Inundation Hazard Assessment in a Chinese Lagoon Area under the Influence of Extreme Storm Surge

Abstract

Assessing the hazard of inundation due to extreme storm surges in low-lying coastal areas and fragile ecosystems has become necessary and important. In this study, Xincun Lagoon and Li’an Lagoon in the Lingshui area of Hainan, China, were selected as the study areas, a high-resolution storm surge inundation numerical model was established, and the model reliability was tested. Based on data on typhoons affecting the study area from 1949 to 2022, the typhoon parameters for the extreme storm surge scenario were set and used for model numerical simulation and hazard assessment. The results revealed that in the extreme storm surge scenario, the average maximum tidal level, average maximum flow velocity, maximum inundation area, and average maximum inundation depth in the lagoon area were 2.29 m, 1.03 m/s, 14.8124 km², and 1.20 m, respectively. Under the extreme storm surge scenario, a flow velocity of 2.0 m/s off the coasts of the lagoons could damage coastal aquaculture facilities, harbors, and ecosystems, while an inundation depth exceeding 1 m along the coasts of the lagoons could lead to the salinization of inundated land and severely affect the safety of residents. The hazard analysis of storm surge inundation in the land area of the lagoons revealed that hydrographic nets and coastal wetlands are the major land types inundated by storm surges, with the two accounting for approximately 70% of the total inundation area. According to China’s technical guidelines, the hazard levels of the inundated land area of the lagoons are mostly level 3 (moderate hazard) and level 2 (high hazard), together accounting for approximately 90% of the total inundation area. If the government deems the measures feasible based on strict estimation and scientific evaluation of economic benefits and disaster prevention, planting mangroves in coastal wetlands and/or establishing adjustable tidal barriers at narrow entrances to lagoons could minimize disaster losses.

1. Introduction

Storm surge is the abnormal rise in seawater level during a storm, measured as the height of the water above the normal predicted astronomical tide. The surge is caused primarily by a storm’s winds pushing water onshore combined with the inverse barometer effect. In coastal areas, especially in low-lying coastal areas, the superposition of a storm surge caused by the landfall of a typhoon and high astronomical tides can easily lead to storm surge inundation, causing enormous amounts of damage and threatening coastal infrastructure and ecosystems. The northwestern Pacific Ocean is an area with a high incidence of typhoons, accounting for approximately 30% of the total number of typhoons globally [1]. China’s southeastern coastal region, on the coast of the northwestern Pacific Ocean, is one of the most economically dynamic regions in China and is among the areas most severely affected by typhoons worldwide [2,3]. The heavy rains and storm surges triggered by typhoons can cause considerable economic losses and human casualties in the affected area. In 2017, super typhoon “Hato” made landfall in the Pearl River Delta, causing economic losses of up to USD 7 billion and more than 800 casualties in China and Vietnam [4]. Super Typhoon Mangkhut made landfall in the Pearl River Delta in 2018, and the Hong Kong area alone suffered direct economic disasters of HKD 4.6 billion, 3.8 times greater than the losses induced by Typhoon Hato. However, this loss occurred even after early warning capabilities and public awareness increased after Typhoon Hato [5].

Hazard assessment and corresponding disaster reduction strategies are important topics for mitigating storm surge disasters. The technological advancement of numerical models plays an important role in storm surge hazard assessment, such as the SLOSH model [6], CH3D-SSMS model [7], HiReSS model [8], HEC-RAS2D model [9], etc. Each storm surge model has its own advantages and applicable conditions. Many scholars have conducted effective research on storm surge inundation and hazard assessment in coastal areas using numerical models, including the study of hazard assessment methods, the optimization of model grids, and the study of the coupling between storm surges and waves. Ebersole [10] integrated seawalls into a high-precision grid to simulate the impact of Hurricane Katrina on St. Bernard Polder. Adequate observations of high water levels, inundation, and seawall damage enabled the model to be fully validated. By reproducing this hurricane through modeling, the source of floodwater was identified, and the spatial variation in the storm surge was discussed. Based on the validation of excellent models of Typhoons Hato and Mangkhut, multiple scenarios combining extreme typhoon intensity and different tidal conditions were used for storm surge inundation numerical simulations. Yang analyzed the impact of extreme scenarios on the Macau area [11]. The combination of hurricane data and numerical simulations could be used to determine hazard assessment scenarios and compare storm surge return periods based on mathematical probability distributions, which provides a basis for storm surge hazard assessment in coastal areas under the influence of climate change [12]. In the process of conducting numerical simulations of coastal storm surge inundation, Thomas mapped predictions made in coarse grids to fine grids with increasing resolution near the forecasted landfall location of a hurricane. The results showed a 53% increase in efficiency and the smallest accuracy loss compared to those of a static simulation [13]. Many scholars have emphasized the necessity of storm surge, tide, and wave coupling when conducting research on coastal areas with seawalls [14,15,16,17,18], which is a very reasonable approach. Especially in coastal areas with seawalls and dense buildings, it is necessary to consider the total water level and wave overtopping (WOT). Suh and Lee conducted numerical simulations of storm surge inundations in different sea level rise scenarios using the ADCIRC + SWAN + EurOtop model and a Coastal–Seawall–Terrestrial Seamless Grid System to evaluate the vulnerability to WOT, which was crucial for seawall design, infrastructure protection, and storm surge warning [19].

