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NASA NTRS2020

Eastern Washington Disasters: Integrating NASA Earth Observations to Analyze Spatiotemporal Distributions of Lightning-Caused Wildfires in Eastern Washington

According to the Washington Department of Natural Resources, roughly 36% of large fires in the statesince 2010 were caused by lightning. General trends also show a greater increase in the number of lightning-ignited fires over the last three decades. The NASA DEVELOP Eastern Washington Disasters team partnered with The Nature Conservancy’s Washington Chapter to analyze the relationship between lightning strikes and wildfire events in Eastern Washington, with an emphasis on Kittitas and Yakima Counties. Using the International Space Station Lightning Imaging Sensor, the Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager vegetation moisture index, and Washington Department of Natural Resources historical fire data, the team generated a lightning-caused fire vulnerability index for 2001-2019. Climatology maps of lightning, wildfire, and vegetation moisture of the study area, along with an Esri ArcGIS StoryMap, further communicated project findings. The project results demonstrated that spatiotemporal patterns of lightning-ignited wildfires in Eastern Washington can be useful to inform land management practices and better predict areas that may be more vulnerable to these events.

Disasters↗

NASA NTRSDate not supplied

The Application of Remote Sensing using NASA Earth Observations paired with Sociodemographic Indicators to Identify Communities Most Susceptible to Urban Heat Exposure in Austin, Texas

In recent years, Austin, Texas has experienced an increase in population and urban development. Additionally, the City’s climate—already characterized by periods of extreme heat and drought—continues to change. As temperatures and demand for utilities and cooling resources rise, the number of heat-related deaths and illnesses in socially vulnerable populations (e.g., older, lower-income populations) is expected to increase. The City of Austin, The University of Texas at Austin (UT Austin), and The University of Texas Health Science Center at Houston (UT Health) partnered with NASA DEVELOP to examine the distribution of urban heat throughout the City. This project used land surface temperature, greenness, plant water content, and urban surface material analysis parameters derived from NASA Earth observations from Landsat 8 Operational Land Imager (OLI), Landsat 8 Thermal Infrared Sensor (TIRS), and Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS). The project team produced three different indices to create a spatial analysis for the study area including a social vulnerability index (SoVI), heat exposure index (HEI), and an overall heat priority index (HPI) score. This overall score was determined with a weighted analysis of heat-related environmental variables from NASA Earth observations and socioeconomic data from the 2019 American Community Survey. Based on the HPI score, the project team identified 121 census block groups out of 605 total that are designated as being most at risk of adverse impacts from extreme heat events. To test the sensitivity of the HPI, the team used a Monte Carlo analysis using different approaches for geographic scale, variable inclusion, census uncertainty, and index aggregation. Based on the sensitivity analysis, the resulting HPI score showed the metric was consistent with the baseline HPI. This provided increased confidence the score can be used as a tool to make informed infrastructure improvement plans in targeted areas (e.g., siting of cooling centers) and ensure equitable sustainable development.

Ryan Hammock↗

NASA NTRS2021

Storm Surge and Ponding Explain Mangrove Dieback in Southwest Florida Following Hurricane Irma

Mangroves buffer inland ecosystems from hurricane winds and storm surge. However, their ability to withstand harsh cyclone conditions depends on plant resilience traits and geomorphology. Using airborne lidar and satellite imagery collected before and after Hurricane Irma, we estimated that 62% of mangroves in southwest Florida suffered canopy damage, with largest impacts in tall forests (>10 m). Mangroves on well-drained sites (83%) resprouted new leaves within one year after the storm. By contrast, in poorly-drained inland sites, we detected one of the largest mangrove diebacks on record (10,760 ha), triggered by Irma. We found evidence that the combination of low elevation (median = 9.4 cm asl), storm surge water levels (>1.4 m above the ground surface), and hydrologic isolation drove coastal forest vulnerability and were independent of tree height or wind exposure. Our results indicated that storm surge and ponding caused dieback, not wind. Tidal restoration and hydrologic management in these vulnerable, low-lying coastal areas can reduce mangrove mortality and improve resilience to future cyclones.

