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Observations of stem water storage in trees of opposing hydraulic strategies
Hydraulic capacitance and water storage form a critical buffer against cavitation and loss of conductivity within the xylem system. Withdrawal from water storage in leaves, branches, stems, and roots significantly impacts sap flow, stomatal conductance, and transpiration. Storage quantities differ based on soil water availability, tree size, wood anatomy and density, drought tolerance, and hydraulic strategy (anisohydric or isohydric). However, the majority of studies focus on the measurement of storage in conifers or tropical tree species. We demonstrate a novel methodology using frequency domain reflectometry (FDR) to make continuous, direct measurements of wood water content in two hardwood species in a forest in Michigan. We present results of a two month study comparing the water storage dynamics between a mature red oak and red maple, two species with differing wood densities, hydraulic architecture, and hydraulic strategy. We also include results pertaining to the use of different probe lengths to sample water content only within the active sapwood and over the entire conductive sapwood and the outer portion of heartwood in red oak. Both species studied exhibited diurnal cycles of storage that aligned well with the dynamics of sap flux. Red maple, a diffuse porous, relatively isohydric species showed a strong dependence on stored water during both wet and dry periods. Red oak, a ring porous relatively anisohydric species, was less reliant on storage, and did not demonstrate a dependence on soil water potential. Comparison between long and short FDR probes in the oak revealed that oaks may utilize water stored in the innermost layers of the xylem when soil moisture conditions are limiting. We found the FDR probes to be a reliable, functional means for continuous automated measurement of wood water content in hardwoods at a fast time scale. Application of FDR technology for the measurement of tree water storage will benefit forest ecologists as well as the modeling community as we improve our understanding and simulations of plant hydrodynamic processes on a large scale.
Integration of LIBS with Machine Learning for Real-Time Monitoring of Feedstock in H 2 Gasification Applications
This project, funded by the U.S. Department of Energy (DOE) – Office of Fossil Energy under Award Number DE-FE0032177, aimed to assess the feasibility of an integrated Laser-Induced Breakdown Spectroscopy (LIBS) system with advanced machine learning (ML) models for real-time characterization and potential control of hydrogen gasifiers running on waste materials as feedstocks. This was a multidisciplinary effort that encompassed the acquisition and standardized analysis of individual and blended feedstocks—comprising biomass, coal waste, and plastic waste, followed by the development of a dynamic LIBS bench system for material sample analysis and development of predictive ML models. Comprehensive laboratory testing enabled the creation of a robust elemental dataset that served as the foundation for ML model training. Techniques such as Random Forest, Gradient Boosting, Support Vector Regression, and Neural Networks were employed to predict key feedstock properties, including higher heating value (HHV), moisture content, thermal conductivity, and ash composition with high accuracy. The results were validated against experimental data and demonstrated strong potential for real-time application in gasifier control systems. The project concluded with a study on the integration of the LIBS+ML approach for gasifier control and a techno-economic analysis of the implementation of the approach into hydrogen (H 2 ) gasification systems. Dissemination of results was carried out at a DOE meeting. This work establishes a scalable framework for automated, in-line feedstock quality assessment, offering significant implications for process optimization and emissions reduction in hydrogen production.
Spatially-Explicit Prediction of Capacity Density Advances Geographic Characterization of Wind Power Technical Potential
Mounting interest in ambitious clean energy goals is exposing critical gaps in our understanding of onshore wind power potential. Conventional approaches to evaluating wind power technical potential at the national scale rely on coarse geographic representations of land area requirements for wind power. These methods overlook sizable spatial variation in real-world capacity densities (i.e., nameplate power capacity per unit area) and assume that potential installation densities are uniform across space. Here, we propose a data-driven approach to overcome persistent challenges in characterizing localized deployment potentials over broad extents. We use machine learning to develop predictive relationships between observed capacity densities and geospatial variables. The model is validated against a comprehensive data set of United States (U.S.) wind facilities and subjected to interrogation techniques to reveal that key explanatory features behind geographic variation of capacity density are related to wind resource as well as urban accessibility and forest cover. We demonstrate application of the model by producing a high-resolution (2 km × 2 km) national map of capacity density for use in technical potential assessments for the United States. Our findings illustrate that this methodology offers meaningful improvements in the characterization of spatial aspects of technical potential, which are increasingly critical to draw reliable and actionable planning and research insights from renewable energy scenarios.
