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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Soil porous microstructure control over soil organic matter mobility: A multimethod workflow for understanding chemistry-dependent organic matter binding in soil

Soil organic matter (SOM) has attracted a great deal of interest; particularly for its potential to mitigate human derived CO 2 emissions. Studies have demonstrated that SOM plays a critical role in carbon storage and CO 2 sequestration. However, the sorption properties of SOM, which influence its transport in pore water and stabilization within the soil, remain poorly understood. This study develops a workflow to: (1) examine compound-specific advective and diffusive transport and desorption behaviors, (2) quantify desorption rates through stop-flow and continuous-flow column experiments, and (3) evaluate the impact of soil microporosity on SOM mobility using high-resolution imaging and extractions. Intact core column experiments were conducted on Uncultivated (Natural) and Cultivated soil samples, both were arid soils, collected in Washington State. X-ray computed tomography was employed to measure porosity and pore connectivity, while Fourier-transform ion cyclotron resonance mass spectrometry was used to analyze SOM composition. The findings revealed that cultivation increased total carbon and nitrogen levels due to irrigation and fertilization, enhancing carbon capture potential in arid soils. In contrast, the Natural soil, characterized by higher porosity and connectivity, contained more oxidized carbon. Pore network analysis indicated that soil compaction in the Cultivated soil may lead to longer diffusion pathways, significantly influencing SOM transport and stability.

Hydraulic Properties↗
DOE OSTI2026

Effects of 9.5 Years of Whole-Soil Warming on the Fatty Acid and n-Alkanes Composition in Bulk Soil and Density Fractions at Blodgett Experimental Forest, California, USA

Original data of molecular data (fatty acids and n-alkanes) including concentrations and calculated molecular proxies in a whole-soil warming experiment at the Blodgett Forest Research Station after 9.5 years of warming. The study site has a Mediterranean climate with annual average temperature of 12.5 ℃ and annual average precipitation of 1774 mm. The study site is characterized by a mesic Ultic Alfisol formed from granitic parent material, corresponding to a Dystric Cambisol under the World Reference Base for Soil Resources (WRB) classification system. Experimental warming is applied throughout the soil profile to a depth of 1 m using vertically embedded heating cables that raise soil temperature by 4 °C relative to ambient conditions. Soil samples were collected on 1 May 2023, after the experiment had been operating continuously for about 9.5 years since its initiation in January 2014.The data has been processed from raw data and cross-validated by other peers. The dataset includes: - Bulk_Fattyacid_9.5-year_Soil_Warming_Blodgett, California, USA: fatty acid concentrations and proxies including Carbon Preference Index (CPI) and Average Chain Length (ACL) of bulk soil organic carbon; - Fractions_Fattyacid_9.5-year_Soil_Warming_Blodgett, California, USA: fatty acid concentrations and proxies including CPI and ACL of free particulate organic matter (fPOM) and mineral-associated organic matter (MAOM); - Bulk_Alkanes_9.5-year_Soil_Warming_Blodgett, California, USA: n-alkanes concentrations and proxies including CPI and ACL of bulk soil organic carbon; - Fractions_Alkanes_9.5-year_Soil_Warming_Blodgett, California, USA: n-alkanes concentrations and proxies including CPI and ACL of fPOM and MAOM; - n-Alkanes_All_Monomer_Concentration_9.5-year_Soil_Warming_Blodgett, California, USA: concentration of all the n-alkane monomers identified and integrated for bulk soil, fPOM and MAOM; - Fattyacid_All_Monomer_Concentration_9.5-year_Soil_Warming_Blodgett, California, USA: concentration of all the fatty acid monomers including diacids identified and integrated for bulk soil, fPOM, and MAOM. All data are provided in CSV format and can be viewed using Microsoft Excel. We specifically look at fatty acids (FA) and n-alkanes in bulk soil, fPOM and MAOM and calculated molecular proxies such as CPI and ACL to understand the source of oragnic carbon (with ACL) and degree of decomposition (CPI) of each soil fraction. Due to lack of long-chain fatty acids (carbon number ⩾ 20), microorganism-derived organic carbon is characterized by shorter ACL in comparison to plant-derived organic carbon. Fresh SOC is characterized by even-over-odd dominance for fatty acids and odd-over-even dominance for n-alkanes. Therefore, CPI indicates whether soil organic carbon (SOC) represents fresh input (CPI > 10) or is strongly decomposed (close to 1). The research questions should be then, after 9.5-year warming: 1. whether the relative contribution between microorganism-derived and plant-derived SOC in each soil fraction? 2. whether fPOM became more decomposed whereas MAOM remained relatively persistent in each soil fraction across the soil depth?

