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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Compounding Risks from Natural Phenomena Hazards at U.S. Department of Energy Facilities

U.S. Department of Energy (DOE) facilities are exposed to adverse effects of natural phenomena hazards (NPHs). While the DOE Standard (STD) 1020-2016 provides criteria and guidance for assessing these effects, certain gaps exist, particularly related to combinations of NPHs that can compound the risk to DOE facility assets. This report describes a methodology to systematically consider compounding NPHs, identified gaps in DOE-STD-1020-2016, and recommends ways to update the standard. At any given DOE facility, NPH effects depend on the site-specific hydrometeorologic and geoseismic conditions. Therefore, compounding NPHs should be determined using site-specific assessments. DOE facility assets generally are assessed using site-specific NPHs with the assessment usually limited to the DOE site extents. However, an inventory analysis of a selected DOE facility also revealed that some assets may have off-site dependencies. These dependencies may be part of threat pathways that can lead to on-site asset failures because of off-site effects of the same NPHs that also affect the DOE site. These threat pathways usually are not accounted for in traditional NPH analyses. The report also describes the effects of duration of NPH effects. Duration of disruption, particularly for off-site dependencies can lead to on-site asset failures, disruption of evacuation roadways, and consequent human health and safety effects. Finally, the report recommends some considerations for updating DOE-STD-1020-2016 to include explicit consideration of compounding NPHs.

42 ENGINEERING↗
NASA NTRS2014

Aggregation Tool to Create Curated Data albums to Support Disaster Recovery and Response

Despite advances in science and technology of prediction and simulation of natural hazards, losses incurred due to natural disasters keep growing every year. Natural disasters cause more economic losses as compared to anthropogenic disasters. Economic losses due to natural hazards are estimated to be around $6-$10 billion dollars annually for the U.S. and this number keeps increasing every year. This increase has been attributed to population growth and migration to more hazard prone locations such as coasts. As this trend continues, in concert with shifts in weather patterns caused by climate change, it is anticipated that losses associated with natural disasters will keep growing substantially. One of challenges disaster response and recovery analysts face is to quickly find, access and utilize a vast variety of relevant geospatial data collected by different federal agencies such as DoD, NASA, NOAA, EPA, USGS etc. Some examples of these data sets include high spatio-temporal resolution multi/hyperspectral satellite imagery, model prediction outputs from weather models, latest radar scans, measurements from an array of sensor networks such as Integrated Ocean Observing System etc. More often analysts may be familiar with limited, but specific datasets and are often unaware of or unfamiliar with a large quantity of other useful resources. Finding airborne or satellite data useful to a natural disaster event often requires a time consuming search through web pages and data archives. Additional information related to damages, deaths, and injuries requires extensive online searches for news reports and official report summaries. An analyst must also sift through vast amounts of potentially useful digital information captured by the general public such as geo-tagged photos, videos and real time damage updates within twitter feeds. Collecting and aggregating these information fragments can provide useful information in assessing damage in real time and help direct recovery efforts. The search process for the analyst could be made much more efficient and productive if a tool could go beyond a typical search engine and provide not just links to web sites but actual links to specific data relevant to the natural disaster, parse unstructured reports for useful information nuggets, as well as gather other related reports, summaries, news stories, and images. This presentation will describe a semantic aggregation tool developed to address similar problem for Earth Science researchers. This tool provides automated curation, and creates "Data Albums" to support case studies. The generated "Data Albums" are compiled collections of information related to a specific science topic or event, containing links to relevant data files (granules) from different instruments; tools and services for visualization and analysis; information about the event contained in news reports, and images or videos to supplement research analysis. An ontology-based relevancy-ranking algorithm drives the curation of relevant data sets for a given event. This tool is now being used to generate a catalog of Hurricane Case Studies at Global Hydrology Resource Center (GHRC), one of NASA's Distribute Active Archive Centers. Another instance of the Data Albums tool is currently being created in collaboration with NASA/MSFC's SPoRT Center, which conducts research on unique NASA products and capabilities that can be transitioned to the operational community to solve forecast problems. This new instance focuses on severe weather to support SPoRT researchers in their model evaluation studies

