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Airships as Transformative Tools for Power Systems Logistics: Addressing Infrastructure Challenges and Enhancing Disaster Resilience

Airships represent a transformative advancement in logistics, particularly for the power systems industry. Their unique ability to transport heavy equipment and personnel efficiently over remote or disaster-stricken areas, while requiring minimal infrastructure, addresses critical gaps left by traditional methods. Combining adaptability with environmental sustainability, airships offer a low-carbon alternative for routine operations and emergency responses. This review presents current developments in airship technology, such as rigid airships designed for large payloads, demonstrate their potential to revolutionize supply chains.

Fuhr, Peter L. [ORNL] (ORCID:0000000211627445)↗

Quick Talk: Resilient Communities, Maryland

Groundswell, Inc., in partnership with the Maryland Energy Administration (MEA), Ayika Solutions, the National Renewable Energy Lab (NREL), and the Baltimore Office of Sustainability, is advancing a vision for resilient, community-centered energy systems in Maryland. This effort aligns with Groundswell's commitment to a clean energy economy that equitably benefits all communities, closing gaps in representation, wages, and access to wealth generation, savings, and local jobs within a diverse energy supply chain. Project Vision and Objectives The project emphasizes "energy resilience," defined as a community’s capacity to anticipate, adapt to, and swiftly recover from power disruptions through inclusive planning and technical solutions. The approach centers on Resilience Hubs—designated community facilities equipped to support residents before, during, and after power-related disruptions. These hubs offer emergency resources, coordinate communications, and deliver essential services while reducing carbon emissions, ultimately enhancing residents' quality of life. Additionally, Resilience Hubs foster social connections, support disaster preparedness, and provide spaces for community programming and everyday use. The goals of Resilient Communities Maryland include: Developing a replicable framework for community energy resilience, applicable beyond Maryland. Defining community-led indicators of social impact and resilience. Establishing equitable decision-making processes in project design and implementation. Informing MEA of practical, community-based resilience strategies to shape future state initiatives. Approach Groundswell and its partners prioritize collaborative engagement with local stakeholders, ensuring that solutions are responsive to each community's unique needs. By integrating feedback from residents, local organizations, and government entities, the team has developed a holistic model for resilience that balances immediate benefits, such as improved emergency response, with long-term gains in social equity and environmental sustainability. Key Outcomes This initiative has produced valuable resources and insights, including: A Community Energy Resilience Framework: Designed to guide other communities in establishing energy resilience strategies tailored to their specific needs. Case Studies: Documenting best practices and lessons learned from pilot projects to serve as models for future resilience efforts. Community-Driven Impact Metrics: Highlighting the positive social impacts that resilience initiatives can bring, such as improved social connectedness and local decision-making power. Recommendations for MEA: Offering evidence-based insights to inform policies and funding opportunities that support resilient energy infrastructure in vulnerable communities. Next Steps Groundswell's final report will detail findings, including the potential for scaling resilience hubs across Maryland and other regions, with a focus on equitable access and community-driven outcomes. This project underscores the importance of aligning energy resilience with social equity, reinforcing community agency, and building systems that withstand and recover from future challenges effectively and sustainably.

14 SOLAR ENERGY↗

Modeling evacuation demand during no-notice emergency events: Tour formation behavior

Disastrous events have been drastically increasing – both in frequency and destructive capacity – over the past few years. While advance-notice events have received a great deal of attention in the literature of disaster management, not much attention so far has been given to the no-notice events mainly because of the scarcity of data. As an attempt to address this critical gap, the current study proposes a disaggregate evacuation demand framework to understand evacuees’ travel behavior in case of no-notice emergency events. Here, the proposed framework comprises four main steps of evacuation decision, evacuation planning, tour formation, and activity schedule update. This article is dedicated to the introduction of the framework structure and elaboration on the tour formation step. In this step, we first estimate the total number of intermediate stops, travel time, and distance of the evacuation tours for those who decide to evacuate through a joint modeling structure and then, determine the type of each intermediate stop (if any). It is found that a broad range of factors including evacuees’ demographic profiles, built-environment attributes, and characteristics of the disastrous event plays a significant role in people’s evacuation behavior during no-notice emergency events. The findings of this study can assist responsible agencies in understanding evacuees’ complex behavior, and consequently, in devising effective strategies to alleviate economic damages and casualties resulted by such events.

