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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Generating Flood Probability Map Based on Combined Use of Synthetic Aperture Radar and Optical Imagery

Despite a lot of efforts to respond flood hazards with remote sensing data, it is still difficult to generate an accurate flood map using solely optical or radar imagery. While optical data is relatively high-resolution and does not suffer from speckle noise compared to radar data, it is very likely to be impacted by cloud and shadow. On the other hand, radar imagery can be used in all weather conditions due to its capability of penetrating clouds. Although a significant improvement of flood monitoring capability is achieved by using radar data, it is still challenging to map urban floods because of strong backscattering by man-made structures. Therefore, complementary use of optical and radar imagery in flood response is required, particularly in urban areas. In this study, we have adopted the Bayesian Joint Probability function to combine two different flood products generated from SAR and optical imagery. Flood detection with SAR data relies on the difference of backscatter signals between standing water and rougher land surface, while a Normalized Difference Water Index (NDWI) approach is used for optical data. Specifically, Planet Dove data with its 3m spatial resolution is used with higher weight values to detect flood extent in urban areas.

floods↗
DOE OSTI2025

A Scale‐Adaptive Urban Hydrologic Framework: Incorporating Network‐Level Storm Drainage Pipes Representation

Abstract Below‐ground urban stormwater networks (BUSNs) significantly influence urban flood dynamics, yet their representation at the watershed or larger scales remains challenging. We introduce a scalable urban hydrologic framework that centers on a novel network‐level BUSN representation, balancing the needs for physical basis, parameter parsimony, and computational efficiency. Our framework conceptualizes an urban watershed into four interacting zones: hillslopes (natural), storm‐sewersheds (urban), a sub‐network channel (tributaries), and a main channel. We develop an innovative Graph Theory‐based algorithm to derive network‐level BUSN parameters from publicly available datasets, enabling efficient, scalable parameterization. We demonstrate this framework's applicability at nine representative watersheds in the Houston metropolitan region, USA, with urban imperviousness ranging from 0% to 64% and drainage areas ranging from 24 to 302 . Our model achieves satisfying computational efficiency, completing hourly time step simulations for 18 years in less than 5 sec per watershed on a standard PC. Validation against observed daily streamflow confirms that the model can capture small‐to‐large flood peaks and seasonal and annual water balance over these watersheds. Comparisons with the National Water Model show better performance in predicting flood peaks and overall water balance, underscoring the promises of our new framework for urban hydrologic modeling at large scales. Furthermore, analysis reveals nonlinear relationships between BUSNs' designed capacities and flood reduction effects. Our approach bridges the gap between detailed hydraulic and large‐scale hydrologic models, providing a valuable tool for urban flood prediction and management across broader spatial and temporal scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Re-examining urban rainfall enhancement over North America

Abstract Quantifying intensification/suppression of precipitation over urban areas relative to their rural surroundings can inform efforts to reduce urban flooding. Few studies have systematically addressed whether urban areas exhibit a higher/lower probability of precipitation and/or higher/lower annual total precipitation and/or intensification/weakening of intense precipitation events relative to nearby rural areas across a range of hydroclimatic conditions and urban contexts. Here we address this literature gap using the IMERG V07 data set and analyses of rural and urban samples drawn from 47 conurbations across North America. Specifically, we quantify whether/how precipitation regimes over the urban grid cells differ from those in rural grid cells located 100–250 km from the city center and at a similar elevation. As in previous research, there is evidence that both the probability of precipitation and annual total precipitation are typically higher in the urban grid cells. However, most conurbations have lower upper percentile precipitation rates in the urban sample and lower median precipitation rates above the 95th percentile than are present in samples drawn from rural grid cells. Thus, these conurbations are not, on average, intensifying high-magnitude precipitation events over urban grid cells. Further, the total volume of water accumulated at the surface during events of equivalent duration is not systematically higher over the urban areas, and 20 year return period values of 30 min and wettest pentad precipitation are also not systematically higher over the urban areas. The nature of urban modification of precipitation is a strong function of the prevailing hydroclimate. For example, the heaviest rainfall periods are enhanced over urban grid cells within regional hydroclimates where the overall probability of precipitation and annual total precipitation are low. Conversely, there is evidence for urban suppression of the highest percentile precipitation rates in wetter hydroclimates.

