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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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Satellite-based Hurricane Risk Assessment for Roadways via Vegetation 3D Modeling and Building Detection
Infrastructures such as roadways, power lines, and communications networks play a critical role in our society. However, they are also susceptible to failures, especially after natural events, easily affecting large geographical areas. Predicting where and when these failures will occur with high confidence is very difficult due to the stochastic nature of such events. Nevertheless, it is possible to know which areas are more vulnerable in advance and plan accordingly. This paper aims to use just remote sensing techniques based on satellite images to detect roadways vulnerabilities to hurricanes. The framework exhibits a modular architecture that enables detecting and mapping in 3D vegetation and detecting buildings. We propose a risk function based on the information retrieved from the satellite image which can be used to create a risk map of the area. The study area has been selected in Tallahassee, Florida where a high-resolution satellite image has been acquired in September 2018, before Hurricane Michael main hit. The findings of this work can help the management teams and city responders to identify the most vulnerable regions which are under the risk of disruption and to organize the resources prior to the event. The advantages of our approach are that the entire framework can be use as an end-to-end standalone solution for risk analysis at city level and can be easily expanded with other source of data.
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- PAR ID:
- 10358642
- Date Published:
- Journal Name:
- Publications Transportation Research Board
- ISSN:
- 0276-945X
- Page Range / eLocation ID:
- 1-12
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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