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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2

Massachusetts Water Resources: Assessing Flood Events Resulting from North American Beaver Reintroduction with NASA Earth Observations to Inform Biodiversity and Infrastructure Management

North American beavers (Castor canadensis) are returning to Massachusetts after overhunting decimated their populations in the 1700s. Current regulations have allowed this species to recolonize, resulting in increasingly prevalent human-beaver conflicts. These ecosystem engineers can quickly change their environment through the creation of dams, leading to floods that can adversely affect human infrastructures, such as basements, roads, or septic systems. Conversely, beaver dams can positively influence their environment, modifying the physical and chemical properties of streams and providing crucial habitats to a variety of wildlife. The 2020 Spring Boston NASA DEVELOP team collaborated with the Massachusetts Audubon Society to support their efforts in monitoring beaver impacts and managing human-beaver conflicts. The project-utilized data from Landsat 5 Thematic Mapper, Landsat 7 Enhanced Thematic Mapper Plus, and Landsat 8 Operational Land Imager to map the spectral signature created from beaver-induced flooding. The team created a tool called Beaver-Flood Event Detector (B-FED) in Google Earth Engine using imagery from 1985 to 2019. Ancillary datasets were incorporated into B-FED that allow the tool to highlight flood events in wetland areas and in situ observations of beaver presence. Beaver observations in or near flooded areas indicated likelihood that the flood was beaver induced. Time series and animations were also produced to display key regions of landscape change across Massachusetts. B-FED will allow the partner to identify and assess potential ecosystem changes and infrastructural impacts from beavers across Massachusetts and inform future management practices

Water Resources↗

Radiative Impacts of Further Arctic Sea Ice Melt: Using past Observations to Inform Future Climate Impacts

The Arctic region has seen dramatic changes over the past several decades, from polar amplification of global temperature rise to ecosystem changes to the decline of the sea ice. While there has been much speculation as to when the world will see an ice-free Arctic, the radiative impacts of an eventual disappearance of the Arctic sea ice are likely to be significant regardless of the timing. Using CERES radiation and microwave satellite sea ice data, Pistone et al (2014) estimated the radiative forcing due to albedo changes associated with the Arctic sea ice retreat over the 30 years of the satellite data record. In this study, we found that the Arctic Ocean saw a decrease in all-sky albedo of 4% (from 52% to 48%), for an estimated increase in solar heating of 6.4 W/m(exp 2) between 1979 and 2011, or 0.21 W/m(exp 2) when averaged over the globe. This value is substantial--approximately 25% as large as the forcing due to the change in CO2 during the same period. Here we update and expand upon this previous work and use the CERES broadband shortwave observations to explore the radiative impacts of a transition to completely ice-free Arctic Ocean. We estimate the annually-averaged Arctic Ocean planetary albedo under ice-free and cloud-free conditions to be 14% over the region, or approximately 25% lower in absolute terms than the Arctic Ocean cloud-free albedo in 1979. However, the question of all-sky conditions (i.e. including the effects of clouds) introduces a new level of complexity. We explore several cloud scenarios and the resultant impact on albedo. In each of these cases, the estimated forcing is not uniformly distributed throughout the year. We describe the relative contributions of ice loss by month as well as the spatial distributions of the resulting changes in absorbed solar energy. The seasonal timing and location—in addition to magnitude—of the altered solar absorption may have significant implications for atmospheric and ocean dynamics in the Arctic and at lower latitudes; this observationally-based estimate of the large-scale characteristics of an ice-free Arctic thus provides a valuable tool to complement and validate model-based assessments of future climate.

Pistone, K.↗

Social, economic and political factors associated with earth resources observation and information analyses.

Discussion of some of the interest conflicts between ecology and economics that arise, particularly in riparian environments, when a population-increase entailed growth in public service requirements is met by indiscriminate technology applications. Reviewed instances of such conflicts include the aborted cross-Florida barge canal project and the Florida Power and Light Company facility at Turkey point.

