Simulation of Cyclone-Induced Storm Surges in the Low-Lying Delta of Bangladesh Using Coupled Hydrodynamic and Wave Model (SWAN + ADCIRC) M

Simulation of cyclone-induced storm surges in the low-lying delta of Bangladesh using coupled hydrodynamic and wave model (SWAN + ADCIRC) M. Deb and C.M. Ferreira

Department of Civil, Environmental & Infrastructure Engineering, George Mason University, Fairfax, VA, USA

Correspondence Abstract Mithun Deb, Department of Civil, Environmental & Infrastructure Bangladesh is vulnerable to several natural disasters and cyclone-generated storm Engineering, George Mason University, surges have resulted in the deaths of over 700 000 people since 1960. Advancing Fairfax, VA 22030, USA our capability to model and simulate storm surges using numerical models is Tel: +1 571 265 9815 utmost important to support early warning and emergency response efforts in Emails: [email protected]; the region. This study primarily explored the effectiveness of a hydrodynamic [email protected] model (ADvanced CIRCulation, ADCIRC) coupled with wave model (Simulating WAves Nearshore, SWAN) under a high-performance computing DOI: 10.1111/jfr3.12254 environment to simulate storm surge and inundation in coastal regions of Bangladesh. The modelling framework was validated using data from freely avail- Key words Bangladesh; coastal ﬂooding; cyclone; able historical reports and buoy data. The model-generated storm surge water 2 storm surge; SWAN + ADCIRC. level shows good agreement with the observations with maximum R value of 0.98 and root mean square error of 0.30 m. Ultimately, research ﬁndings have highlighted the importance of the coupled wave and hydrodynamic modelling to calculate storm surges in a region with poor observational coverage.

Introduction the storm surge height, causing disastrous floods along the coast (Murty et al., 1986; Dube et al., 1997; Madsen and In Bangladesh, flooding due to tropical cyclones has Jakobsen, 2004). resulted in the deaths of over 700 000 people since 1960 Several numerical models have been previously devel- (Chowdhury and Karim, 1996) and it is still considered as oped to simulate storm surges associated with cyclonic one of the most destructive meteorological phenomena in storms making landfall on the coast of Bangladesh (e.g. the region. Almost one sixth of tropical cyclones that Flather and Khandoker, 1993; Flather, 1994; Roy, 1995; develop in the Bay of Bengal make landfall on the Henry et al., 1997). Other studies have demonstrated lim- Bangladesh coast (Islam et al., 2011). Approximately 5% of itations in storm surge modelling in the Bay of Bengal (e.g. the global tropical cyclones form over the Bay of Bengal; Ali, 1979; Murty et al., 1986; Das, 1994a, b; Dube et al., however, fatalities were observed to be 80% of the global 1997; Chittibabu, 1999). More recently, depth-averaged record (Debsharma, 2007). Numerous historic super two-dimensional (2D) hydrodynamic models have been cyclones, such as the 1970 Bhola cyclone, the 1991 used to solve the shallow-water continuity equations and Bangladesh cyclone (hereafter referred as BOB 1991) and calculate storm surges in the Bay of Bengal (e.g. Dube et al., the Sidr 2007 cyclone had catastrophic effects in the coastal 1994; Madsen and Jakobsen, 2004; Bhaskaran et al., 2013, areas of Bangladesh by taking thousands of lives and caus- 2014; Murty et al., 2014). These hydrodynamic models ing significant property damage. In November 1970, the require wind and pressure fields derived from dynamic most deadly cyclone of the century passed through Bhola storm models (e.g. Jelesnianski and Taylor, 1973; Holland, and killed nearly 500 000 people in Bangladesh (Madsen 1980) using observed cyclone data (track, central pressure and Jakobsen, 2004; Islam et al., 2011). The coastal region and maximum wind speed). It is worthwhile to note that of Bangladesh is particularly vulnerable to storm surge implementation of a coupled modelling system that gener- flooding because of low-lying heavily inhabited areas and ates combined effects of physical processes such as tides, continental shelf with shallow bathymetry, which amplifies storm surges and waves in Bay of Bengal is the subject of