99 GENERAL AND MISCELLANEOUS↗

Community Resilience Indicator Analysis: Commonly Used Indicators from Peer-Reviewed Research (Updated for Research Published 2003-2021)

In 2017, FEMA’s National Integration Center (NIC) Technical Assistance (TA) Branch identified a need to establish a data-driven basis for prioritizing locations for TA investment and guiding local emergency management planning. To achieve this goal, FEMA tasked Argonne National Laboratory (Argonne) with identifying commonly used indicators of community resilience across the landscape of published peer-reviewed research. FEMA and Argonne completed the first Community Resilience Indicator Analysis (CRIA) in 2018 and repeated the process in 2022. The CRIA process begins with a literature review and cataloguing of published peer-reviewed assessment methodologies on social vulnerability and community resilience. The literature review findings are then filtered by inclusion criteria established by the CRIA research team to ensure the methodologies are: (1) Quantitative, (2) Data and methodology are publicly available, (3) Calculated at the county level or lower, (4) Examine generalized hazard risk (rather than a singular hazard), and (5) Focused on pre-disaster community conditions. After this, the research team identifies the commonly used indicators across these methodologies and selects the best data source for each indicator. Finally, the research team bins the data for visual display, conducts a correlation analysis and creates a composite index, the FEMA Community Resilience Index (FEMA CRI). In 2018, the CRIA identified eight resilience and vulnerability assessment methodologies and 20 commonly used indicators (indicators used in three or more of the eight methodologies). The FEMA CRI in 2018 was created from these 20 indicators and was produced for at the county level. The 2022 CRIA updated the literature review to expand the list of methodologies examined and followed the same process, resulting in an analysis of 14 methodologies published between 2003 and 2021 and 22 indicators identified as commonly used (indicators used in five or more of the 14 methodologies). In 2022, the research team produced the FEMA CRI at the county and the census tract levels. To make the CRIA data more accessible and more actionable, each individual indicator and the FEMA CRI is binned and included in FEMA’s Resilience Analysis and Planning Tool (RAPT). RAPT enables emergency managers and community partners to quickly visualize relative differences in potential resilience by county, tribe and census tract. By reviewing the data for each of these 22 indicators individually, emergency managers can gain insights for targeted outreach strategies, planning, mitigation investments and response and recovery operations. Communities, regional governments and others can use this data to better understand potential challenges to resilience. As the social science field of examining and validating indicators of resilience evolves, FEMA will update RAPT to provide emergency managers and community partners with additional data and tools to inform planning, mitigation, response and recovery. It is important to understand that the role of the emergency manager is not to change or to “improve” the data, but to plan appropriately for the community characteristics reflected in the data. These datasets are community characteristics that researchers have identified as important considerations for resilience. For example, people with disabilities may have greater challenges to be resilient to disasters. If a community has a high population of people with disabilities, the emergency manager(s) may need to create tailored preparedness outreach programs and strategies to ensure those residents have support if evacuation is necessary. Rather than label these indicators as an absolute measure of resilience, FEMA considers “potential challenges to resilience” a better frame to understand these indicators. Everyone is vulnerable to disasters. While scholars theorize that certain characteristics may make an individual or a household more socially vulnerable, the data does not reflect measures that individuals and/or communities have taken to address potential challenges, such as emergency management planning and outreach or household preparedness measures. To aid emergency managers in understanding how to use these indicators, calling them potential challenges to resilience supports a more positive and strategic application of the data in all phases of emergency management.