Retrieving forest soil moisture from SMAP observations considering a microwave polarization difference index (MPDI) to -ω model
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The Pilot Land Data System (PLDS) at the Ames Research Center manages aircraft data in collaboration with an ecosystem research project
The Pilot Land Data System (PLDS) is a data and information system serving NASA-supported investigators in the land science community. The three nodes of the PLDS, one each at the Ames Research Center (ARC), the Goddard Space Flight Center (GSFC) and the Jet Propulsion Laboratory (JPL), cooperate in providing consistent information describing the various data holding in the hardware and software (accessible via network and modem) that provide information about and access to PLDS-held data, which is available for distribution. A major new activity of the PLDS node at the Ames Research Center involves the interaction of the PLDS with an active NASA ecosystem science project, the Oregon Transect Ecosystems Research involves the management of, access to, and distribution of the large volume of widely-varying aircraft data collected by OTTER. The OTTER project, is managed by researchers at the Ames Research Center and Oregon State University. Its principal objective is to estimate major fluxes of carbon, nitrogen, and water of forest ecosystems using an ecosystem process model driven by remote sensing data. Ten researchers at NASA centers and universities are analyzing data for six sites along a temperature-moisture gradient across the western half of central Oregon (called the Oregon Transect). Sensors mounted on six different aircraft have acquired data over the Oregon Transect in support of the OTTER project.
The evolution of the diffuse cosmic ultraviolet background constrained by the Hubble Space Telescope observations of 3C 273
Results are presented of recent HST UV spectroscopy of 3C 273, which revealed more low-redshift Lyman-alpha absorption lines (IGM clouds) than expected from the extrapolation from high-redshift (not less than 1.6) observations. It is shown on the basis of the standard pressure confined cloud model of the Lyman-alpha forest that this result indicates a sharp drop in the diffuse cosmic UV background from 2 to 0 redshift. It is predicted that the H I optical depth will drop slowly or perhaps even increase with decreasing redshift at less than 2 redshift. The implied constraints on the density and pressure of the diffuse IGM at 0 redshift are also derived. The inferred evolution of the diffuse UV flux bears a striking resemblance to the most recent direct determinations of the volume emissivity of the quasar population.
Effects of Forest Disturbances on Forest Structural Parameters Retrieval from Lidar Waveform Data
The effect of forest disturbance on the lidar waveform and the forest biomass estimation was demonstrated by model simulation. The results show that the correlation between stand biomass and the lidar waveform indices changes when the stand spatial structure changes due to disturbances rather than the natural succession. This has to be considered in developing algorithms for regional or global mapping of biomass from lidar waveform data.
TOPEX/Poseidon Microwave Radiometer Performance and In-Flight Calibration
Results of the in-flight calibration and performance evaluation campaign for the TOPEX/Poseidon Microwave Radiometer are presented.
Chile Disasters: Automating Wildfire Risk and Occurrence Mapping in Google Earth Engine to Improve Wildfire Detection and Response Time Efforts
Wildfires in Chile in the last decade were the worst on record, destroying homes and livelihoods, polluting the air, and displacing whole towns. To predict locations where wildfires were likely to start, the Corporación Nacional Forestal (CONAF) created a wildfire risk model within ArcGIS Pro and Google Earth Engine (GEE) that utilized the NOAA Global Forecast System (GFS) and the NASA Shuttle Radar Topography Mission (STRM) 90-meter datasets. The previous CONAF model was very resource-heavy and time-intensive to run. NASA DEVELOP, in partnership with CONAF, automated the previous model and transferred it fully into GEE where all Earth observation datasets could be used without downloading. The new model substantially reduced the runtime. The final model was used to create a near real-time wildfire monitoring application as well as fire severity maps. The end products will be used by CONAF for wildfire prediction and management to prevent more destruction in the future.
