Climate Dynamics https://doi.org/10.1007/s00382-020-05463-4

Mapping of cyclone induced extreme water levels along Gujarat and Maharashtra coasts: a climate change perspective

Jismy Poulose1,2 · A. D. Rao1 · S. K. Dube1

Received: 5 August 2019 / Accepted: 16 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020

Abstract Coastal food mapping associated with tropical cyclone induced extreme water elevations is carried out for the Gujarat and North Maharashtra coasts in the perspective of climate projections for the Arabian Sea. The projections are taken into account by enhancing the present cyclone wind intensity by 7% and 11% based on the IPCC ffth assessment report to study its impact on extreme water elevations and coastal fooding. The coupled ADCIRC + SWAN model is used in this study to map the maximum water elevations resulting from storm surges, astronomical tides, and wind-waves by utilizing the most probabilistic cyclone tracks generated for this region. Results from the study signifes that extreme water elevations ranging between 9.0 and 9.5 m are evident in the Gulf of Khambhat and Kutch under no-climate change scenario, while it enhances to a maximum of 10.0–11.0 m under climate change projections. Maximum extent of coastal inundation is found in the low- lying regions of Great and Little Rann of Kutch, Mumbai, and high-tide mudfats of Bhavnagar. It is notable that climate projections have maximum impact on inundation height, while it is marginal in terms of risk associated with the additional inundation extent.

Keyword Numerical modelling · Storm surge-tide-wind wave interaction · Extreme water elevations · Climate projections · Coastal inundation

1 Introduction surrounding the AS. Out of 41 cyclones that occurred dur- ing 1970–2017, about 23 made landfall, of which 8 are cat- Coastal regions are dynamic in nature comprising low-lying egorized as severe cyclonic storms, 7 categorized as very areas and are exposed to geomorphologic and oceanographic severe cyclonic storms, and one as a Super cyclone, Gonu in changes (Cowell et al. 2006). About 40% of the global 2007. Regions in Gujarat and northern Maharashtra are the population lives within 100 km of coast and below 100 m most cyclone-afected areas along the west coast of India. of topography above mean sea level (Small and Nicholls Major cyclones in 1975, 1977, 1982, 1996, and 1998 made 2003). The coastal regions of India are vulnerable to tropi- landfall at Porbandar, Karwar, Veraval, Diu, and Porbandar, cal cyclone induced storm surges and associated inundation. respectively. Southern regions in the west coast of India, Based on data from 1980 to 2000, on average about 370 mil- such as south Maharashtra, Goa, Karnataka, and Kerala, lion people in India are exposed to cyclones annually (https​ experienced very few cyclones in the past (https​://bmtpc​ ://ncrmp​.gov.in/cyclo​nes-their​-impac​t-in-india​). Although .org/topic​s.aspx?mid=56&Mid1=178). the frequency of cyclonic storms are less over the Arabian Intense cyclonic storms impacting the coast can result Sea (AS) as compared to the Bay of Bengal, there are reports in an abnormal rise of water levels above the astronomical of severe cyclonic storms landfalling along the rim countries tide along the right side of the track, and the resulting water levels penetrate inland causing widespread coastal fooding. * Jismy Poulose Short term implications of such catastrophes may include [email protected] an altered the shoreline confguration (Pye and Blott 2006; Mahapatra and Ratheesh 2014), and its impact can be diverse 1 Centre for Atmospheric Sciences, Indian Institute as it is tightly coupled to morphological development of of Technology Delhi, New Delhi 110016, India these coastal systems. The vulnerability and risk associated 2 Present Address: Department of Civil Engineering, Indian due to fooding depends on the coastal population density, Institute of Technology Bombay, Mumbai 400076, India

Vol.:(0123456789)1 3 J. Poulose et al. coastal topography, presence of estuaries, deltas, and adjoin- further propagate upstream resulting in widespread food- ing rivers in the cyclone-afected region. The presence of ing along the river banks. The intrusion of saline water into low-lying foodplains, high population density, and rapid inland areas and freshwater bodies due to storm surge inun- urbanization along coastal regions pose a threat and are sus- dation severely afects the agriculture sector and livelihood ceptible to cyclone induced coastal fooding (Woodruf et al. of people living in coastal areas. In recent years, the risk 2013). In India, approximately 35% of population lives in the associated with coastal fooding has exponentially increased coastal regions, and about 10% has habitation in low-lying due to high population growth, socio-economic conditions, areas where the coastal topography is below 10 m. North and land subsidence. Also, deforestation along the coast has Maharashtra and Gujarat have a large coastal space below destroyed natural coastal protection systems and increased 10 m of topography, whereas regions in south Maharash- the vulnerability levels. Anthropogenic induced pressure on tra, Goa, Karnataka, and Kerala have narrow coastal belts the coastal belt and deltaic environment have also altered the (Fig. 1a). Low-lying regions of Kutch (Great Rann of Kutch) risk associated with coastal fooding (Syvitski et al. 2009). and extended Gulf of Khambhat (GoK2) covering up to Lit- The carrying capacity of food waters within safety limits tle Rann of Kutch in the Gujarat State are relatively at higher for Tapi river that passes through Surat (Gujarat) city, is risk. The 1982 cyclone battered the Saurashtra coastline of reduced by 60% due to urbanization and encroachment in Gujarat, generating a 6–8 m storm surge from Junagadh to foodplains of the river (Agnihotri and Patel 2011; Parikh Bhavnagar that caused about 600 casualties. The damage et al. 2017). All these aspects contribute to coastal fooding caused by the 1998 cyclone (Fig. 1b) was quite extensive for risk as a result of cyclone activity. By considering these Gujarat, claiming the largest death toll of 1200 along with risks in a holistic manner, it is highly essential to generate a 1800 missing people. The coastal regions of Kutch, Jamna- coastal food map for extreme water levels along the Gujarat gar, Rajkot, and Porbandar have experience fooding due to and Maharashtra coast. storm surges, especially the Kandla-Jamnagar areas causing The severity and extent of coastal food inundation due a loss of rs 18 billion. Also, cyclone induced vulnerability to cyclones also depend on the height of extreme water lev- increases along the Gujarat and Maharashtra coasts due to els along the coast. Maximum water elevation (MWE) is the presence of many small riverine systems and tidal inlets the combination of storm surges, tides, wind-waves, river like Narmada, Sabarmati, Tapi, Mahi, Dhadhar, etc. Cyclone discharge, and rainfall driven run-of. However, the highest generated storm surges penetrate into riverine mouths and MWE in coastal regions is primarily contributed by storm

