A statistical method to quantify the tide-surge interaction effects with application in probabilistic prediction of extreme storm tides along the northern coasts of the South China Sea

Nonlinear interactions between tides and storm surges, i.e. the tide-surge interactions (TSI), play a critical role in modulating extreme coastal water levels. However, the TSI effects are often omitted in probabilistic modeling of extreme water levels, due to the extensive computational efforts caused by simulating numerous combinations of storm timings and random tidal phases. In this study, we proposed a statistical approach for fast estimation of TSI effects with applications in predicting extreme water levels. Storm tides in the northern expanse of the South China Sea (SCS) during typhoon events from 1979 to 2020 are simulated, based on which contributions of TSI to the total water level are extracted and analyzed. The results reveal a predominant negative influence of TSI on the total storm tide levels. It was found that for a given coastal location, statistically significant correlations exist between TSI divided by the peak storm tide and surge divided by the peak storm tide. Based on the correlations, location-dependent, multiple regression models for predicting TSI effects on storm tide levels were established using predicted surge and tidal levels as input, which avoids modeling numerous scenarios of different tidal phases and storm timings. By comparing with long term history data from a tide gauge, the proposed approach was shown to be able to reproduce the TSI contributions to extreme storm tides accurately and efficiently. By superposing the estimated TSI effects with predicted surge and random tides, a method for calculating the extreme water level with certain return periods is developed. Finally, an application of the method was demonstrated by calculating the 50-year return period water levels along the northern coast of the South China Sea.