Search NASA⌕ Search

SEARCH · Search NASA

Results for “flood model”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Hurricane wind field representation shapes storm surge and building-scale flood hazard estimates

Coastal flood hazard estimates rely on precise hurricane wind forecasts to assess damage and risk. Here, we demonstrate that errors in hurricane wind field representation can lead to significant biases in storm surge and property-level damage estimates. Using Hurricane Ian (2022) as a case study, we compare widely used parametric, reanalysis, and hybrid wind datasets. Improved wind field accuracy reduces storm surge and damage estimate bias by up to 70\%. Our results underscore the importance of accurately predicting hurricane wind structure in hazard assessments.

Coastal Flooding↗
DOE OSTI2022

Analytical-based simulation approach for an anion exchange membrane fuel cell

An analytical and empirical-based 1-D, non-isothermal, steady-state model for anion exchange membrane fuel cell capable of capturing two-phase phenomena is presented in this study. Coupled multi-physics including mass and charge transport, electrochemical reactions, heat transfer, and two-phase water transport are considered in the model and the simulated results are compared to experimental data. To better represent actual material properties and localized conditions, the model applies multilayer discretization in the gas diffusion electrode to enhance prediction accuracy. The model successfully predicts the baseline performance at 70 °C, 131 kPa abs., 92% RH with pure H 2 /O 2 gas as well as the limiting current at 10% H 2 . The robust simulation approach allows for simplistic and accurate estimation of cell performance without the complications of applying two-phase parameters and expensive computational need for numerical models. In addition, the results from the sensitivity studies of material properties and operating conditions provide valuable insights on water management strategies and optimal component design for advancing anion exchange membrane fuel cell technology.

1-D model↗
NASA NTRS1991

Generation of topographic terrain models utilizing synthetic aperture radar and surface level data

Topographical terrain models are generated by digitally delineating the boundary of the region under investigation from the data obtained from an airborne synthetic aperture radar image and surface elevation data concurrently acquired either from an airborne instrument or at ground level. A set of coregistered boundary maps thus generated are then digitally combined in three dimensional space with the acquired surface elevation data by means of image processing software stored in a digital computer. The method is particularly applicable for generating terrain models of flooded regions covered entirely or in part by foliage.

Imhoff, Marc L.↗
NASA NTRS1993

Constraints on the rate of discharge and duration of the Mangala Valles flood

Interest in Mangala Valles remains high within the planetary science community. This is justified because the survey mission images provide us with nearly complete coverage of the system at high resolution. Upcoming high resolution topography from the Mars Observer Laser Altimeter will enable the application of flood discharge models with an unprecedented level of detail. Previous work on the Mangala Valles problem has relied on the available low resolution topography. This has limited the ability of workers to constrain discharge calculations to only within several-order-of-magnitude estimates. Local determinations of channel depths via shadow length measurements and photoclinometric profiling are much more accurate, but can only be applied to steep slopes (and/or low sun elevations) in the case of shadow measurements, or across relatively short distances (to avoid changes in albedo along asymmetric photoclinometric profiles). We are taking stereo parallax measurements from medium resolution Viking Orbiter images, which provide a valuable intermediate check of the topography between those measurements made thus far and the upcoming Mars Observer data. The images used are from orbits 034A and 637A, and cover Mangala Valles from the source graben in Memnonia Fossae to the beginning of the bifurcated reach (at 9.5 deg lat., 151.5 deg lon.). These images are about 300 m/pixel and 250 m/pixel, respectively. Both sets of images were orthographically projected to 250 m/pixel. The separation angle between left-right pairs is approximately 52 degrees. This results in a vertical accuracy on the order of plus or minus 260 m. Though this is still somewhat coarse, the channel relief is clearly resolved. Preliminary profiles across Mangala Valles and the large topographic ridge to the east are shown on the following page. An east-west regional tilt that resulted from slight scaling differences in the digital data has been 'removed' by visually estimating a regional datum on the stereo pairs. North-south variations in scale have not yet been adjusted for, so the zero datum for each profile was simply taken to be the high water line of the channel itself. Our depth measurement for the source breach of about 750 m agrees reasonably well with values of 700-1000 m determined from shadow measurements.