The study of disaster reduction strategies for storm surge inundation using numerical models is also important, and these studies are tailored to local conditions. A calibrated and validated numerical model (Delft3D) was used to simulate the inundation hazard of a lagoon area under different scenarios, and the results revealed that the hydrodynamics of the lagoon area changed and that the habitat flooded [20]. Montgomery proposed a model based on a simplified momentum equation combined with a continuity equation to study the interaction between storm surges and mangroves, and the results showed that the density and extent of mangroves determine their ability to reduce storm surges and that mangroves can effectively reduce the duration of storm surges and storm surge extremes [21]. Coastal ecosystems are directly threatened by storm surges, even if they withstand them. For instance, using airborne light detection and ranging (LiDAR) and satellite images before and after hurricane landfall, Lagomasino found that when low-elevation coasts encounter high water levels caused by hurricanes, 60% of mangroves could be destroyed. This damage occurs regardless of mangrove height and wind strength; instead, this damage is mostly due to seawater intrusion and long-term seawater immersion caused by hurricanes [22]. Mel reviewed in detail an extreme storm surge event that affected Venice in November 2022. Due to the establishment of observation systems and artistic barriers in advance, Venice’s coastal infrastructures and residents were protected [23].

The hazard assessment standards for storm surge inundation have rapidly developed internationally, with detailed standards and more diverse assessment methods in countries such as the United States [24,25] and Japan [26], which require a large amount of high-resolution geographic information data. The above research results provide useful ideas for this study, and it is necessary to conduct hazard assessments and develop disaster reduction strategies for extreme storm surge events in low-lying coastal areas and fragile ecosystems. This study focused on storm surge hazard assessment methods, hazard assessment analysis, and disaster prevention strategies in Xincun Lagoon and Li’an Lagoon in Lingshui District, Hainan Province, China. Among them, the hazard assessment method is based on the standards of Chinese guidelines, which differs from other countries and previous research, and a possible and idealized extreme storm surge scenario is established in this study based on historical typhoon data and numerical simulation results of storm surges. In addition, the disaster prevention strategy draws on the research results mentioned above but is based on numerical simulations of the situation and characteristics of lagoon inundation. The original purpose of this study was to propose a method for determining extreme storm surge events and to obtain the distribution of inundation hazards in the lagoon area based on numerical simulation.

2. Model, Data, and Validation

2.1. Storm Surge Inundation Model

The ADCIRC model (Advanced Circulation Model for Oceans, Coasts, and Estuarine Waters), a hydrodynamic model based on the finite element method [27], is widely used for numerical calculations of tides, storm surges, and currents in various complex coastal areas and has been proven to be an effective numerical model. In this study, the ADCIRC model (version v52.30.07) was used for verifying storm surges caused by historical typhoons and inundation numerical simulations, while SMS software (version 11.2) was used for generating and optimizing unstructured triangular meshes. An introduction and formula description of the model were presented in a previous study [28], so in-depth descriptions are not repeated in this study.

Table 1. Main model parameter settings and descriptions.

Model Parameter	Description	Value and Its Meaning
NOLIBF	Parameter of the type of bottom stress parameterization	2 hybrid nonlinear bottom friction law is used
NOLIFA	Parameter of the finite amplitude terms	2 finite amplitude terms included in the model are run and the wetting and drying of elements are enabled
NWS	Parameter of the wind velocity or stress, atmospheric pressure, and wave radiation stress	8 hurricane parameters are calculated at every node using the Holland wind model
τ0	Generalized Wave Continuity Equation (GWCE) weighting factor in the model	−3 this parameter is calculated based on nodal attributes, and it is variable in space and time
DT	Time step (in seconds)	0.8 time interval for the iterative model calculation

shows the main model parameter settings and descriptions. The open boundary is driven by eight major astronomical tidal components (M2, S2, K2, N2, K1, O1, P1, and Q1), whose harmonic constants are taken from the global tidal model NAO99 [29]. To achieve a stable tidal state, the tide for three days is calculated in advance; then, the typhoon information is added, and the storm surge and tide are coupled.

The dry–wet grid algorithm was used for simulating storm surge inundation. H_min (the minimum water depth) was set to 0.05 m and served as the critical value for determining whether the grid was dry or wet. For the grid to change from dry to wet, the flow velocity U must be greater than the minimum velocity U_min (0.05 m/s) to ensure computational stability. U is expressed as follows [27]:

$$U=\frac{g({\zeta }_{i-1}-{\zeta }_i)}{{\tau }_{bi}\Delta x_i}$$

(1)

where ζ_i−1 and ζ_i represent the tidal levels of two adjacent grids, τ_bi is the bottom friction, and ∆x_i is the distance between two adjacent grids.

2.2. Data Sources

Lingshui County is on the southern coast of Hainan Province, China, near the South China Sea, and it is one of the areas severely affected by typhoons and storm surges in Hainan Province (Figure 1a). Storm surge data from 1949 to 2022 indicate that one storm surge event occurs every three years on average. There are six coastal towns in the Lingshui area, namely, Guangpo town, Yelin town, Li’an town, Sancai town, Xincun town and Yingzhou town. Both Xincun town and Li’an town have semi-enclosed tropical lagoons with narrow mouths. The mouth of Xincun Lagoon faces west, while the mouth of Li’an Lagoon faces south (Figure 1a). Multiple marine ecosystems, such as coral communities, seagrass beds, and mangroves, grow in lagoons, which are important marine ecological habitats.

