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At least 253 records · Page 14

AmeriFlux FLUXNET-1F US-LA2 Salvador WMA Freshwater Marsh

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-LA2 Salvador WMA Freshwater Marsh. This is the FLUXNET version of the carbon flux data for the site US-LA2 Salvador WMA Freshwater Marsh produced by applying the standard ONEFlux (1F) software. Site Description - Fresh floatant Marsh, high organic, Davis Pond freshwater diversion, microtidal

Ward, Eric [U.S. Geological Survey]↗

A Comprehensive Greenhouse Gas Assessment of Biomass-Based Carbon Dioxide Removal and Storage

Biomass with Carbon Removal and Sequestration (BiCRS) is a suite of technological pathways that provide a relatively affordable and high-potential way to remove carbon dioxide from the atmosphere and simultaneously re-purpose residual biomass. BiCRS technologies are individually optimized to different biomass feedstocks, temperatures and oxygen, with the goal of capturing a high percentage of biomass-derived carbon dioxide and storing it in permanent below-ground storage (i.e. geologic storage). The current standard for BiCRS life cycle assessments does not account for carbon dioxide, methane and nitrous oxide (CO 2 , CH 4 , and N 2 O) fluxes from surface soil amendment of BiCRS byproducts (e.g. char, ash) and their alternate fates. This is partly due to a paucity of empirical data due to the novelty of BiCRS conversion technologies, the range in feedstocks, and the heterogeneity of soils to which they might be amended. To fill this gap in in the life-cycle assessment (LCA) and provide a realistic range of parameters for a soil amendment component of the BiCRS LCA, we conducted an incubation experiment to measure soil carbon changes, microbial respiration, methane fluxes, and nitrous oxide fluxes from two different soils amended with char- and ash- byproducts from biomass after gasification to hydrogen, fast pyrolysis to bio-oil, and torrefaction.

54 ENVIRONMENTAL SCIENCES↗

FY 2026 Midyear Report: Seismic Monitoring of Underground Vibration Sources Using Distributed Acoustic Sensing and Seismometers

Safeguards-relevant temporal changes in underground facilities can be observed using geophysical monitoring techniques. Seismic waves, in particular, provide valuable insights into subsurface activities and can serve as an important tool for detecting anomalous events that may indicate containment breaches at geological repositories. This midyear report summarizes ongoing efforts to automatically and rapidly detect and locate anomalous vibration signals that could be indicative of potential containment breaches. Previous work during FY25 focused on compiling continuous seismic datasets from two underground sites and developing a database of continuous waveforms and ground-truth event data derived from multiple sensing modalities. Building on this foundation, we are adapting anomaly detection and geolocation algorithms to explore methods for monitoring underground activities using two relatively low-maintenance sensing technologies: a dense surface geophone array deployed at the Pleasant Gap mine in Pennsylvania, and a three-dimensional fiber-optic cable array for distributed acoustic sensing (DAS) installed in the subsurface at the Sanford Underground Research Facility (SURF) in South Dakota. This report summarizes work conducted during the first two quarters of FY26, during which we refined a dynamic power spectral density (PSD)-based detector, applied it independently to each geophone station, and then combined the per‑station detections with density-based spatial clustering of applications with noise (DBSCAN) to cluster events and produce spatial maps over a nine‑day interval. In addition, we outline plans for a field trial at the Waste Isolation Pilot Plant (WIPP) in New Mexico to compare traditional seismic monitoring approaches with DAS techniques and to evaluate the benefits of combined data analysis. Activities during the past two quarters have included the preparation and submission of a Field Test Plan to WIPP for approval, as well as submission to headquarters for review and feedback.

