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At least 145 records · Page 8

All sources NASA NTRS DOE OSTI

NASA NTRS2022

Milwaukee Urban Development: Assessing the Drivers of Urban Flooding Vulnerability in Milwaukee Using NASA Earth Observations

Milwaukee County has experienced an increase in flooding due to climate change and urbanization. The frequency and severity of flooding vary spatially due to differences in land cover, surface permeability, and infrastructure. Marginalized communities tend to experience disproportionately high flooding and damage due to infrastructural inequalities and limited access to resources. To quantify these differences, we used the Natural Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation Model to calculate and create maps of runoff retention, nominal flood depth, and economic damage to buildings in Milwaukee. Our model inputs included land cover, surface permeability, and rainfall. To inform our precipitation inputs, we used NASA’s Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG) and National Weather Service (NWS) data. We assessed the relationship between flood risk and social and environmental spatial data including redlining, racial demographics, greenspace, and community resilience. The data demonstrate that flood risk is higher in historically redlined neighborhoods, majority Hispanic and Black census block groups, areas that lack parks and trees, and areas of low community resilience as measured by the Census Bureau’s Community Resilience Estimates (CRE). These findings will support our partners, Groundwork Milwaukee and Groundwork USA, in their efforts to promote the equitable distribution of resources and support environmental health in urban spaces. The end products of this project provide our partners with tools to assess urban flooding vulnerability, guide future intervention projects, quantify the effects of environmental injustice, and improve stakeholder access to data.

Madeleine Tango↗

NASA NTRSDate not supplied

Milwaukee Urban Development: Assessing the Drivers of Urban Flood Vulnerability in Milwaukee using the Integrated Valuation of Ecosystem Services and Tradeoffs Urban Flood Risk Mitigation Model (InVEST)

Madeleine Tango↗

DOE OSTI2025

Energy Justice Through Energy Storage: Supporting Energy Resilience in Disadvantaged Communities

This paper reviews energy storage technologies as a possible solution to address power outages and mitigate the impacts, enhancing vulnerable communities’ resilience to climate change. More frequent and severe extreme weather events are one of the main consequences of climate change. These events, coupled with the nation’s aging and frail energy infrastructure, are causing many communities throughout the United States to experience more frequent and longer power outages, resulting in economic, health, and social impacts. Impacts from power outages are not equally felt across communities, with disproportionate impacts across communities representing a matter of energy justice. From an energy justice standpoint, programs and policies that facilitate energy storage technologies should be intentionally designed to include distributive, procedural, recognition, and restorative justice.

25 ENERGY STORAGE↗

DOE OSTI2026

Multi‐Objective Urban Observational Strategies: A Risk‐Based Framework for Expanding Flood Sensor Networks

In coupled human and natural systems, developing an observation strategy which maximizes insight into both the natural system and the human system is a challenging multi-objective optimization problem. In this article, we describe the expansion of a flood risk observation system in Southeast Texas designed to improve our understanding of both physical and socioeconomic exposure to hydrological hazards at fine spatial scales, in the context of a structured hazard-exposure-vulnerability risk framework. We describe a new approach for assessing the spatial extent through which a flood sensor's observations can be assumed to be relevant, and estimate the population served within each sensor's area of information using downscaled socio-demographic data. As hydrological observations and modeling move to ever finer scale, assessing the information they contain in the context of both social and natural systems becomes increasingly important for developing actionable scientific insights.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

TPSAS-NF1676L-31944-DND

The Plum Island Estuary (PIE) in Massachusetts is New England’s largest salt marsh. This dynamic ecosystem plays an important role in the surrounding communities by providing ecosystem and social services, acting as a center for education, research, and recreation. However, marshes around the world are threatened by sea level rise. As the equilibrium between sediment supply, erosion rates, and vegetation growth is unbalanced by rising waters, marshes are liable to recede, or even drown. Because sediment is so crucial, researchers use sediment budgets as a metric to assess Plum Island Estuary’s vulnerability to rising seas; however, established data collection methods are limited in scope. The project employed five years of imagery from Landsat 8 Operational Land Imager (OLI) and three years of imagery from Sentinel-2 Multispectral Instrument (MSI), in conjunction with in situ data, to generate and refine a local algorithm that derives suspended sediment concentration from remote sensing reflectance. This information was applied to a flux model to analyze transport patterns and possible sediment sources, particularly the Merrimack River. The use of remote sensing techniques will provide our partners at the US Geological Survey, US Fish and Wildlife Service, and Plum Island Ecosystems Long Term Ecological Research Network with higher spatial and temporal resolution data, which will allow for the development of more effective management practices.