Document Classification Techniques for Aviation Letters of Agreement
Often when working with technical documents, it is helpful to classify them into specific categories. In this paper, we conduct a thorough review of natural language processing techniques to perform this classification task on Letters of Agreement (LOAs), technical aviation documents outlining rules for utilizing US airspace. We evaluate multiple techniques, including Transfer Learning, for representing the text in the documents as embeddings: unigram and bigram Term Frequency Inverse Document Frequency (TFIDF), Word2Vec, Doc2Vec, GloVe and RoBERTa. We investigate a wide range of classification models: K-Nearest Neighbors, Random Forest, Support Vector Machines (SVM), Logistic Regression, Naive Bayes, Feed-Forward Neural Network, Convolutional Neural Networks (CNNs) and Long-Short Term Memory (LSTM). By comparing the different methods, we found the best overall approach for our task was to use unigram TFIDF representations with SVM while also gaining insight into how the other methodologies performed on a small technical datasets.
Document Classification Techniques for Aviation Letters of Agreement
Often when working with historic air traffic management (ATM) documents, it is helpful to classify them into specific categories. In this paper, we conduct a thorough review of natural language processing techniques to perform this classification task on Letters of Agreement (LOAs), technical aviation documents outlining rules for utilizing US airspace. We evaluate multiple techniques for representing the text in the documents as embeddings: unigram and bigram Term Frequency Inverse Document Frequency (TFIDF), Word2Vec, Doc2Vec, GloVe and RoBERTa. We investigate a wide range of classification models: K-Nearest Neighbors, Random Forest, Support Vector Machines (SVM), Logistic Regression, Naive Bayes, Feed-Forward Neural Network, Convolutional Neural Networks (CNNs) and Long-Short Term Memory (LSTM). By comparing the different methods, we found the best overall approach for our task was to use unigram TFIDF representations with SVM while also gaining insight into how the other methodologies performed on a small technical datasets.
Document Classification Techniques for Aviation Letters of Agreement
Often when working with historic air traffic management (ATM) documents, it is helpful to classify them into specific categories. In this paper, we conduct a thorough review of natural language processing techniques to perform this classification task on Letters of Agreement (LOAs), technical aviation documents outlining rules for utilizing US airspace. We evaluate multiple techniques for representing the text in the documents as embeddings: unigram and bigram Term Frequency Inverse Document Frequency (TFIDF), Word2Vec, Doc2Vec, GloVe and RoBERTa. We investigate a wide range of classification models: K-Nearest Neighbors, Random Forest, Support Vector Machines (SVM), Logistic Regression, Naive Bayes, Feed-Forward Neural Network, Convolutional Neural Networks (CNNs) and Long-Short Term Memory (LSTM). By comparing the different methods, we found the best overall approach for our task was to use unigram TFIDF representations with SVM while also gaining insight into how the other methodologies performed on a small technical datasets.
ICRF wave propagation and absorption modelling via machine learning
A surrogate model of the wave absorption in the ion cyclotron range of frequencies is presented. The model is trained to capture the physics of 1D electron and ion power absorption profiles for both the high harmonic fast wave scheme in NSTX, and the minority heating scheme in WEST. The surrogate models, based on both the random forest regressor and the multilayer perceptron algorithms, reduce inference time of 1D power absorption profiles from 1-5 minutes required by TORIC to ∼50 µs with high accuracy (i.e. R2 = 0.71−0.96).
Regionalized Life Cycle Greenhouse Gas Emissions of Forest Biomass Use for Electricity Generation in the United States
This study presents a cradle-to-grave life cycle analysis (LCA) of the greenhouse gas (GHG) emissions of the electricity generated from forest biomass in different regions of the United States (U.S.), taking into consideration regional variations in biomass availabilities and logistics. The regional biomass supply for a 20 MW bioelectricity facility is estimated using the Land Use and Resource Allocation (LURA) model. Results from LURA and data on regional forest management, harvesting, and processing are incorporated into the GHGs, Regulated Emissions, and Energy Use in Technologies (GREET) model for LCA. The results suggest that GHG emissions of mill residues-based pathways can be 15-52% lower than those of pulpwood-based pathways, with logging residues falling in between. Nonetheless, our analysis suggests that screening bioenergy projects on specific feedstock types alone is not sufficient because GHG emissions of a pulpwood-based pathway in one state can be lower than those of a mill residue-based pathway in another state. Furthermore, the available biomass supply often consists of several woody feedstocks, and its composition is region-dependent. Forest biomass-derived electricity is associated with 86-93% lower life-cycle GHG emissions than the emissions of the average grid electricity in the U.S. Key factors driving bioelectricity GHG emissions include electricity generation efficiency, transportation distance, and energy use for biomass harvesting and processing.
