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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
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NASA NTRS2017

A MODIS-Based Automated Flood Monitoring System for Southeast Asia

Flood disasters in Southeast Asia result in significant loss of life and economic damage. Remote sensing information systems designed to spatially and temporally monitor floods can help governments and international agencies formulate effective disaster response strategies during a flood and ultimately alleviate impacts to population, infrastructure, and agriculture. Recent destructive flood events in the Lower Mekong River Basin occurred in 2000, 2011, 16 2013, and 2016 (http://ffw.mrcmekong.org/historical_rec.htm, April 24, 2017). The large spatial distribution of flooded areas and lack of proper gauge data in the region makes accurate monitoring and assessment of impacts of floods difficult. Here, we discuss the utility of applying satellite-based Earth observations for improving flood inundation monitoring over the flood-prone Lower Mekong River Basin. We present a methodology for determining near real-time surface water extent associated with current and historic flood events by training surface water classifiers from 8-day, 250-meter Moderate-resolution Imaging Spectroradiometer (MODIS) data spanning the length of the MODIS satellite record. The Normalized Difference Vegetation Index (NDVI) signature of permanent water bodies (MOD44W; Carroll et al., 2009) is used to train surface water classifiers which are applied to a time period of interest. From this, an operational nowcast flood detection component is produced using twice daily imagery acquired at 3-hour latency which performs image compositing routines to minimize cloud cover. Case studies and accuracy assessments against radar-based observations for historic flood events are presented. The customizable system has been transferred to regional organizations and near real-time derived surface water products are made available through a web interface platform. Results highlight the potential of near real-time observation and impact assessment systems to serve as effective decision support tools for governments, international agencies, and disaster responders.

Mekong River↗
NASA NTRS2019

Comprehensive Severe Weather Impact Assessment and Monitoring using Synthetic Aperture Radar and Auxiliary Data

Remote sensing datasets, particularly acquired by Synthetic Aperture Radar (SAR) sensors, have become increasingly important in severe weather disaster impact studies given their ability to observe the Earth largely irrespective of weather and sunlight conditions. The reliability of existing SAR change detection products applied on a pair of SAR images is constrained by the limitation of current methods to differentiate and classify disaster specific changes from anthropogenic surface alterations. Moreover, the inherent properties of the sensors, variations in SAR backscatter due to changes in surface conditions, and other factors exacerbate these limitations. We proposed a novel procedure expanding on earlier SAR-based change detection methods to exclude anthropogenic alterations and other sources of ambiguity that might lead to inaccurate mapping of the impacts of severe weather disasters. We applied the proposed procedure that is based on long term interferometric and amplitude-based change detection analyses of Sentinel- 1 SAR imagery to two study sites recently impacted by severe weather disasters (Flooding post severe weather events in urban centers; Hailstorm damage on crops). For the first case study, Sentinel-1 SLC scenes from two flood events in the Houston area (April 2016 flooding event and Hurricane Harvey of August-September 2017) were used to construct a flood map depicting areas repeatedly affected by the flood. Pixels with consistent coherence values in the pre-disaster coherence stack were retained for comparison with the pre- and post-disaster coherence stack and pixels with significant decline (greater than 60%) in coherence values were retained in the final flood map. The findings of the applied technique were calibrated and validated through datasets from NOAA/NWS Service storm reports, aerial imaging (NOAA and Civil Air Patrol), Federal Emergency Management Agency (FEMA) reporting, and targeted collections of NASA’s L-band UAVSAR data. Findings and products derived from the adopted methodology can be useful in disaster response and mitigation activities.

Gebremichael, Esayas↗
NASA NTRS1997

Flood Management Enhancement Using Remotely Sensed Data

SENTAR, Inc., entered into a cooperative agreement with NASA Goddard Space Flight Center (GSFC) in December 1994. The intent of the NASA Cooperative Agreement was to stimulate broad public use, via the Internet, of the very large remote sensing databases maintained by NASA and other agencies, thus stimulating U.S. economic growth, improving the quality of life, and contributing to the implementation of a National Information Infrastructure. SENTAR headed a team of collaborating organizations in meeting the goals of this project. SENTAR's teammates were the NASA Marshall Space Flight Center (MSFC) Global Hydrology and Climate Center (GHCC), the U.S. Army Space and Strategic Defense Command (USASSDC), and the Alabama Emergency Management Agency (EMA). For this cooperative agreement, SENTAR and its teammates accessed remotely sensed data in the Distributed Active Archive Centers, and other available sources, for use in enhancing the present capabilities for flood disaster management by the Alabama EMA. The project developed a prototype software system for addressing prediction, warning, and damage assessment for floods, though it currently focuses on assessment. The objectives of the prototype system were to demonstrate the added value of remote sensing data for emergency management operations during floods and the ability of the Internet to provide the primary communications medium for the system. To help achieve these objectives, SENTAR developed an integrated interface for the emergency operations staff to simplify acquiring and manipulating source data and data products for use in generating new data products. The prototype system establishes a systems infrastructure designed to expand to include future flood-related data and models or to include other disasters with their associated remote sensing data requirements and distributed data sources. This report covers the specific work performed during the seventh, and final, milestone period of the project, which began on 1 October 1996 and ended on 31 January 1997. In addition, it provides a summary of the entire project.

