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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Machine Learning-Based Regression Models for Ironmaking Blast Furnace Automation

Computational fluid dynamics (CFD)-based simulation has been the traditional way to model complex industrial systems and processes. One very large and complex industrial system that has benefited from CFD-based simulations is the steel blast furnace system. The problem with the CFD-based simulation approach is that it tends to be very slow for generating data. The CFD-only approach may not be fast enough for use in real-time decisionmaking. To address this issue, in this work, the authors propose the use of machine learning techniques to train and test models based on data generated via CFD simulation. Regression models based on neural networks are compared with tree-boosting models. In particular, several areas (tuyere, raceway, and shaft) of the blast furnace are modeled using these approaches. The results of the model training and testing are presented and discussed. The obtained R 2 metrics are, in general, very high. The results appear promising and may help to improve the efficiency of operator and process engineer decisionmaking when running a blast furnace.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Simulation and Optimization of Volatile Fatty Acid Upgrading Strategies for Sustainable Transportation Fuel Production

The multicomponent nature of and variability of biomass makes chemical upgrading via a single process stream to a single end use infeasible for most feedstocks. A more promising approach is to identify upgrading strategies (encompassing bioprocessing, catalysis, and separations) which valorize varied biomass fractions to distinct products to which each is best suited. A robust procedure to carry out this goal should incorporate comparison of various upgrading procedures as well as suitability of products to a slate of identified end uses. We applied this strategy to one biomass-derived feedstock, volatile fatty acids (VFAs) derived from wet waste arrested anaerobic digestion, by developing a computer program, VFA Upgrading to Liquid Transportation fUels Refinery Estimation (VULTURE) which evaluates VFA catalytic upgrading to liquid transportation fuels. VULTURE considers multiple separations, catalysis (ketonization, hydrogenation), and fuel application options, generating hundreds of candidate scenarios for a given VFA stream, then selects several promising strategies that optimize bio-content of products with properties best suited for target fuel types. We find that VFAs are upgraded most efficiently when separate light alcohol (C3-6) and heavy hydrocarbon or alcohol (C7-13) fractions are targeted to create gasoline and heavy-duty (diesel or jet) fuels or fuel blends. Surrogate property testing of VULTURE-recommended fuels reveals that most predictive models employed are robust, while rigorous process simulation shows that the simple unit operation assumptions used in VULTURE are largely accurate, especially for heavy-duty fuel synthesis. Techno-economic and life-cycle analyses show that VFA-derived fuels are profitable and have dramatically (=57%) lower carbon intensities than fossil analogs.

BIOMASS FUELS↗
DOE OSTI2016

Developing Materials Processing to Performance Modeling Capabilities and the Need for Exascale Computing Architectures (and Beyond)

Additive Manufacturing techniques are presenting the Department of Energy and the NNSA Laboratories with new opportunities to consider novel component production and repair processes, and to manufacture materials with tailored response and optimized performance characteristics. Additive Manufacturing technologies already are being applied to primary NNSA mission areas, including Nuclear Weapons. These mission areas are adapting to these new manufacturing methods, because of potential advantages, such as smaller manufacturing footprints, reduced needs for specialized tooling, an ability to embed sensing, novel part repair options, an ability to accommodate complex geometries, and lighter weight materials. To realize the full potential of Additive Manufacturing as a game-changing technology for the NNSA’s national security missions; however, significant progress must be made in several key technical areas. In addition to advances in engineering design, process optimization and automation, and accelerated feedstock design and manufacture, significant progress must be made in modeling and simulation. First and foremost, a more mature understanding of the process-structure-property-performance relationships must be developed. Because Additive Manufacturing processes change the nature of a material’s structure below the engineering scale, new models are required to predict materials response across the spectrum of relevant length scales, from the atomistic to the continuum. New diagnostics will be required to characterize materials response across these scales. And not just models, but advanced algorithms, next-generation codes, and advanced computer architectures will be required to complement the associated modeling activities. Based on preliminary work in each of these areas, a strong argument for the need for Exascale computing architectures can be made, if a legitimate predictive capability is to be developed.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Defense Waste Processing Facility Nitric- Glycolic Flowsheet Chemical Process Cell Chemistry: Part 2