Forest biomass, canopy structure, and species composition relationships with multipolarization L-band synthetic aperture radar data
The effect of forest biomass, canopy structure, and species composition on L-band synthetic aperature radar data at 44 southern Mississippi bottomland hardwood and pine-hardwood forest sites was investigated. Cross-polarization mean digital values for pine forests were significantly correlated with green weight biomass and stand structure. Multiple linear regression with five forest structure variables provided a better integrated measure of canopy roughness and produced highly significant correlation coefficients for hardwood forests using HV/VV ratio only. Differences in biomass levels and canopy structure, including branching patterns and vertical canopy stratification, were important sources of volume scatter affecting multipolarization radar data. Standardized correction techniques and calibration of aircraft data, in addition to development of canopy models, are recommended for future investigations of forest biomass and structure using synthetic aperture radar.
Forest structure and solar-induced fluorescence across intact and degraded forests in the Amazon
Tropical forest degradation (e.g., anthropogenic disturbances such as selective logging and fires) alters forest structure and function and influences the forest's carbon sink. In this study, we explored structure-function relationships across a variety of degradation levels in the southern Brazilian Amazon by 1) investigating how forest structural properties vary as a function of degradation history using airborne lidar data; 2) assessing the effects of degradation on solar-induced chlorophyll fluorescence (SIF) seasonality using TROPOMI data; and 3) quantifying the contribution of structural variables to SIF using multiple regression models with stepwise selection of lidar metrics. Forest degradation history was obtained through Landsat time-series classification. We found that fire, logging, and time since disturbance were major determinants of forest structure, and that forests affected by fires experienced larger variability in leaf area index (LAI), canopy height and vertical structure relative to logged and intact forests. Moreover, only recently burned forests showed significantly depressed SIF during the dry season compared to intact forests. Canopy height and the vertical distribution of foliage were the best predictors of SIF. Unexpectedly, we found that wet-season SIF was higher in active regenerating forests (~ 4 years after fires or logging) compared with intact forests, despite lower LAI. Furthermore, our findings help to elucidate the mechanisms of carbon accumulation in anthropogenically disturbed tropical forests and indicate that they can capture large amounts of carbon while recovering.
Interpretation of Ground Temperature Anomalies in Hydrothermal Discharge Areas
Abstract Researchers have long noted the potential for shallow hydrothermal fluids to perturb near‐surface temperatures. Several investigators have made qualitative or semiquantitative use of elevated surface temperatures; for example, in snowfall calorimetry, or for tracing subsurface flow paths. However, a quantitative framework connecting surface temperature observations with conditions in the subsurface is currently lacking. Here, we model an area of shallow subsurface flow at Burgdorf Hot Springs, a rustic commercial resort in the Payette National Forest, north of McCall, ID, USA. We calibrate the model using shallow (0.2 m depth) ground temperature measurements and overburden thickness estimates from seismic refraction studies. The calibrated model predicts negligible loss of heat energy from the laterally migrating fluids at the Burgdorf site, in spite of the fact that thermal anomalies are observed in the unconsolidated near‐surface alluvium. Although elevated near‐surface ground temperatures are commonly assumed to result from locally high heat flux, this conflicts with the small apparent heat loss during lateral flow inferred at the Burgdorf site. We hypothesize an alternative explanation for near‐surface temperature anomalies that is only weakly dependent on heat flux, and more strongly controlled by the Biot number, a dimensionless parameter that compares the rate at which convection carries heat away from the land surface to the rate at which it is supplied by conduction to the interface.