Linking socioeconomic development, sea level rise, and climate change impacts on urban growth in New York City with a fuzzy cellular automata-based Markov chain model

This study aims to conduct a multi-temporal change analysis of land use and land cover in New York City via a cellular automata-based Markov chain model that uses fuzzy set theory and multi-criteria evaluation to predict the city’s future land use changes for 2030 and 2050 under potential sea level rise and long-term rainfall-runoff flooding impacts driven by climate change. To determine the future natural forcing impacts on land use in New York City, this study highlights the need for integrating spatiotemporal modeling analyses, such as a statistical downscaling model driven by climate change with remote sensing and GIS to support urban growth assessment. The research findings indicate that the mean rainfall will increase in the future and sea levels will rise near New York City; however, open space is expected to decrease by 1.51% and 2.51% and the urban area is expected to expand by about 1.36% and 2.63% in 2030 and 2050 respectively, taking into account the climate change and sea level rise.