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All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

NASA's Applied Sciences: Natural Disasters Program

Fully utilize current and near-term airborne and spaceborne assets and capabilities. NASA spaceborne instruments are for research but can be applied to natural disaster response as appropriate. NASA airborne instruments can be targeted specifically for disaster response. Could impact research programs. Better flow of information improves disaster response. Catalog capability, product, applicable disaster, points of contact. Ownership needs to come from the highest level of NASA - unpredictable and irregular nature of disasters requires contingency funding for disaster response. Build-in transfer of applicable natural disaster research capabilities to operational functionality at other agencies (e.g., USFS, NOAA, FEMA...) at the outset, whenever possible. For the Decadal Survey Missions, opportunities exist to identify needs and requirements early in the mission design process. Need to understand additional needs and commitments for meeting the needs of the disaster community. Opportunity to maximize disaster response and mitigation from the Decadal Survey Missions. Additional needs or capabilities may require agency contributions.

Kessler, Jason L.↗
NASA NTRS2011

Assessment of NASA's Physiographic and Meteorological Datasets as Input to HSPF and SWAT Hydrological Models

This paper documents the use of simulated Moderate Resolution Imaging Spectroradiometer land use/land cover (MODIS-LULC), NASA-LIS generated precipitation and evapo-transpiration (ET), and Shuttle Radar Topography Mission (SRTM) datasets (in conjunction with standard land use, topographical and meteorological datasets) as input to hydrological models routinely used by the watershed hydrology modeling community. The study is focused in coastal watersheds in the Mississippi Gulf Coast although one of the test cases focuses in an inland watershed located in northeastern State of Mississippi, USA. The decision support tools (DSTs) into which the NASA datasets were assimilated were the Soil Water & Assessment Tool (SWAT) and the Hydrological Simulation Program FORTRAN (HSPF). These DSTs are endorsed by several US government agencies (EPA, FEMA, USGS) for water resources management strategies. These models use physiographic and meteorological data extensively. Precipitation gages and USGS gage stations in the region were used to calibrate several HSPF and SWAT model applications. Land use and topographical datasets were swapped to assess model output sensitivities. NASA-LIS meteorological data were introduced in the calibrated model applications for simulation of watershed hydrology for a time period in which no weather data were available (1997-2006). The performance of the NASA datasets in the context of hydrological modeling was assessed through comparison of measured and model-simulated hydrographs. Overall, NASA datasets were as useful as standard land use, topographical , and meteorological datasets. Moreover, NASA datasets were used for performing analyses that the standard datasets could not made possible, e.g., introduction of land use dynamics into hydrological simulations

Alacron, Vladimir J.↗
NASA NTRS2017

Contribution of Asteroid Generated Tsunami to the Impact Hazard

The long-standing uncertainty about the importance of asteroid-generated tsunami was addressed at a workshop in August 2016, co-sponsored by NASA and NOAA. Experts from NASA, NOAA, the DoE tri-labs (LLNL, SNL, and LANL), DHS, FEMA, and academia addressed the hazard of tsunami created by asteroid impacts, focusing primarily on NEAs with diameter less than 250m. Participants jointly identified key issues and shared information for nearly a year to coordinate their results for discussion at the workshop. They used modern computational tools to examine 1) Near-field wave generation by the impact; 2) Long-distance wave propagation; 3) Damage from coastal run-up and inundation, and associated hazard. The workshop resulted in broad consensus that the asteroid impact tsunami threat is not as great as previously thought.

