Skip to main content

Impact of varying landfall time and cyclone intensity on storm surges in the Bay of Bengal using ADCIRC model


This study focuses on the impact of varying landfall timing in a tidal cycle (i.e., the spring-neap phase) and varying wind speeds (i.e., cyclone intensities) on the surge tides for the tropical cyclone Fani in the Bay of Bengal using a hydrodynamic finite element-based 2D (ADvanced CIRCulation) ADCIRC model setup. For atmospheric forcing, the Cyclostrophic Symmetric Holland wind Model (H80) and Generalized Asymmetric Holland Model (GAHM) model are used to estimate the wind fields from the IMD best track data. Comparisons with in-situ winds from moored buoys within the proximity of cyclone track showed that H80 simulated winds underestimate the observed winds in terms of magnitude followed by a mismatch in the wind directions as well. In contrast, the GAHM simulated wind fields, which are statistically better in terms of both magnitude and direction are used for different wind experiments. To understand the impact of the varying landfall timing and varying wind speeds on storm surges, a series of sensitivity experiments have been performed during a tidal cycle with modulated high and low winds along the cyclone track. The experiments considering varying landfall timing during a tidal cycle indicate the strongest surge tides (1.99 m) during the spring high tide phase, whereas the lowest surge tide of 0.94 m is observed during spring low tide. However, the surge tide at the actual time of landfall is 1.20 m which is during the transition from low tide to high tide. On the other hand, the combined impact of wind speeds and varying landfall timing indicated the strongest surge tides of 2.25 m during high wind conditions associated with spring high tides. In contrast, the surge tides decrease significantly during low tide and low wind conditions. This study confirms the importance of both winds and landfall timing on the storm surges, which will be crucial to forecast the storm surges associated with the tropical cyclones.


The northern Indian Ocean (NIO) is extremely vulnerable to tropical cyclones (TC). Insights show that about 15% of the worldwide tropical cyclones are over the north Indian Ocean, and the average number of TCs in the north Indian Ocean is about 5–6% of the global annual average of cyclones (source:, of which the frequency of cyclones that make landfall over the coastal regions of Bay of Bengal (BoB) is relatively higher than those over the Arabian Sea (Mohapatra et al. 2012; Sahoo and Bhaskaran 2015). The intensification of these cyclones in the BoB is mostly dependent on the sea surface temperature, ocean heat content, distribution of the eddies in the open ocean, and stratification in the upper ocean (Ali et al. 2007; Tory et al. 2013; Mandal et al. 2018a). These TCs generate surges, extreme wind waves, and coastal floods during their landfall, thereby causing a fundamental threat to the human population and damage to the coastal ecosystem. These destructions are primarily dependent on the cyclone intensities, the maximum radius of curvature of cyclonic winds, geometry, and bathymetry of the landfall region (Dube et al. 1982; Johns et al. 1983; Peng et al. 2004; Peng and Reynolds 2006; Zhong et al. 2010).

The storm surges of higher amplitudes induced by the cyclones were reported in the past, mostly along the north-western and head bay. The genesis and propagation of these surges are predominantly influenced by the basin characteristics (coastal geometry and width of the continental shelf), approach angle and translation speed of an impinging cyclone, and local astronomical tides (Zhang and Chen 2012; Poulose et al. 2018; Pandey and Rao 2019). Since the tidal range variations are quite large from south to north in the western BoB (Murty and Henry 1983; Sindhu and Unnikrishnan 2013), one can expect the interaction among surges, tides, and waves to be prominent along the northern part of the coast during extreme weather events. Similarly, another study by Poulose et al. (2018) has analyzed the role and dependence of varying continental shelf characteristics on the non-linear interaction of storm surges and wind waves along the west coast of India. The results recommend an amplification of the highest surge of nearly 12 cm for every 10 km increase in the continental shelf width.

The BoB has a purely semi-diurnal tidal regime, with the M2 tides having the highest amplitudes (Murty and Henry 1983; Sindhu and Unnikrishnan 2013). The tides and the tidal current amplitudes of the major constituents (M2 and S2) increase northward in the BoB, with the highest of the amplitudes along the northern and north-western BoB (Mandal et al. 2018b, 2020). The interaction of the storm surges along the northern BoB during the high, and low tides may significantly alter the water level elevations, which may lead to severe damage and devastation of the coastal ecosystem. Li et al. (2019) conducted an idealized experiment to study the impact on storm surges by varying the maximum wind speed and radius of maximum wind associated with a cyclone along the northern East China Sea and concluded that both the cyclone intensity and radius of maximum wind play a significant role in determining the storm surge. Bennet and Mulligan (2017) used the parametric wind fields from Symmetric Holland Model (H80) and Generalized Asymmetric Holland Model (GAHM) to compute the significant wave heights, which showed higher correlations of 0.82 and 0.75, respectively (averaged over nine sites), when compared with the observed wave heights from wave rider buoys. The accuracy of surge tide forecast depends not only on the cyclonic track and its intensity, but also on the spatial distribution of winds which includes its speed and direction (Pandey and Rao 2018).

The impact of varying landfall time and intensity on the generation of storm surges, and non-linear interaction of surges and tides are important for coastal management. Thus, it is necessary to understand the variations in the hydrodynamic characteristics associated with tropical cyclone intensity on the surge tides. The accurate presentation of the spatial wind field is necessary. Hence, the objective of this study is to adopt an accurate wind scheme for atmospheric forcing to the ADCIRC model, which ensures better spatial distribution of the wind field relating to the observed wind field to understand the surge variations induced by the tropical cyclone Fani. A series of sensitivity experiments using the ADCIRC model is carried out by allowing the cyclone to make landfall at different phases of a tidal cycle, and further investigate the role of varying wind speeds on the storm surges. A brief discussion on the cyclone Fani is given in section 2. Model experiments, data, and methodology are explained in section 3. Results are discussed in detail in section 4, followed by a summary and conclusions in section 5.

