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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Wood‐density has no effect on stomatal control of leaf‐level water use efficiency in an Amazonian forest

Forest disturbances increase the proportion of fast-growing tree species compared to slow-growing ones. To understand their relative capacity for carbon uptake and their vulnerability to climate change, and to represent those differences in Earth system models, it is necessary to characterise the physiological differences in their leaf-level control of water use efficiency and carbon assimilation. We used wood density as a proxy for the fast-slow growth spectrum and tested the assumption that trees with a low wood density (LWD) have a lower water-use efficiency than trees with a high wood density (HWD). We selected 5 LWD tree species and 5 HWD tree species growing in the same location in an Amazonian tropical forest and measured in situ steady-state gas exchange on top-of-canopy leaves with parallel sampling and measurement of leaf mass area and leaf nitrogen content. We found that LWD species invested more nitrogen in photosynthetic capacity than HWD species, had higher photosynthetic rates and higher stomatal conductance. Furthermore, contrary to expectations, we showed that the stomatal control of the balance between transpiration and carbon assimilation was similar in LWD and HWD species and that they had the same dark respiration rates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Mycorrhizal associations of tree species influence soil nitrogen dynamics via effects on soil acid–base chemistry

Across all spatial scales, ECM-dominated forests were associated with greater soil acidity. In particular, ECM-dominated soils exhibited lower soil pH and base cations, although the magnitude of mycorrhizal-associated differences in soil acid–base chemistry depended on the biomes, with differences being more pronounced in temperate than in sub/tropical forests. Higher lignin and lower base cations in ECM tree leaf litter were related to greater soil acidity in ECM-dominated forests. Moreover, the lower inorganic N concentrations and slower N transformation rates in ECM-dominated forests were associated with their greater soil acidity. Our results indicate that the scale-invariant feedbacks between plant nutrient-use strategies and soil properties have the potential to impact forest community assembly and ecosystem processes, particularly in the context of global change.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Disturbance-accelerated succession increases the production of a temperate forest

Many secondary deciduous forests of eastern North America are approaching a transition in which maturing early successional tree species are declining, resulting in an uncertain future for these forests’ century-long carbon (C) sink. We initiated the Forest Accelerated Succession Experiment (FASET) at the University of Michigan Biological Station to examine the patterns and mechanisms underlying forest C cycling following the stem-girdling induced mortality of >6,700 early successional Populus spp. (aspen) and Betula papyrifera (paper birch). Since 2008, meteorological flux tower-based C cycling observations from the 33-ha treatment forest have been paired with those from a nearby unmanipulated forest. Following over a decade of observations, herein we revisit our core hypothesis: that net ecosystem production (NEP) would increase following the transition to mid-late successional species dominance due to increased canopy structural complexity. Supporting our hypothesis, NEP was stable, briefly declined, and then increased relative to the control in the decade following disturbance; however, increasing NEP was not associated with rising structural complexity but rather the rapid 1-year recovery of total LAI as mid-late successional Acer, Quercus, and Pinus assumed canopy dominance. The transition to mid-late successional species dominance improved carbon-use efficiency (CUE = NEP/gross primary production) as ecosystem respiration declined. Similar soil respiration rates in control and treatment forests, along with species differences in leaf physiology and the rising relative growth rates of mid-late successional species in the treatment forest, suggest changes in aboveground plant respiration and growth, respectively, were primarily responsible for increases in NEP. We conclude that deciduous forests transitioning from early to middle succession are capable of sustained or increased NEP, even when experiencing extensive tree mortality. This adds to mounting evidence that aging deciduous forests in the region will function as C sinks for decades to come.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Estimating Individual Tree Mortality in the Sierra Nevada Using Lidar and Multispectral Reflectance Data

