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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

AmeriFlux Measurement Component Instrument Handbook

An AMC system was installed at the Atmospheric Radiation Measurement (ARM) Climate Research Facility’s North Slope Alaska (NSA) Barrow site, also known as NSA C1 at the ARM Data Archive, in August 2012. A second AMC system was installed at the third ARM Mobile Facility deployment at Oliktok Point, also known as NSA M1. This in situ system consists of 12 combination soil temperature and volumetric water content (VWC) reflectometers and one set of upwelling and downwelling PAR sensors, all deployed within the fetch of the Eddy Correlation Flux Measurement System. Soil temperature and VWC sensors placed at two depths (10 and 30 cm below the vegetation layer) at six locations (or microsites) allow soil property inhomogeneity to be monitored across a landscape. The soil VWC and temperature sensors used at NSA C1 are the Campbell Scientific CS650L and the sensors at NSA M1 use the Campbell Scientific CS655. The two sensors are nearly identical in function, and vendor specifications are based on the CS650 unless otherwise stated.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Data from: Lowland Tropical Forests Remain a Methane Sink Under Warming and Long-Term Hurricane Disturbance Recovery

The repository folder contains spreadsheets and script for soil greenhouse gas (GHG) fluxes, soil moisture, soil temperature, air temperature, and precipitation measurements collected from the Tropical Responses to Altered Climate Experiment (TRACE) at the Sabana Research Field Station, El Yunque National Forest (USDA Forest Service; 18°19′28.74″ N, 65°43′50.09″ W) — an open-air field warming experiment located in a lowland tropical forest in Puerto Rico within the Luquillo Experimental Forest (LEF) — six to seven years after Hurricanes Irma and Maria (2017). All spreadsheets for soil and air microclimate data, as well as soil greenhouse gas data, are included as csv files. Air temperature data are also included as Excel spreadsheets (.xlsx). The script is built in R Studio, which is the only software required to run data analysis. This dataset is associated with the manuscript “Larocca Conte G ; Zuvela L ; Cruz-Pérez R ; Barreto-Vélez T ; Becerra-Santillan N ; Campbell S ; Chu H ; Dam T ; Grullón-Penkova I ; Kleit M ; Ortiz-Iglesias D ; Rubio-Lebrón L ; Cavaleri M ; Reed S ; Sihi D ; Wood T ; O'Connell C., 2026. Lowland Tropical Forests Remain a Methane Sink Under Warming and Long-Term Hurricane Disturbance Recovery. Agricultural and Forest Meteorology. In review". The dataset was used to test the effect of warming on soil CH4 dynamics following long-term legacy effects of hurricane disturbance. The dataset includes: - An overall README file in word and pdf format describing methodology and spreadsheets’ structure. - Continuous measurements of soil temperature and moisture from January 2023 to July 2024 measured with Campbell CS655 probes (“TRACE_soil_temperature_and_moisture_2023_cleaned(in).csv” and “TRACE_soil_temperature_and_moisture_2024_cleaned. csv”). - Air temperature data measured with a HOBO MX23O1A data logger (“Hobo air temperature 2023 Sep 2024” and “Hobo air temperature 2023 Sep 2024” – “CSV FILES folders”). - Precipitation data from a nearby weather tower downloaded from González et al. (2025; “sabana_2020-2025.csv”). - Soil CH4 and CO2 effluxes measured intermittently in two summer campaigns (June – August 2023 and June – July 2024) with a LI-COR 8200-01S Portable Smart Chamber coupled with a LI-COR LI-7810 CH4/ CO2/H2O Trace Gas Analyzer (“23_24COMBO2.0.csv”). - R markdown script for data analysis (“Trace new_PLOTS.Rmd”).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Geophysical survey associated with NEON AOP survey, East River, CO 2018

