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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Corynebacterium glutamicum as an Efficient Omnivorous Microbial Host for the Bioconversion of Lignocellulosic Biomass

Corynebacterium glutamicum has been successfully employed for the industrial production of amino acids and other bioproducts, partially due to its native ability to utilize a wide range of carbon substrates. We demonstrated C. glutamicum as an efficient microbial host for utilizing diverse carbon substrates present in biomass hydrolysates, such as glucose, arabinose, and xylose, in addition to its natural ability to assimilate lignin-derived aromatics. As a case study to demonstrate its bioproduction capabilities, L-lactate was chosen as the primary fermentation end product along with acetate and succinate. C. glutamicum was found to grow well in different aromatics (benzoic acid, cinnamic acid, vanillic acid, and p-coumaric acid) up to a concentration of 40 mM. Besides, 13 C-fingerprinting confirmed that carbon from aromatics enter the primary metabolism via TCA cycle confirming the presence of β-ketoadipate pathway in C. glutamicum . 13 C-fingerprinting in the presence of both glucose and aromatics also revealed coumarate to be the most preferred aromatic by C. glutamicum contributing 74 and 59% of its carbon for the synthesis of glutamate and aspartate respectively. 13 C-fingerprinting also confirmed the activity of ortho-cleavage pathway, anaplerotic pathway, and cataplerotic pathways. Finally, the engineered C. glutamicum strain grew well in biomass hydrolysate containing pentose and hexose sugars and produced L-lactate at a concentration of 47.9 g/L and a yield of 0.639 g/g from sugars with simultaneous utilization of aromatics. Succinate and acetate co-products were produced at concentrations of 8.9 g/L and 3.2 g/L, respectively. Our findings open the door to valorize all the major carbon components of biomass hydrolysate by using C. glutamicum as a microbial host for biomanufacturing.

13C-fingerprinting↗
NASA NTRS1996

Epidemiology of Staphylococcus aureus during space flight

Staphylococcus aureus was isolated over 2 years from Space Shuttle mission crewmembers to determine dissemination and retention of bacteria. Samples before and after each mission were from nasal, throat, urine, and feces and from air and surface sampling of the Space Shuttle. DNA fingerprinting of samples by digestion of DNA with SmaI restriction endonuclease followed by pulsed-field gel electrophoresis showed S. aureus from each crewmember had a unique fingerprint and usually only one strain was carried by an individual. There was only one instance of transfer between crewmembers. Strains from interior surfaces after flight matched those of crewmembers, suggesting microbial fingerprinting may have forensic application.

Flight Experiment↗
DOE OSTI2025

Universal Electronic‐Structure Relationship Governing Intrinsic Magnetic Properties in Permanent Magnets

An electronic-structure-centered perspective is presented on permanent-magnet (PM) design, highlighting two key levers, that is, saturation magnetization (M s ), governed by 3d-band filling and exchange physics, and magnetocrystalline anisotropy energy (MAE), arising from spin-orbit coupling (SOC) on anisotropic orbital populations. Reviewing current practices, including DFT-based MAE/J ij extraction, atomistic-spin and micromagnetic modeling, and high-throughput machine learning (ML) pipelines, three bottlenecks limiting predictive discovery is identified that is i) electronic-structure accuracy for small MAE (sensitive to functional choice, Hubbard U, and many-body effects), ii) finite-temperature and kinetic realism (phonon/magnon renormalization, ordering kinetics), and iii) descriptor and multiscale decoupling (lack of SOC-weighted and orbital-resolved fingerprints). Deep dives into the electronic-structure of Nd─Fe─B and Fe─N show how these fingerprints govern magnetic performance, motivating DFT- and quantum-mechanics-based descriptors for discovery. Unbiased, structure-driven exploration, coupled with high-throughput simulations, ML, generative AI, and reasoning models, accelerates candidate identification and propagates insights across scales. Addressing supply-chain risks, on future needs of designing “critical-element-free” magnets with tailored microstructure and high energy products is emphasized. By integrating electronic fingerprints, AI reasoning, and multiscale modeling, a practical roadmap is provided for rare-earth-lean or rare-earth free, high-performance, sustainable PMs.