Hall, J. J.↗

Program on stimulating operational private sector use of Earth observation satellite information

Ideas for new businesses specializing in using remote sensing and computerized spatial data systems were developd. Each such business serves as an 'information middleman', buying raw satellite or aircraft imagery, processing these data, combining them in a computer system with customer-specific information, and marketing the resulting information products. Examples of the businesses the project designed are: (1) an agricultural facility site evaluation firm; (2) a mass media grocery price and supply analyst and forecaster; (3) a management service for privately held woodlots; (4) a brokerage for insulation and roofing contractors, based on infrared imagery; (5) an expanded real estate information service. In addition, more than twenty-five other commercially attractive ideas in agribusiness, forestry, mining, real estate, urban planning and redevelopment, and consumer information were created. The commercial feasibility of the five business was assessed. This assessment included market surveys, revenue projections, cost analyses, and profitability studies. The results show that there are large and enthusiastic markets willing to pay for the services these businesses offer, and that the businesses could operate profitably.

Eastwood, L. F., Jr.↗

User observations on information sharing (corporate knowledge and lessons learned)

The sharing of 'corporate knowledge' and lessons learned in the NASA aerospace community has been identified by Johnson Space Center survey participants as a desirable tool. The concept of the program is based on creating a user friendly information system that will allow engineers, scientists, and managers at all working levels to share their information and experiences with other users irrespective of location or organization. The survey addresses potential end uses for such a system and offers some guidance on the development of subsequent processes to ensure the integrity of the information shared. This system concept will promote sharing of information between NASA centers, between NASA and its contractors, between NASA and other government agencies, and perhaps between NASA and institutions of higher learning.

Montague, Ronald A.↗

Toa Baja Disasters: Utilizing Open-Source Earth Observations to Inform the Toa Baja Municipality’s Flood Mitigation Efforts and Educate the Public

Toa Baja, located just west of San Juan in Puerto Rico, is known as “the underwater city” due to its propensity to flood. The city contains the mouth of the island’s longest river, Río de la Plata, which drains into the Atlantic Ocean on the northern edge of the municipality. Proximity to these major water features and the flat, low terrain contribute to the flood-prone nature of the area. During tropical storm events, such as Hurricane Maria in 2017, Toa Baja experienced inundation of up to 20 feet. Changes in the global climate system are causing more intense and frequent tropical storms, making places like Toa Baja subject to irreparable damage. This NASA DEVELOP project collaborated with the Municipio Autónomo de Toa Baja, ResilientSEE, and the Massachusetts Institute of Technology Urban Risk Lab to supplement recent 2018 Federal Emergency Management Agency Hydraulic Engineering Centers-River Analysis System flood maps, which designated 63% of the area as a flood plain. The analysis provides a high-resolution interpretation of flood susceptibility using a variety of factors that collectively influence the likelihood of flooding. Sentinel-1 C-Band Synthetic Aperture Radar (C-SAR) data processed with Google Earth Engine scripting identified historical inundation and was used for validation purposes. Socioeconomic factors were combined with the inundation layer producing a final risk output. These outputs will improve public understanding of exposure to flood risk in Toa Baja and provide scientific evidence for flood mitigation advocacy.

Disasters↗

Toa Baja Disasters: Utilizing Open-Source Earth Observations to Inform the Toa Baja Municipality’s Flood Mitigation Efforts and Educate the Public

Toa Baja, located just west of San Juan in Puerto Rico, is known as “the underwater city” due to its propensity to flood. The city contains the mouth of the island’s longest river, Río de la Plata, which drains into the Atlantic Ocean on the northern edge of the municipality. Proximity to these major water features and the flat, low terrain contribute to the flood-prone nature of the area. During tropical storm events, such as Hurricane Maria in 2017, Toa Baja experienced inundation of up to 20 feet. Changes in the global climate system are causing more intense and frequent tropical storms, making places like Toa Baja subject to irreparable damage. This NASA DEVELOP project collaborated with the Municipio Autonómo de Toa Baja, ResilientSEE, and the Massachusetts Institute of Technology Urban Risk Lab to supplement recent 2018 Federal Emergency Management Agency Hydraulic Engineering Centers-River Analysis System flood maps, which designated 63% of the area as a flood plain. The analysis provides a high-resolution interpretation of flood susceptibility using a variety of factors that collectively influence the likelihood of flooding. Sentinel-1 C-Band Synthetic Aperture Radar (C-SAR) data processed with Google Earth Engine scripting identified historical inundation and was used for validation purposes. Socioeconomic factors were combined with the inundation layer producing a final risk output. These outputs will improve public understanding of exposure to flood risk in Toa Baja and provide scientific evidence for flood mitigation advocacy.

