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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A Frequency Domain Methodology for Quantitative Evaluation of Diffuse Wavefield With Applications to Seismic Imaging

Abstract Ambient Noise Imaging (ANI) of subsurface structures relies on seismic interferometry of diffuse seismic wavefields. However, the lack of effective methods to quantify and identify highly diffuse waves hampers applications of ANI, particularly in evaluating seismic attenuation and monitoring structural changes with high temporal resolution. Conventional ANI approaches require data normalization, which effectively suppresses the non‐diffuse component with large amplitude but also results in significant loss of amplitude and phase information in the continuous seismic records. In this study, we propose a frequency domain method to quantitatively evaluate the degree of diffuseness of seismic wavefields by analyzing their statistical characteristics of modal amplitudes for stationarity and randomness. Tests on synthetic waveform and field nodal records show that the proposed method can effectively distinguish between diffuse and non‐diffuse waveforms for either single‐ or three‐component data. As an application, we identify a 60‐s‐long diffuse coda of a local M 2.2 earthquake recorded by a dense nodal array on the San Jacinto Fault Zone, and successfully extract high‐quality dispersion curve andQ‐value without performing data normalization. These results are consistent with those obtained by conventional methods that assess the correlation between coherency and the Green's function, and by modeling ballistic waves generated by road traffic. Our proposed method can advance the imaging of subsurface velocity and attenuation structures as well as monitoring temporal changes for scientific studies and engineering applications.

Geochemistry & Geophysics↗
DOE OSTI2022

Benchmarking structural evolution methods for training of machine learned interatomic potentials

When creating training data for machine-learned interatomic potentials (MLIPs), it is common to create initial structures and evolve them using molecular dynamics (MD) to sample a larger configuration space. Here, we benchmark two other modalities of evolving structures, contour exploration (CE) and dimer-method (DM) searches against MD for their ability to produce diverse and robust density functional theory training data sets for MLIPs. We also discuss the generation of initial structures which are either from known structures or from random structures in detail to further formalize the structure-sourcing processes in the future. The polymorph-rich zirconium-oxygen composition space is used as a rigorous benchmark system for comparing the performance of MLIPs trained on structures generated from these structural evolution methods. Using Behler–Parrinello neural networks as our MLIP models, we find that CE and the DM searches are generally superior to MD in terms of spatial descriptor diversity and statistical accuracy.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Iterative multi-task learning and inference from seismic images

Seismic interpretation aims to extract quantitative and interpretable attributes from a seismic image produced using some migration method to inform characteristics of a subsurface reservoir or target of interest. Current paradigms for computing seismic attributes mostly rely on single-task algorithms. We develop an iterative, multi-task machine learning method to learn and infer multiple attributes from a seismic image. This method is composed of two stages: a multi-task inference stage and a multi-modal, multi-task refinement stage. The basic mechanism of this method is that we train a multi-task inference neural network (NN) to estimate a set of attributes, including a relative geological time (RGT), a denoised higher-resolution (DHR) seismic image, and multiple fault attributes (including probability, dip, and strike), from a low-resolution, noisy seismic image; then we input the inferred attributes to a multi-task refinement NN to enhance the raw inference results iteratively. The two multi-task NNs are trained separately based on synthetic seismic images and associated attributes generated by a geological modeling algorithm. The software we intend to release is a PyTorch implementation of this multi-task learning method for both 2D and 3D cases along with scripts to run the training/validation. The algorithm and software can be a useful tool for automatic seismic interpretation.

Gao, Kai↗
DOE OSTI2021

Object-space optimization of tomographic reconstructions for additive manufacturing

Volumetric 3D printing motivated by computed axial lithography enables rapid printing of homogeneous parts but requires a high dimensionality gradient-descent optimization to calculate image sets. Here we introduce a new, simpler approach to image-computation that algebraically optimizes a model of the printed object, significantly improving print accuracy of complex parts under imperfect material and optical precision by improving optical dose contrast between the target and surrounding regions. Quality metrics for volumetric printing are defined and shown to be significantly improved by the new algorithm. The approach is extended beyond binary printing to grayscale control of conversion to enable functionally graded materials. The flexibility of the technique is digitally demonstrated with realistic projector point spread functions, printing around occluding structures, printing with restricted angular range, and incorporation of materials chemistry such as inhibition. Finally, simulations show that the method facilitates new printing modalities such as printing into flat, rather than cylindrical packages to extend the applications of volumetric printing.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine tool cross beam design, fabrication, and testing using metal big area additive manufacturing