Fig. 1 a Onshore topography and bathymetry of the domain along with synthetic tracks and b model grid for the west coast of India

1 3 Mapping of cyclone induced extreme water levels along Gujarat and Maharashtra coasts: a climate… surges. The storm surge height along the coast is mainly confned to the Gujarat and North Maharashtra coasts based infuenced by tropical cyclone (TC) characteristics such as on the signifcance of the region for cyclones, as discussed the wind speed, storm size, storm translation speed, land- earlier. The objective of the present study is to prepare a fall location, angle of approach, coastal geometry including potential storm surge fooding map associated with extreme coastline confguration and depth, and topographic charac- water elevations, including the height and horizontal extent teristics. The occurrence of peak storm surge coinciding of inundation, resulting from the combined efect of storm with astronomical high tide conditions along with wave- surges, tides, and wind-waves under diferent CC scenarios. induced setup can lead to extreme water levels causing the This map will represent current conditions (no CC) as well worst possible inundation scenario. The ‘Phyan Cyclone’ as 7%, and 11% intensifcation of cyclonic wind speed as in 2009 caused major devastation through fooding in the moderate and extreme scenarios of the efect of CC on TC Bombay-high region due to prevailing high-tides (~ 2.4 m) at projections. the time of peak storm surge. The presence of shallow waters and a wide continental shelf produces high tidal range and surges that enhances MWE in the afected region (Poulose et al. 2018). The north Maharashtra coast has a wide shelf 2 Data and methodology of about 330 km, and the Gujarat coast includes the Gulf of Kutch and Khambhat (GoK1 and GoK2) with shallow of- 2.1 Model shore waters (< 50 m depth) and a tidal amplitude ranging from 7 to 12 m in these gulf regions. The fnite-element and hydrodynamic framework of the In addition, the efect of climate change (CC) increases advanced circulation model, ADCIRC (Luettich et al. 1992) the risk of coastal flooding. The IPCC (2014a) report is considered in this study to compute MWE and associated projects an increase in the frequency of intense cyclones coastal inundation. The depth-integrated two-dimensional in response to a rise in sea surface temperature. Various mode of ADCIRC uses incompressible Navier–Stokes equa- studies are conducted regarding the increase in the number tions to simulate water elevations and depth-averaged cur- and intensity of cyclones in the AS. A recent study of Deo rents in an unstructured gridded domain. The equations are and Ganer (2014) indicates an increase in the intensity of formulated using the assumption of hydrostatic pressure tropical cyclones in the North Indian Ocean over the past and Boussinesq approximations. The elevation and currents 15 years. Anthropogenic global warming increases the prob- are obtained from the solution of depth-integrated continu- ability of post-monsoon extreme severe cyclonic storms over ity and momentum equations, respectively. The ADCIRC the AS as compared to the Bay of Bengal (Murakami 2017). boundary condition includes harmonic tidal constituents at A study by Evan et al. (2011) reveals that weakening of the open boundary, zero normal fow at the bottom, no-slip vertical wind shear in monsoon circulation due to increase condition for the velocity at the lateral boundary, and the in anthropogenic emissions of aerosols favors pre-monsoon wind distribution at the free surface. Also, the ADCIRC and post-monsoon TC intensifcation in the AS. As per the model uses the wetting and drying algorithm to simulate the supplementary material of AR5 (Fifth Assessment Report), extent of spatial inundation due to maximum water elevation IPCC (2014b), the expected percentage change in mean Life- (Luettich and Westerink 1999). time Maximum Intensity of TCs over the period 2081–2100 The third-generation wave model, SWAN (Simulat- relative to 2000–2019 ranges between − 10 and + 10% for ing Waves Nearshore), is dynamically coupled with the the North Indian Ocean. The projected tropical cyclone wind ADCIRC model (ADCIRC + SWAN) to compute the efect speed increment for the end-century (RCP8.5, 2081–2100) of short-period wind waves on the MWE (Dietrich et al. in the Arabian Sea is ~ 2–8 m/s (IPCC 2014b), which is 2012). The SWAN model is mainly intended to estimate considered in the present study by increasing the maximum wave parameters in coastal areas and estuaries from given winds by 7% and 11% (Knutson et al. 2010). wind, bottom roughness, and water current conditions Considering the risk criteria, identifcation and evalua- (Holthuijsen et al. 1993; Booij et al. 1996). The model is tion of coastal inundation zones forms a prime necessity to based on the wave action balance equation with sources and provide short-term and long-term policy planning for coastal sinks (https​://falk.ucsd.edu/model​ing/swant​ech.pdf). The management authorities for effective coastal protection SWAN and ADCIRC models are tightly coupled and share works and appropriate foodplain zonation. The localized the same unstructured grid and wind feld. The water levels information concerning the inundation height for CC sce- and currents computed by the ADCIRC model are mutu- narios enables for better preparedness and disaster manage- ally transferred to SWAN at the prescribed coupling time ment. Studies for the east coast of India in this regard pro- step. The SWAN model updates the radiation stress based vide an insight for high-risk regions along the coast in terms on information from ADCIRC to predict the water levels and of CC scenarios (Rao et al. 2015, 2019). The present study is currents in presence of wind-waves.