Widespread tree mortality events occur during periods of severe drought in temperate conifer forests and are expected to become more frequent in many areas due to climate change. Improved mapping of individual tree mortality is needed to identify risk factors and design effective conservation strategies. In this study, we used National Ecological Observatory Network (NEON) lidar and multispectral reflectance airborne observations to map individual tree mortality over a 160 km 2 area during and after the 2012–2016 drought for two sites in California's Sierra National Forest. We used NEON lidar to derive tree locations and crown perimeters and multispectral data to map tree mortality for more than 1 million trees. We found that 25.4% of the trees in our study area died between 2013 and 2017, with considerably higher mortality at the lower-elevation Soaproot Saddle site. Between 2017 and 2019, an additional 2.0%–2.8% of the trees died each year. Two wildfires in 2020 and 2021 increased tree mortality within burned area perimeters by 49%–89% between 2019 and 2021. Consistent with previous work, we found that tree mortality risk increased as a function of tree height. Tree mortality was positively associated with distance from rivers, trees per hectare, and decreasing slope at the lower elevation site. In contrast, increasing slope was positively associated with tree mortality at the higher elevation site. Our approach and dataset provide a means to study the combined effects of drought and wildfire on tree mortality and may improve projections of forest resilience under a changing climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Improving E3SM Land Model Photosynthesis Parameterization via Satellite SIF, Machine Learning, and Surrogate Modeling

The parameterization of key photosynthesis parameters is one of the key uncertain sources in modeling ecosystem gross primary productivity (GPP). Solar-induced chlorophyll fluorescence (SIF) offers a good proxy for GPP since it marks the actual process of photosynthesis; while machine learning (ML) provides a robust approach to model the GPP-SIF relationship. Here, we trained the boosted regressing tree (BRT) and the Random Forest ML models with Greenhouse Gases Observing Satellite SIF data and in situ GPP observations from 49 eddy covariance towers. These trained ML GPP-SIF models were fed into the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to generate ELM-simulated global SIF estimates, which were then benchmarked against satellite SIF observations with a surrogate modeling approach. Our results indicated good modeling performance of the ML-based GPP-SIF relationship. The ELM model when fed with the ML GPP-SIF models also can well predict the spatial-temporal variations in SIF. We also found high model accuracy for the surrogate modeling. Model parameter sensitivity analysis suggested that the fraction of leaf nitrogen in RuBisCO (flnr) is the most sensitive parameter to the SIF; other sensitive parameters include the Ball-Berry stomatal conductance slope (mbbopt) and the vcmax entropy (vcmaxse). The posterior uncertainty in simulated GPP was greatly reduced after benchmarking, and the model produced improved spatial patterns of mean GPP relative to FLUXCOM GPP. Our integrated approach provides a new avenue for improving land models and using remote-sensing SIF, which can be further improved in the future with more ground- and satellite-based observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Effects of Eucalyptus wood and leaf litter on saproxylic insects in the southeastern United States

Although Eucalyptus is widely planted outside its native range for timber and pulp production, the effects of these exotic plantations on biodiversity relative to native semi-natural forests or plantations of native tree species remain incompletely understood. Here, we compare the diversity of saproxylic beetles (Coleoptera) and true bugs (Hemiptera) between non-native Eucalyptus benthamii Maiden and Cambage (Camden white gum) and native Pinus taeda L. (loblolly pine) stands on the upper Coastal Plain of South Carolina, U.S.A. We sampled insects emerging from logs of both species placed in both stand types after 1, 2, 6, and 12 months in the field. Beetle and true bug richness and diversity were both significantly lower from eucalypt than from pine wood. Moreover, the two communities were compositionally distinct. Whereas pine supported many species of host-specific phloeoxylophagous beetles, most species collected from eucalypts were mycophagous or predatory taxa capable of utilizing a wide range of hosts. Species richness did not differ between logs placed in eucalypt vs. pine stands but Shannon’s diversity was significantly higher in the eucalypt stands, possibly due to greater sun exposure in the latter. Contrary to a previous study, we found no support for the idea that eucalypt litter reduces the diversity of saproxylic insects. Our findings add to the growing body of evidence that non-native plantations are less favorable to biodiversity than those consisting of native tree species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Genome resequencing reveals demographic history and genetic architecture of seed salinity tolerance in Populus euphratica