Parker, T. J.↗
NASA NTRS2005

Lunar and Planetary Science XXXVI, Part 2

Topics covered include: Ringwoodite-olivine assemblages in Dhofar L6 melt veins; Amorphization of forsterite grains due to high energy heavy ion irradiation: Implications for grain processing in ISM; Validation of AUTODYN in replicating large-scale planetary impact events; A network of geophysical observatories for mars; Modelling catastrophic floods on the surface of mars; Impact into coarse grained spheres; The diderot meteorite: The second chassignite; Galileo global color mosaics of Io; Ganymede's sulci on global and regional scales; and The cold traps near the south pole of the moon.

Source record↗
NASA NTRSDate not supplied

Assessing Environmental and Socioeconomic Factors of Urban Flood Vulnerability in Kansas City, Kansas

Pluvial flooding, over-saturated ground, and drainage systems disproportionately impact historically marginalized urban neighborhoods during extreme rainfall events. These communities are impacted by physical and socioeconomic factors that make them vulnerable to flooding events, such as high concentrations of impervious landcover, high precipitation rates, and a combined sewer system framework. Despite known vulnerability to environmental hazards, understanding potential pluvial street-level flooding events are largely unknown. Using the open-source National Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation model, NASA DEVELOP examined neighborhood scale runoff retention and potential economic damages for risk mapping throughout Kansas City, Kansas. The generated outputs aid in identifying vulnerable neighborhoods susceptible to flooding and in need of future intervention. Previous studies have applied this model framework to understand urban flood vulnerability regarding ecosystem services, urban planning, and flood mitigation strategies. We utilized the InVEST outputs to develop indices pertaining to environmental justice factors of race, socioeconomic status, social vulnerability, and health. The findings indicate that historically redlined neighborhoods in Kansas City, Kansas face disproportional impacts from flood events and are subject to greater environmental stressors. This research provides an approach to utilizing an open-source flood vulnerability model to empower neighborhood-scale environmental justice analysis, enhancing the local communities' understanding of present-day impacts of historical environmental injustices.

Hadwynne Gross↗
NASA NTRSDate not supplied

Dynamical Mechanisms Underlying the 2022/23 California Flooding: Analysis With A Stationary Wave Model

In late December 2022 and the first half of January 2023, much of California experienced an unprecedented series of atmospheric rivers that produced heavy rains and near-record flooding. Previous work shows that a chain of dynamical events contributed to the extreme precipitation, including the development of a Rossby wave (as a result of forcing linked to the MJO) that emerged from the Indian Ocean in mid-December, and the subsequent development of a persistent positive Pacific North American (PNA) pattern that ultimately directed moisture onto the US West Coast starting in late December. Here, we use a stationary wave model (SWM) to further elucidate the dynamical and thermodynamical processes that drove the aforementioned chain of events. The results reveal the following: 1) The mid-December Rossby wave was likely induced by vorticity stretching and advection in the middle East linked indirectly to the MJO, 2) The initial development of the PNA in late December was triggered by transient and stretching sources of vorticity in the Pacific that were themselves induced by the aforementioned Rossby wave, and 3) The PNA was maintained through mid-January in part by diabatic heating west of Hawaii that was associated with anomalous precipitation influenced by the PNA circulation anomalies, thus representing a feedback on the PNA. One key finding from the SWM analysis is the limited direct role of tropical heating for inducing any of the dynamical mechanisms related to the California extreme event.

