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At least 595 records · Page 33

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DOE OSTI2020

Projecting spatiotemporally explicit effects of climate change on stream temperature: A model comparison and implications for coldwater fishes

Conservation planners and resource managers seek information about how the availability and locations of cold-water habitats will change in the future and how these predictions vary among models. In this work, we used a physical process-based model to demonstrate the implications of climate change for streamflow and water temperature in two watersheds with distinctive flow regimes: the Snoqualmie watershed (WA) and Siletz watershed (OR), USA. Our model incorporated a downscaled ensemble of global climate model outputs and was calibrated with in situ and remotely sensed water temperatures. Furthermore, we compared predictions from our processed-based model to those from a publicly available and widely used statistical model. The process-based model projected greater changes in summer maximum water temperatures for the mixed-rain-snow Snoqualmie watershed than for the rain-dominated Siletz watershed as a result of the near-complete loss of winter snowpack and significant reduction in summer flow in the Snoqualmie watershed expected by the 2080s. Both models projected generally similar future spatial patterns of maximum water temperature in the two rivers, with cool reaches distributed farther upstream and fewer in number. However, the process-based model projected higher spatial heterogeneity in water temperature due to our spatially explicit simulation of streamflow and because we calibrated the model with spatially continuous remotely sensed water temperature data. We used stream temperature projections to assess the vulnerability of Pacific salmon and trout to changes in the spatial distribution of cold-water habitats during August by the 2080s. Results suggest that salmonids may have fewer summertime cold-water habitats in both watersheds. Projected stream warming may further limit particular species and life stages, especially in the Snoqualmie watershed. Our comparison of models highlights the importance of considering what might be gained by using a process-based model for evaluating and prioritizing management actions that mitigate climate impacts on cold-water habitats for stream fishes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Multiscale framework for modeling multicomponent reactive transport in stream corridors

Here, travel time–based representations of transport, a highly successful strategy for modeling conservative tracer transport in stream corridors, are extended to accommodate multicomponent reactive transport. Specifically, convolution representations used to model exchange of solute with the hyporheic zone are shown to be equivalent to solving one–dimensional subgrid models in Lagrangian form coupled to the advection dispersion equation for the stream channel. Unlike the convolution–based representations of previous travel time–based stream transport models, the subgrid model generalizes to include multicomponent reactive transport with general nonlinear reactions. An example involving biomass growth, the establishment of redox zonation, and the resulting impact on denitrification rates demonstrate reach–scale application of the new approach. Although simplified, those example simulations show some of the key phenomena associated with hyporheic–zone denitrification that are not represented with conventional first–order estimates.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Stream Temperature Prediction in a Shifting Environment: Explaining the Influence of Deep Learning Architecture

Stream temperature is a fundamental control on ecosystem health. Recent efforts incorporating process guidance into deep learning models for predicting stream temperature have been shown to outperform existing statistical and physical models. This performance is in part because deep learning architectures can actively learn spatiotemporal relationships that govern how water and energy propagate through a river network. However, exploration of how spatiotemporal awareness and process guidance influence a model's generalizability under shifting environmental conditions such as climate change is limited. Here, we use Explainable Artificial Intelligence (XAI) to interrogate how differing deep learning architectures affect a model's learned spatial and temporal dependencies, and how those learned dependencies affect a model's ability to maintain high accuracy when applied to unseen environmental conditions. Using the Delaware River Basin in the northeastern United States as a test case, we compare two spatiotemporally aware process–guided deep learning models for predicting stream temperature (a recurrent graph convolution network—RGCN, and a temporal convolution graph model—Graph WaveNet). Both models achieve equally high predictive performance when testing data are well represented in the training data (test root mean squared errors of 1.64°C and 1.65°C); however, Graph WaveNet significantly outperforms RGCN in 4 out of 5 experiments where test partitions represent different types of unseen environmental conditions. XAI results show that the architecture of Graph WaveNet leads to learned spatial relationships with greater fidelity to physical processes, and that this fidelity improves the generalizability of the model when applied to shifting and/or unseen environmental conditions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

STREAMS guidelines: standards for technical reporting in environmental and host-associated microbiome studies