Ramachandran, Rahul↗
NASA NTRS2001

Dealing with the Asteroid Impact Hazard

The small fraction of the asteroids with Earth-crossing or Earth-approaching orbits is of special interest to us because many will eventually impact our planet. The time-averaged impact flux as a function of projectile energy can be derived from lunar cratering statistics, although we have little information on the possible variability of this flux over time. The effects of impacts of various energies can be modeled, using data from historic impacts (such as the KT impactor 65 million years ago), nuclear explosive testing, and the observed 1994 bombardment of Jupiter by fragments of comet Shoemaker-Levy 9. It is of particular interest to find from such models that the terrestrial environment is highly vulnerable to perturbation from impacts, so that even such a small event as the KT impact (by a projectile roughly 15 km in diameter) can lead to a mass extinction. Combining the impact flux with estimates of environmental and ecological effects reveals that the greatest contemporary hazard is associated with impactors near one million megatons energy. The current impact hazard is significant relative to other natural hazards, and arguments can be developed to illuminate a variety of public policy issues. These include the relative risk of different impact scenarios and the associated costs and probability of success of countermeasures. It is generally agreed that the first step is to survey and catalogue the thousand-or-so Near Earth Asteroids (NEAs), and we review the status of the Spaceguard NEA Survey. We compare the efficiency of various ground and space-based approaches and consider the challenges of international coordination and the problems and opportunities associated with communicating the results with the press and the public. It is also important to reflect on how the impact hazard might be dealt with by both national governments and international decision-making bodies, and to anticipate ways of mitigating the danger if a NEA were located on an apparent Earth-impact trajectory. As the most extreme known example of a natural hazard with low probability but severe global consequences. the NEA impact hazard calls for the most careful consideration and planning.

Morrison, David↗
NASA NTRS2003

Impacts and evolution: future prospects

The discipline of astrobiology includes the dynamics of biological evolution. One of the major ways that the cosmos influences life is through the catastrophic environmental disruptions caused when comets and asteroids collide with a planet. We now recognize that such impacts have caused mass extinctions and played a major role in determining the evolution of life on Earth. The time-averaged impact flux as a function of projectile energy can be derived from lunar cratering statistics as well as the current population of near Earth asteroids (NEAs). Effects of impacts of various energies can be modeled, using data from historic impacts [such as the Cretaceous-Tertiary (KT) impactor 65 million years ago] and the observed 1994 bombardment of Jupiter by fragments of Comet Shoemaker-Levy 9. It is of particular interest to find from such models that the terrestrial environment is highly vulnerable to perturbation from impacts, so that even such a small event as the KT impact (by a projectile 10-15 km in diameter) can lead to a mass extinction. Similar considerations allow us to model the effects of still smaller (and much more likely) impacts, down to the size of the asteroid that exploded over Tunguska in 1908 (energy approximately 10 megatons). Combining the impact flux with estimates of environmental and ecological effects reveals that the greatest contemporary hazard is associated with impactors near 1 million megatons in energy (approximately 2 km in diameter for an asteroid). The current impact hazard is significant relative to other natural hazards, and arguments can be developed to illuminate a variety of public policy issues. The first priority in any plan for defense against impactors is to survey the population of Earth-crossing NEAs and project their orbits forward in time. This is the purpose of the Spaceguard Survey, which has already found more than half of the NEAs >1 km in diameter. If there is an NEA on a collision course with Earth, it can be discovered and the impact predicted with decades or more of warning. It is then possible to consider how to deflect or disrupt the NEA. Unlike other natural hazards, the impact risk can be largely eliminated, given sufficient advanced knowledge to take action against the threatening projectile.

