Nonstationary Bi-Variate Frequency Analysis of Extreme Sea Level and Rainfall Under Climate Change Impacts: South Carolina Coastal Area

Statistical analysis of hydrologic variables is of great importance for water resources systems. Design and operation of these systems is often based on the assumption of data stationarity. However, long-term average of variables such as rainfall as well as sea level is observed to shift over time, mostly attributed to the climate change. These changes, in turn, affect flood volume, peak value and frequency. In this study, a framework was proposed for bi- variate frequency analysis of extreme sea level and rainfall. The analysis was performed on rainfall for the coastal area of Charleston and Savannah, and sea level for the coastal area of Charleston and Fort Pulaski, South Carolina, USA. Extreme values were selected based on the peak over threshold method. To determine the most appropriate distribution, AIC and BIC goodness of fit tests were used. Frequency analysis was then carried out using nonstationary Generalized Extreme Value probability distribution function. Results showed an increase in the sea level long term average, significant trends and outliers (specifically in recent decades), while although the analysis of rainfall data confirms the presence of outliers in the time series, it does not indicate significant trends or heterogeneity. Therefore, in performing bi-variate frequency analysis of extreme rainfall and sea level, non-stationary approaches should be used to provide a more reliable prediction of the joint probability of these variables.