Sydney Neugebauer↗

DOE OSTI2025

AI-based Cyber Event OSINT via Twitter Data

Open-Source Intelligence (OSINT) is largely regarded as a necessary component for cybersecurity intelligence gathering to secure network systems. With the advancement of artificial intelligence (AI) and increasing usage of social media, like Twitter, we have a unique opportunity to obtain and aggregate information from social media. In this study, we propose an AI-based scheme capable of automatically pulling information from Twitter, filtering out security-irrelevant tweets, performing natural language analysis to correlate the tweets about each cybersecurity event (e.g., a malware campaign), and validating the information. This scheme has many applications, such as providing a means for security operators to gain insight into ongoing events and helping them prioritize vulnerabilities to deal with. To give examples of the possible uses, we present three case studies demonstrating the event discovery and investigation processes.

Dale, Dakota↗

DOE OSTI2025

Open Source Intelligence for Cybersecurity Events via Twitter Data

Dale, Dakota↗

DOE OSTI2024

Leverage demand-side policies for energy security

Energy security is a top priority for governments, companies, and households because energy systems and the critical functions that they support are threatened by disruptions from wars, pandemics, climate change, and other shocks (1). More often than not, governments rely on policies focused on energy supply to enhance energy security while generally ignoring demand-side possibilities. Further, the indicators traditionally used to measure energy security are also tilted toward the supply side; this fails to capture the full spectrum of vulnerability to energy crises. Energy security assessments need to reflect the wider benefits of security related interventions more accurately. To that end, we develop a systematic approach to measuring the energy security impacts of policy interventions that explicitly considers energy demand (buildings, transport, and industry). Here, we determine that demand-side actions outperform conventional supply-side approaches at making countries more resilient. Energy demand links more directly than supply to the satisfaction of critical social functions and human well-being that are at the core of energy security. Yet, demand-side perspectives tend to be neglected or underrepresented in analysis and policy debates on energy security. Factors that contribute to this supply-side bias include the traditional sectoral organization of industries and policy institutions along fuels (coal, oil, and gas) and energy forms (electric utilities) as well as the decentralized and multivaried activities characteristic of energy demand (from vehicles to household appliances to manufacturing and more), which leads to a multitude of actors and institutional fragmentation. The basic fundamentals of energy systems and markets, where demand and supply are intricately linked, have also not yet risen from vague awareness to a central organizing principle among policy-makers for structuring the energy security discourse.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

A Typology for Characterizing Human Action in MultiSector Dynamics Models

The role of individual and collective human agency is increasingly recognized as a prominent and arguably paramount determinant in shaping the behavior, trajectory, and vulnerability of multisector systems. This human influence operates at multiple scales: from short-term (hourly to daily) to long-term (annually to centennial) timescales, and from the local to the global, pushing systems towards either desirable or undesirable outcomes. However, the effort to represent human systems in multisector models has been fragmented across philosophical, methodological, and disciplinary lines. To cohere insights across diverse modeling approaches, we present a new typology for classifying how human actors are represented in the broad suite of coupled human-natural system models that are applied in MultiSector Dynamics (MSD) research. The typology conceptualizes a “sector” as a system-of-systems that includes a diverse group of human actors, defined across individual to collective social levels, involved in governing, provisioning, and utilizing products, goods, or services towards some human end. We trace the salient features of modeled representations of human systems by organizing the typology around three key questions: 1) Who are the modeled actors in MSD systems? 2) What are their modeled actions? 3) How and for what purpose are these actors and actions operationalized in a computational model? The typology is used to critically examine existing models and chart the frontier of future human systems modeling for MSD research.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS2012