58 GEOSCIENCES↗

Patch-level chamber-based CH4 and CO2 flux measurements in freshwater and salt marshes of coastal Louisiana

This dataset contains chamber-based carbon dioxide and methane flux measurements from ecohydrological patches in two coastal wetlands in Louisiana (within the footprint of AmeriFlux sites US-LA2 and US-LA3). Measurements span four patches, two per wetland. At the freshwater marsh (US-LA2), patches were dominated by Sagittaria lancifolia or co-dominated by Sagittaria lancifolia and Typha latifolia. At the salt marsh site (US-LA3), patches were dominated by Spartina alterniflora or Juncus roemerianus. Flux measurements included (i) wetland surface-level measurements encompassing the entire soil-water-vegetation column and (ii) soil-water surface chamber measurements that excluded emergent vegetation. At US-LA3, open-water pools were common and were also measured as a patch type. With this data we aimed to assess differences in gas flux pathways within ecohydrological patches across different wetland types. Files can be read in any program or code typical of processing .csv file types. "PatchChambers_LA2LA3.csv" covers flux data from patch level chambers which includes bulk CO2/CH4 flux encompassing water, soil, and vegetation within each chamber. "Water_Soil_SurfaceChamber_LA2LA3.csv" has CO2/CH4 flux data from chambers measuring only the water-soil surface without the effects of vegetation. Metadata can be found in the similarly named files for each data sheet ("xxxx_md.csv").

54 ENVIRONMENTAL SCIENCES↗

Insights into the thermo-hydraulic properties of compacted MX80 bentonite during hydration under elevated temperature

In high-level radioactive waste geological repositories, compacted bentonite undergoes coupled thermo-hydraulic processes due to heat released from a central waste canister and groundwater imbibition from the surrounding host rock. An understanding of these processes is essential for long-term simulations of radionuclide migration and canister corrosion, which requires an understanding of temperature effects on the coupled thermo-hydraulic properties governing these processes. In this study, a tank-scale radial infiltration test was used to investigate water imbibition processes in compacted bentonite under a central heater temperature of 200 °C that simulates high thermal gradients in a repository. Interpretation of this test focuses on evaluation of the liquid water wetting front during hydration and the interpretation of the temperature-dependent transient soil water retention curve (SWRC), thermal conductivity function (TCF), and hydraulic conductivity function (HCF). The SWRC during imbibition follows a temperature-dependent wetting path. Temperature effects on the hydraulic conductivity of bentonite in saturated conditions had the greatest effect on the shape of the HCF, with minimal temperature effects at higher suctions. The transient thermal conductivity data matched well with a new TCF linked with the SWRC shape. Although the bentonite layer was restrained, local deformations during hydration may have affected the shapes of the TCF and HCF.

Engineering↗

Identifying recharge sources and their impacts on a North Central New Mexico shallow aquifer using unsupervised machine learning

In this article, shallow aquifers are important but highly variable resources in arid to semi-arid regions. Limited shallow aquifer volume results in high sensitivity to recharge fluctuations, which can impact the local fauna and flora, and transport of contaminants in the aquifer or vadose zone. Aquifer response to external forcing (e.g., precipitation) is usually solved by estimating aquifer parameters and running physics-based models to match known fluctuations of hydraulic head. However, this technique is time and computationally expensive. Furthermore, high aquifer complexity decreases precision in physics-based models. Alternatively supervised machine learning is used to predict aquifer dynamics. However, these techniques rely on input data and struggle to interpret aquifer response for missing sources (i.e., snowpack data). To counter these problems, we propose an unsupervised machine learning technique (NMFk) to estimate the impact of different sources on aquifer recharge. NMFk is used to understand the influence of external forcing on shallow aquifer recharge in the Pajarito Plateau (Los Alamos, NM, USA). The results show how NMFk can be used to reduce the data dimension in a complex field dataset to three recharge signals that cause fluctuations within the field data. Here, the source signals are interpreted as rainfall, snowmelt, and a delayed aquifer response to the previous two signals. These results evidence how heterogeneous aquifers delimited by canyons incised into the Pajarito Plateau respond in similar ways to the source signals identified by NMFk. Furthermore, results show the importance of the local geology where faults act as sinks, and anthropogenic disturbances can facilitate infiltration amplifying the interpreted signal.

