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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Consequence Based Framework for Deployment of Cloud Solutions in the Digital Energy Transition

This study proposes a framework for evaluating cloud computing deployment in the electric sector, focusing on the digital transition of energy systems. It assesses the implications of cloud technology adoption, particularly in terms of security, operational resilience, and efficiency. The paper introduces a framework for consequence-driven applied risk analysis, enabling utilities to prioritize and mitigate potential threats effectively, and responsibly deploy cloud applications. It also discusses the shared responsibility model in cloud computing, highlighting the need for collaborative security efforts. The research aims to provide utilities with a strategic assessment tool for cloud adoption, emphasizing the importance of security culture in enhancing cloud computing's role in critical infrastructure.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Evaluating SWAT + model uncertainties for human and natural outcomes: Application in a Great Lakes agricultural watershed

Nutrient exports from agricultural lands in the Great Lakes Region pose significant threats to water quality and ecological health through eutrophication, hypoxia, and harmful algal blooms. Climate change and agricultural adaptation practices complicate future nutrient loading due to intensified hydrologic cycles and land use decisions. Our research focuses on evaluating the Soil and Water Assessment Tool (SWAT) plus model parametric uncertainties for human and natural outcomes across different scales. These factors are integral to ensuring a balance between productive agricultural practices and maintaining the health of watershed hydrology. However, uncertainties in modeling such complex interactions pose significant challenges, limiting our ability to precisely determine critical factors that influence crop yield and soil moisture. Our analysis employs Sobol global sensitivity analysis to evaluate first-order, second order, and total-order indices for SWAT crop growth parameter, ensuring comprehensive assessment of individual and interactive effects on model outputs. The objective is to identify the parameters that significantly affect model outputs for crop yield and soil moisture and improve our understanding of their interactions at the basin and hydrological response unit (HRU) scale. Our case study, the Portage River Watershed, which drains into Lake Erie, is chosen to better capture finer scale interactions crucial for predicting nutrient loading under future climate scenarios. This foundational work is aimed at setting the stage for the future development of an agent-based model (ABM). The ABM model would incorporate SWAT outputs to dynamically simulate decision-making processes.

Bunyon, Enock↗
DOE OSTI2020

A new soil mixing layer model for simulating conservative solute loss from initially saturated soil to surface runoff

Surface water pollution due to solute loss from soil to surface runoff is a serious threat to the environment, and it is of great significance to predict the solute loss. While soil mixing layer theory has been widely used to predict the solute loss, it has been observed that the conventional soil mixing layer models with constant mixing layer depth underestimate solute concentration in surface runoff, and thus underestimate the solute loss after surface runoff starts. This study introduced a new soil mixing layer model with a new concept that the mixing layer depth increases downward with time after surface runoff occurs. This new concept was implemented by adding a mixing front between the soil mixing layer and its underlying soil layer, which is conceptually similar to the wetting front of soil infiltration given by the Green-Ampt equation. This is equivalent to treating the soil mixing layer as a deforming control volume. A mechanistic explanation for the new model was presented. This study also developed a closed form solution of the new model. The new model was evaluated by simulating five laboratory experiments conducted by two groups of researchers. The first two experiments have ponding water, and consider different drainage and runoff conditions at both early and late runoff times. The other three experiments do not have ponding water, and consider different infiltration conditions at the bottom of soil boxes. Simulation results of the five experiments show that the new model resolves the underestimation problem of conventional soil mixing layer models. The new model is subject to a total of six limits such as that the new model only considers conservative solute. Three limits are related to the assumptions used to derive the closed form solution, and they may be alleviated in a future research as briefly discussed at the end of the paper.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A Cybersecurity Threat Profile for a Connected Lighting System

In this paper we analyze a threat profile performed on a fault-detection use case for streetlights. A threat profile establishes security requirements, justifies security measures, yields actionable controls, and effectively communicates risk to stakeholders. This effort provides critical information for making threat-based decisions to increase security at a reasonable cost, and can effectively be used by development teams, software architects, and managers to make cybersecurity a part of their ongoing culture of awareness, training, and prevention. This leads to more secure systems and better-understood security. On-premise, cloud, and hybrid architectures with different authentication mechanisms were modeled and later categorized using the Microsoft STRIDE framework. An analysis of the recommended controls for each threat was performed to determine which controls could and should be put in place by manufacturers or third-party suppliers, and which controls need to be left up the end-user to implement.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Estimating Future Surface Water Availability Through an Integrated Climate‐Hydrology‐Management Modeling Framework at a Basin Scale Under CMIP6 Scenarios