Populus euphratica is a dominant tree species in desert riparian forests and possesses extraordinary adaptation to salinity stress. Exploration of its genomic variation and molecular underpinning of salinity tolerance is important for elucidating population evolution and identifying stress-related genes. Here, we identify approximately 3.15 million single nucleotide polymorphisms using whole-genome resequencing. The natural populations of P. euphratica in northwest China are divided into four distinct clades that exhibit strong geographical distribution patterns. Pleistocene climatic fluctuations and tectonic deformation jointly shaped the extant genetic patterns. A seed germination rate-based salinity tolerance index was used to evaluate seed salinity tolerance of P. euphratica and a genome-wide association study was implemented. A total of 38 single nucleotide polymorphisms were associated with seed salinity tolerance and were located within or near 82 genes. Expression profiles showed that most of these genes were regulated under salt stress, revealing the genetic complexity of seed salinity tolerance. Furthermore, DEAD-box ATP-dependent RNA helicase 57 and one undescribed gene (CCG029559) were demonstrated to improve the seed salinity tolerance in transgenic Arabidopsis. These results provide new insights into the demographic history and genetic architecture of seed salinity tolerance in desert poplar.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Approximate Mixed-Integer Programming Solution with Machine Learning Technique and Linear Programming Relaxation

Security constrained unit commitment (SCUC) is solved in the day-ahead electricity market to determine electricity generation schedule for the following date. The associated mixed integer programming (MIP) problem is known computationally hard. Heuristics like rounding can be computationally efficient for solving large MIP problems however often produces lower quality solutions due to discrepancy between MIP solution and linear programming relaxation (LPR) solution, i.e., variables taking different discrete values between MIP and LPR solutions. In this work, we applied the machine learning-based algorithms—Classification and Regression Tree (CART) model and random forest (RF) model—to obtain an LPR-based approximation that close to an MIP solution without actually solving the MIP problem. By computing feature importance derived from the CART and RF models, we assessed the link between coefficients in the MIP model, the LPR solution, and the optimal solution of the MIP problem. We applied the CART and RF approaches to solve SCUC problems using day-ahead energy market cases from Midcontinent Independent System Operator. The results show that our method is computationally efficient and could achieve an approximation sufficiently close to the SCUC solution with <0.15% error and ~78% reduction in the discrepancies between LPR and MIP solutions.

Lin, Xinming↗
DOE OSTI2022

AmeriFlux US-SSH Susquehanna Shale Hills Critical Zone Observatory

This is the AmeriFlux version of the carbon flux data for the site US-SSH Susquehanna Shale Hills Critical Zone Observatory. Site Description - The Susquehanna Shale Hills Critical Zone Observatory is comprised of one first-order catchment in the Susquehanna River basin. This catchment, known as Shale Hills, is about 8 hectares in total area. The stream that defines the Shale Hills catchment flows into Shavers Creek in the Juniata River sub-basin. The vegetation cover at Shale Hills is dominated by deciduous broadleaf forest, with some evergreen needleleaf trees along the stream.

Forsythe, Brandon R.↗
DOE OSTI2023

AmeriFlux FLUXNET-1F US-xUK NEON The University of Kansas Field Station (UKFS)

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-xUK NEON The University of Kansas Field Station (UKFS). This is the FLUXNET version of the carbon flux data for the site US-xUK NEON The University of Kansas Field Station (UKFS) produced by applying the standard ONEFlux (1F) software. Site Description - Along an eastern deciduous forest and tallgrass prairie transition zone just north of Lawrence, Kansas, this 6 km2 terrestrial field site is located at the University of Kansas Field Station. A 116' tall flux tower rises out of a mixed hardwood forest dominated by white ash trees. Field obdservations of plants, animals, soils and microbes are collected around the tower and in the nearby tallgrass prairie.

Network), NEON (National Ecological Observatory↗
DOE OSTI2024

AmeriFlux FLUXNET-1F CA-SCC Scotty Creek Landscape

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site CA-SCC Scotty Creek Landscape. This is the FLUXNET version of the carbon flux data for the site CA-SCC Scotty Creek Landscape produced by applying the standard ONEFlux (1F) software. Site Description - The Scotty Creek flux tower is located in an organic-rich boreal forest-wetland landscape about 50 km south of Fort Simpson in the Taiga Plains of the Mackenzie watershed. The tower was installed in 2013 and operates an open-path EC system year-round running on solar power only. Flux footprints contain about 50 % forested peat plateaus and 50 % wetlands (i.e., collapse-scar bogs). The forests are underlain by permafrost, while the treeless wetlands are permafrost-free. The tower itself is located on a forested peat plateau. Black spruce tree density on plateaus is sparse and the mean canopy height is ca. 5 m.

