An Evaluation of Climate Change Impacts on Extreme Sea Level Variability: Coastal Area of New York City

Abstract

Climate change has resulted in sea level rise and increasing frequency of extreme storm events around the world. This has intensified flood damage especially in coastal regions. In this study, a methodology is developed to analyze the impacts of climate change on sea level changes in the coastal regions utilizing an artificial neural network model. For simulation of annual extreme sea level, climate signals of Sea Surface Temperature, Sea Level Pressure and SLP gradient of the study region and some characteristic points are used as predictors. To select the best set of predictors as neural network model input, feature selection methods of MRMR (Minimum Redundancy Maximum Relevance) and MI (Mutual Information) are used. Future values of the selected predictors under greenhouse gas emission scenarios of B1, A1B and A2 are used in the developed neural network model to project water level for the next 100 years. Sea levels with different recurrence intervals are determined using frequency analysis of historical and projected water level as well, and the impact of climate change in extreme sea level is investigated. The developed methodology is applied to New York City to determine the coastal region vulnerability to water level changes. The results of this study show remarkable increase in sea level in the New York City, which is an indicative of coastal areas vulnerability and the need to take strategic actions in dealing with climate change.