J Flood Risk Management (2016) © 2016 The Chartered Institution of Water and Environmental Management (CIWEM) and John Wiley & Sons Ltd Deb and Ferreira ongoing research. It has been shown that wave radiation we developed an unstructured high-resolution numerical stresses can affect the wave-induced currents and increase mesh for the Bangladesh coast. We examined the effective- water level by 5%–20% in regions with broad continental ness of SWAN + ADCIRC model in assessing waves and shelf and nearly 35% in regions of steep slope (Funakoshi storm surge in a data-poor region and showed the model et al., 2008; Dietrich et al., 2010). A coupled wave and performance considering the limitations with data availabil- hydrodynamic model can provide better understanding of ity and accuracy. nearshore hydrodynamics during extreme events such as cyclones (Dietrich et al., 2010). Although a few recent studies have employed a coupled wave-tide circulation model- Study area ling framework for the South China Sea and Bay of Bengal (e.g. Moon, 2005; Bhaskaran et al., 2013, 2014; Murty et al., The Bay of Bengal is among the most vulnerable and least 2014), further development of this framework is needed for studied areas when compared with other cyclone-prone effective implementation. parts of the world. The coastal regions of Bangladesh, India With the advancement of computational resources and and Myanmar suffer the most in terms of casualties by numerical models, comprehensive validations of coupled storm surges, while the actual occurrence of extreme tropi- hydrodynamic and wave models have been established for cal cyclones is not relatively high (Debsharma, 2007). The the Atlantic Ocean (e.g. Dietrich et al., 2011). For example, model domain introduced here includes the entire the 2D, depth-integrated and finite element hydrodynamic Bangladesh coast and part of the Northern Indian Ocean, circulation model ADCIRC (ADvanced CIRCulation) the east coast of India and part of Myanmar (Burma) (Westerink et al., 1993) has been validated for several hur- (Figure 1). The Meghna estuary along the coastal belt of ricanes in the Atlantic basin (Westerink et al., 2008; Bunya Bangladesh has frequently suffered from impacts of tropical et al., 2010), and it has been used by the US Army Corps of cyclones causing heavy loss of life and property. This Engineers (USACE) to estimate hurricane flooding risk in research has prioritised the low-lying Ganges delta, coastal the United States (e.g. Cialone et al., 2008). Similarly, a rivers and mangrove forests in the coast of Bangladesh for third-generation wind-wave model SWAN (Simulating storm surge vulnerability. Among numerous contributing WAves Nearshore), which describes the evolution of the factors in the coastal stretch of the Bangladesh shallow 2D wave energy spectrum, is being used extensively nowa- water shelf, densely populated small islands and countless days for simulating shallow water waves (Dietrich et al., number of inlets are considered as the most sensitive 2011; Ferreira et al., 2014a). The coupled SWAN + elements for disastrous storm surges. Therefore, a high- ADCIRC model is highly scalable and performs well to resolution unstructured numerical mesh with approxi- compute waves and circulation during their transmission mately 200 000 nodes and 400 000 elements has been gen- fi from deep to shallow water zones (Dietrich et al., 2010). erated by keeping a ne resolution of 300 m in the shallow Recently, the SWAN + ADCIRC model has been validated water zones of the Bangladesh coast and 52 km in the deep for the Atlantic basin, Gulf of Mexico and several coastal ocean boundary. Several numerical experiments were car- areas in the United States with a significant number of ried out using the meteorological forcing representative of National Oceanic and Atmospheric Administration the BOB 1991 and the 2007 Sidr cyclones, which travelled – – (NOAA) water-level recording gauges and National Data across the Ganga Brahmaputra Meghna deltaic system of Buoy Center (NDBC) stations (e.g. Ferreira et al., 2014a). Bangladesh and were among the most hazardous natural While the availability of high-resolution data, such as light calamities of the century. detection and ranging (LiDAR), land cover and extensive water-level monitoring, has supported the rapid advance- Methodology ment of the implementation and validation of storm surge models in some areas of the world, it is as yet difficult to Mesh development implement accurate coupled hydrodynamic models in coastal regions of developing countries like Bangladesh, The precision of coupled wave and hydrodynamic models where high-resolution topographic and bathymetric data depends on the quality of data available (Sebastian et al., sets are not available and the number of water-level record- 2014). Higher-resolution topographic data (e.g. LiDAR) ing gauges is limited for model validation. can support the simulation of storm surge propagation In this study, we have applied a tightly coupled SWAN + accurately (Wang et al., 2014), but is not yet publically ADCIRC model on a single unstructured mesh to explore available for the Bangladesh coastal zones. For this analysis, its performance under various meteorological forcing con- global topographic and bathymetric data used to generate ditions and the subsequent storm surge flooding in the the numerical mesh for SWAN + ADCIRC simulation coastal areas of Bangladesh. During the validation process, were collected from the freely available General

Figure 1 Study area (Bay of Bengal: Bangladesh coast).

