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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Static and Dynamic Correlations in Water: Comparison of Classical Ab Initio Molecular Dynamics at Elevated Temperature with Path Integral Simulations at Ambient Temperature

It is a common practice in ab initio molecular dynamics (AIMD) simulations of water to use an elevated temperature to overcome the overstructuring and slow diffusion predicted by most current density functional theory (DFT) models. The simulation results obtained in this distinct thermodynamic state are then compared with experimental data at ambient temperature based on the rationale that a higher temperature effectively recovers nuclear quantum effects (NQEs) that are missing in the classical AIMD simulations. In this work, we systematically examine the foundation of this assumption for several DFT models as well as for the many-body MB-pol model. Here, we find for the cases studied that a higher temperature does not correctly mimic NQEs at room temperature, which is especially manifest in significantly different three-molecule correlations as well as hydrogen bond dynamics. In many of these cases, the effects of NQEs are the opposite of the effects of carrying out the simulations at an elevated temperature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Global River Topology (GRIT): A Bifurcating River Hydrography

Existing global river networks underpin a wide range of hydrological applications but do not represent channels with divergent river flows (bifurcations, multi‐threaded channels, canals), as these features defy the convergent flow assumption that elevation‐derived networks (e.g., HydroSHEDS, MERIT Hydro) are based on. Yet, bifurcations are important features of the global river drainage system, especially on large floodplains and river deltas, and are also often found in densely populated regions. Here we developed the first raster and vector‐based Global RIver Topology that not only represents the tributaries of the global drainage network but also the distributaries, including multi‐threaded rivers, canals and deltas. We achieve this by merging a 30 m Landsat‐based river mask with elevation‐generated streams to ensure a homogeneous drainage density outside of the river mask for rivers narrower than approximately 30 m. Crucially, we employ the new 30 m digital terrain model, FABDEM, based on TanDEM‐X, which shows greater accuracy over the traditionally used SRTM derivatives. After vectorization and pruning, directionality is assigned by a series of elevation, flow angle and continuity approaches. The new global network and its attributes are validated using gauging stations, comparison with existing networks, and randomized manual checks. The new network represents 19.6 million km of streams and rivers with drainage areas greater than 50 km 2 and includes 67,495 bifurcations. With the advent of hyper‐resolution modeling and artificial intelligence, GRIT is expected to greatly improve the accuracy of many river‐based applications such as flood forecasting, water availability and quality simulations, or riverine habitat mapping.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

High-temperature compressive creep tests of U 3 Si 2 with spark plasma sintering: Experiments and finite element modeling

This paper reports a systematic high-temperature creep behavior of dense U 3 Si 2 pellets using a spark plasma sintering (SPS) apparatus at elevated temperatures and pressures under vacuum conditions. The stress exponent was subsequently derived to be 3.21 at 1173 K and 2.17 at 1223 K, respectively, indicating a grain boundary sliding creep mechanism. The creep activation energy was determined to be 203.6 ± 19.0 kJ/mol, which agrees well with the literature. Finite element modeling was performed using the creep parameters fitted from the strain-time plot. Additionally, the results suggest an excellent match with the experimental data, confirming the validity of the experiments. Microstructure characterizations indicate that the main phase of the specimens after creep tests remains to be U 3 Si 2 , with a 4 μm thick layer of nano-sized particles induced from the diffusion between U 3 Si 2 and alumina disc used to avoid electric current passing through the sample. The successful conduct of creep experiments demonstrates the great potential of SPS to perform high-temperature mechanical testing of nuclear fuels under vacuum conditions. The subsequent finite element modeling exhibits excellent capabilities for accurately predicting material performance in the creep tests and provides a practical tool in evaluating nuclear fuels’ performance for a much-extended time scale.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Evaluation of an Improved Convective Triggering Function: Observational Evidence and SCM Tests

