SEARCH · Engineering Papers

Results for “ecosystem model”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 937 records · Page 52

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Development of Explainable, Knowledge-Guided AI Models to Enhance the E3SM Land Model Development and Uncertainty Quantification

Focal Area(s): (2)Predictive modeling using AI techniques and AI-derived model components; use of AI and other tools to design a prediction system comprising of a hierarchy of models. (3) Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge- guided AI. Science Challenge: The Energy Exascale Earth System Model (E3SM) is a fully coupled, state-of-the-science Earth system model that uses code optimized for DOE's advanced computers to address the most critical scientific questions facing our nation and society (Golaz et al., 2019). The E3SM Land model (ELM) is designed to understand how the changes in terrestrial land surfaces will interact with other Earth system components and has been used to understand hydrologic cycles, biogeophysics, and ecosystem dynamics. In spite of great successes, the ELM has several known issues that restrain rapid improvements. For example, the ELM uses equilibrium models to simulate dynamic land-climate interactions and it requires long model spin-up time to identify suitable initial conditions for transient simulations. The ELM lacks built-in uncertainty mechanisms that can improve the robustness of model predictions. The ELM is a holistic, deterministic model system with a rigid design, and in many situations, it is hard to modify the ELM system to incorporate new theory/hypothesis and new data across scales to address emerging science problems (such as predicting the impacts of water cycle extremes). In addition, The ELM is technically optimized for traditional CPU-centric computers and it cannot fully utilize the current and incoming leadership computers for model simulations and uncertainty quantification (UQ). The success of artificial intelligence (AI) has inspired scientists to use AI models to discover intrinsic features from simulation data (Chattopadhyay et al., 2020) and observational data (Reichstein et al., 2019) to gain further process understanding of Earth science problems. However, autonomous AI model training through deep learning usually requires a huge amount of annotated data. To overcome the limitations from the data and computing resources, knowledge-guided AI models are necessary where human-knowledge is ingested in model construction (Banino et al., 2018) and training process (Silver et al., 2016) for efficient learning. Herein, we present a new way that leverages the process understanding from the ELM to guide AI model development for the ELM enhancement and UQ. We hope this study can inspire further Earth and environmental system model developments and transformations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Virus diversity and activity is driven by snowmelt and host dynamics in a high-altitude watershed soil ecosystem

Background: Viruses impact nearly all organisms on Earth, including microbial communities and their associated biogeochemical processes. In soils, highly diverse viral communities have been identified, with a global distribution seemingly driven by multiple biotic and abiotic factors, especially soil temperature and moisture. However, our current understanding of the stability of soil viral communities across time and their response to strong seasonal changes in environmental parameters remains limited. Here, we investigated the diversity and activity of environmental soil DNA and RNA viruses, focusing especially on bacteriophages, across dynamics’ seasonal changes in a snow-dominated mountainous watershed by examining paired metagenomes and metatranscriptomes. Results: We identified a large number of DNA and RNA viruses taxonomically divergent from existing environmental viruses, including a significant proportion of fungal RNA viruses, and a large and unsuspected diversity of positive single-stranded RNA phages ( Leviviricetes ), highlighting the under-characterization of the global soil virosphere. Among these, we were able to distinguish subsets of active DNA and RNA phages that changed across seasons, consistent with a “seed-bank” viral community structure in which new phage activity, for example, replication and host lysis, is sequentially triggered by changes in environmental conditions. At the population level, we further identified virus-host dynamics matching two existing ecological models: “Kill-The-Winner” which proposes that lytic phages are actively infecting abundant bacteria, and “Piggyback-The-Persistent” which argues that when the host is growing slowly, it is more beneficial to remain in a dormant state. The former was associated with summer months of high and rapid microbial activity, and the latter with winter months of limited and slow host growth. Conclusion: Taken together, these results suggest that the high diversity of viruses in soils is likely associated with a broad range of host interaction types each adapted to specific host ecological strategies and environmental conditions. As our understanding of how environmental and host factors drive viral activity in soil ecosystems progresses, integrating these viral impacts in complex natural microbiome models will be key to accurately predict ecosystem biogeochemistry.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Spatiotemporal Metabolic Network Models Reveal Complex Autotroph-Heterotroph Biofilm Interactions Governed by Photon Incidences

