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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Observations of particle number size distributions and new particle formation in six Indian locations

Atmospheric new particle formation (NPF) is a crucial process driving aerosol number concentrations in the atmosphere; it can significantly impact the evolution of atmospheric aerosol and cloud processes. This study analyses at least 1 year of asynchronous particle number size distributions from six different locations in India. We also analyze the frequency of NPF and its contribution to cloud condensation nuclei (CCN) concentrations. We found that the NPF frequency has a considerable seasonal variability. At the measurement sites analyzed in this study, NPF frequently occurs in March–May (pre-monsoon, about 21% of the days) and is the least common in October–November (post-monsoon, about 7% of the days). Considering the NPF events in all locations, the particle formation rate (J SDS ) varied by more than 2 orders of magnitude (0.001–0.6 cm –3 s –1 ) and the growth rate between the smallest detectable size and 25nm (GR SDS-25nm ) by about 3 orders of magnitude (0.2–17.2nm h –1 ). We found that J SDS was higher by nearly 1 order of magnitude during NPF events in urban areas than mountain sites. GR SDS did not show a systematic difference. Our results showed that NPF events could significantly modulate the shape of particle number size distributions and CCN concentrations in India. The contribution of a given NPF event to CCN concentrations was the highest in urban locations (4.3 × 10 3 cm –3 per event and 1.2 × 10 3 cm –3 per event for 50 and 100nm, respectively) as compared to mountain background sites (2.7 × 10 3 cm –3 per event and 1.0 × 10 3 cm –3 per event, respectively). We emphasize that the physical and chemical pathways responsible for NPF and factors that control its contribution to CCN production require in situ field observations using recent advances in aerosol and its precursor gaseous measurement techniques.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Correlating processing variables to material properties in recycled polypropylene: A data‐driven approach

Abstract Polypropylene (PP) is one of the most widely used plastics, yet its recycling remains limited, with less than 1% of solid waste PP being reprocessed. Mechanical recycling through extrusion is the most practical method, but inconsistent reprocessing conditions introduce variability in material properties. While temperature, screw speed, and residence time influence the thermomechanical stress applied during reprocessing, there are no standardized guidelines for optimizing these parameters. This study examines how these factors shape the properties of recycled PP, using conditions designed to mimic post‐industrial recycled (PIR) scrap. Residence time was measured using colorimetric tracking and correlated with molecular weight, viscosity, and mechanical properties over multiple extrusion cycles. Data‐driven modeling, including response surface methodology, support vector machines, and artificial neural networks, identified processing temperature as the dominant factor in material degradation, followed by residence time. Mechanical properties remained stable, while viscosity decreased predictably with increasing residence time. By linking reprocessing conditions to property evolution, this study provides a method to optimize processing parameters and reduce variability in recycled PP. These findings help manufacturers improve process control, making recycled PP more predictable for reuse in manufacturing. Highlights Study of PIR‐quality PP without additives or compatibilizers. Residence time analysis shows processing temperature drives PP property changes. Mark‐Houwink enables quick molecular weight checks for quality control. Models predict mechanical and rheological shifts in reprocessing. Optimized processing parameters minimize property degradation in recycling.

Estela‐García, John E. [Polymer Engineering Center↗
DOE OSTI2022

The impact of forest-controlled snow variability on late-season streamflow varies by climatic region and forest structure

Previous studies have documented how forests influence snow at fine spatial scales, but none have documented the influence that existing forest-snow variability has on streamflow. Here to test how much forest-controlled snow variability influences streamflow, a tiling parameterization based on classifications from high-resolution (1–3 m) vegetation maps was incorporated into the Distributed Hydrology and Soil Vegetation Model (DHSVM). Within each grid cell (90–150 m), the tiling parameterization simulated forest-snow variability with four independently evolving snowpacks. Each tile had unique radiation conditions to represent conditions underneath the canopy, in exposed areas, and along north- and south-facing forest edges. This tiled parameterization was used to test where and when detailed forest-snow modelling should be considered further and where and when the impacts are too small to be worth the effort. To test this, tiled model simulations of streamflow were compared to non-tiled model simulations in the Sierra Nevada, CA, the Jemez Mountains, NM, and the Eastern Cascades, WA. In Tuolumne, CA, the tiled model simulated little difference in grid cell average SWE, and late-season streamflow decreased by only 3%–4% compared to the non-tiled model. In Jemez, NM, the tiled model decreased late-season streamflow by 18% due to increased sublimation. In Chiwawa, WA, the tiled model increased late-season streamflow by 15% due to high shortwave radiation attenuation and less longwave radiation enhancement from the forest. Furthermore, within the Chiwawa, a substantial silvicultural practice was synthetically implemented to increase the north-facing edge's fractional area. This silvicultural experiment, which used the same fractional forest area in all simulations increased late-season streamflow by 35% compared to tiled model simulations that did not represent forest edges. In conclusion, representing forest-SWE variability had an effect on late-season streamflow in some watersheds but not in others based on the fractional area of the forest edges, forest characteristics, and climate conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Impact of anatomical fractionation of corn stover on hammer mill throughput and energy consumption

