Analysis and prediction of sea level rise along the U.S. East and Gulf coasts and its socio-economic impacts on the nearby inland areas

Abstract

Floods are among the most frequent and devastating natural disasters, causing severe global economic damage, yet timely and accurate warnings of flash flood impacts on ungauged locations remain challenging. Sea level rise (SLR) is a substantial factor that contributes to flooding, particularly along the coastal regions of the United States. This study presents a comprehensive analysis of historical tide gauge records from 1900 to 2021 to investigate spatio-temporal dynamics of mean sea level (MSL) along the U.S. East and Gulf coasts and develops a forecasting model to predict future MSL using these dynamics. We employed empirical orthogonal functions (EOF) analysis and dynamic mode decomposition (DMD) with time delay embedding to analyze and forecast MSL data. SLR dynamics and trend vary across different parts the U.S. coasts. Our proposed approach aids in identifying the regions most susceptible to SLR. To assess the socio-economic impact on the coastal regions due to SLR, we propose a framework that integrates the mean sea level data from tide-gauge stations with socio-economic variables of neighboring counties through interaction structure learning techniques. Furthermore, we empirically demonstrate the implications of our proposed framework in highlighting socio-economic factors affected by SLR. In conclusion, our predictive method elucidates the spatio-temporal dynamics of mean sea level, while our interaction learning framework reveals SLR’s impact on coastal socio-economic attributes.