Asteroid↗
NASA NTRS2017

Coping with Higher Sea Levels and Increased Coastal Flooding in New York City

The 837 km New York City shoreline is lined by significant economic assets and dense population vulnerable to sea level rise and coastal flooding. After Hurricane Sandy in 2012, New York City developed a comprehensive plan to mitigate future climate risks, drawing upon the scientific expertise of the New York City Panel on Climate Change (NPCC), a special advisory group comprised of university and private-sector experts. This paper highlights current NPCC findings regarding sea level rise and coastal flooding, with some of the City's ongoing and planned responses. Twentieth century sea level rise in New York City (2.8 cm/decade) exceeded the global average (1.7 cm/decade), underscoring the enhanced regional risk to coastal hazards. NPCC (2015) projects future sea level rise at the Battery of 28 - 53 cm by the 2050s and 46 - 99 cm by the 2080s, relative to 2000 - 2004 (mid-range, 25th - 75th percentile). High-end SLR estimates (90th percentile) reach 76 cm by the 2050s, and 1.9 m by 2100. Combining these projections with updated FEMA flood return period curves, assuming static flood dynamics and storm behavior, flood heights for the 100-year storm (excluding waves) attain 3.9-4.5 m (mid-range), relative to the NAVD88 tidal datum, and 4.9 m (high end) by the 2080s, up from 3.4 m in the 2000s. Flood heights with a 1% annual chance of occurrence in the 2000s increase to 2.0 - 5.4% (mid-range) and 12.7% per year (high-end), by the 2080s. Guided by NPCC (2013, 2015) findings, New York City has embarked on a suite of initiatives to strengthen coastal defenses, employing various approaches tailored to specific neighborhood needs. NPCC continues its collaboration with the city to investigate vulnerability to extreme climate events, including heat waves, inland floods and coastal storms. Current research entails higher-resolution neighborhood-level coastal flood mapping, changes in storm characteristics, surge height interactions with sea level rise, and stronger engagement with stakeholders and community-based organizations.

Coastal flooding↗
NASA NTRS2017

Automated Mapping of Flood Events in the Mississippi River Basin Utilizing NASA Earth Observations

The Mississippi River Basin is the fourth largest drainage basin in the world, and is susceptible to multi-level flood events caused by heavy precipitation, snow melt, and changes in water table levels. Conducting flood analysis during periods of disaster is a challenging endeavor for NASA's Short-term Prediction Research and Transition Center (SPoRT), Federal Emergency Management Agency (FEMA), and the U.S. Geological Survey's Hazards Data Distribution Systems (USGS HDDS) due to heavily-involved research and lack of manpower. During this project, an automated script was generated that performs high-level flood analysis to relieve the workload for end-users. The script incorporated Landsat 8 Operational Land Imager (OLI) tiles and utilized computer-learning techniques to generate accurate water extent maps. The script referenced the Moderate Resolution Imaging Spectroradiometer (MODIS) land-water mask to isolate areas of flood induced waters. These areas were overlaid onto the National Land Cover Database's (NLCD) land cover data, the Oak Ridge National Laboratory's LandScan data, and Homeland Infrastructure Foundation-Level Data (HIFLD) to determine the classification of areas impacted and the population density affected by flooding. The automated algorithm was initially tested on the September 2016 flood event that occurred in Upper Mississippi River Basin, and was then further tested on multiple flood events within the Mississippi River Basin. This script allows end users to create their own flood probability and impact maps for disaster mitigation and recovery efforts.

Bartkovich, Mercedes↗
NASA NTRS2019

New York City Panel on Climate Change 2019 Report Chapter 4: Coastal Flooding

Coastal flooding from storm surge is one of the most dangerous and damaging natural hazards that societies face. It was responsible for half of all hurricane related mortalities in the United States from 1963 to 2012, far more than any other factor. Coastal extreme water levels are increasing globally, mainly driven by rises in mean sea level. Sea level rise is also causing rapid increases in the annual number of shallow “nuisance floods” for low-lying neighborhoods. The objectives of this chapter are to review the latest knowledge on New York City flood risk from storms and tides, and to evaluate how climate change will affect this risk between now and the end of the century. Methods used by NPCC (2015) for assessing storm-driven extreme floods are generally repeated here, including the use of the Federal Emergency Management Agency (FEMA, 2013) baseline flood hazards (e.g., the 100-year flood(sup a)) and the methods for adding sea level rise and mapping the resulting hazard. New advancements include an innovative analysis of monthly tidal flooding based on a dynamic model, a broadened set of sea level rise scenarios supplemented with the Antarctic Rapid Ice Melt (ARIM) scenario (see Chapter 3), and sensitivity analyses that show how differing methods would affect our results. Wind is a primary factor for coastal storm surge, and a brief review is given in Appendix 4.A, with the latest scientific knowledge on what drives extreme wind events in the New York City area and how they may change in the future.