Fani cyclone

The extremely severe cyclone storm Fani is one of the most powerful and destructive tropical cyclones since the 1999 super cyclone that dwelled in BoB from 26th April to 3rd May 2019 (IMD 2019). The system has experienced the lowest pressure drop to 934 hPa with a maximum 3 min sustained wind speed of 59 m/s. The low-pressure system originated as a depression on 26th April 2019 under favourable oceanic and atmospheric conditions and started moving northwestwards (figure 1). It further intensified into a deep depression on 27th April 2019, followed by a quick intensification to cyclonic storm over the southeastern BoB on the same day. Moving northwestwards, it escalated to a very severe cyclonic storm on 30th April 2019 over the southwestern BoB with a maximum wind speed of 41.15 m/s and 970 hPa pressure drop. It moved northwards and further intensified into an extremely severe cyclonic storm on 1st May 2019 with a maximum pressure drop of 934 hPa and the highest sustained wind speed of 59 m/s. The system has undergone a rapid intensification to this stage due to the interaction with very favourable oceanic conditions with sea surface temperatures ranging between 30° and 31°C and low vertical wind shear until the northwestern BoB. However, the system has comparatively weakened in the offshore regions Odisha coast just before landfall on 3rd May 2019. It continued to move northeastwards, weakened to a severe cyclonic storm over north Odisha of the same day, and ultimately dissipating by 4–5th May 2019 (figure 1a).

Figure 1

(a) Best-estimated track (source: IMD) of the tropical cyclone Fani (black line) with centers at every 0000 UTC from 26th April 2019 to 4th May 2019. Observational sea-level data are taken from tide gauges (square dot) at Vizag and Paradeep. The colour bar denotes the bathymetry depth of the whole basin from ETOPO2. Buoys BD08, BD10, and BD13 locations are marked by black square dots. (b) The temporal variation of wind speed (in m/s) and surface pressure (in hPa) from the IMD track data during 26th April 2019–4th April 2019 and, the red dot indicates landfall time.

Model, data, and methodology

This section gives a brief description of the finite-element based ADCIRC (ADvanced CIRCulation) hydrodynamic model, the various in-situ and bathymetry datasets used and the methodology framed for the study.

ADCIRC model

ADCIRC is a two-dimensional depth-integrated shallow water hydrodynamic model that solves the shallow‐water equations on an unstructured triangular mesh to calculate the water level and currents at different scales (Kolar et al. 1994a, b; Luettich and Westerrink 2004). The water levels are calculated by solving the vertically integrated generalized wave continuity equation (GWCE). The GWCE based on finite element solutions to the shallow water equations provides stability to the model (Kolar et al. 1994a, b; Luettich and Westerrink 2004; Dawson et al. 2006). The fully parallelized version of ADCIRC using the message-passing interface (MPI) is used in the present study. The MPI library calls to allow it to operate at high efficiency (typically better than 90%) on parallel computer architectures (source: The model has been used in the BoB domain in earlier studies to understand the wave and hydrodynamics characteristics associated with the tropical cyclones, Phailin and Hudhud (Murty et al. 2014, 2016) and further determined the extent of coastal inundation associated with the different cyclones (Bhaskaran et al. 2014; Gayathri et al. 2015; Sahoo and Bhaskaran 2019; Rao et al. 2020). Also, the model has been utilized to understand the tidal current characteristics in the northern Indian Ocean and the asymmetry has been studied in the Hooghly estuary, the northern BoB by (Jena et al. 2018).

Atmospheric forcing models

The parametric cyclone model can be further divided into a symmetric model and an asymmetric model. The idealized wind vortex is taken from the symmetric Holland wind model 1980 (henceforth denoted H80) as proposed by Holland (1980). The asymmetric wind vortex is taken from the Generalized Asymmetric Wind Model (henceforth denoted GAHM) proposed by Gao et al. (2013). Further, the GAHM is adapted from the H80 model by better taking into account the asymmetry of a cyclone. Here, we take different spatially varying surface wind fields from the parametric H80 and GAHM and compared them to wind observations using buoy winds for the recent tropical cyclone Fani formed in the BoB.

Symmetric Holland model 1980 (H80)

The wind field and atmospheric pressure are calculated at each of the nodes internally by ADCIRC using the symmetric Holland model, which has become the most widely used cyclostrophic model in several hurricane-related studies (Mattocks and Forbes 2008; Bhaskaran et al. 2013, 2014; Muis et al. 2016; Wang et al. 2020). The radial pressure and wind profiles for the H80 expression are as follows:


where \({P}_{c}\) is the minimum central atmospheric pressure, \({P}_{n}\) is the ambient pressure (theoretically at infinite radius), \(P\left(r\right)\) is the pressure at radius r from the center of the cyclone, \({R}_{\mathrm{max}}\) is the radius of maximum wind, \({V}_{g}\) is the gradient wind at radius r, f is Coriolis force and, B is shape parameter which is given as:

$$B=\frac{{[({V}_{m}-{V}_{T})/{W}_{PBL}]}^{2}\mathrm{\rho}_ e}{\left({P}_{n}-{P}_{c}\right)},$$

where \({V}_{m}\) is the maximum sustained wind speed, \({V}_{T}\) is the speed cyclone forward motion \(\rho \) is the air density (1.225 kg/m3) and, \({W}_{PBL}\) is a wind reduction factor whose value is set to 0.90 (Powell et al. 2003). H80 requires the ‘Best Track’ parameters of the cyclone including storm eye location, \({R}_{\mathrm{max}}\), \({V}_{m}\), \({P}_{c}\) and \({P}_{n}\). In this study, the information on storm eye location, \({V}_{m}\), and \({P}_{c}\) are extracted from the India Meteorological Department (IMD) best-track data to be given as an input to the H80 model.

The poor approximation of the radial profile in the inner core of some cyclone cases in the H80 model has been noted by Willoughby and Rahn (2004) amd Willoughby et al. (2006). Owing to the drift motion, the wind field is not intensified on the right-hand side of the cyclone as it should be in the northern hemisphere. However, the cyclostrophic approximation may induce inaccurate wind estimation away from the region of maximum winds. Thus, some asymmetric wind field models were developed by including the elimination of cyclostrophic assumption, frictional inflow corrections, and superimposing the translation component of a cyclone to the H80 model (Zheng et al. 2017; Musinguzi et al. 2019; Qiao et al. 2019).