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The model domain for the computation of surge-tide- 2.2 Synthetic TC tracks wave induced effects covers the region of Gujarat to North Maharashtra, including the Gulfs of Khambhat There are diferent methods to develop extreme sea-level and Kutch (Fig. 1a). The model bathymetry is obtained related coastal food risk zones. One method is to extend the from GEBCO 30 s data (https​://www.gebco​.net) and feld historical record of coastal foods. Upscaling paleoclimatic observation data by NIOT (National Institute of Ocean records of past coastal foods from coastal sediments (Lin Technology, India). The NIOT data is available only for et al. 2014; Nott et al. 2001) is not feasible along the Indian the GoK2 region (Giardino et al. 2014). The topography coasts due to insufcient past recorded TC events. The num- for the model domain is derived from the SRTM 90 m ber of tide-gauge records during extreme events are also very Digital Elevation Database (https://srtm.csi.cgiar​ .org/​ ). A few along the Indian coasts. Inadequacy in tide-gauge obser- highly refned unstructured triangular mesh is generated vations also makes it difcult to use conventional extreme using this bathymetry/topography data for the study region value analysis methods (Haigh et al. 2014) for the study (Fig. 1b) and then used in the ADCIRC + SWAN model. region. Since the return period of the major cyclone events The grid resolution in the computational domain has a are very low, there is paucity in spatial and temporal data size of about 20 m in the nearshore regions relaxing to for the interpolation of extreme sea levels. One of the best 35 km towards the open ocean boundary. The rigid-land- ways to overcome these issues is by generating synthetic TC ward boundary of the computational domain is prescribed tracks (Vickery et al. 2000; Emmanuel et al. 2006; Powell at 15 m topography contour to accommodate inundation et al. 2005). In this method, TC tracks and their intensities efects, and the grid size relaxes to 500 m towards this are re-sampled and modelled from the historical records boundary. The maximum distance from the coast to the (Haigh et al. 2014; Casson and Coles 2002). These synthetic open boundary is about 1300 km, and the latitudinal dis- tracks provide the most probable tracks of the region. tance is about 2500 km. The total number of grid points All the available past cyclone tracks, that made landfall covered in the computational domain is 581,522. in the vicinity of Gujarat and North Maharashtra, covering The ADCIRC model is used to compute the surge-tide the entire coastal stretch from north Gujarat to Dapoli, are interaction. The fnite amplitude and convective accelera- synthesized. The data used for this study involves 100 years tion terms are activated in the computations. The non-lin- (1917–2016) cyclone information of TC tracks, collected ear bottom friction term is applied to the model using the from the best-track data (www.rsmcn​ewdel​hi.imd.gov. hybrid bottom friction formulation, with minimum bottom in; www.metoc​.navy.mil/jtwc/jtwc.html?best-track​s) and drag coefcient prescribed as 0.0022. The spatially con- Cyclone eAtlas (www.rmcchennai​ eatla​ s.tn.nic.in​ ), produced stant horizontal eddy viscosity coefcient is set at 5 m2 s−1 by the India Meteorological Department. These data sets are for model computations. The explicit scheme is used in reconciled to make a uniform database for cyclones. The time discretization maintained at a model time-step of inverse distance weighting (IDW) method is used for the 0.5 s. A minimum depth of 0.2 m, is pre-set to delineate construction of synthetic tracks from actual cyclone tracks the wet and dry grid elements during model simulation. for each zone in the analysis area. The IDW is a determin- The weighing factor, τ0, is set to − 5, which provides the istic method used frequently in spatial interpolation. The spatially varying function τ0 and constant in time, and is idea is based on the calculation of values of unknown dependent on the local friction. In the present study, the points using the weighted average of known points within major tidal constituents such as S2, M2, K2, T2, N2, K1, the neighborhood (Collins and Bolstad 1996). The weights O1, P1, and Q1 extracted from the TPXO model (Egbert are inversely related to the distance between the known and and Erofeeva 2002) are provided as the open boundary unknown points. The greatest weight is given to the nearest forcing in the ADCIRC model. Tidal potential ampli- points. The cyclone eye location (latitude and longitude) of tude, frequency, earth tide potential reduction factor of all actual tracks are considered as the known points to predict tidal constituents are used for the tidal computations. A the synthetic track, and it is populated to 0.1° using linear parametric wind module (Jelesnianski and Taylor 1973) interpolation to generate high resolution points. The IDW is employed to generate cyclonic wind stress and pres- method is then used to construct the synthetic tracks for each sure feld at high resolution grid points and subsequently zone. A detailed discussion of the IDW method adapted for provided as the surface stress boundary condition to the present study is given in Sahoo et al. (2015). the ADCIRC model. Relevant parameters such as track Based on the approach angle of past landfall cyclone position, pressure drop, and radius of maximum wind of tracks and their intensities, the study region is conveni- synthesized TCs are given to the wind module. The con- ently divided into five different zones as Zone1, Zone2, struction of synthetic tropical cyclone tracks for the study Zone3, Zone4, and Zone5 from Gujarat to north Maha- region is explained in the following section. rashtra as shown in Fig. 1a. Zone1 covers the northern tip of Gujarat to Porbandar, including the GoK1 and

1 3 Mapping of cyclone induced extreme water levels along Gujarat and Maharashtra coasts: a climate…