The package contains data layers developed and used in Falco et al. 2024: “EcoImaging: Advanced Sensing to Investigate Plant and Abiotic Hierarchical Spatial Patterns in Mountainous Watersheds". The package is part of the DOE Watershed Function Science Focus Area (SFA) project and includes geophysical measurements collected at the East River, Colorado, in conjunction with the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) survey conducted in June 2018. This dataset provide soil geophysical information and were used to investigate soil-plant relationships. The dataset consists of: - NEON_2018_EMI_survey.zip: the electromagnetic induction (EMI) survey as shape-file; - NEON_plot_TDR.csv: plot‑level data from Time‑Domain Reflectometry (TDR) measurements, providing: * volumetric water content (VWC) in percent (%); * soil temperature in degrees Celsius (°C); - file level metadata (flmd.csv) - data dictionary (dd.csv) file This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2024

Thermal Adaptation of Enzyme‐Mediated Processes Reduces Simulated Soil CO2 Fluxes Upon Soil Warming

Abstract Understanding factors influencing carbon effluxes from soils to the atmosphere is important in a world experiencing climatic change. Two important uncertainties related to soil organic carbon (SOC) stock responses to a changing climate are (a) whether soil microbial communities acclimate or adapt to changes in soil temperature and (b) how to represent this process in SOC models. To further explore these issues, we included thermal adaptation of enzyme‐mediated processes in a mechanistic SOC model (ReSOM) using the macromolecular rate theory. Thermal adaptation is defined here to encompass all potential responses of soil microbes and microbial communities following a change in temperature. To assess the effects of thermal adaptation of enzyme‐mediated processes on simulated SOC losses, ReSOM was applied to data collected from a 13‐year soil warming experiment. Results show that a model omitting thermal adaptation of enzyme‐mediated processes substantially overestimates observed CO 2 effluxes during the initial years of soil warming. The bias against observed CO 2 effluxes was lower for models including thermal adaptation of enzyme‐mediated processes. In addition, for a simulated linear 3°C soil warming over 100 years, models including thermal adaptation of enzyme‐mediated processes simulated SOC losses of a factor of three smaller than models omitting this process. As thermal adaptation of microbial community characteristics is generally not included in models simulating feedback between the soil, biosphere and atmosphere, we encourage future studies to assess the potential impact that microbial adaptation has on soil carbon – climate feedback representations in models. Plain Language Summary A major uncertainty in projecting how much soil organic carbon (SOC) will be converted to CO 2 as a consequence of climate change is related to how soil microbes may adapt to increasing soil temperatures. While this “microbial thermal adaptation” has been shown to occur in short‐term lab incubation experiments, its effect on SOC cycling on a decadal timescale is not clear. To address this knowledge gap, a mechanistic SOC model was used to simulate data collected from a 13‐year soil warming experiment, to assess how microbial thermal adaptation affects predicted SOC losses upon soil warming. The model results show that incorporating microbial thermal adaptation into the model led to reduced CO 2 effluxes from the soil to the atmosphere compared to the common approach of omitting this mechanism. Our results imply that projected SOC losses for the decades to come may be reduced when this mechanism is incorporated in land models. We therefore advocate for more research on the mechanisms controlling microbial thermal adaptation, and how to implement this mechanism in SOC models. Key Points A crucial aspect of soil organic carbon (SOC) models is the representation of soil microbes Predicted soil CO 2 fluxes upon soil warming are reduced when accounting for microbial thermal adaptation On a centennial time scale, this thermal adaptation results in up to a factor of three lower predicted SOC loss

Van de Broek, Marijn↗
DOE OSTI2024

Impact of fire on chemical properties and respiration of boreal forest soils

We collected both organic-rich and sandy soil cores from 12 sites (site_location_and_texture.csv) within Wood Buffalo National Park, Alberta, Canada. Laboratory burns were conducted by exposing intact soil cores to 60 kW m^-2 in a mass loss calorimeter to simulate boreal forest crown fires in order to measure the effects of burning (temp_data.csv) on soil properties including pH, total C, and total nitrogen (N) (metadata.csv). We used 70-day soil incubations and two-pool exponential decay models to characterize the impacts of burning and burn-induced changes in soil properties on soil respiration (soil_moisture_at_end_of_incubation.csv; soil_mass_at_end_of_incubation.csv; respiration_data.csv). Laboratory burns successfully captured a range of soil temperatures that were realistic for natural wildfire events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Data from: Coupled machine learning-ecosystem ensemble models substantially improve predictions of nitrous oxide (N 2 O) fluxes from US croplands