David Lagomasino↗

NASA NTRS2022

Compound Effects of Climate Change on Future Transboundary Water Issues in the Middle East

The Middle East is one of the world’s most vulnerable areas to climate change, which has exacerbated environmental, agricultural, water conflict, and public health issues in the region. Here we analyze the latest climate model projections of precipitation and temperature for the very high emissions scenario, SSP5-8.5,to detect potential future changes in this region. A baseline period (1981-2010) is compared with the middle(2040-2069) and end (2070-2099) of the 21stcentury. The results, representing the worst-case scenario, identify the Tigris-Euphrates headwaters as the hotspot of future compounding effects of climate change in the Middle East. Those effects result from the coincidence of elevated temperature, reduced precipitation, and enhanced interannual variability of precipitation. The hotspot overlays the location of the Southeastern Anatolia Project (in Turkish, GAP) irrigation initiative. In this climate context, risks to GAP viability and downstream water security, and associated potential for water-related conflicts and migration are considerable and demand a reconsideration of the risk-benefit assessment of GAP. This need has become more urgent after the recent widespread and deadly climate-related conflicts and wildfires in summer 2021 across the Middle East that further underlined vulnerability of the region to climate extremes.

Tigris-Euphrates headwater↗

NASA NTRSDate not supplied

Assessing Alaskan boreal forest landcover affected by climate-wildfire interactions from ground truth surveys and NASA airborne remote sensing

Alaska’s boreal forest is facing unprecedented challenges under rapid climate warming (increasingly severe fires, droughts, pest/disease outbreaks) that may destabilize its function as a global carbon sink. Forests near Fairbanks may be especially vulnerable, impacting air quality and ecosystem services. We combined GT (ground truthing) with Airborne Visible InfraRed Imaging Spectrometer (AVIRIS-NG) images collected by the NASA Arctic-Boreal Vulnerability Experiment (ABoVE) program (2017-2019) to assess landcover change at five recently burned sites (2001-2019) of different fire severities and moisture regimes within 30 miles of Fairbanks. GT included tree seedling counts, understory % cover and >50% leaf canopy color assessment. 36 circular plots (1/30 ha radius) including 6 moderate to severely burned plots were selected across sites. 31 additional sites including 12 burned sites were geotagged in photos. AVIRIS images were processed from 29 spectral bands selected to identify changes in chlorophyll and water content. Images were segmented into natural boundaries (polygons) using ENVI 5.5 software. A spectral library of 8 AVIRIS bands with high between-class/low within-class variation was used in two random forest models to predict vegetation classes (model 1: 12 classes, model 2: 14 classes) in each AVIRIS scene, using 20% of the data as training data. Model 2 classified 20% more polygons overall, but only 42% of GT/geotagged polygons were correctly classified by both models. More forest sites were correctly classified (63%) than open vegetation (32%) or post-fire sites (46%). 50% of aspen forest and post-fire polygons were misclassified as shrubland. GT revealed that post-fire plots supported 134,000 (± 48,000) tree seedlings and saplings ha-1 (0.2 - 4 m height, 64% deciduous) versus 2500 (± 2100) shrubs ha-1 (1-6 m height). > 50% canopy browning was observed in conifer forest (8 plots) with no signs of insect infestation. Canopy herbivory > 50% (leaf miner, leaf beetle) and moose herbivory of tree bark was seen across aspen sites. Our study suggests: 1) low canopy vegetation presents challenges for improved landcover classification, and 2) aspen forest should be differentiated in vegetation maps which would aid in tracking herbivory.