Zone2 covers from Porbandar to Diu. The entire GoK2 2.3 Model validation for tides and storm surges is located within Zone3 that extends up to Valsad. Zone4 is a small stretch of coast from Valsad to the north of The model is validated for tides, storm surges, and surge- Mumbai (Nandgaon), and the Mumbai region is enclosed wave interaction. Initially, the model is run only with tidal in Zone5 up to Dapoli in Maharashtra. The IPCC (2014a) constituents for 90 days to obtain a steady state condition. report suggests that small variations in cyclone tracks can Further simulations are carried out to validate water levels lead to large differences in associated impacts in coastal at selected locations using tide-gauge stations of Mumbai regions. Hence, the synthetic tracks are shifted from south (JNPT), Nirma, Dahej, and Mahi for the period April 10–20, to north at every 10 km interval within the zone in order 2013 (Fig. 2). The simulated tidal range is ~ 10.3 m at Nirma, to compute the extreme MWE (EMWE) and associated while the observed value is ~ 11 m. The model simulations inundation. The total number of synthetic tracks con- are in good agreement with the observations at Dahej and sidered from Zone1 to Zone5 are 25, 13, 13, 9, and 14, JNPT, and the RMSE error is about 0.08 m and 0.019, respectively. respectively. Even though the tides are better simulated at The maximum pressure-drop for all the cyclones that Nirma, Dahej, and JNPT, the model is unable to capture crossed each zone during the past 100 years are identified the actual water levels at Mahi as it is located in the inte- from the reconciled database. From archived records of rior of GoK2. Therefore, the maximum diference between cyclones, it is noted that the 1975 Porbandar cyclone was observed and modeled tidal range varies between 0.8 and the most intense event that impacted Gujarat coast with 1.2 m across the GoK2. Based on this preliminary valida- a maximum pressure-drop (ΔP) of 66 hPa (https://bmtpc​ ​ tion, the model is further simulated to obtain the maximum .org/topic​s.aspx?mid=56&Mid1=178). Also, the wind high spring tide conditions along the coast, as illustrated in hazard map provided by GSDMA (https​://www.gsdma​ Fig. 3, and discussed subsequently. .org/) for 100 years return period showed a maximum Model simulations are performed for the 1998 cyclone to wind speed of > 55 m/s for GoK2 and Porbandar regions. compute total water elevation (TWE) resulting from surge- Hence, a uniform pressure-drop of 66 hPa is considered tide (ST) and surge-tide-wave (STW) interactions. The for all the synthetic tracks in Zone1 and Zone2 (Table 1) cyclone occurred during June 4–10, 1998, and made landfall and provided in the wind model to generate wind distribu- near Porbandar on 9th June and thereafter progressed further tion for the coupled ADCIRC + SWAN model. The wind to cross the GoK1 region (Fig. 1a). As per the IMD best- hazard map categorized Zone3 as high damage to a very track data, the cyclone was categorized as a Very Severe high damage risk zone with a maximum wind speed of Cyclonic Storm (VSCS), with a reported maximum pressure 45–55 m/s and Zone4 and Zone5 as moderate damage risk drop of 40 hPa during landfall and continued with the same zone with a maximum wind speed of 40–45 m/s, during intensity until it crossed the GoK1. The observed radius of 1891–2015. These wind speed categories are represented maximum winds was 30 km. The standalone ADCIRC and in the model simulations as 50 hPa for Zone3 and 40 hPa the coupled ADCIRC + SWAN models were used to simu- for Zone4 and Zone5. The translation speed of cyclones late the TWE from ST and STW interactions, respectively. in all zones is considered as uniform with 10 km/h, which After the model reached a steady state with tidal bound- is the observed average speed of a cyclone for this region. ary condition, further simulations were performed with the The uniform radius of maximum wind of 35 km is used in cyclone induced wind stress. all model simulations. The only available tide-gauge observation during the cyclone period is at Vadinar (22.45° N, 69.12° E), which is located 100 km left from the cyclone track at

Table 1 Zone-wise information Zones No. cyclone Max. pressure drop (∆P) Maximum wind speed (m/s) on cyclone intensity tracks Present 7% Wind inten- 11% wind scenario sifcation intensifca- tion

Zone1 25 66 hPa 60 64 66.6 Zone2 13 Zone3 13 50 hPa (Tracks 1–9) 52 56 58 66 hPa (Tracks 10–13) 60 64 66.6 Zone4 9 40 hPa 45 49.2 51 Zone5 14

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Fig. 2 Tide validation at selected tide gauge locations

the mouth of GoK1. The tidal signal is removed from the 2.4 Model simulations observed and simulated time series data of TWE in order to validate the surge residual and surge-wave residual. Numerical experiments are performed using the climate The maximum observed residual is about 60 cm, and the projections of TCs in terms of wind speed intensifca- modelled surge and surge-wave residuals are about 60 cm tion to study its impact on MWE and associated coastal and 65 cm, respectively (Fig. 4). The modelled peak surge inundation. The EMWE and associated inundation along and surge-wave residuals are in good agreement with the the coast are computed using the following three diferent observed residuals. There is no other tide-gauge data scenarios of wind intensifcation: (a) no-climate change available for any cyclone in the study region. It is to be (present scenario), which uses maximum wind speed with noted that the model is not validated for inundation due a 100-year return period, (b) increase in wind speed by 7%, to lack of observational data for past cyclones. which is an average value of climate projections (moderate

1 3 Mapping of cyclone induced extreme water levels along Gujarat and Maharashtra coasts: a climate…

about 4–5 m/s and 6–7 m/s, respectively. Cyclone size and translation speed are considered invariant in all the scenarios as there are no climate projections given by IPCC (2014a). The synthetic tracks, which represent the present scenario, are also assumed to be the same for the impact study of climate projections. Numerical experi- ments are carried out using a total of 74 synthetic tracks for all zones. Each zone represents 3 sets of experiments for present, moderate, and extreme scenarios of wind intensifcation. A quantitative analysis of MWE and associated inun- dation are performed using synthetic cyclone tracks. The non-linear interaction of storm surges, tides, and wind- waves for three diferent scenarios are simulated using the coupled ADCIRC + SWAN model. It is initially forced only with tides along the open boundary. After attaining a steady state, model simulations are performed with cyclonic wind stress to compute MWE and associ- ated coastal inundation resulting from the STW interac- tion. The surge heights are modifed with tidal phases and wind-waves during STW interaction, and it is referred to as TWE in this study. Interaction of tidal amplitudes and its phases with peak storm surge height infers that TWE is maximum during the time of high tide (Poulose et al. 2018). The open boundary condition is adjusted such Fig. 3 Modelled maximum high spring tide along the coast of analy- that the high-tide occurs at the time of peak storm surge sis area to obtain the highest EMWE and corresponding impact on inundation for each track. The composite picture of EMWE and inundation generated for each track at a par- scenario) and (c) an increase of 11%, which is the extreme ticular zone depicts the probable EMWE (PEMWE) and case (extreme scenario). As given in Table 1, the maxi- associated maximum horizontal extent of inundation along mum wind speed in the normal scenario varies between with its height. The inundation height is computed by sub- 45 m/s (40 hPa) and 60 m/s (66 hPa) from Zone5 to Zone1. tracting the local topography from EMWE as the model The table also gives enhanced maximum wind speed for computes only from the mean sea level, and is referred to moderate and extreme scenarios, and the increment is as inundated water level.

