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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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At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A multirate mass transfer model to represent the interaction of multicomponent biogeochemical processes between surface water and hyporheic zones (SWAT-MRMT-R 1.0)

Abstract. Surface water quality along river corridors can be modulated by hyporheic zones (HZs) that are ubiquitous and biogeochemically active. Watershed management practices often ignore the potentially important role of HZs as a natural reactor. To investigate the effect of hydrological exchange and biogeochemical processes on the fate of nutrients in surface water and HZs, a novel model, SWAT-MRMT-R, was developed coupling the Soil and Water Assessment Tool (SWAT) watershed model and the reaction module from a flow and reactive transport code (PFLOTRAN). SWAT-MRMT-R simulates concurrent nonlinear multicomponent biogeochemical reactions in both the channel water and its surrounding HZs, connecting the channel water and HZs through hyporheic exchanges using multirate mass transfer (MRMT) representation. Within the model, HZs are conceptualized as transient storage zones with distinguished exchange rates and residence times. The biogeochemical processes within HZs are different from those in the channel water. Hyporheic exchanges are modeled as multiple first-order mass transfers between the channel water and HZs. As a numerical example, SWAT-MRMT-R is applied to the Hanford Reach of the Columbia River, a large river in the United States, focusing on nitrate dynamics in the channel water. Major nitrate contaminants entering the Hanford Reach include those from the legacy waste, irrigation return flows (irrigation water that is not consumed by crops and runs off as point sources to the stream), and groundwater seepage resulting from irrigated agriculture. A two-step reaction sequence for denitrification and an aerobic respiration reaction is assumed to represent the biogeochemical transformations taking place within the HZs. The spatially variable hyporheic exchange rates and residence times in this example are estimated with the basin-scale Networks with EXchange and Subsurface Storage (NEXSS) model. Our simulation results show that (1), given a residence time distribution, how the exchange fluxes to HZs are approximated when using MRMT can significantly change the amount of nitrate consumption in HZs through denitrification and (2) source locations of nitrate have a different impact on surface water quality due to the spatially variable hyporheic exchanges.

58 GEOSCIENCES↗
DOE OSTI2023

Oxygen Management to Avoid Photo-Inactive Cd(S,Se) for Efficient Cd(Se,Te) Solar Cells

Our previous work has demonstrated that the formation of a penternary cadmium chalcogenide Cd(O,S,Se,Te) region can significantly reduce the front interface recombination in Cd(Se,Te)-based thin-film solar cells. In this work, we have shown that oxygen management during the device fabrication is crucial to form this region. While both the CdS and CdSe layer depositions and the postdeposition CdCl2 treatment should be conducted in the presence of oxygen, the CdTe deposition should be conducted in an oxygen-free atmosphere. Improper oxygen management leads to low device performance due to the formation of a photoinactive Cd(S,Se) region and reduced absorber quality. Additionally, we investigated the carrier transport and collection properties in devices with photoinactive Cd(S,Se) and photoactive Cd(O,S,Se,Te) at the front interface to gain comprehensive understanding of the mechanisms that resulted in improved efficiencies approaching 20%.

CdTe deposition↗
DOE OSTI2025

Modeling the Air Quality Impacts of Future Energy Scenarios

Many greenhouse gas (GHG) emission reduction measures achieve simultaneous reductions in air pollutants. Human-Earth system models can estimate such emission changes in the energy system but using them in chemistry-transport models (CTMs) to study their air quality impacts involves resource-intensive emissions processing. This is greatly simplified by an emissions scaling approach linking state-level emissions estimated by a human-Earth system model to a CTM. A scenario continuing pre-2022 energy policy in the U.S. to 2050 shows widespread air quality improvements over the 2015 baseline from SO 2 and NO x emission reductions of 50–80% from electricity generation and light-duty vehicles. Scenarios of GHG mitigation and vehicle electrification at the state and national level add further benefits. However, PM 2.5 increases from increased use of wood heating and bioenergy suggest that additional PM 2.5 management may be needed when using biofuels. In conclusion, this approach helps assess multiple future energy scenarios efficiently without sacrificing chemical detail in the air quality simulations.

air quality↗
DOE OSTI2020

Indicator-directed Dynamic Power Management for Iterative Workloads on GPU-Accelerated Systems

