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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Focal area: Insights gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics or knowledge-guide AI.
A collaborative team from Texas A&M AgriLife Research, ABQMR Inc., the Soil Health Institute, the Athinoula A. Martinos Center for Biomedical Imaging, and NIST developed low-field magnetic resonance imaging (LF-MRI) instrumentation capable of imaging intact soil-root systems. The system measured root biomass, architecture, 3D mass distribution, and growth rates, providing a non-destructive means to evaluate ideal plant characteristics based on root metrics. It also successfully generated three-dimensional images of soil water content, a key property influencing root growth and exploration. Operating much like an MRI used in a medical setting, the system functioned in field conditions without damaging plants, overcoming the limitations of traditional methods such as trenching, soil coring, and root excavation. Over the course of the project, the team designed and built three functional prototype systems. These prototypes provided new insights into root–water–soil interactions that drive processes such as nutrient uptake, water use, and carbon management. This information contributed to efforts to optimize plants for carbon sequestration without sacrificing economic yield. The project also supported the identification of desirable traits for energy sorghum, including high root growth rates, more vertical root angles, and enhanced drought resilience under water-limiting conditions.
FVCOM-ICM is a water quality model that simulates the influence of nutrients and carbon pollution on biogeochemical oceanographic processes such as phytoplankton primary productivity, nutrient consumption, mineralization, decay and settling of particulate organic matter and computes resulting dissolved oxygen (DO) concentrations and pH levels. An “offline” approach of coupling Finite Volume Coastal Ocean Model (FVCOM) hydrodynamic solutions to CE-QUAL-ICM biogeochemical model was used. FVCOM and CE-QUAL-ICM are public domain Fortran Codes that were developed independently by US Army Corps of Engineers and University of Massachusetts. PNNL developed FVCOM-ICM that has the ability to conduct water quality calculations using a previously computed hydrodynamic solution. The biogeochemical calculations using CE-QUAL-ICM kinetics are conducted over the same finite-volume mesh and in the FVCOM framework.
Coastal ecosystems are among the most biologically and biogeochemically active and diverse systems on Earth. Because they act as important linkages between terrestrial ecosystems and the open ocean, their incorporation in Earth system models (ESMs) is critical to predict coastal and global responses to environmental changes. However, they vary greatly in the magnitude of tides and the volume and timing of freshwater input from land, making it challenging to model the major biogeochemical reactions that control productivity and greenhouse gas emissions across coastal terrestrial aquatic interfaces (TAIs). Our overall objective was to improve mechanistic process understanding and modeling of tidal wetland hydro-biogeochemistry in coastal TAIs. We established a new flux tower site (Ameriflux US-PLo) in the oligohaline part of the Parker River to continuously monitor ecosystem-scale carbon fluxes under temporally varying salinity conditions. The site is co-located with long-term monitoring plots of the Plum Island Ecosystems LTER project. We installed wells and redox sensors in the marsh interior and creek bank, established biomass monitoring plots and deployed novel optode sensors in both locations. We used this data to parameterize plant-mediated transport in PFLOTRAN and tested the impact of soil heterogeneity on porewater constituents and gas fluxes. We collected observations of root oxygen release with a novel planar optode system in the field. Flux data collected during the measurement period encompasses a large variation in salinity ranging from drought to record precipitation years. We developed a method to extract functional relationships from the flux data using artificial neural networks, identifying salinity thresholds for CH 4 fluxes. Finally, we are using the coupled ELM-PFLOTRAN model to test the impact of antecedent hydrological conditions on the salinity-CH 4 flux relationship. This grant contributed to the professional development of one postdoc, three research assistants and one graduate student. The sensor data has been shared with external collaborators.