Fig. 4 Validation of surge and surge-wave residual during 1998 cyclone at Vadinar tide gauge location

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3 Results and discussion coastal vulnerability conducted by the Government of India shows high vulnerability indices for GoK1 and GoK2 (https​ The study region extending from north Maharashtra to ://pib.nic.in/newsi​te/Print​Relea​se.aspx?relid​=11197​8) and Gujarat along the west coast of India is the potential risk localized vulnerability in the inlets of Kutch. The report also zone associated with cyclones. The PEMWE is computed signifes the importance by including additional parameters for all the scenarios from the northern tip of Gujarat (India- such as cyclones, storm surges, and coastal fooding, which Pakistan border) to Dapoli (Maharashtra) using a total of will provide an additional dimension to the coastal vulner- 74 synthetic tracks. Figure 5a depicts the PEMWE for the ability aspects at a very local level. present scenario, while the increment in PEMWE due to 7% In the present study, the area from the northern tip of and 11% wind speed intensifcation are shown in Fig. 5b and Gujarat to Porbandar (Fig. 6a), which also covers the GoK1 c, respectively. The highest PEMWE occurs along the coast is included in Zone1. The GoK1 is a shallow region, and the of GoK1 and GoK2. Our simulations are in agreement with maximum depth inside the gulf is below 100 m. The shal- the study by Muis et al. (2016) in which extreme sea levels low water characteristics of the enclosed gulf basin cause a are slightly underestimated due to the coarse resolution of large variation in the tidal heights. The tide increases from extreme event forcing, but provide an overall range of sea the mouth of the gulf to the interior, and the maximum tidal levels. The PEMWE and associated coastal inundation are height is about 3.2 m at Kandla (Fig. 3), which is located discussed zone-wise in detail for each scenario in the fol- in the north interior of the region. The presence of an open lowing sections. coast and the marginal width of the continental shelf result in decreased tidal heights near Porbandar (1.2 m) and Mandvi (1.5 m), which are located on either side of the gulf’s mouth. 3.1 Zone1 and Zone3 Strong tidal currents in this region infuence the TWE during its interaction with storm surges and wind-waves (Poulose Gujarat state experienced many food events in the past. It et al. 2018), and hence the associated inundation. Cyclone has the longest coastline (~ 1650 km) amongst all the mari- tracks of Zone1 covering from Porbandar to India-Pakistan time states of India. This coastal state comprises two gulf border make landfall at every 10 km interval. There are 25 regions, Kutch and Khambhat, and both of them have shal- tracks in Zone1, and a total of 25 × 3 simulations are car- low intertidal zones. The world’s second largest tidal height ried out to include all the CC scenarios. The EMWE asso- of 5–6 m is observed in GoK2, which has a vast area of ciated with STW interaction is simulated initially for each tidal mudfats of about 22,600 km2. The GoK1 region has a track, and thereafter the PEMWE is calculated. The high- chain of islands and possesses the richest marine biodiver- est PEMWE is computed inside the GoK1 for all scenarios sity. Also, the wide stretch of very fat terrain of river plains (Fig. 5a–c). The value is about 3.5 m at the mouth of the and low-land of lower river basins in the state are highly gulf for the present scenario and increases to 9.5 m towards prone to nearshore fooding. Mapping of multi-hazard and the interior near Kandla (Fig. 7a). The funneling shape of

Fig. 5 a Computed composite PEMWE along the coast for present scenario, b additional water elevation for moderate scenario and c additional water elevation for extreme scenario

1 3 Mapping of cyclone induced extreme water levels along Gujarat and Maharashtra coasts: a climate…