Modern high-performance and warehouse computing centers show strong interest in minimizing system power consumption while satisfying customers’ quality of service (QoS). Dynamic voltage and frequency scaling (DVFS) is effective for achieving this goal. Nevertheless, automating the process online and making it transparent to users must address three major challenges: (1) Complexity — today’s hardware components (e.g., CPUs, GPUs, memory, network, etc.) can be configured in several or dozens of frequency/voltage states for satisfying divergent system demands. Given their combination and the emergence of heterogeneity, searching the optimal configuration in the design space online can be timing consuming. (2) QoS guarantee — user-defined objectives such as power constraint and performance target must be monitored, predicted and ensured at the best effort. (3) Adaptability — various known and unknown workloads run on systems. Workloads characteristics should be quickly determined and configurations dynamically adjusted in accord with workloads and QoS. In this work, we focus on applications exhibiting an interesting feature – iterative or periodic, which is common among conventional HPC and emerging machine learning workloads. We propose an online dynamic power-performance (ODPP) management framework to dynamically adjust GPU DVFS configurations to meet performance and power objectives and constraints, without any code annotation or intrusion. Particularly, ODPP extracts the performance and power indicators for applications from their resources utilization profiles in a short episode. It further automatically constructs an accurate model that infers from the indicators how the application's performance and power vary with GPU core and memory frequencies. Aided with the model, for both seen and unseen applications, ODPP can quickly determine the most appropriate DVFS configuration for their execution. We evaluate ODPP on an NVIDIA GPU using multiple exascale computing (ECP) and deep learning applications.

Zou, Pengfei↗
DOE OSTI2024

Environmental Performance Report 2023: Annual Site Environmental Report per the U.S. Department of Energy Order 231.1B Chg 1

The National Renewable Energy Laboratory's (NREL's) Environmental Performance Report provides a description of the laboratory's environmental management activities in calendar year 2023, including information on environmental and sustainability performance, environmental compliance activities and status, and environmental protection programs, highlights, and successes. The purpose of this report is to ensure that the U.S. Department of Energy (DOE) and the public receive timely, accurate information about events that have positively affected or could adversely affect the health and safety of the public or workers, the environment, or the operations of DOE facilities. This report meets the DOE requirements of the Annual Site Environmental Report (ASER) and has been prepared in accordance with DOE Order 231.1B Chg 1, Environment, Safety and Health Reporting.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

RCSB Protein Data Bank: supporting research and education worldwide through explorations of experimentally determined and computationally predicted atomic level 3D biostructures

The Protein Data Bank (PDB) was established as the first open-access digital data resource in biology and medicine in 1971 with seven X-ray crystal structures of proteins. Today, the PDB houses >210 000 experimentally determined, atomic level, 3D structures of proteins and nucleic acids as well as their complexes with one another and small molecules ( e.g. approved drugs, enzyme cofactors). These data provide insights into fundamental biology, biomedicine, bioenergy and biotechnology. They proved particularly important for understanding the SARS-CoV-2 global pandemic. The US-funded Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) and other members of the Worldwide Protein Data Bank (wwPDB) partnership jointly manage the PDB archive and support >60 000 `data depositors' (structural biologists) around the world. wwPDB ensures the quality and integrity of the data in the ever-expanding PDB archive and supports global open access without limitations on data usage. The RCSB PDB research-focused web portal at https://www.rcsb.org/ (RCSB.org) supports millions of users worldwide, representing a broad range of expertise and interests. In addition to retrieving 3D structure data, PDB `data consumers' access comparative data and external annotations, such as information about disease-causing point mutations and genetic variations. RCSB.org also provides access to >1 000 000 computed structure models (CSMs) generated using artificial intelligence/machine-learning methods. To avoid doubt, the provenance and reliability of experimentally determined PDB structures and CSMs are identified. Related training materials are available to support users in their RCSB.org explorations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Water Temperature, Prey Concentration and Salmonid Density Influence Daily Growth of Wild Juvenile Salmonids in Tributaries of the Upper Salmon River, Idaho ( USA )