Nitrous oxide (N₂O) is a potent and persistent greenhouse gas, with rising atmospheric concentrations driven in part by inefficient use of synthetic nitrogen (N) fertilizers in agriculture. Predicting soil N₂O emissions is challenging due to high spatial and temporal variability arising from complex soil biogeochemical processes. Process-based ecosystem models and standalone machine learning (ML) approaches without extensive site-specific calibration often miss high emission episodes. Here, we show how an Ensemble Modeling System (EMS) based on outputs from an ensemble of ecosystem models coupled to an ensemble of ML models can improve predictions and understanding of N2O fluxes from US cropland. Trained and validated on approximately 12,000 N2O chamber measurements at 17 U.S. Midwest sites (six crops, 35 management practices), the EMS accurately predicted daily fluxes of N2O at both training (R² = 0.84, RMSE = 16.4 g N ha⁻¹ d⁻¹) and held-out testing sites (R² = 0.84, RMSE = 6.2 g N ha⁻¹ d⁻¹). Analyses identified six dominant N₂O drivers: soil organic carbon (SOC), NH₄⁺, NO₃⁻, water-filled pore space (WFPS), soil temperature, and biomass production. Wet, warm soils produced large N₂O peaks only with sufficient SOC and mineral N; in low-SOC soils, fluxes remained low. Incorporating these drivers into process-based models might significantly improve their predictive capacity. The EMS demonstrates a strong potential to predict N₂O fluxes at unseen sites, enabling more reliable regional inventories, improved gap-filling where measurements are sparse, and enhanced understanding of mechanisms to advance targeted mitigation strategies in food, feed, and bioenergy crops.

agricultural sciences↗
DOE OSTI2025

Soil Moisture Data for TRACER project (Houston, TX)

The purpose of this study was to collect and distribute ground-truth soil water content and meteorological data in the Houston, TX, area, supporting the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility, and the 2022 field campaign for the Tracking Aerosol Convection Interactions ExpeRiment (TRACER). The files herein contain soil water content and meteorological data for three stations that the Bureau of Economic Geology at UT Austin installed in the Houston, TX, area during the period of performance. The data includes soil moisture, volumetric water content, electrical conductivity of soil, soil temperature, rain precipitation, air temperature and other parameters. Data files within this data set contain measurements at both sub-hourly and 1-hour resolution measurements. The sub-hourly meteorological data file names end with “TRACER_SubHourly_met.dat”, and the sub-hourly soil data files end with “TRACER_SubHourly_soil.dat”. For the 1-hour resolution data, the files ending with "_Soil_flagged.dat” contain mean hourly volumetric soil water content and temperature measured at 5, 10, 20 and 50 cm depths. The files ending with “_Meteoro_flagged.dat” contain mean hourly measured precipitation, air temperature and humidity, wind speed and direction, and solar radiation. All data have undergone QA/QC procedures that are described by Caldwell et al. (2019) and Dorigo et al. (2013) for the soil-specific data, and EPA (2008) for the meteorological data.

