The impact of different historical typhoon tracks on storm surge: A case study of Zhejiang, China

Journal Pre-proof The impact of different historical typhoon tracks on storm surge: A case study of Zhejiang, China

Mei Du, Yijun Hou, Peng Qi, Kai Wang PII:

S0924-7963(20)30014-2

DOI:

https://doi.org/10.1016/j.jmarsys.2020.103318

Reference:

MARSYS 103318

To appear in:

Journal of Marine Systems

Received date:

14 April 2019

Revised date:

8 February 2020

Accepted date:

12 February 2020

Please cite this article as: M. Du, Y. Hou, P. Qi, et al., The impact of different historical typhoon tracks on storm surge: A case study of Zhejiang, China, Journal of Marine Systems(2020), https://doi.org/10.1016/j.jmarsys.2020.103318

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© 2020 Published by Elsevier.

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The impact of different historical typhoon tracks on storm surge: A case study of Zhejiang, China Mei DU a,b,d, Yijun HOU a,b,c,d*, Peng QI a,c,d, Kai WANG a,b,d a

Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Nanhai

Road, 7, 266071 Qingdao, China b

University of Chinese Academy of Sciences, Beijing 100049, China

Laboratory for Ocean and Climate Dynamics, Qingdao National Laboratory for Marine Science and Technology,

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c

Wenhai Road, 1, 266237 Qingdao, China

Center for Ocean Mega-Science, Chinese Academy of Sciences, 7 Nanhai Road, Qingdao, 266071, P. R. China

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d

*

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Corresponding author. E-mail address: [email protected].

Abstract: A typhoon-induced storm surge simulation system was developed for the Zhejiang coast consisting of an

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assimilation wind-pressure model and the ADCIRC+SWAN model. The simulated peak and phase of the storm

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surge were found to match the observed data very well. We discussed the sensitivity of the modeled storm surge to changes in the computational domain, and compared the simulation results from the assimilation wind-pressure

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model and the symmetric Holland model. The sensitivity of 23 newly-constructed typhoon tracks to the Wenzhou City storm surge was investigated using the validated storm surge simulation system. The storm surges vary significantly when the typhoon tracks are at different positions relative to the station. Given this, the impacts of 55 historical tracks from 1951 to 2017 on the coast of Zhejiang Province were simulated and analyzed. Although the typhoon tracks with no landfall on China’s eastern mainland posed a threat to the entire Zhejiang coast, the tracks making landfall at Fujian were most likely to cause a storm surge in Wenzhou City, Zhejiang Province. The results of this study will help to increase the understanding of typhoon storm surge along China’s coastal areas and may serve as reference material in studies of disaster reduction and prevention techniques in the Zhejiang region.

Keywords: typhoon track; assimilation wind-pressure model; ADCIRC+SWAN model; storm surge; Zhejiang coastal area

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1 Introduction Typhoons originating in the northwestern Pacific Ocean sweep across China’s coast annually, where the population is extremely dense, the economy is highly developed, and social wealth is notably concentrated (Chan and Shi, 1996). With the continuous development of the coastal economy, losses resulting from storm surges caused by typhoons are on the rise, making storm surge currently one of the most serious marine disasters for China. Typhoon Fitow (2013) made landfall in the northern part of Fujian Province, causing a storm surge of 3.75 m at Aojiang station in Zhejiang Province. The high tide level exceeded the local warning level by 1.48 m, which is the

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highest recorded tide in history. This severe storm surge and wave disaster affected 6 million Zhejiang Province inhabitants, with direct economic losses amounting to 2.338 billion RMB (approximately 360 million US dollars),

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of which the main loss occurred in Wenzhou City (SOA, 2013). Severe storm surge can occur on China’s coast

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even for typhoon tracks that remain far offshore. For example, during Typhoon Malakas (2016), which did not make landfall on the Chinese mainland, the affected population in Zhejiang was nonetheless 426,000, the affected

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aquaculture area was 33.2 km2, and the direct economic loss was 157 million RMB (approximately 24 million US

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dollars) (SOA, 2016), demonstrating that the impacts of typhoon path are critical in the study of storm surge. Consequently, the public has begun to pay more attention to the dangers of storm surge. At the same time, relevant

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governmental departments are becoming more sensitive to the importance of understanding storm surge mechanisms, thus making improvements to the capacity of storm surge models necessary for their application in

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coastal engineering and disaster prevention.