Bathymetric Chart of the Oceans (GEBCO) database (e.g. of 300 m (Figure 2(a)). This unstructured numerical mesh Bhaskaran et al., 2013). The GEBCO data set consists of a comprises almost 200 000 nodes and 400 000 triangular 30 arc-second (900 m) resolution grid generated by joining elements that can represent many of the tortuous rivers quality-controlled ship depth soundings (Henstock et al., and smaller islands in the estuary (Figure 2(b,c)). This wide 2006) and land data based on the 3 arc-second (90 m) reso- coverage of ocean boundary allows tides to be specified at lution Shuttle Radar Topography Mission (SRTM30) the deeper basin of the Bay and storms to propagate for a (Jarvis et al., 2004). A numerical mesh with high resolution reasonable time inside the domain. Bathymetry in some in shallow water zones is the most critical factor in storm nearshore water bodies, such as rivers and channels, was surge computations (Blain et al., 1994). The numerical extracted from local riverine studies (e.g. Ali et al., 2007), mesh boundary of earlier Bay of Bengal models was as the 900 m GEBCO data set has poor definition of these restricted to the vicinity of the coast only (Flather, 1994; smaller streams. All of the collected data sets were observed Salek, 1998), excluding coastal districts of Bangladesh. Roy to have the same datum, mean sea level (MSL) similar to (1995) has defined a model domain for the Bay of Bengal GEBCO. For example, Lewis and Bates (2013) have pro- of about 1.4 × 1.3 km2 resolution for shallow water zones vided the dredging information of Pussur River channel to consider small islands of the estuary and 22.2 × 21 km2 which kept the bathymetry at constant of 6 m. Additional of coarse resolution for the outer ocean boundary with information about Meghna estuary bathymetry has been 10 612 grid points. In a related study, Mandal et al. (1996) derived from Ali et al. (2007). considered a nested finite element mesh consisting of 5686 grid points, with 46 × 81 points in the fine mesh scheme and 40 × 49 grid points in the coarse mesh scheme. More- ADCIRC + SWAN over, in a recent study, Mashriqui et al. (2006) have estab- We performed the hurricane storm surge simulations using fi lished an unstructured nite element mesh including about the coupled hydrodynamic and wave model SWAN + 363 399 elements and 186 981 nodes for the Bay of Bengal, ADCIRC (Dietrich et al., 2011). ADCIRC is a finite- consisting part of the Northern Indian Ocean, the east element hydrodynamic model that generates water levels coast of India and all of the coasts of Bangladesh. The and current velocities, and is widely used for storm surge model domain introduced in this analysis also contains the modelling in the east coast of United States and Gulf of entire Bangladesh coast and part of the Northern Indian Mexico (e.g. Ferreira et al., 2014b). We used the 2D, depth- Ocean but uniquely focusing on coastal districts and low- integrated version of ADCIRC (Luettich and Westerink, fi fi lying Ganges delta for the rst time with a ne resolution 2004) that solves the vertically integrated generalised wave

∂h ! + rhUh =0 ð1Þ ∂t

! ! ! ∂U ! pxðÞ,y _ ! τs τb + ðÞU:rh U = −grh ζ + + f k × U + − ∂t gρ hρ hρ ð2Þ

where h is bathymetric depth, t is the time, ζ is surge eleva- ! tion above MSL, U is depth-averaged horizontal velocity vector, p is barometric pressure, f is the Coriolis force coef- _ ﬁcient, k is a vertical unit vector, τs is the free surface shear stress, τb is the bottom shear stress, ρ is the water density and g is gravitational acceleration. The wave model SWAN follows a fully implicit ﬁnite difference method solving the wave action balance equation (Zijlema, 2010). SWAN computes the wave action density spectrum N (x,t,σ,θ) in geographical space x and time t, with σ the relative frequency and θ the wave direction, as governed by the action balance equation (Booij et al., 1999): i ∂N * * * ∂CθN ∂CσN S + r x C + U ÞN + + = tot ð3Þ ∂t g ∂θ ∂σ σ