Autotroph-heterotroph interactions are ubiquitous in natural environment and play a key role in controlling various essential ecosystem functions, such as production and utilization of organic matter, cycling of nitrogen, sulfur, and other chemical elements. Understanding how these biofilm metabolic interactions are constrained in space and time remains challenging because fully predictive models designed for this purpose are currently limited. Toward filling this gap, here we developed community metabolic network models for two autotroph-heterotroph biofilm consortia (termed UCC-A and UCC-O), which share a suite of common heterotrophic members but have a single distinct photoautotrophic cyanobacterium (Phormidesmis priestleyi str. ANA and Phormidium sp. OSCR) that provides organic carbon and nitrogen sources to support the growth of heterotrophic partners. After determining model parameters by data fitting using the spatiotemporal distributions of microbial abundances, we comparatively analyzed the resulting biofilm models to examine any fundamental differences in microbial interactions between the two consortia under the variation of key environmental variables: CO2 and photon levels. The UCC-A model predicted generally expected responses, i.e., the autotroph population increased in response to elevated levels of CO2 and photon, followed by increase in the heterotroph population. In contrast, the UCC-O model showed somewhat complicated dynamics, e.g., higher photon incidence rates resulted in the increase in autotroph population but decrease in heterotroph population due to the lowered provision of glucose from the autotroph. A further analysis showed that species coexistence was governed by the photon incidences rather than the carbon availability for UCC-O, which was the opposite for UCC-A.

Phalak, Poonam↗

DOE OSTI2022

Simulation of high-frequency dissolved oxygen dynamics in a shallow estuary, the Corsica River, Chesapeake Bay

Understanding shallow water biogeochemical dynamics is a challenge in coastal regions, due to the presence of highly variable land-water interface fluxes, tight coupling with sediment processes, tidal dynamics, and diurnal variability in biogeochemical processes. While the deployment of continuous monitoring devices has improved our understanding of high-frequency (12 - 24 hours) variability and spatial heterogeneity in shallow regions, mechanistic modeling of these dynamics has lagged behind conceptual and empirical models. The inherent complexity of shallow water systems is represented in the Corsica River estuary, a small basin within the Chesapeake Bay ecosystem, where abundant monitoring data have been collected from long-term monitoring stations, continuous monitoring sensors, synoptic sensor surveys, and measurements of sediment-water fluxes. A state-of-the-art modeling system, the Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM), was applied to the Corsica domain with a high-resolution grid and nutrient loads from the most recent version of the Chesapeake Bay watershed model. The Corsica SCHISM model reproduced observed high-frequency variability in dissolved oxygen, as well as seasonal variability in chlorophyll-a and sediment-water fluxes. Time-series signal analyses using Empirical Model Decomposition and spectral analysis revealed that the diurnal and M2 tide frequencies are the dominant high-frequency modes and physical transport contributes a larger share to dissolved oxygen budgets than biogeochemical processes on an hourly time scale. Heterogeneity and patchiness in dissolved oxygen resulting from phytoplankton distributions and geometry-driven eddies amplify the physical transport effect, and on longer time scales oxygen is controlled more by photosynthesis and respiration. Our simulation demonstrates that interactions among physical and biological dynamics generate complex high-frequency variability in water quality and non-linear reposes to nutrient loading and environmental forcing in shallow water systems.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

UAS remote sensing (3DR SOLO platform): multispectral reflectance and normalized difference vegetation index, Seward Peninsula, Alaska, 2022

Airborne remote sensing data collected using a Parrot Sequoia+ multispectral sensor installed on a 3DR SOLO unoccupied aerial system (UAS) – operated by the Terrestrial Ecosystem Science & Technology group https://www.bnl.gov/envsci/testgroup/ at Brookhaven National Laboratory. This package includes data from 19 flights flown over the NGEE-Arctic, Kougarok Mile Marker (MM) 80, Kougarok Fire Complex (KFC) and Teller MM 27 sites in July 2022. Derived image products include point cloud, ortho-mosaiced multispectral image, a digital surface model (DSM) using the structure from motion (SfM) technique, and a normalized difference vegetation index (NDVI) map. Unprocessed and processed data products are included in this package (processing levels 0-2). Data and metadata are provided as text (*.txt, *.json, *hdr,), tabular (*.dat, *.csv), point cloud (*.laz), Cloud Optimized GeoTIFF (COG, *.tif), and image (*.jpg, *.tif, *png) formats.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 OSTI2021