Sonnentag, Oliver↗
DOE OSTI2024

AmeriFlux FLUXNET-1F US-SSH Susquehanna Shale Hills Critical Zone Observatory

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-SSH Susquehanna Shale Hills Critical Zone Observatory. This is the FLUXNET version of the carbon flux data for the site US-SSH Susquehanna Shale Hills Critical Zone Observatory produced by applying the standard ONEFlux (1F) software. Site Description - The Susquehanna Shale Hills Critical Zone Observatory is comprised of one first-order catchment in the Susquehanna River basin. This catchment, known as Shale Hills, is about 8 hectares in total area. The stream that defines the Shale Hills catchment flows into Shavers Creek in the Juniata River sub-basin. The vegetation cover at Shale Hills is dominated by deciduous broadleaf forest, with some evergreen needleleaf trees along the stream.

Forsythe, Brandon R.↗
NASA NTRS1988

Isoprene over the Amazon Basin

Data obtained during the 1985 ABLE expedition to the Amazon are used to describe the diurnal and vertical variations of isoprene. Isoprene is a natural hydrocarbon emitted by many species of trees, particularly those in tropical forests. The concentrations of isoprene at lower levels in the atmosphere undergo large diurnal variations, with the highest concentrations during midday and the lowest during the night. At ground level, outside the forest, peak concentrations of about 3-parts per billion by volume (ppbv) of isoprene were observed around midday. Concentrations were nearly zero before sunrise, increased to their maximum values during the day, and declined after sunset. Concentrations of 1-2 ppbv of isoprene were observed up to 300 m. Near the canopy level, up to 8 ppbv of isoprene were observed. In the forest, concentrations are generally quite low below the canopy and are highest at the level of the canopy. Since the reaction of isoprene with OH radicals is extremely fast, its concentrations fall off rapidly with altitude, so that practically none of it was seen above the boundary layer. During nighttime, however, concentrations comparable to daytime values were observed at altitudes of 300 m and above.

Rasmussen, R. A.↗
NASA NTRS1990

Advanced Solid-State Array Spectroradiometer (ASAS) support of 1989 field experiments

An overview of the ASAS data acquired in support of the 1989 field experiments is presented and data quality is discussed. The precision of the ASAS data is considered through the presentation of SNRs derived from both field and laboratory data. ASAS is an airborne, off-nadir pointing, imaging spectroradiometer that acquires digital image data for 29 visible and near-infrared spectral bands (465 to 871 nm) with a spectral resolution of 15 nm. Surfaces observed for the field experiments include volcanic surfaces and a playa within a sparsely vegetated semiarid ecosystem, grass canopies within a prairie ecosystem, and tree canopies within a northern forest ecosystem. It is shown that calibrated ASAS data are sufficiently exact for investigations of the directional distribution of radiation scattered from terrestrial surfaces.

Irons, James R.↗
NASA NTRS1999

Validation of Land-Surface Mosaic Heterogeneity in the GEOS DAS

The Mosaic Land-surface Model (LSM) has been included into the current GEOS Data Assimilation System (DAS). The LSM uses a more advanced representation of physical processes than previous versions of the GEOS DAS, including the representation of sub-grid heterogeneity of the land-surface through the Mosaic approach. As a first approximation, Mosaic assumes that all similar surface types within a grid-cell can be lumped together as a single'tile'. Within one GCM grid-cell, there might be 1 - 5 different tiles or surface types. All tiles are subjected to the grid-scale forcing (radiation, air temperature and specific humidity, and precipitation), and the sub-grid variability is a function of the tile characteristics. In this paper, we validate the LSM sub-grid scale variability (tiles) using a variety of surface observing stations from the Southern Great Plains (SGP) site of the Atmospheric Radiation Measurement (ARM) Program. One of the primary goals of SGP ARM is to study the variability of atmospheric radiation within a G,CM grid-cell. Enough surface data has been collected by ARM to extend this goal to sub-grid variability of the land-surface energy and water budgets. The time period of this study is the Summer of 1998 (June I - September 1). The ARM site data consists of surface meteorology, energy flux (eddy correlation and bowen ratio), soil water observations spread over an area similar to the size of a G-CM grid-cell. Various ARM stations are described as wheat and alfalfa crops, pasture and range land. The LSM tiles considered at the grid-space (2 x 2.5) nearest the ARM site include, grassland, deciduous forests, bare soil and dwarf trees. Surface energy and water balances for each tile type are compared with observations. Furthermore, we will discuss the land-surface sub-grid variability of both the ARM observations and the DAS.