This paper describes the application of metal Big Area Additive Manufacturing (mBAAM) to the fabrication of a machine tool cross beam. The replacement of a traditional box design weldment with a new design printed by wire arc additive manufacturing using the MedUSA system at Oak Ridge National Laboratory (ORNL) is detailed. This requires a new design strategy based on the unique mBAAM capabilities. The intent of the new design is to reduce mass, while maintaining the dynamic stiffness. To compare the two designs, the natural frequencies and mode shapes are measured using impact testing and predicted using finite element analysis. It is confirmed that the printed structure dynamics agreed with the numerical model predictions, which demonstrates that it is feasible to model a large-scale mBAAM part and understand its behavior prior to printing. Another notable outcome of this study is that the significant residual stress and distortion in the print indicate that knowledge gaps remain for widespread implementation of mBAAM.

42 ENGINEERING↗
DOE OSTI2024

Multi-modal characterisation of early-stage, subclinical cardiac deterioration in patients with type 2 diabetes

Abstract Background Type 2 diabetes mellitus (T2DM) is a major risk factor for heart failure with preserved ejection fraction and cardiac arrhythmias. Precursors of these complications, such as diabetic cardiomyopathy, remain incompletely understood and underdiagnosed. Detection of early signs of cardiac deterioration in T2DM patients is critical for prevention. Our goal is to quantify T2DM-driven abnormalities in ECG and cardiac imaging biomarkers leading to cardiovascular disease. Methods We quantified ECG and cardiac magnetic resonance imaging biomarkers in two matched cohorts of 1781 UK Biobank participants, with and without T2DM, and no diagnosed cardiovascular disease at the time of assessment. We performed a pair-matched cross-sectional study to compare cardiac biomarkers in both cohorts, and examined the association between T2DM and these biomarkers. We built multivariate multiple linear regression models sequentially adjusted for socio-demographic, lifestyle, and clinical covariates. Results Participants with T2DM had a higher resting heart rate (66 vs. 61 beats per minute,p < 0.001), longer QTc interval (424 vs. 420ms,p < 0.001), reduced T wave amplitude (0.33 vs. 0.37mV,p < 0.001), lower stroke volume (72 vs. 78ml,p < 0.001) and thicker left ventricular wall (6.1 vs. 5.9mm,p < 0.001) despite a decreased Sokolow-Lyon index (19.1 vs. 20.2mm,p < 0.001). T2DM was independently associated with higher heart rate (beta = 3.11, 95% CI = [2.11,4.10],p < 0.001), lower stroke volume (beta = −4.11, 95% CI = [−6.03, −2.19],p < 0.001) and higher left ventricular wall thickness (beta = 0.133, 95% CI = [0.081,0.186],p < 0.001). Trends were consistent in subgroups of different sex, age and body mass index. Fewer significant differences were observed in participants of non-white ethnic background. QRS duration and Sokolow-Lyon index showed a positive association with the development of cardiovascular disease in cohorts with and without T2DM, respectively. A higher left ventricular mass and wall thickness were associated with cardiovascular outcomes in both groups. Conclusion T2DM prior to cardiovascular disease was linked with a higher heart rate, QTc prolongation, T wave amplitude reduction, as well as lower stroke volume and increased left ventricular wall thickness. Increased QRS duration and left ventricular wall thickness and mass were most strongly associated with future cardiovascular disease. Although subclinical, these changes may indicate the presence of autonomic dysfunction and diabetic cardiomyopathy. Graphical Abstract

Cardiovascular System & Cardiology↗
DOE OSTI2023

Why the observed spin evolution of older-than-solar-like stars might not require a dynamo mode change

ABSTRACT The spin evolution of main-sequence stars has long been of interest for basic stellar evolution, stellar ageing, stellar activity, and consequent influence on companion planets. Observations of older-than-solar late-type main-sequence stars have been interpreted to imply that a change from a dipole-dominated magnetic field to one with more prominent higher multipoles might be necessary to account for the data. The spin-down models that lead to this inference are essentially tuned to the Sun. Here, we take a different approach that considers individual stars as fixed points rather than just the Sun. We use a time-dependent theoretical model to solve for the spin evolution of low-mass main-sequence stars that includes a Parker-type wind and a time-evolving magnetic field coupled to the spin. Because the wind is exponentially sensitive to the stellar mass over radius and the coronal base temperature, the use of each observed star as a separate fixed point is more appropriate and, in turn, produces a set of solution curves that produces a solution envelope rather than a simple line. This envelope of solution curves, unlike a single line fit, is consistent with the data and does not unambiguously require a modal transition in the magnetic field to explain it.