Generalized asymmetric Holland model (GAHM)

Gao et al. (2013) developed GAHM, which differs from H80 with modifications that include eliminating the assumption of cyclostrophic balance at \({R}_{\mathrm{max}}\) (i.e., where the Coriolis force is negligible compared to the pressure gradient and centripetal force in the gradient wind equation). This generates a more accurate representation of the wind field for a wider range of cyclones and ensures that the predicted winds match all available isotach information (Gao 2018). The radial pressure and wind profile for the GAHM expression are as follows:

$$P\left(r\right)={P}_{c}+\left({P}_{n}-{P}_{c}\right){e}^{{-\mathbf{\varphi }(\frac{{R}_{\mathrm{max}}}{r})}^{{B}_{g}}},$$

where \({R}_{0}\) is the Rossby number at r = \({R}_{\mathrm{max}}\), given as \({R}_{0}={V}_{\mathrm{max}}/{R}_{\mathrm{max}}f\). Moreover,\({V}_{\mathrm{max}}\) is defined as \({V}_{\mathrm{max}}=[({V}_{m}-{\upgamma V}_{T})/{W}_{PBL}]\), where, \(\gamma\,\,{\text{ is the damp factor}}={V}_{g}/{V}_{\mathrm{max}}\) and, \({B}_{g}\) is the shape parameter (same as H80, without assuming the cyclostrophic balance), given as, \({B}_{g}=B\left(1+1/{R}_{0}\right) {e}^{\mathrm{\varphi }-1}/\mathrm{\varphi }\), where \(\varphi \) is the scaling parameter introduced in GAHM, given as \(\varphi =1+(1/{R}_{0})/{B}_{g}\left(1+1/{R}_{0}\right)\).

The GAHM is integrated within the ADCIRC source code and uses the ‘Best Track’ information in the Automated Tropical Cyclone Forecast (ATCF) format. The information on the location of the eye (latitude and longitude), maximum sustained wind speed, and minimum sea-level pressure is extracted from the best track details obtained from IMD (Shankar and Behera 2021), the \({B}_{g}\) and \({R}_{\mathrm{max}}\) are pre-computed in four storm quadrants for the available isotaches in the Asymmetric Wind Input Preprocessor (ASWIP) program (in-built in ADCIRC) and appended to the input file before running an ADCIRC simulation.

In addition, the storm translational speed \({\upgamma V}_{T}\) is added to the vortex winds and tangential winds are rotated by frictional wind inflow. Angle (β) approximation were applied to the GAHM wind field according to Harper et al. (2001), in which approximating the inflow angle increases linearly from 0 at the storm center to \(10^\circ \) at \({R}_{\mathrm{max}}\) and then to \(25^\circ \) at 1.2\({R}_{\mathrm{max}}\), and remains at \(25^\circ \) beyond 1.2 \({R}_{\mathrm{max}}\) (Lin and Chavas 2012)

$$\beta =\left\{\begin{array}{l} 10^{\circ} \,\,\,\, {\mathrm{for}} \,\, r<{R}_{\mathrm{max}}\\ 10^\circ +75 (r-{R}_{\mathrm{max}})/{R}_{\mathrm{max}} \,\, {\mathrm{for}} \,\,{R}_{\mathrm{max}}\le r\le 1.2{R}_{\mathrm{max}}\\25^\circ\,\,\,\, {\mathrm{for}}\,\, r\ge 1.2{R}_{\mathrm{max}}.\end{array}\right. $$


The best-estimated track of the tropical cyclone Fani and other relevant information on the atmospheric parameters are obtained from the India Meteorological Department (IMD 2019). The gridded bathymetry of the study domain is extracted from the NOAA ETOPO2 (NOAA 2006) database with a resolution of a 2-min grid. To validate the tides from the model, tide gauges at Paradeep and Vizag are used. The winds from deep-water moored buoys (BD08, BD10, and BD13) are used to compare with the model-simulated winds. The observed data were obtained from INCOIS and NIOT, Ministry of Earth Sciences (MoES). Three buoys are considered as they are in the proximity of the outer core region of the tropical cyclone Fani (figure 1a). The locations of buoys are mentioned in table 1. The details of the sensors used in the buoy are given in Venkatesan et al. (2013).

Table 1 Details of the buoy locations.


A finite-element unstructured mesh was constructed using the scalar paving density method from a software package surface modeling system (SMS) using the ETOPO2 bathymetry data. The unstructured mesh constructed considering geographical longitude/latitude projection and datum set for Indian mean value, which consists of 116,822 elements and 229,914 nodes (figure 2). The unstructured grid resolution, i.e., the node spacing varies between 3 km very near the coast and a maximum of 60 km in the open ocean, as the continental shelf width increases from the southern to the northwestern BoB (source:

Figure 2

The unstructured mesh using the finite element method (a) for the entire study domain and (b) enlarged view of the Odisha coast. BAN and SL indicate Bangladesh and Sri Lanka, respectively. The red dots denote the tide gauges installed at Vizag and Paradeep.

In the present study, the standalone ADCIRC model simulations use a hybrid bottom friction formulation with a drag coefficient of 0.0028 to compute the storm surges. The values corresponding to the horizontal eddy viscosity coefficient and weighting factor (τ0) in GWCE are considered as 4 m2/s and 0.05, respectively. The time step in the model is set to 20 s. This time step satisfies the Courant–Friedrichs–Lewy (CFL) stability condition which avoids numerical instability associated with the explicit numerical methods used in the model. The Ramp function for all the simulations is about 1 day. Ramping is a way by which terms can be steadily increased over some time in a simulation. This is most often done for model forcing terms like tides or winds, to avoid applying a shock to the model. Also, the wetting and drying algorithm is activated in these simulations at a minimum depth of 0.5 m to delineate the wet and dry elements, and wind drag formulation is applied (Garratt 1977). Le Provost’ tidal database is used to compute tidal forcing implicated to the domain (Le Provost et al. 1998). The major tidal constituents, K1, O1, Q1, P1, M2, N2, S2, K2, L2, 2N2, MU2, NU2, and T2, are used as open boundary forcings to force the model. Two main forces that generate storm surges on the sea surface are the surface wind stress and pressure gradient force.

In the present study, the wind field at the sea surface for the cyclone Fani is derived by using H80 and GAHM models from the IMD best track data. For the accuracy of surge tide prediction, the spatial wind distribution profile obtained from both wind models has been analyzed. A time series (three hourly) comparison of wind speed and wind direction is made with the wind observation at BD08, BD10, and BD13. It is found that the winds in the outer core region of the cyclone are underestimated by H80, whereas the GAHM produced strong and broader wind fields giving fewer errors related to mean bias and root mean square near the buoy locations (see section 4.2). Hence, further experiments of predictions of surge tide for various forcing phenomena (impact of landfall time and cyclone intensity) consisted of 21 simulations carried out using GAHM for atmospheric forcing in the ADCIRC.