54 ENVIRONMENTAL SCIENCES↗

Carbon Utilization and Storage Partnership of the Western United States

This technical report documents research conducted under DOE Award No. DE-FE0031837 focused on evaluating the feasibility of carbon capture, utilization, and storage (CCUS) systems in the central and western United States. The project integrated geologic characterization, reservoir simulation, infrastructure modeling, and economic analysis to assess CO₂ storage potential near industrial sources and develop strategies for transport and sequestration. The work included subsurface modeling, risk assessment, monitoring and verification (MRV) planning, and evaluation of regulatory pathways such as EPA Underground Injection Control (UIC) Class VI permitting and IRS 45Q tax credit eligibility. Results demonstrate the viability of multiple storage approaches, including saline formations, enhanced coalbed methane recovery, and basalt mineralization, supported by data-driven workflows and regional analyses. The project also produced permitting templates, technology transfer activities, and stakeholder engagement efforts to support deployment readiness. These findings contribute to the development of scalable, economically viable CCUS systems and provide a repeatable framework for future carbon management projects.

20 FOSSIL-FUELED POWER PLANTS↗

Benchmarking CO₂ storage simulations: Results from the 11 th Society of Petroleum Engineers Comparative Solution Project

The 11 th Society of Petroleum Engineers Comparative Solution Project (shortened SPE11 herein) benchmarked simulation tools for geological carbon dioxide (CO 2 ) storage. A total of 45 groups from leading research institutions and industry across the globe signed up to participate, with 18 ultimately contributing valid results that were included in the comparative study reported here. This paper summarizes the SPE11 results. A comprehensive introduction and qualitative discussion of the submitted data are provided, together with an overview of online resources for accessing the full depth of data. A global metric for analyzing the relative distance between submissions is proposed and used to conduct a quantitative analysis of the submissions. This analysis attempts to statistically resolve the key aspects influencing the variability between submissions. The study shows that the major qualitative variation between the submitted results is related to thermal effects, dissolution-driven convective mixing, and resolution of facies discontinuities. Moreover, a strong dependence on grid resolution is observed across all three versions of the SPE11. However, our quantitative analysis suggests that the observed variations are predominantly influenced by factors not documented in the technical responses provided by the participants. We therefore identify that unreported variations due to human choices within the process of setting up, conducting, and reporting on the simulations underlying each SPE11 submission are at least as impactful as the computational choices reported.

Nordbotten, Jan M. [Univ. of Bergen (Norway); Norw↗

A Comprehensive Greenhouse Gas Assessment of Biomass-Based Carbon Dioxide Removal and Storage

Biomass-based Carbon Removal and Storage (BiCRS) is a suite of technological pathways that provide a relatively affordable and high-potential way to remove carbon dioxide from the atmosphere and simultaneously re-purpose residual biomass. BiCRS technologies are individually optimized to different biomass feedstocks, temperatures and oxygen, with the primary goal of capturing a high percentage of biomass-derived carbon dioxide and storing it in permanent below-ground storage (i.e. geologic storage), and a secondary goal of producing renewable energy. The current standard for BiCRS life cycle assessments does not account for greenhouse gas (carbon dioxide, methane and nitrous oxide) fluxes from surface soil amendment of BiCRS byproducts (e.g. char, ash) and their alternate fates. This is partly due to a paucity of empirical data due to the novelty of BiCRS conversion technologies, the range in feedstocks, and the heterogeneity of soils to which they might be amended. To fill this gap in in the life-cycle assessment and provide a realistic range of parameters for a soil amendment component of the BiCRS life cycle assessment, we conducted an incubation experiment to measure soil carbon changes, microbial respiration, methane fluxes, and nitrous oxide fluxes from two different soils amended with char- and ash- byproducts from biomass after gasification to hydrogen, fast pyrolysis to bio-oil, and torrefaction. Our results suggest that nitrous oxide and methane emission or consumption from BiCRS char amendments to soil are context dependent. Nitrous oxide emissions from amendments are higher in soils with higher pH, whereas some char amendments to low pH soil can reduce N 2 O emissions relative to control. In contrast, methane is emitted from BiCRS char amended to low pH soil, but consumed or neutral relative to control when amended to high pH soil. We present carbon and nitrogen mass balance throughout the experiment to help understand short-term durability of BiCRS chars and their counterfactuals, and preliminary suggestions for revisions to the broader BiCRS Measurement, Reporting, and Verification framework.