Disasters↗

Utilizing Open-Source Earth Observations to Inform the Toa Baja Municipality’s Flood Risk Mitigation Efforts and Educate the Public

Global climate changes contribute to more intense and frequent tropical storms, subjecting places like Toa Baja, Puerto Rico to critical damage. Known as “the underwater city” due to its propensity to flood, residents of Toa Baja face constant flood risk. During extreme tropical storm events, such as Hurricane Maria in 2017, residents experienced up to 20 feet of inundation. The NASA DEVELOP National Program collaborated with the Municipio Autónomo de Toa Baja, ResilientSEE-PR, and the MIT Urban Risk Lab to supplement 2018 FEMA HEC-RAS flood maps that designate 63% of Toa Baja as a flood plain. This analysis provides a high-resolution interpretation of flood risk through two lenses; susceptibility and vulnerability. For this analysis, susceptibility consists of nine weighted layers: NDVI, landcover, slope, elevation, topographic wetness index, height above nearest drainage, saturated hydraulic conductivity, distance to water, and storm surge. These factors are consistently used to evaluate susceptibility to flood, but their weights vary by analysis. Vulnerability consists of population, informal settlements, and building density, which were given equal weight. Susceptibility and vulnerability were combined to map flood risk. This analysis used a bivariate legend to understand the different levels of risk along a spectrum from low susceptibility and low vulnerability (low risk) to high susceptibility and high vulnerability (high risk). Data processed in Google Earth Engine, which identified historical inundation on various occasions, were used to validate the flood susceptibility layers. Results showed 89% of areas designated as high susceptibility are located within the floodway designated by the FEMA HEC-RAS maps. The eastern region of Toa Baja is most at risk for flooding due to high susceptibility to flooding along with a high density of population, buildings, and informal settlements. The resulting map also reveals the presence of smaller high-risk areas all around the municipality. This analysis provides scientific evidence for flood risk mitigation in Toa Baja by highlighting areas that might be impacted by strong floods in the future. Additionally, these results are communicated in an Esri ArcGIS StoryMap, an accessible platform that can easily inform the public about the flood risk in their neighborhood.

Adriana Le Compte↗

NASA's Earth Observing Data and Information System

NASA's Earth Observing System Data and Information System (EOSDIS) has been a central component of NASA Earth observation program for over 10 years. It is one of the largest civilian science information system in the US, performing ingest, archive and distribution of over 3 terabytes of data per day much of which is from NASA s flagship missions Terra, Aqua and Aura. The system supports a variety of science disciplines including polar processes, land cover change, radiation budget, and most especially global climate change. The EOSDIS data centers, collocated with centers of science discipline expertise, archive and distribute standard data products produced by science investigator-led processing systems. Key to the success of EOSDIS is the concept of core versus community requirements. EOSDIS supports a core set of services to meet specific NASA needs and relies on community-developed services to meet specific user needs. EOSDIS offers a metadata registry, ECHO (Earth Observing System Clearinghouse), through which the scientific community can easily discover and exchange NASA s Earth science data and services. Users can search, manage, and access the contents of ECHO s registries (data and services) through user-developed and community-tailored interfaces or clients. The ECHO framework has become the primary access point for cross-Data Center search-and-order of EOSDIS and other Earth Science data holdings archived at the EOSDIS data centers. ECHO s Warehouse Inventory Search Tool (WIST) is the primary web-based client for discovering and ordering cross-discipline data from the EOSDIS data centers. The architecture of the EOSDIS provides a platform for the publication, discovery, understanding and access to NASA s Earth Observation resources and allows for easy integration of new datasets. The EOSDIS also has developed several methods for incorporating socioeconomic data into its data collection. Over the years, we have developed several methods for determining needs of the user community including use of the American Customer Satisfaction Index and a broad metrics program.