This study provides a strong observational support for a recently developed convective triggering function that uses the large-scale dynamic convective available potential energy (dCAPE) as a constraint combined with an unrestricted air parcel launch level (ULL) to relax the unrealistic strong coupling of convection to surface heating and capture nocturnal elevated convection. Both case study and statistical analysis are conducted using the observations collected from the Department of Energy (DOE)'s Atmospheric Radiation Measurement (ARM) program at its Southern Great Plains (SGP) and Manaus (MAO) sites. They show that dCAPE has a much stronger correlation with precipitation than CAPE and ULL is essential to detect elevated convection above boundary layer under both midlatitude and tropical conditions and for both afternoon and nighttime deep convection regimes. Sensitivity tests with the single-column model (SCM) of DOE's Energy Exascale and Earth System Model (E3SM) indicate that the role of dCAPE in suppressing daytime convection is more effective for tropical convection than midlatitude convection. Even though the dCAPE can suppress the overestimated convection, ULL plays a much bigger role in improving the diurnal cycle of precipitation than dCAPE. It not only helps capture nocturnal elevated convection, but also significantly removes the spurious morning precipitation seen in the default model, due to the release of unstable energy at night. However, the use of ULL has led to an overestimation of light-to-moderate precipitation (1-10 mm/day) due to more convection being triggered above boundary layer.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Experimental Combustion and Flame Characterization of a Chemical Looping-Based Oxidative Dehydrogenation Byproduct Fuel Mixture Containing High CO2 Dilution

Abstract This study investigates the combustion performance of a CO2-rich fuel mixture containing ethane and methane as active species using a constant volume combustion chamber. This fuel is obtained as a byproduct of a chemical looping-based oxidative dehydrogenation (Cl-ODH) process ethylene production. The byproduct gas mixture has 40.79% CO2, 39.49% ethane, and 4.88% methane by weight with other minor compounds. Using this fuel for energy extraction would improve the process efficiency of the ethane to ethylene conversion. After initial combustion modeling, the gas fuel mixture was reduced to just the major species: CO2, ethane, and methane. The mixture was then tested for flammability limits and combustion performance under spark-ignition conditions. Effects of ambient conditions like temperatures between 300 and 400 K with initial pressures from 1 to 10 bar were tested. The effects of stoichiometry were tested to understand flame velocities and heat release. The fuel mixture showed an overall reduced flame velocity compared to gasoline. Instability in combustion was believed to be caused by the dissociation of ethane under elevated conditions. At higher pressures, the flame produces lower cumulative heat release. Simulations were also performed using a model tuned to replicate the operations of the combustion chamber used in the experiments. Heat release and unburnt fuel mass data were calculated to identify the discrepancies in the combustion completeness at elevated pressures. The effects of CO2 quenching the flame coupled with the increased dissociation of the fuel species can lead to up to more than 75% of the fuel mixture being unburnt. Data from this study were used to modify a small-scale spark-ignition engine to use this fuel and produce usable energy.

Energy & Fuels↗
DOE OSTI2023

Carbon Organisms Rhizosphere and Protection in Soil Environment model script and input data for soil moisture-respiration responses in tropical forests