Singh, Prashant [Ames Laboratory, and Iowa State U↗
DOE OSTI2025

BCARS Simulated Phantom Dataset for Evaluation of Processing Pipelines

Broadband coherent anti-Stokes Raman scattering (BCARS) microscopy is a powerful label-free biological imaging technique, but the raw signal requires careful processing. The vibrationally resonant (Raman) fingerprint signal is usually small compared with instrumental noise sources and the nonresonant background (NRB) inherent in the BCARS signal. Fortunately, the NRB exhibits a systematic phase relationship with the coherent Raman response, acting as a heterodyne amplifier for the weak fingerprint signal. Due to this heterodyne effect, the Raman response can be recovered quantitatively and invariantly across different instruments, provided the NRB shape is known. Even with heterodyne amplification, the amplitudes of fingerprint signal components are often comparable to system noise. Singular value decomposition (SVD), which utilizes spatial information, is often employed for additional noise filtering. Consequently, finding optimal processing parameters to properly distinguish the NRB and Raman responses and suppress noise in the complex BCARS signal requires a reference system that realistically represents the spectral and spatial properties of BCARS signals obtained from biological samples. We present a digital tissue phantom that meets these criteria as a tool for testing candidate signal processing pipelines. The digital phantom is generated with simulated hyperspectral Raman images having system-specific noise and background characteristics. Here, we analyze phantom datasets with differing background and signal-to-noise conditions to evaluate their impact on the performance of multiple signal processing pipelines. Specifically, we investigate the application of a Butterworth filter-based routine to directly estimate the NRB from the BCARS signal. Additionally, we evaluate a Lorentzian wavelet transform as an alternative to the Hilbert transform for extracting the Raman spectrum from the BCARS signal. While we demonstrate this phantom for BCARS, it can be used for any spectroscopic Raman imaging approach.

Dixon, Jessica Z. [Georgia Institute of Technology↗
DOE OSTI2023

Uncertainty quantification of a deep learning fuel property prediction model

Deep learning models are being widely used in the field of combustion. Given the black-box nature of typical neural network based models, uncertainty quantification (UQ) is critical to ensure the reliability of predictions as well as the training datasets, and for a principled quantification of noise and its various sources. Deep learning surrogate models for predicting properties of chemical compounds and mixtures have been recently shown to be promising for enabling data-driven fuel design and optimization, with the ultimate goal of improving efficiency and lowering emissions from combustion engines. In this study, UQ is performed for a multi-task deep learning model that simultaneously predicts the research octane number (RON), Motor Octane Number (MON), and Yield Sooting Index (YSI) of pure components and multicomponent blends. The deep learning model is comprised of three smaller networks: Extractor 1, Extractor 2, and Predictor, and a mixing operator. The molecular fingerprints of individual components are encoded via Extractor 1 and Extractor 2, the mixing operator generates fingerprints for mixtures/blends based on linear mixing operation, and the predictor maps the fingerprint to the target properties. Two different classes of UQ methods, Monte Carlo ensemble methods and Bayesian neural networks (BNNs), are employed for quantifying the epistemic uncertainty. Combinations of Bernoulli and Gaussian distributions with DropConnect and DropOut techniques are explored as ensemble methods. All the DropConnect, DropOut and Bayesian layers are applied to the predictor network. Aleatoric uncertainty is modeled by assuming that each data point has an independent uncertainty associated with it. The results of the UQ study are further analyzed to compare the performance of BNN and ensemble methods. Although this study is confined to UQ of fuel property prediction, the methodologies are applicable to other deep learning frameworks that are being widely used in the combustion community.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Optical conductivity-based ultrasensitive mid-infrared biosensing on a hybrid metasurface

