Results for “Urban Flooding”

NASA NTRSDate not supplied

Milwaukee Urban Development: Assessing the Drivers of Urban Flood Vulnerability in Milwaukee using the Integrated Valuation of Ecosystem Services and Tradeoffs Urban Flood Risk Mitigation Model (InVEST)

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 OSTI2026

Community-Informed Urban Flood Modeling for Impact Mitigation

Climate change is intensifying the hydrologic cycle, leading to more frequent and severe rainfall-driven (pluvial) flooding in urban areas. In the mid-Atlantic US cities, aging and under-designed stormwater infrastructure is increasingly strained by these events, resulting in recurring damage to property and disruptions to transportation networks. In this study, we combine community engagement with hydrologic modeling to develop and evaluate potential urban flood adaptation strategies. Over a three-year period, local technical experts and community representatives met regularly to discuss flooding concerns, identify priorities, and co-develop adaptation strategies. These discussions informed the development of an urban flooding model (EPA Storm Water Management Model) for the Baltimore Harbor watershed, the focus location of this study. The flooding model integrates complex surface and subsurface stormwater infrastructure data, local expert knowledge, and community insights. We simulate stakeholder-prioritized adaptations, such as green and gray infrastructure strategies. Model results demonstrate that enhanced infrastructure maintenance is the most effective adaptation for reducing flood depths, but has varied effects across the watershed, and can increase flooding in some locations. Spatially concentrated greening provides limited benefit to the watershed as a whole, but moderate benefit in community priority areas. Together, these adaptations have the potential to reduce flood depths by as much as 58% in some locations, greatly reducing property damage and mobility impacts, primary concerns of stakeholders. Future work will implement robust optimization tools to search for adaptations which meet stakeholder objectives and perform highly under varied future climate conditions. This work contributes to the expanding literature on collaborative modeling and demonstrates that community-engaged approaches can enhance model credibility and generate more actionable insights for communities seeking to strengthen climate resilience.

Baltimore MD↗

DOE OSTI2026

Community-Engaged Modeling of Urban Flood Adaptation Pathways

Climate change is intensifying the hydrologic cycle, leading to more frequent and severe rainfall-driven (pluvial) flooding in urban areas. In the mid-Atlantic US cities, aging and under-designed stormwater infrastructure is increasingly strained by these events, resulting in recurring damage to property and disruptions to transportation networks. In this study, we combine community engagement with hydrologic modeling to develop and evaluate potential urban flood adaptation strategies. Over a three-year period, local technical experts and community representatives met regularly to discuss flooding concerns, identify priorities, and co-develop adaptation strategies. These discussions informed the development of an urban flooding model (EPA Storm Water Management Model) for the Baltimore Harbor watershed, the focus location of this study. The flooding model integrates complex surface and subsurface stormwater infrastructure data, local expert knowledge, and community insights. We simulate stakeholder-prioritized adaptations, such as green and gray infrastructure strategies. Model results demonstrate that enhanced infrastructure maintenance is the most effective adaptation for reducing flood depths, but has varied effects across the watershed, and can increase flooding in some locations. Spatially concentrated greening provides limited benefit to the watershed as a whole, but moderate benefit in community priority areas. Together, these adaptations have the potential to reduce flood depths by as much as 58% in some locations, greatly reducing property damage and mobility impacts, primary concerns of stakeholders. Future work will implement robust optimization tools to search for adaptations which meet stakeholder objectives and perform highly under varied future climate conditions. This work contributes to the expanding literature on collaborative modeling and demonstrates that community-engaged approaches can enhance model credibility and generate more actionable insights for communities seeking to strengthen climate resilience.

