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At least 739 records · Page 41

AmeriFlux FLUXNET-1F US-PFb NW1 Pine-1 CHEESEHEAD 2019

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-PFb NW1 Pine-1 CHEESEHEAD 2019. This is the FLUXNET version of the carbon flux data for the site US-PFb NW1 Pine-1 CHEESEHEAD 2019 produced by applying the standard ONEFlux (1F) software. Site Description - This tower (32m, trailer-based, telescoping) is the northwestern most tower in the 10 x 10km study domain. It is located in a red pine forest (canopy height ~25m). There is a regrowing/trap layer consisting of broadleaf aspen and other deciduous vegetation.

Desai, Ankur↗

AmeriFlux FLUXNET-1F US-PFk SW1 Aspen-2 CHEESEHEAD 2019

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-PFk SW1 Aspen-2 CHEESEHEAD 2019. This is the FLUXNET version of the carbon flux data for the site US-PFk SW1 Aspen-2 CHEESEHEAD 2019 produced by applying the standard ONEFlux (1F) software. Site Description - This tower (32m, trailer-based, telescoping) is located in the southwestern quadrant of the 10 x 10km study domain. It is located in an aspen forest (canopy height: immediate vicinity - 10m; greater area - 24.4 m).

Desai, Ankur↗

AmeriFlux FLUXNET-1F US-PFm SW3 Hardwood-2 CHEESEHEAD 2019

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-PFm SW3 Hardwood-2 CHEESEHEAD 2019. This is the FLUXNET version of the carbon flux data for the site US-PFm SW3 Hardwood-2 CHEESEHEAD 2019 produced by applying the standard ONEFlux (1F) software. Site Description - This tower (32m, trailer-based, telescoping) is located in the southwestern quadrant of the 10 x 10km study domain. It is located in an aspen and maple forest (canopy height: 15 m).

Desai, Ankur↗

AmeriFlux FLUXNET-1F US-PFn SW4 Hardwood-3 CHEESEHEAD 2019

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-PFn SW4 Hardwood-3 CHEESEHEAD 2019. This is the FLUXNET version of the carbon flux data for the site US-PFn SW4 Hardwood-3 CHEESEHEAD 2019 produced by applying the standard ONEFlux (1F) software. Site Description - This tower (32m, trailer-based, telescoping) is located in the southwestern quadrant of the 10 x 10km study domain. It is located in a mixed hardwood forest - aspen, oak, birch, some pines (canopy height: 20 - 26 m).

Desai, Ankur↗

AmeriFlux FLUXNET-1F US-PFp SE2 Hardwood-4 CHEESEHEAD 2019

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-PFp SE2 Hardwood-4 CHEESEHEAD 2019. This is the FLUXNET version of the carbon flux data for the site US-PFp SE2 Hardwood-4 CHEESEHEAD 2019 produced by applying the standard ONEFlux (1F) software. Site Description - This tower (32m, trailer-based, telescoping) is located in the southeastern quadrant of the 10 x 10km study domain. It is located in a mixed hardwood forest - black ash and red maple (canopy height: 20 - 24.4 m).

Desai, Ankur↗

AmeriFlux FLUXNET-1F US-PFq SE3 Aspen-4 CHEESEHEAD 2019

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-PFq SE3 Aspen-4 CHEESEHEAD 2019. This is the FLUXNET version of the carbon flux data for the site US-PFq SE3 Aspen-4 CHEESEHEAD 2019 produced by applying the standard ONEFlux (1F) software. Site Description - This tower (32m, trailer-based, telescoping) is located in the southeastern quadrant of the 10 x 10km study domain. It is located in an aspen forest (canopy height: 10 - 14.3 m).

Desai, Ankur↗

AmeriFlux FLUXNET-1F US-PFt SE6 Pine-4 CHEESEHEAD 2019

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-PFt SE6 Pine-4 CHEESEHEAD 2019. This is the FLUXNET version of the carbon flux data for the site US-PFt SE6 Pine-4 CHEESEHEAD 2019 produced by applying the standard ONEFlux (1F) software. Site Description - This tower (32m, trailer-based, telescoping) is located in the southeastern quadrant of the 10 x 10km study domain. It is located in a red pine forest (canopy height: 15 - 21.6 m).