Abstract Optical devices are highly attractive for biosensing as they can not only enable quantitative measurements of analytes but also provide information on molecular structures. Unfortunately, typical refractive index-based optical sensors do not have sufficient sensitivity to probe the binding of low-molecular-weight analytes. Non-optical devices such as field-effect transistors can be more sensitive but do not offer some of the significant features of optical devices, particularly molecular fingerprinting. We present optical conductivity-based mid-infrared (mid-IR) biosensors that allow for sensitive and quantitative measurements of low-molecular-weight analytes as well as the enhancement of spectral fingerprints. The sensors employ a hybrid metasurface consisting of monolayer graphene and metallic nano-antennas and combine individual advantages of plasmonic, electronic and spectroscopic approaches. First, the hybrid metasurface sensors can optically detect target molecule-induced carrier doping to graphene, allowing highly sensitive detection of low-molecular-weight analytes despite their small sizes. Second, the resonance shifts caused by changes in graphene optical conductivity is a well-defined function of graphene carrier density, thereby allowing for quantification of the binding of molecules. Third, the sensor performance is highly stable and consistent thanks to its insensitivity to graphene carrier mobility degradation. Finally, the sensors can also act as substrates for surface-enhanced infrared spectroscopy. We demonstrated the measurement of monolayers of sub-nanometer-sized molecules or particles and affinity binding-based quantitative detection of glucose down to 200 pM (36 pg/mL). We also demonstrated enhanced fingerprinting of minute quantities of glucose and polymer molecules.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Predictive machine learning approaches for the microstructural behavior of multiphase zirconium alloys

Abstract Zirconium alloys are widely used in harsh environments characterized by high temperatures, corrosivity, and radiation exposure. These alloys, which have a hexagonal closed packed (h.c.p.) structure thermo-mechanically degrade, when exposed to severe operating environments due to hydride formation. These hydrides have a different crystalline structure, than the matrix, which results in a multiphase alloy. To accurately model these materials at the relevant physical scale, it is necessary to fully characterize them based on a microstructural fingerprint, which is defined here as a combination of features that include hydride geometry, parent and hydride texture and crystalline structure of these multiphase alloys. Hence, this investigation will develop a reduced order modeling approach, where this microstructural fingerprint is used to predict critical fracture stress levels that are physically consistent with microstructural deformation and fracture modes. Machine Learning (ML) methodologies based on Gaussian Process Regression, random forests, and multilayer perceptrons (MLP) were used to predict material fracture critical stress states. MLPs, or neural networks, had the highest accuracy on held-out test sets across three predetermined strain levels of interest. Hydride orientation, grain orientation or texture, and hydride volume fraction had the greatest effect on critical fracture stress levels and had partial dependencies that were highly significant, and in comparison hydride length and hydride spacing have less effects on fracture stresses. Furthermore, these models were also used accurately predicted material response to nominal applied strains as a function of the microstructural fingerprint.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Impact of environmental factors on biometric matching during human decomposition

Automatic recognition systems are a valuable tool for identifying unknown deceased individuals. Immediately af- ter death fingerprint and face biometric samples are easy to collect using standard sensors and cameras and can be easily matched to anti-mortem biometric samples. Even though post-mortem fingerprints and faces have been used for decades, there are no studies that track these biomet- rics through the later stages of decomposition to determine the length of time the biometrics remain viable. This paper discusses a multimodal dataset of fingerprints, faces, and irises from 14 human cadavers that decomposed outdoors under natural conditions. Results include predictive models relating time and temperature, measured as Accumulated Degree Days (ADD), and season (winter, spring, summer) to the predicted probably of automatic verification using a commercial algorithm.

Bolme, David S↗
DOE OSTI2018

How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions?

In the last few years, we have seen the rise of deep learning applications in a broad range of computational chemistry research problems. Using human-engineered chemical features, such as molecular descriptors and fingerprints, deep learning models have shown similar, if not better performance that most traditional machine learning algorithms. Recently, we reported on the development of Chemception, a deep convolutional neural network (CNN) architecture for general-purpose small molecule property prediction. On average, Chemception matched the performance of expert-developed QSAR/QSPR models trained on chemical features (molecular fingerprints), despite that it was trained on just 2D images of molecular drawings with minimal chemical information. Here, we investigate the effects of systematically removing and adding basic chemical information to the image channels of the 2D images used to train Chemception. By augmenting our images with only 3 additional basic chemical information, we demonstrate the improvement of Chemception performance – that it is now more accurate than contemporary deep learning models trained on ECFP fingerprints for the prediction of toxicity, activity and solvation free energy, as well as physics-based free energy simulation methods for computing solvation properties. By altering the chemical information content in the image channels, and examining the resulting performance of Chemception, we also identify to two different “learning patterns” in toxicity/activity as compared to solvation free energy, and it parallels the fundamental differences in contemporary chemistry research for predicting toxicity/activity and solvation free energy.