Directed Molecular Evolution↗
NASA NTRS1996

(abstract)

The paper will review the application of NASA developed remote sensing technology towards the monitoring and mitigation of natural hazards. The overview will be followed by recent data on three specific natural hazard applications.

remote sensing natural hazards fire hazards infrar↗
DOE OSTI2024

Using Infrasound to Inform Avalanche Hazard Forecasts

Avalanches are natural hazards that occur when an unstable mass of snow breaks away from a mountain slope. It is expected that climate change will lead to increased avalanche activity, which can cause interruptions to water and power infrastructure, transportation blockages, higher risk for loss of life, and changes to ecosystems. Avalanche forecasts are key to mitigating hazards, and observations of recent avalanches comprise one of the key observations for deciding danger level. It is well understood that infrasound can be used to detect and locate snow avalanches in transitional snowpacks, even during snowstorms, but similar studies are lacking for maritime snowpacks. Here we show results from an infrasound field experiment in Tutl’uh (Turnagain Arm), Alaska, USA between January 31 – April 30 field deployment campaign. We show that (1) methods developed for transitional snowpacks can be applied to maritime snowpacks in Alaska, (2) background noise may be higher in this region due to natural and anthropogenic influences, and (3) low-cost infrasound sensors can withstand the harsh Alaskan winter and successfully collect data. We also discuss the impact of this work and a path forward.

58 GEOSCIENCES↗
NASA NTRS2017

Bitcoin, Blockchains and Efficient Distributed Spacecraft Mission Control

Earth Observing 1 (E0-1) satellite has an imaging spectrometer (hyperspectral) instrument called Hyperion. The satellite is able to image any spot on Earth in the nadir looking direction every 16 days. With slewing of the satellite and allowing for up to a 23 degree view angle, any spot on the Earth can be imaged approximately every 2 to 3 days. EO-1 has been used to track many natural hazards such as wildfires, volcanoes and floods. An enhanced capability that is sought is the ability to image natural hazards in a daily time series for space based imaging spectrometers. The Hyperion can not provide this capability on EO-1 with the present polar orbit. However, a constellation of cubesats each powered with Intelligent Payload Modules, and each with copies of a commercial imaging spectrometer, positioned strategically in the same orbit, can be used to provide daily coverage, cost-effectively.

Blockchain in spac↗
NASA NTRS2006

Coordinating an Autonomous Earth-Observing Sensorweb

A system of software has been developed to coordinate the operation of an autonomous Earth-observing sensorweb. Sensorwebs are collections of sensor units scattered over large regions to gather data on spatial and temporal patterns of physical, chemical, or biological phenomena in those regions. Each sensor unit is a node in a data-gathering/ data-communication network that spans a region of interest. In this case, the region is the entire Earth, and the sensorweb includes multiple terrestrial and spaceborne sensor units. In addition to acquiring data for scientific study, the sensorweb is required to give timely notice of volcanic eruptions, floods, and other hazardous natural events. In keeping with the inherently modular nature of the sensory, communication, and data-processing hardware, the software features a flexible, modular architecture that facilitates expansion of the network, customization of conditions that trigger alarms of hazardous natural events, and customization of responses to alarms. The soft8 NASA Tech Briefs, July 2006 ware facilitates access to multiple sources of data on an event of scientific interest, enables coordinated use of multiple sensors in rapid reaction to detection of an event, and facilitates the tracking of spacecraft operations, including tracking of the acquisition, processing, and downlinking of requested data.

Sherwood, Robert↗
DOE OSTI2026

Resilience Metrics for Solar Photovoltaics

This workshop presentation proposes the development of solar photovoltaic (PV) system resilience metrics and a methodology and framework for evaluation of PV resilience metrics. PV resilience metrics are needed to establish a consistent basis for reporting, evaluation, and data collection by industry, evaluate performance of PV systems that have been subject to natural hazards, correlating resilience to system attributes, and predicting resilience for any PV system. PV resilience metrics can guide improved system design, standards, and insurance coverage. Establishing consistent metrics can foster data collection on impacts of natural hazards on PV systems.

14 SOLAR ENERGY↗
NASA NTRS2019

New Era, New Opportunity, Is GES DISC Ready for Big Data Challenge?