Anthony M. DeAngelis↗
NASA NTRSDate not supplied

Dynamical Mechanisms Underlying the 2022/23 California Flooding: Analysis With A Stationary Wave Model

In late December 2022 and the first half of January 2023, much of California experienced an unprecedented series of atmospheric rivers that produced heavy rains and near-record flooding. Previous work shows that a chain of dynamical events contributed to the extreme precipitation, including the development of a Rossby wave (as a result of forcing linked to the MJO) that emerged from the Indian Ocean in mid-December, and the subsequent development of a persistent positive Pacific North American (PNA) pattern that ultimately directed moisture onto the US West Coast starting in late December. Here, we use a stationary wave model (SWM) to further elucidate the dynamical and thermodynamical processes that drove the aforementioned chain of events. The results reveal the following: 1) The mid-December Rossby wave was likely induced by vorticity stretching and advection in the middle East linked indirectly to the MJO, 2) The initial development of the PNA in late December was triggered by transient and stretching sources of vorticity in the Pacific that were themselves induced by the aforementioned Rossby wave, and 3) The PNA was maintained through mid-January in part by diabatic heating west of Hawaii that was associated with anomalous precipitation influenced by the PNA circulation anomalies, thus representing a feedback on the PNA. One key finding from the SWM analysis is the limited direct role of tropical heating for inducing any of the dynamical mechanisms related to the California extreme event.

Anthony M. DeAngelis↗
DOE OSTI2023

Bayesian-Motivated Probabilistic Model of Hurricane-Induced Multimechanism Flood Hazards

Multimechanism floods (MMFs) are caused by the simultaneous occurrence of more than one flood mechanism such as storm surge, precipitation, tides, and waves. MMFs can lead to more severe or differing impacts than single-mechanism floods. As a result, comprehensive risk assessments require the ability to assess the multivariate probabilistic behaviors of hazards from MMFs. Here this study introduces a novel Bayesian-motivated approach for the probabilistic assessment of hurricane-induced hazards from the combination of the surge, precipitation, tides, and river antecedent flow. A Bayesian network (BN) is developed to capture the physical (conditional) relationship between variables and facilitate the generation of a hazard curve for river discharge that captures the contributions from multiple flood drivers. A case study located along the Delaware River is used to illustrate the proposed approach. Five computationally efficient representative predictive models are developed to estimate the conditional distributions required for the BN as a means of demonstrating the overall framework. The predictive models used in this study act as placeholders and can be replaced with more sophisticated and high-fidelity models depending on the desired accuracy level. While the predictive models are intended to be representative and illustrative, the model performance is evaluated using three historical storms that affected the area. Overall, the proposed framework is shown to be transparent, effective, and adaptable.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2022

Milwaukee Urban Development: Assessing the Drivers of Urban Flooding Vulnerability in Milwaukee Using NASA Earth Observations

Milwaukee County has experienced an increase in flooding due to climate change and urbanization. The frequency and severity of flooding vary spatially due to differences in land cover, surface permeability, and infrastructure. Marginalized communities tend to experience disproportionately high flooding and damage due to infrastructural inequalities and limited access to resources. To quantify these differences, we used the Natural Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation Model to calculate and create maps of runoff retention, nominal flood depth, and economic damage to buildings in Milwaukee. Our model inputs included land cover, surface permeability, and rainfall. To inform our precipitation inputs, we used NASA’s Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG) and National Weather Service (NWS) data. We assessed the relationship between flood risk and social and environmental spatial data including redlining, racial demographics, greenspace, and community resilience. The data demonstrate that flood risk is higher in historically redlined neighborhoods, majority Hispanic and Black census block groups, areas that lack parks and trees, and areas of low community resilience as measured by the Census Bureau’s Community Resilience Estimates (CRE). These findings will support our partners, Groundwork Milwaukee and Groundwork USA, in their efforts to promote the equitable distribution of resources and support environmental health in urban spaces. The end products of this project provide our partners with tools to assess urban flooding vulnerability, guide future intervention projects, quantify the effects of environmental injustice, and improve stakeholder access to data.