Pryor, Sara C. (ORCID:0000000348473440)↗
DOE OSTI2025

Effectiveness of nature-based solutions to reduce flooding in Quad Cities Metro Area (QCMA) using SWMM-HEC based flood model

Nature-based solutions (NbS) have gained significant attention as strategies for addressing urban environmental challenges, particularly since the establishment of the UN Sustainable Development Goals (SDGs) for 2030. However, the current research on NbS for urban flood management lacks comprehensive methodological approaches for identifying suitable areas and evaluating their effectiveness across different urban settings. Here, this study attempts to fill this gap by proposing a methodological framework integrating multi-criteria analysis with a SWMM-HEC-based hydrologic and hydraulic (HH) model to assess the suitability of NbS for the Quad Cities Metro Area (QCMA), consisting of Davenport, Bettendorf, Moline, and Rock Island. Eight NbS options-green roofs, rain gardens, infiltration trenches, permeable pavements, vegetative swales, dry detention basins, retention ponds, and rain barrels/cisterns - were considered based on volumetric efficiency and runoff reduction efficiency. The study reveals that implementing the proposed NbS could have substantially reduced flood depths in key historical flood events by 21% in 1993, 15% in 2008, 16% in 2011, 23% in 2014, 40% in 2019, and 10% in 2023. The findings highlight a critical trade-off between peak runoff and NbS implementation: while NbS effectively reduce flood impacts, they also enhance volumetric efficiency by approximately 43%. In high-density areas of the QCMA, flood depth reductions of around 20% suggest that NbS are a viable solution for dense urban environments with limited space. This shows the potential for integrating NbS into existing infrastructure, offering a promising approach for cities facing increasing flooding risks. The proposed methodology provides a practical framework for incorporating NbS into urban stormwater management, addressing gaps in optimizing NbS performance, and offering a pathway to scale their application in other urban areas with various environmental and social contexts.

CMIP6↗
NASA NTRS2020

Potential of GPM IMERG Precipitation Estimates to Monitor Natural Disaster Triggers in Urban Areas: the Case of Rio deJaneiro, Brazil

Extreme rainfall can be a catastrophic trigger for natural disaster events at urban scales. However, there remains large uncertainties as to how satellite precipitation can identify these triggers at a city scale. The objective of this study is to evaluate the potential of satellite-based rainfall estimates to monitor natural disaster triggers in urban areas. Rainfall estimates from the Global Precipitation Measurement (GPM) mission are evaluated over the city of Rio de Janeiro, Brazil, where urban floods and landslides occur periodically as a result of extreme rainfall events. Two rainfall products derived from the Integrated Multi-satellite Retrievals for GPM (IMERG), the IMERG Early and IMERG Final products, are integrated into the Noah Multi-Parameterization (Noah-MP) land surface model in order to simulate the spatial and temporal dynamics of two key hydrometeorological disaster triggers across the city over the wet seasons during 2001-2019. Here, total runoff (TR) and rootzone soil moisture (RZSM) are considered as flood and landslide triggers, respectively. Ground-based observations at 33 pluviometric stations are interpolated, and the resulting rainfall fields are used in an in-situ precipitation-based simulation, considered as the reference for evaluating the IMERG-driven simulations. The evaluation is performed during the wet seasons (November-April), when average rainfall over the city is 4.4mm/day. Results show that IMERG products show low spatial variability at the city scale, generally overestimate rainfall rates by 12-35%, and impacts on TR and RZSM vary spatially mostly as a function of land cover and soil types. Results based on statistical and categorical metrics show that IMERG skill in detecting extreme events is moderate, with IMERG Final performing slightly better for most metrics. By analyzing two recent storms, we observe that IMERG detects mostly hourly extreme events, but underestimates rainfall rates, resulting in underestimated TR and RZSM. An evaluation of normalized time series using percentiles shows that both satellite products have significantly improved skill in detecting extreme events when compared to the evaluation using absolute values, indicating that IMERG precipitation could be potentially used as a predictor for natural disasters in urban areas.