The conversions of nitrite to nitrate, the destruction of glycolate, and the conversion of glycolate to formate and oxalate were modeled for the Nitric-Glycolic flowsheet using data from Chemical Process Cell (CPC) simulant runs conducted by Savannah River National Laboratory (SRNL) from 2011 to 2016. The goal of this work was to develop empirical correlation models to predict these values from measureable variables from the chemical process so that these quantities could be predicted a-priori from the sludge or simulant composition and measurable processing variables. The need for these predictions arises from the need to predict the REDuction/OXidation (REDOX) state of the glass from the Defense Waste Processing Facility (DWPF) melter. This report summarizes the work on these correlations based on the aforementioned data. Previous work on these correlations was documented in a technical report covering data from 2011-2015. This current report supersedes this previous report. Further refinement of the models as additional data are collected is recommended.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

Predicting large-scale pool fire dynamics using an unsteady flamelet- and large-eddy simulation-based model suite

A low-Mach, unstructured, large-eddy-simulation-based, unsteady flamelet approach with a generalized heat loss combustion methodology (including soot generation and consumption mechanisms) is deployed to support a large-scale, quiescent, 5-m JP-8 pool fire validation study. The quiescent pool fire validation study deploys solution sensitivity procedures, i.e., the effect of mesh and time step refinement on capturing key fire dynamics such as fingering and puffing, as mesh resolutions approach O(1) cm. A novel design-order, discrete-ordinate-method discretization methodology is established by use of an analytical thermal/participating media radiation solution on both low-order hexahedral and tetrahedral mesh topologies in addition to quadratic hexahedral elements. Here, the coupling between heat losses and the flamelet thermochemical state is achieved by augmenting the unsteady flamelet equation set with a heat loss source term. Soot and radiation source terms are determined using flamelet approaches for the full range of heat losses experienced in fire applications including radiative extinction. The proposed modeling and simulation paradigm are validated using pool surface radiative heat flux, maximum centerline temperature location, and puffing frequency data, all of which are predicted within 10% accuracy. Simulations demonstrate that under-resolved meshes predict an overly conservative radiative heat flux magnitude with improved comparisons as compared to a previously deployed hybrid Reynolds-averaged Navier–Stokes/eddy dissipation concept-based methodology.

42 ENGINEERING↗
DOE OSTI2018

Multi-configuration Membrane Distillation Model (MCMD)

Many membrane distillation models have been created to simulate the heat and mass exchange process involved but most of the literature only validates models to a couple of cases with minor configuration changes. Tools are needed that allow tradeoffs between many configurations. The multiconfiguration membrane distillation model handles many configurations. This report introduces membrane distillation, provides theory, and presents the work to verify and validate the model against experimental data from Colorado School of Mines and a lower resolution model created at the National Renewable Energy Laboratory. Though more data analysis and testing are needed, an initial look at the model to experimental comparisons indicates that the model correlates to the data well but that design comparisons are likely to be incorrect across a broad range of configurations. More accurate quantification of heat and mass transfer through computational fluid mechanics is suggested.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

FY2017 annual report: Roll-to-Roll Advanced Materials Manufacturing DOE Laboratory Consortium Project

The following report provides a description of the program structure and technical accomplishments made during FY 2017 to overcome challenges for expanding use of R2R technologies and processing for clean energy applications. Technologies are under development that enable enhanced manufacturing of battery electrodes, fuel cells and water separation membranes that take advantage of advancements in new functional materials. Data mining, visualization, modeling and simulation for the materials, technologies and processes being developed allow for improvements in R2R manufacturing applications. This report documents the research conducted by four DOE national laboratories as a consortium in collaboration with industry partnerships. The research is directly applicable to AMM strategies that apply the National Science and Technology Council’s Materials Genome Initiative (NSTC 2011) approach to enhance the performance of battery, fuel cell and water membrane materials. This effort supports building the foundation of technologies, processes and a U.S. manufacturing base that will enable an order of magnitude in shorter process development cycles with the pathway for initial commercialization within months instead of years.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Radiation Source Localization Using Surrogate Models Constructed from 3-D Monte Carlo Transport Physics Simulations