The goal of this Case Study was to quantify the impacts of variable moisture and ash on hammer mill throughput and energy consumption and on loss of very wet stover that causes failures in the first stage grinder and that are not able to be fed to conversion, as compared to a status quo Base Case system. Also considered was convertible carbohydrate content (minimum total carbohydrate specification) and maximum ash content and the delivered feedstock cost impacts of not being able to feed stover not meeting the total carbohydrate specification to the conversion reactor. Laboratory data on the impacts of moisture content and tissue fraction on throughput and energy consumption in a stage 2 hammer mill were received from FCIC Subtask 5.1. Additional air classifier throughput, energy consumption and separation efficiency data were obtained from FCIC Subtask 5.1 for the new air classifier, which has three exit streams (lights, middle and heavies). These data were utilized to develop the necessary response surface equations to perform throughput analysis using discrete event simulation. Because the ash contents and particle sizes had not been analyzed in the laboratory at the time of the model runs, we assumed that the ash distributed proportionally with total mass into the lights and heavies in an air classifier having two exit streams (lights and heavies) and that the lights fraction from the air classifier was not removed. Key takeaways from this Case Study are that due to lower energy consumption, it is more cost effective to hammer mill fractionated corn stover tissues than whole stover. Reduction of grinding energy was significant and may possibly be connected to particle-particle interactions in the grinder that lead to increased residence time of leaves and husks, resulting in decreased throughput and higher generation of fines when milling whole stover. While we did not see significant impacts to throughput, this was due to moisture failures of the first stage grinder in each system dominating failures and downtime. The operating cost savings of reduced grinding energy savings in the second stage hammer mills alone was high enough to offset the added capital cost of the air classifier and extra grinding line.

09 BIOMASS FUELS↗
DOE OSTI2022

Drivers of Phytoplankton Variability in and Near the Pearl River Estuary, South China Sea During Typhoon Hato (2017): A Numerical Study

The Pearl River estuary-coastal system in the Northern South China Sea is frequently affected by tropical cyclones (TCs) every year. Previous in-situ and remote sensing studies have found typhoons can enhance phytoplankton biomass and induce blooms in this region. However, the mechanistic links between phytoplankton blooms and typhoons have not been well elucidated due to the interplay of multiple processes along the land-ocean-atmosphere interfaces. Unravelling these interactions will have to rely on an integrated modelling system. In this work, we constructed a realistic, 3-dimensional, land-ocean-atmosphere modelling system with the marine ecosystem and sediment components for the China Great Bay Area. By using the integrated modelling system, we quantitatively investigated phytoplankton response to hydrological conditions variations under Typhoon Hato (2017), a strong typhoon case. Passive tracer experiments showed that with high river discharge induced by heavy rainfall, the residence time of Lingding Bay is as short as 15-day, less than half of that under the climatological discharge. The increase in freshwater pulse washed out the phytoplankton biomass within Lingding Bay. While for the offshore region, the source and sink terms analysis showed that the increase of phytoplankton biomass in the first week was because of the uplift of nutrient-rich subsurface water, while in the second week was because of the seaward propagated nearshore high phytoplankton biomass water. While riverine nutrients support phytoplankton growth in the third week, a large part of phytoplankton biomass was lost to zooplankton grazing, showing the system shifted from the bottom-up control to the top-down control.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A comparison between residential relocation timing of Sydney and Chicago residents: A Bayesian survival analysis