Objectives: Climatic drying is predicted for many tropical forests, yet models remain poorly parameterized for tropical forests, hampering predictions of forest-climate feedbacks. We applied an integrated model–experiment approach, parameterizing an ecosystem model Carbon Organisms Rhizosphere and Protection in the Soil Environment (CORPSE) with tropical forest observational data, and comparing model predictions with a field drying manipulation. We hypothesized that drying would suppress soil CO2 fluxes (i.e., respiration) in already-drier tropical forests, but increases CO2 fluxes in wetter tropical forests by alleviating anaerobiosis. We measured soil CO2 fluxes, soil moisture, soil temperature, and forest floor biomass during wet-dry cycles (2015 – 2022) in four Panamanian forests that vary in rainfall and soil fertility. We used the field data to parameterize and run tests in the model.Results: Measured CO2 fluxes declined in the dry season and peaked in the early wet season ahead of peak soil moisture, resulting in a lower soil moisture optimum for respiration than previously modeled. We used this data to parameterize the model, which then predicted increased soil CO2 fluxes in wetter and fertile forests with drying, and decreased fluxes in drier, infertile forests. In contrast to model predictions, a chronic throughfall exclusion experiment in the forests initially suppressed soil CO2 fluxes across forests, with sustained suppression after four years in the wettest forest only (-28 ± 4% during the dry season), but elevated soil CO2 fluxes in a fertile forest after four years (+75 ± 28% during the late wet season), as predicted by the model. The unexpected negative drying effect in the wettest, most infertile forest could have resulted from reduced vertical flushing of nutrients into soils. Including hydro-nutrient interactions in ecosystem models could improve predictions of tropical forest-climate feedbacks (results presented in Cusack et al. 2023). Datasets included: Code files:CORPSE_array.py: Defines the equations of the CORPSE modelCORPSE_solvers: Functions for running the CORPSE model using either iterative or ordinary differential equation (ODE) solversrun_Panama_sims.py: Read in datasets and run the model simulations for this studyInput data:PanamaGradientEcosystemChem_BT_CPools_20152016CO2_DC_20190615.xlsx: Plot characteristics used in running model simulationsLiCor compiled surface flux only to 2020_03 DC_20200825.xlsx: Surface gas exchange fluxes used in model-data comparisonsPARCHED litterfall data for Ben Sulman LD 20200902.xlsx: Litterfall data used to drive model simulationsInitialization data:state_500y_20190823.csv: Initial state of model pools based on previous spinup runsOutput data:Outputs/prev_moisture_response.csv: Simulations of multiple sites using original model moisture response function.Outputs/updated_moisture_response.csv: Simulations of multiple sites using updated model moisture response function.Outputs/dry15_prev_moisture_response.csv: Simulations with soil moisture reduced by 15%, using original moisture response function.Outputs/dry15_updated_moisture_response.csv: Simulations with soil moisture reduced by 15%, using updated moisture response function.Outputs/dry30_prev_moisture_response.csv: Simulations with soil moisture reduced by 30%, using original moisture response function.Outputs/dry30_updated_moisture_response.csv: Simulations with soil moisture reduced by 30%, using updated moisture response function.Outputs/latestart_prev_moisture_response.csv: Simulations with extended dry season, using original moisture response function.Outputs/latestart_updated_moisture_response.csv: Simulations with extended dry season, using updated moisture response function.Outputs/[site name]_oneyear.csv: One-year simulation for each site in expanded site list using original moisture response function.Outputs/[site name]_oneyear_dried.csv: One-year simulation for each site in expanded site list using original moisture response function, with soil moisture reduced by 25%.Outputs/[site name]_oneyear_updated_moisture_response.csv: One-year simulation for each site in expanded site list using updated moisture response function.Outputs/[site name]_oneyear_updated_moisture_response_dried.csv: One-year simulation for each site in expanded site list using updated moisture response function, with soil moisture reduced by 25%.Field plot location data:There is also a .kml file that includes coordinates for all 32 plots included in the study of four forests (n = 4 throughfall reduction and n = 4 control plots per site).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Evaluation of aerosol–cloud interactions in E3SM using a Lagrangian framework

A Lagrangian framework is used to evaluate aerosol–cloud interactions in the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) version 1 (E3SMv1) for measurements taken at Graciosa Island in the Azores where a U.S. Department of Energy Atmosphere Radiation Measurement (ARM) site is located. This framework uses direct measurements of cloud condensation nuclei (CCN) concentration (instead of relying on satellite retrievals of aerosol optical depth) and incorporates a suite of ground-based ARM measurements, satellite retrievals, and meteorological reanalysis products that when applied to over a 1500 trajectories provides key insights into the evolution of low-level clouds and aerosol radiative forcing that is not feasible from a traditional Eulerian analysis framework. Significantly lower concentrations (40 %) of surface CCN concentration are measured when precipitation rates in 48 h back trajectories average above 1.2 mm d -1 in the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) product. The depletion of CCN concentration when precipitation rates are elevated is nearly twice as large in the ARM observations compared to E3SMv1 simulations. The model CCN concentration bias remains significant despite modifying the autoconversion and accretion rates in warm clouds. As the clouds in trajectories associated with larger surface-based CCN concentration advect away from Graciosa Island, they maintain higher values of droplet number concentrations (N d ) over multiple days in observations and E3SM simulations compared to trajectories that start with lower CCN concentrations. The response remains robust even after controlling for meteorological factors such as lower troposphere stability, the degree of cloud coupling with the surface, and island wake effects. E3SMv1 simulates a multi-day aerosol effect on clouds and a Twomey radiative effect that is within 30 % of the ARM and satellite observations. However, the mean cloud droplet concentration is more than 2–3 times larger than in the observations. While Twomey radiative effects are similar amongst autoconversion and accretion sensitivity experiments, the liquid water path and cloud fraction adjustments are positive when using a regression model as opposed to negative when using the present-day minus pre-industrial aerosol emissions approach. This result suggests that tuning the autoconversion and accretion alone is unlikely to produce the desired aerosol susceptibilities in E3SMv1.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