Fig. 6 Topography of a Zone1 and b Zone3 the gulf traps the storm surge and the shallower of-shore inland for the entire Zone1. All synthetic TCs in Zone1 depths can lead to an increase in surge heights, attributed cause fooding in the regions of Kandla-Gandhidham, Jam- due to the bottom friction efect and wave shoaling. The high nagar, and Little Rann of Kutch, as observed in the destruc- tidal range and small river inlets also contribute to a higher tive 1998 cyclone. MWE. It is observed in Fig. 6a that there are many inlets that For a moderate scenario, the cyclonic maximum wind meet the northeast tip of the GoK1, and the mouth of these speed is increased from 60 to 64 m/s. It resulted in an inlets are concave-shaped, which efectively increase the increase of PEMWE by ~ 0.7 m (Fig. 5b), and the maximum MWE. Elevated surge waters propagate through the inlets simulated PEMWE is about 10.2 m. This increment in and can inundate the adjoining areas (Fig. 7b). These inlets PEMWE causes additional water level (AWL) of 0.5–1.0 m also have low-lying basins, where the local topography is higher as compared to the present scenario in the “Little within 3–8 m. The simulated maximum water level is ~ 9.4 m Rann of Kutch” area (Fig. 7c), while it is between 0 and along the lower basin of inlets around Gandhidham. The 0.5 m AWL for the other regions except near Adhav where entire “Little Rann of Kutch’’ is a low-lying region that can the value is ~ 2–3 m. The neighboring landward high-land food during extreme storm events, and the inundated water regions restrict further inland intrusion of fooded water. level in this area ranges between 3 and 7 m. The maximum During extreme scenarios, the PEMWE is increased by extent of inundation in this region is seen up to 70 km for the 1.5 m (Fig. 5c), and in general, the AWL is about 0.5–2.0 m present scenario. Zone1 cyclones produce 4–5 m of PEMWE above the present scenario (Fig. 7d). The accumulation of for present scenario along the open coast covering Mandvi water in “Rann of Kutch Lake” and surrounding areas for to Narayan Sarovar and 2–4 m from Dwarka to Veraval. This moderate and extreme scenarios resulted in AWL of ~ 3–4 m PEMWE leads to inundation in the narrow strip of low-lying and 4–5 m, respectively. The maximum increment in areas in the coastal environment. The elevated water level PEMWE is only about 0.5 m along the southern part of near Porbandar enters through the creek and inundates the Zone1 from Dwarka to Porbandar. The AWL varies between surrounding regions. Though the PEMWE is only 2–3 m, 0.5 and 1.0 m in the adjacent regions of Porbandar during the elevated water level can enter into the salt marshlands moderate and extreme scenarios. The experiments suggest of “Great Rann of Kutch” through the Kori creek. The small that a small increase in PEMWE can lead to a large varia- tributaries of the Indus river basin located on the left side tion in the inundated water levels. This is mainly attributed of Kori creek can also cause inundation. The entire “Great due to irregular local topography of the inundated regions. Rann of Kutch’’ is inundated during the present scenario, The entire GoK2 from Diu to Valsad is enclosed within and the inundated water levels are within the range of 1–3 m. Zone3 (Fig. 6b). The width of the gulf varies from 100 km The fooded water levels in the “Rann of Kutch Lake’’ and at the mouth to 30 km near Bhavnagar, and the depth var- adjoining areas are ~ 2–4 m. The presence of Kathiawar and ies between 1 and 50 m inside the gulf. Tidal amplifcation Kutch peninsula inhibits further intrusion of surge water varies from 1 m at Diu to ~ 6 m at Bhavnagar as it enters

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Fig. 7 a PEMWE of Zone1 for present scenario, b associated probable maximum coastal inundation extent and water levels, c AWL for moder- ate scenario and d AWL for extreme scenario the GoK2, due to the funneling efect and shallowness of surge propagates further inland to food the surrounding the region. The PEMWE is computed using thirteen Zone3 areas. Storm surges are able to penetrate into the rivers and synthetic TCs, and the PEMWE increases from 3 m at the lead to fooding along river banks. It can be seen in Fig. 8b mouth of the gulf to 10 m in the interior near Bhavnagar/ that river banks are the most afected regions by inundation. Nirma for the present scenario (Fig. 8a). Furthermore, the The Sabarmati river plain is fooded with the water levels of MWE gets enhanced at the concave-shaped estuaries and 3–5 m. The eastern part of the gulf has four major river estu- also at other small inlets. The highly elevated surge waters aries (Tapi, Narmada, Mahi, and Dhadhar), and the major along the northwest part of the gulf coast cause fooding extent of inundation can occur due to overfow of surge in the neighboring low-land regions. Maximum inundated waters from these rivers. Also, the presence of low-lying water levels of about 9 m (Fig. 8b) is noticed very close to regions for these river basins makes the coast highly suscep- the coast, and 5–7 m in the high-tidal mudfats near Bhavna- tible to extreme water levels. The Tapi river and its tributar- gar as the local topographic height is within 5 m, and the ies fow through fat terrain, where the local topography is

1 3 Mapping of cyclone induced extreme water levels along Gujarat and Maharashtra coasts: a climate…

Fig. 8 a PEMWE of Zone3 for present scenario, b associated probable maximum coastal inundation extent and water levels, c AWL for moder- ate scenario and d AWL for extreme scenario only 1–2 m near the lower basin, and the height of the inun- gulf region for the extreme scenario. About 1–2 m AWL is dated water levels ranges up to 6 m in the area around Surat. generated over the northwest high-tidal mudfat of the gulf, The study indicates that the surge water discharges through whilst it is only 0–0.5 m over the eastern part of the gulf the Tapi river foods the entire Surat city even when MWE during moderate scenarios (Fig. 8c). Adjoining areas of Nar- is about 4 m due to its low-terrain. Surat has a population mada experienced inundation during the moderate scenarios, of 5.6 million as of 2016, and is projected to grow to 8.6 wherein the water depths were between 1 and 3 m and fur- million by 2030 (UNDESA 2015) making the city highly ther increased to 4 m for extreme scenarios (Fig. 8c, d). The vulnerable to storm surge-induced fooding. Though there is Surat city is another region afected by both the scenarios no record of cyclone induced coastal fooding for this zone, with maximum AWL of 1–2 m for extreme scenarios. The our study is in agreement with the MODIS food inundation experiments for Zone3 suggest that the impact of CC sce- map of Gujarat for Aug–Sep 2006 (https://www.dartm​ outh.​ narios is observed more near the river banks in terms of both edu/~food​s/20061​59NwI​ndia.html). the extent of inundation and the height of water levels. It is The increase in wind speed during moderate and extreme evident from these experiments that though the number of scenarios resulted in the amplifcation of PEMWE in the reported cyclones is less for this zone, the impact of climate inner gulf of ~ 10.6 m and ~ 11.2 m at Bhavnagar (Fig. 5b, c). change makes the region highly susceptible to coastal foods. The PEMWE value increases by 1–1.5 m in the entire inner

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Fig. 9 Topography of a Zone2, b Zone4 and c Zone5