Air temperature↗
DOE OSTI2025

SPRUCE Surface N2O fluxes measured with LI-7820, 2024

This dataset contains N2O (nitrous oxide) efflux rates measurements from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experimental site within the Marcell Experimental Forest in northern Minnesota, USA. Measurements were made manually with a LiCor N2O/H2O analyzer (LI-7820) and paired SmartChamber (LI-8200-01S) in June, August, and October (2024-06-24 to 2024-10-22). During each measurement, the SmartChamber was placed on 8” PVC collars that were installed in May 2024. N2O flux was derived from 10-minute flux measurements processed using SoilFluxPro software (v5.3.1) and fit to a linear model. Model slope and R2 are reported along with soil water, soil temperature, and air temperature observations made with SmartChamber sensors. N2O is a gaseous N species formed during the microbial processes of denitrification and ammonia oxidation, and is a powerful greenhouse gas. This dataset contains one data file in comma-separate values (*.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma-separate values (.csv) format and a user guide in PDF (*.pdf) format.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Processed Soil Respiration at the TRACE experimental Warming project, Aug 2015 - Sep 2017, Sabana, Luquillo, Puerto Rico

This data package contains processed measurements of soil carbon dioxide (CO₂) efflux collected using LI-COR LI-8100 soil respiration chambers at the Tropical Responses to Altered Climate Experiment (TRACE) located at the Sabana Field Research Station near Luquillo, Puerto Rico. The TRACE site is a mature, closed-canopy tropical wet forest within the Luquillo Experimental Forest. These data quantify soil surface CO₂ fluxes from both ambient (control) and experimentally warmed plots to evaluate how long-term soil warming affects belowground carbon cycling in tropical ecosystems. The data files include time-series tables of CO₂ flux (µmol CO₂ m⁻² s⁻¹), soil temperature (°C), and ancillary environmental variables, stored in comma-separated values (CSV) format and viewable with any text editor, spreadsheet, or statistical software (e.g., R, Python, Excel). Associated metadata describe plot identifiers, measurement intervals, and processing steps. These data were generated to address the research question: How does sustained soil warming influence soil respiration and carbon flux dynamics in tropical wet forests?

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Fungal mat growth and leaf colonization at the TRACE warming experiment, Mar - Aug 2024, Luquillo, Puerto Rico

This data package contains processed measurements on the growth of litter mat-forming fungi and the time to leaf colonization at the Tropical Responses to Altered Climate Experiment (TRACE). Located near the Sabana Field Research Station in Luquillo, Puerto Rico, the TRACE site is located in a mature, closed-canopy tropical rainforest within the Luquillo Experimental Forest (LEF). These data quantify fungal mat growth and the time to leaf colonization of fungi species Gymnopus johnstonii and Marasmius crinis-equi. The experiment was conducted in ambient (control) and experimentally warmed plots (4°C above ambient) during spring and summer periods to assess how litter mat-forming fungi respond to a range of environmental conditions of tropical wet forests. The data files include tables of relative fungal mat growth rates, time to leaf colonization, averages of soil temperature (°C), and number of dry days before leaf attachment. The data are stored in comma-separated values (CSV) format and viewable with any text editor, spreadsheet, or statistical software (e.g., R, Python, Excel). Associated metadata describe plot identifiers, measurement descriptions, and processing steps.

Agaric fungi↗
DOE OSTI2024

Monitoring of ground water table depth and soil moisture at the Point Reyes field site

Ground water table (GWT) depth and soil moisture (SM) have been monitored at several locations at the Point Reyes field site (Californian coastal grassland) from 2021 to 2024. Monitoring is still on-going and data may be added to this archive at later time. The SM data have been acquired using Teros 12 Meter soil moisture sensors placed at 10, 30, 60 and 90 cm depth at 5 locations along a small hillslope. These sensors also collect soil temperature and bulk conductance. In addition, some collocated sensors provide pore pressure and Photochemical Reflectance Index (PRI). The GWT depth has been inferred from various type of Onset pressure transducers. The pressure measurements have been corrected for atmospheric pressure variations and sensor position relative to the ground surface to infer GWT depth, as well as with RTK GPS data to infer GWT elevation. The GWT data have been acquired at 5 distinct locations from 2020 to 2024 with the sensors placed at about 4 m depth. In addition, GWT data has been acquired for the 2023-2024 period with sensors located in 1 m deep shallow wells installed near each deeper well. This data is intended to evaluate possibly different dynamic in shallow (perched) and deep aquifer. The datasets are all provided in csv format. Please note that the interpretation of the GWT data needs to be done with consideration of environmental and well characteristics at the site and uncertainty in various variables. For more information on GWT and SM data, please contact the author.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Continuous snow depth and temperature measurements from dense network of above-ground distributed temperature profiling systems from 2021-09-23 to 2024-08-23, Seward Peninsula, Alaska