99 GENERAL AND MISCELLANEOUS↗

Investigating Resiliency of Transportation Network under Targeted and Potential Climate Change Disruptions

Ensuring robustness and resilience in intermodal transportation systems is essential for the continuity and reliability of global logistics. These systems are vulnerable to various disruptions, including natural disasters and technical failures. Despite significant research on freight transportation resilience, investigating the robustness of the system after targeted and climate-change-driven disruption remains a crucial challenge. Drawing on network science methodologies, this study models the interdependencies within the rail and water transport networks and simulates different disruption scenarios to evaluate system responses. Here, we use the data from the U.S. Department of Energy Volpe Center for network topology and tonnage projections. The proposed framework quantifies deliberate, stochastic, and climate-driven infrastructure failure, using higher resolution downscaled multiple Earth System Models’ simulations from Coupled Model Intercomparison Project Phase version 6. We show that the disruptions of a few nodes could have a larger impact on the total tonnage of freight transport than on network topology. For example, the removal of targeted 20 nodes can bring the total tonnage carrying capacity to 30% with about 75% of the rail freight network intact. This research advances the theoretical understanding of transportation resilience and provides practical applications for infrastructure managers and policymakers. By implementing these strategies, stakeholders and policymakers can better prepare for and respond to unexpected disruptions, ensuring sustained operational efficiency in transportation networks.

Climate Change Disruptions↗

Essential Function Analysis Capability

The Essential Function Analysis Capability (EFAC) is an extension of the All Hazards Knowledge Framework (AHA) and is a dynamic analytical framework that enables critical infrastructure knowledge discovery and decision support across the five mission areas – prevention, protection, mitigation, response and recovery. EFAC provides the ability to store and model infrastructure systems as a linked multigraphs providing an intuitive and natural representation. These infrastructure systems can then be tied to the organizational and essential function breakdown of an entity. This capability provides the foundation to rapidly evaluate and understand the potential consequences of manmade and natural disaster on these infrastructure systems and essential functions.

Hoover, MichaelJ.↗

A Method for Measuring Coupled Individual and Social Vulnerability to Environmental Hazards

Although models of social vulnerability to environmental hazards are commonly developed to support policy interventions in emergencies and disasters, their utility is hindered by a lack of contextual information on individuals exposed to and affected by hazards. We develop a novel approach to model social vulnerability that couples individuals and their varying forms of protective capacity with the social fabric of the communities in which they reside. The backbone of our model is the Public-Use Microdata Sample (PUMS), a product of the U.S. Census Bureau that preserves a representative sample of completed responses to the American Community Survey (ACS). The PUMS enables us to understand the full range of individual protective capacities against a hazard in an exposed area, which we term individual vulnerability profiles (IVPs). In this case, we examine IVPs in the Coney Island-Brighton Beach section of New York City, which suffered severe impacts during Hurricane Sandy in 2012. To manage the large number of unique IVPs in Coney Island-Brighton Beach, we perform a segmentation analysis to generalize them into thematic cohort vulnerability profiles (CVPs) representing a typology of vulnerable people in Coney Island-Brighton Beach during Sandy. From synthetic populations of CVPs, we then estimate how individuals in varying housing types were coexposed to Sandy at the census tract level by classifying these areas into community social vulnerability profiles (SVPs). Our results provide a topology of social vulnerability that simultaneously links individual, community, and population-wide concerns, enabling a more holistic understanding of resources and interventions beneficial to human security during events like Sandy than is attainable with area-level metrics.

54 ENVIRONMENTAL SCIENCES↗

Resilience of electric utilities during the COVID-19 pandemic in the framework of the CIGRE definition of Power System Resilience