In this study, Xincun Lagoon and Li’an Lagoon in Lingshui were taken as the study area (Figure 1a). The higher the resolution of the input data, the more accurate the simulation results will be. High-resolution basic geographic data, which included water depth data, digital elevation model (DEM) data, land use data, typhoon data, and tidal level data, were obtained in this study. The data sources and descriptions are shown in

Table 2. Data sources and descriptions.

Data	Source	Description
Water depth	National Catalogue Service for Geographic Information of China [30]	The water data observed in 2021 have a spatial resolution of 50 m.
Geographic information	National Catalogue Service for Geographic Information of China [30]	The DEM data and the land use data observed in 2021 have a spatial resolution of 10 m.
Typhoon	China Meteorological Administration (CMA) [31,32]	The typhoon data are in six-hour intervals and include information on time, location, center pressure, and maximum wind speed.
Tidal level	National Marine Data Center of China [33]	The tidal level data with hourly intervals from the Gangbei and Sanya tidal gauge stations in Hainan Province are used.

.

The water depth data are as follows: data covering the sea area of the study area were measured in 2021 (Figure 1b). The following geographic information was used: DEM and land use data from 2021 were used (Figure 1b). The land use data included different types of residential areas (including infrastructure), hydrographic nets, forests, low shrubs, farmlands, and coastal wetlands (Figure 1c). Among them, hydrographic nets included marine aquaculture areas (grid systems that require seawater exchange) and freshwater lakes along the coast. It can be seen that the study area is mainly composed of natural shorelines and landscapes, with few coastal artificial structures.

The following typhoon data were used: annual typhoon data from the China Meteorological Administration (CMA) were used to construct typhoon wind fields. The tidal level data from the Gangbei and Sanya tidal gauge stations near Lingshui were used, which are 80 km and 30 km from the study area, respectively, with the Sanya tidal gauge station being closer to the study area.

The bottom friction of different surface features in coastal areas varies during the storm surge inundation process. The Manning coefficient n was introduced in the bottom friction term of the momentum equation in the model to characterize the difference in bottom friction for different surface features. The formula for the bottom friction term is given as follows:

$${\tau }_b(U,V)=g\frac{n^2}{H_0^{1/3}}\frac{\sqrt{U^2+V^2}}{H}(U,V)$$

(2)

where H₀ is the critical water depth. According to the classification and values of the U.S. National Land Cover Database (NLCD), combined with high-resolution topographic image data, the major land types that may be inundated by storm surges in the study area were classified (Figure 1c), namely, residential areas (buildings, schools, government agencies, and roads), forests, low shrubs, farmlands, coastal wetlands, hydrographic nets, and oceans, with Manning coefficient values of 0.120, 0.170, 0.07, 0.035, 0.040 and 0.020, respectively [27].

2.3. The Calculation of the Typhoon Wind Field

Generally, the wind field structure of a severe landfall typhoon is nearly circular, and the wind distribution is relatively uniform, which is suitable for the wind field depicted by the Holland wind model [34]. The formulas for typhoon pressure and wind speed are given as follows:

$$P(r,\theta )=P_c+(P_n-P_c)e^{-{[R_{\max }/r]}^B}$$

(3)

$$V(r)=\sqrt{\frac{B}{{\rho }_a}{(\frac{R_{\max }}{r})}^B(P_n-P_c)e^{-{[R_{\max }/r]}^B}+{(\frac{rf}{2})}^2}-(\frac{rf}{2})$$

(4)

where $P(r,\theta )$ is the sea surface pressure at a location $r$ from the typhoon center, which is a function of the radial distance $r$ and azimuth angle $\theta$. $P_c$ is the typhoon central pressure, $P_n$ is the background pressure, and the value in this study is 1010 hPa. $R_{\max }$ is the typhoon’s maximum wind speed radius, $V(r)$ is the tangential wind speed at a distance $r$ from the center of the typhoon, ${\rho }_a$ is the air density, $f$ is the Coriolis parameter, and $B$ is the typhoon profile parameter that characterizes the diameter and tangential velocity gradient of the typhoon eye area, for which the value is 2.0 in this study.

The extreme storm surge events determined by historical typhoon data and numerical simulations in this study include only central pressure and typhoon paths (Chapter 3.1). The maximum wind speed and maximum wind speed radius of the typhoon need to be calculated with Formulas (5) and (6). The typhoon’s maximum wind speed and central pressure are correlated, and the formula is as follows (the unit is m/s) [35]:

$$V_{\max }=6.7\times 0.51\times {(P_n-P_c)}^{0.644}$$

(5)

There are differences in the parameterization schemes of the Holland typhoon wind model parameters in different research areas, and for the northwest Pacific Ocean, the parameterization scheme verified by Luo [36] was used in this study, and the $R_{\max }$ formula is shown as follows:

$$R_{\max }=1119\times {(P_n-P_c)}^{-0.805}$$

(6)

2.4. Model Validation

Unstructured triangular meshes are used in the model to depict complex coasts. The calculation area was located at 106° E–118° E and 13.5° N–22.5° N, roughly including the central and northern parts of the South China Sea. The resolution of the grid with open boundaries was 20 km. A densified grid was used for the land area, and the grid resolution of the land area was approximately 25 m, which was determined by balancing the computational stability of the model and the data resolution. The entire computational grid consists of 521,014 grid cells and 266,004 grid points, with approximately 60% of the high-resolution grid in the land areas. The 7 m contour line of the land was used as the land boundary. The grids used for the calculations for Xincun Lagoon and Li’an Lagoon are shown in Figure 2a and Figure 2b, respectively.