The new era of Big Data has opened doors for many new opportunities, as well as new challenges, for both Earth science research/application and data communities. As one of the twelve NASA data centers - Goddard Earth Sciences Data and Information Services Center (GES DISC), one of our great challenges has been how to help research/application community efficiently (quickly and properly) accessing, visualizing and analyzing the massive and diverse data in natural hazard research, management, or even prediction. GES DISC has archived over 2000 TB data on premises and distributed over 23,000 TB of data since 2010. Our data has been widely used in every phase of natural hazard management and research, i.e. long term risk assessment and reduction, forecasting and predicting, monitoring and detection, early warning, damage assessment and response. The big data challenge is not just about data storage, but also about data discoverability and accessibility, and even more, about data migration/mirroring in the cloud. This paper is going to demonstrate GES DISC’s efforts and approaches of evolving our overall Web services and powerful Giovanni (Geospatial Interactive Online Visualization ANd aNalysis Infrastructure) tool into further improving data discoverability and accessibility. Prototype works will also be presented.

Li, A.↗
NASA NTRSDate not supplied

NASA MUREP-DEAP Institutes - Year 1 Updates on Capacity Building, Community Building and Research Initiatives

Through the Minority University Research and Education Project (MUREP), NASA engages underrepresented populations via various initiatives, including by means of three year competitive awards for Data Science Equity, Access, and Priority in Research and Education (DEAP) Institutes led by Historically Black Colleges and Universities (HBCUs). MUREP DEAP Institutes, multi-institutional consortia, conduct data science research and capacity building initiatives that aim to enhance the research, academic, and technological capabilities of the participating HBCUs while providing NASA-specific knowledge, skills and opportunities for students and faculty who have historically been underrepresented and underserved in the STEM workforce. Three MUREP DEAP Institutes, led by Bethune-Cookman University, North Carolina A&T University, and North Carolina Central University, are conducting data science and natural hazard related research utilizing remote sensing and earth observations, with mentorship for the Institutes provided by scientists from NASA’s Marshall Space Flight Center. Each Institute has unique capabilities, needs, and goals, but overlap in their common interest to conduct cutting edge data science and natural hazard related research and to build capacity, broaden participation, and increase retention of a diverse group of participating students and faculty​. Here, successes and challenges from the first year of activities at each MUREP DEAP institute are discussed, where activities focused primarily on the recruitment of a diverse group of students and the upskilling of students and faculty in data science and remote sensing concepts. The planned activities and goals of each DEAP Institute for the upcoming two years of activities will also be discussed, both from research and community-building perspectives.

Ronan Lucey↗
DOE OSTI2023

Hydropower Potential at Non-Powered Dams: A Multi-Criteria Decision Analysis Tool based on Grid, Community, Industry, and Environmental Impacts

Non-powered dams (NPDs) are dams that do not include hydraulic turbine (hydropower) equipment. Currently, there are more than 80,000 such dams in the United States, which provide a variety of non-energy benefits, including flood control, water supply, navigation, and recreation. Approximately 500 of these NPDs are identified as having the potential to add hydropower generation (totaling up to a capacity of more than 8200 MW). A large share of investment costs and environmental impacts of dam construction have already been incurred at these NPDs. Hence, adding power to the existing dam structure is hypothesized to be achieved at a lower cost, with less risk, and a shorter timeframe than the development required for new dam construction. The abundance of NPDs, the associated environmental favorability, and cost advantages, combined with the reliability, predictability, and dispatchability of hydropower, make NPDs a strong candidate in the nation’s renewable energy portfolio. To assess the NPD to hydropower conversion potential, in this study, we developed a GIS-based multi-criterial decision analysis tool, which allows users to rank these NPDs based on the grid, community, industry, and environmental impacts (i.e., GCIE impacts). This web-based interactive tool (developed using open-source Python and JavaScript) lets the user choose from a wide range of features to define each of the GCIE impact scores through a user-friendly graphical user interface. These features are related to dam operation, hydropower generation opportunity, power market economy, social vulnerability and risk, proximity to critical infrastructure and energy generating facilities, environmental concerns (air, water, and critical habitat), and exposure to natural hazards. The overall priority score of NPDs is calculated based on user-defined weights for each of the GCIE impact scores. Besides ranking NPDs, the tool can also be used to estimate the energy-storage feasibility (battery, hydrogen, and pump-storage hydropower) at each of the potential sites.