Recent research has focused on the development of surrogate models for radiation source localization in a simulated urban domain. We employ the Monte Carlo N-Particle (MCNP) code to provide high- delity simulations of radiation transport within an urban domain. The model is constructed to employ a source location (x, y, z) as input and return the estimated count rate for a set of speci ed detector locations. Because MCNP simulations are computationally expensive, we develop e cient and accurate surrogate models of the detector responses. We construct surrogate models using Gaussian processes (GP) and neural networks (NN) that we train and verify using the MCNP simulations. The trained surrogate models provide an e cient framework for Bayesian inference and experimental design. We employ Delayed Rejection Adaptive Metropolis (DRAM), a Markov Chain Monte Carlo (MCMC) algorithm, to infer the location and intensity of an unknown source. The DRAM results yield a posterior probability distribution for the source's location conditioned on the observed detector count rates. The posterior distribution exhibits regions of high and low probability within the simulated environment identifying potential source locations. In this manner, we can quantify the source location to within at least one of these regions of high probability in the considered cases. Employing these methods, we are able to reduce the space of potential source locations by at least 60%.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

UO 2 Grain Growth: Developing Phase Field Models for Pore Dragging, Solute Dragging and Anisotropic Grain Boundary Energies

A detailed phase field model for the effect of pore drag on grain growth kinetics was implemented in MARMOT. The model takes into consideration both the curvature-driven grain boundary motion and pore migration by surface diffusion. As such, the model accounts for the interaction between pore and grain boundary kinetics, which tends to retard the grain growth process. Our 2D and 3D simulations demonstrate that the model capture all possible pore-grain boundary interactions proposed in theoretical models. For high enough surface mobility, the pores move along with the migrating boundary as a quasi-rigid-body, albeit hindering its migration rate compared to the pore-free case. For less mobile pores, the migrating boundary can separate from the pores. For the pore-controlled grain growth kinetics, the model predicts a strong dependence of the growth rate on the number of pores, pore size, and surface diffusivity in agreement with theroretical models. An evolution equation for the grain size that includes these parameters was derived and showed to agree well with numerical solution. It shows a smooth transition from boundary-controlled kinetics to pore-controlled kinetics as the surface diffusivity decreases or the number of pores or their size increases. This equation can be utilized in BISON to give accurate estimate for the grain size evolution. This will be accomplished in the near future. The effect of solute drag and anisotropy of grain boundary on grain growth will be investigated in future studies.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Influence of surface relaxation on solute atoms positioning within atom probe tomography reconstructions

Highlights: • APT measurement of Al/Cu alloy is simulated with a combined electrostatics and molecular dynamics (MD) approach. • Results show segregation of dilute Cu at [111] poles in correspondence to experimental measurements. • No temperature is involved. The effect originates in both disparate evaporation field and bond-energies in the alloy. While thermal or field-induced surface migration has been invoked often in past literature to rationalize the experimental observations of solute and impurity atoms segregation to specific poles and zones lines in atom probe tomography (APT) reconstructions, it does not satisfactorily explain all experimental results. Using a combined finite-element and molecular dynamics simulation approach that models the field evaporation process from mechanically relaxed emitter structures, we show that an additional mechanism, athermal relaxation, can also drive significant solute migration. The mechanism is illustrated for Cu in solid solution in Al, which exhibits segregation of Cu to 111 poles and along [110] zone lines in reconstructed APT data. The higher bonding energy of Cu atoms on the surface, confirmed using density-functional theory calculations, causes longer retention on the surface and preferential evaporation of neighboring Al atoms with subsequent athermal relaxation of Cu into higher-neighbor-count sites left vacant by evaporating Al neighbors. This in turn results in barrier-free long-range migration of the Cu atoms by a series of relaxation events that follow the receding terrace ledges towards high-neighbor count poles and zone lines. This mechanism fundamentally limits spatial resolution in the limit of zero temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Investigation of desorption of hydrogen gas from polymer matrix using thermal desorption analysis and finite element modeling

Polymer materials are used widely as sealing materials and liners in hydrogen storage and transportation infrastructure. During operating conditions, polymers are exposed to high pressure hydrogen gas periodically. When in contact, the hydrogen gas diffuses through the polymer material with ease due to its smaller sized molecules compared to the polymer molecules. In the event of rapid decompression of outside hydrogen gas, the hydrogen molecules within the polymer diffuse out. This process is known as the desorption of polymer and can cause blisters or cracking causing permanent damage to the polymer. Here, we used a combined experimental and modeling approach to investigate the desorption of the hydrogen gas in the polymer. Thermal desorption analysis (TDA) experiments were performed on the polymer sample exposed to the high-pressure hydrogen gas to measure the diffusion coefficient and equilibrium hydrogen concentration. Experiments also provided the quantity of hydrogen within the polymer during the desorption process which followed Fick’s law. However, for higher pressurization values, some anomaly was observed in the quantity of hydrogen remaining in the polymer sample. A continuum mechanics based coupled diffusion–deformation-damage model was deployed to simulate the gas desorption process in the material using the finite element method to understand its effect on the material. It was found that the anomaly in TDA results were due to the blister formation on the polymer surface which resulted in longer hydrogen retention. Simulations also showed that blister formation can be affected by multiple parameters such as the addition of filler particles, and pre-existing cavity size.