54 ENVIRONMENTAL SCIENCES↗

Machine Learning Models for Mapping Groundwater Pollution Risk: Advancing Water Security and Sustainable Development Goals in Georgia, USA

The widespread use of pesticides, such as atrazine and malathion, in agricultural systems raises significant concerns regarding the contamination of groundwater, which serves as a critical resource for drinking water. This study applies machine learning techniques to predict the concentrations of atrazine and malathion in groundwater across Georgia, USA, using 2019 data. A Random Forest classifier was employed to integrate various environmental and demographic factors, including pesticide application rates, precipitation, lithology, and population density, to predict pesticide contamination in groundwater. The models demonstrated high training accuracies of 100% and moderate average testing accuracy of 55% for atrazine and 60% for malathion across five iterations. The low test accuracy of the model, ranging from 50% to 75%, is likely due to overfitting, which can be attributed to the small dataset size and the complex nature of pesticide-contamination patterns, making it challenging for the model to generalize to unseen data. Feature importance analysis revealed that average pesticide usage emerged as the most influential factor for atrazine, while aquifer lithology and precipitation played crucial roles in both models. These results provide valuable insights into the dynamics of pesticide contamination, highlighting areas at greater risk of contamination. The findings underscore the importance of integrating environmental, geological, and agricultural variables for more effective groundwater management and sustainable agricultural practices, contributing to the protection of water resources and public health.

54 ENVIRONMENTAL SCIENCES↗

Self-potential tomography preconditioned by particle swarm optimization—Self-potential monitoring and streamflow data acquired March 26–September 14, 2023 at East Fork Poplar Creek near Oak Ridge Tennessee

This data release contains self-potential (SP) monitoring data measured on the flood plain of East Fork Poplar Creek (East Fork) in Oak Ridge, Tennessee and streamflow data measured at streamgage EFK5.4 about 310 meters upstream from the SP monitoring site. Additionally, forward and inverse numerical modeling scripts used to model the electrical-potential field on the East Fork flood plain are provided. SP monitoring data included in this data release were measured at 39 different data-collection points on the east flood plain; 30 points were spaced 3-m apart along an 87-m profile parallel to the edge of the streambank, and 9 points were spaced 5-m apart along a 40-m profile approximately perpendicular to the streambank. The two profiles of SP data-collection points intersected at the approximate midpoint of the profile parallel to the streambank. Transient voltages were measured at each data-collection point every 60 seconds between 16:13 Eastern Standard Time (EST) on March 26, 2023, and 11:41 EST on September 14, 2023. Streamflow data included in this data release overlap the time-period of self-potential monitoring and were measured every 900 seconds between 16:23 on March 26, 2023, and 23:53 on September 14, 2023.

54 ENVIRONMENTAL SCIENCES↗

Boosting Barlow Twins Reduced Order Modeling for Machine Learning‐Based Surrogate Models in Multiphase Flow Problems

Abstract We present an innovative approach called boosting Barlow Twins reduced order modeling (BBT‐ROM) to enhance the reliability of machine learning surrogate models for multiphase flow problems. BBT‐ROM builds upon Barlow Twins reduced order modeling that leverages self‐supervised learning to effectively handle linear and nonlinear manifolds by constructing well‐structured latent spaces of input parameters and output quantities. To address the challenge of high contrast data in multiphase flow problems due to injection wells and faults, we employ a boosting algorithm within BBT‐ROM. This algorithm sequentially trains a set of weak models (i.e., inaccurate models), improving prediction accuracy through ensemble learning. To evaluate the performance of BBT‐ROM, we conduct three three‐dimensional multiphase flow problems, including waterflooding and geologic carbon storage (GCS), with varying numbers of input parameter cases and model domain features. The results demonstrate that BBT‐ROM excels at predicting non‐wetting phase saturation (e.g., oil or saturation) and fluid pressure, with average relative errors ranging from 0.5% to 3%. Importantly, BBT‐ROM showcases robustness when faced with limited input parameter space during GCS testing.