Romanowski, Gregory J.↗
NASA NTRS2014

Colombia Mi Pronostico Flood Application: Updating and Improving the Mi Pronostico Flood Web Application to Include an Assessment of Flood Risk

Colombia is a country with highly variable terrain, from the Andes Mountains to plains and coastal areas, many of these areas are prone to flooding disasters. To identify these risk areas NASA's Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) was used to construct a digital elevation model (DEM) for the study region. The preliminary risk assessment was applied to a pilot study area, the La Mosca River basin. Precipitation data from the National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM)'s near-real-time rainfall products as well as precipitation data from the Instituto de Hidrologia, Meteorologia y Estudios Ambientales (the Institute of Hydrology, Meteorology and Environmental Studies, IDEAM) and stations in the La Mosca River Basin were used to create rainfall distribution maps for the region. Using the precipitation data and the ASTER DEM, the web application, Mi Pronóstico, run by IDEAM, was updated to include an interactive map which currently allows users to search for a location and view the vulnerability and current weather and flooding conditions. The geospatial information was linked to an early warning system in Mi Pronóstico that can alert the public of flood warnings and identify locations of nearby shelters.

Rushley, Stephanie↗
NASA NTRS2022

Mexico Disasters: Comparing Feasibility of Flood Detection Methods Using Google Earth Engine and Open Data Cube for Flood Mitigation in Mexico

In coordination with out partner's, we identified three case studies for the application of the flood detection algorithms. The first case study analyzes the impacts of Hurricane Sally on Villahermosa, Tabasco resulting in urban flooding and impacting adjacent cropland. The second case study investigates floods in Salto de Agua, Chiapas caused by Hurricane Eta, where the Xanil River level exceeded 14 meters, pouring into the surrounding villages. Finally, we look further north to Tula de Allende, Hidalgo during Hurricane Ida, where floods collapsed the power supply citywide and caused numerous deaths.

John Willis↗
NASA NTRS2020

Ellicott City Disasters II: Enhancing a Statistical Flood Risk Model to Continue Improving Early Warning Systems and Public Safety in Ellicott City, Maryland

As flooding events in the United States grow in frequency and intensity, the use of technological advancements and applied science are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters II project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, the project improved the original statistical flood risk model, FLuME (Flood Learning Model Environment), programmed by the first DEVELOP term. The enhancements incorporated an additional six years of precipitation and soil moisture data from the North American Land Data Assimilation System (NLDAS), modeled using Aqua Advanced Microwave Scanning Radiometer for EOS and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager. These Earth observations were supplemented by stream gauge data from the OEM and the US Geological Survey. The resultant flood risk model FLASH (Flood Learning Environment and Severity Assessment Hub) was trained to evaluate input variables and predict stage height in Ellicott City in real time. The addition of an advanced deep learning framework known as long short-term memory improved the model’s ability to capture relationships between variables. To assess the effectiveness of the new model, FLASH produced a model efficiency metric of 0.99, a significant improvement over the 0.85 value produced by the previous model. The project assisted the OEM in pursuing the integration of open data and NASA Earth observations into a threat matrix capable of informing near real-time decision making.

Disasters↗
NASA NTRS2020

Ellicott City Disasters II: Enhancing a Statistical Flood Risk Model to Continue Improving Early Warning Systems and Public Safety in Ellicott City, Maryland

As flooding events in the United States grow in frequency and intensity, the use of technological advancements and applied science are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters II project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, the project improved the original statistical flood risk model, FLuME (Flood Learning Model Environment), programmed by the first DEVELOP term The enhancements incorporated an additional six years of precipitation and soil moisture data from the North American Land Data Assimilation System (NLDAS), modeled using Aqua Advanced Microwave Scanning Radiometer for EOS and Tropical Rainfall Measuring Mission TRMM Microwave Imager. These Earth observations were supplemented by stream gauge data from the OEM and the US Geological Survey. The resultant flood risk model FLASH (Flood Learning Environment and Severity Assessment Hub) was trained to evaluate input variables and predict stage height in Ellicott City in real time. The addition of an advanced deep learning framework known as long short-term memory improved the model’s ability to capture relationships between variables. To assess the effectiveness of the new model, FLASH produced a model efficiency metric of 0.99, a significant improvement over the 0.85 value produced by the previous model. The project assisted the OEM in pursuing the integration of open data and NASA Earth observations into a threat matrix capable of informing near real-time decision making.