Climate Change Adaptation Science Activities at NASA Johnson Space Center

The Johnson Space Center (JSC), located in the southeast metropolitan region of Houston, TX is the prime NASA center for human spaceflight operations and astronaut training, but it also houses the unique collection of returned extraterrestrial samples, including lunar samples from the Apollo missions. The Center's location adjacent to Clear Lake and the Clear Creek watershed, an estuary of Galveston Bay, puts it at direct annual risk from hurricanes, but also from a number of other climate-related hazards including drought, floods, sea level rise, heat waves, and high wind events all assigned Threat Levels of 2 or 3 in the most recent NASA Center Disaster/Risk Matrix produced by the Climate Adaptation Science Investigator Working Group. Based on prior CASI workshops at other NASA centers, it is recognized that JSC is highly vulnerable to climate-change related hazards and has a need for adaptation strategies. We will present an overview of prior CASI-related work at JSC, including publication of a climate change and adaptation informational data brochure, and a Resilience and Adaptation to Climate Risks Workshop that was held at JSC in early March 2012. Major outcomes of that workshop that form a basis for work going forward are 1) a realization that JSC is embedded in a regional environmental and social context, and that potential climate change effects and adaptation strategies will not, and should not, be constrained by the Center fence line; 2) a desire to coordinate data collection and adaptation planning activities with interested stakeholders to form a regional climate change adaptation center that could facilitate interaction with CASI; 3) recognition that there is a wide array of basic data (remotely sensed, in situ, GIS/mapping, and historical) available through JSC and other stakeholders, but this data is not yet centrally accessible for planning purposes.

Stefanov, William L.↗

NASA NTRS2009

Severe Space Weather Events--Understanding Societal and Economic Impacts: A Workshop Report - Extended Summary

The effects of space weather on modern technological systems are well documented in both the technical literature and popular accounts. Most often cited perhaps is the collapse within 90 seconds of northeastern Canada's Hydro-Quebec power grid during the great geomagnetic storm of March 1989, which left millions of people without electricity for up to 9 hours. This event exemplifies the dramatic impact that severe space weather can have on a technology upon which modern society critically depends. Nearly two decades have passed since the March 1989 event. During that time, awareness of the risks of severe space weather has increased among the affected industries, mitigation strategies have been developed, new sources of data have become available, new models of the space environment have been created, and a national space weather infrastructure has evolved to provide data, alerts, and forecasts to an increasing number of users. Now, 20 years later and approaching a new interval of increased solar activity, how well equipped are we to manage the effects of space weather? Have recent technological developments made our critical technologies more or less vulnerable? How well do we understand the broader societal and economic impacts of severe space weather events? Are our institutions prepared to cope with the effects of a 'space weather Katrina,' a rare, but according to the historical record, not inconceivable eventuality? On May 22 and 23, 2008, a one-and-a-half-day workshop held in Washington, D.C., under the auspices of the National Research Council's (NRC's) Space Studies Board brought together representatives of industry, the federal government, and the social science community to explore these and related questions. The key themes, ideas, and insights that emerged during the presentations and discussions are summarized in 'Severe Space Weather Events--Understanding Societal and Economic Impacts: A Workshop Report' (The National Academies Press, Washington, D.C., 2008), which was prepared by the Committee on the Societal and Economic Impacts of Severe Space Weather Events: A Workshop. The present document is an expanded summary of that report.

Source record↗

NASA NTRS2008

Warming of the Indian Ocean Threatens Eastern and Southern Africa, but could be Mitigated by Agricultural Development

Since 1980, the number of undernourished people in eastern and southern Africa has more than doubled. Rural development stalled and rural poverty expanded during the 1990s. Population growth remains very high and declining per capita agricultural capacity retards progress towards Millennium Development goals. Analyses of in situ station data and satellite observations of precipitation identify another problematic trend. Main growing season rainfall receipts have diminished by approximately 15% in food insecure countries clustered along the western rim of the Indian Ocean. Occurring during the main growing seasons in poor countries dependent on rain fed agriculture, these declines are societally dangerous. Will they persist or intensify? Tracing moisture deficits upstream to an anthropogenically warming Indian Ocean leads us to conclude that further rainfall declines are likely. We present analyses suggesting that warming in the central Indian Ocean disrupts onshore moisture transports, reducing continental rainfall. Thus late 20th century anthropogenic Indian Ocean warming has probably already produced societally dangerous climate change by creating drought and social disruption in some of the world's most fragile food economies. We quantify the potential impacts of the observed precipitation and agricultural capacity trends by modeling millions of undernourished people as a function of rainfall, population, cultivated area, seed and fertilizer use. Persistence of current tendencies may result in a 50% increase in undernourished people. On the other hand, modest increases in per capita agricultural productivity could more than offset the observed precipitation declines. Investing in agricultural development can help mitigate climate change while decreasing rural poverty and vulnerability.