BAWLD-CH 4 : a comprehensive dataset of methane fluxes from boreal and arctic ecosystems

Methane (CH 4 ) emissions from the boreal and arctic region are globally significant and highly sensitive to climate change. There is currently a wide range in estimates of high-latitude annual CH 4 fluxes, where estimates based on land cover inventories and empirical CH 4 flux data or process models (bottom-up approaches) generally are greater than atmospheric inversions (top-down approaches). A limitation of bottom-up approaches has been the lack of harmonization between inventories of site-level CH 4 flux data and the land cover classes present in high-latitude spatial datasets. Here we present a comprehensive dataset of small-scale, surface CH 4 flux data from 540 terrestrial sites (wetland and non-wetland) and 1247 aquatic sites (lakes and ponds), compiled from 189 studies. The Boreal–Arctic Wetland and Lake Methane Dataset (BAWLD-CH 4 ) was constructed in parallel with a compatible land cover dataset, sharing the same land cover classes to enable refined bottom-up assessments. BAWLD-CH 4 includes information on site-level CH 4 fluxes but also on study design (measurement method, timing, and frequency) and site characteristics (vegetation, climate, hydrology, soil, and sediment types, permafrost conditions, lake size and depth, and our determination of land cover class). The different land cover classes had distinct CH 4 fluxes, resulting from definitions that were either based on or co-varied with key environmental controls. Fluxes of CH 4 from terrestrial ecosystems were primarily influenced by water table position, soil temperature, and vegetation composition, while CH 4 fluxes from aquatic ecosystems were primarily influenced by water temperature, lake size, and lake genesis. Models could explain more of the between-site variability in CH 4 fluxes for terrestrial than aquatic ecosystems, likely due to both less precise assessments of lake CH 4 fluxes and fewer consistently reported lake site characteristics. Analysis of BAWLD-CH 4 identified both land cover classes and regions within the boreal and arctic domain, where future studies should be focused, alongside methodological approaches. Overall, BAWLD-CH 4 provides a comprehensive dataset of CH 4 emissions from high-latitude ecosystems that are useful for identifying research opportunities, for comparison against new field data, and model parameterization or validation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

A Unified Framework to Reconcile Different Approaches of Modeling Transpiration Response to Water Stress: Plant Hydraulics, Supply Demand Balance, and Empirical Soil Water Stress Function

Plant responses to water stress is a major uncertainty to predicting terrestrial ecosystem sensitivity to drought. Different approaches have been developed to represent plant water stress. Empirical approaches (the empirical soil water stress (or Beta) function and the supply-demand balance scheme) have been widely used for many decades; more mechanistic based approaches, that is, plant hydraulic models (PHMs), were increasingly adopted in the past decade. However, the relationships between them—and their underlying connections to physical processes—are not sufficiently understood. This limited understanding hinders informed decisions on the necessary complexities needed for different applications, with empirical approaches being mechanistically insufficient, and PHMs often being too complex to constrain. Here we introduce a unified framework for modeling transpiration responses to water stress, within which we demonstrate that empirical approaches are special cases of the full PHM, when the plant hydraulic parameters satisfy certain conditions. We further evaluate their response differences and identify the associated physical processes. Finally, we propose a methodology for assessing the necessity of added complexities of the PHM under various climatic conditions and ecosystem types, with case studies in three typical ecosystems: a humid Midwestern cropland, a semi-arid evergreen needleleaf forest, and an arid grassland. Notably, Beta function overestimates transpiration when VPD is high due to its lack of constraints from hydraulic transport and is therefore insufficient in high VPD environments. With the unified framework, we envision researchers can better understand the mechanistic bases of and the relationships between different approaches and make more informed choices.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Ecosystem Science with NISAR: Final Preparations in The Pre-Launch Period