Two typhoon events were selected to calibrate and validate the storm surge model in this study. The bottom friction coefficient was calibrated for the verification of the astronomical tide. After the bottom friction coefficient was fixed, the drag coefficient of the wind stress term and the maximum wind speed radius of the typhoon were also calibrated for storm surge verification. The maximum wind speed radii in

Table 3. Typhoon Kelly information and disaster description.

Time (Year-Month-Day-Hour)	Position	Central Air Pressure (hPa)	Maximum Wind Speed (m/s)	Maximum Wind Speed Radius (km)	Description
1981070214	115.6° E, 14.4° N	981	30	65	Typhoon Kelly caused a maximum storm surge of over 1.0 m at three tidal gauge stations in Guangdong and Hainan Provinces, with the highest level at Gangbei tidal gauge station in Hainan, i.e., 1.62 m. The storm surge disaster information was not collected [37].
1981070220	114.5° E, 14.9° N	980	30	65
1981070302	113.9° E, 15.7° N	975	35	60
1981070308	112.8° E, 16.8° N	964	45	45
1981070314	111.4° E, 17.3° N	962	45	45
1981070320	110.7° E, 17.7° N	965	45	45
1981070402	109.7° E, 18.3° N	965	45	45
1981070408	108.5° E, 18.7° N	980	35	55
1981070414	107.6° E, 19.0° N	985	30	60
1981070420	106.8° E, 19.2° N	985	30	60
1981070502	105.6° E, 19.3° N	985	30	60
1981070508	105.0° E, 19.3° N	994	20	70
1981070514	103.5° E, 19.3° N	995	15	80

and

Table 4. Typhoon Niki information and disaster description.

Time (Year-Month-Day-Hour)	Position	Central Air Pressure (hPa)	Maximum Wind Speed (m/s)	Maximum Wind Speed Radius (km)	Description
1996082002	121.5° E, 17.6° N	990	23	85	Affected by Typhoon Niki, the storm surge at the Gangbei tidal gauge station in Hainan was the highest, reaching 1.57 m, with the highest recorded tidal level exceeding the warning level. According to statistics, 200 hectares of seawater aquaculture facilities were damaged in the Lingshui area, with 948 tons of farmed fish lost, four fishing boats sunk or run aground, and one person deceased [37].
1996082008	119.9° E, 17.6° N	985	25	85
1996082014	118.3° E, 17.3° N	985	25	75
1996082020	116.8° E, 17.1° N	985	25	75
1996082102	115.7° E, 17.1° N	980	30	75
1996082108	114.2° E, 17.3° N	980	30	75
1996082114	112.7° E, 17.5° N	975	33	75
1996082120	111.5° E, 17.8° N	975	33	70
1996082202	110.4° E, 18.2° N	970	35	70
1996082208	109.4° E, 18.5° N	975	30	70
1996082214	108.0° E, 18.9° N	975	30	70
1996082220	106.9° E, 19.5° N	980	30	85
1996082302	105.4° E, 20.0° N	985	25	85

are artificially adjusted values, ensuring an excellent simulation accuracy of storm surges and total water levels. In addition, the root mean square error (RMSE), the average maximum storm surge error, and the average maximum total water level error were used to evaluate the accuracy of the model.

Typhoons Kelly in 1981 and Niki in 1996, which caused severe storm surge disasters, were selected as example typhoons for model validation (the tracks are shown in Figure 3a). The tidal level data from the Gangbei and Sanya tide gauge stations near Lingshui were used for model verification (Figure 3a shows the locations of the tidal gauge stations).

Typhoon Kelly, i.e., No. 5 of 1981, made landfall on the coast of Sanya city, Hainan, in the early morning of 4 July. At the time of landfall, the typhoon’s central pressure was 965 hPa, and the maximum wind speed was 45 m/s. The typhoon information and disaster description are presented in Table 3.

Figure 4 and Figure 5 show comparisons of the simulated storm surge results and measurements for typhoons Kelly and Niki. The hourly curves show that the simulated results matched well with the actual measurements. The RMSE of the simulated storm surges and total water levels at the Gangbei and Sanya tidal gauge stations during the two typhoons were 0.16 m and 0.07 m, and the average errors of the simulated maximum storm surges and maximum total water levels at the Gangbei and Sanya tidal gauge stations were 2.6% and 6.8%, indicating that the numerical storm surge model was reliable.

Typhoon Niki, i.e., No. 12 of 1996, made landfall on 22 August along the coast of Lingshui, Hainan. At the time of landfall, the typhoon’s central pressure was 970 hPa, and the maximum wind speed was 35 m/s. The typhoon information and disaster description are presented in Table 4.