IMERG↗
DOE OSTI2026

Compound flood simulations indicate rising public exposure to sewage-contaminated waters in Waikīkī, Hawai‘i

Waikīkī, Hawai‘i serves as a valuable case study for flood vulnerability due to its low elevation, present-day exposure to multiple flood sources, and role as the state’s economic engine. This study applies the high-resolution, open-source WRF-Hydro-CUFA (Coastal Urban Flood Application) model to simulate compound flood interactions among pluvial, fluvial, coastal, storm drain-driven, and subsurface processes, and to evaluate how flood characteristics in Waikīkī evolve under projected sea-level rise (SLR). Simulated water levels in an estuarine drainage canal, serving as the primary outlet for gravity-flow drainage from Waikīkī, show strong agreement with observations (r ≥ 0.935, R 2 ≥ 0.874), accurately reproducing flooding observed during recent 5-year and 50-year storm events. Simulations incorporating SLR demonstrate a shift in dominant flood drivers as sea levels rise. In addition, results identify critical thresholds at which elevated estuarine tailwater conditions cause widespread drainage backflow, eventually occurring in the absence of rainfall. These findings suggest that tidally driven flooding will progressively dominate over rainfall-driven events, increasing public exposure to highly contaminated canal and groundwater sources. The results underscore the urgent need to integrate water quality issues and drainage failure into coastal flood management strategies for low-lying urban environments, particularly those reliant on polluted estuarine waterways for stormwater management.

Compound flooding↗
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↗
DOE OSTI2022

Short-Term Rainfall Prediction Based on Radar Echo Using an Improved Self-Attention PredRNN Deep Learning Model

Accurate short-term precipitation forecast is extremely important for urban flood warning and natural disaster prevention. In this paper, we present an innovative deep learning model named ISA-PredRNN (improved self-attention PredRNN) for precipitation nowcasting based on radar echoes on the basis of the advanced PredRNN-V2. We introduce the self-attention mechanism and the long-term memory state into the model and design a new set of gating mechanisms. To better capture different intensities of precipitation, the loss function with weights was designed. We further train the model using a combination of reverse scheduled sampling and scheduled sampling to learn the long-term dynamics from the radar echo sequences. Experimental results show that the new model (ISA-PredRNN) can effectively extract the spatiotemporal features of radar echo maps and obtain radar echo prediction results with a small gap from the ground truths. From the comparison with the other six models, the new ISA-PredRNN model has the most accurate prediction results with a critical success index (CSI) of 0.7001, 0.5812 and 0.3052 under the radar echo thresholds of 10 dBZ, 20 dBZ and 30 dBZ, respectively.

Wu, Dali (ORCID:0000000231177074)↗
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 NTRSDate not supplied

Enabling Interoperability in Earth System Digital Twins (ESDT): Integrating Observations, Models, and AI for Actionable Insights Through NASA'S Intelligent Systems Technology Program

NASA’s Intelligent Systems Technology Program (IST) is driving a paradigm shift in Earth science through the development of Earth System Digital Twins (ESDT). These integrated information systems create a dynamic "digital replica" of the Earth by harmonizing continuous, multi-source observations with high-fidelity models and state-of-the-art artificial intelligence (AI) that enable “What now?”, “What next?”, and “What if?” scenario building. These scenarios are reflected in NASA IST’s series of ESDTs, from the Coastal Zone Digital Twin that integrates complex data on the current state of the Chesapeake Bay to the Terrestrial Environmental Rapid-Replication and Assimilation Hydrometeorological (TerraHydro) AI-based ESDT that forecasts water movement across Earth’s surface, to the Agriculture Land Information System (AgLIS) which can be used to assess optimal planting dates and crop yield estimates. By bridging the gap between vast data archives and actionable insights, these projects enable a system-of-systems approach to understanding complex, interacting Earth processes. This poster will highlight recent innovations and future directions from NASA’s ESDT initiatives: Continuous Data Assimilation & Multi-Source Fusion. A core requirement of the ESDT work is the transition from static models to dynamic "living" replicas. This involves creating frameworks for the continual assimilation of near-real-time data from uncoordinated, heterogeneous sources, including satellite observations and airborne assets, and ground-based Internet of Things (IoT) sensors. These systems link design, operational status, and environmental data, ensuring the digital twin accurately reflects the current state of the physical Earth system. High-Fidelity Hybrid Modeling & Computational Acceleration to enable interactive "what-if" explorations, programs are moving beyond traditional, slow physical solvers by developing fast surrogate machine learning models and Deep Generative Models (DGMs). These hybrid approaches use neural networks to emulate complex physics, such as cloud feedback or ocean dynamics, at a fraction of the original computing cost, often leveraging advanced hardware like Graphics Processing Units (GPUs) to achieve the necessary scale. Federated Ecosystems & Interoperable Frameworks rather than building isolated tools, NASA IST is moving toward federated ESDTs and reusable analytic collaborative frameworks. This theme focuses on interoperability standards and common ontologies that allow specialized digital twins to interact and share data. This system-of-systems architecture supports multi-discipline investigations, such as analyzing how upstream watershed changes impact downstream urban flooding or how wildfire emissions affect regional air quality. By leveraging these advancements, ESDTs empower researchers and decision-makers to conduct real-time analysis and run complex hypothetical scenarios, ultimately improving our understanding of Earth’s evolving systems and informing critical real-world applications.

