Study of storm surge trends in typhoon-prone coastal areas based on observations and surge-wave coupled simulations

https://doi.org/10.1016/j.jag.2018.01.006Get rights and content

Highlights

  • An unstructured-grid, storm surge-wave-tide coupled model was applied in this study.

  • The simulated storm surge was validated by situ observations and satellite altimeter data.

  • This study identified the storm surge trends for the full complex coastline of the study area.

  • Correlation between the storm surge and the annual ENSO index was investigated.

Abstract

This is a study of the storm surge trends in some of the typhoon-prone coastal areas of China. An unstructured-grid, storm surge-wave-tide coupled model was established for the coastal areas of Zhejiang, Fujian and Guangdong provinces. The coupled model has a high resolution in coastal areas, and the simulated results compared well with the in situ observations and satellite altimeter data. The typhoon-induced storm surges along the coast of the study areas were simulated based on the established coupled model for the past 20 years (1997–2016). The simulated results were used to analyze the trends of the storm surges in the study area. The extreme storm surge trends along the central coast of Fujian Province reached up to 0.06 m/y, significant at the 90% confidence level. The duration of the storm surges greater than 1.0 and 0.7 m had an increasing trend along the coastal area of northern Fujian Province, significant at confidence levels of 70%–91%. The simulated trends of the extreme storm surges were also validated by observations from two tide gauge stations. Further studies show that the correlation coefficient (RTE) between the duration of the storm surge greater than 1 m and the annual ENSO index can reach as high as 0.62, significant at the 99% confidence level. This occurred in a location where the storm surge trend was not significant. For the areas with significant increasing storm surge trends, RTE was small and not significant. This study identified the storm surge trends for the full complex coastline of the study area. These results are useful both for coastal management by the government and for coastal engineering design.

Introduction

Among the variety of marine disasters in China, storm surges cause the most significant economic losses and casualties (Feng et al., 2015; Dong et al., 2016). Many studies have been conducted on prediction skill and disaster assessment (Yin et al., 2009; Zhang et al., 2010; Feng et al., 2012; Gan et al., 2012; Feng et al., 2016) to help reduce the losses caused by storm surges in China. The study of trends in storm surges is also very important for long-term forecasting and coastal management. Paprotny (2014) studied the trends in the probability of storm surge occurrence along the coast of the Polish Baltic Sea by analyzing long-term water level records at three tide gauge stations. Androulidakis et al. (2015) investigated the trends of the extreme storm surges in the Mediterranean Sea, and found a general decreasing trend. Sang et al. (2016) found an increasing trend in extreme storm surges in Busan harbor based on observations. For the coastal areas of Brazil, Chou et al. (2016) showed that the storm surge frequency and intensity are increasing. Studies on the storm surge trends along the coastal areas of China have also been conducted based on observations and simulations (Feng and Tsimplis, 2004; Feng et al., 2015; Oey and Chou, 2016).

Studies of storm surge trends based on observations are limited for the coastal areas of China because most of the water level observations are still kept confidential (Feng et al., 2015). Furthermore, the distribution of the tide gauge stations is sparse, and a comprehensive trend study of the full coastal area of China is not possible. Previous storm surge trend studies based on observations have used data mainly from the University of Hawaii Sea Level Center (Feng and Tsimplis, 2004; Feng et al., 2015). Besides the sparse distribution of the stations, another limitation of this dataset is that the data records for most of the stations are only available before 1997. Therefore, it is not possible to analyze the trends over the past 20 years.

Numerical simulations can be used to make up for the lack of observations. Recent studies show that the interaction between tides and a storm surge can cause a difference of up to 20 cm in a storm surge (Zhang et al., 2010). Feng and Tsimplis (2004) showed that the tide-surge interaction is significant at many tide gauge stations. Therefore, the storm surge-tide interaction should be included in the storm surge simulations. Another physical mechanism that can influence the storm surge is the wave-current interaction (Xie et al., 2008; Moon et al., 2009; Kim et al., 2010; Huang et al., 2010; Feng et al., 2016). Xie et al. (2008) found that the contribution of waves to the storm surge reached up to 0.76 m during hurricane Hugo in Charleston Harbor. Kim et al. (2010) also found that the contribution of the wave-induced radiation stress to the peak sea level rise reached as high as 40% during typhoon Anita (1970) on the coast of Tosa Bay, Japan. Feng et al. (2016) showed that the storm surge-wave-tide coupled model can simulate the wave height and storm surge better than the model components alone. Therefore, this type of coupled model is applied in this study.

Many storm surge disasters in China are caused by strong typhoons. Emanuel (2013) predicted an increasing trend in typhoon intensity and frequency in the western North Pacific. According to the statistics of Feng and Tsimplis (2004), the annual number of typhoon-influenced events in China between 20°N and 30°N is greater than 10, making this region the most typhoon-affected area. We therefore focused on the coastal areas of Fujian, Zhejiang, and parts of Guangdong provinces. Considering the complex coastline of this area, the numerical model was based on an unstructured grid, which includes the effect of the complex geometry better than models based on a structured grid.

The objective of this study was to study storm surge trends for the typhoon-prone coastal areas of China. A state-of-the-art unstructured-grid storm surge-wave-tide coupled model was applied to simulate typhoon-induced storm surges for the past 20 years (1997–2016). The model has high resolution in the coastal area, and the simulated results can be used to analyze the storm surge trends for the full coastline of the study area. The coupled model, the wind field forcing, and the model validation are described in Section 2. Section 3 presents the analysis of the trends of the storm surge based on the simulation results. Discussion on the results is provided in Section 4. The conclusions are presented in Section 5.