Some progress has been made in the study of typhoon-induced storm surge using numerical simulation. Lin et al. (2010) utilized the ADCIRC model to simulate the storm surge resulting from tropical cyclones and then employed a frequency-analysis method to calculate the increase in water level resulting from the storm surge, together with the tidal level in different return periods, subsequently comparing the results to those obtained with the SLOSH model. Feng et al. (2012) performed a sensitivity study on the influence of typhoon wind field evolution and air pressure distribution on the sea-level extremes for Tianjin, thereby determining the most dangerous cyclone trajectories for that city. In addition, advances in coupled models and results visualization have enabled the more accurate simulation and analysis of nearshore hydrodynamic processes. For example, Xie et al. (2008) coupled the Princeton Ocean Model (POM) and the Simulating WAves Nearshore (SWAN) model to study the effect of storm surge inundation during Hurricane Hugo (1989). Feng et al. (2011) coupled the Regional Ocean 2

Journal Pre-proof Modeling System (ROMS) and the SWAN model to study the effects of wave radiation stress on the storm surge during Typhoon Saomai (2006). Dietrich et al. (2011) used the moving wind and pressure field of a tropical cyclone to drive the ADCIRC+SWAN model to simulate the storm surge and wave processes caused by Hurricanes Katrina (2005) and Rita (2005). Some researchers have utilized the ADCIRC+SWAN model to simulate storm surges occurring in the northwestern Pacific Ocean but did not focus on typhoon storm surges affecting Zhejiang, China (Suh et al., 2015; Wang et al., 2018). In numerical simulation, the grid setting of the computational domain affects the accuracy of the model simulation results. The unstructured mesh needs to be extended to a suitable domain in order to enable the transfer

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of energy from deep to shallow waters (Choi et al., 2013). Kerr et al. (2013a, b) used the ADCIRC+SWAN model

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in surge analysis based on different grid structures. For an equivalent domain size, a better-quality and

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higher-resolution mesh enables a more accurate typhoon simulation (Kerr et al., 2013a). The high accuracy of the coupled model has been demonstrated in simulations of coastal storm surge resulting from cyclones (Choi et al.,

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2013; Cruz-Castro, 2015; Suh et al., 2015), including historical hurricanes, such as Hurricanes Katrina (2005) and

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Ike (2008) (Dietrich et al., 2012). Besides the mesh resolution, the wind and pressure fields of the typhoon process also play significant roles in model simulation accuracy. Sun et al. (2013) made the simulated wind field closer to

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the actual typhoon process by adding the Cross-Calibrated Multi-Platform (CCMP) sea surface wind provided by the Asia Pacific Data Research Center (APDRC) to the model wind field.

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The impact of historical typhoon trajectories on storm surge is investigated here using a storm surge simulation system developed for China’s coastal area based on the Holland model (Holland, 1980) and the ADCIRC+SWAN model (Dietrich et al., 2011). This system consists of an assimilation wind-pressure model, used to reconstruct the meteorological field of the typhoon, and a coupled model, used to simulate the astronomical tides and storm surges. Section 2 describes the development and verification of the storm surge simulation systems. Section 3 delineates some sensitivity experiments and demonstrates the effects of typhoon track on storm surges experienced in Zhejiang, China. Finally, the concluding remarks are provided in Section 4.

2 Materials and methods 2.1 Data 2.1.1 Meteorological data

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Journal Pre-proof The typhoon tracks utilized in this study were sourced from the China Meteorological Administration (CMA) Tropical Cyclone Best Track Dataset from 1951 to 2017 (Ying, 2014). The analysis products were derived from the hourly time-series dataset of the Climate Forecast System Reanalysis (CFSR) Selected Hourly Time-Series Products and the Climate Forecast System Version 2 (CFSV2) Selected Hourly Time-Series Products (Saha et al., 2010; Saha et al., 2011). Notably, the CFSR only contains data before 2011, while data after 2011 are in the CFSV2. Thus, together these 2 data products cover the time range of data needed. 2.1.2 Topographic and hydrological data

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The bathymetry data were derived from the General Bathymetric Chart of the Oceans (GEBCO) data with a

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resolution of 30 s and the naval electronic nautical charts with a resolution of 50 m (ZHOUSHAN Chart

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Information Technology Co., Ltd.). The coastline data used in the construction of the computational mesh were

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provided by the Global Self-consistent, Hierarchical, High-resolution Geography (GSHHG) shoreline database. The actual coastline conditions and the nearshore terrain of the simulated area play important roles in the simulation of

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storm surge (Ding and Wei, 2017). To ensure data accuracy, the coastline selected above was manually modified

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and the bathymetry data were replaced with chart data in the nearshore area, based on the electronic charts of the

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naval version from ZHOUSHAN Chart Information Technology Co., Ltd. (http://www.zshaitu.com/index.aspx). Additionally, several tide stations (Ganpu, Haimen, Kanmen, Wenzhou, and Dongtou) and buoys (QF209 and QF210) provided the tidal and wave observation data used to validate the simulated results of the model; their locations are shown in Fig. 1. The typhoon parameters at the time each storm was closest to the mainland are listed in Table 1.

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Fig. 1. Distribution map of 2 typhoon tracks, along with buoy locations and tide stations. The black squares, red dots, and red

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diamonds in the figure represent the positions of provinces, cities, and buoys, respectively. The blue curves show the 2 typhoon

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tracks and the locations of the moving center (every 6 hours), represented by blue “o” symbols.