Resilience is a vital concept in engineering, business, and natural sciences, and is a measure of the ability of an entity to withstand High Impact Low Probability (HILP) events. During the COVID-19 pandemic, which started in late 2019/early 2020, power system utilities around the globe have responded in effective and efficient ways to enhance the resilience of their organisations, both in terms of real-time operations and prudent management of its infrastructure, in order to continue their mandate in providing reliable supply to meet customer demands. Here, this paper presents the CIGRE definition for power system resilience, established by the C4.47 Working Group in 2018, and demonstrates the application of resilience-oriented thinking within the electrical sector. The response and recovery efforts are described, with respect to the key actionable measures integral to the power system resilience definition, taken before, during and after the COVID-19 pandemic. A practical conceptual framework is also presented for thinking about resilience in terms of three key components of resilience strategies: organisational, infrastructure and operational resilience. The paper also discusses the different strategies adopted in response to COVID-19, based on the C4.47 members’ experiences during the pandemic. Finally, a case study is presented, which proves the effectiveness of a set of response measures, using graph theory and the characteristics of the staff-asset interactions.

42 ENGINEERING↗

An open-access simulated earthquake ground-motion database for an M7 Hayward Fault earthquake in the San Francisco Bay Region

Comprehensive understanding of earthquake ground motions, particularly in the near-fault region of large-magnitude events, is limited by gaps in strong-motion data. This challenge is prominent in areas with high seismic hazard but infrequent large earthquakes where data is sparse and difficult to interpret. These data limitations lead to uncertainties in the development of site-specific ground motions, which are crucial for engineering risk assessments. To address these challenges, physics-based regional-scale ground-motion simulations have been developed. With the emergence of exaflop-scale computing ecosystems, it is now possible to simulate regional earthquake processes at unprecedented fidelity and generate the large number of fault rupture realizations necessary to characterize both intra- and inter-event ground-motion variability. This article introduces a new database of simulated earthquake ground motions, created for applications in earthquake engineering, earthquake planning, and emergency response. The inaugural version of the database features simulated ground motions for a magnitude 7 Hayward Fault earthquake in the San Francisco Bay Region (SFBR), using the EarthQuake SIMulation (EQSIM) simulation framework and the Graves–Pitarka kinematic rupture model. The aim is to provide high-fidelity, spatially dense, three-component motions generated on the Department of Energy’s (DOE) newest generation of graphics processing unit (GPU)-accelerated supercomputers. These motions are being made openly available to the engineering, scientific, and disaster planning communities. In addition, this work develops protocols for the efficient dissemination of these large data sets and emphasizes community engagement to build confidence in their application. This article discusses the methodology behind the data, underlying software verification and validation, scalable data management, and a user interface for data access. The goal is to facilitate widespread use and elicit expert feedback to maximize the utility and exploitation of simulated motions. While the initial focus is on the San Francisco Region, simulations for additional regions will be added as the DOE program progresses.

Simulated ground-motion database↗

SpaceNet 8 - The Detection of Flooded Roads and Buildings

The frequency and intensity of natural disasters (i.e. wildfires, storms, floods) has increased over recent decades. Extreme weather can often be linked to climate change, and human population expansion and urbanization have led to a growing risk. In particular floods due to large amounts of rainfall are of rising severity and are causing loss of life, destruction of buildings and infrastructure, erosion of arable land, and environmental hazards around the world. Expanding urbanization along rivers and creeks often includes opening flood plains for building construction and river straightening and dredging speeding up the flow of water. In a flood event, rapid response is essential which requires knowledge which buildings are susceptible to flooding and which roads are still accessible. To this aim, SpaceNet 8 is the first remote sensing machine learning training dataset combining building footprint detection, road network extraction, and flood detection covering 850km 2, including 32k buildings and 1,300km roads of which 13% and 15% are flooded, respectively.