We report that understanding households' behaviour in residential relocation timing is of great importance in the field of transport engineering and economics. This research aims to develop a residential relocation model by considering the potential dynamic impacts of other households' decisions and variables, including economic and demographic attributes, housing features, intra-household decision-making structures, travel mode choice, and other life-course attributes. A multivariate parametric survival model with both fixed and time-varying covariates is developed. To the best of the authors' knowledge, this study is the first paper in the literature of residential relocation timing to propose the use of a Bayesian model in contrast to the widely used classic frequentist approach and have conducted a discussion on its advantages. An emerging residential relocation dataset collected for two cities in Australia and the USA (Sydney and Chicago cities) has been used, which covers residence, vehicle ownership, occupation, education, economic and demographic attributes of respondents. A comprehensive comparison between the results of two cities and a comparison between two Bayesian and frequentist approaches are made. This study confirms the impact of life-course variables, intra-household decision-making behaviours, and sociodemographic attributes on home mobility. According to the model outputs, the accelerating or decelerating impact of explanatory variables on the relocation timing has been almost the same in the two cities. The Bayesian model was confirmed to have some advantages over the frequentist model, including being straightforward to interpret, availability of making inferences on the results, and ease of handling complex models, and optimisation convergence complexities.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Machine-to-machine variability of roughness and corrosion in additively manufactured 316L stainless steel

Numerous studies on the corrosion response of metal AM have been conducted. Nonetheless, the specimens being tested are commonly ground or polished to remove the outer as-built surface. If metal AM is truly going to be employed as a transformative technology that can produce complex shapes that do not require traditional machining, then the material needs to be evaluated in the as-built state. The reality is that AM alloys have shown significant inconsistencies regarding as-built surface texture, topology, and residual stress. One metric that has shown significant unpredictability in the literature is the susceptibility to localized corrosion of typically passive alloys, such as stainless steel (SS). There are a large number of studies that have attempted to understand the corrosion response of metal AM materials, but these studies are typically performed on materials that have been printed on a single machine and often mechanically polished to a smooth finish. This study compares the corrosion response of as-built AM, laser-beam powder bed fusion (LB-PBF), 316L SS parts that have been fabricated on five different machines. The majority of this work focused on understanding the susceptibility to localized corrosion of AM metals with respect to machine-dependent variables, namely surface roughness, and build angle. Surface roughness data was collected using scanning white light triangulation, laser scanning confocal microscopy, and coherence scanning interferometry. The results show that there is significant variability (p two-tail < 0.05) in the susceptibility to local corrosion initiation of LB-PBF 316L SS samples built on different machines. Surface oxides were probed with electron dispersive spectroscopy and revealed that variations in local corrosion susceptibility likely arise from differences in the stability of the passive film caused by chemical segregation, unique microstructure, and tortuous roughness features at the as-built surfaces. The variability of roughness and corrosion properties from test samples printed on different machines was corroborated by property measurements performed at five different testing sites, proving reproducibility of the data. Most importantly, this study shows that if the as-built surface layer is removed through grinding or electropolishing the machine-to-machine variation observed in the corrosion susceptibility is reduced.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Constraining the Aerosol Effects on Deep Convective Clouds by Considering the Coupling Between Clouds and the Planetary Boundary Layer

Several mechanisms have been proposed for the aerosol invigoration effect. Although their principles are well established, their actual magnitudes and roles in cloud development remain uncertain and debatable. This uncertainty partly stems from observational-based studies, in which it has been challenging to separate the co-variability between aerosols and meteorology. Addressing this problem requires large data samples. To this end, this study employs the Atmospheric Radiation Measurement data set expanding to 16 years (some up to 17 years, compared to 10 years in previous work) in the U.S. Southern Great Plains. It also conducts a more careful and rigorous analysis to isolate the influences of convective available potential energy (CAPE) and synoptic patterns to address a previously raised concern. We incorporated a new key process affecting aerosol-cloud interaction: cloud-surface coupling. The state/degree of the coupling relationship turns out to play an important role in the invigoration effect. Our analysis reinforces earlier findings of a robust positive relationship between cloud thickness and aerosol loading across CAPE percentiles—but only under cloud-surface coupled conditions. The increase in cloud thickness with aerosol loading is most pronounced in coupled clouds with high CAPE and bases below 1 km. Coupled clouds with bases below 1 km thicken between 1 and 4 km, depending on the CAPE percentile. Decoupled clouds show no such systematic changes. Synoptic patterns also lead to different strengths of the invigoration effect. Clean and polluted air masses are predominantly associated with northerly and southerly winds, respectively, with a stronger invigoration effect in cleaner air masses.