Abstract Climate change and increasing water demand due to population growth pose serious threats to surface water availability. The biggest challenge in addressing these threats is the gap between climate science and water management practices. Local water planning often lacks the integration of climate change information, especially with regard to its impacts on surface water storage and evaporation as well as the associated uncertainties. Using Texas as an example, state and regional water planning relies on the use of reservoir “Firm Yield” (FY)—an important metric that quantifies surface water availability. However, this existing planning methodology does not account for the impacts of climate change on future inflows and on reservoir evaporation. To bridge this knowledge gap, an integrated climate‐hydrology‐management (CHM) modeling framework was developed, which is generally applicable to river basins with geographical, hydrological, and water right settings similar to those in Texas. The framework leverages the advantages of two modeling approaches—the Distributed Hydrology Soil Vegetation Model (DHSVM) and Water Availability Modeling (WAM). Additionally, the Double Bias Correction Constructed Analogues method is utilized to downscale and incorporate Coupled Model Intercomparison Project Phase 6 GCMs. Finally, the DHSVM simulated naturalized streamflow and reservoir evaporation rate are input to WAM to simulate reservoir FY. A new term—“Ratio of Firm Yield” (RFY)—is created to compare how much FY changes under different climate scenarios. The results indicate that climate change has a significant impact on surface water availability by increasing reservoir evaporation, altering the seasonal pattern of naturalized streamflow, and reducing FY.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Enhancing Biopreparedness through a Model System to Understand the Molecular Mechanisms that Lead to Pathogenesis and Disease Transmission: NW-BRaVE

The science of biopreparedness to counter biological threats hinges on understanding the fundamental principles and molecular mechanisms that lead to pathogenesis and disease transmission. Our vision to address this challenge is to create a powerful and user-friendly platform to elucidate the fundamental principles of how molecular interactions drive pathogen-host relationships and host shifts. We will enable groundbreaking discoveries by integrating a wide range of structural, genomics, proteomics, and other advanced omics measurements, along with evolutionary and artificial intelligence predictions. To make sure the system is applicable to real-world problems, we will develop it in the context of a tractable model system, the small, abundant, and accessible photosynthetic cyanobacteria and their constantly co-adapting viral pathogens, cyanophages. This model will maintain the system’s applicability to real-world problems and techniques, but the overall focus will be on elucidating general principles of detecting, assessing, and surveilling molecular interaction, adaptation, and coevolution that are system agnostic and therefore extensible to other viral-host interactions. Our overall objectives are to (1) identify the molecular complexes that comprise the cyanobacteria redox macromolecular subsystem and how they dynamically change with bacteriophage infection in situ, using cryo-electron tomography; (2) profile regulatory changes during infection using proteomics, multiomics, and experimental validation, and integrate the data with in situ structures; (3) use genomics and metagenomics to determine environmental and population factors across time scales that impact the interactions between marine cyanobacteria and their cyanophage parasites, predicting the evolutionary origins of in situ structural and functional interactions, convergence and coevolution; and (4) develop a data integration and transformation platform that facilitates the integration of in situ, proteomic, and evolutionary measurements of molecular interactions to surveil diverse hosts and parasites in various environmental contexts. These objectives address Focus Area 2 Reveal Molecular Interactions Across Biological Scales for Design of Targeted Interventions. Our powerful and user-friendly platform will enhance connections between the often-siloed fields of structure, molecular phenotype, and evolutionary genomics that are key to biopreparedness, but in need of integration (Figure 1). We will build an integrated navigation tool to facilitate the effective use of globally distributed experimental data for integrated analysis and predictive modeling. The project will develop, implement, and test a platform to assess host-pathogen molecular interactions, adaptation to hosts and host shifts, and coevolution between hosts and pathogens, successfully impacting the research community by revolutionizing abilities to study any host-pathogen interaction, encourage diverse community contributions, and gain fundamental insights into how proteins adapt to new contexts. This ability will be critical for designing early interventions to address future threats. We will build surveillance training capability, aiming for a fair and equitable response to future pandemics and biothreats.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