The NISAR mission which in its most recent round of launch preparations was set to launch in the spring of 2024, and now delayed until later in the fall or early spring of 2025, will serve as an unprecedented resource for the Remote Sensing of Ecosystems Science community. The two frequency, L- and S-band will full-polarimetric capability over a 250 km wide swath using the SweepSAR technique [1] will collect reliable set of observations (60 per year; 30 each for ascending and descending passes) on a continuing basis that will allow for the modeling and observation of time-varying processes that are prevalent in the living environment broadly described as Ecosystems. Among the prime science goals of the NISAR Ecosystems disciplines are in the characterization of agriculture, disturbance, biomass, forest structure and water dynamics seen in the world’s rivers, coasts, and permafrost regions. In this paper we provide an overview of the Ecosystem science that will be enabled by the NISAR mission and give a status of the basic algorithms that are being used to provide a basic set of tools to the community to make use of the data that NISAR will provide.

Siqueira, Paul↗

DOE OSTI2025

Multiscale maps of Active Layer Depth for Teller site Mile Marker 27 and Kougarok Mile Marker 80, Seward Peninsula, AK

Remote sensing maps of active layer depth derived from Unmanned Areal System (UAS) data. The UAS datasets were stepwise scaled until matching the AVIRIS-NG (Airborne Visible / Infrared Imaging Spectrometer - Next Generation) and Sentinel-2 spatial resolutions. Using the field observed Active Layer Depth (ALD) measurement in combination with spectral and topographic predictors derivatives from DJI UAS imagery, we used a spatially explicit RF regression model to predict and map ALD across our study landscapes. This package includes maps for Next-Generation Ecosystem Experiment Arctic (NGEE Arctic)’s Teller Mile Marker (MM) 27, and Kougarok MM80 (aka Mile 80) watersheds. The field, map data, and metadata are provided as geoTIF and text (*.csv) formats. These datasets are provided in support of Hantson et al., 2024 (accepted) “Scaling Arctic landscape and permafrost features improves active layer depth modeling”

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Development of an Energy Services Interface for the EGoT

The Energy Services Interface (ESI) is a set of rules that ensure private, secure, and trustworthy information exchange between Grid Service Providers (GSPs) and utility customers. Large-scale adoption of Distributed Energy Resources (DERs) will be necessary for GSPs to dispatch effective grid services, and to stimulate technological innovations in DER and DERMS (DER Management Systems) technologies, as well as novel grid service programs. The ESI promotes these objectives by advancing a set of rules and interoperability requirements that define bi-directional, service-oriented, logical interfaces between GSPs and customers’ DERs, with expectations for privacy, security, and trust. The ESI rules and interoperability requirements establish boundaries between customers and GSPs that delineate the functions and responsibilities that must be implemented by the developers of Energy Grid of Things (EGoT) ecosystem products. These rules impose constraints on the implementation of a DERMS. The ESI interoperability requirements are based on the Interoperability Maturity Model (IMM), developed by the Grid Modernization Laboratory Consortium. By emphasizing private, secure, and trustworthy information exchange, and by mandating a service-oriented and interoperable architecture, the ESI promotes the development of an EGoT ecosystem that motivates customer participation and technological innovation. Interoperability will encourage innovation by reducing barriers to entry and increasing confidence of stakeholders. Customers will be willing to participate in DER service programs that establish trust and emphasize customer choice. Large-scale customer participation ensures GSPs have ample DER resources to provide grid services that have significant impact on grid reliability and reduce electricity cost for consumers. This in turn signals economic opportunities that encourage innovation, resulting in the development of a robust EGoT ecosystem.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Multi-resolution Arctic Shrub Cover Dataset Derived from UAS and Airborne SfM and LiDAR (2013-2025)