A Physics-Based Digital Twin for Wave Elevation and Seabed Moment Estimation of Offshore Monopiles: Preprint

In this work, we present a proof of concept of a physics-based digital twin for a monopile structure (with overhead inertia) subjected to wave loading. The digital twin is formulated using reduced-order models derived from first principles and combined with a Kalman filter for state estimation. The proposed framework estimates the monopile top motion, the wave elevation, and the section forces and moments along the pile using primarily acceleration measurements at the monopile top. Key innovations include the use of a hydrodynamic shape function to represent distributed wave loading in a compact and computationally efficient manner, and the introduction of a shaping filter to augment the state-space with wave kinematics. Synthetic measurement data are generated using OpenFAST and used as a reference to assess the performance of the digital twin. Results demonstrate that the wave elevation can be accurately reconstructed without direct sea-state measurements as long as the wave regime is inertia-dominated. Under the ideal tested conditions, the total hydrodynamic force and sea-bed bending moment are estimated with relative errors on the order of 1% and correlation coefficients exceeding 96%. Future work will evaluate the estimator's performance under operational uncertainties and more complex loading conditions.

17 WIND ENERGY↗
DOE OSTI2026

Thermal loading effects on chalk hydromechanical behavior for nuclear waste disposal

Safe disposal of heat-generating nuclear waste depends on host rock stability under thermal, hydrological, and mechanical stresses. This study investigates the effect of thermal loading on mechanical behavior of the shallowly buried Ghareb formation chalk through triaxial and hydrostatic constant strain rate and creep tests at temperatures up to 100 ˚C and effective pressures up to 20.7 MPa. Experimental results show that thermal loading reduces the elastic moduli of chalk by 50–75%, and a transition occurs above 60 ˚C where creep rates increase rapidly. Water saturation nearly doubles the thermally induced strain compared to dry conditions and strongly decreases material rigidity. Thermal loading also leads to significant pore pressure increases under undrained conditions and leads to reductions in the apparent permeability during drained conditions. Laboratory experimental data were used to parameterize and develop a preliminary constitutive model for predicting future deformation during repository operations in the Ghareb. The strongly coupled effects – mechanical weakening, fluid pressure fluctuations, and permeability modification – demonstrate that elevated repository temperatures will have a pronounced effect on the near field Ghareb behavior during waste disposal operations. The findings indicate that the coupled interactions must be considered in predictive models and repository design to ensure long-term nuclear waste isolation and safety.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Developing a Redox Network for Coastal Saltmarsh Systems in the PFLOTRAN Reaction Model