3. Results

3.1. Typhoon Parameters for an Extreme Scenario

The typhoon conditions for an extreme scenario were idealized and artificially constructed based on historical typhoon statistics and numerical simulations of storm surges. First, the circulation size of historical typhoons was not recorded in typhoon yearbooks, as determining this parameter was difficult; therefore, empirical formulas were used to calculate the maximum wind speed radius (Formula (6)), which determines the circulation range of typhoons. Second, the intensity of typhoons was classified using the Pearson III model, and the extreme values were selected. Third, the typhoon movement speed was not a decisive factor for storm surges and was set as a statistical average. However, the direction of movement is important for storm surges. Therefore, based on the statistical data of the direction of typhoon movement in the study area, an idealized set of typhoon paths was constructed for storm surge simulation, and the typhoon path with the highest storm surge was selected for hazard assessment.

The strength of a typhoon is determined based on statistical data from historical typhoons. The annual minimum central pressures of typhoons within 300 km of Lingshui from 1949 to 2022 were selected as the statistical samples [31,38]. The Pearson-III (P-III) distribution was used to calculate the probability of occurrence of typhoon intensities (Figure 6). Most of the coastal infrastructure in Lingshui has been designed and constructed based on probabilities of occurrence of 1% and 2% for typhoon intensities to defend against extreme typhoon disasters. The intensities of the 1% probability and 2% probability of a typhoon occurring within 300 km of Lingshui were 908 hPa and 918 hPa, respectively. The strongest typhoon to occur in this area from 1949 to 2022 was the 2014 super typhoon Rammasun, which made landfall in Wenchang city, Hainan Province, approximately 200 km from Lingshui, and the landfall intensity was 910 hPa, which is between the 1% and 2% probability levels of typhoon occurrence and meets the building design defense standards of the study area. Therefore, 910 hPa was adopted as the extreme typhoon intensity.

The path of the modeled typhoon was determined based on statistical data from historical typhoons and numerical simulations of storm surges. A total of 114 tropical cyclones landed in Hainan Province from 1949 to 2022 [30], with an annual average of 1.6. Among them, 25 typhoons (with central pressures of less than 980 hPa) occurred in Lingshui and nearby areas, and their movement directions were W, WNW, NW, and NNW. The average central pressure, maximum wind speed, and moving speed of these 25 typhoons were 963 hPa, 40 m/s, and 20 km/h, respectively (

Table 5. Information on the 25 typhoons that caused significant storm surges in the Lingshui area between 1949 and 2022.

Typhoon Number (Year-No.)	Name	Central Air Pressure (hPa)	Maximum Wind Speed (m/s)	Maximum Wind Speed Radius (km)	Direction of Movement
1952 No. 10	Lois	960	40	20	W
1954 No. 01	Elsie	960	35	15	NNW
1956 No. 09	Vera	975	35	30	WNW
1956 No. 07	Charlotte	960	45	13	WNW
1962 No. 19	Carla	975	35	25	WNW
1964 No. 26	Clara	965	40	18	WNW
1968 No. 08	Rose	970	35	15	WNW
1971 No. 30	Elaine	965	40	16	WNW
1973 No. 14	Marge	938	60	25	WNW
1973 No. 18	Ruth	973	35	18	NW
1981 No. 05	Kelly	965	45	20	WNW
1982 No. 23	Nancy	965	45	20	WNW
1985 No. 21	Dot	970	40	28	WNW
1987 No. 10	Cary	970	35	25	NW
1989 No. 05	Dot	960	40	15	WNW
1989 No. 26	Elsie	975	30	25	WNW
1991 No. 06	Zeke	960	45	15	WNW
1992 No. 04	Chuck	965	40	18	NW
1996 No. 12	Niki	970	35	18	WNW
2000 No. 16	Wukong	970	35	15	W
2005 No. 18	Damrey	930	55	18	W
2010 No. 02	Conson	970	35	16	NW
2012 No. 23	Son-tinh	950	45	18	NW
2013 No. 30	Haiyan	960	40	30	NNW
2016 No. 21	Sarika	960	45	18	WNW
Average		963.2	40.4	19.8

). Based on the movement directions of W, WNW, NW, and NNW, typhoon tracks were generated every 10 km, and a set of typhoon tracks affecting the Lingshui area was constructed based on historical statistics (a total of 32 typhoon scenarios, Figure 3b). The above typhoon parameters were used to drive the storm surge model. According to the numerical calculation results (

Table 6. The simulated results of the maximum storm surge in 32 typhoon scenarios in the lagoon area. “+” indicates the northern side of Lingshui, and “−” indicates the southern side of Lingshui.