The impact of varying winds and non-linear interactions with the bathymetry during the different phases of the tidal cycle may cause significant modulations in the surge levels. With the above-mentioned model configurations, an attempt has been made to investigate the storm surge variations by means of a series of sensitivity experiments and thereby understand the impact of varying landfall timing during the different phases of the tidal cycle in combination with the modulations in wind speeds.

In the first part (see section 4.3), multiple simulations have been performed during the different phases of a tidal cycle, namely, the nearest neap low tide (NLT), the nearest neap high tide (NHT), the nearest spring high tide (SHT), the nearest spring low tide (SLT), nearest same day high tide (SDHT), the nearest same day low tide (SDLT), and the actual surge landfall time (AS). Therefore, the study consists of seven simulations during different phases of a tidal cycle at the observed (actual) wind speed (refer to table 3). In the second part (see section 4.4), another 14 sets of experiments have been done to quantify the tide surge by increasing the wind speed (high wind) and decreasing the wind speed (low wind) by a constant magnitude of value 7.7 m/s (15 Knots) to the actual wind speed value for the same tropical cyclone track, Fani (see figure 3). And thus, imposing modulated wind intensity (high wind and low wind) forcing on seven sets of different phases of the tidal cycle.

Figure 3

The time series of the wind speeds associated with the cyclone tracks with different modulations. The red dot represents the landfall time.


This section presents the results of this study in four subsections as the comparison of the observed sea level from the tide gauges with the model simulated sea level from the standalone ADCIRC model (in section 4.1), comparison of atmospheric wind models (in section 4.2), the impact of varying landfall timing on the surge heights (in section 4.3), and the combined effect of both the varying wind speeds and the varying landfall timings during a tidal cycle on the surge heights (in section 4.4).

Comparison with tide gauges

The sea level data from the ADCIRC model (with astronomical tidal forcing only) is compared with the observed sea level data from the tide gauges installed at Paradeep and Vizag (see figure 4). Owing to the lack of tide gauge observed sea level data during the cyclone period, the comparisons have been made during normal conditions for a period of about 25 days. Both the model simulated and tide gauge records at Paradeep and Vizag indicate the significant signatures of two spring and neap tides.

Figure 4

Time series comparison (a and c) and scatter plots (b and d) of sea levels from the tide gauge and ADCIRC model at Paradeep and Vizag. ‘R’ and ‘D’ denote correlation coefficient and RMSE, respectively.

The sea level at Paradeep varies within the range of –1 to 1 m, with the evident signatures of a tidal cycle during January 2012, well captured from both the model and tide gauge (figure 4a). A strong correlation of 0.99 is observed with a low root-mean-square error (RMSE) of 7 cm (figure 4b). Similarly, the statistics at Vizag are also significant, with a correlation coefficient of 0.99 and a slightly low RMSE of 5 cm (figure 4c and d). The RMSEs vary in the range 5–7 cm, which is attributed to the lack of wind forcing in the initial conditions of the standalone ADCIRC simulation. Furthermore, the harmonic analysis indicates the dominance of the M2 tides, followed by the other semi-diurnal tidal constituents (S2 and N2) and diurnal tidal constituents (K1 and O1), which is in well accordance with the earlier results (Murty and Henry 1983; Mandal et al. 2018b, 2020).

Comparison of atmospheric wind models

The maximum wind speeds (in m/s) at all nodal locations in the domain, spatial wind distribution, and water levels in H80 and GAHM are represented in figure 5(a and b), which shows the extent of high wind speeds caused by tropical cyclone Fani. These figures clearly illustrate higher winds along the right side of the cyclone track (blue line) than the left side. However, the wind field from GAHM is much stronger and wider than that of H80. The H80 has a noticeably weaker and narrower wind field near the landfall area than that of GAHM.

Figure 5

Maximum wind speed track from (a) H80 and (b) GAHM for the tropical cyclone Fani. Spatial distribution of cyclonic wind fields just before the landfall time (2nd May 17:00 hrs) from (c) H80 and (d) GAHM. The spatial maps of model-simulated water level elevation (m), the contours depict range from –0.2 to 1.2 m for every 0.2 m rise of water level and at the landfall time (3rd May 0300 hrs) using (e) H80 and (f) GAHM. Square black dots indicate the stations along the coast which have got affected due to storm, namely, Srikakulam (SK), Ganjam (GJ), Puri (PU), Jagatsinghpur (JP) and Kendrapara (KP) and, thick black line denotes tropical cyclone track for Fani.

The distance from the center of the cyclone to the 10 ms−1 wind contour (shown in figure 5c and d) indicates that GAHM wind formulation leads to a reduction in exponential decay and an increase in the radial extent of the wind in the outer core region of the cyclone, which is about 20% of the H80 wind profile. The GAHM wind profile showed the radial extent of winds to be stronger and more directed towards the coast on the right side of the cyclone track due to the asymmetric nature of the wind profile. It is adopted by considering the elimination of the assumption of cyclostrophic balance at the radius of maximum wind, imposing forward motion and frictional inflow angle correction that allows for a better representation of a wide range of cyclones (Gao 2018), which plays a significant role in the development of surges along the coast.

A closer investigation shows that H80 (figure 5e and f) causes a peak surge in a narrower geographical area and more localized near landfall location along the coast, whereas the GAHM causes a peak in surge broader geographical area along the coast mostly on the right side of the track. Also, the simulated surge tide at the landfall location (85.67°E, 19.70°N) is 0.97 m for the simulated winds from H80, and 1.20 m for the winds from GAHM, which leads to significantly lower water surface elevations for H80 than that of GAHM.

An inter-comparison of winds between H80 and GAHM is performed with the buoy winds. The wind field for the entire domain is constructed using the GAHM model based on the IMD cyclone track information and it is compared with the observed data recorded from the moored buoys BD08, BD10, and BD13 (figure 6). The comparisons of the model computed wind speeds (figure 6a, b, and c) with the observed data indicate that the GAHM has captured the wind speeds and profiles very well along with the maximum wind intensities (as the cyclones progressed towards the coast) as compared to H80 and, the results are in good accordance with the earlier results (Bennett and Mulligan 2017).