An accurate and interpretable model for antimicrobial resistance in pathogenic Escherichia coli from livestock and companion animal species

Understanding the microbial genomic contributors to antimicrobial resistance (AMR) is essential for early detection of emerging AMR infections, a pressing global health threat in human and veterinary medicine. Here we used whole genome sequencing and antibiotic susceptibility test data from 980 disease causing Escherichia coli isolated from companion and farm animals to model AMR genotypes and phenotypes for 24 antibiotics. We determined the strength of genotype-to-phenotype relationships for 197 AMR genes with elastic net logistic regression. Model predictors were designed to evaluate different potential modes of AMR genotype translation into resistance phenotypes. Our results show a model that considers the presence of individual AMR genes and total number of AMR genes present from a set of genes known to confer resistance was able to accurately predict isolate resistance on average (mean F 1 score = 98.0%, SD = 2.3%, mean accuracy = 98.2%, SD = 2.7%). However, fitted models sometimes varied for antibiotics in the same class and for the same antibiotic across animal hosts, suggesting heterogeneity in the genetic determinants of AMR resistance. We conclude that an interpretable AMR prediction model can be used to accurately predict resistance phenotypes across multiple host species and reveal testable hypotheses about how the mechanism of resistance may vary across antibiotics within the same class and across animal hosts for the same antibiotic.

Chung, Henri C.↗
DOE OSTI2022

Development of Numerical Model of Metal Foam with PCM for the Estimation of Effective Thermal Conductivity

Global warming due to climate change is a threat to humankind. Nuclear energy is one of the promising solutions to reduce fossil fuel usage. Nuclear energy can handle the base load, compensating for the volatility of renewable energy. If nuclear energy could achieve load following capability, the combination with renewable energy would be more suitable. Thermal energy storage (TES) is one of the options for enabling load following of nuclear reactors. The TES makes it possible to store surplus nuclear thermal energy and release it later as needed. In Idaho National Laboratory (INL), a new concept of latent heat TES integrated with high-temperature heat pipe has been proposed and is under development, which is called Heat pipe-Integrated Thermal Battery (HITB). HITB exchanges thermal energy between the reactor system and TES via heat pipe. The heat transferred to TES medium, made of phase change material (PCM), stores energy as sensible heat and/or latent heat. As PCM typically has poor thermal conductivity, however, various heat transfer enhancement techniques are required to achieve a rapid charging cycle. There are many techniques to enhance the heat transfer ability of TES medium such as disk, fin, and metal foam. Among them, metal foam is an appropriate option to enhance the heat transfer because it maximizes the heat transfer area through metal wicks. Metal foam is a lightweight metal structure that has a high porosity of over 0.9. The typical materials for metal foam are Aluminum, Copper, Nickel, and Silicon Carbide (SiC). Metal foam not only enhances heat transfer via conduction but also increases contact surface area. In the HITB design , the metal foam is being considered as one of the options to enhance the heat transfer of TES medium (PCM) [1]. To predict the enhanced thermal performance of TES, one should properly estimate the effective thermal conductivity of metal foam combined with PCM material or calculate heat transfer in distributed model. There are many experimental works that provides effective thermal conductivity of metal foam with various PCM [2,3]. Also, many theoretical models were developed based on the unit cell model of metal foam [4,5]. With a distributed model, on the other hand, detail heat transfer characteristics between metal foam and PCM material can be analyzed considering the geometry or buoyancy effect. However, due to the complex geometry of metal foam pores, the computational cost for three-dimensional modeling highly increases. Therefore, if metal foam structure can be modeled in simple and repetitive design, the computational cost would decrease Among the various metal foam models [2], lattice model is one of the simple and extendable design. The porosity and pores per inch (PPI) can be characterized by the size and spatial distance of lattice structure. If the three-dimensional metal foam model consists of lattice structure could properly estimate the heat transfer, which is characterized by effective thermal conductivity, it would be a good option to assess the thermal performance of metal foam with PCM. In this study, a three-dimensional numerical model was developed to simulate conductive heat transfer between metal foam and PCM. The three-dimensional lattice structure of square pillars was selected as a basic structure of the metal foam. The calculation result was characterized by the effective thermal conductivity of the whole domain. A sensitivity study was conducted for mesh size, domain size, and PPI to check whether the calculation result gives a converged result or not. Lastly, the effective thermal conductivity from the lattice model was compared with existing experimental data to validate the model result

