detect change in soil-C pools; however, radiocarbon data suggest a small but persistent sink of 10–30 g C·m –2 ·yr –1 . Net ecosystem production (NEP) in hardwood stands averaged ~300 g C·m –2 ·yr –1 . NEP in hemlock-dominated forests averaged ~450 g C·m –2 ·yr –1 until infestation by the hemlock woolly adelgid turned these stands into a net C source. Since 2000, NPP has increased by 26%. For the period 1992–2015, NEP increased 93%. The increase in mean annual temperature and growing season length alone accounted for ~30% of the increase in productivity. Interannual variations in GPP and NEP were also correlated with increases in red oak biomass, forest leaf area, and canopy-scale light-use efficiency. Compared to long-term global change experiments at the Harvard Forest, the C sink in regrowing biomass equaled or exceeded C cycle modifications imposed by soil warming, N saturation, and hemlock removal. Furthermore, the results of this synthesis and comparison to simulation models suggest that forests across the region are likely to accrue C for decades to come but may be disrupted if the frequency or severity of biotic and abiotic disturbances increases.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Classification of bacterial plasmid and chromosome derived sequences using machine learning

Plasmids are important genetic elements that facilitate horizonal gene transfer between bacteria and contribute to the spread of virulence and antimicrobial resistance. Most bacterial genome sequences in the public archives exist in draft form with many contigs, making it difficult to determine if a contig is of chromosomal or plasmid origin. Using a training set of contigs comprising 10,584 chromosomes and 10,654 plasmids from the PATRIC database, we evaluated several machine learning models including random forest, logistic regression, XGBoost, and a neural network for their ability to classify chromosomal and plasmid sequences using nucleotide k-mers as features. Based on the methods tested, a neural network model that used nucleotide 6-mers as features that was trained on randomly selected chromosomal and plasmid subsequences 5kb in length achieved the best performance, outperforming existing out-of-the-box methods, with an average accuracy of 89.38% ± 2.16% over a 10-fold cross validation. The model accuracy can be improved to 92.08% by using a voting strategy when classifying holdout sequences. In both plasmids and chromosomes, subsequences encoding functions involved in horizontal gene transfer—including hypothetical proteins, transporters, phage, mobile elements, and CRISPR elements—were most likely to be misclassified by the model. This study provides a straightforward approach for identifying plasmid-encoding sequences in short read assemblies without the need for sequence alignment-based tools.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Quantifying the effect of forest age in annual net forest carbon balance

Forests dominate carbon (C) exchanges between the terrestrial biosphere and the atmosphere on land. In the long term, the net carbon flux between forests and the atmosphere has been significantly impacted by changes in forest cover area and structure due to ecological disturbances and management activities. Current empirical approaches for estimating net ecosystem productivity (NEP) rarely consider forest age as a predictor, which represents variation in physiological processes that can respond differently to environmental drivers, and regrowth following disturbance. Here, we conduct an observational synthesis to empirically determine to what extent climate, soil properties, nitrogen deposition, forest age and management influence the spatial and interannual variability of forest NEP across 126 forest eddy-covariance flux sites worldwide. The empirical models explained up to 62% and 71% of spatio-temporal and across-site variability of annual NEP, respectively. An investigation of model structures revealed that forest age was a dominant factor of NEP spatio-temporal variability in both space and time at the global scale as compared to abiotic factors, such as nutrient availability, soil characteristics and climate. These findings emphasize the importance of forest age in quantifying spatio-temporal variation in NEP using empirical approaches.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Machine learning models for maintenance cost estimation in delivery trucks using diesel and natural gas fuels

The maintenance costs can represent about 15%–60% of the cost of produced goods depending on the type of goods transported. To comply with stringent emissions regulations, diesel engines are incorporated with complex after-treatment systems that demand increased maintenance. The availability of alternative fuels such as natural gas and propane has fostered the natural gas and propane powertrain systems as well as electrification options for heavy- and medium-duty vehicles. A critical barrier to adopting alternative fuel vehicles has been the lack of knowledge on comparative vehicle maintenance/repair costs with conventional diesel. Moreover, the region of operation, the type of vehicle operation, and seasonal temperature changes also affect the duty cycle which impacts the maintenance and repair costs. This study focuses on estimating the cost-per-mile for heavy-duty vehicles using machine learning models such as random forest, xgboost, neural networks, and a super-learner model. The super-learner model achieved an error as low as 0.0068 $/mile for mean absolute error and 0.0086 $/mile for root mean square error with a coefficient of determination/R-Squared of 97.28%. Specifically, the paper investigates the data collected from the maintenance and repair costs associated with delivery trucks using diesel and natural gas fuels. Since the availability of data is the major constraint, we leveraged the data collected by West Virginia University and the partnership with fleet companies. This allows for additional information related to maintenance costs and fleet-specific maintenance practices of alternative fuel vehicles. This study promotes clean fuel technologies and enables fleet management companies to adopt alternative fuel vehicles in case of similar or lower cost of maintenance compared to diesel vehicles resulting in reduced emissions and total cost of ownership.

Katreddi, Sasanka↗