Figure 6

Comparison of H80 and GAHM wind speed (ac) and wind direction (df) with the observed data for tropical cyclone Fani at different buoy locations BD07, BD10, and BD13. The red dot represents the landfall time.

Also, a comparison of observed wind direction has been performed with that of H80 and GAHM simulated wind directions. The wind direction from H80 shows a mismatch with the observed wind direction. In contrast, the GAHM simulated wind directions match better with the observed wind data from the moored buoys. The consideration frictional inflow angle approximation according to Harper et al. (2001) in GAHM (as presented in equation 9) gives a better result. The H80 does not provide wind inflow angle correction and correction of the forward motion of the cyclone to account for the asymmetry (Shen et al. 2006), hence the H80 model results are deviating from the true wind fields in terms of wind direction.

The validations results are estimated in terms root-mean-square error (RMSE) and mean bias Error (MBE) for all the three buoys, as shown in table 2. The errors corresponding to wind speed at all the buoy locations are considerably less for GAHM compared to H80. The averaged RMSE has reduced from 53.07° to 24.16° for wind directions and 6.08 to 2.63 ms−1 from wind speeds using GAHM. Hence, further experiments (following from section 4.3 to 4.4) consisting of a total of 21 simulations to quantify the tide surge on various forcing (impact of landfall time and varying cyclone intensity) are carried out using GAHM.

Table 2 Mean bias error (MBE) and root-mean-square error (RMSE) at different buoy locations for the tropical cyclone Fani.

To quantify the impact of cyclonic winds over the tidal forcing during 30th April 2019 03:00 hrs to 4th May 2019 06:00 hrs, two distinct simulations carried out are: (i) only tidal forcing, and (ii) combined effect of tidal and atmospheric forcing (using GAHM). The cyclone was on its intensified stage near the landfall location (85.67°E, 19.70°N) near Puri coast, post the neap tide phase on 3rd May 2019 (figure 7a). Predominant signatures of the tropical cyclone are significantly observed from the different physical parameters (water level elevation, wind speed, and depth-integrated velocity). A significant difference is observed for the water level elevations from the two simulations indicating a storm-induced surge of 1.19 m (figure 7a and b), which is underestimating but nearer to the value issued by the IMD (about 1.5 m). The storm surge is also associated with a high wind speed of 41 m/s and strong currents of 1.33 m/s (figure 7c and d). Furthermore, the spatial distribution of both the parameters also indicates the increased wind speeds and high-water levels in the nearshore regions (figure 5e and f).

Figure 7

(a) Model simulated water level elevation (in m) with only tides (blue line) and with winds (red line) during 30th April 2019 12:00 hrs – 4th May 2019 06:00 hrs. (b) The storm-induced surge (in m) due to the actual wind speeds (i.e., the water level difference between storm tide and tide). The temporal variations of model-simulated (c) wind speed (in m/s) and (d) depth-averaged velocity (in m/s) during the same time. The red dot on temporal plots (a–d) indicates the landfall time (3rd May 2019 03:00 hrs) at the location 85.67°E, 19.70°N.

Impact of varying landfall timing in a tidal cycle

This section focuses on the quantification of the actual surge due to the cyclone and further investigates the impact of varying landfall timing on the induced surges during the different phases of a tidal cycle. The standalone ADCIRC model with tide forcing captures the observed tidal signals during 21st April 2019 to 19th May 2019.

In order to quantify the effect of this varying landfall timing on storm surges, seven simulations with tidal forcing using cyclone winds associated with the cyclone track, namely, landfall at (i) actual surge (AS), (ii) neap low tide (NLT), (iii) neap high tide (NHT), (iv) spring high tide (SHT), (v) spring low tide (SLT), (vi) same day high tide (SDHT), and (vii) same day low tide (SDLT) (red dots in figure 8). The actual landfall happened on 3rd May 2019 at 0300 hrs near the Puri coast, and an induced surge tide of 1.20 m is observed. The landfall timing has been shifted to the high tide and low tide on the same day (SDHT and SDLT), and respective surge tides are 1.61 and 1.06 m (table 3). Also, the signatures of spring and neap tides are predominantly observed from the model and thus, a significant change in the water levels is observed when the landfall timings are shifted to the high and low tides during the spring and neap phases (figure 8). A storm tide of 1.14 m is observed during NLT, whereas, the same is 1.49 m during NHT. On the other hand, the water levels are quite high during the spring tides, which are nearly 1.99 and 0.94 m during SHT and SLT, respectively. Thus, it can be concluded that the tide–surge interactions depend predominantly on the landfall timing in a tidal cycle.

Figure 8

The sea level variability near Puri coast from ADCIRC model (with only tide forcing) during 21st April 2019 to 19th May 2019, denoting the different phases of a tidal cycle. The red dots indicate the landfall timings for the experiments.

Table 3 Quantification of storm-induced tide surge (in m) at varying landfall times in a tidal cycle assuming the cyclone track is not changed.

On the other hand, the difference between the water levels observed during the storm high tide and astronomical high tide, similarly storm low tide and astronomical low tide conditions distinctly during the actual, spring, and neap phase timings are calculated from the two simulations (table 4). The difference in water level differences during different phases of the tidal cycle for the respective high tide and low tides that values are close and range from 1.21 to 1.48 m. These minute sea level differences indicate the non-linear interaction performed by the model during the different phases of a tidal cycle.

Table 4 Water levels simulated with actual wind during high and low tide of spring, neap, and actual phase of the tidal cycle.

The Hovmöller diagram for water level (in m) for the different landfall time showed that the water level is more surging towards the right side as compared to the left side along the coast from landfall location for all the tidal phases (figure 9). The highest water level of 1.99 m is observed during the spring high tide phase (SHT) and the lowest water level of 0.94 m is observed during the spring low tide phase (SLT) for the combined tidal and wind forcing along the coast. Moreover, the water level gradually decreases on the left side from the landfall locations as compared to the right side for all the landfall times with respect to a different phase of the tidal cycle.

Figure 9

Hovmöller diagram indicating water level (m) for different landfall times (as shown on y-axis). The distance along the coast (referring zero as landfall point (85.67°E, 19.70°N). Negative (positive) values of x-axis represent distance on the left (right) of landfall.