Astronomy & Astrophysics↗
DOE OSTI2023

Optical Properties of Individual Tar Balls in the Free Troposphere

Tar balls are brown carbonaceous particles, highly viscous, spherical, amorphous, and light absorbing. They are believed to form in biomass burning smoke plumes during transport in the troposphere. Tar balls are believed to have a significant impact on the Earth’s radiative balance, but due to poorly characterized optical properties, this impact is highly uncertain. Here, we investigate the chemical composition and optical properties of individual tar balls transported in the free troposphere to the Climate Observatory “Ottavio Vittori” on Mt. Cimone, Italy (2165 meters above sea level) using multi-modal micro-spectroscopy. Our results show that tar balls contributed 50% of carbonaceous particles by number in the size range from 0.25 to 1.8 µm. Of those tar balls, 16% were inhomogeneously mixed with other constituents. Using electron energy loss spectroscopy, we retrieved the complex refractive index from 200 to 1200 nm for both inhomogeneously and homogeneously mixed tar balls. We found no significant difference in the average refractive index of inhomogeneously and homogenously mixed tar balls (1.40 - 0.03i, and 1.36 - 0.03i at 550 nm, respectively). Furthermore, we estimated the top of the atmosphere radiative forcing using the Santa Barbara DISORT Atmospheric Radiative Transfer model (SBDART) and found that a layer of only tar balls with an optical depth of 0.1 above vegetation would exert a positive radiative forcing ranging from 2.8 Wm-2 (on a clear sky day) to 9.5 Wm-2 (when clouds are below the aerosol layer). In conclusion, understanding the optical properties of tar balls can help reduce uncertainties associated with the contribution of biomass-burning aerosol in current climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Contrast and Predictability of Island‐Scale El Niño Influences on Hawaii Wave Climate

Abstract The El Niño‐Southern Oscillation (ENSO) influences ocean wave activity across the Pacific, but its effects on island shores are modulated by local weather and selective sheltering of multi‐modal seas. Utilizing 41 years of high‐resolution wave hindcasts, we decipher the season‐ and locality‐dependent connections between ENSO and wave patterns around the Hawaiian Islands. The north and west‐facing shores, exposed to energetic northwest swells during boreal winters, experience the most pronounced ENSO‐related variability, with increased high‐surf activity during El Niño years. While the year‐round trade wind waves exhibit moderate correlation with ENSO, the basin‐wide climate influence is masked by locally accelerated trade winds in channels and around large headlands. The remarkable global‐to‐local pathway through the high‐resolution hindcast enables development of an ENSO‐based semi‐empirical wave model to statistically describe and predict severe wave conditions on vulnerable shores with potential application in coastal risk management and hazard mitigation for Pacific Islands and beyond.

Zhao, Sen [Department of Atmospheric Sciences Scho↗
DOE OSTI2021

TRUST Nonlinear Dynamics Testbed Assessment

The following assessment evaluates the efficacy of the control script for carrying out a linear signal to generate a linear mechanical response of the system: the TRUST nonlinear dynamics (TRUSTND) testbed. The hardware has three main components: the controller (NI PXIe 8861) embedded in the National Instruments chassis (NI PXIe 1092), the signal amplifier (The Modal Shop Linear Power Amplifier 2050E09) to amplify the output of a custom LabVIEW script, and a shaker (The Modal Shop Electrodynamic Exciter 2075E) where wave spring specimens live and are tested within the attached aluminum testbed (center mass). Different kinds of signals (single-tone, swept frequency and white noise) were sent through this hardware in the E-1 lab space at TA-53. First, the linearity of the setup was tested by specifying a sine wave in the control script and checking the quality of the oscillations in a wave spring (McMaster-Carr 9714K19 [1]). When passing a sine wave through the amplifier was succeeded, the frequency response of the wave spring was tested with white noise for comparison to finite-element model predictions. The testbed outputs a repeatable, linear response to the input excitation when used with the wave spring. These measurements reduce the uncertainty associated with the testbed and test procedure to better characterize uncertainty associated with material behavior (SX358 foam samples) in future tests