Typhoon Track	P1 (m)	P2 (m)	Typhoon Track	P1 (m)	P2 (m)	Typhoon Track	P1 (m)	P2 (m)	Typhoon Track	P1 (m)	P2 (m)
W, +20 km	0.51	0.66	WNW, +20 km	0.58	0.73	NW, +20 km	0.56	0.70	NNW, +20 km	0.54	0.68
W, +10 km	0.59	0.77	WNW, +10 km	0.64	0.84	NW, +10 km	0.62	0.82	NNW, +10 km	0.59	0.81
W, +0 km	0.63	0.83	WNW, +0 km	0.66	0.89	NW, +0 km	0.65	0.88	NNW, +0 km	0.62	0.86
W, −10 km	0.66	0.88	WNW, −10 km	0.68	0.93	NW, −10 km	0.67	0.92	NNW, −10 km	0.64	0.88
W, −20 km	0.67	0.91	WNW, −20 km	0.70	0.95	NW, −20 km	0.69	0.93	NNW, −20 km	0.68	0.90
W, −30 km	0.66	0.88	WNW, −30 km	0.69	0.93	NW, −30 km	0.69	0.89	NNW, −30 km	0.67	0.87
W, −40 km	0.65	0.83	WNW, −40 km	0.68	0.91	NW, −40 km	0.67	0.87	NNW, −40 km	0.67	0.85
W, −50 km	0.63	0.81	WNW, −50 km	0.66	0.89	NW, −50 km	0.65	0.84	NNW, −50 km	0.64	0.83

; P1 is the output point inside Xincun Lagoon, P2 is the output point inside Li’an Lagoon, and the positions of P1 and P2 are shown in Figure 1b), the typhoon track causing the largest storm surge is the one oriented to the WNW and passing 20 km south of Lingshui (shown by the thick red line in Figure 3b).

The extreme storm surge scenario was defined as the combination of typhoon landfall with a central pressure of 910 hPa and an astronomical high tide. The maximum wind speed and maximum wind speed radius were determined using Formulas (5) and (6), respectively, and the movement speed of the typhoon was set to the average value (20 km/h). The typhoon parameters used for inundation hazard assessment under the extreme storm surge scenario are listed in

Table 7. Typhoon parameters for the extreme storm surge scenario.

Time (Hour)	Position	Central Air Pressure (hPa)	Maximum Wind Speed (m/s)	Maximum Wind Speed Radius (km)	Speed (km/h)	Direction of Movement
0	117.6° E, 16.3° N	910	66	27	20	WNW
6	116.5° E, 16.6° N	910	66	27	20	WNW
12	115.4° E, 16.8° N	910	66	27	20	WNW
18	114.3° E, 17.1° N	910	66	27	20	WNW
24	113.3° E, 17.4° N	910	66	27	20	WNW
30	112.2° E, 17.7° N	910	66	27	20	WNW
36	111.1° E, 18.0° N	910	66	27	20	WNW
42	110.0° E, 18.2° N	910 (landfall)	66	27	20	WNW
48	108.9° E, 18.5° N	950	48	41	20	WNW

.

3.2. An Analysis of the Differences in the Numerical Simulations between the Two Lagoons

Due to the use of a cold start in the model, the tides were calculated in advance for three days to reach a normal state, and then, by integrating Holland’s wind field model, the storm surge and tide were coupled. By adjusting the model parameters, the maximum storm surge caused by a typhoon can be superimposed on the astronomical high tide (i.e., the average monthly astronomical high tide from June to October—0.8 m). Figure 7 shows the hourly curves of the total water levels at output points P1 in Xincun Lagoon and P2 in Li’an Lagoon under the extreme scenario. The storm surge at points P1 and P2 during the astronomical high tide stage is 1.14 m and 1.71 m, respectively.

Figure 8 shows the distributions of the highest tidal level, maximum flow velocity, and maximum inundation range and depth in the lagoon area. Under the extreme scenario, the average highest tidal level in the lagoon area was 2.29 m, with the highest tidal levels in Li’an Lagoon being 2.3–2.7 m and those in Xincun Lagoon being 1.6–2.2 m. Because the movement direction of the typhoon affecting the study area was NW, the difference in the orientation of the two lagoon entrances resulted in the difference in the highest tidal level. The entrance of Li’an Lagoon faced south, and when the typhoon approached, southeasterly winds were more likely to push the storm surge into Li’an Lagoon. Then, with the entrance of Xincun Lagoon facing west, when the typhoon approached, the southeast wind could not push a large amount of storm surge into Xincun Lagoon. The average highest tidal level of Li’an Lagoon was approximately 0.5 m greater than that of Xincun Lagoon.

The maximum flow velocities in the lagoon area were 1.8–2.2 m/s, and the average maximum flow velocity was 1.03 m/s. The difference in the maximum flow velocity between the two lagoons was not significant, and the maximum flow velocity (more than 2.0 m/s) was distributed in shallow-water areas near the coast, which could easily destroy fishing boats, aquaculture facilities, and coastal roads. The water depth in the middle of the two lagoons exceeded 10 m, as shown in Figure 1b, and the flow velocity caused by the storm surge was significantly lower in the deep-water areas than in the shallow-water areas. When the storm surge propagated from deep-water areas to shallow-water areas, as the terrain slope increased, the flow velocity caused by the storm surge increased.

The maximum inundation area of the lagoon area was 14.8124 km², and the average maximum inundation depth was 1.20 m. The inundation depth in the land area near the coastline of the lagoons was relatively large. Because the highest tidal level in Li’an Lagoon was greater than that in Xincun Lagoon, the inundation depth in the land area of Li’an Lagoon was generally greater than that in Xincun Lagoon.

3.3. Inundation Hazard Assessment under Extreme Storm Surges

Under the extreme storm surge scenario, assessing the inundation hazard in the land area of a lagoon was the key research topic in this paper. First, the storm surge inundation hazard level was defined. According to the Technical Guidelines for Risk Assessment and Zoning of Marine Disaster Part 1: Storm Surge [38], the effect of storm surge inundation depth on residents, land vehicles, and buildings was divided into four hazard levels (

Table 8. Hazard levels corresponding to storm surge inundation depth.

