Spatial–temporal distribution of storm surge damage in the coastal areas of China

Shi, Xiaoyan; Liu, Shan; Yang, Saini; Liu, Qinzheng; Tan, Junlei; Guo, Zhixing

doi:10.1007/s11069-015-1838-z

Cited by 49 publications

(31 citation statements)

References 15 publications

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“…Thus, the focus on storm surge disasters has changed from reduction in disaster losses to mitigation of disaster risks. Therefore, research on storm surge risk has attracted more and more attention (Shi et al, 2020a).…”

Section: Introductionmentioning

confidence: 99%

Simulation of storm surge inundation under different typhoon intensity scenarios: case study of Pingyang County, China

Shi

Guo³

et al. 2020

Nat. Hazards Earth Syst. Sci.

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Abstract. China is one of the countries that is most seriously affected by storm surges. In recent years, storm surges in coastal areas of China have caused huge economic losses and a large number of human casualties. Knowledge of the inundation range and water depth of storm surges under different typhoon intensities could assist predisaster risk assessment and making evacuation plans, as well as provide decision support for responding to storm surges. Taking Pingyang County in Zhejiang Province as a case study area, parameters including typhoon tracks, radius of maximum wind speed, astronomical tide, and upstream flood runoff were determined for different typhoon intensities. Numerical simulations were conducted using these parameters to investigate the inundation range and water depth distribution of storm surges in Pingyang County considering the impact of seawall collapse under five different intensity scenarios (corresponding to minimum central pressure values equal to 915, 925, 935, 945, and 965 hPa). The inundated area ranged from 103.51 to 233.16 km2 for the most intense typhoon. The proposed method could be easily adopted in various coastal counties and serves as an effective tool for decision-making in storm surge disaster risk reduction practices.

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Section: Introductionmentioning

confidence: 99%

Simulation of storm surge inundation under different typhoon intensity scenarios: case study of Pingyang County, China

Shi

Guo³

et al. 2020

Nat. Hazards Earth Syst. Sci.

Self Cite

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show abstract

“…TCs can cause hazardous disasters, such as strong wind, heavy rainfall, and storm surge, which are strongly related to the TC intensity and structure [7,8]. Thus, in order to reduce losses caused by these disasters in terms of human life and economic interests, which are especially serious in Southern China [9,10], it is very important and urgent to improve the forecasts of TC intensity and the related rainfall and wind.…”

Section: Introductionmentioning

confidence: 99%

Assimilation of Data Derived from Optimal-Member Products of TREPS for Convection-Permitting TC Forecasting over Southern China

Zhang

Chen

2019

Atmosphere

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To improve the landfalling tropical cyclone (TC) forecasting, the pseudo inner-core observations derived from the optimal-member forecast (OPT) and its probability-matched mean (OPTPM) of a mesoscale ensemble prediction system, namely TREPS, were assimilated in a partial-cycle data assimilation (DA) system based on the three-dimensional variational method. The impact of assimilating the derived data on the 12-h TC forecasting was evaluated over 17 TCs making landfall on Southern China during 2014–2016, based on the convection-permitting Global/Regional Assimilation and Prediction System (GRAPES) model with the horizontal resolution of 0.03°. The positive impacts of assimilating the OPT-derived data were found in predicting some variables, such as the TC intensity, lighter rainfall, and stronger surface wind, with statistically significant impacts at partial lead times. Compared with assimilation of the OPT-derived data, assimilation of the OPTPM-derived data generally brought improvements in the forecasts of TC track, intensity, lighter rainfall, and weaker surface wind. When the data with higher accuracy was assimilated, the positive impacts of assimilating the OPTPM-derived data on the forecasts of heavier rainfall and stronger surface wind were more evident. The improved representation of initial TC circulation due to assimilating the derived data improved the TC forecasting, which was intuitively illustrated in the case study of Mujigae.

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“…Storm surges have the potential to cause loss of life and financial damage [ 9 , 10 ] and have done so on many occasions in the past [ 11 , 12 ]. Globally, China is one of the countries most frequently and severely affected by storm surge disasters [ 13 , 14 ]. During the period of 1949–2016, storm surges have occurred 602 times in China, resulting in direct economic losses of up to 313.64 billion yuan.…”

Section: Introductionmentioning

confidence: 99%

“…Current disaster prevention and mitigation measures against storm surges are generally divided into two aspects: non-engineering measures and engineering measures. Non-engineering measures include formulating emergency rescue programs, the rational utilization of coastal resources, the strengthening management of coastal areas, and disaster monitoring and forecasting systems [ 4 ]; engineering measures include strengthening the construction of storm surge coastal monitoring stations, increasing the embankment construction, building mangroves and sand belts, and combining engineering measures with biological measures to form a system to protect against storm surge disasters [ 13 , 14 , 37 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Loss Due to Storm Surge Disasters in China Based on Econometric Model Groups

Jin

Shi

Jin-tian

et al. 2018

IJERPH

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Storm surge has become an important factor restricting the economic and social development of China’s coastal regions. In order to improve the scientific judgment of future storm surge damage, a method of model groups is proposed to refine the evaluation of the loss due to storm surges. Due to the relative dispersion and poor regularity of the natural property data (login center air pressure, maximum wind speed, maximum storm water, super warning water level, etc.), storm surge disaster is divided based on eight kinds of storm surge disaster grade division methods combined with storm surge water, hypervigilance tide level, and disaster loss. The storm surge disaster loss measurement model groups consist of eight equations, and six major modules are constructed: storm surge disaster in agricultural loss, fishery loss, human resource loss, engineering facility loss, living facility loss, and direct economic loss. Finally, the support vector machine (SVM) model is used to evaluate the loss and the intra-sample prediction. It is indicated that the equations of the model groups can reflect in detail the relationship between the damage of storm surges and other related variables. Based on a comparison of the original value and the predicted value error, the model groups pass the test, providing scientific support and a decision basis for the early layout of disaster prevention and mitigation.

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Spatial–temporal distribution of storm surge damage in the coastal areas of China

Cited by 49 publications

References 15 publications

Simulation of storm surge inundation under different typhoon intensity scenarios: case study of Pingyang County, China

Simulation of storm surge inundation under different typhoon intensity scenarios: case study of Pingyang County, China

Assimilation of Data Derived from Optimal-Member Products of TREPS for Convection-Permitting TC Forecasting over Southern China

Evaluation of Loss Due to Storm Surge Disasters in China Based on Econometric Model Groups

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