* where N is the wave action density spectrum, C g is the * wave group velocity, U is ambient current vector, Cσ is the propagation velocity and Cθ is the wave propagation veloc-

ity. The source term, Stot represents all physical processes that generate, dissipate or redistribute wave energy. Detailed information regarding governing equations related to ADCIRC and SWAN can be found in Luettich and Wes- terink (2004) and Booij et al. (1999), respectively. Meteoro- logical wind data can be incorporated into ADCIRC in a variety of formats, and can be adjusted directionally to consider surface roughness effects (Bunya et al., 2010). ADCIRC interpolates data to the mesh vertices after spatial and temporal adjustments, and then passes them to SWAN to calculate wave radiation stress gradients. Then, ADCIRC uses the SWAN results to extrapolate forward the wave forcing in time. After completion of the coupled time interval, ADCIRC passes on the wind velocity, water levels and currents to SWAN to further update the radiation stress. In this way, the radiation stress gradients used by ADCIRC and the wind speeds, water levels and currents used by SWAN are mutually exchanged between both models. Fur- ther details about the coupled ADCIRC + SWAN model Figure 2 (a) Finite element grid land and ocean boundaries. can be found in Dietrich et al. (2011). In this study, we ran (b) Grid resolution (in metres). (c) Mesh bathymetry and elevation the coupled model in a parallel computational environment data (in metres) collected from General Bathymetric Chart of the with 600 cores from STAMPEDE super computing envi- Oceans (GEBCO). ronment (e.g. Mandli and Dawson, 2014). SWAN and ADCIRC utilise the same local submeshes decomposed continuity equation (GWCE) and the momentum equa- over the number of cores and pass the information stored tions (Eqns (1) and (2), respectively): at the vertices through local memory.

Spatial attributes bottom friction and other relevant spatial attributes for the ADCIRC simulation for the Bay of Bengal region, land Land cover is expected to play a significant role in the forc- cover data set of 1 km resolution was collected from the ing and dissipation mechanisms of storm surges in near- Global Land Cover 2000 Project (GLC2000). The land shore coastal bays (Westerink et al., 2008). Recent studies cover maps used to generate GLC2000 are all based on discuss how land cover types such as wetlands and man- daily data collected from the vegetation sensor on-board of groves can effectively attenuate hurricane storm surges (e.g. earth observation satellite SPOT 4 and land cover cate- Zhang et al., 2012; Barbier et al., 2013; Liu et al., 2013; gories identified and mapped in detailed regional scale pro- Ferreira et al., 2014a). Land cover information can be ducts were then generalised into global scale GLC2000 incorporated into hydrodynamic modelling by quantifying (Bartholomé and Belward, 2005). For this study, we have fl its in uence on the bottom friction and interferences on reclassified the GLC2000 data set according to the National the momentum transmitted by the wind to the water Land Cover Database 2001 (NLCD 2001) (Figure 3(a–c)). column (Westerink et al., 2008; Wamsley et al., 2009; Land cover classes such as mangroves and irrigated agricul- Bunya et al., 2010). To compute the spatially varying tures from GLC2000 were considered as woody wetlands

Figure 3 (a) Reclassiﬁed Global Land Cover 2000 Project (GLC2000) data for Bangladesh coast. (b) Manning’s N values for the entire Bangladesh coast. (c) Vegetation canopies recognised from GLC2000.