Table 1. Characteristics of Typhoons Trami and Fitow Maximum wind speed (m/s)

Minimum sea-level pressure (hPa)

Trami Fitow

35 42

956 965

Radius of maximum wind (km)

Translation speed (m/s)

Time (UTC)

Landfall location

39.52 41.91

6.08 7.16

20130821 13:00 20131006 10:00

Fuzhou, Fujian Ningde, Fujian

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Typhoon

2.2 Combined wind-pressure model

In this study, the assimilation wind-pressure model was driven by a combined wind-pressure field composed of the analysis products and the typhoon wind model. The typhoon wind model was the Holland circular symmetric typhoon wind model (Holland, 1980), expressed in Eqs. (1) and (2):









Vg  AB  pn  pn  exp  A r B 

Pg  Pc  Pn  Pc  exp  A r B ,

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 r B  r 2f

2

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4 

 r f 2,

(1)

(2)

Journal Pre-proof where

Vg

is the gradient wind at radius r ,

pressure, and where

Rmax

Pc

Pg

is the gradient pressure at radius r ,

Pn

is the ambient

is the central pressure. Here, A and B are scaling parameters expressed by

is the radius of the maximum wind speed.

Rmax

Rmax =A1/ B ,

and B are from Willoughby and Rahn (2004).

The assimilation wind-pressure model was then applied to the wind-pressure field from the Holland model and the analysis data. A weight coefficient was applied for the new wind-pressure field data. The assimilation equation

Vt

is the moving speed of the typhoon center in the Holland model,

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where

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c  r ( nRmax )

(5)

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  c 4 (1  c 4 )

(3)

(4)

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P  1    Pg   Pb

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V  1    Vg  Vt   Vb

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is described in Eqs. (3)–(6):

(6)

Pb

is the pressure from the analysis

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dataset, Vb is the wind speed from the analysis dataset, and  is the weight coefficient. When n  4 , the combined wind-pressure field can yield a relatively realistic description of the dynamic characteristics of the typhoon (Sun et al., 2013). Notably, both the wind and the pressure are assimilated here, which is different from previous studies, which only assimilated the wind field (Chu and Cheng, 2008; Sun et al., 2013). We used the above formulas to calculate the wind-pressure field as the input file of the ADCIRC+SWAN model. In order to clearly show the changes in the wind field and the pressure field after processing, the details are illustrated in Figs. 2 and 3. According to the measured data, Typhoon Trami (2013) had a maximum wind speed of 35 m/s and appeared at (25.9°N, 120.9°E) at 13:00 UTC 21 August 2013, while Typhoon Fitow (2013) had a maximum wind speed of 45 m/s and appeared at (26.6°N, 122.2°E) at 10:00 UTC 6 October 2013. In Fig. 2a and d, the maximum wind speed from the analysis products is much lower than the actual data (from the CMA), and the typhoon center is also inaccurate. In Fig. 2b and e, the maximum wind speed and the typhoon center location from the Holland model are accurate, but its ambient wind speeds are much lower than those from the analysis products. 6

Journal Pre-proof Analogously, the above descriptions can also be applied to the pressure fields in Fig. 3. The combined wind fields (Fig. 2c and f) and the combined pressure fields (Fig. 3c and f) can well describe the typhoons’ processes. In this

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study, the wind forcing and the atmospheric forcing were provided by the assimilation wind-pressure model.

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Fig. 2. Planar views of wind (units: m; vector). (a–c): Wind vectors of Typhoon Trami at 13:00 UTC 21 August 2013; (d–f): Wind vectors of Typhoon Fitow at 10:00 UTC 6 October 2013. (a and d) are from analysis products, (b and e) are from the

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Holland model, and (c and f) are from the combined wind field.

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Fig. 3. Planar views of the pressure (units: hPa; shaded). (a–c): Pressure of Typhoon Trami at 13:00 UTC 21 August 2013; (d–f): Pressure of Typhoon Fitow at 10:00 UTC 6 October 2013. (a and d) are from analysis products, (b and e) are from the Holland

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model, and (c and f) are from the combined pressure field.

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2.3 Hydrodynamic model of storm surge

In this study, we sought to simulate the storm surges caused by typhoons in coastal areas by running the

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ADCIRC+SWAN model. The ADCIRC+SWAN model is an advanced coupled model for waves and tides. The ADCIRC model is a finite-element model developed by Luettich et al. (1992) and Westerink et al. (1994), while the SWAN model is a third-generation wave model (Booij et al., 1999; Ris et al., 1999). The ADCIRC model has the significant advantage of flexible coverage, especially of the complex coastlines and tidal hydrodynamics in coastal areas (Suh et al., 2014), while the SWAN model is suitable for coastal areas, estuaries, and lakes. Most noteworthy, both models can run on the same unstructured triangular mesh, which reduces the error caused by interpolation and improves computational efficiency (Dietrich et al., 2011).