Crop phenology regulates seasonal agroecosystem carbon, water, and energy exchanges, and is a key component in empirical and process-based crop models for simulating biogeochemical cycles of farmlands, assessing gross and net primary production, and forecasting the crop yield. The advances in phenology matching models provide a feasible means to monitor crop phenological progress using remote sensing observations, with a priori information of reference shapes and reference phenological transition dates. Yet the underlying geometrical scaling assumption of models, together with the challenge in defining phenological references, hinders the applicability of phenology matching in crop phenological studies. The objective of this study is to develop a novel hybrid phenology matching model to robustly retrieve a diverse spectrum of crop phenological stages using satellite time series. The devised hybrid model leverages the complementary strengths of phenometric extraction methods and phenology matching models. It relaxes the geometrical scaling assumption and can characterize key phenological stages of crop cycles, ranging from farming practice-relevant stages (e.g., planted and harvested) to crop development stages (e.g., emerged and mature). To systematically evaluate the influence of phenological references on phenology matching, four representative phenological reference scenarios under varying levels of phenological calibrations in terms of time and space are further designed with publicly accessible phenological information. The results indicate that the hybrid phenology matching model can achieve high accuracies for estimating corn and soybean phenological growth stages in Illinois, particularly with the year- and region-adjusted phenological reference (R-squared higher than 0.9 and RMSE less than 5 days for most phenological stages). The inter-annual and regional phenological patterns characterized by the hybrid model correspond well with those in the crop progress reports (CPRs) from the USDA National Agricultural Statistics Service (NASS). Compared to the benchmark phenology matching model, the hybrid model is more robust to the decreasing levels of phenological reference calibrations, and is particularly advantageous in retrieving crop early phenological stages (e.g., planted and emerged stages) when the phenological reference information is limited. This innovative hybrid phenology matching model, together with CPR-enabled phenological reference calibrations, holds 3 considerable promise in revealing spatio-temporal patterns of crop phenology over extended geographical regions.
The importance of including directional and spectral light in simulations of ocean radiative transfer was investigated using a coupled biogeochemical-circulation-radiative model of the global oceans. The effort focused on phytoplankton abundances, nutrient concentrations and vertically-integrated net primary production. The importance was approached by sequentially removing directional (i.e., direct vs. diffuse) and spectral irradiance and comparing results of the above variables to a fully directionally and spectrally-resolved model. In each case the total irradiance was kept constant; it was only the pathways and spectral nature that were changed. Assuming all irradiance was diffuse had negligible effect on global ocean primary production. Global nitrate and total chlorophyll concentrations declined by about 20% each. The largest changes occurred in the tropics and sub-tropics rather than the high latitudes, where most of the irradiance is already diffuse. Disregarding spectral irradiance had effects that depended upon the choice of attenuation wavelength. The wavelength closest to the spectrally-resolved model, 500 nm, produced lower nitrate (19%) and chlorophyll (8%) and higher primary production (2%) than the spectral model. Phytoplankton relative abundances were very sensitive to the choice of non-spectral wavelength transmittance. The combined effects of neglecting both directional and spectral irradiance exacerbated the differences, despite using attenuation at 500 nm. Global nitrate decreased 33% and chlorophyll decreased 24%. Changes in phytoplankton community structure were considerable, representing a change from chlorophytes to cyanobacteria and coccolithophores. This suggested a shift in community function, from light-limitation to nutrient limitation: lower demands for nutrients from cyanobacteria and coccolithophores favored them over the more nutrient-demanding chlorophytes. Although diatoms have the highest nutrient demands in the model, their relative abundances were generally unaffected because they only prosper in nutrient-rich regions, such as the high latitudes and upwelling regions, which showed the fewest effects from the changes in radiative simulations. The results showed that including directional and spectral irradiance when simulating the ocean light field can be important for ocean biology, but the magnitude varies with variables and regions. The quantitative results are intended to assist ocean modelers when considering improved irradiance representations relative to other processes or variables associated with the issues of interest.
The primary objectives of this project are to quantify the spatio‐temporal linkages between physical climate and ocean biogeochemical variables in the U.S. Department of Energy (DOE) Energy Exascale Earth System Model (E3SM), in CMIP6 Earth System Models (ESMs), and in historical observations, to evaluate the E3SM’s ability to reproduce the observations, and to develop a solid diagnostic package to do so. It will revolve around four hypotheses and will focus on key physical and biogeochemical processes that regulate the upper ocean carbon and oxygen cycling: water mass distribution, ventilation, and biological production.