J Flood Risk Management (2016) © 2016 The Chartered Institution of Water and Environmental Management (CIWEM) and John Wiley & Sons Ltd Deb and Ferreira and cultivated crops, respectively, to match the NLCD the JTWC database. The minimum sea-level pressure and 2001. A detailed description of the methodology for storm track data for the BOB 1991 have been collected from extracting friction parameters such as Manning’s N, surface a previous study by Salek (1998), who conducted a sensitiv- canopy and surface directional effective roughness from the ity analysis using storm tracks from JTWC and SPARRSO. NLCD 2001 database is presented in Atkinson et al. (2011) For BOB 1991 validation, model results generated with and Bunya et al. (2010). storm track from SPARRSO showed good agreement with the observed data. Moreover, radius of maximum wind data for both the BOB 1991 and Sidr 2007 cyclones were Meteorological forcing extracted from a similar study by Dube et al. (2009). Finally, this wind field data were adjusted with JTWC best A cyclone’s wind and pressure fields are the most important track format file. The wind field and atmospheric pressure atmospheric parameters for storm surge modelling (Dube were then calculated at each node internally by ADCIRC et al., 2009). For this study, meteorological forces describing using the Holland symmetrical model and finally contribu- the BOB 1991 and Sidr 2007 cyclones that hit the coast of ted towards information on sea-level pressure distribution Bangladesh were provided by US Navy Joint Typhoon and gradient wind within a tropical cyclone. Warning Center (JTWC) (http://www.usno.navy.mil/ JTWC/). Meteorological attributes such as storm tracks, central pressure and wind speed were used as inputs into ADCIRC’s Holland symmetrical wind model (Bhaskaran Validation data et al., 2014). To generate the cyclone wind and pressure To validate the model results with tidal water levels and fields over the entire model domain, the dynamic Holland storm surges, several publicly available sources have been wind model (Holland, 1980) has been used based on the explored. The University of Hawaii Sea Level Center best track data from JTWC and modified JTWC data from (UHSLC) was operating seven stations along the continental Space Research and Remote Sensing Organization shelf offshore of Bangladesh coast. Each station utilised float- (SPARRSO), Bangladesh (Figure 4). The data set provided and well-type gauges and water levels were collected by by JTWC for BOB 1991 lacked the minimum sea-level pres- Department of Hydrography, Bangladesh Inland Water sure and radius of maximum wind values, which led to the Transport Authority (BIWTA) based on a local Public use of a symmetrical wind model. Similarly for Sidr 2007, Works Department’s vertical datum mPWD which is 0.6 m attribute such as radius of maximum wind was absent in below the MSL (Roy, 1995). Among these water-level

Figure 4 Space Research and Remote Sensing Organization (SPARRSO) and US Navy Joint Typhoon Warning Center (JTWC) best track for BOB 1991 and Sidr 2007, respectively.

Figure 5 Locations used for model validation along with University of Hawaii Sea Level Center (UHSLC) gauges-observed data.

Figure 6 Freely available water-level data during BOB 1991 and Sidr 2007 cyclones at University of Hawaii Sea Level Center (UHSLC) gauges. stations, only one is currently operational (Chittagong) and For storm surge validation with the BOB 1991 and Sidr all others have historic water-level data sets of different time 2007 cyclones, UHSLC stations were found to be unreliable series. According to UHSLC, the hourly residuals of these because of the inconsistency in water-level data recordings data sets contain large periodic ﬂuctuations about which during high surge period. Figure 6 illustrates water-level there is no speciﬁc information; this could be due to either data during BOB 1991 and Sidr 2007 cyclones at some of phase shifts in the timing of the gauge or the inaccuracy of the available UHSLC stations, where recorded data at Cox’s the tidal analysis (http://uhslc.soest.hawaii.edu/data/down Bazar, Charchanga and Khepupara during BOB 1991 had load/rq#uh136a). Owing to the uncertainty of the collected completely missed the storm surges. In addition, geo- data sets, two of these stations, Cox’s Bazar and Hiron point, graphic information about UHSLC gauge on Chittagong were chosen for tidal validation as they had continuous found to be misrepresenting the actual location, which was water-level data for the entire month of June 1990 (Figure 5). observed to operate throughout the Sidr 2007 period.

Ultimately, previous studies related to the BOB 1991 and Sidr 2007 cyclones (e.g. Salek, 1998; JSCE, 2007; Dube et al., 2009; Lewis and Bates, 2013) have been explored and used in this study to validate model-generated storm surge results. These studies were established based on different non-governmental agency reports and data sets, which are not publicly available.