25 ENERGY STORAGE↗
DOE OSTI2024

Model acetylcholinesterase‐Fc fusion glycoprotein biotechnology system for the manufacture of an organophosphorus toxicant bioscavenging countermeasure

Abstract Organophosphate (OP) toxicants remain an active threat to public health and to warfighters in the military. Current countermeasures require near immediate administration following OP exposure and are reported to have controversial efficacies. Acetylcholinesterase (AChE) fused to the human immunoglobulin 1 (IgG1) Fc domain (AChE‐Fc) is a potential bioscavenger for OP toxicants, but a reproducible AChE‐Fc biomanufacturing strategy remains elusive. This report is the first to establish a comprehensive laboratory‐scale bioprocessing strategy that can reproducibly produce AChE‐Fc and AChE(W86A)‐Fc which is a mutated AChE protein with reduced enzymatic activity. Characterization studies revealed that AChE‐Fc and AChE(W86A)‐Fc are N ‐glycosylated dimeric fusion glycoproteins but only AChE‐Fc had the capability to bind to paraoxon (a model OP). This AChE‐Fc fusion glycoprotein bioprocessing strategy can be leveraged during industrial biomanufacturing development, while the research‐grade AChE‐Fc proteins can be used to determine the potential clinical relevance of the countermeasure against OP toxicants.

Biel, Thomas G.↗
DOE OSTI2021

Represent precipitation-induced geological hazards in Earth system models using artificial intelligence

Precipitation-induced geological hazards, such as debris flow, landslides, mudflow and rockfalls (hereafter referred to as landslides), pose serious threats to public safety in many areas through the world. As residential properties and infrastructure in the US have increasingly expanded into landslide-prone areas and the drivers of landslides (e.g., wildfires and hurricanes) are predicted to intensify under climate warming, losses and fatalities from landslides are likely to increase in the future. Although our understanding of geoenvironmental factors and mechanisms contributing to landslides has greatly improved, only moderate progress has been made in predicting landslides out of well-studied watersheds. Furthermore, explicitly representing various landslide-related processes in Earth system models (ESMs), from the buckling of local bearing elements in granular materials, to frictional sliding between grains, formation of microcracks in the soil matrix, rupture of capillary bridges, or breakage of plant roots3 is still very unlikely within the next decade, even with the help of exascale computers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Modeling the microplastic distribution along the Delaware River Estuary: Accumulation patterns and hydrodynamic influences

Microplastic pollution is an escalating environmental concern, particularly in densely populated estuary regions, where it poses significant threats to aquatic life and human health. The dispersion patterns of microplastic particles along estuaries are influenced and complicated by multiple environmental factors such as river flow, tidal mixing, salt intrusion, and estuarine circulation. This study examines the accumulation and dispersion patterns by modeling three typical classes of microplastics in the Delaware River Estuary: synthetic fibers, sinking plastic films, and rising plastic pellets. Our findings reveal specific areas with high microplastic accumulation for each type. Notably, the upper estuary regions exhibit significant retention of rising microplastics, associated with a region with reduced along-thalweg velocities downstream of Trenton, NJ and upstream of Philadelphia, PA. Conversely, synthetic fibers and sinking plastic films accumulate in the flow convergence zone near the bottom salinity front, typically downstream of Philadelphia. All of the microplastic accumulation hot spot locations are controlled by the balance of river discharge and salinity intrusions. During the dry season, microplastic accumulation hot spots shift upstream in the estuary, whereas in the wet season, the strong river discharge pushes them downstream. Furthermore, on the other hand, tidal mixing, settling, and resuspension processes strongly impact the spreading of microplastics along the river.