The dataset contains temperature measurements from distributed temperature profiling (DTP) systems (Dafflon et al., 2022; Wielandt et al., 2022; Wang et al., 2024a; Fiolleau et al., 2024) deployed vertically above the ground surface at a large number of locations from 2021 to 2024. The research is designed to improve understanding of the local heterogeneity in snow depth and snow thermal insulation dynamics, as well as their interactions in a discontinuous permafrost region (Wang et al., 2025). The DTP systems were deployed at 96 locations in a watershed along the Nome-Teller road at mile marker 27 (T27) and at 54 locations on a hillslope along the Kougarok road at mile marker 64 (K64) in the Seward Peninsula, Alaska. The probe location information is stored in Probe_locations_T27.csv and Probe_locations_K64.csv. Temperature measurements were recorded at 15-minute intervals using high-precision digital sensors (accuracy: ±0.1°C, resolution: 0.0078°C). The temperature probes, either 1.4 m or 1.6 m long, contain sensors spaced every 5 cm or 10 cm along their length. The temperature data are stored in compressed files following the format: DTP_snow_air_temperature_(site)_(start)_(end).zip, where site is either T27 or K64, and start and end represent the time series period. Within each ZIP file, individual CSV files are named by probe ID and contain temperature records at different heights above the ground surface.This dataset also includes derived snow depth time series over three snow seasons, estimated from temperature measurements. Snow depth was estimated by identifying the consecutive sensor pair that exhibited the largest drop in high-frequency temperature fluctuations (detailed in the methods). These data are stored in: Snow_depths_flags_(site)_(start)_(end).csv, which includes snow depth time series and corresponding quality flags (defined in the methods) from different probes. Additionally, the dataset includes derived metrics and supporting measurements at selected locations over two snow seasons, contributing to the manuscript of Wang et al., 2025. These locations were chosen based on the availability of high-quality snow depth time series during both seasons. The additional data include: (1) Air temperature proxies measured from the top sensors on the pole when they were not buried by snow, stored in Air_temperature_proxies_(site)_(start)_(end).csv (2) Ground interface temperature, recorded at 3 cm above the ground, stored in Ground_interface_temperature_(site)_(start)_(end).csv (3) Site characteristics, including vegetation height, elevation, and the topographic position index (TPI) within a 50 m radius, stored in Selected_probe_locations_gps_vegheight_tpi_elevation_(site).csv. These metrics were derived from 1 m resolution summer LiDAR-based digital elevation models and digital surface models from Singhania et al., 2023, DOI:10.5440/1832016. Metadata files include data descriptions (_dd.csv) for tabular data. All included files are listed and described in xxxx_flmd.csv.This dataset is an updated version of a previous archive (Wang et al., 2024b, DOI: 10.15485/2475020), incorporating multiple seasons and improved snow depth estimation. Please note that due to large amount of information present in this dataset, many specificities associated with the acquisition of snow temperature, air temperature proxy and estimation of snow depth, and the future archiving of additional datasets on the soil temperature, thaw depth and soil characteristics at these locations, the author would welcome being contacted by people planning to use this dataset.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Representing cropping systems with the MEMS 2 ecosystem model