Results and discussion

Tidal validation In this analysis, we have only used two UHSLC stations (Hiron point and Cox’s Bazar) for validation because of vague and inconsistent data sets found in other water-level recording stations. The main challenge during the process was to locate these stations with available geographic information, as most of them were found to be at narrow streams with width less than a grid resolution of 300 m. In addition, UHSLC water-level recording stations at Khepupara and Charchanga were discarded for model comparison because of the lack of accurate bathymetric data in the regions. At small channels, due to a very shallow bathymetry, ADCIRC underestimates water levels during harmonic analysis in its simulation (Funakoshi, 2006). Water-level information from Hiron point and Cox’s Bazar had been examined care- fully and a data set of a certain period was selected from both stations based on data availability. The standalone Figure 7 (a) Water levels during June 1990 at University of Hawaii ADCIRC run was performed for the entire month of June Sea Level Center (UHSLC) gauge, Cox’s Bazar. (b and c) Water 1990, with a ramp function of 2 days, which is the model levels during June 1990 at UHSLC gauge, Hiron point. spin up time. The numerical mesh and the ADCIRC model forced by the tidal constituents (M2, N2, K1, K2, O1, P1, S2 and Q1) were observed to perform well during the valida- period of the BOB 1991 and the Sidr 2007 cyclones. The tion period as shown by the good agreement with observed simulation also includes the tidal constituents (M2, N2, K1, – gauge data. Figure 7(a c) shows the periods of model vali- K2, O1, P1, S2 and Q1) from open ocean boundary during − dations where the tidal water levels varied between 1.0 and both events. Time step for SWAN and the coupling time 1.5 m approximately at both the stations. interval was set to 600 s. For BOB 1991, the simulation ran Gauge water levels have been adjusted to MSL from from April 23 to April 30 (8.0 days with a ramp function mPWD, a local datum according to Salek (1998). At of 1.0 day). This event was triggered as a tropical depres- UHSLC station, Cox’s Bazar, the correlation coefficient 2 sion on April 22, 1991, in the Bay of Bengal, which turned (R ) for water-level data with ADCIRC model result is into a tropical storm on April 24 and finally made landfall found to be 0.92 and the root mean square error (RMSE) during 2000 UTC, April 29, 1991. Wetting and drying algo- of 0.23 m. Good correlation between model and recorded rithm was applied, which enabled computation of coastal data was also observed for the Hiron point UHSLC gauge, inundation and friction was incorporated into the model where we found the RMSE of approximately 0.18 m and with Manning’s N, canopy and surface directional rough- fi 2 correlation coef cient (R ) of 0.94. ness. The total computational runtime was 52 min with 600 cores from STAMPEDE which would be equivalent to 22 days with a single central processing unit (CPU). The Storm surge validation model-computed maximum wind speed from the For storm surge validation, considering the coupled model SPARRSO cyclone track for BOB 1991, based on Holland (SWAN + ADCIRC), runs were executed for the BOB 1991 model, was 63 m/s and minimum central pressure of and the Sidr 2007 cyclones. The model run includes the full 962 hPa during the landfall time (Figure 8(a,c)).

Figure 8 (a and b) Wind speed (m/s) of BOB 1991 and Sidr 2007 cyclones, respectively, generated using Holland dynamic model. (c and d) Minimum central pressure for BOB 1991 and Sidr 2007.

A maximum wind radius of 40.0 km has been used to cases (Table 1). For the Sidr 2007 cyclone, the Holland wind create a symmetric wind ﬁeld (Dube et al., 2009) for the model calculated maximum wind speeds of 72 m/s during

BOB 1991 because of the absence of Rmax data in JTWC landfall and JTWC estimated the lowest central pressure of storm database. The model results show surge height of the cyclone to be 918 hPa (Figure 8(b,d)). The coupled 5.0–7.6 m in the entire coastal stretch of nearly 160 km of model (SWAN + ADCIRC) run was performed for the the Chittagong division (Figure 11(a)). The computed surge entire cyclone period of 5 days from its generation at 0000 values near the landfall location at Chittagong and Cox’s UTC, November 11, 2007 to dissipation on 0000 UTC,

Bazar are 6.2 and 4.0 m, respectively, which show good November 16, 2007. A maximum wind radius (Rmax)of agreement with previous findings of high water levels by 25 km has been used to generate the symmetric wind field Dube and Murty (2009) and Salek (1998) (Figure 9(a,b)). for the Holland wind model according to Dube et al. (2009). Computed water levels slightly varied by 0.5–1.0 m with The coupled model (SWAN + ADCIRC) results were previous studies at Sandwip, Khepupara, Galachipa and observed to perform well while calculating the inundation Hiron point in southwestern coastal zones (Table 1). How- extent for Sidr 2007 (Figure 11(b)). Moreover, model results ever, in general, the model result showed good agreement were validated against the storm surge contours from the with the existing studies from Salek (1998) and Dube et al. Japan Society of Civil Engineers (JSCE) model results and (2004) with the correlation coefficient (R2) of 0.91 and 0.93, field investigations on the Sidr 2007 (JSCE, 2008). High respectively, and RMSE of approximately 0.65 m at both water-level values were also extracted for the same locations