Madeleine Tango↗
DOE OSTI2020

A Novel Modeling Framework for Computationally Efficient and Accurate Real-Time Ensemble Flood Forecasting With Uncertainty Quantification

A novel modeling framework that simultaneously improves accuracy, predictability, and computational efficiency is presented. It embraces the benefits of three modeling techniques integrated together for the first time: surrogate modeling, parameter inference, and data assimilation. The use of polynomial chaos expansion (PCE) surrogates significantly decreases computational time. Parameter inference allows for model faster convergence, reduced uncertainty, and superior accuracy of simulated results. Ensemble Kalman filters assimilate errors that occur during forecasting. To examine the applicability and effectiveness of the integrated framework, we developed 18 approaches according to how surrogate models are constructed, what type of parameter distributions are used as model inputs, and whether model parameters are updated during the data assimilation procedure. We conclude that (1) PCE must be built over various forcing and flow conditions, and in contrast to previous studies, it does not need to be rebuilt at each time step; (2) model parameter specification that relies on constrained, posterior information of parameters (so-called Selected specification) can significantly improve forecasting performance and reduce uncertainty bounds compared to Random specification using prior information of parameters; and (3) no substantial differences in results exist between single and dual ensemble Kalman filters, but the latter better simulates flood peaks. The use of PCE effectively compensates for the computational load added by the parameter inference and data assimilation (up to ~80 times faster). Therefore, the presented approach contributes to a shift in modeling paradigm arguing that complex, high-fidelity hydrologic and hydraulic models should be increasingly adopted for real-time and ensemble flood forecasting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Advancing Multi-Hazard Risk and Safety Considerations for Aging Nuclear Facilities

While probabilistic risk assessment (PRA) of nuclear facilities is expected to include internal and external hazards for a risk-informed and performance-based design, the current state of practice treats each hazard independently. However, such an independent treatment of hazards may not account for the correlations between different hazards and their response of and damage to the structures, systems, and components (SSCs) in a plant resulting in underestimating the overall risk. This project proposes to advance the multi-hazard PRA of nuclear facilities to more adequately evaluate concurrent hazards and contribute to an increased safety of nuclear plants. A framework for multi-hazard PRA will be developed by identifying concurrent hazard events (both internal and external) and event sequences that include interdependencies through the response of SSCs. An example application of the multi-hazard PRA framework will be demonstrated by considering a generic pressurized water reactor (PWR) subjected to seismic and internal flooding hazards. Computational models for the response of components will be developed to generated multi-hazard fragility surfaces under seismic and flooding loads. A PRA model consisting of event and fault trees will also be developed to quantify the multi-hazard risk profile and compare it with the independent hazard risk profile. Overall, by advancing the multi-hazard PRA of nuclear facilities, this project enhances nuclear safety and reduces costs by mitigating unforeseen consequences caused by correlations between concurrent hazards.

97 - MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

FloodPlanet: High-Resolution Commercial Imagery for Training and Validation of Deep Learning-Based Models of Inundation Extent