Carbon↗
DOE OSTI2026

Data for Impacts of Legacy and Contemporary Nitrogen Inputs on N2O and CO2 Emissions in Miscanthus and Maize Cultivated Soils

Nutrient inputs influence the sustainability of bioenergy crop production through contemporary (shortly after addition) and legacy effects (persisting over years) on microbial nitrogen (N) and carbon cycling, which contribute to greenhouse gas emissions. However, the relative importance of contemporary and legacy effects and how that could vary by crop functional types is poorly understood. Considering its rhizomatous roots and perennial growth, we hypothesized that Miscanthus × giganteu s ( M × g ) would be more sensitive to legacy N fertilization and the historical context of its environment than an annual crop like maize. To test this hypothesis, we examined the effects of legacy and contemporary N inputs on nitrous oxide (N2O) and carbon dioxide (CO2) emissions, as well as key N cycling genes in soils where M × g and maize were grown. A 150-day soil incubation experiment was conducted using soils from a long-term M × g and maize fertility experiment with three historic N fertilization rates (0, 112, and 336 kg N ha−1 year−1) and a contemporary amendment (60 mg N kg−1) with negative control (0 mg N kg−1). We observed significant increases in cumulative N2O emissions in M × g soils relative to maize soils, particularly at higher legacy fertilization rates, while contemporary N had no significant effect. Bacterial amoA gene abundance, which plays a significant role in nitrification in nutrient-rich soils, also increased with higher legacy fertilization rates in M × g soils but was unaffected by the contemporary N. In maize soils, legacy and contemporary N did not significantly affect N2O emissions, but cumulative CO2 emissions and amoA gene abundance significantly increased. The abundances of norB genes were not significantly influenced by either legacy fertilization or contemporary N amendments in either soil. Our findings demonstrate the greater importance of fertilization history over contemporary N in mediating soil N2O emissions, particularly for perennial bioenergy crops.

Carbon↗
DOE OSTI2026

Soil Characterizations of Five Urban Sites in Knoxville, Tennessee. 2024-2025

This dataset includes soil characterizations from five urban parks in Knoxville, Tennessee, USA: Cumberland Estates Park (CEP), Socially Equal Energy Efficient Development (SEED), West View Park (WVP), Victor Ashe Park (VAP), and West Hills Park (WHP). The dataset consists of seven CSV files reporting the data from the measurements of gravimetric moisture content, pH, total carbon and nitrogen, soil texture, dissolved organic carbon and nitrogen, and microbial biomass carbon and nitrogen derived from these soil cores. During five separate sampling events conducted in 2024 and 2025, five soil cores were collected at each site within 3 meters of the remote soil monitoring equipment. In 2025, an additional three soil cores were collected adjacent to the monitoring equipment at each site to assess soil bulk density. This dataset is part of a larger study investigating the effects of soil moisture and plant evapotranspiration on ambient temperature and relative humidity across multiple urban parks in Knoxville, Tennessee.