Geosciences↗
DOE OSTI2020

Experimental and Numerical Investigation of a Miniature Additively Manufactured Vortex Tube

Ranque–Hilsch vortex tubes are simple devices that can produce a cooling effect using compressed air. A key advantage of vortex tubes is the lack of moving solid parts; however, their efficiencies are relatively low. The present study focuses on the development of a miniature variable-diameter tube using additive manufacturing. A metal-based 3D printing technique was utilized to fabricate this vortex tube monolithically. Computational fluid dynamics simulations employing software STAR-CCM + with a compressible Reynolds-Averaged Navier–Stokes (RANS) approach and the elliptic-blending lag k-epsilon turbulence model have been applied to model thermofluid processes inside the vortex tube, to good agreement with the experiment. In conclusion, a temperature decrease of 13.3 °C and a cooling power of approximately 4 W were experimentally achieved with a pressure ratio of 4 in the air at normal conditions. This result shows promise for the goal of utilizing additive manufacturing to design and build complex-geometry vortex tubes intended for use with cryogenic fluids.

08 HYDROGEN↗
DOE OSTI2024

Managing negative values is reservoir inflow computation: A case study

Reservoir inflow is conventionally estimated using the water balance method, which involves the reservoir release and the change in storage during the period considered. As a result, the estimated inflow may sometimes be negative as the errors involved in each input variable build-up to the output. In our study, the fleet data was provided by the Tennessee Valley Authority (TVA) for their Norris Hydropower facility. Unlike the flow release data, which was readily accessible, the change in storage had to be calculated using the reservoir elevation and volume relationship. The original inflow estimates produced a wide range of negative values with large outliers, making it difficult to visualize the current trends. This paper describes a methodology to remove the negative values encountered during the inflow computation, and the results were analyzed by correlating with the nearby streamflow gaging stations.

Shibu, Asha↗
DOE OSTI2022

Role of Ocean and Atmosphere Variability in Scale-Dependent Thermodynamic Air-Sea Interactions

This study investigates the influence of oceanic and atmospheric processes in extratropical thermodynamic air-sea interactions resolved by satellite observations (OBS) and by two climate model simulations run with eddy-resolving high-resolution (HR) and eddy-parameterized low-resolution (LR) ocean components. Here, in this work, spectral methods are used to characterize the sea surface temperature (SST) and turbulent heat flux (THF) variability and co-variability over scales between 50 and 10,000 km and 60 days to 80 years in the Pacific Ocean. The relative roles of the ocean and atmosphere are interpreted using a stochastic upper-ocean temperature evolution model forced by noise terms representing intrinsic variability in each medium, defined using climate model data to produce realistic rather than white spectral power density distributions. The analysis of all datasets shows that the atmosphere dominates the SST and THF variability over zonal wavelengths larger than ~2,000–2,500 km. In HR and OBS, ocean processes dominate the variability of both quantities at scales smaller than the atmospheric first internal Rossby radius of deformation (R 1 , ~600–2,000 km) due to a substantial ocean forcing coinciding with a weaker atmospheric modulation of THF (and consequently of SST) than at larger scales. The ocean forcing also induces oscillations in SST and THF with periods ranging from intraseasonal to multidecadal, reflecting a red spectrum response to ocean forcing similar to that driven by atmospheric forcing. Such features are virtually absent in LR due to a weaker ocean forcing relative to HR.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Understanding Hailstone Temporal Variability and Contributing Factors over the U.S. Southern Great Plains

Abstract Hailstones are a natural hazard that pose a significant threat to property and are responsible for significant economic losses each year in the United States. Detailed understanding of their characteristics is essential to mitigate their impact. Identifying the dynamic and physical factors contributing to hail formation and hailstone sizes is of great importance to weather and climate prediction and policymakers. In this study, we have analyzed the temporal and spatial variabilities of severe hail occurrences over the U.S. southern Great Plains (SGP) states from 2004 to 2016 using two hail datasets: hail reports from the Storm Prediction Center and the newly developed radar-retrieved maximum expected size of hail (MESH). It is found that severe and significant severe hail occurrences have a considerable year-to-year temporal variability in the SGP region. The interannual variabilities have a strong correspondence with sea surface temperature anomalies over the northern Gulf of Mexico and there is no outlier. The year 2016 is identified as an outlier for the correlations with both El Niño–Southern Oscillation (ENSO) and aerosol loading. The correlations with ENSO and aerosol loading are not statistically robust to inclusion of the outlier 2016. Statistical analysis without the outlier 2016 shows that 1) aerosols that may be mainly from northern Mexico have the largest correlation with hail interannual variability among the three factors and 2) meteorological covariation does not significantly contribute to the high correlation. These analyses warrant further investigations of aerosol impacts on hail occurrence.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A machine learning pipeline for identifying infiltration managed aquifer recharge locations from satellite imagery in the San Joaquin Valley, California