Spangler, Ava [Pennsylvania State University] (ORC↗

DOE OSTI2025

Integrating Intelligent Hydro-informatics into an effective Early Warning System for risk-informed urban flood management

The urban drainage system constantly facing flooding issues in coastal and urban areas. Robust and accurate urban flood management, particularly considering fast-moving compound floods, is crucial to minimize the impact of flood disasters in coastal cities. Till now, Ho Chi Minh City (HCMC) lacks an effective means of urban flood management because of flood risk communication among residents. Existing flood risk communication tools rely on post-disaster flood model outcomes and data. Therefore, this research proposes a real-time Early Urban Flooding Warning System (EUFWS) integrated with a user-friendly web and app interface. The backbone of this system consists of flood models developed using machine learning (ML) algorithms, combined with big data and Web-GIS visualization, with ML serving as the core for constructing the EUFWS. EUFWS offer several key advantages: they are available at all times, accessible from anywhere, and provide a real-time, multi-user working platform. Additionally, the system is flexible, allowing for the easy addition of components and services and scalable, adjusting to workload demands. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. Research results indicate that EUFWS supported decision-makers to be effectively risk informed and make intelligent decisions during urban flood emergencies. Finally, this underscores the significant potential of integrating ML and information technology to enhance the management of smart urban drainage systems in flood-prone cities worldwide.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Collaborative Design of Robust Adaptation Pathways for Urban Flooding Resilience in Baltimore

Urban pluvial flooding poses a growing threat to the city of Baltimore, driven by changes in climate and rainfall, increased impervious area, aging infrastructure, and severe social inequity. Adaptations to urban flooding should strengthen community-scale resilience while navigating the uncertainties associated with future actions. However, the highly uncertain future climate and conflicting stakeholder objectives complicate the selection of effective and socially acceptable strategies. This study addresses these challenges through a community-informed Multi Objective Robust Decision Making (MORDM) process, coupled with Dynamic Adaptation Policy Pathways (DAPP). Community stakeholders are engaged on a large scale through community partnerships, including priority identification sessions, focus groups, and metric identification workshops. The combination of community partnership, MORDM, and DAPP will create an original set of adaptation pathways which are aligned with community goals and values, and furthermore are adjustable under changing future constraints and conditions. This framework will enhance the city's capacity to respond to evolving pluvial flood conditions while accounting for diverse stakeholder interests and will provide guidance for addressing both the immediate and long-term challenges of urban flooding in Baltimore.

BSEC↗

DOE OSTI2026

Significant Rural Contributions to Severe Coastal Urban Flooding Through Flow Connectivity

Coastal cities are increasingly threatened by hurricane-induced compound flooding. However, the relative contributions of rainfall-surge interactions and other flood drivers, such as runoff from surrounding rural and local impervious areas, in shaping urban flooding remain unclear. Here, we use a multi-scale Earth system modeling framework to disentangle the contributions of diverse flooding drivers. The results show that urban flooding is primarily affected by topographic factors (low elevation, flat slopes, and localized flow constrictions), rather than increased runoff from their impervious surfaces. Importantly, runoff from rural areas contributes more to severely flooded urban areas with inundation depth exceeding 1.7 m than local runoff. Although rainfall–surge interactions and sea-level rise exacerbate inundation along the coastline, over 90% of the compounding effects are absorbed by wetlands. By resolving flooding processes across scales and components, this study emphasizes the role of runoff from surrounding rural areas in shaping zones of severe urban flooding.

58 GEOSCIENCES↗

NASA NTRS2023

InVEST Urban Development: Incorporating Earth Observation Data into the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation Model Python API

Urban flooding poses as one of the biggest issues for cities today as its impacts are amplified by both climate change and urbanization. The Natural Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation (UFRM) model, which benefits from its simplicity and robustness, is commonly used in NASA DEVELOP projects for disaster mitigation, urban planning, and environmental justice issues. While the InVEST UFRM model was able to produce the surface water runoff and retention map sufficient for the scopes of past projects, the model’s accuracy and spatial variability need improvement. Since the current InVEST UFRM model employs constant rainfall depth for all pixels in the area of interest (AOI), the model suffers from inaccurately estimating rainfall depth, runoff volume, and flood depth. Therefore, we adapted the model so that satellite-based precipitation raster datasets (i.e., Integrated Multi-satellitE Retrievals for Global Precipitation Measurement [GPM IMERG]) can be used instead of a single constant value. We simulated the flood events on August 21st and August 22nd, 2017, in Wyandotte County, Kansas using both our modified and the original InVEST UFRM model and then compared the results after incorporating rainfall raster into the model. Areas with developed land on the land use map predicted moderate to high flood volume in the original volume regardless of the actual amount of precipitation. The modified model considered the rainfall depth’s spatial variation achieving less overestimation of flood runoff and volume at low-to-moderate rainfall area.