The interdisciplinary nature of microbiome research, coupled with the generation of complex multi-omics data, makes knowledge sharing challenging. The Strengthening the Organization and Reporting of Microbiome Studies (STORMS) guidelines provide a checklist for the reporting of study information, experimental design and analytical methods within a scientific manuscript on human microbiome research. Here, in this Consensus Statement, we present the standards for technical reporting in environmental and host-associated microbiome studies (STREAMS) guidelines. The guidelines expand on STORMS and include 67 items to support the reporting and review of environmental (for example, terrestrial, aquatic, atmospheric and engineered), synthetic and non-human host-associated microbiome studies in a standardized and machine-actionable manner. Based on input from 248 researchers spanning 28 countries, we provide detailed guidance, including comparisons with STORMS, and case studies that demonstrate the usage of the STREAMS guidelines. In conclusion, STREAMS, like STORMS, will be a living community resource updated by the Consortium with consensus-building input of the broader community.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2014

Self-field effects on instability of wave modes in a two-stream free-electron laser with an axial magnetic field

Free electron lasers (FEL) play major roles in the Raman Regime, due to the charge and current densities of the beam self-field. The method of perturbation has been applied to study the influence of self-electric and self-magnetic fields. A dispersion relation for two-stream free electron lasers with a helical wiggler and an axial magnetic field has been found. This dispersion relation is solved numerically to investigate the influence of self-fields on the FEL coupling and the two-stream instability. It was found that self-fields can produce very large effects on the FEL coupling, but they have almost negligible effects on two-stream instability.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2015

Constraints for the aperiodic O-mode streaming instability

In plasmas, where the thermal energy density exceeds the magnetic energy density (β{sub ∥} > 1), the aperiodic ordinary mode (O-mode) instability is driven by an excess of parallel temperature A = T{sub ⊥}/T{sub ∥} < 1 (where ∥ and ⊥ denote directions relative to the uniform magnetic field). When stimulated by parallel plasma streams, the instability conditions extend to low beta states, i.e., β{sub ∥} < 1, and recent studies have proven the existence of a new regime, where the anisotropy threshold decreases steeply with lowering β{sub ∥} → 0 if the streaming velocity is sufficiently high. However, the occurrence of this instability is questionable especially in the low-beta plasmas, where the electrostatic two-stream instabilities are expected to develop much faster in the process of relaxation of the counterstreams. It is therefore proposed here to identify the instability conditions for the O-mode below those required for the onset of the electrostatic instability. A hierarchy of these two instabilities is established for both the low β{sub ∥} < 1 and large β{sub ∥} > 1 plasmas. The conditions where the O-mode instability can operate efficiently are markedly constrained by the electrostatic instabilities especially in the low-beta plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2015

VISCOUS BOUNDARY LAYERS OF RADIATION-DOMINATED, RELATIVISTIC JETS. II. THE FREE-STREAMING JET MODEL

We analyze the interaction of a radiation-dominated jet and its surroundings using the equations of radiation hydrodynamics in the viscous limit. In a previous paper we considered the two-stream scenario, which treats the jet and its surroundings as distinct media interacting through radiation viscous forces. Here we present an alternative boundary layer model, known as the free-streaming jet model—where a narrow stream of fluid is injected into a static medium—and present solutions where the flow is ultrarelativistic and the boundary layer is dominated by radiation. It is shown that these jets entrain material from their surroundings and that their cores have a lower density of scatterers and a harder spectrum of photons, leading to observational consequences for lines of sight that look “down the barrel of the jet.” These jetted outflow models may be applicable to the jets produced during long gamma-ray bursts and super-Eddington phases of tidal disruption events.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Production Application Performance Data Streaming for System Monitoring

In this article, we present an approach to streaming collection of application performance data. Practical application performance tuning and troubleshooting in production high-performance computing (HPC) environments requires an understanding of how applications interact with the platform, including (but not limited to) parallel programming libraries such as Message Passing Interface (MPI). Several profiling and tracing tools exist that collect heavy runtime data traces either in memory (released only at application exit) or on a file system (imposing an I/O load that may interfere with the performance being measured). Although these approaches are beneficial in development stages and post-run analysis, a systemwide and low-overhead method is required to monitor deployed applications continuously. This method must be able to collect information at both the application and system levels to yield a complete performance picture. In our approach, an application profiler collects application event counters. A sampler uses an efficient inter-process communication method to periodically extract the application counters and stream them into an infrastructure for performance data collection. We implement a tool-set based on our approach and integrate it with the Lightweight Distributed Metric Service (LDMS) system, a monitoring system used on large-scale computational platforms. LDMS provides the infrastructure to create and gather streams of performance data in a low overhead manner. We demonstrate our approach using applications implemented with MPI, as it is one of the most common standards for the development of large-scale scientific applications. We utilize our tool-set to study the impact of our approach on an open source HPC application, Nalu. Our tool-set enables us to efficiently identify patterns in the behavior of the application without source-level knowledge. We leverage LDMS to collect system-level performance data and explore the correlation between the system and application events. Finally, we demonstrate how our tool-set can help detect anomalies with a low latency. We run tests on two different architectures: a system enabled with Intel Xeon Phi and another system equipped with Intel Xeon processor. Our overhead study shows our method imposes at most 0.5% CPU usage overhead on the application in realistic deployment scenarios.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Stream discharge and temperature data collected within the East and Taylor Watershed, Colorado for the Lawrence Berkeley National Laboratory Watershed Function Science Focus Area (water years 2019 to 2025)

