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At least 415 records · Page 23

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DOE OSTI2025

Advancing stream temperature prediction with a generalizable large-sample framework across CONUS river reaches

Accurately predicting stream temperature in ungauged basins remains a critical challenge for water resource management, thermoelectric power plant cooling, and ecosystem conservation. Large-sample machine learning models trained on hundreds of well-monitored river basins have shown remarkable performance; however, such models have yet to be developed solely using forcing data that can be readily extracted to simulate stream temperatures anywhere in the contiguous United States (CONUS). In this study, we present a scalable, large-sample deep learning framework using Long Short-Term Memory (LSTM) networks to simulate daily stream temperatures in ungauged basins across the CONUS. The framework leverages both modeled reanalysis of meteorological and streamflow inputs as well as static attributes available for all 2.7 million CONUS river reaches in the National Hydrography Dataset Plus (NHDPlusV2). By generating dynamical inputs from predefined thermally relevant upstream contributing areas, rather than the entire upstream basin, the model also offers improvements in very large basins where full-basin averaging can dilute the most important influences on stream temperature. Evaluated across 300 basins, the model achieves a median Mean Absolute Error (MAE) of 1.1 °C and a Nash-Sutcliffe Efficiency (NSE) of 0.95 on temporally and spatially distinct test folds—comparable to models trained exclusively using meteorological and streamflow observational data. The flexible, high-performing framework generalizes to any unmonitored river reach without significant regulation or unnatural thermal input immediately upstream, substantially expanding predictive capabilities in data-scarce regions.

Hydrology↗

DOE OSTI2020

Fast shared-memory streaming multilevel graph partitioning

In this report we show that a fast parallel graph partitioner can benefit many applications by reducing data transfers. The online methods for partitioning graphs have to be fast and they often rely on simple one-pass streaming algorithms, while the offline methods for partitioning graphs contain more involved algorithms and the most successful methods in this category belong to the multilevel approaches. In this work, we assess the feasibility of using streaming graph partitioning algorithms within the multilevel framework. Our end goal is to come up with a fast parallel offline multilevel partitioner that can produce competitive cutsize quality. We rely on a simple but fast and flexible streaming algorithm throughout the entire multilevel framework. This streaming algorithm serves multiple purposes in the partitioning process: a clustering algorithm in the coarsening, an effective algorithm for the initial partitioning, and a fast refinement algorithm in the uncoarsening. Its simple nature also lends itself easily for parallelization. The experiments on various graphs show that our approach is on the average up to 5.1x faster than the multi-threaded MeTiS, which comes at the expense of only 2x worse cutsize.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Tidal Stream Energy Resource Characterization in the Salish Sea

Tidal stream energy holds great potential as a renewable energy source in regions of the world where tidal currents are strong and predictable. The tidal stream energy resource of a region is strongly controlled by its tidal wave characteristics, the local bathymetry, and coastal geometric features. The Salish Sea, a large estuary on the Pacific Northwest coast, represents a great tidal stream resource because of its strong tidal currents in many tidal channels. However, the tidal resource in the Salish Sea has not been systematically characterized, primarily because of its large area and complex bathymetry and coastlines. This paper presents a modeling study conducted to characterize the tidal energy resource of the Salish Sea based on a high-resolution three-dimensional tidal hydrodynamic model of the Salish Sea, which was extensively validated using data derived from 10 tidal gauge stations and 132 Acoustic Doppler Current Profiler stations. Model validation results indicated the Salish Sea hydrodynamic model is skillful in simulating tidal wave propagation and velocity distributions in the Salish Sea. Based on model results, a total of 16 tidal channels with strong tidal currents, 9 in the San Juan Islands and Rosario Strait regions and 7 in the Puget Sound, were identified as hotspots for potential tidal energy development. Velocity probability distributions and exceedance curves of cross-channel average velocity magnitudes were calculated at all 16 channels based on the recommendations of the International Electrotechnical Commission Technical Specifications for tidal energy resource characterization. The tidal energy resource at the 16 hotspots was also characterized using power density distributions and kinetic energy fluxes. The ranking of the kinetic energy fluxes suggested that Admiralty Inlet, Rosario Strait, and Middle Channel are the top three hotspot sites in the Salish Sea for tidal stream energy development. The study demonstrated the need for a high-resolution 3-D modeling framework for accurate simulation of tidal currents in large complex estuarine systems, in the context of tidal energy resource characterization.