Abstract Background Biogeochemical exports from watersheds are modulated by the activity of microorganisms that function over micron scales. Here, we tested the hypothesis that meander-bound regions share a core microbiome and exhibit patterns of metabolic potential that broadly predict biogeochemical processes in floodplain soils along a river corridor. Results We intensively sampled the microbiomes of floodplain soils located in the upper, middle, and lower reaches of the East River, Colorado. Despite the very high microbial diversity and complexity of the soils, we reconstructed 248 quality draft genomes representative of subspecies. Approximately one third of these bacterial subspecies was detected across all three locations at similar abundance levels, and ~ 15% of species were detected in two consecutive years. Within the meander-bound floodplains, we did not detect systematic patterns of gene abundance based on sampling position relative to the river. However, across meanders, we identified a core floodplain microbiome that is enriched in capacities for aerobic respiration, aerobic CO oxidation, and thiosulfate oxidation with the formation of elemental sulfur. Given this, we conducted a transcriptomic analysis of the middle floodplain. In contrast to predictions made based on the prominence of gene inventories, the most highly transcribed genes were relatively rare amoCAB and nxrAB (for nitrification) genes, followed by genes involved in methanol and formate oxidation, and nitrogen and CO 2 fixation. Within all three meanders, low soil organic carbon correlated with high activity of genes involved in methanol, formate, sulfide, hydrogen, and ammonia oxidation, nitrite oxidoreduction, and nitrate and nitrite reduction. Overall, the results emphasize the importance of sulfur, one-carbon and nitrogen compound metabolism in soils of the riparian corridor. Conclusions The disparity between the scale of a microbial cell and the scale of a watershed currently limits the development of genomically informed predictive models describing watershed biogeochemical function. Meander-bound floodplains appear to serve as scaling motifs that predict aggregate capacities for biogeochemical transformations, providing a foundation for incorporating riparian soil microbiomes in watershed models. Widely represented genetic capacities did not predict in situ activity at one time point, but rather they define a reservoir of biogeochemical potential available as conditions change.
Numerical models have been developed to investigate and understand responses of biogeochemical cycle to global changes. Steady state, when a system is in dynamic equilibrium, is generally required to initialize these model simulations. However, the spin-up process that is used to achieve steady state pose a great burden to computational resources, limiting the efficiency of global modeling analysis on biogeochemical cycles. This study introduces a new Semi-Analytical Spin-Up (SASU) to tackle this grand challenge. We applied SASU to Community Land Model version 5 and examined its computational efficiency and accuracy. At the Brazil site, SASU is computationally 7 times more efficient than (or saved up to 86% computational cost in comparison with) the traditional native dynamics (ND) spin-up to reach the same steady state. Globally, SASU is computationally 8 times more efficient than the accelerated decomposition spin-up and 50 times more efficient than ND. In summary, SASU achieves the highest computational efficiency for spin-up on site and globally in comparison with other spin-up methods. It is generalizable to wide biogeochemical models and thus makes computationally costly studies (e.g., parameter perturbation ensemble analysis and data assimilation) possible for a better understanding of biogeochemical cycle under climate change.
Estimates of marine net primary production (NPP) commonly rely on limited in situ 14C incubations or satellite-based algorithms mainly constrained to the surface ocean. Here we combine data from biogeochemical Argo floats with a carbon-based productivity model (CbPM) to compute vertically-resolved estimates of NPP. Inferred NPP profiles derived by informing the CbPM with float-based, depth-resolved, bio-optical data are able to qualitatively reproduce the vertical structure in NPP inferred from in situ 14C incubations at various ocean regions. At station ALOHA, float-based estimates agree within uncertainty with productivity observations at depth, but underestimate surface NPP. We test the ability of the CbPM to infer the depth-resolved structure in NPP from bio-optical properties in the mixed layer, in similar fashion as how remote sensing algorithms of ocean productivity operate. In Southern Ocean waters, the depth-reconstructing implementation of the CbPM overestimates phytoplankton division rates and Chl:C below the mixed layer, resulting in artificially high subsurface NPP when compared with the fully float-informed implementation of the model. The CbPM subsurface extrapolation of phytoplankton Chl, Chl:C division rates, and NPP improves by accounting for deep nutrient (iron) stress impacts on photoacclimation in the Southern Ocean. This improvement is also observed in vertically- integrated NPP, where the mean bias between model implementations in depth-integrated productivity south of 30◦S is reduced by 62 % when account for deep iron limitation. Our results demonstrate that profiling data from biogeochemical Argo floats can serve to inform regional adjustments that lead to the improvement of marine productivity algorithms.