Delaware River Estuary↗
DOE OSTI2020

Automated Discovery for Emulytics

Sandia has an extensive background in cybersecurity research and is currently extending its state-of-the-art modeling via emulation capability. However, a key part of Sandia's modeling methodology is the discovery and specification of the information-system under study, and the ability to recreate that specification with the highest fidelity possible in order to extrapolate meaningful results. This work details a method to conduct information system discovery and develop tools to enable the creation of high-fidelity emulation models that can be used to enable assessment of our infrastructure information system security posture and potential system impacts that could result from cyber threats. The outcome are a set of tools and techniques to go from network discovery of operational systems to emulating complex systems. As a concrete usecase, we have applied these tools and techniques at Supercomputing 2016 to model SCinet, the world's largest research network. This model includes five routers and nearly 10,000 endpoints which we have launched in our emulation platform.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Scenario Generation for Built Environment Decision Support under Uncertainty: Case Studies of Airflow Modeling and Climate-Resilient Infrastructure System Design

When confronted with unforeseen challenges, practicing informed decision making is crucial for enhancing resilience in the built environment. While scan-to-building information modeling (BIM) is a well-established approach for creating detailed digital representations of physical assets, its application in assessing and improving infrastructure resilience remains underexplored. This study addresses this gap by proposing a novel application of scan-to-BIM, namely, scan-to-BIM-to-digital twin (S-BIM-DT) workflow. By integrating reality capture and digital twin technologies, this workflow creates continuously updated and accurate digital representations of physical assets, enabling the generation of various scenarios. Unlike traditional methods, the S BIM-DT workflow facilitates continuous model refinement, supporting informed resilience strategies. By combining these technologies into a cohesive process, the workflow facilitates decision making under uncertainty, enabling stakeholders to evaluate and respond to various scenarios effectively. We demonstrate the implementation of the S-BIM-DT workflow through two use cases that highlight its capability to enhance resilience at different scales. The first use case involves the Combined Transportation, Emergency, and Communications Center (CTECC) in Austin, Texas. BIM-enriched computational fluid dynamics (CFD) modeling simulates airflow and develops alternative scenarios for optimizing the heating, ventilation, and air conditioning (HVAC) systems. This approach enhances resilience against airborne health threats in a postCOVID context. The second use case focuses on designated areas within Beaumont, Texas, as part of the Southeast Texas Urban Integrated Field Laboratory (SETx-UIFL) research. By developing inundation maps to assess extreme weather events, this modeling aids in preparedness efforts and informs the development of climate-resilient infrastructure in vulnerable neighborhoods. Results indicate that the S-BIM-DT workflow effectively generates scenarios that enhance resilience in the built environment by facilitating informed decision making. Furthermore, this study serves as a bridge between advanced scan-to-BIM methodologies and the practical strategies needed to improve built infrastructure resilience.

Built environment↗
DOE OSTI2023

Cyber-Resilient Automatic Generation Control for Systems of AC Microgrids

In this paper we propose a co-design of the secondary frequency regulation in systems of AC microgrids and its cyber security solutions. We term the secondary frequency regulator a Micro-Automatic Generation Control (μ AGC) for highlighting its same functionality as the AGC in bulk power systems. We identify sensory challenges and cyber threats facing the μ AGC. To address the sensory challenges, we introduce a new microgrid model by exploiting the rank-one deficiency property of microgrid dynamics. This model is used to pose an optimal μ AGC control problem that is easily implemented, because it does not require fast frequency measurements. An end-to-end cyber security solution to the False Data Injection (FDI) attack detection and mitigation is developed for the proposed μ AGC. The front-end barrier of applying off-the-shelf algorithms for cyber attack detection is removed by introducing a data-driven modeling approach. Finally, we propose an observer-based corrective control for an islanded microgrid and a collaborative mitigation scheme in systems of AC microgrids. We demonstrate a collaborative role of systems of microgrids during cyber attacks. Furthermore, the performance of the proposed cyber-resilient μ AGC is tested in a system of two networked microgrids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Coupling of the Electricity and Transportation Sectors - Part II: Risk Assessment