This study focuses on an agricultural region in California’s Central Valley, USA, where Managed Aquifer Recharge (MAR) is widely implemented to mitigate groundwater depletion under increasing water demand and climate variability. A deep learning and machine learning framework was developed to identify infiltration-MAR locations using satellite imagery and environmental data. The framework integrates surface water detection from Sentinel-2 imagery, geospatial delineation of water bodies, spatiotemporal tracking of water body dynamics, and supervised classification using meteorological, environmental, and topographic variables. The framework was applied to a 2379 km² study area southwest of Fresno, where 765 water bodies were detected, including 139 identified MAR sites based on publicly available datasets and expert knowledge. The classification model achieved an accuracy of 0.94 and an F1 score of 0.85. Feature importance analysis indicates that cropland, normalized difference vegetation index (NDVI), and evaporation are among the most influential predictors for infiltration-MAR. Notably, the framework suggests that engineered water management in infiltration-MAR systems can disrupt or even reverse the expected positive correlation between surface water extent and precipitation. These findings provide physically interpretable insights into the characteristics of existing infiltration-MAR facilities and demonstrate the potential of the proposed framework as a reproducible, interpretable, and potentially transferable tool for data-driven infiltration-MAR identification and inventory development under growing climatic and hydrological uncertainty.

Classification↗
DOE OSTI2023

Assessing decadal variability of subseasonal forecasts of opportunity using explainable AI

Abstract Identifying predictable states of the climate system allows for enhanced prediction skill on the generally low-skill subseasonal timescale via forecasts with higher confidence and accuracy, known as forecasts of opportunity. This study takes a neural network approach to explore decadal variability of subseasonal predictability, particularly during forecasts of opportunity. Specifically, this work quantifies subseasonal prediction skill provided by the tropics within the Community Earth System Model Version 2 (CESM2) Large Ensemble and assesses how this skill evolves on decadal timescales. Utilizing the networks’ confidence and explainable artificial intelligence, physically meaningful sources of predictability associated with periods of enhanced skill are identified. Using these networks, we find that tropically-driven subseasonal predictability varies on decadal timescales during forecasts of opportunity. Further, we investigate the drivers of the low frequency modulation of the tropical-extratropical teleconnection and discuss the implications. Analysis is extended to ECMWF Reanalysis v5 data, revealing that the relationships learned within the CESM2-Large Ensemble holds in modern reanalysis data. These results indicate that the neural networks are capable of identifying predictable decadal states of the climate system within CESM2 that are useful for making confident, accurate subseasonal precipitation predictions in the real world.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Anderson acceleration stability in NDA-accelerated k-eigenvalue problems

Anderson acceleration (AA) has been used to improve the stability and convergence rate of multiphysics iterative methods for reactor analysis. Most applications studied assume a tightly converged solution for the different physics problems, and AA is usually applied to state variables like temperature, density, and heat generation rate. In this paper, we study the theoretical performance of AA in NDA-accelerated k-eigenvalue problems. The problems and algorithms studied are simplified from the coupled iteration scheme adopted by MPACT and many other high-fidelity whole-core reactor codes. Compared to previous analyses of AA for these iteration schemes, we study the case with a partially converged neutronics solution and possibly partially converged nonlinear diffusion acceleration (NDA)/coarse mesh finite difference (CMFD) solutions. We observe that the performance of the iteration scheme with AA is very sensitive to the initial guess and is affected by the partially converged CMFD solutions. When the NDA solution is fully converged, using AA cannot achieve the optimal convergence rate in large-sized problems. Conversely, if the NDA solution is partially converged, the iteration scheme with AA can diverge or converge extremely slowly. It is found that the loss of robustness for AA is due to the fact that it is applied to the iterative subspace of state variables rather than the fundamental unknowns of the governing equations. To improve the robustness, the scalar flux should also be considered in the implementation of AA. After considering the residuals of flux, we observe that the stability is regardless of the partial convergence of NDA solutions. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Idealized simulations of wind farm interactions with intermittent turbulence in stable boundary layer conditions