Abstract Croplands have been the focus of substantial investigation due to their considerable potential for sequestering carbon. Understanding the potential for soil organic carbon (SOC) sequestration and necessary management strategies will be enabled with accurate process‐based models. Accurately representing crop growth and agricultural practices will be critical for realistic SOC modeling. The MEMS 2 model incorporates a current understanding of SOC formation and stabilization, measurable SOC pools, and deep SOC dynamics and is seen as a highly promising tool to inform management intervention for SOC sequestration. Thus far, MEMS 2 has been developed to represent grasslands. In this study, we further developed MEMS 2 to model annual grain crops and common agricultural practices, such as irrigation, fertilization, harvesting, and tillage. Using four Ameriflux sites, we demonstrated an accurate simulation of crop growth and development. Model performance was strong for simulating aboveground biomass (index of agreement [d] range of 0.89–0.98) and green leaf area index (dfrom 0.90 to 0.96) across corn, soybean, and winter wheat. Good agreement with observations was also achieved for net ecosystem CO 2 exchange (dfrom 0.90 to 0.96), evapotranspiration (dfrom 0.91 to 0.94), and soil temperature (dof 0.96), while discrepancy with the available soil water content data remain (dfrom 0.14 to 0.81 at four depths to 100 cm). While we will continue model testing and improvement, MEMS 2 (version 2.14) has now demonstrated its ability to effectively simulate the growth of common grain crops and practices.

Agriculture↗
DOE OSTI2025

What regulates decomposition in agroecosystems? Insights from reading the tea leaves

Litter decomposition is a critical Earth process, recycling nutrients and setting a portion of plant tissue on a path toward soil organic matter. Despite this importance, we still lack a good understanding of local factors that regulate decomposition, especially in agroecosystems where management plays an outsized role. Using a narrow range of climate and soils, we buried 1,308 pre-manufactured “litter bags” of differing residue quality (i.e., green and rooibos tea leaves) in 109 plots across several management practices to (1) explore the local controls on decomposition in agroecosystems and (2) test the robustness of the Tea Bag Index (TBI). We found that management practices intended to increase soil ecosystem services, that is, soil health, altered the decomposition of both teas. For example, adding nitrogen fertilizer and implementing perennial cropping decreased the extent of green tea decomposition (carbon-to-nitrogen ratio, or C:N = 12.8). No-tillage increased, but perennial cropping decreased, the rate of rooibos tea decomposition (C:N = 50.1). Cropped prairie accelerated green tea decomposition and increased the extent of red tea decomposition. A random forest regression model showed that soil temperature was the strongest predictor of green tea decomposition, but a soil health score also played a significant role in predicting the mass remaining. Soil texture and nutrient availability best predicted rooibos tea decomposition. Finer textured soils seemed to decelerate rooibos decomposition but increased the extent of decomposition. Furthermore, we demonstrated that the TBI metrics correlated somewhat well with empirically derived decomposition constants and were similarly sensitive to the effects of management. Still, the green tea stabilization factor had a substantial prediction bias. Our study increased our basic understanding of what regulates decomposition in agroecosystems. It also showed that the TBI can be a scientifically rigorous citizen science approach to monitoring changes in soil health.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Soil moisture and temperature from 2019 to 2024 along northeast- and southwest-facing hillslopes at the Lower Montane site in the East River Watershed, Colorado

Soil moisture, temperature, and electrical conductivity have been monitored at multiple depths (between 10 and 50 cm) at 4 locations along a northeast-facing slope and 3 locations on the opposite southwest-facing slope at the Lower Montane site in the East River Watershed, Colorado, from Oct 2019 to Oct 2024. The purpose of this data is to inform hydro-biogeochemical analyses for the Watershed Function Scientific Focus Area (SFA). Two locations on the northeast-facing slope were reinstalled in 2020 due to damage from wildlife, and thus data for these sites are provided in two distinct files. Overall, the data are reported in 9 CSV files containing the measurements, and the locations are provided in the Sensor_Location.csv file. There is a total of 10 *.csv data files and 3 *.csv metadata files. Older datasets associated with the northeast-facing slope are provided in another archive (see reference). These data products are part of the Watershed Function Scientific Focus Area collection effort to further scientific understanding of biogeochemical dynamics from genome to watershed scales. Feel free to contact the author with any questions or collaboration interests.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