Orton, Philip↗
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 NTRS2019

Rapid Generation of Flood Maps Using Dual-Polarimetric Synthetic Aperture Radar Imagery

Rapid generation of synthetic aperture radar (SAR) based flood extent and flood depth maps provide valuable data in disaster response efforts. We present a simple but powerful method using dual polarimetric SAR imagery. A RGB false-color map is generated using pre- and post-flooding imagery, allowing operators to distinguish between existing standing water in pre-flooding data and recently flooded areas. This method works very well in areas of standing water, while large omission errors can be seen in urban areas due to the double-bounce effect. A flood depth map is also estimated by using an external DEM. Compared with FEMA flood product, flood water depth from the proposed method showed low bias with small dispersion. This automatic flood mapping system will contribute to the rapid assessment for disaster relief efforts.

Jo, MinJeong↗
NASA NTRS2019

Rapid Generation of Flood Maps Using Dual-Polarimetric Synthetic Aperture Radar Imagery

Rapid generation of synthetic aperture radar (SAR) based flood extent and flood depth maps provide valuable data in disaster response efforts. We present a simple but powerful method using dual-polarimetric SAR imagery. A RGB false-color map is generated using pre- and post-flooding imagery, allowing operators to distinguish between existing standing water in pre-flooding data and recently flooded areas. This method works very well in areas of standing water, while large omission errors can be seen in urban areas due to the double-bounce effect. A flood depth map is also estimated by using an external DEM. Compared with FEMA flood product, flood water depth from the proposed method showed low bias with small dispersion. This automatic flood mapping system will contribute to the rapid assessment for disaster relief efforts.

Jo, MinJeong↗
NASA NTRS2021

Northern Great Plains Disasters: Using Earth Observations to Enhance Flood Monitoring on Tribal Lands in the Northern Great Plains

In 2019, the Great Plains experienced unprecedented catastrophic flooding. Large flood events are predicted to increase in frequency and severity, posing risks to communities in this region, particularly Tribal Nations. We used data from Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), imagery from the Sentinel-2 MultiSpectral Instrument (MSI), and digital elevation models (DEMs) from the Shuttle Radar Topography Mission (SRTM) within Google Earth Engine to map historical floods in the region beginning in 2014 with particular attention to the Rosebud Sioux Reservation and the tribal lands of other Great Plains Tribal Water Alliance members. This historical mapping used C-SAR for a combined method approach with a Z-score algorithm in addition to an index for flooded short vegetation. We also developed a flood risk map by weighting different flood predictor variables according to flood risk literature. These variables included soil drainage from the Soil Survey Geographic Database (SSURGO); elevation, slope, and Topographic Wetness Index (TWI) derived from digital elevation models; precipitation from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS); land cover from the National Land Cover Database (NLDC); and Normalized Difference Vegetation Index (NDVI) derived from Landsat 8 Operational Land Imager (OLI). From the flood extent and risk maps, we identified widespread flooding in short vegetation (including cropland) and noted flood susceptibility in regions exhibiting high social vulnerability and low community resilience (FEMA indices). We created an ArcGIS Online StoryMap to share project background, results, and data. Additionally, we provided a written tutorial so partners may replicate the flood mapping for future flood events.

Anna Ballasiotes↗
NASA NTRSDate not supplied

Dissemination of Global Flood Information Through Nasa Disasters Program Global Initiative for Flood Forecasting and Alerting (GIFFT)

The Global Initiative for Flood Forecasting and Alerting (GIFFT) is an initiative to disseminate flood products from different sensors to global stakeholders via the Pacific Disaster Center’s (PDC) DisasterAWARE®, the NASA Disasters Mapping Portal and potentially other mechanisms. GIFFT will also generate an integrated product(s) using ensembled hydrologic models and Earth observation derived flood outputs to stakeholders globally. This initiative combines the results of several Disasters Program funded projects that focus on flood forecasting, flood severity estimation, post-event flood mapping, flooddepth estimation and resulting flood severity using remote sensing-based flood information derived from SAR and optical imagery. PDC’s DisasterAWARE platform is a global multi-hazard alerting platform that currently incorporatesModel of Models (MoM) outputs as flood “incidents,” visually depicting potential floods in the context ofpopulation and infrastructure that may become affected. Automated procedures are being developed tocategorize MoM outputs as DisasterAWARE “hazards,” allowing for their dissemination to users alongwith other flood products that assess potential impacts. PDC’s stakeholders include UN, DoD, FEMA andtheir equivalents around the world. Additionally, the DisasterALERT mobile platform has 2 million usersworldwide and is a fully functional and sustained platform used to disseminate alerts and analyticalproducts for 18 different hazard types. GIFFT has incorporated HydroSAR products that will bedistributed via DisasterAWARE.