Coastal ecosystems have been largely ignored in Earth system models but are important zones for carbon and nutrient processing. Interactions between water, microbes, soil, sediments, and vegetation are important for mechanistic representation of coastal processes and ecosystem function. To investigate the role of these feedbacks, we used a reactive transport model (PFLOTRAN) that has the capability to be connected to the Energy Exascale Earth System Model (E3SM). PFLOTRAN was used to incorporate redox reactions and track chemical species important for coastal ecosystems as well as define simple representations of vegetation dynamics. Here our goal was to incorporate oxygen flux, salinity, pH, sulfur cycling, and methane production along with plant-mediated transport of gases and tidal flux. Using porewater profile and incubation data for model calibration and evaluation, we were able to create depth-resolved biogeochemical soil profiles for saltmarsh habitat and use this updated representation to simulate direct and indirect effects of elevated CO 2 and temperature on subsurface biogeochemical cycling. We found that simply changing the partial pressure of CO 2 or increasing temperature in the model did not fully reproduce observed changes in the porewater profile, but the inclusion of plant or microbial responses to CO 2 and temperature manipulations was more accurate in representing porewater concentrations. This indicates the importance of characterizing tightly coupled vegetation-subsurface processes for developing predictive understanding and the need for measurement of plant-soil interactions on the same time scale to understand how hotspots or moments are generated.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Uncertainty-Guided Prediction Horizon of Phase-Resolved Ocean Wave Forecasting Under Data Sparsity: Experimental and Numerical Evaluation

Accurate short-term wave forecasting is critical for the safe and efficient operation of marine structures that rely on real-time, phase-resolved ocean wave information for control and monitoring purposes (e.g., digital twins). These systems often depend on environmental sensors (e.g., waverider buoys, wave-sensing LIDAR). Challenges arise when upstream sensor data are missing, sparse, or phase-shifted due to drift. This study investigates the performance of two machine learning models, time-series dense encoder (TiDE) and long short-term memory (LSTM), for forecasting phase-resolved ocean surface elevations under varying degrees of data degradation. We introduce the τ-trimming algorithm, which adapts the prediction horizon based on uncertainty thresholds derived from historical forecasts. Numerical wave tank (NWT) and wave basin experiments are used to benchmark model performance under short- and long-term data masking, spatially coarse sensor grids, and upstream phase shifts. Results show under a 50% probability of upstream data loss, the τ-trimmed TiDE model achieves a 46% reduction in error at the most upstream target, compared to 22% for LSTM. Furthermore, phase misalignment in upstream data introduces a near-linear increase in forecast error. Under moderate model settings, a ±3 s misalignment increases the mean absolute error by approximately 0.5 m, while the same error is accumulated at ±4 s using the more conservative approach. These findings inform the design of resilient, uncertainty-aware wave forecasting systems suited for realistic offshore sensing environments.

42 ENGINEERING↗
DOE OSTI2023

Evaluation of Copper Chelation Therapy in a Transgenic Rat Model of Cerebral Amyloid Angiopathy

Cerebral amyloid angiopathy (CAA) is characterized by the accumulation of the amyloid β (Aβ) protein in blood vessels and leads to hemorrhages, strokes, and dementia in elderly individuals. Recent reports have shown elevated copper levels colocalized with vascular amyloid in human CAA and Alzheimer’s disease patients, which have been suggested to contribute to cytotoxicity through the formation of reactive oxygen species. Here, we treated a transgenic rat model of CAA (rTg-DI) with the copper-specific chelator, tetrathiomolybdate (TTM), via intraperitoneal (IP) administration for 6 months to determine if it could lower copper content in vascular amyloid deposits and modify CAA pathology. In this work, results showed that TTM treatment led to elevated Aβ load in the hippocampus of the rTg-DI rats and increased microbleeds in the wild type (WT) animals. X-ray fluorescence microscopy was performed to image the distribution of copper and revealed a surprising increase in copper colocalized with Aβ aggregates in TTM-treated rTg-DI rats. Unexpectedly, we also found an increase in the copper content in unaffected vessels of both rTg-DI and WT animals. These results show that IP administration of TTM was ineffective in removing copper from vascular Aβ aggregates in vivo and increased the development of disease pathology in CAA.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Using Temporal Deep Learning Models to Estimate Daily Snow Water Equivalent Over the Rocky Mountains