Combined impact of both the varying wind speeds and landfall timing

To further gain insights into the impact of varying intensities of tropical cyclones along with the varying landfall timings in a tidal cycle, 14 more simulations have been carried out using two manipulated wind speeds along the same cyclone track. The wind speeds associated with the tropical cyclone have been modified from the actual wind speed by increasing the wind speed amount of 7.7 m/s (+15 Knots), which is considered to be as high wind, and by decreasing by wind speed amount of 7.7 m/s (–15 Knots) which is considered to be as low wind. Moreover, the surge tides are the highest for the high winds, followed by the actual winds and low winds. In the actual wind conditions and at actual landfall timing, the surge tide of 1.20 m is observed. However, an increase in water level (1.45 m) is observed due to higher winds, and the same decreases to 0.96 m during the lower wind conditions. Similar results are observed for all the phases of the tidal cycle for the respective wind modulations (figure 10a–g).

Figure 10

The time series of water levels associated with the cyclone track for the tropical cyclone Fani with different wind modulations for the different phases of the tidal cycle (as presented in table 3).

The simulated surge tides at different tidal phases with three different winds are shown in figure 11(a). The maximum surge tide (2.25 m) is obtained in the case of spring high tide with high wind conditions. The minimum surge tide (0.78 m) is obtained from the spring low tide with low wind. The results showed that for a particular tidal phase, the tide surge increases with an increase in wind speeds. As Odisha coast is vulnerable to coastal erosion due to the frequent occurrence of the tropical cyclone, so computation of alongshore sediment transport is an integral part of coastal scientists for sustainable management of its coastal resources, for which quantification of the depth-averaged current field is also presented in figure 11(b) in this study, which is essential for a cyclonic event. It is evident that varying forcing by atmospheric wind and astronomical tide predominantly acts on ocean currents, resulting in water level changes.

Figure 11

The bars indicate (a) the tide surge and (b) depth-averaged currents during different phases of a tidal cycle (according to ascending landfall time order), for low, actual, and high wind conditions (as presented in colour bars) at the landfall location 85.67°E, 19.70°N.

Thus, it can be concluded that not only the tides and winds play a significant role in the intensification of the surge tides, but also the combined impact of both the higher winds and high tide conditions lead to an increased surge which could cause potential damage to the coastal living environment.

Summary and conclusions

The accuracy of storm surge forecast depends not only on the cyclonic track and its intensity, but also on the spatial distribution of winds which include its speed and direction. Hence, in this study, a two-dimensional hydrodynamic ADCIRC model has been configured for the BOB. The simulated sea level data match well with the observed sea level data from the tide gauges (higher correlation and lower errors), suggesting that the reliability of the tide model studies the coastal hydrodynamics processes. Then, the recent tropical cyclone Fani was simulated from the pressure data from IMD using symmetric H80 and asymmetric GAHM wind models. Comparisons with the observations available at moored ocean buoys indicate that the H80 symmetric wind model underestimates the cyclonic wind speeds in the outer core region and also shows a mismatch in its direction. The asymmetric winds from GAHM simulated relatively better winds with less bias and RMSEs. The advantage of GAHM over H80 is the elimination of cyclostrophic assumption and, frictional inflow corrections, which generates an asymmetric and more accurate representation of the wind field for a wider range of cyclones for GAHM. Further, 21 simulations are carried out using GAHM to quantify the impact of landfall time and varying cyclone intensity on storm surges.

This study investigates the coastal hydrodynamics associated with the tropical cyclone ‘Fani’ to particularly focus on (i) the impact of variable landfall timing on the surge tides in a tidal cycle, and also (ii) to elucidate the combined impact of varying winds and different landfall timings in a tidal cycle on the surge tides. The major results of this study are summarized below:

  • The signatures of the tropical cyclone are well captured in the model with an asymmetric wind speed of 41 m/s, and a strong depth-integrated velocity of 1.33 m/s. Also, a storm-induced surge of 1.19 m is observed from the model, which is nearer to the surge values forecasted by the India Meteorological Department (1.50 m).

  • A series of sensitivity experiments considering varying landfall timing during a tidal cycle indicate the strongest tide surge (~1.99 m) during the spring high tide phase, followed by a tide surge of 1.61 m during the same day high tide (SDHT). However, the tide surges are comparatively less for landfall during the neap tidal phases.

  • The combined impact of wind speeds and varying landfall timing indicated the strongest surge tides of 2.25 m during high wind conditions associated with spring high tides. On the other hand, the surge tides of 1.99 and 1.75 m are observed during the actual wind and low wind conditions, respectively. Surges are comparatively less during the low wind and low tide combinations during the neap tidal phase.

Forecasting the storm surges is very crucial to prevent the huge devastation of coastal ecosystems, habitat, and economic losses. However, the prediction of the surge during a high tide or a low tide condition is very imperative, which is highly dependent on the cyclone landfall time. Thus, the tide-surge interactions in the prediction of storm surge are essential. This study presents insights into the stronger impacts of tides and winds on the surges depending on the varying landfall timing (during high and low tides) during high and low wind conditions. This particular result is quite important and valuable to be incorporated in the development of an operational forecasting system along the different coasts around the world to predict the coastal inundation and surge tides to disseminate proper warnings to the coastal zone authorities to adopt evacuation policies.


  1. Ali M M, Jagadeesh P V and Jain S 2007 Effects of eddies on Bay of Bengal cyclone intensity; EOS 88(8) 93–95,

    Article  Google Scholar 

  2. Bennett V C and Mulligan R P 2017 Evaluation of surface wind fields for prediction of directional ocean wave spectra during hurricane sandy; Coast. Eng. 125 1–5,

    Article  Google Scholar 

  3. Bhaskaran P K, Gayathri R, Murty P L, Bonthu S and Sen D A 2014 Numerical study of coastal inundation and its validation for Thane cyclone in the Bay of Bengal; Coast. Eng. 83 108–118,

    Article  Google Scholar 

  4. Bhaskaran P K, Nayak S, Bonthu S R, Murty P N and Sen D 2013 Performance and validation of a coupled parallel ADCIRC–SWAN model for THANE cyclone in the Bay of Bengal; Environ. Fluid Mech. 13(6) 601–623,

    Article  Google Scholar 

  5. Dawson C, Westerink J J, Feyen J C and Pothina D 2006 Continuous, discontinuous and coupled discontinuous–continuous Galerkin finite element methods for the shallow water equations; Int. J. Numer. Methods Fluids 52(1) 63–88,

    Article  Google Scholar 

  6. Dube S K, Sinha P C and Rao A D 1982 The effect of coastal geometry on the location of peak surge; Mausam 33(4) 445–450.