2.4 Model configuration In the Northern Hemisphere summer, China’s southeastern coastal areas are seriously threatened by typhoon storm surge, particularly in locations south of the Yangtze River. Therefore, we focused on the coastal areas of Zhejiang Province, since it is one of the region’s major disaster zones. The tracks of Typhoons Trami (2013) and 8

Journal Pre-proof Fitow (2013), shown in Fig. 1, were selected for model validation. The simulation of Typhoon Trami commenced at 00:00 UTC 16 August 2013 and ended at 00:00 UTC 23 August 2013; the simulation of Typhoon Fitow commenced at 00:00 UTC 30 September 2013 and ended at 00:00 UTC 7 October 2013. According to the combined wind-pressure field, the maximum wind speed and minimum central pressure of Trami (2013) were 35 m/s and 960 hPa, respectively. Similarly, for Fitow (2013), the maximum wind speed was 45 m/s, and the minimum central pressure was 950 hPa. In the ADCIRC+SWAN model, the simulations of Trami and Fitow both lasted 168 hours and both initiated with a cold start. In a suitable domain, models can accurately simulate the dynamic processes of waves, tides, and ocean

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currents (Wang et al., 2020a; Wang et al., 2020b). Consequently, in this study the computational domain size

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extended from 19°N to 31°N and 115°E to 129°E, as shown in Fig. 4. In order to capture the important

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characteristics of water depth and coastline shape, the resolution of the unstructured triangular mesh for the entire domain should have a gradient based on actual topographic changes and computational requirements. In this case,

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the mesh had a horizontal resolution ranging from 100 m to 3 km along the coast and 3–22 km at the open

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boundary (Fig. 4). The highest resolution area was located along the coast of Wenzhou, Zhejiang Province (Fig. 5). In the ADCIRC+SWAN model, the only open boundary is driven by the harmonic constants of the 8 main

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astronomical constituents (K1, K2, M2, N2, O1, P1, Q1, and S2) obtained from the Oregon State University Tidal Prediction Software (OTPS). Here, data from nautical surveys and the GEBCO product were integrated and

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interpolated into the computational mesh.

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Fig. 4. Mesh structure (a) and bathymetry (b, units: m; shaded) within the overall computational domain for the ADCIRC+SWAN model. There are 113,266 nodes and 221,418 triangular elements in total. The area with the highest resolution

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is demarcated by a red box. Most of the coastline within the box belongs to Wenzhou, Zhejiang Province.

Fig. 5. Enlarged view of the mesh (a) and bathymetry (b, units: m, shaded) along Wenzhou, Zhejiang Province; this domain is demarcated by the red box in Fig. 4. The mesh has a horizontal resolution ranging from 100 to 200 m along the coast in this area. 10

Journal Pre-proof 2.5 Model validation 2.5.1 Astronomical tide validation Accurately simulating the astronomical tide is the basis for simulating storm surges (Wang, 2018). Therefore, for the processes of Typhoons Fitow and Trami, the tidal levels in the computational domain were simulated in order to evaluate model performance. Fig. 6 shows the comparison of simulated and observed values of the astronomical tides for 3 stations along the Zhejiang coast. As this figure clearly shows, the model could accurately simulate the tidal rise and fall at all stations during the typhoons. The root-mean-square error (RMSE) of the

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astronomical tide levels during the 2 typhoon processes is listed in Table 2. The RMSE values are acceptable, and

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the phase difference between the simulated and observed values is less than 1 hour, indicating excellent agreement in terms of the temporal and hydrographic features (See Table 2 and Fig. 6 for details). The accuracy of the

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astronomical simulation results ensures the accuracy of the model in the computational domain, thereby laying the

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foundation for storm surge simulation.

Trami

Fitow

0.54 0.31 0.17

0.40 0.37 0.39

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Kanmen Wenzhou Dongtou

RMSE of typhoon processes (m)

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Station

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Table 2. RMSE of astronomical tide levels during Typhoons Trami and Fitow

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Fig. 6. Simulated (black “-”) and observed (red “.”) values of astronomical tides for Typhoon Trami (2013) and Typhoon Fitow

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(2013). (a–c): comparison of the Typhoon Trami; (d–f): comparison of the Typhoon Fitow.

2.5.2 Storm surge and significant wave height validation Two surge events were simulated in order to evaluate the performance of storm surge simulation using the ADCIRC+SWAN model. In the case of Typhoons Trami and Fitow, the wind forcing and atmospheric forcing came from the combined wind-pressure model, as described in Section 2.2. The simulated storm surge results were obtained by subtracting the simulated astronomical tide from the simulated water-level. Fig. 7 compares the simulated and observed storm surges at 2 stations; Table 3 lists the absolute and relative errors of each simulated maximum surge. From Fig. 7, it can be seen that variations in water level were consistent for both the Kanmen and Wenzhou stations during the 2 typhoons. Fig. 7a and b compare the simulated surge of Trami to the observed surge. The relative error of the simulated maximum storm surge was 7.1% at Kanmen station and approximately 3 h later than actually observed. The relative error was 4.5% at Wenzhou station, and the phase 12

Journal Pre-proof error of the maximum surge was 0. As for the processes of Typhoon Fitow, the simulated time of maximum surge arrival was 1–2 h earlier than the measured time (Fig. 7c and d). The relative error of the simulated maximum storm surge was 1.6% at Kanmen station and 6.9% at Wenzhou station. Table 3. Absolute and relative errors of simulated maximum storm surges at 4 stations during 2 typhoon events Absolute error (m)

Relative error

Typhoon

Kanmen

0.05

7.1%

Trami

Wenzhou

0.03

4.5%

Trami

Kanmen

0.03

1.6%

Fitow

Wenzhou

0.09

6.9%

Fitow

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Station

Fig. 7. Simulated (black “-”) and observed (red “-o-”) values of storm surges for Typhoon Trami (2013) and Typhoon Fitow (2013). (a–c): comparison of the Typhoon Trami; (d–f): comparison of the Typhoon Fitow.

