Simulation And Risk Analysis Of Typhoon And Storm Surge In The Southeast China Coastal Region

Southeast coastal region is the most developed and populated area in China,and is also one of the regions most seriously impacted by typhoons in the world.Analyzing typhoon and storm surge risks and predicting maximum wind speeds and surge levels of typhoons are vital for the design of critical structures and typhoon disaster mitigation in these areas.Firstly,taking the southeastern coastal city of Shenzhen as an example,this study employ the traditional Monte-Carlo simulation to analyze of the typhoon wind hazard of Shenzhen.Based on the historical typhoons obtained from the China Meteorological Administration(CMA)Best Track Dataset for Tropical Cyclones over the Western North Pacific,typhoon key parameters are extracted and optimal statistical distributions are established for these parameters in relation to Shenzhen.Then,the Monte-Carlo method is employed to sample from each distribution to generate 1000-year virtual typhoons.The Yan Meng(YM)wind field model is introduced,and the sensitivity of the YM model to several parameters is discussed.We find that the model is very sensitive to the surface roughness and Holland pressure profile parameter B,and the two have opposite effects on the wind speed.We use the YM model to simulate the wind field of the virtual typhoons and extract the extreme wind speeds.Different extreme value distributions are used to fit the extreme wind speed series,and the Weibull distribution is better than Gumbel distribution through the goodness-of-fit test.Finally,the extreme wind speeds for different return periods are predicted and compared with the results from current structural code and other references.We find the main reason for the difference is the different B model.From single point to entire area,this paper describes a technique for analyzing the areawide typhoon risk for the southeast China coastal region based on the Monte-Carlo method.The whole region is divided into 0.25??0.25?grid cells and each grid became a site of interest.A Monte-Carlo method is used to generate virtual typhoons and 1000 years of typhoons are simulated for the different grid cells.YM wind field model is adopted to simulate the maximum wind speeds of 100 historical typhoons.By minimizing the errors between these maximum wind speeds and the observed maximum wind speeds,a new set of formulas is established to calculate the radius to maximum winds(R_max)and Holland pressure profile parameter(B).Using this newly developed scheme which incorporated the YM wind field model,region-specific statistical models for the decay rate of typhoons after reaching land,and the appropriate extreme value distribution,we predict the site-specific extreme wind speeds associated with various return periods and propose a new map of wind speeds for the typhoon-prone coasts of China.Because the Monte-Carlo method relies on an assumption of uniform climatology over the subregion,it is more suitable to study the typhoon risk at a single point.In order to properly model typhoon wind risk over large regions,a simplified empirical track model approach is used to simulate 1000-year storms in the whole Northwest Pacific Basin.The extreme wind speeds of different return periods for different locations are predicted and a new map of wind speeds for the typhoon-prone coasts of China is proposed which can provide the completely new reference for assessing the risk of large-scale systems.Besides we compare the extreme wind speed predicted by the empirical track model and Monte-Carlo method.And we find the difference of extreme wind speed is mainly caused by the difference of the central pressure of the virtual typhoons constructed by two methods and the uncertainty of the model itself.We investigate the influence of typhoon decay model,track model,Holland pressure profile parameter,the radius to maximum winds,and the extreme value distribution on the predicted extreme wind speed.We find the different typhoon decay models have least influence on the predicted extreme wind speed.This is mainly because the central pressures obtained by the different decay models have little difference.Over most of the southeast coast of China,the predicted wind speed by the non-simplified empirical track model is larger than that from the simplified tracking model.This is mainly due to the difference in the typhoon central pressure and minimum approch distance constructed by the two track models.Different B models will have a greater impact on the prediction of extreme wind speed.When the B value is larger,the predicted extreme wind speed is also larger.The extreme wind speed predicted by different extreme value distribution is quite different which is caused by the characteristics of the extreme value distribution itself.Generally,the extreme wind speed predicted by the distribution with the"thick tail"is larger.Based on the generated 1000-year virtual typhoons for Northwest Pacific basin,we predict the the return periods of typhoon wind speeds along the China southeast coast caused by four super typhoons Meranti(2016),Hato(2017),Mangkhut(2018)and Lekima(2019)in order to assess the typhoon wind hazard.Finally,based on the 1,000 years of full-track typhoon events by empirical track model,the YM wind field model and SWAN+ADCIRC(Simulating Waves Nearshore+Advanced Circulation)coupled model,we investigate typhoon wind-surge-wave risk for Shenzhen City.Statistical analysis is carried out on the simulated wind-surge-wave data.It is observed that the probability distribution of maximum surge heights at the Shenzhen exhibited a heavy tail,and that of the peak wind speeds and SWHs exhibited a thin tail.The generalized Pareto distribution(GPD)is applied to estimate the upper tail of the storm wind-surge-wave.The resulting return periods of wind-surge-wave are predicted,respectively.The joint typhoon wind-surge-wave hazard maps for Shenzhen are also developed to consider the combined effects of the wind,surge and wave.This risk assessment methodology may be applied to other coastal areas and can be extended to consider the effect of future climate change.