We synthesized 177 unoccupied aerial system flights and 77 airborne flights across the Arctic and created a multi-resolution benchmark data of low-to-tall shrub fractional cover leveraging Structure-from-Motion and Light Detection and Ranging. The resulting dataset covered a total of 1899 km2 across Alaska, Western Canada, Sweden, and Siberian Arctic, including key sites from the Oro Arctic to the High Arctic. The dataset is organized into 6 primary data collection directories (“Abisko,” “AWI,” “ERE,” “Fairbanks,” “NGEE,” “Toolik”), each containing site and flight subdirectories. Flight directories include shrub cover rasters (*.tifs) at 1 m, 5 m, and 30 m resolution, the canopy height model at 1 m resolution (*.tifs), and a bounding box *.kml file. For the AWI, Abisko, NGEE, and Fairbanks collections, we also include the GCC raster at 1 m resolution (*.tif). Files are organized by Collection > Site > Flight Name > Data Files. Flight rasters are in the local UTM zone and the .kml files are in the geographic coordinate system EPSG 4326. We also include a .csv file that details the source datasets for every flight. The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research. Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.

canopy height model↗

DOE OSTI2026

Vegetation responses to air dryness amplify future land surface warming

Temperature exerts a first-order control on vegetation photosynthesis and transpiration. Yet most studies investigating temperature impacts on plants rely on near-surface air temperature, rather than canopy temperature—the temperature plants actually experience. Because canopy temperature directly regulates ecosystem function, it provides a more accurate measure of vegetation–climate interactions. Combining Earth System Model (ESM) simulations and satellite observations in a dual emergent constraint, here we show that canopy temperature is projected to increase substantially more than air temperature (~0.11-degrees more or a 16% increase in their difference) over the 21st century. The ESM ensemble median fails to capture these stronger increases in the majority of vegetated regions. We find that the largest projected increases in the difference between canopy and air temperature are predicted to occur in regions where elevating moisture stress—particularly rising vapor pressure deficit—increasingly constrains vegetation growth and transpiration. This implies that future warming will impose stronger constraints on plant function than currently estimated. Relying on air temperature alone will therefore lead to systematic underestimation of temperature effects on photosynthesis, vegetation growth, and the land carbon sink. Accurate representation of canopy temperature in ESMs is thus essential to improve projections of ecosystem responses and feedbacks to climate change.

Green, Julia K↗

DOE OSTI2021

Intensified Soil Moisture Extremes Decrease Soil Organic Carbon Decomposition: A Mechanistic Modeling Analysis

Earth system models have predicted that there will be more frequent and severe precipitation and drought events in terrestrial ecosystems. Microbially mediated decomposition of soil organic carbon (SOC) tends to increase as soils wet and decrease as soils dry. However, the long-term SOC change under intensified moisture extremes remains poorly known as it depends on the frequency and intensity of soil drying and wetting. In this study, we explored long-term SOC dynamics under scenarios of alternating drying-wetting cycles using the Microbial-ENzyme Decomposition model, a mechanistic microbial model. The model was parameterized with 11 years of observations from a temperate deciduous broadleaf forest site, showing satisfactory model performance in both model calibration (R 2 = 0.67) and validation (R 2 = 0.69) against heterotrophic respiration. We then used the model to simulate the long-term SOC dynamics under five scenarios of alternating drying-wetting cycles with different frequencies and severities over a period of 100 years. Results showed that the changes in active microbial biomass C and the corresponding turnover rates of SOC pools were more sensitive to soil drying than soil wetting. As a result, the cumulative soil carbon emission from microbial respiration decreased by 433.7 g C m -2 after the 100-year simulation in the highest frequency and intensity moisture scenario, but was not significantly affected by the lowest frequency and intensity scenario. This study emphasizes the nonlinear response of SOC decomposition to soil moisture changes, which causes decreased decomposition by microbes under drying that is, not compensated by increased decomposition under wetting conditions.