This dataset contains stream discharge and temperature data for water years 2019 to 2025 from the East and Taylor Watersheds in Colorado, United States. This data was collected to understand hydrological processes occurring in the East River and Taylor River Watersheds, Colorado, which is part of the Lawrence Berkeley National Laboratory Watershed Function Scientific Focus Area. Data includes instantaneous observed discharge using salt dilution and acoustic doppler velocimeter techniques, raw pressure transducer downloaded data, sub-hourly temperature as well as corrected water level and associated stream discharge and mean daily values. Notes on water level corrections, rating curve development and metadata provided. A rating curve is the translation of depth to streamflow. The rating curve can be used as a quantitative measure of the “quality of the data.” Data within this dataset is formatted using ESS-DIVE’s Hydrological Monitoring Reporting Format. This data package contains (1) a zip file (Stream_Discharge_Data_WY19-WY25.zip) containing stream discharge and temperature data organized by location; (2) an InstallationMethods file (InstallationMethods.csv) describing metadata about the installation; (3) a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; (4) a data dictionary (dd.csv) file that contains terms/column_headers used throughout the files along with a definition, units, and data type; (5) a locations metadata file (locations.csv); (6) and a sensor metadata file (sensors.csv). All data files are in non-proprietary formats (csv, png, or pdf formats). Please contact Rosemary Carroll, Curtis Beutler, or Austin Shirley for any support in accessing the files. Update on 2023-05-12: Additional data from WYs 2021 and 2022 were added. Additionally, the dataset was converted using ESS-DIVE’s Hydrological Monitoring Reporting Format. Data files were reformatted to match reporting format guidance, new metadata files were added, and files were converted from excel to CSV. Update on 2025-05-16: Additional data from WYs 2022 (for locations not previously included), 2023, and 2024 were added. An additional descriptive PDF (WFSFA_Streamflow_Hydrograph_Disclaimer.pdf) was added. Metadata files were updated to reflect the addition of new data and locations. Update on 2026-05-18: Additional data from WY 2025 were added, including a new location Upper Trail Creek (TR-TCG2). Metadata files were updated to reflect the addition of new data.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Data and scripts associated with “Non-random processes impacting organic matter chemistry are maximized in mid-order streams”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the publication “Non-random processes impacting organic matter chemistry are maximized in mid-order streams” submitted to Limnology and Oceanography (L&O) by Danczak et al. (in review). This package contains data and scripts used to investigate dissolved organic matter (DOM) molecular chemistry and diversification processes across 47 surface-water sampling sites in the Yakima River Basin, Washington, USA, during an August 2021 sampling campaign. The package contains analyses of ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), geochemical measurements, geospatial attributes, molecular diversity, and meta-metabolome ecological null models needed to reproduce the main manuscript results. The underlying field data were pulled from exising data packages at https://doi.org/10.15485/1892052 (Fulton et al., 2022) and https://doi.org/10.15485/1898914 (Grieger et al., 2022). For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. We thank the following organizations for providing access to field locations for sample collection: the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, the Confederated Tribes and Bands of the Yakama Nation, and the Cowiche Canyon Conservatory. Research was conducted under Washington State Parks and Recreation Commission Scientific Research Permit #210901. We are grateful to the Yakama Nation Tribal Council and Yakama Nation Fisheries for their collaboration in facilitating sample collection and ensuring data usage aligns with their values and worldview. This data package contains an R-Markdown file for analyses and five folders: (1) Data, (2) Geospatial Data, (3) Supplemental_Files, (5) Figures_pdf, (4) and src. The Data folder contains tabular inputs and derived files used in the manuscript analysis. The Geospatial Data folder contains climate and water-balance, hydrologic, land-cover, population/regional water-use, stream, topographic, and stream-order attribute CSV files. The src folder contains scripts used to process data, run analyses, and generate figures. The Figures_pdf folder contains manuscript figure outputs. The Supplemental_Files folder contains supplemental analysis products. All files are .csv, .pdf, .html, .png, .R, .Rmd, .svg, or .tre. This data package is associated with the rcfsa-RC2-SPS_Null_Modeling repository found at https://github.com/river-corridors-sfa/rcfsa-RC2-SPS_Null_Modeling.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