36 MATERIALS SCIENCE↗
DOE OSTI2022

On multidomain multiscale modeling and simulation of a novel partial pressure and temperature swing adsorptive reactor ($\mathrm{PPTSAR}$) with application to the water gas shift reaction

Here in this work, a dynamic, multidomain, multiscale model of a novel Partial Pressure and Temperature Swing Adsorptive Reactor (PPTSAR) Process is developed and simulated for a Water Gas Shift Reaction (WGSR) application. The proposed model couples the reactor wall, catalyst/adsorbent pellet, and fluid mixture domains at their respective scales to comprehensively model mass, momentum, and energy transport phenomena occurring within each domain. The resulting model elucidates the PPTSAR’s start-up, transient, and long-term, periodic, dynamic behavior, during both adsorption/reaction and desorption operating modes. In the WGSR application considered, the use of two PPTSARs is shown to allow continuous operation that delivers CO conversion greater than 95% with simultaneous complete CO 2 capture.

42 ENGINEERING↗
DOE OSTI2019

Maize yield under a changing climate: The hidden role of vapor pressure deficit

Temperatures over the next century are expected to rise to levels detrimental to crop growth and yield. As the atmosphere warms without additional water vapor input, vapor pressure deficit (VPD) increases as well. Increased temperatures and accompanied elevated VPD levels can both lead to negative impacts on crop yield. The independent importance of VPD, however, is often neglected or conflated with that from temperature due to a tight correlation between the two climate factors. We used a coupled process-based crop (MAIZSIM) and soil (2DSOIL) model to gain a mechanistic understanding of the independent roles temperature and VPD play in crop yield projections, as well as their interactions with rising CO 2 levels and changing precipitation patterns. We found that by separating out the VPD effect from rising temperatures, VPD increases had a greater negative impact on yield (12.9 ± 1.8%, increase in VPD associated with 2 °C warming) compared to that from warming (8.5 ± 1.4%, the direct effect of 2 °C warming). The negative impact of these two factors varied with precipitation levels and influenced yield through separate mechanisms. Warmer temperatures caused yield loss mainly through shortening the growing season, while elevated VPD increased water loss and triggered several water stress responses such as reduced photosynthetic rates, lowered leaf area development, and shortened growing season length. Elevated CO 2 concentrations partially alleviated yield loss under warming or increased VPD conditions through water savings, but the impact level varied with precipitation levels and was most pronounced under drier conditions. These results demonstrate the key role VPD plays in crop growth and yield, displaying a magnitude of impact comparative to temperature and CO 2 . A mechanistic understanding of the function of VPD and its relation with other climate factors and management practices is critical to improving crop yield projections under a changing climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Burial-driven methane recycling in marine gas hydrate systems

Natural gas hydrate may be buried with sediments until it is no longer stable at a given pressure and temperature, resulting in conversion of hydrate into free gas. This gas may migrate upward and recycle back into the hydrate stability zone to form hydrate. As of yet, however, no quantitative description of the methane recycling process has been developed using multiphase flow simulations to model burial-driven gas hydrate recycling. In this study, we present a series of 1D multiphase transport simulations to investigate the methane recycling process in detail. By invoking the effects of capillary phenomena on hydrate and gas formation in pores of varying size, we find that a free gas phase can migrate a significant distance above the bulk base of hydrate stability. Since the top of the free gas occurrence is often identified as the base of the hydrate stability zone from seismic data, our results demonstrate that not only could this assumption mischaracterize a hydrate system, but that under recycling conditions the highest hydrate saturations can occur beneath the top of the free gas occurrence. We show that the presence of pore size distributions requires a replacement zone through which hydrate saturations progressively decrease with depth and are replaced with free gas. This replacement zone works to buffer against significant gas buildup that could lead to fracturing of overlying sediments. In conclusion, this work provides a framework for simulating flow and transport of methane within the 3-phase stability zone from a mass conservation perspective.