Mayes, Melanie A [ORNL] (ORCID:0000000163689210)↗
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 OSTI2026

How deep is your soil? Quantifying and spatially analyzing understudied deep soil in the United States

Deep soil is largely understudied and important in understanding biogeochemical processes in soil. Here, understudied soil is defined as the difference between soil studied to a known depth and the estimated bedrock depth. To understand more about deep soil, the understudied soil in the US was quantified and spatially analyzed using soil survey data and model estimates of bedrock depth. An equation was derived to find understudied soil using the dataset parameters “max lower depth studied”, “depth to bedrock”, and “likelihood of bedrock in the top 200 cm”. The survey data and bedrock model revealed that soil has been studied to an average depth of 1-2 meters, and the average depth to bedrock is 20 meters. Soil data density in the soil surveys was greatest in the West Coast, Midwest, and areas historically managed for agricultural, while the non-contiguous US and interior West were underrepresented. The soil had been studied deeper than the estimated soil depth in 455 out of 56,889 observation points concentrated in Alaska, California, Texas, Florida, Puerto Rico, and the US Virgin Islands. To understand the diversity and any taxonomic bias of the global soil data available, soil order was compared to US-based National Resource Conservation Service percentages and it was found that Oxisols, Alfisols, Ultisols, Andisols, and Histosols were overrepresented while Gelisols, Aridisols, Vertisols, Entisols, and Spodosols are underrepresented. Soil depth is important in exploring the complexity of biogeochemical processes that take place in soil.

Bedrock↗
DOE OSTI2025

Soil biogeochemical properties and metrics of tree-mycorrhizal dominance for a 25-Ha forest in South Central Indiana, USA.

This data package contains a dataset used in the papers “Seeing the forest for all the trees: Mycorrhizal-associated nutrient economies are modulated by stem density and the synchrony between overstory and understory communities” and “Mycorrhizal associations of tree species influence soil nitrogen dynamics via effects on soil acid–base chemistry”. Four csv files are included along with a dataset. The dataset features chemical soil properties for a single sampling campaign within the 25 Ha Lilly-Dickey Woods Smithsonian Forest Global Earth Observatory (ForestGEO) plot in South Central Indiana, USA (ldw_dat_raw.csv). Also included are separate files focused on pH (pH_data.csv), carbon and nitrogen (CN_data.csv), and nitrification rates (Nitrification_data.csv). These variables are commonly associated with the tree-mycorrhizal dominance of forest stands. In these data subsets, each soil variable was matched to a 10 meter radius neighborhood wherein metrics of tree-mycorrhizal dominance (basal area, stem count, importance value, etc.) were calculated. Models between these soil variables and dominance metrics were used to investigate how different assessments of mycorrhizal associated nutrient economies (MANE) capture these relationships. This research was performed as a part of the Smithsonian ForestGEO project. This data package can be used to explore spatial variability in soil chemistry within a mature hardwood forest, or it can be combined with the included tree data, other fine-scale spatial information, or other tree inventory data for the site to evaluate how soil chemistry varies with tree community composition or edaphic or topographic properties.

Craig, Matthew [ORNL] (ORCID:0000000288907920)↗
DOE OSTI2025

Regional-scale soil carbon predictions can be enhanced by transferring global-scale soil–environment relationships

Accurate modelling and mapping soil organic carbon are crucial for supporting soil health restoration and climate change mitigation at both regional and global scales. However, regional soil predictions often suffer from data scarcity and high prediction uncertainty. Utilizing a pre-trained global-to-regional soil carbon predictive model can be a potential solution to address this challenge. Despite its promise, how to construct and apply the global-scale model to enhance regional-scale soil carbon mapping remains largely unexplored. Here, we propose the Global Soil Carbon Pre-trained Model (GSoilCPM), a deep-learning-based domain adaptative model, to enhance regional-scale soil carbon predictions. Based on large amount of environmental covariate data and 106,167 soil samples across the globe, we verify our hypothesis of the effectiveness of this 'global-to-regional' modelling strategy. The pre-trained model can be then transferred and fine-tuned to bridge the regional- and global-scale soil–environment relationships. We applied and validated this modelling strategy in four regional-scale study areas, three in the Northern Hemisphere and one in the Southern Hemisphere, each with distinct environmental background. Compared to traditional modelling approaches as a baseline, four case studies all demonstrated significant improvement in prediction accuracy across diverse environments and varying data availabilities. The average percentage improvement across all regions is 10.93% (absolute values decreased by 1.20 g kg−1 averagely) in MAE and 29.04% (absolute values increased by 0.10 averagely) in CCC. The applicability and future horizons of using GSoilCPM were further discussed. We further reveal that regions with fewer soil samples or lower baseline accuracy benefit more from the pre-trained global model. Our findings highlight the advantages of leveraging the generalized knowledge from global models to enhance specifically localized soil modelling, positioning a potential paradigm shift in digital soil mapping, and far-reaching implications for soil monitoring and land management.