Next, we compared the significant wave height at each buoy to the computed values from the ADCIRC+SWAN model (Fig. 8). The absolute and relative errors for each simulated maximum significant wave height are listed in Table 4. From Fig. 8a and b, the simulated significant wave height was 8.45 m at buoy QF209 and 6.26 m at buoy QF210 during Typhoon Trami. The relative error of the simulated maximum wave was 1.6% at 13

Journal Pre-proof buoy QF209 and 4.3% at buoy QF210. These results indicate that the maximum significant wave simulations were in good agreement with the observations. Similar behavior was found for Typhoon Fitow. The simulated significant wave height was 10.82 m at buoy QF209 and 11.72 m at buoy QF210 (Fig. 8c and d), and the relative errors were 2.7% at buoy QF209 and 1.1% at buoy QF210. It should to be noted that the initial wave height values in Figs. 7 and 8 were 0 due to the cold start of the ADCIRC+SWAN model. In summary, the simulated results from the ADCIRC+SWAN coupled model were found to be in good agreement with actual observations, not only for astronomical tide and storm surge trend but also for peak wave height. There were deviations in the simulation values from the observations, but the results were

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methods for this model were deemed to be reasonable and reliable.

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considered to be acceptable. Based on the above verification experiments, the parameter settings and calculation

Absolute error (m)

QF209

0.13

QF210

0.26

QF209

0.28

QF210

0.13

Relative error

Typhoon process

1.6%

Trami

4.3%

Trami

2.7%

Fitow

1.1%

Fitow

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Buoy number

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Table 4. Absolute and relative errors for the simulated maximum significant wave heights at buoys during Typhoons Trami and Fitow

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Fig. 8. Simulated (black “-”) and observed (red “.”) significant wave heights for Typhoon Trami (2013) and Typhoon Fitow

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3 Results and discussion

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(2013). (a–b): comparison of the Typhoon Trami; (c–d): comparison of the Typhoon Fitow.

3.1 Comparison of computational domain and wind-pressure model 3.1.1 Effect of computational domain

To find a more suitable computational mesh for the simulations in this paper, we compare the simulated storm surges of the domains in Fig. 4 and Fig. 9. Notably, the resolution of Zhejiang’s coastal area in Fig. 9 is the same as that in Fig. 4. Taking the Typhoon Trami as an example, the comparison of simulated storm surges is shown in Fig. 10. The results indicate that the simulated surges are not sensitive to the extent of the domain. Given the significant difference in number of nodes and triangular mesh elements we carried out the simulations over the smaller domain (shown in Fig. 4).

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Fig. 9. A larger domain including the Bohai Sea, Yellow Sea and East China Sea. There are 159,164 nodes and 278,408

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triangular elements in total.

Fig. 10. Comparison of the simulated storm surges of Typhoon Trami in two different domains.

3.1.2 Storm surges from an asymmetric Holland model

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Journal Pre-proof We consider the asymmetric Holland model (Xie et al., 2006) before choosing the assimilation wind-pressure model to finish all experiments. Taking Typhoon Trami for an example, Fig. 11 shows the comparison between the simulated storm surges of asymmetric Holland model and observations. However, the simulated storm surge of the asymmetric Holland model is much lower than the actual storm surge during the whole typhoon process. Therefore, we select the symmetric Holland (Holland, 1980) and analysis products to construct the assimilation wind-pressure model which is given in the Section 2.2. The model validations in Section 2.5 indicate that the assimilation

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wind-pressure model is more suitable for simulating storm surge.

Fig. 11. Simulated (black „-‟) and observed (red „-o-‟) values of surges for the typhoon Trami. The coordinate axis 0 data of Figs.

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11a and b corresponds to 12:00 UTC on 16 August 2013.

3.2 Effect of different historical typhoon tracks Since the purpose of this study was to investigate the impacts of various typhoon tracks on storm surge, it was necessary to perform a set of experiments concerning the impact of different distances between the typhoon center and the station. When a typhoon makes landfall, the onshore wind is on the right side of the typhoon path, and the offshore wind is on the left side. Moreover, the different underlying surfaces of typhoons also induced different storm surges. Therefore, the discussion here focuses on the effects of the onshore winds, offshore winds and underlying surfaces of typhoons.