Arndt, Jacob↗

The detection and attribution of extreme reductions in vegetation growth across the global land surface

Abstract Negative extreme anomalies in vegetation growth (NEGs) usually indicate severely impaired ecosystem services. These NEGs can result from diverse natural and anthropogenic causes, especially climate extremes (CEs). However, the relationship between NEGs and many types of CEs remains largely unknown at regional and global scales. Here, with satellite‐derived vegetation index data and supporting tree‐ring chronologies, we identify periods of NEGs from 1981 to 2015 across the global land surface. We find 70% of these NEGs are attributable to five types of CEs and their combinations, with compound CEs generally more detrimental than individual ones. More importantly, we find that dominant CEs for NEGs vary by biome and region. Specifically, cold and/or wet extremes dominate NEGs in temperate mountains and high latitudes, whereas soil drought and related compound extremes are primarily responsible for NEGs in wet tropical, arid and semi‐arid regions. Key characteristics (e.g., the frequency, intensity and duration of CEs, and the vulnerability of vegetation) that determine the dominance of CEs are also region‐ and biome‐dependent. For example, in the wet tropics, dominant individual CEs have both higher intensity and longer duration than non‐dominant ones. However, in the dry tropics and some temperate regions, a longer CE duration is more important than higher intensity. Our work provides the first global accounting of the attribution of NEGs to diverse climatic extremes. Our analysis has important implications for developing climate‐specific disaster prevention and mitigation plans among different regions of the globe in a changing climate.

59 BASIC BIOLOGICAL SCIENCES↗

Deep Reinforcement Learning for Resilient Power and Energy Systems: Progress, Prospects, and Future Avenues

In recent years, deep reinforcement learning (DRL) has garnered substantial attention in the context of enhancing resilience in power and energy systems. Resilience, characterized by the ability to withstand, absorb, and quickly recover from natural disasters and human-induced disruptions, has become paramount in ensuring the stability and dependability of critical infrastructure. This comprehensive review delves into the latest advancements and applications of DRL in enhancing the resilience of power and energy systems, highlighting significant contributions and key insights. The exploration commences with a concise elucidation of the fundamental principles of DRL, highlighting the intricate interplay among reinforcement learning (RL), deep learning, and the emergence of DRL. Furthermore, it categorizes and describes various DRL algorithms, laying a robust foundation for comprehending the applicability of DRL. The linkage between DRL and power system resilience is forged through a systematic classification of DRL applications into five pivotal dimensions: dynamic response, recovery and restoration, energy management and control, communications and cybersecurity, and resilience planning and metrics development. This structured categorization facilitates a methodical exploration of how DRL methodologies can effectively tackle critical challenges within the domain of power and energy system resilience. The review meticulously examines the inherent challenges and limitations entailed in integrating DRL into power and energy system resilience, shedding light on practical challenges and potential pitfalls. Additionally, it offers insights into promising avenues for future research, with the aim of inspiring innovative solutions and further progress in this vital domain.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Valuing Resilience for Microgrids: Challenges, Innovative Approaches, and State Needs

The United States depends on the delivery of reliable, affordable, clean, and safe electricity. Electric utilities invest billions of dollars each year in generation, transmission, and distribution assets to meet this need. However, experiences with recent natural disasters of increasing frequency and duration demonstrate the shortcomings of this approach in the face of modern threats. Further, as customers rely on electricity for a broader range of important needs, such as transportation, as well as critical life-saving services and mission critical facilities such as water treatment, medical care, shelters, telecommunications, and more, the need to minimize the likelihood and impacts of outages grows. Against this backdrop, resilience has emerged as a key consideration to guide electricity spending, whether from utilities, customers, or taxpayers. Although reliability has been defined and measured for decades with broadly accepted metrics that measure how many customers lose power and at what frequency and duration, resilience considers the electricity system’s response to a disruption and its subsequent impacts on customers. Developing tools and methods to accurately assess the costs and benefits of resilience investments is a critical step toward the goal of mitigating the impacts of outages on customers and society. Today, electric system resilience is largely treated as an externality due to challenges estimating the costs of long-duration outages, impacts of outages on society, and increasing reliance on electricity for a growing set of interdependent services. These interdependencies include the water, wastewater, telecommunications, natural gas, and health sectors. Without knowing how much a given resilience investment will benefit customers or society more broadly, investors, policymakers, and regulators are less likely to make or approve such investments, and less able to prioritize those investments. State Energy Offices and public utility commissions (PUCs) lead the development of state-level energy policy and utility regulation, respectively, and each has interests in encouraging appropriate public and private investments in resilience. To this end, the National Association of State Energy Officials (NASEO) and National Association of Regulatory Utility Commissioners (NARUC), with the support of the U.S. Department of Energy (DOE) Office of Electricity (OE), formed a joint Microgrids State Working Group to explore the costs and benefits of microgrids, barriers to broader deployment of microgrids to meet resilience and other objectives, and policy and regulatory strategies to optimize investments in resilience, including but not limited to microgrids. Although no universally accepted valuation tool for resilience exists, National Laboratories, utilities, researchers, and state and federal agencies have collaborated to develop, apply, and improve a number of approaches to quantify resilience, several of which are still in progress at the time this report is published. This report seeks to share these important advances by discussing current definitions of resilience (Section 1), how microgrids are defined and used to meet resilience objectives (Section 2), new approaches to valuing resilience (Section 3), steps State Energy Offices and PUCs have taken to further resilience valuation efforts (Section 4), and finally, considerations and suggested next steps for State Energy Offices and PUCs (Section 5). Relevant examples of specific microgrid projects and resilience valuation efforts are included throughout the report. While this report is written specifically for NASEO and NARUC members, it may be useful for utilities, local governments, and individual customers interested in improving the way public and private dollars are spent to achieve resilience outcomes.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Integrated Model of Models for Global Flood Alerting

