A great challenge has been appeared on if the assumption of data stationary for flood frequency analysis is justifiable. Results for frequency analysis (FA) could be substantially different if non-stationarity is incorporated in the data analysis. In this study, extreme water levels (annual maximum and daily instantaneous maximum) in a coastal part of New York City were considered for FA. Annual maximum series (AMS) and peak-over threshold (POT) approaches were applied to build data timeseries. The resulted timeseries were checked for potential trend and stationarity using statistical tests including Man-Kendall, Augmented Dickey-Fuller (ADF) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS). Akaike information criterion (AIC) was utilized to select the most appropriate probability distribution models. Generalized Extreme Value (GEV) distribution and Generalized Pareto Distribution (GPD) were then applied as the probability distribution functions on the selected data based on AMS and POT methods under non-stationary assumption. Two methods of maximum likelihood and penalized maximum likelihood were applied and compared for the estimation of the distributions' parameters. Results showed that by incorporating non-stationarity in FA, design values of extreme water levels were significantly different from those obtained under the assumption of stationarity. Moreover, in the non-stationary FA, consideration of time-dependency for the distribution parameters resulted in a range of variation for design floods. The findings of this study emphasize on the importance of FA under the assumptions of data stationarity and nonstationarity, and taking into account the worst case flooding scenarios for future planning of the

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<p>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.</p>