3.2.1 Effect of onshore wind on storm surge Based on the track of Typhoon Sinlaku (2002), we constructed 10 new typhoon tracks, as shown in Fig. 12. 17

Journal Pre-proof The new typhoon tracks were created by shifting the track of Sinlaku 10 times without modification (Table 5). Except for the position of the typhoon center, the characteristics of track Nos. 1–10 were identical to those of Typhoon Sinlaku when their associated combined wind-pressure fields were constructed. Fig. 13 shows the 11 simulated surges at Wenzhou station. Based on the maximum surge times in Fig. 13, we determined that the maximum surge always occurred at the time of landfall. The maximum surge associated with track No. 3 was higher than the surges from the other tracks shown in Fig. 13. This was because when the maximum surge occurred, the distance between the typhoon center of track No. 3 and Wenzhou Station was almost equal to the maximum wind speed radius of the typhoon. In the following discussion, we refer to the distance between the typhoon center

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and Wenzhou when the surge was obviously abnormal as the ―effective distance‖. It can be clearly seen that the

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surges caused by track Nos. 3–10 decreased as the effective distance from the Wenzhou station increased. The effect of the onshore wind on the water level increase gradually decreased when the effective distance between

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Wenzhou station and the typhoon center was less than the maximum wind speed radius, such as track No. 1 and No.

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2. These results demonstrate that typhoon wind speed is the main factor affecting storm surge in the onshore wind area on the right side of the typhoon track, and the most severe storm surge occurs at the maximum wind speed

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radius of the typhoon.

1

Direction of track shift Distance of shift (km)

NE 75

2

3

4

5

6

7

8

9

10

NE 60

NE 45

NE 30

NE 15

SW 15

SW 30

SW 45

SW 60

SW 75

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Track number

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Table 5. Direction and distance for each shifted track of Typhoon Sinlaku

NE: northeast of actual Sinlaku track; SW: southwest of actual Sinlaku track.

Fig. 12. The 10 tracks (blue “-”) constructed from the actual track of Typhoon Sinlaku (red “-”). The constructed tracks are 18

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designated Nos. 1–10 from north to south. The maximum wind speed radius of the typhoon was 46 km when it made landfall.

Fig. 13. Simulated storm surge values for the typhoon tracks in Fig. 12. Time = 0 hours corresponds to the initial time of the

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typhoon simulations: 00:00 UTC 4 October 2002.

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3.2.2 Effect of offshore wind on storm surge

Fig. 14 shows 10 new tracks obtained by shifting the actual track of Typhoon Haikui (2012), as seen in the

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design scheme listed in Table 6. The characteristics of Typhoon Haikui were used to construct the wind and pressure fields of the new tracks. The simulated surges at Wenzhou for these 11 processes are shown in Fig. 15. As

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the effective distance between Wenzhou and the typhoon center decreased, the effect of the offshore wind on the water-level reduction for track Nos. 11–16 gradually increased. This is because the maximum wind speed radius of the typhoon gradually approached the station, which increased the wind speed at Wenzhou. However, track Nos. 17–20 exhibited results that were the opposite of track Nos. 11–16. The reason for this phenomenon was that Wenzhou Station had entered the interior of the typhoon’s maximum wind speed circle, which caused the wind speed in Wenzhou to gradually decrease. In addition, it was found that the greatest water-level reduction occurred at the maximum wind speed radius of the typhoon. Table 6. Direction and distance for each shifted track of Typhoon Haikui Track number

11

12

13

14

15

16

17

18

19

20

Direction of track shift Distance of shift (km)

SW 13

SW 26

SW 39

SW 52

SW 65

SW 78

SW 91

SW 104

SW 117

SW 130

SW: southwest of actual Haikui track. 19

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Fig. 14. The 10 tracks (blue “-”) constructed from the actual track of Typhoon Haikui (red “-”). The constructed tracks are

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designated Nos. 11–20 from north to south. The maximum wind speed radius of the typhoon was 50 km when it made landfall.

Fig. 15. Simulated storm surge values for the typhoon tracks in Fig. 14. Time = 0 hours corresponds to the initial time of the typhoon simulations: 12:00 UTC 4 August 2012.

3.2.3 Effects of the surface underlying the track Firstly, 3 new tracks were constructed based on the track of Typhoon Morakot (2009), as shown in Fig. 16. Track No. 21 was generated by shifting the actual track of Morakot 0.5º longitude west; track No. 22 was generated by shifting the actual track of Morakot eastward twice the distance between Morakot and Wenzhou, and track No. 23 was generated by shifting track No. 22 0.5º longitude east. Secondly, the movement speed, central air pressure, 20

Journal Pre-proof and maximum wind speed radius of Typhoon Morakot were used to construct the combined wind-pressure field for the new tracks. Finally, we used the ADCIRC+SWAN model to simulate storm surges. Fig. 17 shows the simulated significant wave heights of the 4 tracks at 00:00 UTC 10 August 2009. The maximum wave heights in Fig. 17c and d are higher than those in Fig. 17a and b. This is because the ocean surface traversed by track Nos. 22 and 23 is greater than that traversed by track No. 21 and Typhoon Morakot, making the waves more favorable for propagation and accumulation in the calculation process. Fig. 18 shows the simulations of significant wave heights and storm surges at Wenzhou station. Among the 4 typhoon processes, track No. 22 induced the highest wave at Wenzhou station, as opposed to track No. 23 (Fig. 18a). This is because track No. 22 is closer to Wenzhou than

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track No. 23. In Fig. 18b, it can be seen that the surges of track No. 21 and the actual track of Morakot were smaller

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than those of track Nos. 22 and 23. These differences are due to differences in the underlying surfaces of the typhoon tracks. When the underlying surface environments of 2 typhoon tracks are similar, the distance between the

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typhoon center and the station is the decisive factor for wave and surge. For example, the simulated surge of track

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No. 22 is greater than that of track No. 23.