Desai, Ankur↗

AmeriFlux FLUXNET-1F US-PFe NW4 Lake-1 CHEESEHEAD 2019

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-PFe NW4 Lake-1 CHEESEHEAD 2019. This is the FLUXNET version of the carbon flux data for the site US-PFe NW4 Lake-1 CHEESEHEAD 2019 produced by applying the standard ONEFlux (1F) software. Site Description - This tower (32m, trailer-based, telescoping) is located in the northwestern quadrant of the 10 x 10km study domain. It is located in a red pine and aspen forest (20.1 m height) on the bank of a lake (500m across).

Desai, Ankur [University of Wisconsin-Madison]↗

AmeriFlux FLUXNET-1F US-PFg NE1 Pine-2 CHEESEHEAD 2019

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-PFg NE1 Pine-2 CHEESEHEAD 2019. This is the FLUXNET version of the carbon flux data for the site US-PFg NE1 Pine-2 CHEESEHEAD 2019 produced by applying the standard ONEFlux (1F) software. Site Description - This tower (32m, trailer-based, telescoping) is located in the northeastern quadrant of the 10 x 10km study domain. It is located in a forest with red pine (~32m) overstory and maple (10-15m) understory.

Desai, Ankur [University of Wisconsin-Madison]↗

AmeriFlux FLUXNET-1F US-PFh NE2 Pine-3 CHEESEHEAD 2019

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-PFh NE2 Pine-3 CHEESEHEAD 2019. This is the FLUXNET version of the carbon flux data for the site US-PFh NE2 Pine-3 CHEESEHEAD 2019 produced by applying the standard ONEFlux (1F) software. Site Description - This tower (32m, trailer-based, telescoping) is located in the northeastern quadrant of the 10 x 10km study domain. It is located in a forest jack pine forest (18 - 20m), with some aspens.

Desai, Ankur [University of Wisconsin-Madison]↗

AmeriFlux FLUXNET-1F US-PFi NE3 Hardwood-1 CHEESEHEAD 2019

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-PFi NE3 Hardwood-1 CHEESEHEAD 2019. This is the FLUXNET version of the carbon flux data for the site US-PFi NE3 Hardwood-1 CHEESEHEAD 2019 produced by applying the standard ONEFlux (1F) software. Site Description - This tower (32m, trailer-based, telescoping) is located in the northeastern quadrant of the 10 x 10km study domain. It is located in a mixed hardwood forest (canopy height: 18 - 20m).

Desai, Ankur [University of Wisconsin-Madison]↗

AmeriFlux FLUXNET-1F US-PFj NE4 Maple-1 CHEESEHEAD 2019

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-PFj NE4 Maple-1 CHEESEHEAD 2019. This is the FLUXNET version of the carbon flux data for the site US-PFj NE4 Maple-1 CHEESEHEAD 2019 produced by applying the standard ONEFlux (1F) software. Site Description - This tower (32m, trailer-based, telescoping) is located in the northeastern quadrant of the 10 x 10km study domain. It is located in a maple forest (canopy height: 18 - 20m). Some conifers mixed in, more conifers to the NNW.

Desai, Ankur [University of Wisconsin-Madison]↗

Grid Resiliency with a 100% Renewable Microgrid

San Diego Gas & Electric Company (SDG&E) installed America’s first and largest utility-scale microgrid in Borrego Springs in 2013. The first generation Borrego Springs Microgrid utilized diesel generators to form and stabilize the microgrid island, with support from grid-scale batteries and local solar photovoltaic (PV) generation. In this project, SDG&E in partnership with National Renewable Energy Laboratory (NREL) demonstrated through modeling, simulation and utility field testing that blackstart and islanding of the microgrid can be led with 100% renewable, inverter based resources (IBRs), to help reduce community reliance on conventional generation resources. Through equipment upgrades, grid-forming island leader capability was transitioned to a battery IBR instead of the Borrego Springs Microgrid diesel generators. A new microgrid controller was integrated to the microgrid and programmed to control and manage multiple energy storage systems. Synchrophasor and other power quality data verified autonomous, high-speed response of the IBRs through blackstart, islanding, and load step testing. Results of project field evaluations provide distribution systems operators (DSO) with increased confidence that renewable, IBR can replace traditional generators to blackstart and island microgrids and rapidly establish stable island frequency with rapid changes in peak power demand. Importantly, the project validated the integration feasibility of a distributed energy resource management system (DERMS) controller that manages multiple grid-forming and grid-following IBRs, establishing a standard design interface to reduce the complexity of integrating new DERs in the future and supporting replication by the industry. As a result of learnings in this project, SDG&E has implemented the microgrid controller strategy at multiple other microgrid sites, thereby validating the replicability of the solution. Hardware-in-the-loop (HIL) simulations including power and controller HIL hardware — along with electromagnetic transient (EMT) simulations of Borrego Springs Microgrid —informed adjustments to inverter parameters and were important to characterize the performance of the IBRs in relevant operating conditions before deployment. The EMT and HIL simulations of islanding the entire community are important contributions in providing confidence in IBR performance prior to future islanding of the community in the field. High-fidelity EMT and/or HIL simulation of IBRs can de-risk field operations, and its relevance and importance as a tool is increasing as distribution grids and microgrids become more complex and dynamic with an increasing proportion of renewable generation, distributed energy storage, and two-way power and energy flows.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Integrated Hydro-Terrestrial Modeling: Development of a National Capability