Flooding events are becoming increasingly frequent worldwide and are known to cause extensive damage. Public optical and radar satellite imagery can be used to detect large areas of inundation in rural areas, however, long revisit times and coarse spatial resolution limit applications for short-lived events and urban areas. Commercial constellations such as those operated by Planet offer increased spatial and temporal resolution and can supplement mapping efforts to provide more information to disaster response, relief, and mitigation efforts. Deep learning requires high quality labeled data for training across coincident sensors. The FloodPlanet dataset presented here contains labeled surface water for 18 events across the world based on Planetscope imagery with coincident Harmonized Landsat Sentinel-2 ( HLS) or Sentinel-1 and builds upon the previously existing Sen1Floods11, xBD, and NASA Sentinel-1 datasets. Sen1Floods11 includes 4,831 512x512 pixel overlapping tiles of coincident Sentinel-1 and Sentinel-2 data observing 11 flood events across the world from 2017-2019. The dataset contains a combination of automated and hand-labeled surface water for use in training and validation of inundation modeling efforts. The xBD dataset identifies flood-damaged buildings and indicates the scale of damage to each (none, minor, moderate, and major) from four flood events which occurred in the United States, India, Nepal, and Bangladesh from the same time period. The NASA dataset contains hand-labeled water bodies observed in Sentinel-1 imagery during five flood events within the 2017-2019 period. The effort presented here utilizes observations from these previously investigated flood events to generate labels of surface water at the 3-5m spatial resolution provided by Planetscope and facilitate the comparison between public and commercial data. A data pipeline was built which uses clustering algorithms to pick the most suitable overlapping chips between the public data and PlanetScope data for manual labeling. Labels were created manually using NASA’s ImageLabeler tool and include areas of high- and low-confidence water. The high confidence designation is reserved for areas of open, unobstructed water while low confidence is used for areas of suspected water beneath vegetation, clouds, or cloud shadows. Expected to be released in late 2022, the FloodPlanet dataset will include tiled imagery with a unique ID for each 1024x1024 pixel tile, 7 bands of HLS data, and high- and low-confidence flood labels in both shapefile and tiff formats. The authors will follow Spatial Temporal Access Catalog (STAC) guidelines to release FloodPlanet on the Radiant Earth ML hub, which hosts public datasets for machine learning.

Alexander Melancon↗
DOE OSTI2023

Scaling of Floods With Geomorphologic Characteristics and Precipitation Variability Across the Conterminous United States

Abstract Accurate flood risk assessment requires a comprehensive understanding of flood sensitivity to regional drivers and climate factors. This paper presents the scaling of floods (duration, peak, volume) with geomorphologic characteristics of the basin (i.e., drainage area, slope, elevation) and precipitation patterns (rainfall accumulation, variability). Long‐term daily streamflow observations over the 20th and early 21st centuries from Hydro‐Climatic Data Network streamgages across the conterminous United States are used to create a flood event database based on their flood stage information. Antecedent daily rainfall accumulation and variability corresponding to these floods are computed using Global Historical Climatology Network daily data set. Two Bayesian scaling models are developed, and the spatial organization of scaling exponents is investigated. The baseline model quantifies the scaling of floods to geomorphologic characteristics. The dynamic model quantifies the scaling of floods to antecedent precipitation distribution which is further conditioned on geomorphologic characteristics. Results show that small and low‐elevation basins have a stronger response to antecedent rainfall distribution in amplifying flood peaks, while high‐elevation steeper basins have a lower response for flood duration and volume. The dynamic models demonstrate that there are significant variations in the flood scaling rates, with the largest rates up to 40% and 4.5% for flood duration, 64% and 44% for peak, and 98% and 40% for volume found across the Northeast, Coastal Southeast, and Northwest with intensifying rainfall accumulation and variability, respectively. This study advances flood predictions by better informing the flood attributes in the context of dynamical land‐atmosphere perturbations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2017

Hydrological Modeling of the Jezero Crater Outlet-Forming Flood

Jezero crater is a site of prime scientific interest because it was a lake early in Mars history. Preserved clay- and carbonate-bearing sedimentary fans on Jezero's western and northwestern margin (Fig. 2) are accessible to future exploration. Geologic context [1] and stratigraphic analysis of the western fan strongly support the interpretation that these fans were deposited as deltas into the lake. This has helped establish Jezero as one of the final candidate landing sites for Mars 2020. The high level of certainty that Jezero was a lake results from the existence of its outlet valley, which required filling of the crater to form [e.g., 1,4]. Here, we specifically focus on how this outlet valley was carved by the dam breach flood that eroded the eastern crater rim. We have completed preliminary modeling in both 1D and 2D of the outlet's formation.

Geology↗