Section snippets

Methods

The storm surge-wave-tide coupled model was used to simulate the typhoon induced storm surges in the study area from 1997 to 2016. The model results were validated by tide-gauge observations and remote sensing data. The dataset of storm surges over the 20 years was then used for trend analysis.

Results

The simulated storm surges induced by the typhoons during 1997–2016 were used to analyze the storm surge trends. Regression analysis was adopted to calculate the storm surge trend:y = α + βx + ε,where y represents the storm surge extreme or duration of the storm surge at corresponding year x, and ε is the residual error. Thus, slope β is the trend of the storm surge extreme or duration of the storm surge. After the residual error analysis and F test, the confidence level of the regression model

Discussion

This study analyzed the storm surge trends from 1997 to 2016. There is some overlap with the studies of Feng and Tsimplis (2004), Feng et al. (2015), and Oey and Chou (2016) in the time period and study area. Oey and Chou (2016) found an increasing trend in the extreme storm surges (ηSmax) from 1982 to 2013 in the coastal area of the western North Pacific, in which ηSmax is defined as the seasonally mean maxima of the storm surge for the whole study area. Conversely, the increasing trend of the

Conclusions

We studied the typhoon-induced storm surge trends in the typhoon-prone coastal areas of China for the past 20 years from 1997 to 2016, based on an unstructured-grid storm surge-wave-tide coupled model. The simulated storm surges agreed well with the observed results from tide gauge stations and satellite altimeter data. One implication of this study is that the altimeter can be used to validate the storm surge model, although only limited data can be used.

By analyzing the simulated storm surges

Acknowledgements

This work was supported by the National Key Research and Development Program of China (Grant Nos. 2016YFC1402000, 2016YFC1401500), National Natural Science Foundation of China (Grant Nos. 41406018, 41476019), Foundation for Innovative Research Groups of the National Natural Science Foundation of China (Grant No. 41421005), and NSFC-Shandong Joint Fund for Marine Science Research Centers (Grant No. U1406401). This work was also supported by the High Performance Computing Center, IOCAS. The

References (38)

  • B. Yin et al.

    Simulating a typhoon storm surge in the East Sea of China using a coupled model

    Prog. Nat. Sci.

    (2009)
  • N. Booij et al.

    A third-generation wave model for coastal regions, part I, model description and validation

    J. Geophys. Res.

    (1999)
  • S.C. Chou et al.

    Projections of storm surge trend on the coast of Southeast Brazil

    Clivar Open Science Conference

    (2016)
  • A. Cid et al.

    Long-term changes in the frequency, intensity and duration of extreme storm surge events in southern Europe

    Clim. Dyn.

    (2016)
  • J.C. Dietrich et al.

    Performance of the unstructured-mesh, SWAN + ADCIRC model in computing hurricane waves and surge

    J. Sci. Comput.

    (2012)
  • M.A. Donelan et al.

    On the dependence of sea surface roughness on wave development

    J. Phys. Oceanogr.

    (1993)
  • M.A. Donelan et al.

    On the limiting aerodynamic roughness of the ocean in very strong winds

    Geophys. Res. Lett.

    (2004)
  • J. Dong et al.

    Analysis on the spatial and temporal distribution characteristics of the storm surge of Fujian Province

    Mar. Sci. Bull.

    (2016)
  • K.A. Emanuel

    Downscaling CMIP5 climate models shows increased tropical cyclone activity over the 21 st century

    Proc. Natl. Acad. Sci. U. S. A.

    (2013)
  • Cited by (33)

    • A survey of disaster management and SAR operations using sensors and supporting techniques

      2022, International Journal of Disaster Risk Reduction
      Citation Excerpt :

      Fig. 5 shows an example of remote sensing architecture where F and G are used to store and analyze the data and information received from D and E after changing them to an understandable form using graphs or raster, the reflected energy from A to B and C represented the information and features in D and E [7]. Sensors and monitoring using cameras are widely used by researchers to manage different types of disasters such as floods [27–29], earthquakes [30–35], storms [36], landslides [37,38], typhoons [39] and cyclones [40]. In pre-disaster, Earthquake estimation can occur using vibration sensors in wireless sensor network techniques, satellite image data sets, or monitoring [7,13].

    • Investigating typhoon-induced storm surge and waves in the coast of Taiwan using an integrally-coupled tide-surge-wave model

      2020, Ocean Engineering
      Citation Excerpt :

      The results indicated that the complex variability in total water levels driven by tides, ocean storm surge, surge from wind, and overwash had a crucial influence on the circulation. Feng et al. (2018) and Sahoo et al. (2019) established a storm surge-wave-tide coupled model for the coastal areas of Zhejiang, China and for the Bahamas archipelago in the Atlantic Ocean basin, respectively. To fully understand the tide, surge, and wave characteristics, the objective of this study is to establish a tide-surge-wave coupled model for predicting water level and surge height around the Taiwan coast.

    • Modeling wave effects on storm surge from different typhoon intensities and sizes in the South China Sea

      2020, Estuarine, Coastal and Shelf Science
      Citation Excerpt :

      Superstorm Sandy (2012) caused severe flooding along the northeastern coast of the United States, resulting in at least $50 billion in economic losses and 159 deaths (Blake et al., 2013). Moreover, owing to global warming and rising sea levels, warming oceans could provide more energy for the development of stronger typhoons than at present, resulting in an increase in typhoon intensity and landfall frequency in the western North Pacific (Emanuel, 2013; Guan et al., 2018) and leaving coastal regions increasingly vulnerable to extreme flooding (Oey and Chou 2016; Feng et al., 2018). Therefore, efficient and accurate marine forecasting through the accurate modeling of the storm surge induced by typhoons is important for mitigating typhoon-induced disasters in coastal regions.

    View all citing articles on Scopus
    View full text