Alaska↗

NASA NTRS2022

Grand Canyon Ecological Forecasting: Using NASA Earth Observations to Monitor and Model Juniper Woodland Mortality in Grand Canyon National Park

Significant die-offs of the drought tolerant species Utah juniper (Juniperus osteosperma) and one-seeded juniper (Juniperus monosperma) have been observed throughout central and northern Arizona, including Grand Canyon National Park (GCNP). As climate models project rising temperatures and continuous drought, land managers are concerned for the future of juniper in and around GCNP. This project incorporated data from Landsat 8 Operational Land Imager (OLI), the Shuttle Radar Topography Mission (SRTM), and ocular samples of the National Agriculture Imagery Program (NAIP) in a random forest model to identify patterns between characteristics of the landscape and locations of juniper woodland mortality and to model areas subject to vulnerability. This study found no significant correlation between ocularly sampled juniper tree mortality and remotely sensed environmental variables used thus, accurately modeling mortality vulnerability in the future was not feasible. The ocular sampling, however, allows the partners at the GCNP’s Science & Resource Management Division to better understand areas of juniper tree woodland mortality and the relative amount of mortality in the park. Additionally, the areas of juniper tree mortality found in this project provide the partners with guidance for future field sampling.

Scarlet Jackson↗

NASA NTRS2023

The Hazard Components of Representative Key Risks. The Physical Climate Perspective

The framework of Representative Key Risks (RKRs) has been adopted by the Intergovernmental Panel on Climate Change Working Group II (WGII) to categorize, assess and communicate a wide range of regional and sectoral key risks from climate change. These are risks expected to become severe due to the potentially detrimental convergence of changing climate conditions with the exposure and vulnerability of human and natural systems. Other papers in this special issue treat each of eight RKRs holistically by assessing their current status and future evolution as a result of this convergence. However, in these papers, such assessment cannot always be organized according to a systematic gradation of climatic changes. Often the big-picture evolution of risk has to be extrapolated from either qualitative effects of “low”, “medium” and “high” warming, or limited/focused analysis of the consequences of particular mitigation choices (e.g., benefits of limiting warming to 1.5 or 2C), together with consideration of the socio-economic context and possible adaptation choices. In this study we offer a representation – as systematic as possible given current literature and assessments – of the future evolution of the hazard components of RKRs. We identify the relevant hazards for each RKR, based upon the WGII authors’ assessment, and we report on their current state and expected future changes in magnitude, intensity and/or frequency, linking these changes to Global Warming Levels (GWLs) to the extent possible. We draw on the assessment of changes in climatic impact-drivers relevant to RKRs described in the 6th Assessment Report by Working Group I supplemented when needed by more recent literature. For some of these quantities - like regional trends in oceanic and atmospheric temperature and precipitation, some heat and precipitation extremes, permafrost thaw and Northern Hemisphere snow cover - a strong and quantitative relationship with increasing GWLs has been identified. For others - like frequency and intensity of tropical cyclones and extra-tropical storms, and fire weather - that link can only be described qualitatively. For some processes - like the behavior of ice sheets, or changes in circulation dynamics - large uncertainties about the effects of different GWLs remain, and for a few others - like ocean pH and air pollution - the composition of the scenario of anthropogenic emissions is most relevant, rather than the warming reached. In almost all cases, however, the basic message remains that every small increment in CO2 concentration in the atmosphere and associated warming will bring changes in climate phenomena that will contribute to increasing risk of impacts on human and natural systems, in the absence of compensating changes in these systems’ exposure and vulnerability, and in the absence of effective adaptation. Our picture of the evolution of RKR-relevant climatic impact-drivers complements and enriches the treatment of RKRs in the other papers in at least two ways: by filling in their often only cursory or limited representation of the physical climate aspects driving impacts, and by providing a fuller representation of their future potential evolution, an important component – if never the only one – of the future evolution of risk severity.