Urban flooding↗

DOE OSTI2025

Comparing multi-source urban flood indicators: satellite, simulation, and citizen-reported data

Urban flooding arises from complex mechanisms, making it challenging to capture accurately with a single detection method. This study evaluates three complementary approaches to detect flooding across three Chicago neighborhoods: (i) Sentinel-1 synthetic aperture radar (SAR), offering weather-independent, high-resolution (10 m) imagery of surface inundation; (ii) the storm water management model (SWMM), simulating combined sewer overflow and drainage performance; and (iii) citizen-generated 311 service requests, capturing observed flooding impacts. By analyzing six storms ranging from severe to mild, we examine how each source uniquely contributes to identifying urban flood events. SAR imagery effectively identifies standing water but can miss brief flooding due to satellite revisit constraints. SWMM provides detailed insights into system-wide drainage behavior yet may underestimate localized street-level flooding. Meanwhile, 311 calls reflect real-world flooding impacts but are vulnerable to underreporting. Statistical overlap analysis highlights chronic flood hotspots repeatedly identified across multiple detection methods, indicating persistent infrastructure and topographic vulnerabilities. Temporal analysis further reveals that while SWMM flooding aligns closely with rainfall peaks, 311 calls typically precede or persist beyond these peaks. Our findings emphasize the value of using satellite observations, hydrological modeling, and resident-reported data in a complementary manner to better interpret patterns in flood timing, severity, and spatial distribution—providing insights that can inform targeted infrastructure improvements and contribute to urban flood resilience planning.

311↗

NASA NTRSDate not supplied

InVEST Urban Development: Incorporating Earth Observation Data into the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation Model Python API

Urban flooding poses as one of the biggest issues for cities today, as its impacts are amplified by both climate change and urbanization. The Natural Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation (UFRM) model, which benefits from its simplicity and robustness, is commonly used in NASA DEVELOP projects for disaster mitigation, urban planning, and environmental justice issues. While InVEST UFRM model was able to produce the surface water runoff and retention map sufficient for the scopes of past projects, the model accuracy and spatial variability need improvement. Since the current InVEST UFRM model employs constant rainfall depth for all pixels in the area of interest (AOI), the model suffers from inaccurately estimating rainfall depth, runoff volume, and flood depth. Therefore, we adapted the model so that satellite-based precipitation raster datasets (i.e., Integrated Multi-satellitE Retrievals for Global Precipitation Measurement [GPM IMERG]) can be used instead of a single constant value. We simulated the flood events on August 21st and August 22nd, 2017 in Wyandotte County, Kansas using both our modified and the original InVEST UFRM model and then compared the results after incorporating the rainfall raster into the model. Areas with developed land on the land use map predicted moderate to high flood volume in the original volume regardless of the actual amount of precipitation. The modified model considered the rainfall depth’s spatial variation achieving less overestimation of flood runoff and volume at low-to-moderate rainfall area.

Son Do↗

DOE OSTI2026

CROCUS Tipping Bucket Rain Gauge Data from Argonne Deployable Mast Deployed at Argonne National Laboratory During Urban Flooding Campaign

The Tipping Bucket Rain Gauge (TBRG) dataset contains data from a non-heated Met One 12-inch tipping bucket rain gauge that was mounted on the Argonne Deployable Mast (ADM). The ADM is a rapid deployable meteorological trailer that can be outfitted with instrumentation to measure urban heat island effects, urban flooding or urban flux measurements. During the urban flooding field campaign, the ADM was outfitted with multiple precipitation measurement systems, including the TBRG. This dataset contains one minute measurements for precipitation accumulation during the ADM's deployment at the Argonne Testbed for Multiscale Observational Science (ATMOS) site. These data are helpful for identifying periods of precipitation, leading to potential flooding. TBRGs can be used to validate optical rain gauge data and disdrometer data collected during the CROCUS urban flooding campaign. Data were collected at ATMOS, a 20-acre prairie site at Argonne National Laboratory in Lemont, Illinois. The data is presented as daily NetCDF (.nc) files, each containing approximately 24 hours of observations. Files follow the naming convention of: the project (CROCUS), location (ADM-atmos), instrument name (tbrg), data level (raw, a1), and date (year, month, day). The NetCDF format can be accessed using common scientific software such as Python using xarray, netCDF4 or act-doe.