This comprehensive report explores the intricate interplay through “sector coupling” between the Transportation Sector, Energy Sector, and the critical infrastructure challenges faced by the United States. Aligned with the ambitious federal goal of reducing greenhouse gas emissions, the report analyzes the present vulnerabilities in the fossil fuel supply chain, highlighting instances where the dependence on this industry has been susceptible to local and global disruptions. With a focus on risk assessment, this report establishes a framework to compare the fossil fuel-based transportation sector with the burgeoning electrified sector, considering the evolving technologies in electric vehicles (EVs) and the nascent charging infrastructure. This study delves into the historical reliability of fossil fuel-based transportation and identifies opportunities for strengthening sector coupling between transportation and the electric sector. Assessments of the gasoline-diesel supply chain provide insights into both opportunities and threats, offering a roadmap for creating a more reliable and resilient electrified transportation system. We modeled sectoral coupling scenarios for the current internal combustion engine (ICE)-based and future EV-dominant landscapes, leveraging qualitative sector attributes to identify relative strengths and weaknesses in Part 1 of the report. In this report (Part 2) we continue to understand the role of the electricity sector on transportation and develop a semi-quantitative methodology is employed to evaluate and compare the risks associated with the fossil fueled and electric transportation sector during emergency scenarios, drawing from real-world events like hurricanes and geopolitical disruptions. Different operational scenarios for the fossil fuel and electrified sectors have been developed to understand the risks associated with them. This report concludes with recommendations for the architectural designs of electric charging infrastructure, underlining the need for informed decisions in the transition towards a sustainable and resilient transportation future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Variational data augmentation for a learning-based granular predictive model of power outages

As the trend in climate change continues, extreme weather events are expected to occur with increasing frequency and severity and pose a significant threat to the electric power infrastructure. Regardless of the efforts a utility puts towards hardening the grid, storm-induced damage to the utility assets such as cables and distributed energy resources (DERs) that are particularly vulnerable to such events is unavoidable. Access to a highly granular, in space and time, outage forecasting tool with long lead times (i.e., days ahead) will enhance the efficiency of service restoration efforts. Here, in this study, we propose to develop and implement a multi-model framework as an operational tool based on a granular and multi-day outage forecasting model using operational numerical weather prediction model forecasts and detailed component outage information. An innovative two-layered recurrent neural network, i.e., a long-short-term-memory (LSTM)-based variational autoencoder (VAE) framework and a sliding window are used to address the uneven distribution of different types of weather events and make better use of the time-series data. Case studies are performed to demonstrate the performance of the new framework.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Defensive Cybersecurity Architecture Design Using Force-on-Force Cyber-Physical Modeling

Currently, nuclear power plant physical security systems are highly dependent on air-gaps as a protective measure against cyber-threats. Cyber-physical threats become more likely as advanced cyber-threat capabilities to jump air-gaps transition into common use. Defending against the emerging threat of cyber-enabled physical intrusions is poorly understood. The consequence of these cyber-physical attacks has no quantitative analysis method to inform risk-informed, performance-based cybersecurity approaches. By modifying the physical security simulation tool Dante, cyber-physical threat consequence was able to be analyzed on a notional facility. The results of this analysis are used to design a Defensive Cybersecurity Architecture (DCSA) for the physical security system to produce example resilience measures for this notional facility. A DCSA defines security levels to provide a graded approach for defending plant functions, and security zones for trusted communication between systems. This approach can be applied to real world systems to produce physical protection systems and response measures that are resilient to cyber-physical threats.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

Modeling the Water Systems of the Western US to Support Climate‐Resilient Electricity System Planning

Abstract Electricity and water systems in the Western US (WUS) are closely connected, with hydropower comprising 20% of total annual WUS generation, and electricity related to water comprising about 7% of total WUS electricity use. Because of these interdependencies, the threat of climate change to WUS resources will likely have compounding electricity impacts on the Western Interconnect grid. This study describes a WUS‐wide water system model with a particular emphasis on estimating climate impacts on hydropower generation and water‐related electricity use, which can be linked with a grid expansion model to support climate‐resilient electricity planning. The water system model combines climatically‐driven physical hydrology and management of both water supply and demand allocation, and is applied to an ensemble of 15 climate scenarios out to 2050. Model results show decreasing streamflow in key basins of the WUS under most scenarios. Annual water‐related electricity use increases up to 4%, and by up to 6% during the summer months, driven by growing agricultural demands met increasingly through a shift toward energy‐intensive groundwater to replace declining surface water. Total annual hydropower generation changes by +5% to −20% by mid‐century but declines in most scenarios, with decreases in summer generation by up to nearly −30%. Water‐related electricity use increases tend to coincide with hydropower generation declines, annually and seasonally, demonstrating the importance of concurrently evaluating the climate signal on both water‐for‐energy and energy‐for‐water to inform planning for grid reliability and decarbonization goals.

13 HYDRO ENERGY↗