Interactions between ocean circulation and biogeochemistry (BGC) affect a wide range of important Earth System processes.
Methane production and emission from riverbed sediments constitute a significant yet underexplored component of the river methane budget. Despite their importance, a mechanistic and quantitative understanding of these processes under dynamic flow conditions remains elusive, particularly at the basin scale. In this study, we investigate the multiscale (site-to-basin) mechanisms governing methane emissions from riverbeds, focusing on the interplay between biogeochemical processes and hydrological dynamics. We developed a reactive transport model that integrates methane production with hydrodynamic and ebullitive pathways and coupled it with a basin-scale groundwater flow model to simulate methane emissions across scales. By incorporating key factors such as vertical hydrological exchange flow (VHEF), particulate organic carbon availability, sediment properties, and temperature sensitivity, our model captures the spatiotemporal variability of methane ebullition at both the site and basin scales. Our results reveal that methane ebullition is strongly modulated by VHEF, with lower flow rates promoting methane accumulation and subsequent ebullition. Sensitivity analyses underscore nonlinear responses of methane fluxes to environmental drivers. Furthermore, these findings emphasize the critical roles of sediment composition, hydrological exchange, and environmental conditions in shaping methane dynamics. Overall, this work advances our understanding of riverine methane emissions and offers insights for guiding climate models and mitigation strategies.
We used a numerical model to investigate the effects of sea-level rise (SLR) on the biogeochemical processes in the York River Estuary with extensive tidal marshes. The fully-coupled hydrodynamic-water quality-marsh model accounts for the spatial and temporal variations of physical-biogeochemical interactions between the tidal marshes and surrounding waters. This study focuses on an SLR scenario where the vertical accretion of tidal marshes keeps pace with the rising sea levels. Results show that SLR amplifies the tidal range and prolongs flooding duration, which results in enhanced porewater exchanges of materials between the tidal marshes and the surrounding waters. The increased availability of shallow-water habitats and enhanced light utilization in the shallow areas under SLR promote phytoplankton production (PP) in the shallow-water regions of the York River. Consequently, the organic carbon in the open water is fueled by the contributions from shallow waters and the enhanced export of organic carbon from the marshes. The change in the dissolved oxygen (DO) budget in the York River Estuary is attributed to changes in water column respiration, net metabolism of the benthic layer, reaeration, PP, and increased stratification under SLR. The net DO flux out of the York River increases at the York River mouth. Diel DO variation, especially in the marshes in the upper estuary, promotes phosphorus release from the sediment. The changes in dissolved nitrogen under SLR are relatively minimal.
The main objective of our research was to characterize biogeochemical cycles at continental and global scales in both terrestrial and aquatic ecosystems. This characterization applied to both natural ecosystems and those disturbed by human activity. The primary elements of interest were carbon and nitrogen and the analysis sought to quantify standing stocks and dynamic cycling processes. The translocation of major nutrients from the terrestrial landscape to the atmosphere (via trace gases) and to fluvial systems (via leaching, erosional losses, and point source pollution) were of particular importance to this study. Our aim was to develop the first generation of Earth System Models. Our research was organized around the construction and testing of component biogeochemical models which treated terrestrial ecosystem processes, aquatic nutrient transport through drainage basins, and trace gas exchanges at the continental and global scale. A suite of three complementary models were defined within this construct. The models were organized to operate at a 1/2 degree latitude by longitude level of spatial resolution and to execute at a monthly time step. This discretization afforded us the opportunity to understand the dynamics of the biosphere down to subregional scales, while simultaneously placing these dynamics into a global context.
Developing and validating data records from operational ocean color satellite instruments requires substantial volumes of high quality in situ data. In the absence of broad, institutionally supported field programs, organizations such as the NASA Ocean Biology Processing Group seek opportunistic datasets for use in their operational satellite calibration and validation activities. The publicly available, global biogeochemical dataset collected as part of the two and a half year Tara Oceans expedition provides one such opportunity. We showed how the inline measurements of hyperspectral absorption and attenuation coefficients collected onboard the R/V Tara can be used to evaluate near-surface estimates of chlorophyll-a, spectral particulate backscattering coefficients, particulate organic carbon, and particle size classes derived from the NASA Moderate Resolution Imaging Spectroradiometer onboard Aqua (MODISA). The predominant strength of such flow-through measurements is their sampling rate-the 375 days of measurements resulted in 165 viable MODISA-to-in situ match-ups, compared to 13 from discrete water sampling. While the need to apply bio-optical models to estimate biogeochemical quantities of interest from spectroscopy remains a weakness, we demonstrated how discrete samples can be used in combination with flow-through measurements to create data records of sufficient quality to conduct first order evaluations of satellite-derived data products. Given an emerging agency desire to rapidly evaluate new satellite missions, our results have significant implications on how calibration and validation teams for these missions will be constructed.