Water is one of our most important natural resources and is essential to our national economy and security. Multiple federal government agencies have mission elements that address national needs related to water. Each water-related agency champions a unique science and/or operational mission focused on advancing a portion of the nation’s ability to meet our water-related challenges. These diverse mission needs have engendered a rich and extensive base of water-related data and modeling capabilities. While useful for their intended purposes, these capabilities are not well integrated to address complex regional problems and overarching national problems. These major investments by a number of federal agencies, however, lay the foundation for an integrated hydro-terrestrial modeling and data infrastructure that will enhance knowledge, understanding, prediction, and management of the nation’s diverse water challenges. Creating a more seamless national hydro-terrestrial modeling and data capacity presents an enormous opportunity to advance operational as well as research capabilities leading to more effective water management. Advances are necessary not only in operational tools for forecasting but also in research to identify and resolve knowledge and data gaps that lead to unacceptable uncertainties in forecast outcomes. As such, close coordination across scientific, operational, and resource management communities is required. To this end, an interagency workshop on “Integrated Hydro-Terrestrial Modeling: Development of a National Capacity” was held at the National Science Foundation (NSF) headquarters in Alexandria, Virginia in September 2019, led jointly by the NSF, the U.S. Department of Energy (DOE) and the U.S. Geological Survey (USGS) and with broader interagency support provided through an interagency steering committee. This workshop provided a venue to bring together representatives of water-related agencies and their scientific partners (including university researchers) to initiate and refine a vision for a national Integrated Hydro-Terrestrial Modeling (IHTM) and data infrastructure and advance ideas towards its development. The workshop was designed to address three critical foci to advance the development of a national IHTM capacity: “Priority Water Challenges” around which to motivate and initiate development; Technical and methodological obstacles related to data and modeling; Organizational, structural, and cultural barriers that heretofore have impeded integration of capabilities across the federal and research landscapes. The following “Priority Water Challenge” domain areas represent targets for initiating development of the IHTM and were identified and selected in alignment with priorities of the administration’s Water Sub-Cabinet: (1) Nutrient loading, hypoxia, and harmful algal blooms; (2) Water availability in the western United States; and (3) Extreme weather-related water hazards. These water challenges span agency mission boundaries and encompass a broad range of geographies, complex system dynamics and feedbacks, and critical processes spanning hydrological, climatic, and biophysical systems as well as land-use/land-cover, agricultural, built infrastructure, societal, economic, and decisional environments. These three Priority Water Challenges cannot be fully addressed without leveraging complementary and synergistic capabilities across multiple agencies.

99 GENERAL AND MISCELLANEOUS↗

STREAM: A Scalable Federated HPC Telemetry Platform

Obtaining and analyzing high performance computing (HPC) telemetry in real time is a complex task that can impact algo- rithmic performance, operating costs, and ultimately scientific outcomes. If your organization operates multiple HPC systems, filesystems, and clusters, telemetry streams can be synthesized in order to ease operational and analytics burden. In order to collect this telemetry, the Oak Ridge Leadership Computing Facility (OLCF) has deployed STREAM (Streaming Telemetry for Resource Events, Analytics, and Monitoring), which is a distributed and high-performance message bus based on Apache Kafka. STREAM collects center-wide performance information and must interface with many sources, including five HPE deployed supercomputers, each with their own Kafka cluster which is managed by HPCM. OLCF Supercomputers and their attached scratch filesystems currently send more than 300 million messages to over 200 topics producing around 1.3 Terabytes per day of telemetry data to STREAM. This paper describes the architectural principles that enable STREAM to be both resilient and highly performant while supporting multiple upstream Kafka clusters and other data sources. It also discusses the design challenges and decisions faced in adapting our existing system- monitoring infrastructure to support the first Exascale computing platform.