Representative Key Risks↗

NASA NTRS2023

Climate and Human Impacts on Hydrological Processes and Flood Risk in Southern Louisiana

Satellite observations of coastal Louisiana indicate an overall land loss over recent decades, which could be attributed to climate and human-induced factors, including sea level rise (SLR). Climate induced hydrological change (CHC) has impacted the way flood control structures are used, altering the spatiotemporal water distribution. Based on “what-if” scenarios, we determine relative impacts of SLR and CHC on increased flood risk over southern Louisiana and examine the role of water management, via flood control structures, in mitigating flood risk over the region. Our findings show that CHC has increased flood risk over the past 28 years. The number of affected people increases as extreme hydrological events become more exceptional. Water management reduces flood risk to urban areas and croplands, especially during exceptional hydrological events. For example, currently (i.e., 2016-2020 period), CHC-induced flooding puts an additional 73km2 of cropland under flood risk at least half of the time (median flood event) and 65km2 once a year (annual flood event), when compared to a past period (1993-1997). A ten- to twenty-fold increase relative to SLR-induced flooding. CHC also increases population vulnerability in southern Louisiana to flooding; additional 9900 residents currently live under flood risk at least half of the time, and that number increases to 27,400 for annual flood events. Residents vulnerable to SLR induced flooding is lower (6000 and 3300 residents, respectively). Conclusions are that CHC is a major factor that should be accounted for flood resilience and that water management interventions can mitigate risks to human life and activities.

Augusto Getirana↗

NASA NTRS2022

Neurosymbolic Hybrid Approach to Driver Collision Warning

There are two main algorithmic approaches to autonomous driving systems: (1) An end-to-end system in which a single deep neural network learns to map sensory input directly into appropriate warning and driving responses. (2) A mediated hybrid recognition system in which a system is created by combining independent modules that detect each semantic feature. While some researchers believe that deep learning can solve any problem, others believe that a more engineered and symbolic approach is needed to cope with complex environments with less data. Deep learning alone has achieved state-of-the-art results in many areas, from complex gameplay to predicting protein structures. In particular, in image classification and recognition, deep learning models have achieved accuracies as high as humans. But sometimes it can be very difficult to debug if the deep learning model doesn't work. Deep learning models can be vulnerable and are very sensitive to changes in data distribution. Generalization can be problematic. It's usually hard to prove why it works or doesn't. Deep learning models can also be vulnerable to adversarial attacks. Here, we combine deep learning-based object recognition and tracking with an adaptive neurosymbolic network agent, called the Non-Axiomatic Reasoning System (NARS), that can adapt to its environment by building concepts based on perceptual sequences. We achieved an improved intersection-over-union (IOU) object recognition performance of 0.65 in the adaptive retraining model compared to IOU 0.31 in the COCO data pre-trained model. We improved the object detection limits using RADAR sensors in a simulated environment, and demonstrated the weaving car detection capability by combining deep learning-based object detection and tracking with a neurosymbolic model.

Wang, Pei↗

NASA NTRS2023

Marin County Wildfires Ii: Improving Fire Suppression Modeling to Inform Fire Prevention and Suppression Decisions in Marin County, Ca

A future of increased wildfires requires greater integration of spatial analysis and local knowledge of emergency responders. We examine the application of a Potential Operational Delineations (PODs) framework for strategic pre-fire planning in Marin County. PODs are spatial units for wildfire management that combine predictive modeling and local firefighter expertise to identify potential control locations as unit boundaries and assess the difficulty of suppression within units. Additionally, this project explores the integration of road networks and social vulnerability to assess environmental justice in evacuation safety. This project constitutes a novel application of the PODs framework as it integrates expertise from Marin County senior firefighters with a Fireline Location Model (FLM) to achieve POD definition and uses a Suppression Difficulty Score (SDS) to rank each POD. The FLM uses network analysis and hydrologic modeling to identify key roads and ridgelines as boundaries and combines them with expert knowledge, in the form of workshops, to construct PODs. Once identified, PODs are classified using SDS, which includes processed inputs such as LiDAR-derived aboveground biomass, ECOSTRESS Evaporative Stress Index, land use cover type from Sentinel-2 Imagery, and a digital elevation model. Environmental justice for evacuation safety incorporated three key road metrics such as connectivity, travel area, and exit capacity, the Social Vulnerability Index from the Center for Disease Control, and cell coverage to determine a final Evacuation Difficulty Score. Results indicate a strong link between road networks as primary POD boundaries, with ridgelines and waterways as secondary and tertiary locations. Specifically, we find 78.5% of expertise-identified POD boundaries align with FLM-determined boundaries. More validation is needed to support this process; however, initial results signal a feasible framework to integrate expertise and spatial analysis in local level strategic fire planning