Earth System↗
DOE OSTI2025

Enhancing 2D hydrodynamic flood models through machine learning and urban drainage integration

Two-dimensional hydrodynamic flood models are commonly employed for simulating flood extent and inundation depth. However, the influence of urban drainage network (UDN) is frequently overlooked in these models, potentially compromising their accuracy. Furthermore, the expensive computational costs and longer processing times make them challenging for large-scale hydrodynamic simulation. To address these challenges, this paper develops a machine learning (ML)-driven emulator for an open-source flood model, the Two-dimensional Runoff Inundation Toolkit for Operational Needs (TRITON). A TRITON-ML Emulator (TR-Emulator) that utilizes Convolutional Long Short-Term Memory is developed to capture the spatiotemporal features of flood events based on the outputs from TRITON. We further enhance the emulator by integrating UDN parameters (TR-UDN), such as the flow capacity of drainage pipes, pipe size, and pipe length, via an ML stacking technique to improve the water surface elevation (WSE) simulation. Hurricane Harvey 2017 in Houston, TX is used as the case study. We compare WSE results from TRITON, TR-Emulator, TR-UDN, and the United States Geological Survey (USGS) observations to evaluate the performance of these models. The results indicate that the TR-Emulator effectively replicates the WSE simulated by TRITON. Additionally, TR-UDN performs well in capturing WSE patterns and peak flows, aligning more closely with USGS observations, except in areas with milder slopes where conveyance discrepancies are observed. We further test the generalizability of our ML-based models using another smaller event. This paper shows that the TR-Emulator is effective for users and engineers to emulate a 2D hydrodynamic model, and the enhanced version of the TR-Emulator, TR-UDN, can be an efficient tool for predicting WSEs during urban flooding.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2021

Cincinnati & Covington Urban Development II: Assessing Flooding and Landslide Susceptibility Along the Ohio-Kentucky Border

Landslides and flooding are reoccurring environmental hazards that lead to health risks and economic burdens in the urban areas of Cincinnati, Ohio and Covington, Kentucky. These communities share underlying natural and artificial conditions that make them vulnerable to these hazards, including excessive precipitation, weak lithology, high impervious surface levels, and steep slopes. Despite the human and economic risks associated with these environmental hazards, the areas of highest vulnerability within the region remain unknown. NASA DEVELOP partnered with Groundwork USA and Groundwork Ohio River Valley (ORV) to assess the region’s susceptibility to landslides and flooding. The team utilized NASA Earth observations, including the Landsat 8 Operational Land Imager (OLI), Landsat 8 Thermal Infrared Sensor (TIRS), and Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrieval for GPM (IMERG), alongside ancillary datasets to map landslide susceptibility and exposure throughout the study area. The team also used ancillary data to map surface runoff and runoff retention using the Natural Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation Model. The resulting landslide susceptibility and exposure maps highlight the neighborhoods around Avondale and Fairmount as areas of particularly high landslide exposure. Meanwhile, the InVEST outputs demonstrate that Downtown Cincinnati and the Queensgate neighborhood retain the least amount of rainfall. This research provides partners with a more complete hazard analysis of the greater Cincinnati area while also producing refined methodologies to enhance future flood and landslide vulnerability mapping throughout Groundwork USA’s nationwide network of communities.