tidal stream energy, resource characterization, Sa↗

DOE OSTI2022

Dynamic Responses of Trace Metal Bioaccessibility to Fluctuating Redox Conditions in Wetland Soils and Stream Sediments

Natural aquatic systems undergo fluctuating redox conditions due to microbial activity, varying water saturation levels, and nutrients dynamics. With fluctuating redox conditions, trace metals can mobilize or sequester in response to changes in iron and sulfur speciation and the concentrations and lability of organic carbon. This study examined the effect of redox fluctuations on trace metal mobility in samples collected from two different natural aquatic systems: riparian wetlands and a stream. The wetland soils contained low sulfur and total Fe contents as compared to stream sediments. The mineral composition at both sites was dominated by quartz. We incubated water-saturated soils under three cycles of anoxic-oxic conditions (τanoxic:τoxic = 3) spanning 24 days and monitored the change in dissolved and bioavailable metal concentrations. For both natural systems, reduction of iron oxides under anoxic conditions caused Co and Zn release. In contrast, oxidation of sulfides mobilized Cu under oxic conditions in both sites. In wetland soils, dissolution of Fe (hydr)oxides increased Ni solubility; however, in stream sediments, Ni release occurred when sulfides or organic matter were oxidized. For stream sediments, each subsequent redox cycle increased the bioavailability of trace metals. Redox fluctuations in wetland soils increased bioavailable Zn and Cu and decreased bioavailable Ni and Co. This study illustrates that different trace metals display distinct bioavailability patterns during redox fluctuations in natural environments. The biogeochemical cycling of nutrients in systems with redox fluctuations may be influenced by these trace metal availability patterns in addition to the availability of electron donors and acceptors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Combined Effects of Stream Hydrology and Land Use on Basin‐Scale Hyporheic Zone Denitrification in the Columbia River Basin

Abstract Denitrification in the hyporheic zone (HZ) of river corridors is crucial to removing excess nitrogen in rivers from anthropogenic activities. However, previous modeling studies of the effectiveness of river corridors in removing excess nitrogen via denitrification were often limited to the reach‐scale and low‐order stream watersheds. We developed a basin‐scale river corridor model for the Columbia River Basin with random forest models to identify the dominant factors associated with the spatial variation of HZ denitrification. Our modeling results suggest that the combined effects of hydrologic variability in reaches and substrate availability influenced by land use are associated with the spatial variability of modeled HZ denitrification at the basin scale. Hyporheic exchange flux can explain most of spatial variation of denitrification amounts in reaches of different sizes, while among the reaches affected by different land uses, the combination of hyporheic exchange flux and stream dissolved organic carbon (DOC) concentration can explain the denitrification differences. Also, we can generalize that the most influential watershed and channel variables controlling denitrification variation are channel morphology parameters (median grain size (D50), stream slope), climate (annual precipitation and evapotranspiration), and stream DOC‐related parameters (percent of shrub area). The modeling framework in our study can serve as a valuable tool to identify the limiting factors in removing excess nitrogen pollution in large river basins where direct measurement is often infeasible.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Train, Inform, Borrow, or Combine? Approaches to Process–Guided Deep Learning for Groundwater–Influenced Stream Temperature Prediction

Although groundwater discharge is a critical stream temperature control process, it is not explicitly represented in many stream temperature models, an omission that may reduce predictive accuracy, hinder management of aquatic habitat, and decrease user confidence. We assessed the performance of a previously-described process-guided deep learning model of stream temperature in the Delaware River Basin (USA). We found lower accuracy (root mean square error [RMSE] of 1.71 versus 1.35°C) and stronger seasonal bias (absolute mean monthly bias of 1.06 vs. 0.68°C) for reaches primarily influenced by deep groundwater as compared to atmospheric conditions. We then tested four approaches for improving groundwater process representation: (a) a custom loss function leveraging the unique patterns of air and water temperature coupling characteristic of different temperature drivers, (b) inclusion of additional groundwater-relevant catchment attributes, (c) incorporation of additional process model outputs, and (d) a composite model. The custom loss function and the additional attributes significantly improved the predictive accuracy in groundwater-dominated reaches (RMSE of 1.37 and 1.26°C) and reduced the seasonal bias (absolute mean monthly bias of 0.44 and 0.48°C), but neither approach could identify holdout groundwater reaches. Variable importance analysis indicates the custom loss function nudges the model to use the existing inputs more efficiently, whereas with the added features the model relies on a broader suite of inputs. This analysis is a substantial step toward more accurately representing groundwater discharge processes in stream temperature models and will improve predictive accuracy and inform habitat management.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Two-dimensional single-stream electron motion in a coaxial diode with magnetic insulation