13 HYDRO ENERGY↗
NASA NTRS2011

Automated Purgatoid Identification: Final Report

Driving on Mars is hazardous: technical problems and unforeseen natural hazards can end a mission quickly at the worst, or result in long delays at best. This project is focused on helping to mitigate hazards posed to rovers by purgatoids: small (less than 1 m high, less than 10 m wide), ripple-like eolian bedforms commonly found scattered across the Meridiani Planum region of Mars. Due to the poorly consolidated nature of purgatoids and multiple past episodes of rovers getting stuck in them, identification and avoidance of these eolian bedforms is an important feature of rover path planning (NASA, 2011).

MatLab↗
NASA NTRS2007

Evaluation of the Potential of NASA Multi-satellite Precipitation Analysis in Global Landslide Hazard Assessment

Landslides are one of the most widespread natural hazards on Earth, responsible for thousands of deaths and billions of dollars in property damage every year. In the U.S. alone landslides occur in every state, causing an estimated $2 billion in damage and 25- 50 deaths each year. Annual average loss of life from landslide hazards in Japan is 170. The situation is much worse in developing countries and remote mountainous regions due to lack of financial resources and inadequate disaster management ability. Recently, a landslide buried an entire village on the Philippines Island of Leyte on Feb 17,2006, with at least 1800 reported deaths and only 3 houses left standing of the original 300. Intense storms with high-intensity , long-duration rainfall have great potential to trigger rapidly moving landslides, resulting in casualties and property damage across the world. In recent years, through the availability of remotely sensed datasets, it has become possible to conduct global-scale landslide hazard assessment. This paper evaluates the potential of the real-time NASA TRMM-based Multi-satellite Precipitation Analysis (TMPA) system to advance our understanding of and predictive ability for rainfall-triggered landslides. Early results show that the landslide occurrences are closely associated with the spatial patterns and temporal distribution of rainfall characteristics. Particularly, the number of landslide occurrences and the relative importance of rainfall in triggering landslides rely on the influence of rainfall attributes [e.g. rainfall climatology, antecedent rainfall accumulation, and intensity-duration of rainstorms). TMPA precipitation data are available in both real-time and post-real-time versions, which are useful to assess the location and timing of rainfall-triggered landslide hazards by monitoring landslide-prone areas while receiving heavy rainfall. For the purpose of identifying rainfall-triggered landslides, an empirical global rainfall intensity-duration threshold is developed by examining a number of landslide occurrences and their corresponding TMPA precipitation characteristics across the world. These early results , in combination with TRMM real-time precipitation estimation system, may form a starting point for developing an operational early warning system for rainfall-triggered landslides around the globe.

Hong, Yang↗
DOE OSTI2024

Offshore application of landslide susceptibility mapping using gradient-boosted decision trees: a Gulf of Mexico case study

Abstract Among natural hazards occurring offshore, submarine landslides pose a significant risk to offshore infrastructure installations attached to the seafloor. With the offshore being important for current and future energy production, there is a need to anticipate where future landslide events are likely to occur to support planning and development projects. Using the northern Gulf of Mexico (GoM) as a case study, this paper performs Landslide Susceptibility Mapping (LSM) using a gradient-boosted decision tree (GBDT) model to characterize the spatial patterns of submarine landslide probability over the United States Exclusive Economic Zone (EEZ) where water depths are greater than 120 m. With known spatial extents of historic submarine landslides and a Geographic Information System (GIS) database of known topographical, geomorphological, geological, and geochemical factors, the resulting model was capable of accurately forecasting potential locations of sediment instability. Results of a permutation modelling approach indicated that LSM accuracy is sensitive to the number of unique training locations with model accuracy becoming more stable as the number of training regions was increased. The influence that each input feature had on predicting landslide susceptibility was evaluated using the SHapely Additive exPlanations (SHAP) feature attribution method. Areas of high and very high susceptibility were associated with steep terrain including salt basins and escarpments. This case study serves as an initial assessment of the machine learning (ML) capabilities for producing accurate submarine landslide susceptibility maps given the current state of available natural hazard-related datasets and conveys both successes and limitations.

Dyer, Alec S. (ORCID:0000000219813904)↗