Wildfire modeling↗

NASA NTRSDate not supplied

Anticipating Hazard Impacts through Capacity Building and Co-development

Improving forecast accuracy, extending lead time, understanding hazard susceptibility, and integrating exposure and vulnerability data to generate impact-based forecasts are critical in mitigating disaster impacts. These efforts enable anticipatory action through enhancing the efficacy of early warning systems to assess potential multi-dimensional hazard impacts. In response to this need, SERVIR has co-developed a series of hazard services providing vital information from national to regional scales. This poster showcases examples of geospatial services co-developed with partners through a capacity building approach, supporting the establishment of hazard early warning / early action systems. These services integrate multidimensional vulnerability and exposure data to comprehensively assess impacts. Furthermore, these services demonstrate how co-development and capacity building can advance the development of impact-based forecasts and multi-hazards services. By prioritizing collaboration, human insights, local knowledge, capacity building, and employing applied science approaches in geospatial service development, this work has helped create inclusive and customized solutions. These solutions are tailored to meet the needs of local communities and are readily adaptable into decision-making processes.

weather↗

DOE OSTI2020

A PROVEN APPROACH FOR EFFECTIVE COMPUTER SECURITY SELF-ASSESSMENTS AT NUCLEAR FACILITIES

A proven method for conducting cybersecurity self-assessments at nuclear power plants is now available for international use. This method was originally developed by Pacific Northwest National Laboratory, under the sponsorship of the U.S. Nuclear Regulatory Commission (NRC), for use at U.S. nuclear power plants. The “Method,” described in NUREG/CR-6847 “Cyber Security Self-Assessment Method for U.S. Nuclear Power Plants,” was originally a limited release document that was withheld from public disclosure but is now publicly available. The Method provides a systematic, phased, and risk-informed approach to help decision makers and security specialists understand their relative cybersecurity posture. Completed Method assessments may be used to support or validate selection of computer security controls to mitigate cyber threats as well as demonstrate compliance with regulations or statutes enacted by competent authorities. The Method assesses the cybersecurity posture of key systems at a nuclear facility with a focus on protection of design base functions. It considers both physical and digital elements of system vulnerabilities and the resulting potential consequences from exploitation. It is well-suited for addressing blended cyberattacks. A semi-quantitative analytical approach is used in the evaluation of potential vulnerabilities, consequences, and risks and provides a technical basis for the selection of security controls to mitigate cyberattacks. The Method’s application at U.S. nuclear power plants has been very encouraging. The only nuclear plant in the United States that did not have adverse findings during its initial NRC computer security inspection prepared for inspection through the diligent application of this self-assessment method and the implementation of recommendations that came out of that self-assessment. Nuclear facilities around the world might find application of the Method extremely helpful for making cost-effective, risk-based decisions regarding computer security and for preparing to pass computer security inspections by their competent authorities.

Cyber security, Nuclear security, Self-assessment↗

DOE OSTI2020

The Nuclear Digital I&C System Supply Chain Cyber-Attack Surface

The nuclear supply chain attack surface is a large, complex network of interconnected stakeholders and activities. The global economy has widened and deepened the supply chain, resulting in larger numbers of geographically dispersed locations and increased difficulty ensuring the authenticity and security of digital assets. Although the nuclear industry has made significant strides in securing facilities from cyber-attacks, the supply chain remains vulnerable. This paper outlines supply chain threats and vulnerabilities and provides a Digital I&C System Supply Chain Cyber-Attack Surface diagram to illustrate the complexity of securing hardware, firmware, software, and system information throughout the entire supply chain lifecycle. The knowledge presented in this paper provides a foundation to use in cybersecurity supply chain risk analysis and to guide future supply chain research and development efforts leading to enhancement of a nuclear facility’s overall security posture.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2020

