Title:Societal Data Insights: Data Integration for Inland Flooding.

Presenter(s): Isabel Lopez

Date: 24 October 2024 2:00 pm – 2:30 pm ET

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About Speaker: Isabel Lopez

Abstract: Urban recurrent flooding presents a complex challenge distinct from nuisance flooding, typically associated with coastal areas. Unlike nuisance flooding, which is often predictable and localized, urban recurrent flooding involves adynamic interplay of factors such as dense infrastructure, varied land use, and heterogeneous topography. These elements contribute to unpredictable flood patterns that are more difficult to model and manage. The complexity of urban environments amplifies the challenges in assessing flood risks and potential impacts, necessitating a more sophisticated analytical approach. This research adapts the Topographic Wetness Index (TWI) to highlight areas prone to flooding based on flow direction and water accumulation. Additionally, it incorporates the Curve Number (CN) method to estimate runoff volumes from precipitation events, providing refined tools for measuring surface runoff and predicting flooding potential. Recognizing that urban flooding significantly impacts communities, this study integrates social data to capture the broader societal effects, particularly on vulnerable populations. The proposed framework is designed for flexibility, allowing its application across diverse urban areas with varying geographic and social characteristics. By combining geospatial analysis with social data, this research offers a comprehensive approach to flood risk assessment, providing valuable insights for policymakers and urban planners.The results are from the NOAA EPP/MSI CSC NERTO graduate internship project that was conducted with NOAA mentors Dr. Jonathon Mote and Dr. Kyle Metta of the Weather ProgramOffice (WPO) in Silver Spring, MD. The NERTO aligns with NOAA CSC CESSRST-II’s goal to understand changes in climate and weather and to share that knowledge and information with others. The NERTO project enhanced the intern’s ability to integrate social data with physical data, providing deeper insights into developing methods that combine social, weather, and climate data for more comprehensive analyses.