Funk, Chris↗

NASA NTRS2023

Huntsville Urban Development II: Utilizing NASA Earth Observations to Map the Urban Heat Island and Evaluate Vulnerability in Huntsville, Alabama

Huntsville, Alabama has seen a boom in growth over recent years. One consequence of this urban expansion is the exacerbation of the Urban Heat Island (UHI) effect across the city. This project identified the areas within Huntsville the greatest potential for heat reduction and community health benefits from tree-planting efforts. The team created maps of land surface temperature (LST), the normalized difference vegetation index (NDVI), and the normalized difference built-up index (NDBI) over June through August from 2019 to 2022 using data from ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station, Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), Landsat 9 OLI-2 and TIRS-2. The team identified areas with high LST, low NDVI, and high NDBI as areas with the greatest potential for heat reduction via tree-planting. Social factors relating to age, race, income, and self-reported health were adapted from Tree Equity Score to map community need for tree cover. When combining social with environmental factors, the team determined areas with the greatest potential for UHI mitigation: west-central and north downtown Huntsville. The team’s partner organization, the City of Huntsville, can use this priority map to guide their future tree-planting, and weigh the factors assessed according to their preference.

James Karroum↗

NASA NTRS1993

Undecidability in macroeconomics

In this paper we study the difficulty of solving problems in economics. For this purpose, we adopt the notion of undecidability from recursion theory. We show that certain problems in economics are undecidable, i.e., cannot be solved by a Turing Machine, a device that is at least as powerful as any computational device that can be constructed. In particular, we prove that even in finite closed economies subject to a variable initial condition, in which a social planner knows the behavior of every agent in the economy, certain important social planning problems are undecidable. Thus, it may be impossible to make effective policy decisions. Philosophically, this result formally brings into question the Rational Expectations Hypothesis which assumes that each agent is able to determine what it should do if it wishes to maximize its utility. We show that even when an optimal rational forecast exists for each agency (based on the information currently available to it), agents may lack the ability to make these forecasts. For example, Lucas describes economic models as 'mechanical, artificial world(s), populated by ... interacting robots'. Since any mechanical robot can be at most as computationally powerful as a Turing Machine, such economies are vulnerable to the phenomenon of undecidability.

Chandra, Siddharth↗

NASA NTRS2011

Climate Change Impacts and Responses: Societal Indicators for the National Climate Assessment

The Climate Change Impacts and Responses: Societal Indicators for the National Climate Assessment workshop, sponsored by the National Aeronautics and Space Administration (NASA) for the National Climate Assessment (NCA), was held on April 28-29, 2011 at The Madison Hotel in Washington, DC. A group of 56 experts (see list in Appendix B) convened to share their experiences. Participants brought to bear a wide range of disciplinary expertise in the social and natural sciences, sector experience, and knowledge about developing and implementing indicators for a range of purposes. Participants included representatives from federal and state government, non-governmental organizations, tribes, universities, and communities. The purpose of the workshop was to assist the NCA in developing a strategic framework for climate-related physical, ecological, and socioeconomic indicators that can be easily communicated with the U.S. population and that will support monitoring, assessment, prediction, evaluation, and decision-making. The NCA indicators are envisioned as a relatively small number of policy-relevant integrated indicators designed to provide a consistent, objective, and transparent overview of major variations in climate impacts, vulnerabilities, adaptation, and mitigation activities across sectors, regions, and timeframes. The workshop participants were asked to provide input on a number of topics, including: (1) categories of societal indicators for the NCA; (2) alternative approaches to constructing indicators and the better approaches for NCA to consider; (3) specific requirements and criteria for implementing the indicators; and (4) sources of data for and creators of such indicators. Socioeconomic indicators could include demographic, cultural, behavioral, economic, public health, and policy components relevant to impacts, vulnerabilities, and adaptation to climate change as well as both proactive and reactive responses to climate change. Participants provided inputs through in-depth discussion in breakout sessions, plenary sessions on break-out results, and several panels that provided key insights about indicators, lessons learned through experience with developing and implementing indicators, and thoughts on how the NCA could proceed to develop indicators for the NCA.