Waves in the continental shelf Waves during cyclonic events create significant impacts throughout the entire continental shelf of the Bangladesh coast (Salek, 1998). Using the coupled SWAN + ADCIRC model, the study was able to identify the most vulnerable areas that suffered significant losses due to wave impacts throughout BOB 1991 and Sidr 2007 cyclones. To evaluate wave action during the course of BOB 1991 and Sidr 2007 cyclones, six places near the landfall locations were identified (Figure 5). Simulation results showed that the significant wave height for BOB 1991 varied between 3.0 and 5.0 m through the coastal belt from Cox’s Bazar to Chitta- gong (Figure 12). Places closer to the landfall location in Chittagong and Sandwip were the most affected by the wave height of approximately 4.0–5.0 m (Figure 12). On a similar study by Salek (1998), significant wave heights at Chittagong and Sandwip Island during BOB 1991 cyclone were found to vary between 3.0 and 5.0 m. In the south- Figure 9 (a and b) Comparison of storm surges during BOB 1991 western zone of the coast (Hiron point, Khepupara and super cyclone with historic studies at Chittagong and Cox’s Bazar, respectively. Galachipa), wave effects during BOB 1991 were less severe and it varied from 0.5 to 2.0 m. From simulation results, from another similar study by Lewis et al. (2013). The com- wave effects during the Sidr 2007 were observed to be less puted storm surge was found to vary between 5.0 and 6.5 m destructive at landfall location (Barisal division) in compar- along the coastal districts and estuaries where Sidr 2007 made ison to BOB 1991, where significant wave height oscillated landfall and previous studies also demonstrated similar between 2.0 and 3.0 m (Figure 13). Interestingly, very simi- results on the same locations (Figure 10(a,b)). Computed lar wave response was also found on other sides of the coast storm surges for Sidr 2007 had presented more robust out- during Sidr 2007 (Chittagong and Sandwip), even though it comes during the model validation, where correlation coeffi- made landfall at the southwestern zone. One of the possible cient values (R2) were 0.95 and 0.98 with existing studies of reasons, as hypothesised by Salek (1998), is the combina- Lewis et al. (2013) and JSCE (2008) correspondingly and tion of coastal trapping and funnelling effects in the minimum RMSE found to be of 0.30 and 0.5 m (Table 1). Meghna estuary, which produces more widespread surges We hypothesise that this improvement in model accuracy for near Sandwip Island and Chittagong coast. The eastern side ’ the Sidr 2007 simulation is a result of the reliable real time of the coast, mainly, Chittagong, Cox s Bazar and Sandwip wind and pressure data from JTWC, whereas atmospheric were observed to be the most vulnerable regions to the data sets to create BOB 1991 wind field were assembled cyclonic wave effects because of the slightly deeper bathym- entirely from literatures and historic case studies (Figure 11). etry than the western coast.

Table 1 Comparison of model outcomes with high water levels (HWLs) extracted from existing studies on storm surge modelling in Bangladesh coast Locations Chittagong Cox’s Bazar Sandwip Khepupara Galachipa Hiron point R2 RMSE Coordinates 22.19 21.46 22.48 21.88 22.03 21.81 91.81 91.92 91.55 90.10 90.34 89.49 HWL during BOB 1991 cyclone (m) Model 6.2 4.0 5.4 1.3 1.4 1.5 Salek, 1998 6.2 4.2 4.0 ~0.9 ~0.9 ~0.9 0.91 0.645 Dube et al., 2004 6.5 4.0 4.0 ~0.9 ~0.9 ~0.9 0.93 0.652 HWL during Sidr 2007 cyclone (m) Model 3.0 2.0 4.5 5.9 6.5 2.5 Lewis et al., 2013 3.2 2.0 3.4 5.5 6.1 2.5 0.95 0.512 JSCE, 2008 3.0 1.7 4.2 6.0 6.0 2.2 0.98 0.297

RMSE, root mean square error.

Figure 10 (a and b) Comparison of storm surges during Sidr 2007 super cyclone with historic studies at Galachipa and Khepupara, respectively.

