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1. Delineating urban flooding when incorporating community stormwater knowledge

   https://doi.org/10.1088/2634-4505/adad11  

   Scolio, Madeline; Kremer, Peleg; Smith, Virginia; Amur, Achira; Wadzuk, Bridget; Homet, Kate; Devlin, Eric; Al_Mehedi, Md_Abdullah; Moore, Laura (February 2025, Environmental Research: Infrastructure and Sustainability)

   Abstract Accurately delineating both pluvial and fluvial flood risk is critical to protecting vulnerable populations in urban environments. Although there are currently models and frameworks to estimate stormwater runoff and predict urban flooding, there are often minimal observations to validate results due to the quick retreat of floodwaters from affected areas. In this research, we compare and contrast different methodologies for capturing flood extent in order to highlight the challenges inherent in current methods for urban flooding delineation. This research focuses on two Philadelphia neighborhoods, Manayunk and Eastwick, that face frequent flooding. Overall, Philadelphia, PA is a city with a large proportion of vulnerable populations and is plagued by flooding, with expectations that flood risk will increase as climate change progresses. An array of data, including remotely sensed satellite imagery after major flooding events, Federal Emergency Management Agency’s Special Flood Hazard Areas, First Street Foundation’s Flood Factor, road closures, National Flood Insurance Program claims, and community surveys, were compared for the study areas. Here we show how stakeholder surveys can illuminate the weight of firsthand and communal knowledge on local understandings of stormwater and flood risk. These surveys highlighted different impacts of flooding, depending on the most persistent flood type, pluvial or fluvial, in each area, not present in large datasets. Given the complexity of flooding, there is no single method to fully encompass the impacts on both human well-being and the environment. Through the co-creation of flood risk knowledge, community members are empowered and play a critical role in fostering resilience in their neighborhoods. Community stormwater knowledge is a powerful tool that can be used as a complement to hydrologic flood delineation techniques to overcome common limitations in urban landscapes. 

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2. Modest flooding can trigger catastrophic road network collapse due to compound failure

   https://doi.org/10.1038/s43247-022-00366-0  

   Dong, Shangjia; Gao, Xinyu; Mostafavi, Ali; Gao, Jianxi (February 2022, Communications Earth & Environment)

   Abstract Compound failures occur when urban flooding coincides with traffic congestion, and their impact on network connectivity is poorly understood. Firstly, either three-dimensional road networks or the traffic on the roads has been considered, but not both. Secondly, we lack network science frameworks to consider compound failures in infrastructure networks. Here we present a network-theory-based framework that bridges this gap by considering compound structural, functional, and topological failures. We analyze high-resolution traffic data using network percolation theory to study the response of the transportation network in Harris County, Texas, US to Hurricane Harvey in 2017. We find that 2.2% of flood-induced compound failure may lead to a reduction in the size of the largest cluster where network connectivity exists, the giant component, 17.7%. We conclude that indirect effects, such as changes in traffic patterns, must be accounted for when assessing the impacts of flooding on transportation network connectivity and functioning. 

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3. Estimating road traffic impacts of commute mode shifts

   https://doi.org/10.1371/journal.pone.0279738  

   Hu, Yue; Barbour, William; Qian, Kun; Claudel, Christian; Samaranayake, Samitha; Work, Daniel B. (January 2023, PLOS ONE)

   Jin, Sheng (Ed.)

   This work considers the sensitivity of commute travel times in US metro areas due to potential changes in commute patterns, for example caused by events such as pandemics. Permanent shifts away from transit and carpooling can add vehicles to congested road networks, increasing travel times. Growth in the number of workers who avoid commuting and work from home instead can offset travel time increases. To estimate these potential impacts, 6-9 years of American Community Survey commute data for 118 metropolitan statistical areas are investigated. For 74 of the metro areas, the average commute travel time is shown to be explainable using only the number of passenger vehicles used for commuting. A universal Bureau of Public Roads model characterizes the sensitivity of each metro area with respect to additional vehicles. The resulting models are then used to determine the change in average travel time for each metro area in scenarios when 25% or 50% of transit and carpool users switch to single occupancy vehicles. Under a 25% mode shift, areas such as San Francisco and New York that are already congested and have high transit ridership may experience round trip travel time increases of 12 minutes (New York) to 20 minutes (San Francisco), costing individual commuters $1065 and $1601 annually in lost time. The travel time increases and corresponding costs can be avoided with an increase in working from home. The main contribution of this work is to provide a model to quantify the potential increase in commute travel times under various behavior changes, that can aid policy making for more efficient commuting. 