Fig. 16. The 3 tracks (blue “-”) constructed from the actual track of Typhoon Morakot (red “-”). The constructed tracks are designated Nos. 21–23 from west to east.

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Fig. 17. Significant wave heights (units: m; shaded) of track No. 21 (a), the track of Morakot (b), track No. 22 (c), and track No. 23 (d) at 00:00 UTC 10 August 2009.

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Fig. 18. Comparison of the significant wave height simulations (a) and surges (b) for the typhoon tracks in Fig. 16. Time = 0 hours corresponds to the initial time of the model: 00:00 UTC 5 August 2009.

3.3 Impact of 55 historical typhoon paths on storm surge in Zhejiang Typhoons in different coastal areas have their own characteristics. In addition, the actual typhoon events have more perfect randomness and uncertainty, especially in the moving speed and shape of the typhoon vortex. On the other hand, the parameters of the historical typhoon track are determined by the interaction of atmosphere and ocean, which contains complex dynamical processes. Although the statistical model can make up for the limitation of the data quantity of the historical tracks, it cannot cover the uncertainty of typhoon vortex’s shape or moving speed very well. Therefore, the randomly generated typhoon track and typhoon parameters from statistical model (Kim et al., 2016 and 2018) cannot completely reproduce the characteristics of real typhoon events. To study the storm surges along the coast of Zhejiang Province, we need to comprehensively analyze the typhoon events 23

Journal Pre-proof occurred here to capture the regional characteristics. Thus, the Zhejiang's storm surge based on historical path analysis is more practical. It is particularly important for the design of disaster prevention and mitigation facilities, such as seawalls, breakwaters and submerged breakwaters. In the Section 3.2, we reconstruct some new typhoons based on three different typhoon tracks to discuss more clearly the influence of onshore wind, offshore wind and the underlying surface of tracks on storm surge. The particularity of these three typhoon tracks paved the way for the analysis of 55 typhoons in this Section. It can be seen that different typhoon tracks result in different storm surges. As for the coast of Zhejiang, more historical typhoon processes were then simulated in order to further explore the impact of different historical typhoon tracks

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on storm surge using the established method. In order to ensure that the numerical simulation results were

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statistically significant, 55 eligible historical typhoon tracks were divided into 3 groups: (A) typhoons tracking

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along China’s eastern coast without making landfall; (B) typhoons making landfall on the Zhejiang coast; and (C) typhoons making landfall in Fujian. These 3 groups of typhoons are referred to below as Group A, Group B, and

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Group C and shown in Fig. 19, corresponding to 14 red tracks, 7 green tracks, and 34 blue tracks. The

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computational domain and mesh settings for the model simulation of these tracks were the same as those described in Section 2.4 (as shown in Figs. 4 and 5). The combined wind-pressure field of these 55 tracks was calculated

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using Eqs. (1)–(6). After simulation, the results at Ganpu, Haimen, Kanmen, and Wenzhou were extracted as samples in order to analyze and compare the impact of the typhoon tracks on the associated storm surges (tracks

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and station locations are shown in Figs. 1 and 19, respectively). For each simulation, the extracted results were compared to the warning water-levels at the 4 stations (Table 7). The number of times that each station’s water-level exceeded the warning water-level was then totaled and plotted in the stacked bar charts shown in Fig. 20.

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Table 7. Warning water-levels at 4 stations (Yu et al., 2015)

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Fig. 19. The 3 groups of 55 historical typhoon tracks along the China coast. See text for details.

Ganpu

Haimen

Warning water-level (m)

6.90

5.60

Kanmen

Wenzhou

7.40

5.80

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Station

Fig. 20. Frequency of water-level warnings at each station for 55 typhoons

The Group A typhoons triggered storm surge warnings at roughly the same frequency at all 4 stations. This indicates that typhoons along the Zhejiang shelf to the north lead to a consistent risk of triggering strong storm surges on the Zhejiang coast. Since the tracks of the typhoons in Group A all had the same underlying surfaces, the explanation in Section 3.2.3 covers this result. The Group B typhoons, however, triggered storm surge warnings at a 25

Journal Pre-proof higher frequency in the northern part of Zhejiang (Ganpu and Haimen) but a lower frequency in the southern part of the Zhejiang (Kanmen and Wenzhou). Most of the typhoons in Group B made landfall in central Zhejiang, which happens to be located between Kanmen and Haimen. Therefore, when these typhoons made landfall, the water level in the onshore wind region increased, while the water level in the offshore wind region decreased. This coincides with the results discussed in Section 3.2.2. As for Group C, the frequency of triggered warnings increased from Ganpu to Wenzhou (i.e., from north to south along the Zhejiang coast). For a typhoon making landfall at Fujian, the storm surge frequency at Zhejiang is directly related to the distance between the station and the typhoon center: the closer the station to the typhoon center, the more frequently the storm surge exceeds the warning water-level. This