This data package supports the publication “Fire-altered Carbon Pools Create Disturbance Memory in Stream Dissolved Organic Carbon” by Li et al. (2026). The package contains processed model inputs, configuration files, restart files, simulation outputs, scripts, and visualization products used to evaluate post-fire dissolved organic carbon (DOC) dynamics in the Naches River Watershed, Washington, USA, following the 2021 Schneider Springs Fire. The modeling workflow couples ELM-BGC, the biogeochemistry-enabled Energy Exascale Earth System Model Land Model; ATS, the Advanced Terrestrial Simulator for integrated surface-subsurface hydrology; and PFLOTRAN, a reactive transport model for multicomponent aqueous geochemistry. Together, these models simulate how wildfire-induced changes in vegetation, litter, coarse woody debris, and soil organic matter influence DOC production, transport, and reaction from burned hillslopes to stream networks. The archive includes preprocessed meteorological, geospatial, hydrologic, and biogeochemical forcing data; ELM-BGC-derived DOC source terms; ATS mesh files; PFLOTRAN reactive-transport inputs; model configuration files; spin-up and transient restart files; watershed-scale diagnostic outputs; stream concentration time series; and figures or visualization files used to inspect and reproduce key results. File types include Hierarchical Data Format 5 (HDF5) files for gridded forcing and model-coupling data, model input and configuration files for ELM-BGC, ATS, and PFLOTRAN, restart and simulation-output files generated by the modeling workflow, tabular or time-series diagnostic outputs, scripts for post-processing and figure generation, and image or visualization products associated with the manuscript. Use of the package depends on the intended task. Re-running the simulations requires the relevant modeling software, including ELM-BGC, ATS, and PFLOTRAN as ATS's geochemical engine. Inspecting outputs and reproducing figures requires Python with scientific plotting libraries such as Matplotlib, and three-dimensional model outputs may be viewed with ParaView. Geographic information system files or maps may be inspected with ArcGIS Pro or comparable GIS software. The data package is intended to enable traceability, reuse, and partial reproduction of the coupled land-to-watershed hydro-biogeochemical modeling workflow used to test how wildfire disturbance affects terrestrial carbon pools and downstream DOC dynamics.
Abstract Dissolved oxygen (DO) is a key biogeochemical control in coastal systems, and its concentration and drivers vary markedly through time and space. This makes it difficult to accurately represent coastal DO and associated biogeochemical processes in models, limiting our ability to predict how these systems will respond to global change. We obtained high‐frequency (5‐min) in situ measurements of DO collected at three locations across the interface of a tidal creek and coastal marsh in the Pacific Northwest, USA. Random Forest machine learning models quantified the importance of three categories of environmental drivers (Aquatic, Climatic, and Terrestrial) of DO variability across the creek–marsh interface. We selected two 4‐month datasets representing Summer and Winter seasonal periods to test two hypotheses on the dominant drivers of DO at the coastal interface. We found that the Terrestrial driver—characterized by long periods of anaerobic conditions and episodic pulses in DO after floods—was most important during the Winter, whereas the Aquatic driver—characterized by variability over tidal, diel, and lunar cycles—was most important during the Summer. We explored how future climate change scenarios could alter the drivers of DO variability using a cumulative sums driver–response framework. Our results suggest that under climate change, Aquatic and Climatic drivers may increase in importance during the Summer, potentially linked to changing metabolic regimes and sea level, with Terrestrial driver importance potentially increasing during the Winter. Our approach highlights useful methods for understanding the spatiotemporal complexity of oxygen across coastal interfaces and quantifying the relative importance of distinct environmental drivers.
Not provided.