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4. Dynamic Modeling of Inland Flooding and Storm Surge on Coastal Cities under Climate Change Scenarios: Transportation Infrastructure Impacts in Norfolk, Virginia USA as a Case Study

   https://doi.org/10.3390/geosciences12060224  

   Shen, Yawen; Tahvildari, Navid; Morsy, Mohamed M.; Huxley, Chris; Chen, T. Donna; Goodall, Jonathan Lee (June 2022, Geosciences)

   Low-lying coastal cities across the world are vulnerable to the combined impact of rainfall and storm tide. However, existing approaches lack the ability to model the combined effect of these flood mechanisms, especially under climate change and sea level rise (SLR). Thus, to increase flood resilience of coastal cities, modeling techniques to improve the understanding and prediction of the combined effect of these flood hazards are critical. To address this need, this study presents a modeling system for assessing the combined flood impact on coastal cities under selected future climate scenarios that leverages ocean modeling with land surface modeling capable of resolving urban drainage infrastructure within the city. The modeling approach is demonstrated in quantifying the impact of possible future climate scenarios on transportation infrastructure within Norfolk, Virginia, USA. A series of combined storm events are modeled for current (2020) and projected future (2070) climate scenarios. The results show that pluvial flooding causes a larger interruption to the transportation network compared to tidal flooding under current climate conditions. By 2070, however, tidal flooding will be the dominant flooding mechanism with even nuisance flooding expected to happen daily due to SLR. In 2070, nuisance flooding is expected to cause a 4.6% total link close time (TLC), which is more than two times that of a 50-year storm surge (1.8% TLC) in 2020. The coupled flood model was compared with a widely used but physically simplistic bathtub method to assess the difference resulting from the more complex modeling presented in this study. The results show that the bathtub method overestimated the flooded area near the shoreline by 9.5% and 3.1% for a 10-year storm surge event in 2020 and 2070, respectively, but underestimated the flooded area in the inland region by 9.0% and 4.0% for the same events. The findings demonstrate the benefit of sophisticated modeling methods compared to more simplistic bathtub approaches, in climate adaptive planning and policy in coastal communities. 

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5. Patch Similarity Convolutional Neural Network for Urban Flood Extent Mapping Using Bi-Temporal Satellite Multispectral Imagery

   https://doi.org/10.3390/rs11212492  

   Peng, Bo; Meng, Zonglin; Huang, Qunying; Wang, Caixia (November 2019, Remote Sensing)

   null (Ed.)

   Urban flooding is a major natural disaster that poses a serious threat to the urban environment. It is highly demanded that the flood extent can be mapped in near real-time for disaster rescue and relief missions, reconstruction efforts, and financial loss evaluation. Many efforts have been taken to identify the flooding zones with remote sensing data and image processing techniques. Unfortunately, the near real-time production of accurate flood maps over impacted urban areas has not been well investigated due to three major issues. (1) Satellite imagery with high spatial resolution over urban areas usually has nonhomogeneous background due to different types of objects such as buildings, moving vehicles, and road networks. As such, classical machine learning approaches hardly can model the spatial relationship between sample pixels in the flooding area. (2) Handcrafted features associated with the data are usually required as input for conventional flood mapping models, which may not be able to fully utilize the underlying patterns of a large number of available data. (3) High-resolution optical imagery often has varied pixel digital numbers (DNs) for the same ground objects as a result of highly inconsistent illumination conditions during a flood. Accordingly, traditional methods of flood mapping have major limitations in generalization based on testing data. To address the aforementioned issues in urban flood mapping, we developed a patch similarity convolutional neural network (PSNet) using satellite multispectral surface reflectance imagery before and after flooding with a spatial resolution of 3 meters. We used spectral reflectance instead of raw pixel DNs so that the influence of inconsistent illumination caused by varied weather conditions at the time of data collection can be greatly reduced. Such consistent spectral reflectance data also enhance the generalization capability of the proposed model. Experiments on the high resolution imagery before and after the urban flooding events (i.e., the 2017 Hurricane Harvey and the 2018 Hurricane Florence) showed that the developed PSNet can produce urban flood maps with consistently high precision, recall, F1 score, and overall accuracy compared with baseline classification models including support vector machine, decision tree, random forest, and AdaBoost, which were often poor in either precision or recall. The study paves the way to fuse bi-temporal remote sensing images for near real-time precision damage mapping associated with other types of natural hazards (e.g., wildfires and earthquakes). 

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