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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Youngstown and Warren Disasters: Mapping Flood Susceptibility, Vulnerability, and Risk and Tree Canopy Coverage in Northern Ohio to Inform Stormwater Management and Flood Mitigation Efforts

Both pluvial and fluvial flooding events pose direct challenges on urban infrastructure and communities across the United States. Heavy rainfall events oversaturate the ground, overflow waterbodies, and overwhelm stormwater infrastructure. Vulnerable areas receive heavy damage from flooding events due to physical factors like increased impervious surfaces, poor stormwater systems, and limited greenspaces. These vulnerable neighborhoods are comprised of aging populations, minority communities, and lower income levels. Lack of data in these communities have made it difficult to implement policymaking and flood mitigation strategies. Using the Urban Flood Risk Mitigation model (InVEST) and the PlanetScope satellite constellation, the team visualized historical flooding and tree canopy coverage as a measure of flood susceptibility. The team also used the Arc-Malstrom model to provide further insight into where flooding accumulates via surface elevation depressions in the study area. To validate these models, the team explored the spatial variation of rainfall events using NASA’s Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG). The resulting maps highlight areas surrounding the cities of Youngstown and Warren as being the most flood susceptible and socially vulnerable, while the city centers contain the lowest tree canopy coverage. The DEVELOP team collaborated with the Environmental Collaborative of Ohio (ECO) to create products for end users within the City of Warren’s Water Pollution Control Department, the Eastgate Regional Council of Governments and the Healthy Community Partnership of Mahoning Valley. These products help identify target areas for preventative flood mitigation measures as well as areas ideal for green infrastructure intervention.

InVEST Urban Flood Risk Mitigation Model↗
DOE OSTI2026

Model data for infrastructure-aware simulation of compound flooding at Alligator Bayou Watershed, southeast Texas

This dataset supports infrastructure-aware hydrologic modeling and flood scenario analysis for the Alligator Bayou Watershed, a highly managed urban watershed in Southeast Texas. It includes Jupyter notebooks for figure reproduction, model configuration files, simulation outputs, and derived products used to quantify the influence of engineered stormwater infrastructure on flood behavior across multiple spatial scales. The dataset was generated using the Watershed Workflow Python package and the Advanced Terrestrial Simulator (ATS), enabling integrated surface–subsurface hydrologic simulations on a channel-aligned mesh with explicit representations of pump stations, gate structures, detention basins, and impervious surfaces. Outputs include time series of gate and pump flows, stage observations, and water balance components, as well as spatially explicit fields of peak ponded depth and flood duration across multiple infrastructure scenarios spanning a single-location detention basin expansion, distributed drainage limitations, and compound coastal flooding. These data facilitate full reproducibility of the manuscript figures and support further research on urban flood dynamics and the role of stormwater infrastructure in shaping watershed-scale flood response.

EARTH SCIENCE > OCEANS > COASTAL PROCESSES↗
DOE OSTI2023

Recreating the California New Year's Flood Event of 1997 in a Regionally Refined Earth System Model

Abstract The 1997 New Year's flood event was the most costly in California's history. This compound extreme event was driven by a category 5 atmospheric river that led to widespread snowmelt. Extreme precipitation, snowmelt, and saturated soils produced heavy runoff causing widespread inundation in the Sacramento Valley. This study recreates the 1997 flood using the Regionally Refined Mesh capabilities of the Energy Exascale Earth System Model (RRM‐E3SM) under prescribed ocean conditions. Understanding the processes causing extreme events informs practical efforts to anticipate and prepare for such events in the future, and also provides a rich context to evaluate model skill in representing extremes. Three California‐focused RRM grids, with horizontal resolution refinement of 14 km down to 3.5 km, and six forecast lead times, 28 December 1996 at 00Z through 30 December 1996 at 12Z, are assessed for their ability to recreate the 1997 flood. Planetary to synoptic scale atmospheric circulations and integrated vapor transport are weakly influenced by horizontal resolution refinement over California. Topography and mesoscale circulations, such as the Sierra barrier jet, are better represented at finer horizontal resolutions resulting in better estimates of storm total precipitation and storm duration snowpack changes. Traditional time‐series and causal analysis frameworks are used to examine runoff sensitivities state‐wide and above major reservoirs. These frameworks show that horizontal resolution plays a more prominent role in shaping reservoir inflows, namely the magnitude and time‐series shape, than forecast lead time, 2‐to‐4 days prior to the 1997 flood onset.

54 ENVIRONMENTAL SCIENCES↗
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↗
DOE OSTI2019

Semantics-enabled Spatio-Temporal Modeling of Earth Observation Data: An application to Flood Monitoring

Extreme events such as urban floods are dynamic in nature, i.e. they evolve with time. The spatiotemporal analysis of such disastrous events is important for understanding the resiliency of an urban system during these events. Remote Sensing (RS) data is one of the crucial earth observation (EO) data sources that can facilitate such spatiotemporal analysis due to its wide spatial coverage and high temporal availability. In this paper, we propose a discrete mereotopology (DM) based approach to enable representation and querying of spatiotemporal information from a series of multitemporal RS images that are acquired during a flood disaster event. We represent this spatiotemporal information using a semantic model called Dynamic Flood Ontology (DFO). To establish the effectiveness and applicability of the proposed approach, spatiotemporal queries relevant during an urban flood scenario such as, show me road segments that were partially flooded during the time interval t1 have been demonstrated with promising results.

Kurte, Kuldeep↗
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 OSTI2019

Flood Inundation Generation Mechanisms and Their Changes in 1953-2004 in Global Major River Basins

Despite the serious threats posed by floods, the driving mechanisms of floods are still not well understood. Here, we apply a physically-based inundation model coupled with a river routing model (Model for Scale Adaptive River Transport, MOSART) within the Energy Exascale Earth System Model (E3SM) framework to investigate flood inundation dynamics. After calibration using observed streamflow and satellite-derived flood extent, the model is used to simulate global flood inundation from 1953 to 2004. The mean date and seasonality of annual maximum flood, defined based on flood extent, exhibit significant regional differences across 16 major basins. Generally, soil moisture and monthly maximum daily rainfall (MMR) are the dominant drivers of flood in tropical basins while monthly maximum daily snowmelt (MMS) is the dominant driver in high latitude basins. From 1953-1982 to 1975-2004, significant changes in flood generation mechanisms are found in some basins such as Amazon, Lena, Yenisey, and Kolyma. Analysis of the rainfall seasonality and water balance at grid scale reveals a stronger rainfall seasonality in the Amazon during the later period that increases the synchrony between extreme rainfall and wet soil. With high antecedent soil moisture coinciding with rainfall, MMR contributes more to flood in the later period. Fewer extreme rainfall events and increasing soil moisture reduced the contribution of MMR and increased the role of MMS in floods in the Lena and Yenisey basins, respectively. Lastly, increased soil moisture and frequency of large MMS reduced the contribution of the latter to floods in the Kolyma basin.

Mao, Yuna↗