Bivariate Frequency Analysis of Extreme Sea Level with Rainfall and Temperature in New York City

Climate change negatively impacts hydrologic patterns, affecting rainfall, temperature extremes, and sea level rise. Long-term averages of these variables may shift over time due to climate change effects. This study conducted trend analysis on rainfall, maximum and minimum temperature, and water level data from Manhattan, Central Park, and Battery Park stations to identify significant changes in means. The Partial Mann-Kendall test was employed for trend analysis. Frequency analysis utilized common probability distribution functions, including Generalized Extreme Value (GEV), normal, log-normal, and Log-Pearson distributions, with goodness-of-fit tests like Kolmogorov-Smirnov to identify the most suitable distributions. While flood frequency analysis typically examines rainfall and water levels separately, their combination can significantly influence floodplain delineation. This study aimed to enhance flood frequency analysis by considering joint probability distributions for rainfall and storm surge. The correlations between variables and joint probabilities of extreme water levels and temperatures were explored to assess the potential impacts of global warming on sea level flooding. Copula functions determined the joint probabilities of water levels with rainfall and temperature across various recurrence intervals. The trend analysis results indicated an increase in long-term averages due to climate change. The GEV distribution emerged as the most appropriate function for extreme climate variables. This joint probability distribution analysis underscored the necessity of incorporating both rainfall and water level data in flood frequency assessments.

کلیدواژه ها:

Climate change ، Climate variables ، Copula ، Joint Probability ، Partial Mann-Kendall