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coincides with the results discussed in Section 3.2.1. In summary, during these typhoon processes, typhoon wind

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speed and the underlying surface of its track both exert important impacts on the increase and decrease of the water level along the coast of Zhejiang, China. While Kanmen station is not far from Wenzhou station, the warning

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frequency at Wenzhou is significantly higher than that at Kanmen. This is due to the fact that Wenzhou station is

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located in the lower reaches of the Ou River, meaning that the water level is also affected by river runoff. During typhoons, the onshore wind forces the seawater to the upper reaches of the river, increasing the water level of

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4 Conclusions

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Wenzhou station more sharply than at Kanmen station.

In this study, the impact of typhoon track on storm surge in Zhejiang’s coastal area was investigated using

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numerical simulation. A typhoon-induced storm surge simulation system was developed for the Zhejiang coast. This system includes an assimilation wind-pressure model and the ADCIRC+SWAN model. In the assimilation wind-pressure model, a new empirical formula—Eq. (4)—was defined for calculating the pressure in the combined wind-pressure field. For model validation, 2 astronomical tides and 2 storm surge disasters that had occurred in Fujian Province were simulated. The simulated storm surge trend was in good agreement with the observed data, indicating that the simulation system was capable of capturing storm surge characteristics. However, there were some peak value errors between the simulated output and the observed data, primarily due to the fact that the river runoff effect was not considered. The phase errors between the simulated and observed storm surges were very small, since, in addition to considering the wave effect in the simulation process, the accuracy of the wind and pressure fields was improved. The storm surge simulation system proposed in this study can also be used to perform similar simulations in other areas, especially the application of the assimilation wind-pressure model.

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Journal Pre-proof We discussed the sensitivity of the modeled storm surge to changes in the computational domain based on the ADCIRC + SWAN model. The results show that the simulated storm surge in Zhejiang coastal area are similar in both the large and the small domains. In order to reduce computing time, we carried out the simulations on the smaller computational domain. In addition, we simulated the storm surges by using the wind-pressure field provided by the symmetric Holland model, which lead to more realistic surge simulations. Three actual typhoon tracks (Sinlaku, Morakot, and Haikui) and 23 hypothetical tracks (Nos. 1–23) were chosen for experimental investigation (see Figs. 12, 14, and 16). The sensitivity of the Wenzhou storm surge to these tracks was investigated using the validated storm surge simulation system. The results revealed that the wind speed and the underlying

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surface each exert a significant influence on storm surge. Given this, the storm surge simulation system was used to

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comprehensively analyze the impacts of 55 historical typhoons from 1951 to 2017 on the coast of Zhejiang, China. When a typhoon made landfall in Fujian, Wenzhou was most likely to be threatened by the storm surge due to its

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location in the south of Zhejiang Province. The track of the typhoons in Group A posed a threat to the entire

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Zhejiang coastal area. Therefore, the spatial relationship between station and typhoon, as well as the particular terrain of the station environment, should be considered in disaster reduction and prevention studies. Moreover,

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taking Wenzhou station as an example, of importance is not only its distance from the typhoon but also the Ou River runoff situation, particularly during flood season, and whether the typhoon arrives at high tide. To summarize,

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Wenzhou storm surge is more dangerous than the surge at other stations in Zhejiang. Therefore, the coastal cities of

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Zhejiang Province should improve the accuracy of storm surge prediction and strengthen measures designed to reduce the chance of suffering destructive storm surges. It should be pointed out that we do not consider the sensitivity of other typhoon characteristics to storm surges. Such as the moving direction and moving speed of typhoons, the size of typhoon central pressure and so on. All of these will be covered in our future research. It should be noted that, due to the limited hydrological data along the China coast, only the effects of tides and waves on storm surge were considered in this study; sea level rise was not taken into account, which, although it has little effect on storm surge, should also be noted in the context of global warming. It is also noteworthy that, due to the limited historical typhoon data, only 55 typhoons were simulated, with additional typhoon simulations needed to ensure more statistically sound results. Future work will include improving the storm surge simulation system by adding sea-level rise and typhoon precipitation.

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5 Data availability statement The observed typhoon data that support the findings of this study are available in the CMA repository (http://tcdata.typhoon.org.cn). The datasets generated during the current study are available from the corresponding author on reasonable request.

Acknowledgements The authors appreciate two anonymous reviewers and editor for their valuable suggestions and are also

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thankful to the public platforms for providing the data listed in section 2. This study was supported by the National

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Key Research and Development Program of China (No. 2016YFC1402000). The numerical work was supported by

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the High Performance Computing Center, Institution of Oceanology, CAS.

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Highlights  A typhoon-induced storm surge simulation system was developed.  An assimilation wind-pressure model was used to force the storm surge model.  A new empirical formula was defined for calculating the pressure field of typhoon.  The wind and the underlying surface exerted a significant influence on storm

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surge.

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 The effects of 55 historical storm surges in Zhejiang Province were analyzed.

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