Disasters↗
NASA NTRS2024

Kentucky Disasters: Multi-Hazard Approach to Mapping Flood Susceptibility and Vulnerability in Kentucky

Flooding is the most common and costly natural disaster in Kentucky, with major flood events in 2022 and 2023 highlighting the need for flood risk assessment. In partnership with the National Weather Service Jackson and Paducah Forecast Offices and the Kentucky Climate Center, we mapped flood risk in Kentucky using a multi-hazard approach that considered two dimensions of risk: flood susceptibility based on a weighted combination of seven physical factors and flood vulnerability based on 13 socioeconomic and infrastructure factors. We additionally analyzed NASA Soil Moisture Active Passive (SMAP) observations of surface soil moisture to explore the utility of SMAP observations for future analysis of flood risk. By analyzing flood susceptibility, we found that with equal rainfall, western Kentucky generally displays a higher propensity to flood than eastern Kentucky. In contrast, our flood vulnerability analysis indicated that more vulnerable areas were generally concentrated in the eastern part of the state. Through a combined perspective, our flood risk analysis identified much of the state as having moderate degrees of flood susceptibility and vulnerability. Our parallel analysis of antecedent soil moisture found that SMAP soil moisture levels were variable in the months leading up to each flood event but were drier than normal in the month prior to the 2023 event, as shown by negative soil moisture anomalies. These results were limited by challenges with weighting input parameters and a lack of validation but overall demonstrate the feasibility of using GIS and Earth observations for mapping flood risk and soil moisture.

analytic hierarchy process↗
NASA NTRS2016

Near Real-Time Flood Monitoring and Impact Assessment Systems

Floods are the costliest natural disaster, causing approximately 6.8 million deaths in the twentieth century alone. Worldwide economic flood damage estimates in 2012 exceed $19 Billion USD. Extended duration floods also pose longer term threats to food security, water, sanitation, hygiene, and community livelihoods, particularly in developing countries. Projections by the Intergovernmental Panel on Climate Change (IPCC) suggest that precipitation extremes, rainfall intensity, storm intensity, and variability are increasing due to climate change. Increasing hydrologic uncertainty will likely lead to unprecedented extreme flood events. As such, there is a vital need to enhance and further develop traditional techniques used to rapidly assess flooding and extend analytical methods to estimate impacted population and infrastructure. Measuring flood extent in situ is generally impractical, time consuming, and can be inaccurate. Remotely sensed imagery acquired from space-borne and airborne sensors provides a viable platform for consistent and rapid wall-to-wall monitoring of large flood events through time. Terabytes of freely available satellite imagery are made available online each day by NASA, ESA, and other international space research institutions. Advances in cloud computing and data storage technologies allow researchers to leverage these satellite data and apply analytical methods at scale. Repeat-survey earth observations help provide insight about how natural phenomena change through time, including the progression and recession of floodwaters. In recent years, cloud-penetrating radar remote sensing techniques (e.g., Synthetic Aperture Radar) and high temporal resolution imagery platforms (e.g., MODIS and its 1-day return period), along with high performance computing infrastructure, have enabled significant advances in software systems that provide flood warning, assessments, and hazard reduction potential. By incorporating social and economic data, researchers can develop systems that automatically quantify the socioeconomic impacts resulting from flood disaster events.

Ahamed, Aakash↗
NASA NTRS2015

Soil Moisture Active Passive (SMAP) Mission Level 4 Surface and Root Zone Soil Moisture (L4_SM) Product Specification Document

This is the Product Specification Document (PSD) for Level 4 Surface and Root Zone Soil Moisture (L4_SM) data for the Science Data System (SDS) of the Soil Moisture Active Passive (SMAP) project. The L4_SM data product provides estimates of land surface conditions based on the assimilation of SMAP observations into a customized version of the NASA Goddard Earth Observing System, Version 5 (GEOS-5) land data assimilation system (LDAS). This document applies to any standard L4_SM data product generated by the SMAP Project. The Soil Moisture Active Passive (SMAP) mission will enhance the accuracy and the resolution of space-based measurements of terrestrial soil moisture and freeze-thaw state. SMAP data products will have a noteworthy impact on multiple relevant and current Earth Science endeavors. These include: Understanding of the processes that link the terrestrial water, the energy and the carbon cycles, Estimations of global water and energy fluxes over the land surfaces, Quantification of the net carbon flux in boreal landscapes Forecast skill of both weather and climate, Predictions and monitoring of natural disasters including floods, landslides and droughts, and Predictions of agricultural productivity. To provide these data, the SMAP mission will deploy a satellite observatory in a near polar, sun synchronous orbit. The observatory will house an L-band radiometer that operates at 1.40 GHz and an L-band radar that operates at 1.26 GHz. The instruments will share a rotating reflector antenna with a 6 meter aperture that scans over a 1000 km swath.