Artificial Intelligence, Cheminformatics, Computat↗
DOE OSTI2018

Classification of Complete Proteomes of Different Organisms and Protein Sets Based on Their Protein Distributions in Terms of Some Key Attributes of Proteins

The existence of complete genome sequences makes it important to develop different approaches for classification of large-scale data sets and to make extraction of biological insights easier. Here, we propose an approach for classification of complete proteomes/protein sets based on protein distributions on some basic attributes. We demonstrate the usefulness of this approach by determining protein distributions in terms of two attributes: protein lengths and protein intrinsic disorder contents (ID). The protein distributions based on L and ID are surveyed for representative proteome organisms and protein sets from the three domains of life. The two-dimensional maps (designated as fingerprints here) from the protein distribution densities in the LD space defined by ln( L ) and ID are then constructed. The fingerprints for different organisms and protein sets are found to be distinct with each other, and they can therefore be used for comparative studies. As a test case, phylogenetic trees have been constructed based on the protein distribution densities in the fingerprints of proteomes of organisms without performing any protein sequence comparison and alignments. The phylogenetic trees generated are biologically meaningful, demonstrating that the protein distributions in the LD space may serve as unique phylogenetic signals of the organisms at the proteome level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Ultra-high Information-content Chemical Imaging with Broadband Coherent Anti-Stokes Raman and Two-photon Fluorescence Lifetime Microscopy

Raman fingerprint spectroscopy and fluorescence lifetime imaging are emerging tools for studying metabolic profiles of biological specimens. While Raman fingerprint spectroscopy detects intrinsic molecular vibrations that reflect the molecular composition and chemical environment of a sample, fluorescence lifetime imaging measures changes in the excited-state lifetime of fluorophores that are sensitive to their microenvironments. Here, we present a multimodal imaging platform combining broadband coherent anti-Stokes Raman scattering (BCARS) and two-photon fluorescence lifetime imaging (2p-FLIM) microscopy that can acquire biologically relevant Raman fingerprint spectra and fluorescence lifetime signals in vivo and simultaneously. The tremendous chemical information obtained from spatially co-registered BCARS and 2p-FLIM images allows us to characterize the subtle differences between sub-cellular compartments and verify the potential false-positive results generated by fluorescence imaging alone. This is demonstrated by directly comparing the BCARS, 2p-FLIM, and two-photon excitation fluorescence(TPEF) signals simultaneously obtained from the same dye-stained organelle in the live, intact C. elegans expressing a green fluorescent protein (GFP) marker. In this work, we introduce the BCARS/2p-FLIM/TPEF setup scheme, the image acquisition steps, data processing, and representative results showing that the cross-modality imaging method enables rigorous characterization and in vivo detection at sub-cellular resolution. Furthermore, this protocol provides a framework for simultaneous chemical and fluorescence lifetime imaging to improve the accuracy of biological interpretation in complex living systems.

Xu, Haoyu [Georgia Institute of Technology, Atlant↗
DOE OSTI2023

Regime-oriented causal model evaluation of Atlantic–Pacific teleconnections in CMIP6

The climate system and its spatio-temporal changes are strongly affected by modes of long-term internal variability, like the Pacific decadal variability (PDV) and the Atlantic multidecadal variability (AMV). As they alternate between warm and cold phases, the interplay between PDV and AMV varies over decadal to multidecadal timescales. Here, we use a causal discovery method to derive fingerprints in the Atlantic–Pacific interactions and to investigate their phase-dependent changes. Dependent on the phases of PDV and AMV, different regimes with characteristic causal fingerprints are identified in reanalyses in a first step. In a second step, a regime-oriented causal model evaluation is performed to evaluate the ability of models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) in representing the observed changing interactions between PDV, AMV and their extra-tropical teleconnections. The causal graphs obtained from reanalyses detect a direct opposite-sign response from AMV to PDV when analyzing the complete 1900–2014 period and during several defined regimes within that period, for example, when AMV is going through its negative (cold) phase. Reanalyses also demonstrate a same-sign response from PDV to AMV during the cold phase of PDV. Historical CMIP6 simulations exhibit varying skill in simulating the observed causal patterns. Generally, large-ensemble (LE) simulations showed better network similarity when PDV and AMV were out of phase compared to other regimes. Also, the two largest ensembles (in terms of number of members) were found to contain realizations with similar causal fingerprints to observations. For most regimes, these same models showed higher network similarity when compared to each other. This work shows how causal discovery on LEs complements the available diagnostics and statistical metrics of climate variability to provide a powerful tool for climate model evaluation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Robust global detection of forced changes in mean and extreme precipitation despite observational disagreement on the magnitude of change