Kenney, Melissa A.↗

DOE OSTI2026

The UCAR Africa Initiative: Recent insights, challenges, and opportunities to foster collaborative research for environmental sustainability

Africa is increasingly being exposed to the negative impacts of climate and environmental change, while having less capacity to respond compared to other continents. The vulnerability partially results from unprecedented demographic growth, urbanization, and industrialization. However, the continent has still largely been underserved by the broader Earth System Science (ESS) community, as evidenced by the limited amount of ESS data and research that cover Africa compared to other areas of the world. Here we present the recent University Corporation for Atmospheric Research (UCAR) Africa Initiative that aims to enhance environmental sustainability in Africa by fostering international collaborative research partnerships co-led by African scientists. Specifically, we outline urgent challenges and opportunities identified through an international workshop in six areas of ESS namely (1) air quality and health, (2) weather, (3) climate, (4) land and water, (5) social science perspectives, and (6) developing equitable collaboration and sustainable infrastructure. We highlight examples of successful partnerships and conclude with recommendations to advance collaborative, actionable ESS research that addresses Africa’s critical environmental challenges.

African weather and land variability↗

DOE OSTI2021

Machine Intelligence to Detect, Characterise, and Defend against Influence Operations in the Information Environment

Social media has enabled a new era of manipulation in the information and cognitive domains. Deceptive content—misleading, falsified, and fabricated—is routinely created and spread in the modern social media environment with the intent to create confusion and widen political and social divides, and exploit the societal conflict exacerbated by these divides in the real-world (aka physical domain). Such disinformation campaigns demonstrate a threat to the integrity of economic, political, cultural, public health, and national security institutions around the world. In this work we overview our artificial intelligence (AI) capabilities to detect, describe, and defend against information operations on Twitter as an example social platform to understand the influence of misleading and falsified content diffusion and better enable those charged with defending against such manipulation to enable responsive parties to counter it. We first present novel linguistically-informed deep learning (DL) models for misinformation and disinformation detection, and present an in-depth linguistic analysis of psycho-linguistic markers across broad deception categories. We then demonstrate how our models perform in the multilingual and multimodal setting and categorize falsified and misleading content based on the intent to deceive. We also provide a large-scale analysis to describe user behavior and spread patterns while engaging with deceptive content and report novel findings about the immediate diffusion of deceptive content by characterizing the vulnerable sub-populations and their demographics, and explicitly measuring speed and scale of deception spread to uncover who shares deceptive content, how quickly, how much, and how evenly. In addition, we measure audience reactions to misinformation and disinformation at scale, distinguishing the reactions of users identified as bots versus humans. Finally, we take advantage of deep translation and generation models to create unique solutions for real-time defense against digital deception and discuss how to apply causal inference to prescribe and intervene into strategic communications jointly across information, cognitive, and physical domains.

artificial intelligence, deep learning, neural lan↗

DOE OSTI2025

Evaluating Direct and Indirect Influence on EV Charging Stations Across the US

The adoption of new technology for electric vehicles (EV) and mobility applications can bring underappreciated vulnerabilities to the power grid. One area of potential fraud and adversarial influence is through the business ecosystem of startups that own and deploy EV technology. Yet, there are no models or analyses that map the network of organizations and people that have direct and indirect influence over technologies currently deployed in the grid. To fill this gap, we develop a multilayer network model to measure direct and indirect influence on EV charging stations. First, we create and adversarial socio-technical network (ASTN) model via a data fusion pipeline for different US regions of interest (ROI). Then, we develop an integrated ASTN for Chicago, Los Angeles, New York, and Philadelphia. We rank EV charging companies direct influence within each geographic region as well as indirect influence via social network analysis. While some companies have strong direct and indirect influence (i.e., ChargePoint) others show a mismatch between their influence over charging stations and their position within the social network. For example, Tesla has strong direct influence on stations and weak indirect influence over competitors. In contrast, 7Charge has weak direct influence over stations, but strong indirect influence over competitors.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