A dramatic increase in frequency of minor to major flooding since 2000 has caused significant economic losses across the world. To mitigate and recover from these losses, actions have been taken to build resilient communities and infrastructures, specifically, by providing situational awareness in near real-time about flood impacts to enhance response and recovery efforts. Several hydrologic and hydraulic flood models are available at various spatial and temporal resolutions to forecast flood events at regional to global scale. Given the global coverage of two operational flood models – GloFAS (Global Flood Awareness System) and GFMS (Global Flood Monitoring System), the purpose of this project is to implement a Model of Models (MoM) approach to integrate the outputs from these two models to classify flood severity at watershed level worldwide, and send alerts based on severity similar to the USGS PAGER (used for severity alerting and impact analysis for earthquakes) to flood impacted communities. The alerts containing flood impacts and severity information will be disseminated through the DisasterAWARE platform, operated by the Pacific Disaster Center (PDC), that provides global multi-hazard alerting and Situational Awareness information to the emergency management community and public. The current version of the MoM approach was implemented for a case study flood event that occurred during January and February of 2020 in South and Central Africa. The findings of the case study event reveal that the approach is effective in identifying potential flood impact areas and the spatio-temporal variation of flood severity, flood depth and extent at watershed level, which will be used to assess infrastructure and societal impacts using earth-observation data and for alerting.

Kar, Bandana↗

Identification of Metal Stresses in Arabidopsis thaliana Using Hyperspectral Reflectance Imaging

Industrial accidents, such as the Fukushima and Chernobyl disasters, release harmful chemicals into the environment, covering large geographical areas. Natural flora may serve as biological sensors for detecting metal contamination, such as cesium. Spectral detection of plant stresses typically employs a few select wavelengths and often cannot distinguish between different stress phenotypes. In this study, we apply hyperspectral reflectance imaging in the visible and near-infrared along with multivariate curve resolution (MCR) analysis to identify unique spectral signatures of three stresses in Arabidopsis thaliana : salt, copper, and cesium. While all stress conditions result in common stress physiology, hyperspectral reflectance imaging and MCR analysis produced unique spectral signatures that enabled classification of each stress. As the level of potassium was previously shown to affect cesium stress in plants, the response of A. thaliana to cesium stress under variable levels of potassium was also investigated. Increased levels of potassium reduced the spectral response of A. thaliana to cesium and prevented changes to chloroplast cellular organization. While metal stress mechanisms may vary under different environmental conditions, this study demonstrates that hyperspectral reflectance imaging with MCR analysis can distinguish metal stress phenotypes, providing the potential to detect metal contamination across large geographical areas.