Inundation Hazard Level	Inundation Depth (m)	Influence
Low (level 4)	<0.5	the movement of vehicles and residents could be affected
Moderate (level 3)	0.5–1.2	the safety of vehicles and children could be threatened
High (level 2)	1.2–3.0	the vehicles and first floors of buildings could be inundated, posing a serious hazard to residents
Extremely high (level 1)	>3.0	the life and property of residents could be greatly threatened

).

Based on the statistics of inundation of different land types (

Table 9. Inundation area statistics of different land types and different inundation hazard levels in the lagoon land area simulated under the extreme storm surge scenario.

Land Type	Inundation Area (km2)					Proportion
	>3.0 m	1.2–3.0 m	0.5–1.2 m	<0.5 m	Total
Coastal wetland	0	2.5049	2.1623	0.3016	4.9688	33.54%
Farmland	0	0.5951	0.5848	0.2034	1.3832	9.34%
Forest	0	1.1665	0.7424	0.3029	2.2117	14.93%
Hydrographic net	0	2.2046	2.1548	0.7812	5.1406	34.70%
Low shrub	0	0.0252	0.0490	0.0656	0.1397	0.95%
Residential area	0	0.3404	0.5162	0.1118	0.9684	6.54%
Total	0	6.8366	6.2094	1.7664	14.8124	100%
Proportion	0%	46.15%	41.92%	11.93%	100%

), the inundation areas of hydrographic nets, coastal wetlands, forests, farmlands, residential areas, and low shrubs were 5.1406, 4.9688, 2.2117, 1.3832, 0.9684, and 0.1397 km², respectively, accounting for 34.70%, 33.54%, 14.93%, 9.34%, 6.54%, and 0.95% of the total inundation area, respectively. Hydrographic nets and coastal wetlands were the major land types inundated due to extreme storm surges, and these two types of land cover accounted for nearly 70% of the total inundation area. Under the extreme storm surge scenario, seawater intrusion could salinize the hydrographic net and coastal wetland areas in the lagoon area, thereby seriously affecting the water safety of coastal residents and destroying mariculture facilities; coastal forest and farmland could suffer seawater intrusion, and some residential areas along the coast could be greatly affected.

Based on the statistics of inundation at the different hazard levels (Table 9), the inundation areas at hazard levels 1, 2, 3, and 4 were 0, 6.8366, 6.2094, and 1.7664 km², respectively, accounting for 0%, 46.15%, 41.92%, and 11.93% of the total inundation area. The land inundation area of the lagoon was mainly subject to hazard level 3 and hazard level 2, and these two levels accounted for nearly 90% of the total inundation area.

The distribution of storm surge inundation hazard levels under the extreme storm surge scenario (Figure 9) shows that the inundation hazard level of the coastal area of the lagoon in the Li’an town was mainly 2 (indicated in orange in Figure 9), and the inundation hazard levels of the coastal area of the lagoon in Xincun town were mainly 3 and 2 (indicated in yellow and orange in Figure 9). The inundation depths of the inundated coastal wetlands and fishponds in the lagoon area were approximately 1.0–2.2 m. The inundated villages included the Chengpo village, Yanjin village, Zaozai village, and Tonghai village in the Xincun town area and the Lingzai village, Lifeng village, and Li’an village in the Li’an town area, and the inundation depths of the villages were 0.9–1.7 m.

4. Discussion

Global warming is changing climate patterns, and low-lying areas and fragile ecosystems in coastal areas could face increasingly serious threats from typhoons and storm surges [39,40,41,42,43,44,45,46], representing a major challenge for coastal areas with large residential populations and rapid economic development. Some research results have been achieved in this study, but there are also some issues that need to be discussed.

First, waves are usually coupled with storm surge models when conducting storm surge inundation assessments. Prior to this study, numerical experiments on the coupling of storm surges, tides, and waves were conducted. However, due to the unique terrain, the entrances to the two lagoons are very narrow (approximately 150 m at the entrance of Xincun Lagoon and approximately 250 m at the entrance of Li’an Lagoon). Under the influence of extreme typhoon conditions, although the SWH (significant wave height) outside the lagoons reaches more than 6 m, waves cannot propagate into the lagoons. The numerical results show that the maximum SWH in the coastal areas of both lagoons is less than 1.0 m (Figure 10a). For extreme cases in which the total water level exceeds 2 m, the contribution rate of the wave setup to the total tide is less than 3%, and its impact on the simulated results of storm surge inundation is minimal and can be ignored in this study. Based on the above experiment, a storm surge and tide coupling scheme was adopted for the numerical simulation, which also saved considerable calculation time. This also shows that the terrain characteristics of the study area are important.