Detection and attribution (D&A) of forced precipitation change are challenging due to internal variability, limited spatial, and temporal coverage of observational records and model uncertainty. These factors result in a low signal-to-noise ratio of potential regional and even global trends. Here, we use a statistical method – ridge regression – to create physically interpretable fingerprints for the detection of forced changes in mean and extreme precipitation with a high signal-to-noise ratio. The fingerprints are constructed using Coupled Model Intercomparison Project phase 6 (CMIP6) multi-model output masked to match coverage of three gridded precipitation observational datasets – GHCNDEX, HadEX3, and GPCC – and are then applied to these observational datasets to assess the degree of forced change detectable in the real-world climate in the period 1951–2020. We show that the signature of forced change is detected in all three observational datasets for global metrics of mean and extreme precipitation. Forced changes are still detectable from changes in the spatial patterns of precipitation even if the global mean trend is removed from the data. This shows the detection of forced change in mean and extreme precipitation beyond a global mean trend is robust and increases confidence in the detection method's power as well as in climate models' ability to capture the relevant processes that contribute to large-scale patterns of change. We also find, however, that detectability depends on the observational dataset used. Not only coverage differences but also observational uncertainty contribute to dataset disagreement, exemplified by the times of emergence of forced change from internal variability ranging from 1998 to 2004 among datasets. Furthermore, different choices for the period over which the forced trend is computed result in different levels of agreement between observations and model projections. These sensitivities may explain apparent contradictions in recent studies on whether models under- or overestimate the observed forced increase in mean and extreme precipitation. Lastly, the detection fingerprints are found to rely primarily on the signal in the extratropical Northern Hemisphere, which is at least partly due to observational coverage but potentially also due to the presence of a more robust signal in the Northern Hemisphere in general.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2004

Decoding Ca2+ signals in plants

Different input signals create their own characteristic Ca2+ fingerprints. These fingerprints are distinguished by frequency, amplitude, duration, and number of Ca2+ oscillations. Ca(2+)-binding proteins and protein kinases decode these complex Ca2+ fingerprints through conformational coupling and covalent modifications of proteins. This decoding of signals can lead to a physiological response with or without changes in gene expression. In plants, Ca(2+)-dependent protein kinases and Ca2+/calmodulin-dependent protein kinases are involved in decoding Ca2+ signals into phosphorylation signals. This review summarizes the elements of conformational coupling and molecular mechanisms of regulation of the two groups of protein kinases by Ca2+ and Ca2+/calmodulin in plants.

Non-NASA Center↗
NASA NTRSDate not supplied

Hyperspectral Sounder Products: SiFSAP and ClimFiSP

Infrared hyperspectral sounders have demonstrated to provide very high value information for a range of weather and climate applications. Traditional retrieval algorithms of hyperspectral sounders use the ‘cloud clearing’ methodology in the retrieval process due to the lack of means to account for cloud scattering effect. The corresponding Level-2 data products are therefore limited by two major factors: the degradation of spatial resolution as compared with the native resolution of the instruments and the lack of ‘radiance closure’ that is critically needed for climate trend studies. The single field-of-view (FOV) sounder atmospheric products (SiFSAP) algorithm has been developed to supplement existing operational products. The SiFSAP algorithm uses the principal component based radiative transfer model (PCRTM) that is ultra-fast and has been extensively validated for the cloud scattering simulation capability. The cloud properties are physically retrieved along with profiles of temperature, moisture, and trace gases of interest, surface properties simultaneously by fitting single FOV spectral measurements. Therefore, SiFSAP ensure the radiometric consistency between the retrieved geophysical properties and the top-of-atmosphere (TOA) spectral measurements. This presentation provides a general introduction of SiFSAP and details about the physical retrieval algorithm. Also introduced here is a derivative data product of SiFSAP, the climate fingerprinting Sounder Products (ClimFiSP). ClimFiSP include the space-time averaged properties of key climate variables that are derived from the space-time averaged radiances via the spectral fingerprinting scheme. The spectral fingerprinting method allows a low latency data update on ClimFiSP following any potential calibration update on satellite measurements. ClimFiSP also include the rigorously defined uncertainty characterization for trends or anomalies of climate variables, being critical for the construction of long-term climate data record. Both SiFSAP and ClimFiSP will be available to users through NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC).