Trade-off between model performance for both the cyclones (Figure 14). This anomaly in storm and CPU time surge elevation of 1.0 m due to exclusion of wave radiation stresses can be disastrous for places such as Galachipa, Khe- For a developing nation, such as Bangladesh, it can be a pupara and Hiron point in the Bangladesh coast where great challenge to implement a computationally expensive topography is almost flat, and the model results would coupled model such as SWAN + ADCIRC, especially at largely misrepresent the actual inundation area. On the forecast mode, running simulations a couple of hours before other hand, SWAN + ADCIRC model runs had taken the storm landfall. The availability of high-performance approximately 21 and 10 days with a single CPU for BOB computing environments can certainly contribute largely to 1991 and Sidr 2007 cyclones, while individual ADCIRC run such storm surge models during real-time forecasts; simulations required only 10 and 5 days, respectively. however, it is still not readily accessible at most of the devel- Therefore, the stand-alone (no waves) hydrodynamic model oping countries. To evaluate the trade-offs between higher can also be beneficial to countries like Bangladesh reducing model accuracy due to more elaborate model configurations computational time by almost 50% compared with coupled and computational time in calculating storm surge levels wave + hydrodynamic models, however with greater losses along the coast, we tested the impact of inclusion of the in the precision of the calculated storm surge data. wave radiation stress components in the model computation. Storm surges from the BOB 1991 and Sidr 2007 cyclones were generated again with stand-alone ADCIRC Concluding remarks model on the same unstructured mesh. Simulation results were used to compare between the coupled hydrodynamic Summary and wave model, and the stand-alone hydrodynamic model scenarios. As expected, the stand-alone hydrodynamic The study demonstrates the implementation of a coupled model showed different results than the coupled model in wave + hydrodynamic model (SWAN + ADCIRC) to com- the shallow water zones and coastal areas, where storm pute storm surge and flooding extent for the coast of surge elevation decreased by almost 1.0 m at some places Bangladesh, which could contribute towards better coastal

Figure 11 (a and b) Spatial distribution of storm surges at BOB 1991 and Sidr 2007 super cyclone landfall locations, respectively.

flooding mitigation, planning, operational forecasting and generated using Holland symmetric model. Computed emergency response during extreme events. The severe his- model outcomes were validated with previous research and toric cyclonic storms BOB 1991 and Sidr 2007 (both cate- studies, which were established using data sets from gory 5) were assembled from different sources because of numerous non-governmental agencies. Computed and the absence of required meteorological attributes, such as observed storm surge data showed good agreement, where minimum central pressure and radius of maximum wind the correlation coefficient (R2) was found to vary between data in the JTWC storm database. An unstructured numer- 0.91 and 0.98 and RMSE of approximately 0.3–0.6 m for ical mesh with high resolution (~300 m) on the shallow both cyclones. This study primarily explored the effective- water region and coarser in deep waters (~52 km) was ness of the SWAN + ADCIRC model in storm-associated developed. Moreover, bottom friction and roughness coeffi- water level and inundation extent calculations in data-poor cients were generated from available global land cover data coastal regions of Bangladesh. Also, it had illustrated the set to better represent coastal wetlands and mangroves in wave response in the shallow continental shelf of our model, which can attenuate storm surges significantly. Bangladesh during extreme cyclonic events and helped to Initially, to check mesh performance, a standalone improve our understanding of storm surge behaviour ADCIRC run was implemented by forcing tidal constitu- throughout the entire region. Finally, advantages and disad- ents from the ocean boundary. Model results for tides were vantages of coupled model implementation in coastal flood- validated against water-level recording gauge data from two ing were compared with stand-alone hydrodynamic model. stations of UHSLC. Then, the coupled SWAN + ADCIRC Results showed that the surge elevation can vary up to model was applied with two different storms (BOB 1991 1.0 m at some places with stand-alone ADCIRC simula- and Sidr 2007). The wind field of these storms was tions and potentially lead to a misrepresentation of the

Figure 12 Signiﬁcant wave heights (m) at different locations during landfall of BOB 1991 where the simulation started from April 23, 1991, 0000 UTC.

Figure 13 Signiﬁcant wave heights (m) at different locations during landfall of Sidr 2007 cyclone where simulation started from November 11, 2007, 0000 UTC. actual inundation conditions. The coupled model elimi- Limitations nates the interpolation between numerous models with het- The bathymetry data collected from the GEBCO data set erogeneous grids and would certainly contribute towards did not represent well small rivers or streams because of the operational forecasting system and decision-making its ultimate resolution of 900 m. The validation of model strategies in Bangladesh. results was restricted to a limited number of water-level

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