Adamson, Ryan↗

Depot-Based Vehicle Data for National Analysis of Medium- and Heavy-Duty Electric Vehicle Charging

Medium- and heavy-duty vehicles (MHDVs) are a major source of greenhouse gases and local criteria air pollutants. Electrifying MHDVs may reduce these harmful emissions, which disproportionately impact disadvantaged communities. Due to their relatively high per-vehicle energy needs, consistent fleet operations, and frequent colocation of multiple vehicles at depots, MHDVs may have more spatially and temporally concentrated charging demands than light-duty passenger electric vehicles. That charging concentration means their electrification may require careful advance planning and coordination to manage potential impacts to the electrical grid via charge management or infrastructure upgrades. However, MHDV duty cycles and parking schedules are highly variable across vocations of operation, and there is a shortage of nationally representative, vocationally diverse public data describing typical MHDV operations. This report summarizes the methodology - designed with national representativeness in mind - used to create a new set of data describing typical daily driving distances, dwell durations, and normalized electric vehicle depot charging load curves for MHDVs. The dataset reflects the subset of MHDV operating patterns that may originate from a consistent depot each day and rely on the same depot for charging. In addition to trucks with depot-centric vocational patterns, the data describes operations of transit buses and school buses, each with a depot-centric focus. The dataset is available to the public and suitable for national analysis. It can inform research, infrastructure planning, and policymaking regarding the electrification of MHDVs.

33 ADVANCED PROPULSION SYSTEMS↗

White Rock Canyon Riparian Monitoring

Lands at Los Alamos National Laboratory (LANL) are owned and managed by the Department of Energy, National Nuclear Security Administration (DOE/NNSA). The Laboratory’s eastern boundary is in White Rock Canyon (WRC) along the Rio Grande within Technical Areas (TAs) 70 and 33, with approximately 4 miles of property along the river (Figure 1). Feral cattle have been documented along the Rio Grande in WRC since the 1980s. Cattle activity is known to be especially harmful to riparian ecosystems, which are hubs for biodiversity and crucial habitat used by multiple endangered species in this region, including the Southwestern Willow Flycatcher and Yellow-billed Cuckoo on LANL property (Poessel 2020, Sanchez 2021). To gain a comprehensive understanding of the status of riparian ecosystems within WRC and the damage feral cattle populations are imposing, ecologists at LANL conducted a month-long vegetation monitoring project in September 2025. Riparian vegetation monitoring protocols and rapid assessments of cattle impacts were used to quantify the intensity and extent of cattle damage. The data collected will be used as a baseline for comparison following the removal of feral cattle from the canyon. Abundant cattle sign indicates current and heavy utilization of the riparian areas that were surveyed. There are multiple wallowing areas where soils are completely denuded of vegetation and highly compacted. Based on the severity and extent of impact observed in these areas, cattle use appears consistent and on-going over an extended period of years, supporting the need for removal and restoration management.

54 ENVIRONMENTAL SCIENCES↗

Bayesian Physics Informed Spatio-Temporal Network for Streamflow Data Imputation

Reliable reconstruction of incomplete streamflow records is critical for improving hydrological forecasting, flood preparedness, and water resource management. However, large observational gaps and uncertainties in governing physical parameters limit the accuracy of traditional statistical and machinelearning imputation frameworks. To address these challenges, we develop a Bayesian Physics-Informed Spatio-Temporal Network (BPI-STNet) that jointly captures spatial and temporal dependencies while enforcing hydrologic consistency through embedded physical constraints. The framework integrates a GraphSAGE-LSTM architecture to model spatial connectivity across gauges and temporal flow dynamics, coupled with a Bayesian update mechanism to estimate uncertain parameters in a simplified water-balance framework. Unlike conventional physics-informed networks that rely on sampling-based posterior estimation, BPI-STNet derives an analytic solution to the inverse problem, allowing closed-form Bayesian updates of uncertain parameters Λ={α,β,k} using Gaussian priors and likelihoods. Applied to daily observations from the Susquehanna River Basin (1980-2022), BPI-STNet achieves substantial improvements over a purely data-driven RGNN baseline, which reduced RMSE by 23 % and MAE by 9 %, and achieving an average NSE values up to 0.96. The results demonstrate that coupling Bayesian inference with physics-informed learning yields physically consistent, uncertainty-aware reconstructions that preserve the temporal persistence and statistical distribution of observed flows. The proposed framework establishes a generalizable paradigm for data-sparse hydrologic systems where both data fidelity and physical interpretability are essential.

Krishnan Kutty Ambika, Anukesh [ORNL] (ORCID:00000↗