Paxton LaJoie↗
DOE OSTI2020

Resilience of Urban Transport Network-of-Networks under Intense Flood Hazards Exacerbated by Targeted Attacks

Natural hazards including floods can trigger catastrophic failures in interdependent urban transport network-of-networks (NoNs). Population growth has enhanced transportation demand while urbanization and climate change have intensified urban floods. However, despite the clear need to develop actionable insights for improving the resilience of critical urban lifelines, the theory and methods remain underdeveloped. Furthermore, as infrastructure systems become more intelligent, security experts point to the growing threat of targeted cyber-physical attacks during natural hazards. Here we develop a hypothesis-driven resilience framework for urban transport NoNs, which we demonstrate on the London Rail Network (LRN). We find that topological attributes designed for maximizing efficiency rather than robustness render the network more vulnerable to compound natural-targeted disruptions including cascading failures. Our results suggest that an organizing principle for post-disruption recovery may be developed with network science principles. Our findings and frameworks can generalize to urban lifelines and more generally to real-world spatial networks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Decomposing a Compound Flood Event in an Urban Pacific Northwest Estuary: Primary Drivers and Projections for the Future

The Duwamish River Estuary (DRE) of Washington is prone to compound flooding during atmospheric river (AR) events. The processes contributing to such flooding (coastal and fluvial) have remained opaque to municipalities that are increasingly impacted. Here, we conduct a suite of coupled atmosphere-hydrology-ocean model simulations with varying forcing combinations (tide, surge, and/or river discharge) to identify the primary drivers of compound flooding during a recent AR event. We also test year 2,100 climate forcing to project how flooding and drivers may change in the future for the same event. We identify a clear distinction between dynamics in the downstream, engineered portion of the DRE compared to the upstream, “natural” river. Downstream, tides dominate water levels but contributions from storm surge and nonlinear tide-surge interaction elevate tide-only high waters from no flooding to major flooding. Upstream, total water levels during the event are ∼6 cm higher than downstream due to an increasing influence of river discharge over surge and tides. Notably, nonlinear surge-river and tide-river interactions act to reduce upstream water levels up to 50% compared to estimates which linearly sum tides, surge, and river, likely reducing flood vulnerability. Under two future climate scenarios: one with only sea level rise (SLR) and another with SLR plus atmospheric warming, we find little change in mechanism contributions to water levels. Expanded flooding in both cases is largely due to SLR, as a ∼50% increase to river discharge under the warming scenario has no impact downstream and marginally increases (∼3 cm) water level upstream.

atmospheric river↗
NASA NTRS2023

An Urban Drainage Scheme for Large-Scale Flood Models

As flood modeling spatial resolutions get finer, physical processes normally neglected, such as urban drainage, must be accounted for. Here, we describe and evaluate an urban drainage scheme for large-scale flood models. The parameterization accounts for urban imperviousness, and water flow over streets and through a prescribed urban drainage network. A parameter sensitivity analysis is performed during three major extreme floods over Rio de Janeiro city, Brazil, at ∼200 m spatial resolution. Results show that, compared to a hypothetical case without urban drainage, representing a drainage network decreases urban flooding during selected extreme events across Rio de Janeiro by 31–53 %. Such a decrease is caused by an underground water storage of up to 2.5 billion m3 across the city during flood peaks. Underground water storage and transport smooth out and delay peak flows by a few hours over major rivers and channels draining the city. Simulations also indicate that the number of residents exposed to flooding drops by 60–80 %, from ∼5 million to 1–2 million, when an urban drainage system is considered during extreme events. Similar proportions are found for social infrastructure (i.e., schools and hospitals) exposed to flooding. Results reveal that racial minority and low-income populations could disproportionally be exposed to extreme floodings across the city. We conclude that representing urban drainage has a substantial impact on flood exposure and should be accounted for in fine resolution modeling. The proposed scheme is particularly useful in poorly monitored cities and where extreme floods are a frequent hazard yet to be tackled.

Urban drainage↗
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↗