One of the most widespread models of electrons drifting around the cathode in magnetrons is the single-stream state, which is the Brillouin stream with purely azimuthal motion. We describe a single-stream state in which electrons not only move in the azimuthal direction, but also along the axial direction, which is useful for consideration, for example, of relativistic magnetrons, MILOs, and coaxial transmission lines. Relations are given for the conditions of magnetic insulation for 2D electron motion, for 1D azimuthal and axial motion, and for synchronism of these streams with the operating waves of M-type microwave sources. Relations are also provided for the threshold of generation in magnetrons with 2D electron motion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2017

Top–down effects of a grazing, omnivorous minnow ( Campostoma anomalum ) on stream microbial communities

We report top–down control exerted by macroconsumers can strongly affect lower trophic levels and ecosystem processes. Studies of effects on primary consumers in streams have been focused on algae, and effects on bacteria are largely unknown. We manipulated the density of an omnivorous, grazing minnow, the central stoneroller (Campostoma anomalum), in experimental stream mesocosms (treatments with 0, 1, 2, 3, 4, 5, 6, or 7 individuals) to understand consumer effects on algal and bacterial abundance (chlorophyll a [Chl a] extraction, bacterial cell counts, biomass measurements) and bacterial diversity and community composition (via Illumina MiSeq sequencing of the V4 region of the 16S ribosomal RNA gene). Increasing C. anomalum density reduced algal biomass until density reached ~2 fish (5 g fish biomass/m 2 ), and higher fish densities did not affect algal biomass. Fish biomass did not affect bacterial cell counts. Biofilm organic matter decreased with increasing C. anomalum biomass. Bacterial community composition was not affected by fish biomass, but variation in community composition was correlated with shifts in bacterial abundances. Evenness of bacterial operational taxonomic units (OTUs) decreased with increasing C. anomalum biomass, indicating that bacterial communities exhibited a greater degree of OTU dominance when fish biomass was higher. These findings suggest that this grazing fish species reduces algal abundance and organic matter in low-nutrient streams until a threshold of moderate fish abundance is reached and that it reduces evenness of benthic bacterial communities but not bacterial biomass. Finally, given the importance of biofilm bacteria for ecosystem processes and the ubiquity of grazing fishes in streams, future researchers should explore both top–down and bottom–up interactions in alternative environmental contexts and with other grazing fish species.

16S rRNA sequencing↗

DOE OSTI2014

Cannibalization and rebirth in the NGC 5387 system. I. The stellar stream and star-forming region

We have identified a low surface brightness stellar stream from visual inspection of Sloan Digital Sky Survey (SDSS) imaging for the edge-on, spiral galaxy NGC 5387. An optically blue overdensity coincident with the stream intersection with the NGC 5387 disk was also identified in SDSS and in the Galaxy Evolution Explorer Deep Imaging Survey contributing 38% of the total far-UV integrated flux from NGC 5387. Deeper optical imaging was acquired with the Vatican Advanced Technology Telescope that confirmed the presence of both features. The stellar stream is red in color, (B – V) = 0.7, has a stellar mass of 6 × 10{sup 8} M{sub ☉}, which implies a 1:50 merger ratio, has a circular radius, R{sub circ} ∼ 11.7 kpc, formed in ∼240 Myr, and the progenitor had a total mass of ∼4 × 10{sup 10} M{sub ☉}. Spectroscopy from LBT+MODS1 was used to determine that the blue overdensity is at the same redshift as NGC 5387, consists of young stellar populations (∼10 Myr), is metal-poor (12 + log (O/H) = 8.03), and is forming stars at an enhanced rate (∼1-3 M{sub ☉} yr{sup –1}). The most likely interpretations are that the blue overdensity is (1) a region of enhanced star formation in the outer disk of NGC 5387 induced by the minor accretion event or (2) the progenitor of the stellar stream experiencing enhanced star formation. Additional exploration of these scenarios is presented in a companion paper.