multivariate curve resolution↗

Advancing process-based flood frequency analysis for assessing flood hazard and population flood exposure

Recent studies have showcased the use of process-based hydrological models with Stochastic Storm Transposition (SST) techniques to conduct Flood Frequency Analysis (FFA). This framework, referred hereby FFA-SST, has proved to be a robust strategy to estimate peak flows of specific annual exceedance probability (e.g., 100-year peak flow) that can reflect natural and anthropogenic disturbances, including changes in land use and meteorological patterns. With the objective of advancing the FFA-SST framework, this study presents for the first time the use of an Integrated Surface-Subsurface Hydrological Model (ISSHM) to conduct FFA-SST by extending the analysis from peak flow responses to flood extent, enabling a unique view and analysis of flood hazard and population flood exposure at the basin scale. As a proof-of-concept, we used the ISSHM, Advanced Terrestrial Simulator (Amanzi-ATS) model, and the SST model, RainyDay, to conduct FFA-SST by simulating the flood response to 5,000 annual synthetic storm events in a 2,227 $km^2$ Southeast Texas watershed. We demonstrate that ATS, without site-specific calibration, provides a robust process-based representation of peak flows, flood extent, streamflow, evapotranspiration, soil moisture content, and water storage changes. Our results and analyses, covering frequency curves up to a 500-year return period for peak flows, basin inundation fractions, and the number of people exposed to flooding, offer a unique perspective to analyze flood impacts across spatial scales. Overall, this study provides critical insights for flood risk management by extending the FFA-SST framework to include both flood hazard and population flood exposure analyses at the basin scale. Such an approach will empower stakeholders and disaster emergency agencies with a more comprehensive understanding of flood impacts across the entire basin domain, facilitating informed decision-making for flood risk assessment and management.

58 GEOSCIENCES↗

Integrated modeling analysis of estuarine responses to extreme hydrological events and sea-level rise

Estuaries are vulnerable to climate change and are particularly sensitive to extreme river discharge (RD) and sea-level rise (SLR). To understand the effects of extreme RD and SLR on estuaries, an integrated modeling approach is adopted to simulate hydrodynamic changes in the Yangtze River Estuary. Model results showed that SLR will intensify the estuarine hydrodynamics in water level, currents, and salinity. Water level, current speed, and salinity increased linearly with the magnitude of SLR. The hydrological model suggested a lower RD in the future. This will increase the saltwater intrusion, which will be more severe during the lower RD and drought extreme hydrological event (EHE) conditions. Model results also suggested that the saltwater intrusion is more sensitive to RD than SLR. In addition, the interplay between the RD and SLR will exacerbate the impacts of coastal oceans on estuarine hydrodynamics, especially during extremely low RD conditions. Moreover, the decrease of RD and deepening of the channel due to human activities will also amplify the effects of SLR on the Estuary, increasing the risk of coastal disasters.

3-D hydrodynamic model, extreme climate, draught, ↗

SolarPlus-Optimizer v0.1

With the falling costs of solar arrays and battery storage and reduced reliability of the grid due to natural disasters, small-scale local generation and storage resources are beginning to proliferate. However, very few software options exist for integrated control of building loads, batteries and other distributed energy resources. The available software solutions on the market can force customers to adopt one particular ecosystem of products, thus limiting consumer choice, and are often incapable of operating independently of the grid during blackouts. In this software package, we present the "Solar+ Optimizer" (SPO), a control platform that provides demand flexibility, resiliency and reduced utility bills, built using open-source software. SPO employs Model Predictive Control (MPC) to produce real time optimal control strategies for the building loads and the distributed energy resources on site. SPO is designed to be vendor-agnostic, protocol-independent and resilient to loss of wide-area network connectivity. The software was evaluated in a real convenience store in northern California with on-site solar generation, battery storage and control of HVAC and commercial refrigeration loads. Preliminary tests showed price responsiveness of the building and cost savings of more than 10% in energy costs alone.

Prakash, AnandKrishnan↗