ABSTRACT Theory, experiments and field studies indicate that the somatic growth rate of freshwater consumers is shaped by the individual, additive and multiplicative effects of multiple factors, including consumer size and condition, temperature, prey resources and biotic interactions. While our understanding of how these factors affect wild populations of freshwater consumers is improving, the topic remains poorly studied, especially with respect to mobile species. Here, we report on an 8‐year, seven‐stream ( n = 49 stream‐year combinations) observational study examining the individual and interactive effects of invertebrate prey concentration (F, mg/m 3 ), mean daily water temperature (T, °C) and juvenile Chinook salmon ( Oncorhynchus tshawytscha ) density (D, fish/100 m 2 ) on summer daily growth rates (%/d) of mobile, anadromous, juvenile Chinook salmon (age‐0+, n = 382) and sub‐yearling (age‐0+, n = 61) and yearling (age‐1+) steelhead trout ( O. mykiss , n = 70) rearing in cold (mean daily summer: 12.1°C, range: 4.2°C–16.7°C) mountain tributaries of the Salmon River basin in central Idaho (USA). AIC c model selection indicated that daily juvenile salmonid growth positively correlated with water temperature, prey biomass concentration, local juvenile Chinook density and the interaction between water temperature and food but with species and age‐specific differences. Water temperature was a covariate in all top‐ranked models, with daily growth (%/day) rate increasing (0.05%–0.23%/d) linearly with mean daily summer water temperature. In addition to a direct positive relationship with daily growth rate, there was evidence that prey concentration positively interacted with water temperature to accelerate daily growth (F × T). The positive relationship between juvenile salmonid daily growth rate and juvenile Chinook density is difficult to explain and could result from confounding factors. The individual success observed in these streams may contribute to population‐level benefits for the focal consumers, as prey‐rich, warm summers may result in larger individuals with higher energy reserves at the end of the summer/autumn growing season, contributing to improved overwinter survival. Our results, taken in combination with evidence from models, experiments and observational studies, have climate change implications. Current and predicted increases in water temperature will necessitate higher rates of prey consumption by aquatic ectothermic consumers to offset accelerated metabolic demands. Thus, to improve the resilience of mobile freshwater consumers in a warming climate, we suggest that natural resource managers not only consider physical and chemical habitat conditions but also biotic conditions, including the spatiotemporal quantity and quality of prey resources.

Kiffney, Peter. M. [Fish Ecology, Northwest Fisher↗
DOE OSTI2022

Performance of New Near-Real-Time PERSIANN Product (PDIR-Now) for Atmospheric River Events over the Russian River Basin, California

Most heavy precipitation events and extreme flooding over the U.S. Pacific coast can be linked to prevalent atmospheric river (AR) conditions. Thus, reliable quantitative precipitation estimation with a rich spatiotemporal resolution is vital for water management and early warning systems of flooding and landslides over these regions. At the same time, high-quality near-real-time measurements of AR precipitation remain challenging due to the complex topographic features of land surface and meteorological conditions of the region: specifically, orographic features occlude radar measurements while infrared-based algorithms face challenges, differentiating between both cold brightband (BB) precipitation and the warmer nonbrightband (NBB) precipitation. It should be noted that the latter precipitation is characterized by greater orographic enhancement. In this study, we evaluate the performance of a recently developed near-real-time satellite precipitation algorithm: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain Rate-Now (PDIR-Now). This model is primarily dependent on infrared information from geostationary satellites as input; consequently, PDIR-Now has the advantage of short data latency, 15–60-min delay between observation to precipitation product delivery. The performance of PDIR-Now is analyzed with a focus on AR-related events for cases dominated by NBB and BB precipitation over the Russian River basin. In our investigations, we utilize S-band (3-GHz) precipitation profilers with Joss/Parsivel disdrometer measurements at the Middletown and Santa Rosa stations to classify BB and NBB precipitation events. In general, our analysis shows that PDIR-Now is more skillful in retrieving precipitation rates over both BB and NBB events across the topologically complex study area as compared to PERSIANN-Cloud Classification System (CCS). Also, we discuss the performance of well-known operational near-real-time precipitation products from 2017 to 2019. Conventional categorical and volumetric categorical indices, as well as continuous statistical metrics, are used to show the differences between various high-resolution precipitation products such as Multi-Radar Multi-Sensor (MRMS).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Building 151 NNMA Clean Laboratory Project Soil Sampling and Analysis Plan

This Soil Sampling and Analysis Plan (SAP) was prepared by the Environmental Function Area (EFA)/Technical Services Department (TSD) of the Environment, Safety & Health (ES&H) Directorate for the Project Management Office (PMO) for the proposed Building 151 NNMA Clean Laboratory Project (project) located immediately south of Building 154. The purpose of the SAP was to describe the procedures for collection and analysis of environmental samples and evaluation of analytical data (chemical and radiological) to determine management options of excavated soil during project construction in accordance with Lawrence Livermore National Laboratory’s (LLNL) Soils Screening and Management Plan (SSMP) (LLNL, 2022), which is consistent with U.S. Environmental Protection Agency (EPA) guidance for developing Data Quality Objectives for environmental data (EPA, 2006). The SAP was developed in accordance with the SSMP and based on preliminary design information provided to EFA by PMO. In addition to environmental samples, geotechnical samples will be collected by Consolidated Engineering Laboratories (CEL) on behalf of PMO. Geotechnical samples will be collected from a dedicated geotechnical boring location or co-located with environmental samples. Collection of geotechnical samples is determined by CEL and is therefore not discussed in this SAP.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Best Practices for NERSC Training