58 GEOSCIENCES↗

AmeriFlux FLUXNET-1F US-UTJ UFLUX Bluff

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-UTJ UFLUX Bluff. This is the FLUXNET version of the carbon flux data for the site US-UTJ UFLUX Bluff produced by applying the standard ONEFlux (1F) software. Site Description - This station is on the edge of an alfalfa field near the San Juan River

Inkenbrandt, Paul [Utah Geological Survey]↗

AmeriFlux US-UTG UFLUX Green River

This is the AmeriFlux version of the carbon flux data for the site US-UTG UFLUX Green River. Site Description - This station is located adjacent to a large pivot that irrigates alfalpha. The pivot is in line with the predominant wind direction.

Inkenbrandt, Paul [Utah Geological Survey]↗

Regularization via f -Divergence: An Application to Multi-Oxide Spectroscopic Analysis

In this paper, we explore the application of convolutional neural networks (CNNs) for predicting the chemical composition of complex geologic samples in a simulated Martian atmospheric environment. Specifically, we aim to characterize oxide weight percentages (wt.%) of rock samples analyzed by remote Laser-Induced Breakdown Spectroscopy (LIBS), framing the problem as a multi-target regression task . Neural networks trained on LIBS spectra are prone to overfitting due to high spectral complexity, limited labeled data, and measurement noise. While regularization is critical for improving generalization, common methods (e.g., ℓ 2 regularization) impose constraints not directly tied to data distribution properties. We propose a novel regularization method based on a specific ƒ-divergence induced by a graph-based estimator, designed to constrain the distributional discrepancy between predictions and targets. This regularizer serves a dual purpose: (a) mitigating overfitting by enforcing a constraint on the distributional difference between predictions and noisy targets, and (b) acting as an auxiliary loss that penalizes large divergences. To enable backpropagation, we develop a differentiable approximation of this particular ƒ-divergence, making the method feasible for neural networks. Experiments on ChemCam and SuperCam LIBS calibration spectra show that mathematical equation-divergence regularization outperforms or matches standard regularization methods (ℓ 1 , ℓ 2 , dropout) and the classical baseline, partial least squares (PLS). Combining ƒ-divergence regularization with standard regularization yields further performance gains, indicating that distributional regularization is useful in this context giving a promising direction for robust model training in planetary science applications. Source code is publicly available at Klein and Li (2025), https://doi.org/10.11578/dc.20250530.7.

58 GEOSCIENCES↗

The 2025 “Hacking Limnology” Workshop Series and DSOS Virtual Summit: A Half Decade of Data‐Intensive Aquatic Science

The 5th Aquatic Ecosystem MOdeling Network—Junior (AEMON-J) “Hacking Limnology” Workshop and 6th Virtual Summit: Incorporating Data Science and Open Science in the Aquatic Sciences (DSOS) convened 21–25 July 2025. As in previous years (Fig. 1; Meyer and Zwart 2020; Meyer et al. 2021b, 2021c, 2022, 2024), the virtual workshops and summit were free of charge, the content was formatted to allow for broad engagement from a globally distributed audience, and workshop materials and recordings were made available on the AEMON-J/DSOS archive (Meyer et al. 2021a). In contrast to previous years, which primarily focused on inland aquatic ecosystems, this year's workshops and summit showcased a notable plurality of ecosystem types, with workshops spanning marine, riverine, and lacustrine environments. The weeklong event brought together researchers and practitioners interested in the nexus of data science, open science, and the aquatic sciences, hosting between 47 and 65 attendees at a single time and a higher number of registrants (n = 389), who might opt to access the material asynchronously.