58 GEOSCIENCES↗

DOE OSTI2026

Flash flourishing of Northern Hemisphere vegetation and its drivers

Rapid surges in vegetation growth—defined by thresholds in growth rate and duration—are critical yet understudied indicators of ecosystem responses to environmental change. Here, we investigate spatiotemporal patterns of such abrupt, short-lived flash flourishing events across the northern extratropical latitudes (NEL) from 2003 to 2022. We find more frequent occurrence of flash flourishing events at high latitudes (≥45° N), where their incidence is 1.6 times higher than at mid-latitudes. Moreover, there is an increasing tendency in frequency, duration, and intensity of flash flourishing events over the past two decades, alongside consistent rises in vegetation indices across onset, post-onset, and entire phases. Model simulations attribute these multiyear increases primarily to elevated atmospheric CO 2 , while temperature and radiation predominantly control phase-specific variability, with onset traits strongly predicting subsequent phenological responses. Together, these findings identify the patterns and drivers of NEL flash flourishing and highlight their large-scale impacts on ecosystem dynamics, offering critical insights for model improvement and the assessment of ecological shifts.

Kong, Xiangxu [Nanjing University of Information S↗

DOE OSTI2022

A coupled ground heat flux–surface energy balance model of evaporation using thermal remote sensing observations

Abstract. One of the major undetermined problems in evaporation (ET) retrieval using thermal infrared remote sensing is the lack of a physically based ground heat flux (G) model and its integration within the surface energy balance (SEB) equation. Here, we present a novel approach based on coupling a thermal inertia (TI)-based mechanistic G model with an analytical surface energy balance model, Surface Temperature Initiated Closure (STIC, version STIC1.2). The coupled model is named STIC-TI. The model is driven by noon–night (13:30 and 01:30 local time) land surface temperature, surface albedo, and a vegetation index from MODIS Aqua in conjunction with a clear-sky net radiation sub-model and ancillary meteorological information. SEB flux estimates from STIC-TI were evaluated with respect to the in situ fluxes from eddy covariance measurements in diverse ecosystems of contrasting aridity in both the Northern Hemisphere and Southern Hemisphere. Sensitivity analysis revealed substantial sensitivity of STIC-TI-derived fluxes due to the land surface temperature uncertainty. An evaluation of noontime G (Gi) estimates showed 12 %–21 % error across six flux tower sites, and a comparison between STIC-TI versus empirical G models also revealed the substantially better performance of the former. While the instantaneous noontime net radiation (RNi) and latent heat flux (LEi) were overestimated (15 % and 25 %), sensible heat flux (Hi) was underestimated (22 %). Overestimation (underestimation) of LEi (Hi) was associated with the overestimation of net available energy (RNi−Gi) and use of unclosed surface energy balance flux measurements in LEi (Hi) validation. The mean percent deviations in Gi and Hi estimates were found to be strongly correlated with satellite day–night view angle difference in parabolic and linear pattern, and a relatively weak correlation was found between day–night view angle difference versus LEi deviation. Findings from this parameter-sparse coupled G–ET model can make a valuable contribution to mapping and monitoring the spatiotemporal variability of ecosystem water stress and evaporation using noon–night thermal infrared observations from future Earth observation satellite missions such as TRISHNA, LSTM, and SBG.

Bhattacharya, Bimal K.↗

DOE OSTI2025

Validating Greater Sage-Grouse Individual-based Model (IBM) Tool (Final Report)

