Storm Surge Disaster Risk Assessment And Impact Forecasting Research

Storm surge is one of the worst oceanic disasters in coastal areas,and causes great losses every year.Risk analysis is one of the most important non-engineering measures for disaster prevention and mitigation,especially in planning of economic development and functional zones.Meanwhile,accurate forecasting on typhoon-caused storm surges could greatly reduce their impact on local and regional society and economy.The core contents of this study are to assess the flooding risk and to predict the flooding impact caused by typhoon-associated storm surges in the coastal area of Guangdong Province,China.The extreme Total Water Level(TWLt)in a certain return period is used to simulate the inundation processes caused by storm surges,and then to assess the flooding risk.One of the most critical challenges of this method is to derive the extreme TWLt,which consists of Astronomic Tide(AT)and Storm Surge(SS),using long-term tide level observations at limited stations.For instance,there is only one long-term and standard/primary tidal station per 350-km coastal line on average in China.Traditionally,TWLt along the entire coast is spatially interpolated using the extreme TWLt computed at limited primary tidal stations.This method is described as a Total Interpolation(TI)here and often generates large uncertainties in the coastal areas without tidal level observations.Meanwhile,there are over 800 secondary tidal stations with tidal datum,whose numeric values usually describe the maximum magnitude of AT.Therefore,a Residual Interpolation(RI)is developed to derive the extreme ATt amd TWLt by making full use of tidal datum at the secondary stations and the long-term tidal level observations at the primary stations.The core algorithm of this method is to compute the extreme ATt for the 2-year return period using the tidal datum at the dense secondary stations,and then the residuals of ATtbetween return periods of T(T=10,20,50,100,200 and 500 years)and 2 years at the primary stations are interpolated into the secondary tide stations,and the extreme Storm Surges(SSt)computed at the primary and other stations are interpolated into the secondary tide stations as well.The sum of ATt=2,ATt residuals and SSt forms the maximum water level and is further converted into the extreme TWLt,which are interpolated into elevation grids for inundation modeling by a static inundation model.A series of flooding risk maps are then generated by overlapping the inundation maps with population and economic data,e.g.,the Gross Domestic Production(GDP).The common damage ratios adopted in literature are applied to compute the flooding-affected population and GDP loss according to different inundation depths.A four-level risk map is made by the quartiles(<35%,35-55%,55-85%,>85%)of the affected population and GDP loss,representing the risk levels of low,moderate,high,and extremely high.Meanwhile,a four-level integrated risk map is also generated by multiplying the risk arrays of affected population and GDP loss for a return period.Cities in the Pearl River Delta face extremely high storm surge risk.Other regions,such as ChaoZhou and ShanTou in the north and ZhanJiang in the south also have large areas within the extremely high risk zones.An automated approach is also developed to compute the TWLt,risk analysis and risk map drawing at the ArcGIS platform.This approach greatly improve the efficiency on assessment of flooding risk due to typhoon-caused storm surges and could be applied in other regions in China and elsewhere as well.Besides flooding risk assessment using long-term tidal observations,this study further integrates several approaches to predict the flooding impact of storm surges.The impact of storm surges for a single typhoon event is closely related to the landfall location and time,which determine the tidal level of the local AT.Previous studies emphasize more on the prediction of storm surge and often fail to provide the astronomic tide levels,thus adding a barrier for final users to understand the impact of the TWL due to storm surges.This study applies the T-tide function to predict AT,and then combine AT with SS predicted by the Guangzhou Storm Surge Model(GZSSM V1.0)operated in the Guangdong Institute of Tropical and Marine Meteorology,China Meteorological Administration(GITMM/CMA),to form the TWL for the next 24 hours.The dynamic TWLs at each hour is then applied to drive the two-dimensional hydraulics model(FloodArea)to simulate the flooding depth and distribution,which are further used to estimate the potentially affected population and GPS loss.The according data collected during the NiDa Typhoon(No.1604)are applied to validate the prediction results as a case study.The predicted TWL is generally in agreement with the observed water level variations at SiShengWei hydrologic stations in the estuary of East Pearl River.The predicted affected population and GDP loss are less than reported numbers since the reported values also include those caused by the winds.In summary,this study develops an approach called Residual Interpolation to derive the extreme TWLt to assess the flooding risk due to typhoon-caused storm surges,and integrates AT and SS prediction to form TWL for flooding modeling and impact estimate.The Residual Interpolations can better capture the spatial variability of the storm tide than the traditionally Total Interpolation.Accordingly,the flooding risk uncertainty is greatly reduced by the residual interpolation.The automated risk analysis tool and the integrated flooding impact prediction model thus offer better supports for coastal development planning,flooding risk assessment and emergent management.