Meyer, Michael F. [US Geological Survey, Portland,↗

ED-cPSD: Fast Phase-Size Distribution via Sequential Erosion-Dilation

The Erosion-Dilation continuous Phase-Size Distribution, ED-cPSD, is an application for calculating continuous pore and particle-size distribution from digital reconstructions and/or image-based structural data. It is based on the erosion-dilation continuous phase-size distribution method. A continuous size distribution is a measure of the probability density of finding a particle or pore of a certain size. These distributions are of interest in any field of study involving porous media, including but not limited to electrochemistry, petroleum engineering, geology, and food science. The algorithm behind the software provides a computationally efficient way to calculate phase-size distributions for large domains. For a 3D battery electrode reconstruction with 1.3 x 10 8 voxels, the particle size distribution is derived in under 2 min on a desktop, while also retaining flexibility and computational efficiency for HPC-scale multi-threading. The software can handle structures with over 10 9 voxels. The algorithm is roughly 280 times faster than a previous version on the same task.

Characterization↗

Complex Dependence of Calcite Crack Kinetics on Salinity: The Role of DLVO and Hydration Forces

Abstract Subcritical crack growth (SCG) plays an important role in many geological processes such as delayed earth rupture and rock weathering. The complex dependency of SCG on the in‐crack fluid chemistry, however, is still poorly understood. In this study, we utilize the newly developed surface force‐based fracture theory (SFFT) to elucidate the relative contributions of surface forces and solute transport to the crack growth kinetics of calcite in NaCl solutions. Expanding on Barenblatt's cohesive crack model, SFFT introduces an effective stress intensity at the crack tip that encompasses all the relevant intermolecular forces across the crack in addition to the external far‐field stresses. The nonlinear system of equations portraying the crack opening profile, the solute distribution in a propagating crack, and the crack growth velocity are numerically solved via an implicit scheme. After carefully calibrating the model for calcite‐water systems, the SFFT is used to predict the SCG response of calcite at different NaCl concentrations, based on various hypotheses. These predictions are then compared to existing SCG data from the literature. We demonstrate that the experimentally observed variation of SCG rate with NaCl concentration cannot be explained solely by DLVO forces (electrostatic and Van der Waals interactions). This can be remediated by introducing an exponentially decaying hydration force with a nonlinear, nonmonotonic dependence on NaCl concentration. Furthermore, we demonstrate that accounting for both diffusive and advective transport of ions is important in explaining the absence of a stage‐II SCG response for calcite in electrolyte solutions. Plain Language Summary Subcritical crack growth (SCG) refers to the slow propagation of cracks in materials under a stress below the threshold for catastrophic failure. SCG is a key process in many geological events, for example, delayed earth ruptures and rock weathering. New initiatives such as underground CO 2 and H 2 storage in carbonate reservoirs further call for better understanding of SCG in carbonate minerals subjected to varying fluid chemistry. This study examines the SCG of calcite, a key mineral found in carbonate rocks, intergranular cement in sandstones, and filling material in mineral veins and faults, determining their deformation and strength. A mathematical model is developed to describe how the crack opens and propagates, how solutes (like salts) distribute within the crack, and how the crack surfaces interact with each other. We used the model to predict calcite SCG in water at different salt concentrations and compared it with experimental data. Our results revealed that the hydration force is the dominating factor in determining the complex, non‐linear dependency of SCG on salinity. We also found that both the movement of ions by diffusion and by bulk water flow are crucial for explaining the SCG rates, especially when the cracks grow quickly. Key Points Surface Force‐Based Fracture Theory predicts the complex subcritical crack growth patterns of calcite crystals immersed in NaCl solutions Results highlight the dominant role of hydration forces in altering the fracture behavior of calcite compared to VdW and electric double‐layer forces Advective solute transport explains the absence of stages‐II and ‐III subcritical crack growth responses in solid‐liquid systems

DLVO↗