Stable atmospheric boundary layer conditions typically correspond to weak turbulence levels, but intermittent periods of elevated turbulence can occur during otherwise quiescent conditions. The interaction between intermittent turbulence and wind turbines is not well understood because of sparse observations, as well as the difficulty in realistically resolving small-scale turbulence during strongly stable conditions with numerical simulations. In this study, an explicit filtering and reconstruction approach for large-eddy simulation (LES) is used to simulate weakly and strongly stable conditions, with surface cooling rates of −0.2 and −2.0 K h −1 , respectively. This approach can sustain resolved background turbulence at relatively coarse grid spacing and stronger stratification compared to conventional closures, permitting more realistic intermittent stable boundary layer (SBL) turbulence. The idealized LES capability of the Weather Research and Forecasting model is employed with turbine rotors parameterized using generalized actuator disks to examine (1) how the presence of turbine wakes affects SBL evolution and (2) the effect of intermittent turbulence on power production and wake recovery. Wakes increase mixing and deepen the SBL, with a stronger effect under strongly stable conditions, primarily because the SBL is shallower and closer to the top of the wind turbine rotor layer. Intermittent turbulence does not have a significant impact on mean power generation and wake recovery because the relevant intermittent turbulent structures in this study only affect the bottom half of the rotor disk. Power production is, however, more variable during periods of elevated turbulence, demonstrating the impact of SBL intermittency. This study uses an idealized configuration, focusing on LES model performance and physical understanding, with the goal of informing future simulations of the conditions observed during the American Wake Experiment.

Energy - Wind↗
DOE OSTI2021

Large Magnetoresistance in Scandium Nitride Magnetic Tunnel Junctions Using First Principles

Abstract The state‐of‐the‐art magnetic tunnel junction, a cornerstone of spintronic devices and circuits, uses a magnesium oxide tunnel barrier that provides a uniquely large tunnel magnetoresistance at room temperature. However, the wide bandgap and band alignment of magnesium oxide‐iron systems increases the resistance‐area product and creates variability and breakdown challenges. Here, the authors study using first principles narrower‐bandgap scandium nitride (ScN) transport properties in magnetoresistive junctions in comparison to magnesium oxide. The results show a high magnetoresistance in Fe/ScN/Fe via Δ 1 and symmetry filtering with low wave function decay rates, suggesting scandium nitride could be a new barrier material for spintronic devices.

Karki, Suyogya↗
DOE OSTI2025

Hidden Features: How Subsurface and Landscape Heterogeneity Govern Hydrologic Connectivity and Stream Chemistry in a Montane Watershed

ABSTRACT Hydrologic connectivity is defined as the connection among stores of water within a watershed and controls the flux of water and solutes from the subsurface to the stream. Hydrologic connectivity is difficult to quantify because it is goverened by heterogeniety in subsurface storage and permeability and responds to seasonal changes in precipitation inputs and subsurface moisture conditions. How interannual climate variability impacts hydrologic connectivity, and thus stream flow generation and chemistry, remains unclear. Using a rare, four‐year synoptic stream chemistry dataset, we evaluated shifts in stream chemistry and stream flow source of Coal Creek, a montane, headwater tributary of the Upper Colorado River. We leveraged compositional principal component analysis and end‐member mixing to evaluate how seasonal and interannual variation in subsurface moisture conditions impacts stream chemistry. Overall, three main findings emerged from this work. First, three geochemically distinct end members were identified that constrained stream flow chemistry: reach inflows, and quick and slow flow groundwater contributions. Reach inflows were impacted by historic base and precious metal mine inputs. Bedrock fractures facilitated much of the transport of quick flow groundwater and higher‐storage subsurface features (e.g., alluvial fans) facilitated the transport of slow flow groundwater. Second, the contributions of different end members to the stream changed over the summer. In early summer, stream flow was composed of all three end members, while in late summer, it was composed predominantly of reach inflows and slow flow groundwater. Finally, we observed minimal differences in proportional composition in stream chemistry across all four years, indicating seasonal variability in subsurface moisture and spatial heterogeneity in landscape and geologic features had a greater influence than interannual climate fluctuation on hydrologic connectivity and stream water chemistry. These findings indicate that mechanisms controlling solute transport (e.g., hydrologic connectivity and flow path activation) may be resilient (i.e., able to rebound after perturbations) to predicted increases in climate variability. By establishing a framework for assessing compositional stream chemistry across variable hydrologic and subsurface moisture conditions, our study offers a method to evaluate watershed biogeochemical resilience to variations in hydrometeorological conditions.

Johnson, Keira [College of Earth, Ocean, and Atmos↗