The project focused on validating the previously developed Greater Sage-Grouse Individual-based Model (GrSG IBM; LaGory et al. 2012, 2021). The objective was to transform this predictive, spatially and temporally explicit model into a portable resource to assist siting/resource managers in proactively assessing the cumulative impacts of wind energy development on the greater sage-grouse. Utilizing a bottom-up, individual-based approach, the GrSG IBM accounts for landscape context and species behavior, aiming to reduce uncertainty in estimating development impacts and support ecologically mindful land-based wind energy development. The validation effort covered approximately 6,540 km 2 near the Seven Mile Hill Wind Project in Wyoming. The GrSG IBM tool, built on the NetLogo platform (Tisue and Wilensky 2004), was executed over a 50-year period, with the analysis focusing on years following a 10-year initialization phase. Key results demonstrated the tool’s biological soundness across five key biological metrics: non-chick age class distribution (older than 10 weeks), adult sex ratio, life expectancy, population size, and overall population growth. For instance, the tool estimated that 58.6% of the non-chick population was reproductively immature, while the reference ranges from 51.4% to 57.8% (Patterson 1952, Rogers 1964). Experts confirmed the tool’s estimate was within a reasonable range for the species. The tool estimated average life expectancy of 1.43 years, while the reference ranges from 0.9 years to 1.1 years (Ammann 1957, Hamerstrom 1949). Experts also supported the model’s life-expectancy estimate as ecologically sound for the species in the study area. In terms of population change, the model estimated an annual shift between a 0.6% decline and a 1.0% increase over 50 years. While the reference suggests 2.9% annual decline in range-wide populations (Cortes et al. 2023), that includes many at-risk populations in South Dakota and Washington, for example. Our study area—in the northeastern part of Carbon County and western-edge of Albany County, Wyoming—is one of the remaining greater sage-grouse habitats supporting some of the most stable populations. Experts confirmed that the range of the annual population change spanning from a 0.6% decline to a 1.0% increase estimated by the tool was reasonable for our study area for this reason and confirmed that aligned with population estimates from existing studies on the greater sage-grouse and wind energy development in the study area (LeBeau et al. 2017a, Smith et al. 2024). Furthermore, the project showed that temporally explicit biological metrics generated by the GrSG IBM tool can complement the USGS’ Prioritizing Restoration of Sagebrush Ecosystems Tool (PReSET; Duchardt et al. 2021) by incorporating habitat restoration strategies into seasonal habitat suitability models to visualize population responses over time.

17 WIND ENERGY↗

DOE OSTI2026

Ecosystems for Scientific Computing in the Age of AI

Scientific computing is at an inflection point. Artificial intelligence (AI) is reshaping how scientific software is developed, how teams collaborate, how projects are governed, and how the next generation is trained. Drawing on insights from a 2025 workshop report, this article argues that the future of discovery will depend on agile, robust ecosystems built through socio-technical co-design—the intentional integration of technical and human systems. This perspective is essential for ensuring that future scientific computing remains trustworthy, sustainable, and scalable. It combines advances in AI, high-performance computing, and software with new models for cross-disciplinary collaboration, education, and workforce development. Key recommendations include building modular, trustworthy AI-enabled software ecosystems; enabling teams to integrate AI into scientific workflows while preserving human creativity, integrity, and rigor; and developing adaptive training pathways that keep pace with rapid technological change. By sharing these perspectives, we hope to stimulate broader community dialogue and encourage coordinated action.

AI↗

DOE OSTI2021

Agricultural Impacts on Nitrogen Cycling: Climate and Air Pollution (Final Technical Report)

We have successfully added a parameterization of ammonia emissions from synthetic fertilizer and manure from agriculture to Earth System Models. We have evaluated how emissions change in the future and the processes driving these changes. We have examined the impact of agricultural ammonia emissions on present-day air quality and have evaluated the impact of resolving the emissions with high temporal resolution. We have also examined the impact of how Earth System Models treat the cycling of emissions in soils. Our code, the Flows of Agricultural Nitrogen – version 2 – model (FANv2) has been run in both the CLM framework and the ELM framework. To my knowledge the code has not yet been fully implemented into ELM. Volatilization of ammonia (NH3) from fertilizers and livestock wastes forms a significant pathway of nitrogen losses in agricultural ecosystems and constitutes the largest source of atmospheric emissions of NH3. We have provided a major update to the process model FAN (Flow of Agricultural Nitrogen), which evaluates NH3 emissions interactively within an Earth system model; in this work, the Community Earth System Model (CESM) is used. The updated version (FANv2) includes a more detailed treatment of both physical and agricultural processes, which allows the model to differentiate between the volatilization losses from animal housings, manure storage, grazed pastures, and the application of manure and different types of mineral fertilizers. The modeled ammonia emissions are first evaluated at a local scale against experimental data for various types of fertilizers and manure, and they are subsequently run globally to evaluate NH3 emissions for 2010–2015 based on gridded datasets of fertilizer use and livestock populations. Comparison of regional emissions shows that FANv2 agrees with previous inventories for North America and Europe and is within the range of previous inventories for China. However, due to higher NH3 emissions in Africa, India, and Latin America, the global emissions simulated by FANv2(48 Tg N) are 30 %–40 % higher than in the existing inventories.

54 ENVIRONMENTAL SCIENCES↗