The National Energy Research Supercomputing Center (NERSC) at Lawrence Berkeley National Laboratory (LBNL) organizes approximately 20 training events per year for its 8,000 users from 800 projects, who have varying levels of High Performance Computing (HPC) knowledge and familiarity with NERSC's HPC resources. Due to the novel circumstances of the pandemic, NERSC began transforming our traditional smaller-scale, on-site training events to larger-scale, fully virtual sessions in March 2020. We treated this as an opportunity to try new approaches and improve our training best practices. This paper describes the key practices we have developed since the start of this transformation, including considerations for organizing events; collaboration with other HPC centers and the DOE ECP Program to increase reach and impact of events; targeted emails to users to increase attendance; efficient management of user accounts for computational resource access; strategies for preventing Zoombombing; streamlining the publication of professional-quality, closed-captioned videos on the NERSC YouTube channel for accessibility; effective communication channels for Q&A; tailoring training contents to NERSC user needs via close collaboration with vendors and presenters; standardized training procedures and publishing of training materials; and considerations for planning HPC training topics. Additionally, most of these practices will be continued after the pandemic as effective norms for training.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Robust Method to Identify Groundwater Affected By Redox Conditions at Los Alamos National Laboratory Legacy Cleanup Site - 20497

To facilitate the mission of the Department of Energy's (DoE's) Environmental Management Program at Los Alamos National Laboratory (LANL), a significant number of groundwater monitoring wells with depths ranging from 152-396 meter were completed for characterization and monitoring. Groundwater at Los Alamos tends to be oxygen saturated with very low concentrations of organic matter. Drilling fluids can introduce residual carbon causing an increase in microbial activity and local reducing conditions around the well, potentially impacting representativeness of groundwater data quality for redox sensitive constituents. We assert that the redox state of the wells can robustly be identified by aqueous solution concentrations of iron and manganese. An automated review process has been built into a computer-based data management system enabling an efficient screening process for identifying reducing conditions that are not caught using standard data-validation protocol, and ensures that important data quality issues are thoroughly identified and addressed in an efficient and timely manner. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Sampling and Analysis Plan for Partial Closure of Solid Waste Management Unit #16; Addendum Part I for Delineating BH-056

On July 11, 2022, Sandia National Laboratories in California (SNL/CA) submitted a Response to Regional Water Quality Control Board Comments on Soil Sampling Results for Closure of a Portion of SWMU #16 in response to the February 16,2022 San Francisco Bay Regional Water Quality Control Board’s (SFRWQCB) letter requesting supporting information for the recommended closure of 7,700 linear feet of abandoned sewer lines. On August 18, 2022, SFRWQCB further requested a Sampling and Analysis Plan (SAP) for additional “step-out” sampling to delineate the potential presence of benzidine near borehole BH-056, which is located near the former sewer line. SNL/CA is in the process of contracting Weiss Associates (Weiss) to perform and oversee the boring, sampling, analysis, and report development to determine the potential presence and extent of benzidine. This document outlines the work that is anticipated, including the development of the SAP, to complete the investigation and submit a final report to the SFRWQCB. The work proposed by Weiss provides an estimated schedule for completing the investigation and developing the addendum Part II SAP for the project. In addition, Weiss provided a preliminary estimate of the sample locations (see Attachment A) which serve as addendum Part I of the SAP requested by the SFRWQCB. The contractor will submit the addendum Part II SAP, to satisfy the SFRWQCB requirement, before proceeding with any work.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Redefining Net-Zero Water for Resilient Aquifers