3.2 Zone2 3.3 Zone4 and Zone5

Zone2 synthetic cyclones make landfall mostly perpen- The coastal plain extending from Daman (south Gujarat) dicular to the straight-line coast covering from Porbandar to Goa is known as the Konkan coast and is bounded with to Diu (Fig. 9a). The continental shelf width along this the Western Ghats (Fig. 1a). Zones 4 and 5 are comprised coast is about 80–100 km, and it breaks at 120 m depth of south Gujarat and north Maharashtra, which have com- (refer Fig. 1b). The tidal range increases from 2 m at Man- paratively narrow coastal land, and the average width of the grol to 2.5 m at Porbandar and Diu (refer Fig. 3). This is coastal plain is ~ 50–80 km (Fig. 1b). This coast has many due to an increase in shelf width of Porbandar and loca- bays, creeks, tidal inlets, estuaries, and headlands. There tion of Diu at the mouth of GoK2. The zone has a narrow are 11 important rivers in Maharashtra, including Vaitarna, strip of low-lying coastal region with a sharp rise of land- Ulhas, Patalganga, Vashisti, Shastri, Karli, Savitri, Kunda- ward topography, and the elevated region is known as the lika, etc. that terminate into the Arabian Sea. Many major Kathiawar Peninsula. The report of Gujarat state cyclonic and minor ports are situated around the coastal city of hazard zonation for 100-year of return period shows that Mumbai, which is the economic capital and the most popu- this coastal zone is the most cyclone-afected region of lous city of India. Mumbai is listed as the ffth most food- the state, and it falls in the category of > 55 m/s cyclonic afected coastal city in the world, and a major part of the wind speed. The PEMWE is calculated in this zone using reclaimed land of Mumbai is below the high-tide level. Even 13 synthetic cyclone tracks for all scenarios. Even though though several cyclones made landfall along the Mumbai the PEMWE of 4.0 m is simulated all along the coast for coast in the past, it has not seen signifcant cyclones recently. the present scenario, it has not caused any fooding in the However, there are clear indications that the impact of CC adjacent region except in a small pocket lying around Diu on extreme events is being felt. As per the AR5, IPCC (IPCC island, and the fooded water level is about 3 m (Fig. 10a). 2014a), Mumbai port city is a high-risk zone in terms of There is a small inlet fowing around this island, and the areas exposed to coastal fooding and increased population surge water can penetrate through the inlet by inundating by 2070. Inadequate drainage systems, destruction of coastal the adjacent low-lying regions. As the minimum topo- mangrove ecosystems and encroachment seaward makes graphic height is about 6 m along the coast, the inland Mumbai more aggravated. The past cyclone data for north inundation is seen less over this region (Fig. 10b). The Maharashtra and south Gujarat shows that the occurrence PEMWE increases to 4.8 m and 5.3 m for moderate and of cyclones is more during the post-monsoon season with a extreme scenarios, respectively (Fig. 5b). The model maximum wind speed ranging between 40 and 44 m/s. simulated AWL of 0.5–1.0 m around Diu island for the The northern part of the Konkan coast from Valsad to moderate scenario (Fig. 10c), and it further enhances to Dapoli is enclosed within zones4 and 5. Zone4 has the 1–2 m for the extreme scenario (Fig. 10d). The simulations shortest coastal stretch, extending from Valsad in the south infer that the coastal region of Zone2 is mostly safe from Gujarat to Nandgaon in Maharashtra (Fig. 9b). The conti- cyclone induced coastal fooding for any climate change nental shelf width along this coast increases from 280 km scenario. in the south to 350 km in the north with an averaged shelf

1 3 Mapping of cyclone induced extreme water levels along Gujarat and Maharashtra coasts: a climate…

Fig. 10 a PEMWE of Zone2 for present scenario, b associated probable maximum coastal inundation extent and water levels, c AWL for moder- ate scenario and d AWL for extreme scenario width of ~ 330 km near Valsad. As shown in Fig. 3, the tidal The PEMWE is higher (5–6 m) in the concave-shaped coast- height increases from 2 to 3 m from Nandgaon to Valsad. lines compared to straight-line coast (4–5 m). Even with The complex shoreline and abruptly changing shelf width a PEMWE of ~ 4–5 m for the present scenario, the coastal along the coast are the main reasons for highly variant tidal region of Zone4 and northern part of Zone5 are protected heights in this zone. The presence of high tidal range along from inundation due to the presence of elevated region the coast in Zone5, that extends from Nandgaon to Dapoli, except near Nandgaon, where the topography is within 3 m including Mumbai, increases the coastal vulnerability due and associated inundated water level is ~ 3–4 m (Fig. 11b). to STW interaction in this region. Tidal height increases at Though many small river inlets fow through these zones, the convergence zones, such as at JNPT (Mumbai), where the river banks are safe from any inundation except in the the tide is about 2.4 m and increased to 2.7 m inside Thane lower basin of Vaitarna estuary (< 3 m topography) and the creek. The tidal height decreases to 1.7 m towards Dapoli highest inundation levels of 5–6 m are observed along these being located along a straight-line coast. river banks. The maximum elevated surge waters of 4–7 m The computations are performed using 9 synthetic TCs to in the Thane, Panvel, and Kanjara creek can inundate the generate PEMWE for Zone4, and 14 TCs are used to gener- neighboring inland regions (< 4 m topography) through ate the same for Zone5. Unlike other zones, the PEMWE well-connected small inlets. The inundated water level associated with Zone4 cyclones is computed predominantly heights vary between 2–5 m over this region for the present along the coast of Zone5 (Fig. 11a). The discussions are scenario. Though there is a lack of recorded cyclone-induced made both for zones 4 and 5. The highest PEMWE of about surge events, the worst storm water food in the history of 7 m is observed at Thane creek and 6 m at Vaitarna estuary Mumbai and Thane occurred in 2005, resulting in the deaths for the present scenario. The local shoreline confguration of approximately 500 people. such as concave-shaped river estuaries, bays, and creeks, The maximum increase in PEMWE of about 1.2 m can result in trapping of water, and enhancement of TWE. and 1.6 m is observed inside Thane creek for moderate

1 3 J. Poulose et al.