    Google Scholar 

  7. Gao J 2018 On the surface wind stress for storm surge modeling. Doctoral dissertation, The University of North Carolina at Chapel Hill.

  8. Gao J, Luettich R and Fleming J 2013 Development and initial evaluation of a generalized asymmetric tropical cyclone vortex model in ADCIRC; In: Proceedings of the ADCIRC Users Group Meeting, Vicksburg, MS, USA (Vol. 16).

  9. Garratt J R 1977 Review of drag coefficients over oceans and continents; Mon. Weather Rev. 105(7) 915–929.

    Article  Google Scholar 

  10. Gayathri R, Bhaskaran P K and Sen D 2015 Numerical study on storm surge and associated coastal inundation for 2009 AILA Cyclone in the head Bay of Bengal; Aquat Procedia 4 404–411,

    Article  Google Scholar 

  11. Harper B A, Hardy T, Mason L, Bode L, Young I and Nielsen P 2001 Queensland climate change and community vulnerability to tropical cyclones: Ocean hazards assessment –Stage 1; Report prepared by Systems Engineering Australia in conjunction with James Cook University Marine Modelling Unit, Queensland Government.

  12. Holland G J 1980 An analytic model of the wind and pressure profiles in hurricanes; Mon. Weather Rev. 108(8) 1212–1218,<1212:AAMOTW>2.0.CO;2.

    Article  Google Scholar 

  13. IMD Report 2019 Extremely severe cyclonic storm Fani over east central equatorial Indian Ocean and adjoining southeast Bay of Bengal, India Meteorological Department.

  14. Jena B K, Sivakholundu K M and Rajkumar J A 2018 Description of tidal propagation in Hooghly estuary using numerical and analytical solutions; Ocean Eng. 169 38–48,

  15. Johns B, Sinha P C, Dube S K, Mohanty U C and Rao A D 1983 Simulation of storm surges using a three-dimensional numerical model: An application to the 1977 Andhra cyclone; Quart. J. Roy. Meteorol. Soc. 109(459) 211–224,

    Article  Google Scholar 

  16. Kolar R L, Gray W G, Westerink J J and Luettich R A Jr 1994 Shallow water modeling in spherical coordinates: Equation formulation, numerical implementation, and application; J. Hydraul. Res. 32(1) 3–24,

    Article  Google Scholar 

  17. Kolar R L, Westerink J J, Cantekin M E and Blain C A 1994 Aspects of nonlinear simulations using shallow-water models based on the wave continuity equation; Comput. Fluids 23(3) 523–538,

    Article  Google Scholar 

  18. Le Provost C, Lyard F, Molines J M, Genco M L and Rabilloud F 1998 A hydrodynamic ocean tide model improved by assimilating a satellite altimeter-derived data set; J. Geophys. Res. Oceans 103(C3) 5513–5529,

    Article  Google Scholar 

  19. Li J, Hou Y, Mo D, Liu Q and Zhang Y 2019 Influence of tropical cyclone intensity and size on storm surge in the Northern East China Sea; Remote Sens. 11(24) 3033,

    Article  Google Scholar 

  20. Lin N and Chavas D 2012 On hurricane parametric wind and applications in storm surge modeling; J. Geophys. Res. Atmos. 117(D9),

  21. Luettich R A and Westerink J J 2004 Formulation and numerical implementation of the 2D/3D ADCIRC finite element model version 44. XX; 74p.

  22. Mandal S, Sil S and Gangopadhyay A 2020 Tide-current-eddy interaction: A seasonal study using high frequency radar observations along the western Bay of Bengal near 16°N; Estuar. Coast. Shelf Sci. 232 106523,

  23. Mandal S, Sil S, Shee A and Venkatesan R 2018 Upper Ocean and subsurface variability in the Bay of Bengal during cyclone ROANU: A synergistic view using in-situ and satellite observations; Pure Appl. Geophys. 175 4605–4624,

    Article  Google Scholar 

  24. Mandal S, Sil S, Gangopadhyay A, Murty T and Swain D 2018 On extracting high-frequency tidal variability from HF radar data in the northwestern Bay of Bengal; J. Oper. Oceanogr. 11(2) 65–81,

    Article  Google Scholar 

  25. Mattocks C and Forbes C 2008 A real-time, event-triggered storm surge forecasting system for the state of North Carolina; Ocean Model. 25(3–4) 95–119,

    Article  Google Scholar 

  26. Mohapatra M, Nayak D P and Bandyopadhyay B K 2012 Evaluation of cone of uncertainty in tropical cyclone track forecast over North Indian Ocean issued by India Meteorological Department; Trop. Cyclone Res. Rev. 1(3) 331–339,

    Article  Google Scholar 

  27. Muis S, Verlaan M, Winsemius H, Aerts J and Ward P 2016 A global reanalysis of storm surges and extreme sea levels; Nat. Commun. 7(1) 1–2,

    Article  Google Scholar 

  28. Murty P L, Bhaskaran P K, Gayathri R, Sahoo B, Kumar T S and Subba Reddy B 2016 Numerical study of coastal hydrodynamics using a coupled model for Hudhud cyclone in the Bay of Bengal; Estuar. Coast. Shelf Sci. 183 13–27,

    Article  Google Scholar 

  29. Murty P L, Sandhya K G, Bhaskaran P K, Jose F, Gayathri R, Nair T B, Kumar T S and Shenoi S S 2014 A coupled hydrodynamic modeling system for PHAILIN cyclone in the Bay of Bengal; Coast. Eng. 93 71–81,

    Article  Google Scholar 

  30. Murty T S and Henry R F 1983 Tides in the Bay of Bengal; J. Geophys. Res. Oceans. 88(C10) 6069–6076,

    Article  Google Scholar 

  31. Musinguzi A, Akbar M K, Fleming J G and Hargrove S K 2019 Understanding hurricane storm surge generation and propagation using a forecasting model, forecast advisories and best track in a wind model, and observed data – Case study Hurricane Rita; J. Mar. Sci. Eng. 7(3) 77,