Deep learning↗
DOE OSTI2026

Using Artificial Soiling to Rank Anti-Soiling Coatings for Arid Climates

A simple method to evaluate (e.g., screen and rank) the performance of coatings that are fully or partially anti-soiling (AS) is lacking within the PV industry. Artificial soiling may be used as a rapid and economical assessment approach, offering an efficient alternative to time-intensive, site-specific field testing. In this study, we present an artificial soiling method to replicate the anti-soiling performance rankings of two groups of coated glass samples supplied by two manufacturers (group C with CA, CP, and CS coatings; group A with AC coating). These are compared to field aging in two climates: semi-arid Lemoore, California over 4 months, and the hot desert in Mesa, Arizona over 7 months. Both field and indoor performance ranking utilize the transmittance ratio, defined as the optical transmittance between a coated sample and an uncoated reference in each group, as an evaluation metric to assess the effectiveness of the artificial soiling approach in replicating field soiling. It is critical to mimic the dominant field meteorological conditions associated with soiling-prone days and vulnerable times of day during the soiling season. Our results reveal that the use of artificial soiling for field performance ranking among coatings strongly depends on the soil type (composition and particle distribution), dust surface density, and prevalent environmental factors (wetting saturation by humidity, dew condensation extent, and prior weathering history of the coating). The similar rank order relative to the field suggests that the artificial soiling approach presented may be used for down-selecting anti-soiling coatings prior to prolonged field validation.

14 SOLAR ENERGY↗
DOE OSTI2026

Vegetation heterogeneity reflects soil thermal state and surface soil displacement in a thawing permafrost landscape

Thawing permafrost has the potential to dramatically alter the physical and ecological structure of northern landscapes. Warming of the Arctic and subsequent degradation of permafrost have created a need to assess the stability and movement of soils on hillslopes and the potential impacts on ecosystem structure. In this work, we explore the relationships among vegetation heterogeneity, soil temperature, and soil surface displacements observed from 2019 to 2022 in a watershed in the discontinuous permafrost region on the Seward Peninsula of Alaska. Vegetation heterogeneity was measured as the standard deviation (SD) of the normalized difference vegetation index (NDVI) from 3 m PlanetScope satellite imagery around each soil temperature and active layer thickness observation. Locations of observations were clustered into three soil thermal groups, warm, intermediate, and cold, based on soil temperature and active layer thickness. Average annual horizontal surface displacements were significantly lower for soils within the warm thermal group (median = 0.033 m yr −1 ) compared to soils within the cold thermal group (median = 0.090 m yr −1 ; p < 0.001). Conversely, vegetation heterogeneity was significantly higher in the warm (median = 0.014 SD NDVI; p = 0.002) and intermediate (median = 0.015 SD NDVI; p = 0.002) groups compared with the cold thermal group (median = 0.012 SD NDVI), suggesting a warming-induced shift in vegetation community complexity. Because of the observed associations of ground surface displacement rates and vegetation heterogeneity with soil thermal state, we hypothesize that warming soil conditions induce changes in the rates and patterns of hillslope erosion due to an increase in surface movement as near-surface permafrost thaws, followed by a decrease as the permafrost table deepens and excess ice content diminishes. The transition to warm soils promotes surface ecosystem transformation, shifting the dominant vegetation at the site, given the warming climatic conditions of the region. We integrated our observations of soil temperature, vegetation heterogeneity, and soil surface displacements into a conceptual model that describes the co-evolution of hillslopes and vegetation in warming permafrost environments, which is currently unrepresented in earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Composition and persistence of soil organic matter along eroding and depositional transects in buried vs. modern soil layers: A case of the Brady paleosol at Wauneta, Nebraska