Wan Wu↗
DOE OSTI2013

Hardware device to physical structure binding and authentication

Detection and deterrence of device tampering and subversion may be achieved by including a cryptographic fingerprint unit within a hardware device for authenticating a binding of the hardware device and a physical structure. The cryptographic fingerprint unit includes an internal physically unclonable function ("PUF") circuit disposed in or on the hardware device, which generate an internal PUF value. Binding logic is coupled to receive the internal PUF value, as well as an external PUF value associated with the physical structure, and generates a binding PUF value, which represents the binding of the hardware device and the physical structure. The cryptographic fingerprint unit also includes a cryptographic unit that uses the binding PUF value to allow a challenger to authenticate the binding.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Signature-based store checking buffer

A system and method for optimizing redundant output verification, are provided. A hardware-based store fingerprint buffer receives multiple instances of output from multiple instances of computation. The store fingerprint buffer generates a signature from the content included in the multiple instances of output. When a barrier is reached, the store fingerprint buffer uses the signature to verify the content is error-free.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

DETERMINING THE TRIBUTARY SEDIMENT CONTRIBUTION TO EXPERIMENTAL FLOOD DEPOSITS ON THE COLORADO RIVER IN MARBLE CANYON, GRAND CANYON, ARIZONA

A multivariate sediment fingerprint was used in a Bayesian mixing model to determine the source contribution to experimental flood deposits along the Colorado River in the Marble Canyon reach of Grand Canyon, AZ. This study provides an independent evaluation, complementary to previous sediment mass balance and morphological studies, of the degree to which sand from the Paria River, the primary source of sand in Marble Canyon, has supplemented total sand storage since upstream dam closure in 1963. Samples of endmember and downstream mixed populations of sand were analyzed using portable XRF to measure the concentration of eleven major and trace elements, from which 55 elemental ratios were calculated. A principal components analysis consolidated those ratios eligible for use in the fingerprint into a multivariate signature used in the mixing model, MixSIAR. Twenty flood deposits from 14 sites in Marble Canyon from the 2013 and 2014 controlled floods were sampled. Six individual mixing models, using sediment fingerprints unique to each of six half-phi grain size fractions returned the percentage of sand, in each size fraction of each deposit, that was sourced from the Paria River. Paria contribution averaged 33.8 ± 4.1% in deposits from the 2013 flood, and 54.7 ± 4.3% in deposits from the 2014 flood. Individual grain size samples ranged 4.9 – 73.1% in 2013 and 9.1 – 90.2% in 2014. In general, iii the four median grain size fractions (90 – 355 μm) in both years have the highest Paria contribution; low Paria contribution in the finest 63 – 90 μm) fraction suggests significant contribution by an unaccounted-for third endmember, likely minor tributaries. The coarsest fraction, 355 – 500 μm, is the only grain size to show a trend in Paria contribution with distance traveled downstream, suggesting a transport-related fractionation effect. Overall scatter in the results speaks to the spatial heterogeneity of intra-rapid storage pools throughout the reach and their capacity, as well as the effects of local hydraulic variability between eddy deposition zones where sandbars form. Mass balance calculations determine that under conditions of constant or decreasing total storage since 1963, approximately 20-30% of the annual Paria flux would need to be retained within Marble Canyon each year to reach the observed concentration of Paria sand. From these results, we conclude that HFEs are conditionally sustainable given frequent enough occurrence to maintain sandbar deposit storage.

13 HYDRO ENERGY↗