Mitchell, Andrew E.↗

Black Hole Mergers as Probes of Structure Formation

Intense structure formation and reionization occur at high redshift, yet there is currently little observational information about this very important epoch. Observations of gravitational waves from massive black hole (MBH) mergers can provide us with important clues about the formation of structures in the early universe. Past efforts have been limited to calculating merger rates using different models in which many assumptions are made about the specific values of physical parameters of the mergers, resulting in merger rate estimates that span a very wide range (0.1 - 104 mergers/year). Here we develop a semi-analytical, phenomenological model of MBH mergers that includes plausible combinations of several physical parameters, which we then turn around to determine how well observations with the Laser Interferometer Space Antenna (LISA) will be able to enhance our understanding of the universe during the critical z 5 - 30 structure formation era. We do this by generating synthetic LISA observable data (total BH mass, BH mass ratio, redshift, merger rates), which are then analyzed using a Markov Chain Monte Carlo method. This allows us to constrain the physical parameters of the mergers. We find that our methodology works well at estimating merger parameters, consistently giving results within 1- of the input parameter values. We also discover that the number of merger events is a key discriminant among models. This helps our method be robust against observational uncertainties. Our approach, which at this stage constitutes a proof of principle, can be readily extended to physical models and to more general problems in cosmology and gravitational wave astrophysics.

Alicea-Munoz, E.↗

NASA's Earth Observing Data and Information System (EOSDIS)

NASA's Earth Observing System Data and Information System (EOSDIS) has been a central component of the NASA Earth observation program since the 1990's. The data collected by NASA represent a significant public investment in research. Consequently, NASA developed a free, open and non-discriminatory policy consistent with existing international policies to maximize access to data. EOSDIS manages data covering a wide range of Earth science disciplines including cryosphere, land cover change, polar processes, field campaigns, ocean surface, geodesy, atmosphere dynamics and composition, and inter-disciplinary research, and many others.

Remote Sensing↗

NASA's Earth Observing Data and Information System (EOSDIS)

NASA's Earth Observing System Data and Information System (EOSDIS) has been a central component of the NASA Earth observation program since the 1990's. The data collected by NASA represent a significant public investment in research. Consequently, NASA developed a free, open and non-discriminatory policy consistent with existing international policies to maximize access to data. EOSDIS manages data covering a wide range of Earth science disciplines including cryosphere, land cover change, polar processes, field campaigns, ocean surface, geodesy, atmosphere dynamics and composition, and inter-disciplinary research, and many others. This presentation will discuss data stewardship and archive activities.

Data Systems↗

Computationally Efficient Modeling and Data Assimilation of Near-Surface Variability

Near-surface (< 20m) ocean exhibits high variability due to coupled interactions, for e.g., with the atmosphere, sea ice, land, etc. Here we focus on atmospheric heat and momentum (wind) forcing, which are known to cause diurnal variability within the mixed layer. Only recently with a combination of sufficiently high vertical/horizontal resolution (75L, 1/4deg) and sub-daily atmospheric forcing fields, ocean models are starting to resolve this diurnal variability. However, the computation expense of such a high vertical resolution is burdensome in the context of coupled modeling and data assimilation. An alternative approach is to parameterize this diurnal variability with a prognostic model, that is embedded into the ocean model.In the first part of this presentation, we will demonstrate results with the above two approaches, by comparing them to profiles of near-surface temperature and salinity. In the context of data assimilation and reanalysis, this modeling capability opens the door to re-examine and perhaps improve specification of background (or, ensemble) error characteristics. The second half of this talk will focus on illustrating diurnally varying errors within an ensemble DA, and possible approaches to improve localization (horizontal/vertical) to extract maximum possible observational information content from in-situ and satellite observations of sea surface temperature.

Akella, Santha↗

Scalable statistical inference of photometric redshift via data subsampling

Handling big data has largely been a major bottleneck in traditional statistical models. Consequently, when accurate point prediction is the primary target, machine learning models are often preferred over their statistical counterparts for bigger problems. But full probabilistic statistical models often outperform other models in quantifying uncertainties associated with model predictions. We develop a data-driven statistical modeling framework that combines the uncertainties from an ensemble of statistical models learned on smaller subsets of data carefully chosen to account for imbalances in the input space. We demonstrate this method on a photometric redshift estimation problem in cosmology, which seeks to infer a distribution of the redshift—the stretching effect in observing the light of far-away galaxies—given multivariate color information observed for an object in the sky. Our proposed method performs balanced partitioning, graph-based data subsampling across the partitions, and training of an ensemble of Gaussian process models.

data subsampling↗