Paleosols form when soils are buried through deposition by aeolian, colluvial, alluvial or other processes. Burial of former topsoil isolates soil organic matter (SOM) from surface conditions, allowing carbon to accumulate and potentially remain stable for millennia. In this study, SOM composition, distribution, and persistence were analyzed in the Brady Soil of Nebraska, USA to compare SOM spatial variability in modern and buried soils, as well as the impact of erosional exposure on SOM stability. The Brady Soil, formed as a surface soil during the Pleistocene-Holocene transition and now a paleosol buried up to 6 m deep (or more) by loess deposition during the Holocene, was sampled along burial (up to 5.8 m depth) and erosional (up to 1.8 m depth) transects to compare SOM dynamics in different geomorphic settings. Fourier Transform Infrared Spectroscopy (FTIR) and Fourier Transform ion cyclotron resonance mass spectrometry (FTICR-MS) were used to analyze SOM composition, while δ 13 C isotope analyses identified SOM sources and radiocarbon values were used to estimate turnover rates. Results confirmed a vegetation shift from C3 to C4 plants after Brady Soil formation, reflecting warming climatic conditions. Increasing SOM age and decreasing δ 13 C and δ 15 N values with depth indicated slowing of decomposition rate in buried soils. Higher pH in the Brady Soil suggested greater base cation content, supporting SOM stabilization through organo-mineral associations and aggregate formation. However, exposure of the Brady Soil due to surface erosion caused faster SOM turnover. This result suggested susceptibility of buried SOM to losses via decomposition upon erosional exposure, possibly accelerated by priming in response to modern SOM inputs. These findings highlight the potential loss of carbon stocks in buried soils under future climate change, as shifts in soil physicochemical properties may destabilize long-preserved SOM.

Environmental sciences↗
DOE OSTI2026

Data for Immediate Impacts of Soybean Cover Crop on Bacterial Community Composition and Diversity in Soil Under Long-Term Saccharum Monoculture

Saccharum yield decline results from long-term monoculture practices. Changes in cropping management can improve soil health and productivity. Below-ground bacterial community diversity and composition across soybean (Glycine max (L.) Merr) cover crop, Saccharum monoculture (30+ year) and fallowed soil were determined. Near full length (~1,400 base pairs) of 16S rRNA gene sequences were extracted from the rhizospheres of sugarcane and soybean and fallowed soil were compared. Higher soil bacterial diversity was observed in the soybean cover crop than sugarcane monoculture across all measured indices (observed operationational taxonomic units, Chao1, Shannon, reciprocal Simpson and Jackknife). Acidocateria, Proteobacteria, Bacteroidetes and Planctomycetes were the most abundant bacterial phyla across the treatments. Indicator species analysis identified nine indicator phyla. Planctomycetes, Armatimonadetes and candidate phylum FBP were associated with soybean; Proteobacteria and Firmicutes were linked with sugarcane and Gemmatimonadetes, Nitrospirae, Rokubacteria and unclassified bacteria were associated with fallowed soil. Non-metric multidimensional scaling analysis showed distinct groupings of bacterial operational taxonomic units (97% identity) according to management system (soybean, sugarcane or fallow) indicating compositional differences among treatments. This is confirmed by the results of the multi-response permutation procedures (A = 0.541, p = 0.00045716). No correlation between soil parameters and bacterial community structure was observed according to Mantel test (r = 211865, p = 0.14). Use of soybean cover-crop fostered bacterial diversity and altered community structure. This indicates cover crops could have a restorative effect and potentially promote sustainability in long-term Saccharum production systems.