High-Resolution WRF-Based Downscaling of Earth System Model Projections for Energy Applications across CONUS

Evaluating energy resources under future scenarios requires meteorological information that adequately resolves regional-scale variability and is suitable for regional energy system studies. Although Earth system model (ESM) outputs provide essential large-scale context, their coarse resolution and inherent biases limit direct use in energy system applications. This work presents a high-resolution dynamical downscaling framework using the Weather Research and Forecasting (WRF) model to generate energy-relevant regional fields for future scenarios over the contiguous United States (CONUS). The framework first identifies an optimal WRF configuration through sensitivity experiments, then evaluates raw and bias-corrected ESM initial and boundary conditions, with soil moisture and soil temperature bias correction implemented as an integral component of the bias-corrected ESM atmospheric forcing prior to WRF dynamical downscaling to improve land-atmosphere interactions. Simulations performed at 4-km resolution show that uncorrected ESM forcing leads to systematically dry and cold soil states, which propagate into elevated near-surface air temperature and solar irradiance biases, particularly during summer for the period 2000-2014. Incorporating bias-corrected atmospheric forcing together with soil state bias correction substantially reduces these errors and improves the representation of surface energy processes in WRF simulations. The results highlight the importance of bias-aware initialization strategies in high-resolution dynamical downscaling for future energy system analysis and planning.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Baltimore Turfgrass Field Experiment Campaign Report

The Baltimore Turfgrass Field Experiment (BTFE) consisted of an observation eddy covariance flux station, measuring CO 2 , H 2 O, and turbulent energy fluxes between the turfgrass field and the atmosphere. The tower was sited before the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility’s arrival, but for the purposes of this report, we can say that the campaign lasted as long as the Coast-Urban-Rural Atmospheric Gradient Experiment (CoURAGE) campaign, 12 months, 12/2024 through 11/2025. As stated, the flux tower was part of the COURAGE campaign on the M1 site. We set out to measure turfgrass because it is a ubiquitous part of the urban environment—in fact, it is the dominant vegetation land cover type in Baltimore and most cities. It contributes small but significantly to greenhouse gas balances as well as the energy balance in the urban environment. We also installed a soil temperature and matric potential profiling system as part of this experiment. Additionally, AmeriFlux asked us to participate in their soil matric potential soil moisture mapping experiment. We collected 12 soil samples to be analyzed by a team in Bloomington, Indiana: results are pending.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Electrothermal mineralization of per- and polyfluoroalkyl substances for soil remediation

Abstract Per- and polyfluoroalkyl substances (PFAS) are persistent and bioaccumulative pollutants that can easily accumulate in soil, posing a threat to environment and human health. Current PFAS degradation processes often suffer from low efficiency, high energy and water consumption, or lack of generality. Here, we develop a rapid electrothermal mineralization (REM) process to remediate PFAS-contaminated soil. With environmentally compatible biochar as the conductive additive, the soil temperature increases to >1000 °C within seconds by current pulse input, converting PFAS to calcium fluoride with inherent calcium compounds in soil. This process is applicable for remediating various PFAS contaminants in soil, with high removal efficiencies ( >99%) and mineralization ratios ( >90%). While retaining soil particle size, composition, water infiltration rate, and cation exchange capacity, REM facilitates an increase of exchangeable nutrient supply and arthropod survival in soil, rendering it superior to the time-consuming calcination approach that severely degrades soil properties. REM is scaled up to remediate soil at two kilograms per batch and promising for large-scale, on-site soil remediation. Life-cycle assessment and techno-economic analysis demonstrate REM as an environmentally friendly and economic process, with a significant reduction of energy consumption, greenhouse gas emission, water consumption, and operation cost, when compared to existing soil remediation practices.

Science & Technology - Other Topics↗