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Advancing the Frontiers of Deep Learning for Low-Dose 3D Cone-Beam CT Reconstruction

X-ray computed tomography (CT) is an important noninvasive medical imaging modality for studying the structural details of internal organs. Image reconstruction in CT is an inverse problem of recovering an object's internal structure from the absorption profile of X-ray beams (sinogram) measured using a detector. The classical variational approach for CT reconstruction minimizes an energy functional using an appropriate iterative algorithm. Motivated by the success of deep learning (DL), researchers have begun to leverage training data and enhanced computing capabilities in recent years to produce high-fidelity reconstructed images. Nonetheless, much of the academic research in DL algorithms for CT has focused primarily on the two-dimensional setting (with simplified forward operators and noise model) for proofs-of-concept, and a comprehensive benchmarking of various classical and data-driven CT reconstruction approaches has not beenundertaken. The key objective of our CT reconstruction grand challenge was to promote methodological advancements for both classical and DL-based approaches for clinical CT with a reasonably accurately simulated 3D CT forward operator and noise model. We have utilized the publicly available LIDC-IDRI dataset and simulated sinograms and FDK images corresponding to two dose levels (clinical- and low-dose, constituting two tracks of the challenge) starting from the normal-dose images as the ground truth. In this paper, we summarize the motivation, context, and results of our challenge, and highlight the future research directions in DL for clinical CT.

X-ray tomography↗
DOE OSTI2021

Mass enhancement in 3 d and s - p perovskites from symmetry breaking

In some d-electron oxides, the measured effective mass m* exptl has long been known to be significantly larger than the model effective mass m* model deduced from mean-field band theory, i.e., m* exptl =βm* model , where β>1 is the “mass-enhancement” or “mass-renormalization” factor. Previous applications of density functional theory (DFT), based on a symmetry-restricted structure with the smallest number of possible magnetic, orbital, and structural degrees of freedom, missed such mass enhancement. This fact has been taken as evidence of strong electronic correlation, often described via the symmetry-restricted dynamic mean-field approach of the many-body theory, being the exclusive enabling physics. This paper uses instead a static density functional approach that does not restrict positional or spin degrees of freedom (symmetry-broken structures). This approach analyzes the contributions of different symmetry-broken modalities to mass enhancement for a few nominally highly correlated d-electron perovskites as well as the nominally uncorrelated, closed-shell s-p bonding perovskites. It shows that the energy-lowering symmetry-broken spin effects (e.g., nonzero local moment in the paramagnetic phase) and structural effects (e.g., atomic displacement) as described in mean-field DFT already manifest mass enhancement for both electrons and holes in a range of d-electron perovskites SrVO 3 , SrTiO 3 , BaTiO 3 , and LaMnO 3 , as well as p-electron perovskites CsPbI 3 and SrBiO 3 , including both metals (SrVO 3 ) and insulators (the rest). This is revealed only when enlarged unit cells of the same parent global symmetry, which are large enough to allow for symmetry-breaking distortions and concomitant variations in spin order, are explored for their ability to lower the total energy. Positional symmetry breaking that leads to mass enhancement includes octahedral rotation in halide perovskites such as CsPbI 3 , Jahn-Teller-like Q${+}\atop{2}$ distortion in LaMnO 3 , and bond disproportionation in SrBiO 3 , while magnetic symmetry breaking resulting in mass enhancement includes the formation of a distribution of local moments in SrVO3 that averages to zero in the paramagnetic phase. Not all symmetry breaking leads to significant mass enhancement, e.g., the rather small octahedral rotations in the nearly perfectly cubic SrTiO 3 cause negligible mass enhancement, as do the paraelectric displacements in BaTiO 3 . In principle, by ergodicity, the two descriptions, i.e., the symmetry-restricted dynamic approach with a single time-fluctuating magnetic moment and the symmetry-broken mean-field approach with a static distribution of spatially fluctuated local moments, are not mutually exclusive but are a choice of representation and consequently, a choice of computational efficiency. In approximate implementations, the symmetry-broken mean-field approach appears to remove much of what was strong correlation in dynamically correlated symmetry-restricted solutions, leaving smaller (“weak”) residual correlation with respect to the exact solution.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Real-time image denoising of mixed Poisson–Gaussian noise in fluorescence microscopy images using ImageJ