Second, another limitation of this study is the lack of land inundation data for major storm surge events in lagoon areas, which means that this study lacks an inundation validation of the model. Objectively, obtaining data on land inundation is somewhat challenging and subject to various factors. Before 2010, the Lingshui area was affected by several storm surge events caused by typhoons that made landfall (Table 5), but post-disaster investigations and measurements were not carried out due to a lack of project funding, professional investigators, and investigation equipment. The situation was the opposite after 2010, and although project funding, investigators, and equipment were available, there were no strong typhoon events that made landfall in the Lingshui area (Table 5), and land inundation data are still missing. Despite many attempts, there has been an inability to collect storm surge inundation data, making it an important future research direction. It is also possible to accurately and reasonably simulate storm surge inundation without land inundation data. Notably, due to the absence of seawalls and dense buildings in the Lingshui Lagoon area, the physical process of storm surge inundation is much simpler. Using the dry–wet algorithm to calculate storm surge inundation on natural coastlines is a well-tested solution. For similar terrains (e.g., natural coastlines), when the simulation of storm surges and total water levels in the study area and nearby areas is accurate, the results of storm surge inundation simulations have been proven to be close to the observed inundation values [11,47,48,49,50]. With excellent model validation (Figure 4 and Figure 5), further improvements in the accuracy of storm surge inundation simulations using Manning coefficients for different land use types could be attempted. In addition, characterizing the coastal inundation hazard by correlating the inundation depth with the impact on people, vehicles, and buildings could also make the assessment results more reasonable. Overall, the storm surge inundation results of this study have a certain degree of credibility.

Third, the extreme storm surge event in this study is an idealized storm surge inundation event based on historical typhoon statistical data and numerical simulations of typhoon scenarios (intensity and path) superimposed on an astronomical high tide. The probability of a typhoon occurring is between 1% and 2%, but the storm surge caused by it occurs during an astronomical high tide period, which makes the probability of total tidal level occurrence much lower than 1%, and it can reach 0.1% in some coastal areas with large tidal ranges. Due to the lack of long-term tidal observation data in this study area, the probability of total water level occurrence caused by such extreme storm surge events cannot be accurately calculated, which is also one of the limitations of this study. However, such a total water level is very rare in the study area based on the return period of the total water level at nearby tidal gauge stations, and it is reasonable to classify it as an extreme storm surge event. The research method of this study follows China’s technical guidelines, focusing on a deterministic and idealized storm surge event and analysis. Another limitation of this study is that different maximum wind speed radii, different movement speeds, and different wind field data were not considered. The authors will also attempt to collect data on the historical maximum wind speed radii of typhoons, select different typhoon movement speeds and wind field sources (such as the EAR5 wind field) for different scenario combinations for storm surge inundation simulations, and obtain new research results in future work.

5. Conclusions

In this study, Xincun Lagoon and Li’an Lagoon in the Lingshui area, Hainan Province, China, were selected as the study area, and a hazard assessment of the inundation of the lagoon area by extreme storm surge event was conducted using a high-resolution storm surge numerical model from the perspective of mitigating the disaster hazard of extreme weather in the coastal lagoon area.

The construction of typhoon parameters for extreme storm surge scenarios is an important foundation for hazard assessment, and scholars have proposed reasonable schemes for constructing typhoon parameters based on different ideas [51,52,53,54,55]. Unlike those of the aforementioned scholars, the extreme typhoon parameter scheme in this study is based on historical typhoon statistical analysis and the results of storm surge numerical simulations and follows the Chinese storm surge disaster hazard assessment standards. This extreme scenario is not randomly generated and could occur in the future. However, this method is a little simple, only proposing one extreme storm surge scenario, rather than a combination of the statistics of various scenarios, which is not rigorous enough. Therefore, although the method of extreme storm surge events has certain rationality, it also has limitations.

Based on the simulation of inundation statistics and hazard level distributions in lagoon areas, targeted plans for reducing disaster losses can be constructed. However, the plans require strict estimation and scientific evaluation by the government based on economic benefits and disaster prevention to determine feasibility. For example, in the extreme scenario, the coastal velocity of 2.0 m/s in the lagoon area is mainly concentrated in coastal waters, which are mainly mudflats and wetlands. A shallow water depth is suitable for planting mangroves, and mangroves have been proven to be effective at reducing the velocity of storm surges [21,22]. If conditions permit, the government can also consider building adjustable tidal barriers at the entrances of the lagoons, which have been crucial for controlling Venice’s extreme storm surges [23]. Furthermore, the results of numerical simulations of adjustable tidal barriers in the Macau region also suggest that they can greatly reduce the storm surge inundation area and depth [56]. Due to the narrow entrances of the two lagoons in Lingshui, adjustable tidal barrier schemes can be considered. The above plans are based on the simulated results of storm surge inundation hazard assessment and the special terrain of the study area’s lagoon, with the aim of minimizing potential future disaster losses from extreme storm surges.

In the future, the authors will conduct in-depth research in the following areas to better complete hazard assessment research. First, finer grids have been proven to improve the accuracy of simulations of storm surge inundation [13,19,57], which helps to enhance the credibility of storm surge hazard assessment results. A more refined computational grid (such as a grid resolution of approximately 5 m) can depict the complex terrain of coastal land, and combined with high-resolution DEM data and remote sensing image inversion of land information, this approach can create block-scale computational grids. Second, the impact of storm surge flooding on coastal facilities can be extended to people by focusing on the impact of storm surge flooding on residents, local communities, and socioeconomics [58], which will make research more practical and humane. In addition, climate change and sea level rise should also be considered in future research on storm surge hazard assessment [59], which could help guide infrastructure construction and defense standards for coastal areas.