Groundwater has long been a critical source of potable water, supplying one-third of all global water withdrawals and providing strategic reserves in times of drought (Konikow, 2012; Famiglietti, 2014). The risk in relying on groundwater is that it is often pumped at rates far greater than it can be naturally replenished. With increasing population and a changing climate, the depletion of potable water supplies has become an ever-growing concern (Srinivasan et al., 2012). Recently, the concept of “resilience” has been applied to groundwater, leaving many questions to be explored in terms of what resilience should mean for aquifers (Foster and MacDonald, 2014; Grönwall and Oduro-Kwarteng, 2017). The term is commonly used to indicate resilience to impacts of climate change specifically, but here we consider resilience in the context of more general water insecurity. Relatedly, the concept of “sustainable yield” has emerged in an attempt to determine a metric that can ensure the long-term resilience of groundwater systems, but the definition is fraught with inherent uncertainties and its quantification poses challenges (Rudestam and Langridge, 2014; Grönwall and Oduro-Kwarteng, 2017). At the heart of these concepts is a focus on sustainable management of aquifers. A sustainable approach to managing groundwater resources usually involves balancing long-term abstraction with the long-term recharge taking into account the needs for environmental flows and implementing drought-preparedness measures (Robins et al. 2006; Foster and MacDonald, 2014). At a high level, a sustainable balance of groundwater abstraction versus aquifer recharge may be considered “Net Zero” (NZ) or “Net Positive” (NP) in cases where recharge is equal to or in excess of abstraction, respectively. However, NZ/NP water management programs that address these concerns have varied definitions of compliance, which include considerations for the application at hand (ILFI, 2020; USGBC, 2020). Locations with high risk of low or poor water supply may be particularly interested in pursuing NZ/NP water. The AQUEDUCT Water Risk Atlas (World Resources Institute, 2019) indicates long-term availability, providing risk levels for quantity and quality of water at specific locations across the globe, for existing and predicted future conditions. This paper follows the U.S. Department of Energy (DOE) Federal Energy Management Program (FEMP) NZ water definition adopted by the U.S. Army for use at their installations, including Army Reserve Centers (ARCs). This definition also relates to the U.S. Army’s current focus on water resilience, which includes short-term and long-term availability of water resources.

net zero water, HUC12, Saipan, American Samoa, Mau↗
DOE OSTI2024

Sampling and Analysis Plan for LSL2 Underground Storage Tank Evaluation of PFAS in Soil: Protection of Groundwater

This SAP gathers all pertinent information relevant to the sampling and analysis of soil at the removal site for a former 10,000-gallon UST and reference sites in the vicinity of the excavation site. Data Quality Objectives establishes the process and steps for the acquisition of data. Data Flow Diagram establishes the decisions and next steps. Quality Assurance Project Plan establishes the quality requirements for data collection, including planning, implementation, and assessment of sampling, field measurements, and laboratory analysis. The work is being completed for DOE-SC Pacific Northwest Site Office.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Deep reinforcement learning control for co-optimizing energy consumption, thermal comfort, and indoor air quality in an office building

With the recent demand for decarbonization and energy efficiency, advanced HVAC control using Deep Reinforcement Learning (DRL) becomes a promising solution. Due to its flexible structures, DRL has been successful in energy reduction for many HVAC systems. However, only a few researches applied DRL agents to manage the entire central HVAC system and control multiple components in both the water loop and the air loop, owing to its complex system structures. Moreover, those researches have not extended their applications by incorporating the indoor air quality, especially both CO2 and PM2.5concentrations, on top of energy saving and thermal comfort, as achieving those objectives simultaneously can cause multiple control conflicts. What's more, DRL agents are usually trained on the simulation environment before deployment, so another challenge is to develop an accurate but relatively simple simulator. Therefore, we propose a DRL algorithm for a central HVAC system to co-optimize energy consumption, thermal comfort, indoor CO2 level, and indoor PM2.5 level in an office building. To train the controller, we also developed a hybrid simulator that decoupled the complex system into multiple simulation models, which are calibrated separately using laboratory test data. The hybrid simulator combined the dynamics of the HVAC system, the building envelope, as well as moisture, CO2, and particulate matter transfer. Three control algorithms (rule-based, MPC, and DRL) are developed, and their performances are evaluated on the hybrid simulator environment with a realistic scenario (i.e., with stochastic noises). The test results showed that, the DRL controller can save 21.4 % of energy compared to a rule-based controller, and has improved thermal comfort, reduced indoor CO2 concentration. The MPC controller showed an 18.6 % energy saving compared to the DRL controller, mainly due to savings from comfort and indoor air quality boundary violations caused by unmeasured disturbances, and it also highlights computational challenges in real-time control due to non-linear optimization. Finally, we provide the practical considerations for designing and implementing the DRL and MPC controllers based on their respective pros and cons.