1-min Precipitation Accumulation↗

DOE OSTI2026

Mapping Philadelphia's Floodscape: A 35‐Year Analysis of Coastal Urban Flood Hazards and Drivers

Low-lying coastal urban cities face significant flooding risks from river flooding (fluvial), storm surges and high tides (coastal), and intense local rainfall (pluvial). Accurately assessing these hazards requires modeling frameworks capable of capturing both the individual and combined effects of multiple flood drivers, as well as the diversity of flood scenarios that can arise from their interactions. In this study, we implemented a physics-based, high-resolution modeling approach to assess flood hazards in Philadelphia, PA, a coastal city exemplifying multi-driver flood risks over multiple decades (1985–2019), by simulating a wide range of flood events at a 10-m resolution. By integrating watershed, coastal, and urban flood models, we explicitly resolved the interaction between fluvial, pluvial, and coastal processes across the city. From these simulations, we identified flood hazard hotspots and systematically attributed the primary drivers of flooding for each event. The results suggested that 44% of Philadelphia's flood events were compound floods, primarily driven by fluvial-surge and fluvial-pluvial combinations. Notably, 77% of these events involved fluvial flooding, either as a single flood driver or in combination with other drivers, underscoring the dominant role of riverine processes in the city's flood hazard. Overall, this study demonstrates the value of a comprehensive, process-based approach for urban flood hazard assessment and highlights the importance of considering the full spectrum of flood scenarios to inform targeted and adaptive flood management strategies in coastal cities.

Compound flooding↗

NASA NTRS2024

Platte River Water Resources: Assessing Urban Flood Vulnerability to Select Restoration Sites for Urban Woods and Prairies in the Great Plains

In the Platte River Basin, wetlands provide ecosystem services such as flood mitigation and wildlife habitat. However, increasing urban development in the area has impacted natural floodplain processes, leading to a decline in wildlife habitat and an elevated flood risk for nearby communities. To address this issue, Audubon Great Plains’ Urban Woods and Prairies (UWP) Initiative focuses on restoring vital habitats within urban areas to protect bird species and reduce flood hazards. Our project used remotely sensed data, including Landsat 8 Operational Land Imagery (OLI), Sentinel-2 Multispectral Instrument (MSI), and Sentinel-1 Synthetic Aperture Radar (SAR), to assess land use and land cover from 2013 to 2023, as well as flood extent. Broad-scale analysis of the LULC showed some changes in land use patterns across the Central Platte River Basin, with the most notable being a decrease in Agricultural land coverage and an increase in Vegetation and Grassland coverage. Land use changes varied across 13 focal cities across the entire basin. In particular, developed land in Grand Island, NE, nearly tripled from 2019 to 2023, making it a good possible candidate for restoration efforts. We overlaid a flood extent map with the LULC classifications in Grand Island to identify possible restoration sites under UWP. This data will inform Audubon Great Plains in identifying potential restoration sites in key cities.

Jennifer Mathis↗

DOE OSTI2025

Community-Informed Urban Flood Modeling for Impact Mitigation

The intensification of the hydrologic cycle due to climate change poses a threat to aging and under-designed water infrastructure systems which cannot adequately manage intense storm events. Developing a comprehensive plan for managing rain-driven flooding events is challenging due to uncertainties in the magnitude and frequency of future storm events and conflicting stakeholder objectives. In the City of Baltimore, Maryland, stormwater infrastructure is struggling to keep up with rainfall-driven (pluvial) flooding events, which regularly damage housing and disrupt transportation for residents. In this study, a hybrid of community engagement, numerical modeling, and artificial intelligence techniques are employed to explore prospective urban flooding adaptations. Community engagement drives the development of an urban flooding model (EPA Storm Water Management Model) for the Baltimore Harbor watershed. The model integrates complex surface and subsurface stormwater infrastructure data from the City, high-resolution spatial data, insights from local public works experts, and the lived experiences of City residents. This co-developed model simulates adaptations of interest to stakeholders in the city, including green and grey infrastructure and operational management strategies. Stormwater management scenarios focused on inlet cleaning and spatially concentrated green infrastructure are found to be the most effective in reducing flood depths in community priority locations. Together, these adaptations can reduce the duration of intersection inundation by more than twenty minutes, allowing for quicker emergency response and restoration of typical transportation systems. Future work will combine this community engaged flooding model with the Deep Uncertainties Pathways framework to explore tradeoffs between adaptations and develop dynamic adaptations which align with community objectives, enhance climate resilience in Baltimore, and can be adjusted in response to changing future conditions.