Field Data↗
DOE OSTI2026

Carbon dioxide, water vapor and methane soil efflux (soil respiration) in a Pinus palustris root exclusion in Georgetown, SC

This dataset contains processed data from a combination of survey flux chambers and long-term automated flux chambers. Soil flux measurements were conducted from June 2023 through December 2025 in a mature longleaf pine forest in Georgetown, SC. Soil respiration measurements were conducted approximately biweekly for two and a half years, before and after a root exclusion that took place on May 5, 2024. Processed, QAQC’d data for the treatment (root exclusion) and control (roots intact) before and after the root exclusion can be found in the file: 1_DATA_ESS_DOE_HR_RS_HB2_QAQC_Survey_Data_20260223.csv. Two multiday deployments were also conducted prior to the root exclusion using long-term automated chambers to continuously monitor greenhouse gas soil efflux. Processed, QAQC’d data for both long-term deployments can be found in the file: 2_DATA_ESS_DOE_HR_RS_HB2_QAQC_Longterm_Data_20260209.csv. Raw and working data files (.json, .81x, & .82z format) from LI-COR equipment are included for reference and can be accessed using SoilFluxPro software. CSV metadata files describe the raw data and modifications made using SoilFluxPro v5 and Matlab R2024b, as well as formatting and units for processed CSVs. Matlab code is included for reading in the processed CSVs, with sample figures comparing treatment and control. This research was performed as part of the project: “Improving models of stand and watershed carbon and water fluxes with more accurate representations of soil-plant-water dynamics in southern pine ecosystems”, which examines in part the effects hydraulic redistribution on soil efflux of carbon dioxide, water vapor and methane, as well as soil moisture and temperature in a southern pine ecosystem with sandy soils and high water table.

CARBON DIOXIDE FLUX↗
DOE OSTI2025

Scaling High-Resolution Soil Organic Matter Composition to Improve Predictions of Potential Soil Respiration Across the Continental United States

Despite the importance of microbial soil organic matter (SOM) respiration in regulating the flux of carbon between soils and the atmosphere, soil carbon cycling models remain primarily based on climate and soil properties, leading to large uncertainty in predictions. To address this knowledge gap, we analyzed high-resolution water-extractable SOM profiles from soil cores collected across the United States by the 1,000 Soils Pilot of the Molecular Observation Network. Our innovation lies in using machine learning to distill thousands of SOM formula into tractable units; and it enables integrating data from molecular measurements into soil respiration models. In surface soils, SOM chemistry provided better estimates of potential soil respiration than soil physicochemistry, and using them combined yielded the best prediction. Overall, we identify specific subsets of organic molecules that may improve predictions of global soil respiration and create a strong basis for developing new representations in process-based models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Experimental Soil Warming Impacts Soil Moisture and Plant Water Stress and Thereby Ecosystem Carbon Dynamics

Experimental soil heating experiments have found a consistent increase in soil-surface CO 2 emissions ( F s ), but inconsistent soil organic carbon (SOC) responses. Interpretation of heating effects is complicated by spatial heterogeneity and soil moisture, nitrogen availability, and microbial and plant responses. Here we applied a mechanistic ecosystem model to interpret heating impacts on a California forest subjected to 1 m deep, 4°C heating. The model accurately simulated control-plot CO 2 fluxes, SOC stocks, fine root biomass, soil moisture, and soil temperature, and the observed increases in F s and decreases in fine root biomass. We show that a complex suite of interactions can lead to a consistent increase in F s (~17%) over the 5-year study period, with very small changes in SOC stocks (<1%). Modeled increases in leaf water stress from soil drying reduced GPP and NPP. The resulting reduction in leaf and fine root allocation increased fine root litter inputs to the soil and reduced root exudation. Soil heating led to about a 50% larger increase in root autotrophic respiration than in heterotrophic respiration, with the heating effect on both these fluxes decreasing over the simulation period. Increased heterotrophic respiration led to increased soil N availability and plant N uptake. These heating responses are mechanistically linked, of magnitudes that can affect ecosystem dynamics, and long-term observations of them are rarely made. Therefore, we conclude that a coupled observational and mechanistic modeling framework is needed to interpret manipulation experiments, and to improve projections of climate change impacts on terrestrial ecosystem carbon dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Changes in soil water content and lateral flow exert large effects on soil thermal dynamics across Alaskan landscapes