Fig. 11 a PEMWE of Zone4 and Zone5 for present scenario, b associated probable maximum coastal inundation extent and water levels, c AWL for moderate scenario and d AWL for extreme scenario and extreme scenarios, respectively (refer Fig. 5b, c). The 3.4 Efect of climate change on the extreme water extremely low-lying coastal regions of Thane and other levels nearby creeks experience the highest AWL and the larg- est extent of inundation for CC scenarios (Fig. 11c, d). In Coastal food risk is higher due to increases in storminess, general, the rise in MWE is ~ 0.5 m and ~ 1.0 m for moderate sea level rise (SLR), and other climatic efects. Recent stud- and extreme scenarios, respectively, all along the coast for ies on CC reveal that SLR is faster than expected, which may zones 4 and 5. This increase in PEMWE results in AWL of impact coastal vulnerability during extreme event conditions 0.5–1.0 m in the neighborhood of Vaitarna estuary and Vasai (Church and White 2006). The global SLR scenario provided creek for the moderate scenario, whilst it varies between by AR5, IPCC (2015) for RCP8.5 is 0.45–0.82 cm, and the 1.0 and 2.0 m for the extreme scenario. Many coastal areas studies show large uncertainty in the SLR at global and of Zone4 and Zone5 are protected from cyclone induced regional levels (Le Bars 2018). SLR is not geographically inundation during all CC scenarios due to the presence of uniform, and an estimation of spatial variability of future the highland. The local shoreline confguration and presence SLR is an important aspect of the future study. Though the of many small inlets and creeks can result in trapping of estimated SLR trend in the North Indian Ocean is consistent waters and thereby cause inundation along low-lying coastal with global SLR, a higher SLR trend is observed in the Bay regions. of Bengal than that in the AS (Unnikrishnan et al. 2015).

1 3 Mapping of cyclone induced extreme water levels along Gujarat and Maharashtra coasts: a climate…

The altimetry data shows higher SLR trend uncertainties of high tidal range and the surge enhancement by shallow in the western part of AS compared to that in the eastern depths. The variation in the PEMWE is relatively large in side. The coastal vulnerability index based on projected sea GoK1 and GoK2 and ranges from 7 to 11 m. The highest level rise shows high to very high-risk levels along GoK1 values are observed along the coast of GoK2 (10 m) and and medium to very high along the GoK2 (Mahapatra et al. in the inner regions of GoK1 (11.2 m), while the lowest of 2015). Due to the lack of large time-scale data (altimeter and about 4–5 m is computed along the coast from Dwarka to tide-gauge) for the North Indian Ocean region, the future Diu and 7–9 m along the coast of Mumbai. projection of the SLR trend is not estimated for the region. The maximum horizontal extent and inundation height A slight change in the evaluation of diferent uncertainty associated with PEMWE (for the extreme scenario) are parameters can lead to large modifcations in the future food observed in the low-lying regions such as Great and Little risk assessments (Woppelmann et al. 2013). A study on the Rann of Kutch, Mumbai, and high-tide mudfats of Bhavna- impact of uncertainty parameters shows that storm surge and gar (Fig. 12b). Whereas, the coastal stretch from south of wave set-up are dominant factors during extreme events (Le Bhavnagar to Jamnagar is protected from inland inunda- Cozannet et al. 2015), and the impact of SLR needs more tion due to the presence of the highland region of Kathia- attention in the future. Also, the interaction study of SLR war Peninsula except in some pockets like Porbandar and with storm tide shows that the storm tide levels outpace the Diu island. The fooded regions with high water levels are SLR impact where the tidal height and currents are large Kandla (10.5 m) in GoK1, Bhavnagar (9.5 m) in GoK2, and during extreme events (Arns et al. 2017). Therefore, the Mumbai coast (9 m). The major part of inundation is con- impact of projected SLR on extreme water levels is omitted tributed by the innumerable number of small inlets present from the present study. in the afected area. Especially in the estuary and banks of As the discussion continues, the study by Muis et al. Dhadhar, Narmada and Tapi rivers along the eastern part (2016) using a global model reports that the cyclone induced of GoK2 where the inundated water level is ~ 5–6 m. The extreme sea levels vary between 5 and 10 m inside the GoK1 uniqueness of topography such as the presence of highly and entire GoK2 for a 100-year return period even with- elevated Kutch and Kathiawar Peninsula around the Guja- out considering the interaction of surge with tide and wind rat coast and also the Western Ghats boundary around waves. However, it is to be noted that the localized mod- Maharashtra coast restricts further intrusion of additional eling of these extreme events may lead to signifcantly dif- water levels generated by the wind enhancement for climate ferent sea levels, which is addressed in the present study. The change projections (Fig. 12c). The impact of CC in terms of PEMWE of all zones is composited and depicted in Fig. 12a an additional area of inundation is minimal except at Dhad- for extreme scenarios to identify vulnerable coastal regions har, Narmada and Tapi river banks, nearby regions of GoK2 in the event of climate change. Most of the coast is afected high-tide mudfats, and Little Rann of Kutch. The total area by high values of PEMWE, which is due to the presence of inundation is ~ 13,400 km2 for the present scenario, and

Fig. 12 a Composite depiction of PMEWE for extreme scenario, b associated probable maximum coastal inundation extent and water levels and c probable maximum extent of coastal inundation for diferent climate change scenarios

1 3 J. Poulose et al. increases by approximately 10% for the moderate scenario, to cyclone induced inundation are Great and Little Rann of and a further 2% for the extreme scenario. Though these Kutch and adjoining areas of GoK2 and Mumbai. low-lying regions are formerly identifed for the greater risk of coastal fooding (Woodruf et al. 2013), quantifcation of Acknowledgements We are very thankful to the Department of Sci- ence and Technology for the fnancial support by awarding the project food heights along the west coast of India is studied for the to IIT Delhi to carry out this work. We are also very grateful to Indian frst time in the perspective of climate change. Institute of Technology Delhi HPC facility and Department of Science and Technology, Government of India, for giving fnancial support (DST-FIST 2014) for computational resources.

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