Fluorescence microscopy imaging speed is fundamentally limited by the measurement signal-to-noise ratio (SNR). To improve image SNR for a given image acquisition rate, computational denoising techniques can be used to suppress noise. However, common techniques to estimate a denoised image from a single frame either are computationally expensive or rely on simple noise statistical models. These models assume Poisson or Gaussian noise statistics, which are not appropriate for many fluorescence microscopy applications that contain quantum shot noise and electronic Johnson–Nyquist noise, therefore a mixture of Poisson and Gaussian noise. In this paper, we show convolutional neural networks (CNNs) trained on mixed Poisson and Gaussian noise images to overcome the limitations of existing image denoising methods. The trained CNN is presented as an open-source ImageJ plugin that performs real-time image denoising (within tens of milliseconds) with superior performance (SNR improvement) compared to conventional fluorescence microscopy denoising methods. The method is validated on external datasets with out-of-distribution noise, contrast, structure, and imaging modalities from the training data and consistently achieves high-performance ( > <#comment/> 8 d B ) denoising in less time than other fluorescence microscopy denoising methods.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Femtosecond wavelength-tunable laser system using gain managed nonlinear amplifier

Optical parametric amplifiers require a seed energy at the spectral range of interest for amplification. In the case of pulsed laser systems, seed pulse characteristics such as spectral energy density and spectral uniformity influence laser system design and performance. In this letter, we present the design and modeling of a few micro-joules, wavelength-tunable, femtosecond parametric amplifier system. We employ a gain-managed nonlinear fiber amplifier output as the seed pulse. This seed pulse is relatively uniform across its bandwidth from 1010 to 1180 nm. Its spectral energy density averages 600 pJ/nm, 30 times higher than conventional counterparts. The overall amplification gain is 175, and the conversion efficiency is 17.5%. The inherent quasi-linear chirp of the seed pulse is utilized to alter the output wavelength. Our tunable femtosecond laser is an enabling technology for high-demand applications such as time-resolved spectroscopy, nonlinear and advanced material research, and biomedical imaging and microscopy modalities.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Strain Gauge Diagnostic Development for use in Vessel Health Monitoring for Hydro-shots

Six-foot vessels are a crucial component to protecting high fidelity equipment at DARHT. Analysis shows these vessels have a limited life span (~10 shots) due to fatigue, ratcheting, and damage accumulation. Vessel health monitoring and diagnostics can help inform decision on a vessel’s fitness for service. Strain gauges are an easy and effective sensor for use in vessel health monitoring. In early March, J-2 executed a qualification shot, uniform blast loading with minimal fragments, with bi-axial strain gauges fielded at four locations around vessel. Several damage features were extracted from the data, and frequency content helps to validate computer modelling of the vessel response. Permanent plastic strain at each location was significant with location one having the most strain at the end state, with 230 micro-strain. However, residual pressure in the vessel could be a major factor in this strain. PEEQ is a measure of total plastic equivalent strain accumulation. The equivalent strain at location one exceeded the plastic strain limit four times and accumulated the most PEEQ with 2036 micro-strain. As expected, this qualification shot did contribute to damage in the vessel, and should be considered in its fitness for service, but not critical enough to pull it from service. Frequency content also gave valuable information on the modal response of the vessel and contributes to the understanding of how the vessel responds to the initial blast load with an expected lower frequency membrane/breathing mode and transitioning to higher frequency bending modes in the vessel wall.

42 ENGINEERING↗
DOE OSTI2021

Simultaneous x-ray and neutron 4D tomographic study of drying-driven hydro-mechanical behavior of cement-based materials at moderate temperatures