58 GEOSCIENCES↗
DOE OSTI2017

Mesoscopic Adaptive Resolution Scheme toward Understanding of Interactions between Sickle Cell Fibers

Understanding of intracellular polymerization of sickle hemoglobin (HbS) and subsequent interaction with the membrane of a red blood cell (RBC) is important to predict the altered morphologies and mechanical properties of sickle RBCs in sickle cell anemia. However, modeling the integrated processes of HbS nucleation, polymerization, HbS fiber interaction, and subsequent distortion of RBCs is challenging as they occur at multispatial scales, ranging from nanometers to micrometers. To make progress toward simulating the integrated processes, we propose a hybrid HbS fiber model, which couples fine-grained and coarse-grained HbS fiber models through a mesoscopic adaptive resolution scheme (MARS). To this end, we apply a microscopic model to capture the dynamic process of polymerization of HbS fibers, while maintaining the mechanical properties of polymerized HbS fibers by the mesoscopic model, thus providing a means of bridging the subcellular and cellular phenomena in sickle cell disease. At the subcellular level, this model can simulate HbS polymerization with preexisting HbS nuclei. At the cellular level, if combined with RBC models, the generated HbS fibers could be applied to study the morphologies and membrane stiffening of sickle RBCs. One important feature of the MARS is that it can be easily employed in other particle-based multiscale simulations where a dynamic coarse-graining and force-blending method is required. As demonstrations, we first apply the hybrid HbS fiber model to simulate the interactions of two growing fibers and find that their final configurations depend on the orientation and interaction distance between two fibers, in good agreement with experimental observations. We also model the formation of fiber bundles and domains so that we explore the mechanism that causes fiber branching.

Biophysics↗
DOE OSTI2018

Particle-in-cell simulations of magnetically driven reconnection using laser-powered capacitor coils

In this study, we propose an experimental scheme to fulfill magnetically driven reconnections. Here, two laser beams are focused on a capacitor-coil target and then strong currents are wired in two parallel circular coils. Magnetic reconnection occurs between the two magnetic bubbles created by the currents in the two parallel circular coils. A two-dimensional particle-in-cell simulation model in the cylindrical coordinate is used to investigate such a process, and the simulations are performed in the ( r , z ) plane. The results show that with the increase of the currents in the two coils, the associated magnetic bubbles expand and a current sheet is formed between the two bubbles. Magnetic reconnection occurs when the current sheet is sufficiently thin. A quadrupole structure of the magnetic field in the θ direction ( B θ ) is generated in the diffusion region and a strong electron current along the r direction ( J e r ) is also formed due to the existence of the high-speed electron flow away from the X line in the center of the outflow region. Finally, because the X line is a circle along the θ direction, the convergence of the plasma flow around r = 0 will lead to the asymmetry of J e r and B θ between the two outflow regions of magnetic reconnection.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Comparison of free vortex wake and blade element momentum results against large-eddy simulation results for highly flexible turbines under challenging inflow conditions

Abstract. Throughout wind energy development, there has been a push to increase wind turbine size due to the substantial economic benefits. However, increasing turbine size presents several challenges, both physically and computationally. Modeling large, highly flexible wind turbines requires highly accurate models to capture the complicated aeroelastic response due to large deflections and nonstraight blade geometries. Additionally, the development of floating offshore wind turbines requires modeling techniques that can predict large rotor and tower motion. Free vortex wake methods model such complex physics while remaining computationally tractable to perform key simulations necessary during the turbine design process. Recently, a free vortex wake model – cOnvecting LAgrangian Filaments (OLAF) – was added to the National Renewable Energy Laboratory's engineering tool OpenFAST to allow for the aerodynamic modeling of highly flexible turbines along with the aero-hydro-servo-elastic response capabilities of OpenFAST. In this work, free vortex wake and low-fidelity blade element momentum (BEM) results are compared to high-fidelity actuator-line computational fluid dynamics simulation results via the Simulator fOr Wind Farm Applications (SOWFA) method for a highly flexible downwind turbine for varying yaw misalignment, shear exponent, and turbulence intensity conditions. Through these comparisons, it was found that for all considered quantities of interest, SOWFA, OLAF, and BEM results compare well for steady inflow conditions with no yaw misalignment. For OLAF results, this strong agreement with the SOWFA results was consistent for all yaw misalignment values. The BEM results, however, deviated significantly more from the SOWFA results with increasing absolute yaw misalignment. Differences between OLAF and BEM results were dominated by the yaw misalignment angle, with varying shear exponent and turbulence intensity leading to more subtle differences. Overall, OLAF results were more consistent than BEM results when compared to SOWFA results under challenging inflow conditions.