A Mathematical Framework for Representing Cyber-Physical System Interdependencies and Resilience

U.S. critical infrastructure is increasingly composed of integrated cyber-physical systems (CPS) whose components are operationally interdependent. These interdependencies can create additional vulnerabilities beyond those that are typically evaluated through independent physical and cyber risk assessments. These additional vulnerabilities, previously undetected at the sub-system and/or component level, could cause cascading effects across both the physical and cyber domains that could lead to infrastructure disruption or failure. This paper will review an initial mathematical framework developed to help understand the dynamics of these interfacing physical and cyber components at a holistic system level. The resulting framework also provides a method for modeling and assessing the resilience of CPS in complex and adaptive systems. It frames the system in terms of mission performance and the potential effects to performance caused by attacks that are cyber, physical, or blended in nature. The framework utilizes a systems-based, state-space modeling approach to reason about risk, resilience, and interdependencies in CPS. Probabilities are applied to each CPS component regarding a threat (both man-made and natural) and the likelihood of that particular component being impacted, along with indirect impact to other interdependent components. These component-level impacts from threats then translate into mission-level impact and systemic state change.

Brigantic, Robert T.↗

DOE OSTI2021

Cybersecurity for Electric Vehicle Fast-Charging Infrastructure: Preprint

The integration of electric vehicles (EVs) into electric grid operations can potentially leave the grid vulnerable to cyberattacks from both legacy and new equipment and protocols, including extreme fast-charging infrastructure. This paper introduces a co-simulation platform to perform cyber vulnerability analysis of EV charging infrastructure and its dependencies on communications and control systems. Grid impact scenarios through linkages to power system simulation tools such as OpenDSS and vehicle infrastructure-specific attack paths are discussed. An adaptive platform that assists with predicting and solving evolving cybersecurity challenges is demonstrated with a cyber-energy emulation that accelerates the analysis of cyberattacks and system behavior.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Cybersecurity for Fast Charging EV Infrastructure

ADVANCED PROPULSION SYSTEMS,POWER TRANSMISSION AND↗

DOE OSTI2020

Deconstructing the Nuclear Supply Chain Cyber-Attack Surface

The nuclear supply chain cyber-attack surface is a large, complex network of interconnected stakeholders and activities. The global economy has widened and deepened the supply chain resulting in larger numbers of geographically dispersed locations and increased difficulty ensuring the authenticity and security of digital assets. Although the nuclear industry has made significant strides in securing facilities from cyber-attacks, the supply chain remains vulnerable. This paper provides further details on each of the elements in the Digital I&C System Supply Chain Cyber-Attack Surface, including supply chain lifecycle activities, key stakeholders, touchpoints, and attack types. Deconstructing this attack surface provides insights into supply chain threats, vulnerabilities, and consequences. These insights will lead to improvements in cybersecurity supply chain risk analysis, development of new cybersecurity supply chain processes and tools, and enhancement of overall supply chain resilience.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Determining diagnostic coverage for memory using redundant execution

Memory, used by a computer to store data, is generally prone to faults, including permanent faults (i.e. relating to a lifetime of the memory hardware), and also transient faults (i.e. relating to some external cause) which are otherwise known as soft errors. Since soft errors can change the state of the data in the memory and thus cause errors in applications reading and processing the data, there is a desire to characterize the degree of vulnerability of the memory to soft errors. In particular, once the vulnerability for a particular memory to soft errors has been characterized, cost/reliability trade-offs can be determined, or soft error detection mechanisms (e.g. parity) may be selectively employed for the memory. In some cases, memory faults can be diagnosed by redundant execution and a diagnostic coverage may be determined.

Bramley, Richard Gavin↗