Risk assessment of coastal flooding disaster by storm surge based on Elevation-Area method and hydrodynamic models:Taking Bohai Bay as an example

The future inundation by storm surge on coastal areas are currently ill-defined.With increasing global sealevel due to climate change,the coastal flooding by storm surge is more and more frequently,especially in coastal lowland with land subsidence.Therefore,the risk assessment of such inundation for these areas is of great significance for the sustainable socio-economic development.In this paper,the authors use Elevation-Area method and Regional Ocean Model System(ROMS)model to assess the risk of the inundation of Bohai Bay by storm surge.The simulation results of Elevation-Area method show that either a 50-year or 100-year storm surge can inundate coastal areas exceeding 8000 km^(2);the numerical simulation results based on hydrodynamics,considering ground friction and duration of the storm surge high water,show that a 50-year or 100-year storm surge can only inundate an area of over 2000 km^(2),which is far less than 8000 km^(2);while,when taking into account the land subsidence and sea level rise,the very inundation range will rapidly increase by 2050 and 2100.The storm surge will greatly impact the coastal area within about 10-30 km of the Bohai Bay,in where almost all major coastal projects are located.The prompt response to flood disaster due to storm surge is urgently needed,for which five suggestions have been proposed based on the geological background of Bohai Bay.This study may offer insight into the development of the response and adaptive plans for flooding disasters caused by storm surge.

Landslide susceptibility assessment in Western Henan Province based on a comparison of conventional and ensemble machine learning

Landslide is a serious natural disaster next only to earthquake and flood,which will cause a great threat to people’s lives and property safety.The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective,difficult to quantify,and no pertinence.As a new research method for landslide susceptibility assessment,machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models.Taking Western Henan for example,the study selected 16 landslide influencing factors such as topography,geological environment,hydrological conditions,and human activities,and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination(RFE)method.Five machine learning methods[Support Vector Machines(SVM),Logistic Regression(LR),Random Forest(RF),Extreme Gradient Boosting(XGBoost),and Linear Discriminant Analysis(LDA)]were used to construct the spatial distribution model of landslide susceptibility.The models were evaluated by the receiver operating characteristic curve and statistical index.After analysis and comparison,the XGBoost model(AUC 0.8759)performed the best and was suitable for dealing with regression problems.The model had a high adaptability to landslide data.According to the landslide susceptibility map of the five models,the overall distribution can be observed.The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest,the Xiaoshan Mountain range in the west,and the Yellow River Basin in the north.These areas have large terrain fluctuations,complicated geological structural environments and frequent human engineering activities.The extremely high and highly prone areas were 12043.3 km^(2)and 3087.45 km^(2),accounting for 47.61%and 12.20%of the total area of the study area,respectively.Our study reflects the distribution of landslide susceptibility in western Henan Province,which provides a scientific basis for regional disaster warning,prediction,and resource protection.The study has important practical significance for subsequent landslide disaster management.

Hydrological response characteristics of landslides under typhoon-triggered rainstorm conditions

Many landslide disasters,which tend to result in significant damage,are caused by typhoon-triggered rainstorms.In this case,it is very important to study the dynamic characteristics of the hydrological response of landslide bodies since it enables the early warning and prediction of landslide disasters in typhoon periods.To investigate the dynamic mechanisms of groundwater in a landslide body under typhoon-triggered rainstorm conditions,the authors selected the landslide occurring in Zhonglin Village,Wencheng County,China(also referred to as Zhonglin Village landslide)as a case study.The transient seepage field characteristics of groundwater in the landslide body were simulated with two different rainfall models by using the finite element method(FEM).The research results show that the impact of typhoon-triggered rainstorms on landslides can be divided into three stages:(i)Rapid rise of groundwater level;(ii)infiltration of groundwater from the surface to deeper level,and(iii)surface runoff erosion.Moreover,the infiltration rate of groundwater in the landslide body is mainly affected by the intensity of typhoon-induced rainfall.It can be deduced that higher rainfall intensity leads to a greater potential difference and a higher infiltration rate.The rainfall intensity also determines the development mode of landslide deformation and destruction.