17 WIND ENERGY↗
DOE OSTI2026

Data From: "Warming and snow loss increase reliance on old groundwater in a Colorado River headwater"

This repository contains the data and code associated with the paper titled "Warming and snow loss increase reliance on old groundwater in a Colorado River headwater," published in Nature Geoscience, 2026. This study seeks to answer how various ages of groundwater interact with mountainous streamflow in mountainous headwaters such as the East River. It includes various model-data processing scripts, primarily for ParFlow-CLM analysis of simulated water years 2015-2021, and two numerical warming experiments (+2.5 and +4.0 degrees C), including run scripts, forcing scripts, and post-processing, as well as comparison to observation datasets, detailed below. This data requires the use of R (.r, .rmd), Python (.py), Jupyter Notebook or Jupyter Lab (.ipynb), ParFLOW-CLM, EcoSLIM. Further information on the use of all file formats mentioned below (e.g. .tff. .nc) are provided within the associated scripts and directory where the files are located. Contents & Usage ASO/: ​​Contains the bash and python scripts used to convert airborne snow observatory (ASO) data (ASO, 2023) in various data formats (georeferenced tiff file, NetCDF, UTM, and to latitude/longitude) then regrided to the ParFlow equivalent grid. Output data are in regrid_regll_data.zip and subsequently visualized and analyzed in plot_and_compare.py for Supplementary Figures A14 and A15. The wksht_ASO_comparison.xlsx spreadsheet is used to calculate the data for Supplementary Figure A16. EcoSLIM/: Contains the scripts and input files to run the EcoSLIM particle tracking simulations (/run_scripts) and the post-processing python script (/plot_scripts/eco_agedist_plots.ipynb). Jasechko et al./: Contains the jupyter notebook (Extract_Elevation.ipynb) to determine the outlet elevations of the 260 watersheds used in Jasechko et al. (2016), and the corresponding table, Table_S1_Watersheds_alt.csv. Used to create Supplementary Information Figure A2. PLM_Wells/: Contains the QA/QC-ed groundwater level time series of the PLM-1 and PLM-6 Monitoring Wells from Faybishenko et al. (2023), reformatted to water years used for Supplementary Figures A19 and and A20. ParFlow/: Contains the input files and run scripts to run ParFlow-CLM (/run_scripts), the python and tool command language (Tcl) scripts to create and distribute the ParFlow forcing simulation files (/forcing), and various scripts and intermediary files to analyze the model outputs (/post_process). SQUIRE/: Contains the processing scripts and intermediary files for the Surface QUantitatIve pRecipitation Estimation (SQUIRE) data (Grover, 2023) used to generate Supplementary Figure A18. USGS_Streamflow/: Contains the raw and gap-filled United States Geological Survey streamflow data (U.S. Geological Survey, 2026) used at the Almont station (site number 09112500). Gap-filling is performed in the R script with data from the Taylor station (site number 09110000). (/USGS_09112500_EAST_RIVER_AT_ALMONT_GAP_FILLED/code_almont_streamflow_gap_fill.Rmd). discharge/: Contains the gap-filled discharge data at the Watershed Function SFA East River pumphouse site (Newcomer et al., 2022) used to generate Supplementary Figure A13 and to compute hourly Nash-Sutcliffe model efficiency coefficients (NSE) in Table A4. snotel_and_flux_tower/: Contains the snow telemetry data (U.S. Department of Agriculture, 2024) from the Butte (site ID 380) and Schofield (site ID 737) stations, reformatted by water year, accessed with the snotelr R package. Used to create Supplementary Figure A17. Also contains the flux tower observational data (FluxTower_Pumphouse_ESS-DIVE.ET_only.h.txt) from Ryken et al. (2022) and sap flux transpiration data (MaxB_Transpiration_5Sites.daily_sums.h.txt) from Ryken (2021), used to create Supplementary Figures A22 and A23, respectively. Raw EcoSLIM model outputs are in excess of 24TB, and are stored on National Energy Research Scientific Computing Center (NERSC) and publicly available via the external link provided in the paper.

atmospheric warming↗