Guo, Fangzhou↗
DOE OSTI2024

Choosing the Right Tool: A Comparative Study of Wetland Assessment Approaches

There are over 700 aquatic ecological assessment approaches across the globe that meet specific institutional goals. However, in many cases, multiple assessment tools are designed to meet the same management need, resulting in a confusing array of overlapping options. Here, we look at six riverine wetland assessments currently in use in Montana, USA, and ask which tool (1) best captures the condition across a disturbance gradient and (2) has the most utility to meet the regulatory or management needs. We used descriptive statistics to compare wetland assessments (n = 18) across a disturbance gradient determined by a landscape development intensity. Factor analysis showed that many of the tools had internal metrics that did not correspond well with overall results, hindering the tool’s ability to act as designed. We surveyed regional wetland managers (n = 56) to determine the extent of their use of each of the six tools and how well they trusted the information the assessment tool provided. We found that the Montana Wetland Assessment Methodology best measured the range of disturbance and had the highest utility to meet Clean Water Act (CWA§ 404) needs. Montana Department of Environmental Quality was best for the CWA§ 303(d) & 305(b) needs. The US Natural Resources Conservation Service’s Riparian Assessment Tool was the third most used by managers but was the tool that had the least ability to distinguish across a disturbance, followed by the US Bureau of Land Management’s Proper Functioning Condition.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Policy Innovation and Governance for Irrigation Sustainability in the Arid, Saline San Joaquin River Basin

This paper provides a chronology and overview of events and policy initiatives aimed at addressing irrigation sustainability issues in the San Joaquin River Basin (SJRB) of California. Although the SJRB was selected in this case study, many of the same resource management issues are being played out in arid, agricultural regions around the world. The first part of this paper provides an introduction to some of the early issues impacting the expansion of irrigated agriculture primarily on the west side of the San Joaquin Valley and the policy and capital investments that were used to address salinity impairments to the use of the San Joaquin River (SJR) as an irrigation water supply. Irrigated agriculture requires large quantities of water if it is to be sustained, as well as supply water of adequate quality for the crop being grown. The second part of the paper addresses these supply issues and a period of excessive groundwater pumping that resulted in widespread land subsidence. A joint federal and state policy response that resulted in the facilities to import Delta water provided a remedy that lasted almost 50 years until the Sustainable Groundwater Management Act of 2014 was passed in the legislature to address a recurrence of the same issue. The paper describes the current state of basin-scale simulation modeling that many areas, including California, are using to craft a future sustainable groundwater resource management policy. The third section of the paper deals with unique water quality issues that arose in connection with the selenium crisis at Kesterson Reservoir and the significant threats to irrigation sustainability on the west side of the San Joaquin Valley that followed. The eventual policy response to this crisis was incremental, spanning two decades of University of California-led research programs focused on finding permanent solutions to the salt and selenium contamination problems constraining irrigated agriculture, primarily on the west side. Arid-zone agricultural drainage-induced water quality problems are becoming more ubiquitous worldwide. One policy approach that found traction in California is an innovative variant on the traditional Total Maximum Daily Load (TMDL) approach to salinity regulation, which has features in common with a scheme in Australia’s Hunter River Basin. The paper describes the real-time salinity management (RTSM) concept, which is geared to improving coordination of west side agricultural and wetland exports of salt load with east side tributary reservoir release flows to improve compliance with river salinity objectives. RTSM is a concept that requires access to continuous flow and electrical conductivity data from sensor networks located along the San Joaquin River and its major tributaries and a simulation model-based decision support designed to make salt load assimilative capacity forecasts. Web-based information dissemination and data sharing innovations are described with an emphasis on experience with stakeholder engagement and participation. The last decade has seen wide-scale, global deployment of similar technologies for enhancing irrigation agriculture productivity and protecting environmental resources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

HPC-FAIR: A Framework Managing Data and AI Models for Analyzing and Optimizing Scientific Applications

The increasing reliance on machine learning (ML) to analyze and optimize large-scale scientific applications on supercomputers faces a significant bottleneck: the lack of readily available, high-quality training datasets and the difficulty in reusing existing AI models. This project was motivated by the urgent need to address the “FAIR” principles (Findability, Accessibility, Interoperability, Reusability) for both training datasets and AI models in the high-performance computing (HPC) domain. The project developed HPC-FAIR, a high-performance computing data management framework designed to centralize HPC-related datasets and AI models within a unified hub. To ensure interoperability, the framework established a standardized representation and vocabulary (ontology) for both data and models. HPC-FAIR also implemented automated workflows to streamline data processing, model access, and benchmarking. Additionally, the project focused on optimizing data harnessing efficiency through advanced techniques like deep reuse and compression-based analytics.

97 MATHEMATICS AND COMPUTING↗