WHONDRS Surface Water Geochemistry and Organic Matter Characterization Data from Streams Distributed across Latin America

This dataset supports a broader study examining global transferability of stream biogeochemistry and was generated in collaboration with the MicroSudAqua (µSudAqua) network (https://microsudaqua.netlify.app/en/). The dataset provides surface water geochemistry (dissolved organic carbon, total dissolved nitrogen, cations) and organic matter characterization (FTICR-MS) from streams in Argentina, Brazil, Chile, and Colombia. Samples were collected across stream orders (1st to 6th order) within five basins. Related data were collected and will be published separately in collaboration with the µSudAqua network. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to this readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) a folder of field photos; (2) a folder of surface water sample data, (3) a folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data; (4) file-level metadata; (5) data dictionary; (6) field metadata; (7) readme; (8) international generic sample number (IGSN) mapping file; and (9) field protocol. The sample data subfolder contains (1) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (2) total dissolved nitrogen data and averages; (3) anions and averages; (4) methods codes; (5) FTICR-MS methods; and (15) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains the processed data and three subfolders, one containing the .xml files, one containing the water CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS). All files are .csv, .pdf, .R, .xml, .d, .html, .Rmd, .py, .cal, .json, .jpg, .jpeg, .png, .mov, or .mp4.

Anions↗

DOE OSTI2024

Energy Efficient Streaming Time Series Classification with Attentive Power Iteration

Efficiently processing time series data streams in real-time on resource-constrained devices offers significant advantages in terms of enhanced computational energy efficiency and reduced time-related risks. We introduce an innovative streaming time series classification network that utilizes attentive power iteration, enabling real-time processing on resource-constrained devices. Our model continuously updates a compact representation of the entire time series, enhancing classification accuracy while conserving energy and processing time. Notably, it excels in streaming scenarios without requiring complete time series access, enabling swift decisions. Experimental results show that our approach excels in classification accuracy and energy efficiency, with over 70% less consumption and threefold faster task completion than benchmarks. This work advances real-time responsiveness, energy conservation, and operational effectiveness for constrained devices, contributing to optimizing various applications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Evaluation of Side Stream Filtration Technology at Oak Ridge National Laboratory

This technology evaluation was performed by Pacific Northwest National Laboratory and Oak Ridge National Laboratory on behalf of the Federal Energy Management Program. The objective was to quantify the benefits side stream filtration provides to a cooling tower system. The evaluation assessed the performance of an existing side stream filtration system at a cooling tower system at Oak Ridge National Laboratory’s Spallation Neutron Source research facility. This location was selected because it offered the opportunity for a side-by-side comparison of a system featuring side stream filtration and an unfiltered system.

side stream filtration↗

DOE OSTI2021

Streaming Generalized Canonical Polyadic Tensor Decompositions

In this paper, we develop a method which we call OnlineGCP for computing the Generalized Canonical Polyadic (GCP) tensor decomposition of streaming data. GCP differs from traditional canonical polyadic (CP) tensor decompositions as it allows for arbitrary objective functions which the CP model attempts to minimize. This approach can provide better fits and more interpretable models when the observed tensor data is strongly non-Gaussian. In the streaming case, tensor data is gradually observed over time and the algorithm must incrementally update a GCP factorization with limited access to prior data. In this work, we extend the GCP formalism to the streaming context by deriving a GCP optimization problem to be solved as new tensor data is observed, formulate a tunable history term to balance reconstruction of recently observed data with data observed in the past, develop a scalable solution strategy based on segregated solves using stochastic gradient descent methods, describe a software implementation that provides performance and portability to contemporary CPU and GPU architectures and integrates with Matlab for enhanced usability, and demonstrate the utility and performance of the approach and software on several synthetic and real tensor data sets.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Walker Branch Watershed: 15-minute and Daily Stream Discharge and Annual Runoff