Ava, Spangler [Pennsylvania State University]↗

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↗

NASA NTRSDate not supplied

Platte River Basin Water Resources: Assessing Urban Flood Vulnerability to Select Restoration Sites for Urban Woods and Prairies in the Great Plains

Explore the source record for details and available documents.

Jennifer Mathis↗

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 NTRS2021

Fairfax Water Resources: Estimating Urban Flood Susceptibility, Historical Flooding Extent, and Land Cover Change in Fairfax County, Virginia to Aid in Flood Mitigation Planning

Between 2000 and 2020, Fairfax County, Virginia experienced extreme weather events that caused severe flooding and degradation of roads, businesses, and other public property. A single flood event on July 8th, 2019 resulted in $14.8 million in damages. These flood events routinely impact the community, often resulting in power outages, school closures, and downed trees. The Fairfax County Department of Public Works and Environmental Services partnered with DEVELOP to explore how remotely sensed data could be integrated to support its current flood mitigation efforts. This project used environmental factors such as elevation, slope, and topographic wetness index from Earth observation derived data to map flood susceptibility. We utilized Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-Band Synthetic Aperture Radar (C-SAR), and Sentinel-2 Multispectral Instrument (MSI) to map historic flooding events in Fairfax County. These maps will support flood management practices for the Fairfax County Department of Public Works and Environmental Services through the integration of remotely sensed data. Our results show that developed areas in the county are more susceptible to flooding, coinciding with analysis of flood factors, which indicated that imperviousness and tree canopy were the most influential drivers in flood susceptibility. Other results show that using Earth observations to map historical flooding is limited in urban areas due to false positives from SAR imagery between water and shadows. Further research is necessary to evolve the historical flood mapping technique if Earth observations are to be incorporated in future historical flood analysis.

Kaitlynn Hietpas↗

DOE OSTI2026

An Integrated Modeling Framework for Sediment Dynamics During Urban Flooding: Application to Hurricane Harvey in Houston

Floodwater can mobilize and redistribute large volumes of sediment from upland to downstream urban areas, threatening infrastructure, water quality, and ecosystem health. However, existing modeling approaches often fail to capture sediment dynamics in urban floodplains due to the lack of integration between upland hydrological processes and riverine sediment transport. This study presents the first integrated modeling framework that couples the Energy Exascale Earth System Model (E3SM) land component, which simulates runoff and hillslope erosion, with TELEMAC-GAIA, a two-dimensional hydrodynamic and sediment transport model. This framework enables the fully distributed, process-based simulation of high-resolution (as fine as 30 m) sediment dynamics from hillslopes to floodplains. Applied to a highly urbanized watershed in Houston during Hurricane Harvey, this framework reproduced observed water levels at 16 USGS gauges (median R 2 = 0.83 and KGE = 0.78), key sediment dynamics such as sediment transport and deposition processes, and reproduced spatial deposition patterns consistent with LiDAR-derived data. Based on the simulation, we estimate 8.0 million m 3 of event-scale sediment deposition, including 5.7 million m 3 trapped in the flood-control reservoirs and 2.3 million m 3 deposited along major channels and floodplains. Using a representative unit removal cost, this corresponds to an estimated dredging cost of $581 million for total deposition. These results provide a first-order, physically based quantification of Harvey-scale sediment impacts. This study provides a valuable tool for the holistic analysis of sediment dynamics triggered by extreme urban flooding, supporting flood-resilience planning. More broadly, it highlights the importance of integrating physically based hydrological processes for urban flooding and sediment research.

Hurricane Harvey↗