Abstract In this study we construct and compare three different deep learning (DL) models for estimating daily snow water equivalent (SWE) from high‐resolution gridded meteorological fields over the Rocky Mountain region. To train the DL models, Snow Telemetry (SNOTEL) station‐based SWE observations are used as the prediction target. All DL models produce higher median Nash‐Sutcliffe Efficiency (NSE) values than a conceptual SWE model and interpolated gridded data sets, although mean squared errors also tend to be higher. Sensitivity of the SWE prediction to the model's input variables is analyzed using an explainable artificial intelligence (XAI) method, yielding insight into the physical relationships learned by the models. This method reveals the dominant role precipitation and temperature play in snowpack dynamics. In applying our models to estimate SWE throughout the Rocky Mountains, an extrapolation problem arises since the statistical properties of SWE (e.g., annual maximum) and geographical properties of individual grid points (e.g., elevation) differ from the training data. This problem is solved by normalizing the SWE with its historical maximum value to alleviate extrapolation for all tested DL models. Our work shows that the DL models are promising tools for estimating SWE, and sufficiently capture relevant physical relationships to make them useful for spatial and temporal extrapolation of SWE values.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Dataset for 'Ombadi et al. (2023). A warming-induced reduction in snow fraction amplifies rainfall extremes, Nature'

This package contains the main codes, sample input data and main result files to reproduce the analysis and results presented in the article: “Ombadi et al. (2023), A warming-induced reduction in snow fraction amplifies rainfall extremes, Nature”. The folder consists of the following: (1) “Raw data”: a folder that contains sample input data which is used in some of the codes for demonstration purposes. It also contains data that was not pre-processed such as Elevation data; (2) “Results”: this folder contains files of the main results presented in the paper including: “Annual-Max-Series”, “Change-rainfall-extremes”, “Change-snow-fraction”, “Warming levels_By scenario_model_year” and “Masks”. Description of these folders is detailed in the "Readme.rtf" file; (3) Python jupyter notebooks (Extract_Annual Max Series (AMS).ipynb, Elevation-dependent amplification of rainfall extremes.ipynb, Sensitivity_to_global_warming.ipynb) demonstrate the main steps of analysis. Further description of those notebooks is provided in the "Readme.rtf" file; (4) R code for extreme value analysis (Extreme_Value_Analysis.R). The sample and pre-processed dataset in "Raw data" is obtained from publicly available repositories of CMIP6 and ERA5 datasets; see Methods for more detail. This research was supported by Office of Science, Office of Biological and Environmental Research of the US Department of Energy under contract no. DE-AC02-05CH11231 for the CASCADE Scientific Focus (funded by the Regional and Global Model Analysis Program area within the Earth and Environmental Systems Modeling Program) and the iNAIADS Early Career Research Project (funded by the Environmental Systems Science program).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Validation and moisture content sensitivity analysis of cross-laminated timber wall assemblies in EnergyPlus

Cross-laminated timber buildings are becoming more common in North America, with many numerical studies showing potential energy savings. However, no studies have validated any EnergyPlus heat transfer algorithms or quantified their accuracy in simulating CLT in building envelopes. This study empirically validates the heat flux predictions for each of EnergyPlus's heat transfer algorithms (Conduction Transfer Functions (CTF), Effective Moisture Penetration Depth (EMPD), Conduction Finite Difference (CondFD), and Heat and Moisture Transfer (HAMT)) for two different CLT ply thicknesses with both summer and winter boundary conditions measured in controlled lab experiments. It also evaluates the model sensitivity of heat flux and heating and cooling loads to moisture content. The 1D validation shows that the HAMT model is the most accurate among all algorithms. All EnergyPlus's heat flux predictions are accurate independent of CLT plate thickness for summer conditions. However, the three constant property algorithms (CTF, EMPD, and CondFD) underpredict heat flux throughout the whole day during winter conditions. The 1D sensitivity analysis indicates that elevated moisture content can increase peak heat fluxes through the material by up to 20 %. Finally, the whole building model sensitivity analysis shows increased heating load and slight cooling load variation due to increased moisture content when using constant property models. The analysis shows significantly lower peak thermal demand (7 % lower heating and 6 % lower cooling) and monthly thermal load (8 % less cooling and 6 % less heating) predictions when using HAMT vs a constant property model.