    Article  Google Scholar 

  32. NOAA National Geophysical Data Center 2006 2-minute Gridded Global Relief Data (ETOPO2) v2; NOAA National Centers for Environmental Information,

    Article  Google Scholar 

  33. Pandey S and Rao A D 2018 An improved cyclonic wind distribution for computation of storm surges; Nat. Hazards 92(1) 93–112,

    Article  Google Scholar 

  34. Pandey S and Rao A D 2019 Impact of approach angle of an impinging cyclone on generation of storm surges and its interaction with tides and wind waves; J. Geophys. Res. Oceans 124(11) 7643–7660,

    Article  Google Scholar 

  35. Peng M S and Reynolds C A 2006 Sensitivity of tropical cyclone forecasts as revealed by singular vectors; J. Atmos. Sci. 63(10) 2508–2528,

    Article  Google Scholar 

  36. Peng M S, Ridout J A and Hogan T F 2004 Recent modifications of the Emanuel convective scheme in the Navy operational global atmospheric prediction system; Mon. Weather Rev. 132(5) 1254–1268.

    Article  Google Scholar 

  37. Poulose J, Rao A D and Bhaskaran P K 2018 Role of continental shelf on non-linear interaction of storm surges, tides and wind waves: An idealized study representing the west coast of India; Estuar. Coast. Shelf Sci. 207 457–470,

    Article  Google Scholar 

  38. Powell M D, Vickery P J and Reinhold T A 2003 Reduced drag coefficient for high wind speeds in tropical cyclones; Nature 422(6929) 279–283,

    Article  Google Scholar 

  39. Qiao W, Song J, He H and Li F 2019 Application of different wind field models and wave boundary layer model to typhoon waves numerical simulation in WAVEWATCH III model; Tellus A 71(1) 1657552,

    Article  Google Scholar 

  40. Rao A D, Upadhaya P, Pandey S and Poulose J 2020 Simulation of extreme water levels in response to tropical cyclones along the Indian coast: A climate change perspective; Nat. Hazards 100(1) 151–172,

    Article  Google Scholar 

  41. Sahoo B and Bhaskaran P K 2015 Synthesis of tropical cyclone tracks in a risk evaluation perspective for the east coast of India; In: International Conference on Water Resources Coastal and Ocean Engineering, (ICWRCOE) 15 389–396,

  42. Sahoo B and Bhaskaran P K 2019 Prediction of storm surge and inundation using climatological datasets for the Indian coast using soft computing techniques; Soft. Comput. 23(23) 12,363–12,383,

    Article  Google Scholar 

  43. Shankar C G and Behera M R 2021 Numerical analysis on the effect of wave boundary condition in storm wave and surge modeling for a tropical cyclonic condition; Ocean Eng. 220 108371,

    Article  Google Scholar 

  44. Shen J, Gong W and Wang H V 2006 Water level response to 1999 Hurricane Floyd in the Chesapeake Bay; Cont. Shelf Res. 26(19) 2484–2502,

    Article  Google Scholar 

  45. Sindhu B and Unnikrishnan A S 2013 Characteristics of tides in the Bay of Bengal; Mar. Geod. 36(4) 377–407,

    Article  Google Scholar 

  46. Tory K J, Dare R A, Davidson N E, McBride J L and Chand S S 2013 The importance of low-deformation vorticity in tropical cyclone formation; Atmos. Chem. Phys. 13(4) 2115–2132,

    Article  Google Scholar 

  47. Venkatesan R, Shamji V R, Latha G, Mathew S, Rao R R, Muthiah A and Atmanand M A 2013 In-situ ocean subsurface time-series measurements from OMNI buoy network in the Bay of Bengal; Curr. Sci. 10 1166–1177,

  48. Wang X, Yao C, Gao G, Jiang H, Xu D, Chen G and Zhang Z 2020 Simulating tropical cyclone waves in the East China Sea with an event-based, parametric-adjusted model; J. Oceanogr. 76(6) 439–457,

    Article  Google Scholar 

  49. Willoughby H E, Darling R W R and Rahn M E 2006 Parametric representation of the primary hurricane vortex. Part II: A new family of sectionally continuous profiles; Mon. Weather Rev. 134 1102–1120,

    Article  Google Scholar 

  50. Willoughby H E and Rahn M E 2004 Parametric representation of the primary hurricane vortex. Part I: Observations and evaluation of the Holland (1980) model; Mon. Weather Rev. 132(12) 3033–3048.

  51. Zhang D L and Chen H 2012 Importance of the upper‐level warm core in the rapid intensification of a tropical cyclone; Geophys. Res. Lett. 39(2),

  52. Zheng J H, Wang J C, Zhou C Y, Zhao H J and Sang S 2017 Numerical simulation of typhoon-induced storm surge along Jiangsu coast, Part II: Calculation of storm surge; Water Sci. Eng. 10(1) 8–16,

    Article  Google Scholar 

  53. Zhong W, Lu H C and Zhang D L 2010 Mesoscale barotropic instability of vortex Rossby waves in tropical cyclones; Adv. Atmos. Sci. 27(2) 243–252,

    Article  Google Scholar 

Download references


The authors acknowledge the financial assistance from the Science and Engineering Research Board (SERB), Government of India (CRG/2019/005842). They thank INCOIS and NIOT, Ministry of Earth Sciences (MoES) for providing the tide gauge and deep water buoy datasets free of cost. The authors thank the Editor and reviewers for their suggestions to improve the quality of the work. Finally, the Indian Institute of Technology Bhubaneswar is acknowledged for providing financial and infrastructural support.

Author information




VGS performed all analyses and wrote the initial manuscript. SM contributed to the methodology, interpretation, and editing of the manuscript. SS supervised the entire work. All authors VGS, SM, and SS have contributed to the discussions and writing the manuscript.

Corresponding author

Correspondence to Sourav Sil.

Additional information

Communicated by C Gnanaseelan

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Shashank, V.G., Mandal, S. & Sil, S. Impact of varying landfall time and cyclone intensity on storm surges in the Bay of Bengal using ADCIRC model. J Earth Syst Sci 130, 194 (2021).

Download citation


  • storm surge
  • tides
  • winds
  • cyclone Fani
  • GAHM