This data set reports 15-minute and daily stream discharge, and annual runoff for the West and East Forks draining Walker Branch Watershed (WBW). The long-term Walker Branch Hydrology Monitoring project was intended to document changes in the water balance (precipitation inputs, stream discharge outputs) for the WBW over time. This data set contains three data files of stream discharge (L/s) for 15-minute and daily intervals, and annual runoff (cm). The daily and annual datasets began in 1969 in both East and West Forks, while the 15-minute datasets began in 1994 in both Forks. Data were collected through 2014 for the West Fork, and through 2012 for the East Fork. The Walker Branch Watershed is located on the U.S. Department of Energy's Oak Ridge Reservation in eastern Tennessee.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Collision tomography: physical properties of possible progenitors of the Andromeda Stellar Stream

To unveil a progenitor of the Andromeda Giant Stellar Stream, we investigate the interaction between an accreting satellite galaxy and the Andromeda Galaxy using an N-body simulation. We perform a comprehensive exploration of the properties of the progenitor dwarf galaxy, using 247 models of varying mass, mass distribution, and size. We show that the binding energy of the progenitor is the crucial parameter in reproducing the Andromeda Giant Stellar Stream and the shell-like structures surrounding the Andromeda Galaxy. As a result of the simulations, the progenitor must satisfy a simple scaling relation between the core radius, the total mass and the tidal radius. Using this relation, we successfully constrain the physical properties of the progenitors to have masses ranging from 5×10{sup 8}M{sub ⊙} to 5×10{sup 9}M{sub ⊙} and central surface densities around 10{sup 3} M{sub ⊙} pc{sup −2}. A detailed comparison between our result and the nearby observed galaxies indicates that possible progenitors of the Andromeda Giant Stellar Stream include a dwarf elliptical galaxy, a dwarf irregular galaxy, and a small spiral galaxy.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

The shape of the inner Milky Way halo from observations of the Pal 5 and GD-1 stellar streams

We constrain the shape of the Milky Way’s halo by dynamical modeling of the observed phase-space tracks of the Pal 5 and GD–1 tidal streams. We find that the only information about the potential gleaned from the tracks of these streams are precise measurements of the shape of the gravitational potential—the ratio of vertical to radial acceleration—at the location of the streams, with weaker constraints on the radial and vertical accelerations separately. The latter will improve significantly with precise proper-motion measurements from Gaia. We measure that the overall potential flattening is 0.95 ± 0.04 at the location of GD–1 ([R,Z]≈[12.5,6.7] kpc) and 0.94 ± 0.05 at the position of Pal 5 ([R,Z]≈[8.4,16.8] kpc). Combined with constraints on the force field near the Galactic disk, we determine that the axis ratio of the dark-matter halo’s density distribution is 1.05 ± 0.14 within the inner 20 kpc, with a hint that the halo becomes more flattened near the edge of this volume. The halo mass within 20 kpc is (1.1±0.1)×10{sup 11} M{sub ⊙}. A dark-matter halo this close to spherical is in tension with the predictions from numerical simulations of the formation of dark-matter halos.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Signature of a Massive Rotating Metal-poor Star Imprinted in the Phoenix Stellar Stream

The Phoenix stellar stream has a low intrinsic dispersion in velocity and metallicity that implies the progenitor was probably a low-mass globular cluster. In this work we use Magellan/Magellan Inamori Kyocera Echelle (MIKE) high-dispersion spectroscopy of eight Phoenix stream red giants to confirm this scenario. In particular, we find negligible intrinsic scatter in metallicity ($σ$([FE II/H]) = ${0.04}_{-0.03}^{+0.11}$) and a large peak-to-peak range in [Na/Fe] and [Al/Fe] abundance ratios, consistent with the light element abundance patterns seen in the most metal-poor globular clusters. However, unlike any other globular cluster, we also find an intrinsic spread in [Sr II/Fe] spanning ~1 dex, while [Ba II/Fe] shows nearly no intrinsic spread ($σ$([Ba II/H]) = ${0.03}_{-0.02}^{+0.10}$). This abundance signature is best interpreted as slow-neutron-capture element production from a massive fast-rotating metal-poor star (15–20 $M$ ⊙ , $v$ ini /$v$ crit = 0.4, [Fe/H] = -3.8). The low inferred cluster mass suggests the system would have been unable to retain supernovae ejecta, implying that any massive fast-rotating metal-poor star that enriched the interstellar medium must have formed and evolved before the globular cluster formed. Furthermore, neutron-capture element production from asymptotic giant branch stars or magneto-rotational instabilities in core-collapse supernovae provide poor fits to the observations. We also report one Phoenix stream star to be a lithium-rich giant ($A$(Li) = 3.1 ± 0.1). At [Fe/H ] = -2.93; it is among the most metal-poor lithium-rich giants known.

79 ASTRONOMY AND ASTROPHYSICS↗