42 ENGINEERING↗
DOE OSTI2026

Analyzing historical snow trends in interior Alaska

Study region The Chena River watershed in Interior Alaska, USA Study focus This study examines 40 years (water years 1982–2021) of snowpack characteristics to consider its hydrological implications in the 5350 km² Chena River basin. Using observations and a fine-scale physics model, we analyzed trends of snow water equivalent (SWE), snow onset and disappearance, and snow cover duration (SCD). New hydrological insights for the region Results indicate a decline in SWE across the modeled domain, averaging a decrease of 3 mm per decade, with larger decreases (up to 10 mm per decade) at lower elevations. While domain-averaged SWE trends were not statistically significant, observed SCD showed statistically significant decreases: −5.2, −5.0, and −4.4 days per decade at Teuchet Creek, Fairbanks F.O., and Little Chena Ridge, respectively. Notably, observations at SNOTEL stations and modeling revealed no statistically significant change in domain-averaged Rain-on-Snow (ROS) events over the 40-year period, contrasting some regional future estimates of increased ROS frequency. Peak streamflow did not consistently correlate with peak SWE levels, suggesting that other environmental factors such as ROS events and rapid temperature increases (e.g., a 10°C spike observed in 1992) are key drivers of hydrological outcomes. These findings improve understanding of complex subarctic hydrological processes impacting permafrost and highlight the need for adaptive water resource management to mitigate multi-factor risks like flooding and wildfire, requiring proactive planning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Assessing the limitations of commercial sensors and models for supporting marine carbon dioxide removal monitoring: a case study

Several unknowns remain surrounding marine Carbon Dioxide Removal (mCDR) monitoring, reporting, and verification (MRV) practices and capabilities. Current in-situ sensor technology is limited (primarily pH and pCO 2 ), requiring calculations and assumptions to estimate changes in carbonate chemistry parameters, including total alkalinity (TA). Considering that cost, energy consumption, and accuracy of commercial sensors can vary by orders of magnitude, understanding how well existing sensors perform in an mCDR context is important for this emerging community. Likewise, documenting sensor limitations and how relatively simple models can optimize sensor deployments will improve MRV efforts and support protocol development. Here we (1) compare performance a variety of commercially available sensors in a blind mesocosm experiment simulating ocean alkalinity enhancement (OAE), and how sensor performance impacted carbonate chemistry estimates; (2) evaluate if sensors can distinguish the OAE signal from natural variability during a small scale OAE field test in Sequim Bay, WA, USA, and (3) use an idealized ocean biogeochemistry model to explore optimal sensor network design based on (1) and (2). Our mesocosm results indicate that correctly constraining pH uncertainty will be critical for accurate TA estimates with current sensor technology compared to the less impactful variation caused by uncertainty in pCO 2 (pH data that are presented throughout are reported on the total scale (pH T ) unless otherwise noted). Our pilot field test demonstrated that sensors were capable of distinguishing mCDR signatures from natural variability under optimal real-world conditions. Idealized modeling simulations of the field test showed that a range of sparse and dense (3 to 100) sensors sampling areas of detectable increases will underestimate the net change in surface pH by at least 35–55%, at both realistic and highly elevated alkalinity input levels. We also highlight the limitations of current sensing technology for MRV, and the importance of ocean biogeochemistry models as critical tools for predicting when and where mCDR signals will be detectable using available sensors. Overall, our findings suggest that commercially available pCO 2 sensors and some pH sensors will form an important backbone for mCDR MRV tasks, though complete MRV characterization will require these data to be used in combination with other tools.

OAE↗
DOE OSTI2020

Comparison of experimental and computational methods for discharge measurements from tidal wetlands

This short communication describes the use and comparison of three methods used to calculate the flow discharge from a tidal wetland stream. The techniques presented are a) bottom-mounted ADCP deployments, b) vessel-mounted ADCP transects, and c) a volume inundation spatial model that relies on a high-resolution LiDAR dataset and time-series of water surface elevation data. The relative agreement between these three methods is used to build confidence in the application of these techniques to the challenge of deducing flow discharge from tidal wetlands.

54 ENVIRONMENTAL SCIENCES↗