Bosilovich, Michael G.↗
NASA NTRS2010

Multiple Spectral-Spatial Classification Approach for Hyperspectral Data

A .new multiple classifier approach for spectral-spatial classification of hyperspectral images is proposed. Several classifiers are used independently to classify an image. For every pixel, if all the classifiers have assigned this pixel to the same class, the pixel is kept as a marker, i.e., a seed of the spatial region, with the corresponding class label. We propose to use spectral-spatial classifiers at the preliminary step of the marker selection procedure, each of them combining the results of a pixel-wise classification and a segmentation map. Different segmentation methods based on dissimilar principles lead to different classification results. Furthermore, a minimum spanning forest is built, where each tree is rooted on a classification -driven marker and forms a region in the spectral -spatial classification: map. Experimental results are presented for two hyperspectral airborne images. The proposed method significantly improves classification accuracies, when compared to previously proposed classification techniques.

Tarabalka, Yuliya↗
NASA NTRS2012

Using Random Forest Models to Predict Organizational Violence

We present a methodology to access the proclivity of an organization to commit violence against nongovernment personnel. We fitted a Random Forest model using the Minority at Risk Organizational Behavior (MAROS) dataset. The MAROS data is longitudinal; so, individual observations are not independent. We propose a modification to the standard Random Forest methodology to account for the violation of the independence assumption. We present the results of the model fit, an example of predicting violence for an organization; and finally, we present a summary of the forest in a "meta-tree,"

Levine, Burton↗
NASA NTRS2016

Prediction of Weather Impacts on Airport Arrival Meter Fix Capacity

This paper introduces a data driven model for predicting airport arrival capacity with a look-ahead time 2-8 hour forecast. The model is suitable for air traffic flow management by explicitly investigating the impact of convective weather on airport arrival meter fix throughput. Estimation of the arrival airport capacity under arrival meter fix flow constraints due to severe weather is an important part of Air Traffic Management (ATM). Airport arrival capacity can be reduced if one or more airport arrival meter fixes are partially or completely blocked by convective weather. When the predicted airport arrival demands exceed the predicted available airport's arrival capacity for a sustained period, Ground Delay Program (GDP) operations will be triggered by ATM system. Serious imbalances between demand and capacity occur most frequently when the airport capacity is severely degraded due to either bad airport terminal surface weather or inclement convective weather around airport arrival fixes. A model that predicts the weather-impacted airport arrival meter fix throughput may help ATM personnel to plan GDP operations more efficiently. This paper identifies the characteristics of air traffic flow across arrival meter fixes at Newark Liberty International Airport (EWR). The proposed approach, based on machine-learning methods, is developed to predict the weather impacted EWR arrival Meter Fix (MF) throughput. Sector forecast coverage is used to envision the weather impact on airport arrival MF flow, and the validation is accomplished by using Convective Weather Avoidance Model (CWAM) 0.5 to 2-hour and Collaborative Convective Forecast Product (CCFP) 4 to 8-hour look-ahead forecast data for the period of April-September in 2014. Furthermore, the regression tree ensemble learning of random forests approach for translating a sector forecast coverage model to an EWR arrival meter fix throughput model is examined. The results suggest that ATM decision makers in charge of MF flow control and GDP planning may benefit from adopting the airport arrival meter capacity prediction models to estimate the inclement weather impacts.

Wang, Yao X.↗