L4-SM↗
NASA NTRS2020

Space-Based Precipitation Measurements in Tropical Cyclones: Past, Present, and Future

Passive and active remote sensing of precipitation from space has led to significant advances in the understanding and prediction of tropical cyclones around the globe. This presentation will highlight the role of past NASA space-based measurements of precipitation by the Tropical Rainfall Measuring Mission (TRMM, 1998-2015), ongoing measurements by the Global Precipitation Measurement (GPM) mission (2014-current), and future measurements from the Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS, nominal launch date in 2020) as well as a potential new mission on Aerosols, Clouds, Convection, and Precipitation (ACCP) from the 2017 NASA Earth Science Decadal Survey. TRMM, which flew the first precipitation radar in space, provided the first systematic descriptions of the radial and azimuthal variations of rainfall in tropical cyclones around the globe and their relationship to storm motion and vertical wind shear. GPM is the lynchpin of a global constellation of precipitation satellites that provides high spatial (0.1°) and temporal (30 min) resolution real-time estimates of precipitation globally, making them essential to applications related to tropical cyclone prediction, disaster response, flood and landslide monitoring, and vector-borne disease monitoring. TROPICS will be a constellation of 6 Cubesat satellites with microwave imaging and sounding channels that will provide information on temperature and humidity in the storm environment, as well as estimates of precipitation and tropical cyclone intensity. ACCP is yet to be fully defined, but is envisioned to potentially carry multi-frequency radar with possible Doppler capability.

Braun, Scott↗
NASA NTRSDate not supplied

NASA Environmental Justice Data Search Interface Overview

NASA’s Earth Science Division (ESD) is committed to empower Environmental Justice (EJ) communities by expanding awareness, accessibility, and use of Earth science data to enable contributions to Earth science research and applications. To that end, the NASA Earth Science Data Systems (ESDS) Program developed an EJ Data Catalog, a simple guide to NASA datasets and socioeconomic datasets that may be useful in EJ research. The EJ Data Catalog is divided by topics—such as disasters, urban flooding, extreme heat, food availability, water availability, climate, and health and air quality—and possible use cases for each dataset. The new version of the EJ Data Catalog is now integrated into NASA’s Science Discovery Engine (SDE), an open-source science infrastructure to enable collaborative and interdisciplinary science. In this workshop you will learn about NASA’s Equity and Environmental Justice (EEJ) activities and opportunities as well as participate on an interactive live demo of the new Science Discovery Engine for Environmental Justice.

environmental justice↗
NASA NTRS2020

Ellicott City Disasters II - Building a Real-Time Predictive Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the uses of applied remote sensing analyses are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters III project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems in Ellicott City, Maryland. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, this term built on the predictive capability of the long-short term memory (LSTM) model created by the second term of this DEVELOP project to create a Sequentially Trained Real-time EstimAted Model (STREAM). Enhancements to the model included the integration of both real-time and predicted weather products from the National Weather Service to increase predictive capacity. These weather products were supplemented by stream gauge data from the OEM as well as real-time radar products. The resultant flood risk model was trained to evaluate input variables and predict stage height in Ellicott City in real time. The model, upgraded to predict stage height up to 8 hours in advance, was incorporated into an online dashboard in a user-friendly interface. The project demonstrated the potential for integration of open data and NASA Earth observations into a flood risk forecasting tool capable of informing real-time decision-making.

Erika Munshi↗
NASA NTRS2020

Ellicott City Disasters III: Building a Real-Time Statistical Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the uses of applied remote sensing analyses are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters III project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems in Ellicott City, Maryland. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, this term built on the predictive capability of the long-short term memory (LSTM) model created by the second term of this DEVELOP project to create a Sequentially Trained Real-time EstimAted Model (STREAM). Enhancements to the model included the integration of both real-time and predicted weather products from the National Weather Service to increase predictive capacity. These weather products were supplemented by stream gauge data from the OEM as well as real-time radar products. The resultant flood risk model was trained to evaluate input variables and predict stage height in Ellicott City in real time. The model, upgraded to predict stage height up to 8 hours in advance, was incorporated into an online dashboard in a user-friendly interface. The project demonstrated the potential for integration of open data and NASA Earth observations into a flood risk forecasting tool capable of informing real-time decision-making.

Erika Munshi↗