The drying of cement-based materials is intimately related to their durability, which has significant economic, social and environmental repercussions. The evolution of the saturation of the pore network and the associated drying shrinkage are in fact leading causes of cracking and of the ensuing penetration of aggressive chemicals. This process is highly heterogeneous, due to the thermo-hydric spatial gradients developing in the material from the exposed surfaces to its core and because of local effects, driven by the intrinsically heterogeneous micro-structure (e.g., by the distribution of pores and aggregates). It follows that macroscopic, sample-scale measurements cannot fully disclose the complexity of the underlying processes. In the last few decades, significant advances in full-field techniques have allowed an unprecedented insight into these local processes. For cement-based materials, x-ray and neutron tomography lend themselves as ideal, and highly complementary, tools for the study of their thermo-hydro-mechanical behavior. Notably, the high sensitivity to density variations of x-ray imaging gives access to the developments of fractures, in 4D (3D + time). On the other hand, neutron tomography allows the study of the evolution of the moisture field in 4D, thanks to its high hydrogen sensitivity. The combination of these two techniques provides a unique insight in thermo-hydro-mechanical couplings, e.g., the effect of cracks on the water content field. This contribution presents novel 5D datasets (3D tomographies along time, plus truly simultaneous x-ray and Neutron rapid acquisitions) in-operando of a cement paste and of a concrete sample heated at moderate temperatures (up to 140{sup ∘}C). The analysis of this 5D data-set (once aligned in time and across modalities) allows for example a quantification of the 4D moisture profiles which were found to predict an overall water loss at hydric equilibrium coherent with the corresponding analytical analysis. In the cement paste sample, the x-ray dataset captures the evolution of an extensive cracking network, opening and propagation toward the core of the sample. A novel analysis procedure is here proposed which allows the extraction of these fractures and the analysis of their interplay with local drying as captured through neutron imaging. This for example reveals the depth of penetration of drying in the vicinity of the fractures along time, which is essential for the assessment and calibration of hydro-mechanical coupled models.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Multimodal representation learning for predicting molecule–disease relations

Motivation: Predicting molecule–disease indications and side effects is important for drug development and pharmacovigilance. Comprehensively mining molecule–molecule, molecule–disease and disease–disease semantic dependencies can potentially improve prediction performance. Methods: We introduce a Multi-Modal REpresentation Mapping Approach to Predicting molecular-disease relations (M2REMAP) by incorporating clinical semantics learned from electronic health records (EHR) of 12.6 million patients. Specifically, M2REMAP first learns a multimodal molecule representation that synthesizes chemical property and clinical semantic information by mapping molecule chemicals via a deep neural network onto the clinical semantic embedding space shared by drugs, diseases and other common clinical concepts. To infer molecule–disease relations, M2REMAP combines multimodal molecule representation and disease semantic embedding to jointly infer indications and side effects. Results: We extensively evaluate M2REMAP on molecule indications, side effects and interactions. Results show that incorporating EHR embeddings improves performance significantly, for example, attaining an improvement over the baseline models by 23.6% in PRC-AUC on indications and 23.9% on side effects. Further, M2REMAP overcomes the limitation of existing methods and effectively predicts drugs for novel diseases and emerging pathogens. Availability and implementation: The code is available at https://github.com/celehs/M2REMAP, and prediction results are provided at https://shiny.parse-health.org/drugs-diseases-dev/.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Hysteresis Patterns of Watershed Nitrogen Retention and Loss Over the Past 50 years in United States Hydrological Basins

Patterns of watershed nitrogen (N) retention and loss are shaped by how watershed biogeochemical processes retain, biogeochemically transform, and lose incoming atmospheric deposition of N. Loss patterns represented by concentration, discharge, and their associated stream exports are important indicators of integrated watershed N retention behaviors. We examined continental United States (CONUS) scale N deposition (e.g., wet and dry atmospheric deposition), vegetation trends, and stream trends as potential indicators of watershed N-saturation and retention conditions, and how watershed N retention and losses vary over space and time. By synthesizing changes and modalities in watershed nitrogen loss patterns based on stream data from 2200 U.S. watersheds over a 50 years record, our work revealed two patterns of watershed N-retention and loss. One was a hysteresis pattern that reflects the integrated influence of hydrology, atmospheric inputs, land-use, stream temperature, elevation, and vegetation. The other pattern was a one-way transition to a new state. We found that regions with increasing atmospheric deposition and increasing vegetation health/biomass patterns have the highest N-retention capacity, become increasingly N-saturated over time, and are associated with the strongest declines in stream N exports—a pattern, that is, consistent across all land cover categories. We provide a conceptual model, validated at an unprecedented scale across the CONUS that links instream nitrogen signals to upstream mechanistic landscape processes. Our work can aid in the future interpretation of in-stream concentrations of DOC and DIN as indicators of watershed N-retention status and integrators of watershed hydrobiogeochemical processes.

58 GEOSCIENCES↗