remote sensing↗
NASA NTRS2023

Georgia Disasters II: Evaluating the Impacts of Hurricane Irma on Georgia Heirs Property Owners Using NASA Earth Observations

Heirs property owners are especially vulnerable to natural and manmade disasters. This group of people have inherited property left with no clear title and thus have unclear group ownership with the other legal owners, which are all spouses, children, etc. of past owners. After Hurricane Irma made landfall in Georgia in September of 2017, heirs property owners became more likely to be denied access to federal relief due to the legal status of their property title. To observe how this group was impacted by Hurricane Irma, the NASA DEVELOP team partnered with The Georgia Heirs Property Law Center (The Center), a non-profit law firm that works with heirs properties owners. The team used computer assisted mass appraisal (CAMA) data to identify likely heirs property owners. They cross referenced this map with a flood map produced with surface reflectance and backscatter imagery from Landsat 8 OLI, Sentinel-2 MSI, and Sentinel-1 C-SAR, sensors to identify communities in need of relief or assistance. The flood extent maps were validated against United States Geological Survey (USGS) Hurricane Irma High Water Mark in situ data taken the same day Irma crossed into Georgia. To further evaluate the impacted group, the team correlated the flood and heirs property likelihood maps to FEMA denials based on titles issues. The team’s end products were handed off to the Georgia Heirs Property Law Center for use in community outreach, educational materials, and to help direct where The Center can work to prioritize its limited legal resources.

Shakirah Rogers↗
NASA NTRSDate not supplied

Planning for a Large Space Weather Event: Preparation for and Lessons Learned from the 2024 Space Weather Interagency Tabletop Exercise

On May 8-9 2024, the Space Radiation Analysis Group (SRAG) at NASA/Johnson Space Center participated in a joint exercise with DoD, FEMA, OSTP, DOE and other agencies to assess their ability to respond to a large hypothetical space weather event. SRAG is a uniquely skilled group of scientists and engineers tasked with mitigating impacts of radiation exposure to the astronaut crew aboard the International Space Station and upcoming missions beyond Low Earth Orbit (LEO). In meeting this objective, the team has developed expertise in both modeling the impacts of an enhanced space environment and in the real-time operational support of the Flight Control Team when assessing a response to the environment. Several members of SRAG worked with the Tabletop Exercise (TTX) planners to define a realistic large event and to model the resulting radiation exposure on an Artemis crew. SRAG members participating in the TTX were blinded to the results of this analysis. As the event unfolded during the TTX, SRAG provided their assessment of the likely impact on the crew and recommendations for crew actions. During the exercise, the team tracked lessons learned for their own planning, currently in work, for Artemis-II and subsequent missions.

Janet Barzilla↗
NASA NTRSDate not supplied

Exploring Flooded Fraction Prediction through Machine Learning Models Focusing on Medical Infrastructure in the Southeast U.S. Coastal Areas

Rising sea levels due to climate change increasingly threaten medical infrastructure through flooding. This study develops machine learning models to predict flood exposure for 11,508 medical facilities in the southeastern coastal regions of the United States by integrating datasets including meteorological, hydrological, topographic, and geological data, the Natural Risk Index, and historical flood records from NASA, HIFLD, and FEMA. Six regression models, namely Linear Regression, Support Vector Regression, Random Forest, k-Nearest Neighbors, XGBoost, and Artificial Neural Networks, are trained using 16 explanatory variables identified through literature review and correlation analysis. Data preprocessing employs the SMOGN for class imbalance and Winsorization for outliers. Model performance is evaluated using MAE, MSE, and RMSE, with Random Forest and XGBoost models achieving the highest performance (MSE of 2.58e-5 and 3.69e-5, respectively). This multifactorial approach allows the models to capture complex flood-influencing relationships, enhancing adaptability and performance across geographic regions. Future work focuses on expanding across the U.S. and developing a near real-time flood monitoring system.

Jihoon Chung↗