Both lateral surface and subsurface water flow affect soil moisture dynamics, yet most land surface models only solve subsurface water movement vertically. Here, we use a 3D ecosystem model that considers both land surface and subsurface hydrologic processes to simulate soil moisture, which is then used to drive a 1-D vertical soil thermal model to simulate the soil moisture effects on soil thermal dynamics in central Alaska. Our coupled model improves soil temperature (ST) estimates by 43.5% in comparison with observational data. Soil moisture has little effect on ST during the wet season (-1.5%) and a substantial influence during the dry season (60%). Spatially, water lateral flow has significant impacts on both soil moisture and ST, causing model estimates for thawed areas in the transition season to increase by ~10% in the study area. Our results highlight the importance of considering dynamical soil moisture, as well as lateral flow effects, on soil thermal dynamics in permafrost regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The Freundlich isotherm equation best represents phosphate sorption across soil orders and land use types in tropical soils of Puerto Rico

Biomass production in the lowland wet tropical forest is greater than in any other biome, and it is typically limited by soil phosphorus (P) availability. However, the mechanisms involved in the P cycle remain poorly represented in Earth System Models (ESMs). Soil P sorption processes are key in the P cycle and for understanding the extent of P limitation for plant productivity. Currently, a few ESMs include isotherm equations to model these processes. Although the Langmuir equation is widely cited, other isotherm equations may better describe sorption in tropical soils. Here, we use a diverse range of soil samples from Puerto Rico to test the validity of the Langmuir, Freundlich, and Temkin equation. We found that across four soil orders (Inceptisols, Mollisols, Oxisols, Ultisols), and forested and cultivated land use types, the Freundlich equation best represented soil P sorption. Furthermore, the Langmuir and the Temkin equations poorly represent soil P adsorption, especially at low P concentrations. Specifically, the Langmuir equation underestimated soil P adsorption by 40% and the Temkin equation overestimated adsorption by 76%. We also found, as expected, that soil clay content and pH were the most important parameters explaining the variability of the Freundlich (K f ) constant. Greater clay content and lower pH, common in highly weathered Ultisols and Oxisols which are abundant in the tropics, led to greater K f values. Overall, our results suggest that a diversity of soils can prompt underestimation of P sorption when using the Langmuir isotherm, which leads to an overestimation of available P that can have repercussions on ESM predictions of the P cycle and tropical forest productivity.

Earth system models↗
DOE OSTI2026

Soil carbon stocks from soil organic carbon at the Teller 47, Teller 27, and Kougarok 86 field sites, Seward Peninsula, Alaska, 2021 – 2023

Carbon stored in permafrost soils represents the largest terrestrial storage and hence has key significance for the global carbon cycle, yet the site-specific estimates of permafrost soil carbon are limited. To better understand permafrost carbon storage, soil organic content (SOC) was measured from soils collected at the Teller 27, Teller 47, and Kougarok 86 field sites on the Seward Peninsula, Alaska in the summers of 2019 and 2021-2023. Soil samples were collected from locations with and without near-surface permafrost to varied depths within the permafrost, where present. In addition to sample collection for soil organic carbon analysis, bulk density samples were also collected within each soil profile to allow for calculation of carbon stocks. Carbon to nitrogen ratios are also reported. Location data were collected at each observation point using a handheld GPS unit with waypoint averaging to reduce location measurement error to within 3 m. SOC was measured using elemental analysis in the laboratory. This dataset contains a *.csv file of soil carbon stocks, a *.kml file of measurement locations, a *.pdf user guide, a *.csv data dictionary, and a *.csv file level metadata.

EARTH SCIENCE > CRYOSPHERE > FROZEN GROUND↗