Brandon Li
Pronouns: he/him/his
Research Mentor(s): Yi Hong
Research Mentor School/College/Department: Cooperative Institute for Great Lakes Research / SNRE
Program: UROP
Session: Session 7 (4:40pm – 5:30pm)
Authors: Brandon Li, Yi Hong
Abstract
Coastal flooding within Great Lakes communities poses serious threats to ecosystem and economic sustainability. To improve predictions and local resilience, it is essential to understand the driving factors among multiple hydrodynamic and meteorological factors for coastal flooding. In this context, our study conducted spatial-temporal analysis of various large datasets from 2010 to 2021, including insurance claims from the National Flood Insurance Program (NFIP), gridded precipitation data at 10 km resolution, hydrodynamic simulations of National Oceanic and Atmospheric Administration’s (NOAA) operational model, and observations of stream flows and water levels from multiple USGS and NOAA gauge stations. By analyzing NFIP’s insurance claim data, we could identify the regions most impacted by recent floods. In this project, we focused on three regions with the highest number of filed insurance claims, including North Chicago, Monroe, and Rochester. Daily data of wave height, stream flow, precipitation, and water level were collected and analyzed at the census tract scale. Spatial temporal variations of these factors were analyzed by generating time-series figures and geo spatial images. Finally, we are completing our statistical analysis to evaluate the driving factors of coastal flood events. By training a model with the daily values of each predictor, we can test how well the model can predict a flood occurrence based on the predictor values. This will help us identify which predictors have the strongest correlation with flooding. As the flooding across the Great Lakes is widespread and beyond these three cities, we can later expand our analysis to more regions. This allows the National Flood Insurance Program (NFIP) to take greater measures in improving flood resilience for regions most heavily affected by flooding.
