Section IV Ocean Hazards and Disasters 17 –Induced Storm Surges and Wind Waves in the

Prasad K. Bhaskaran1, A. D. Rao2, and Tad Murty3

ABSTRACT

The Bay of Bengal and the Gulf of Mexico are the two water bodies on the globe that are most prone to storm surges generated by tropical cyclones. In this chapter, a review has been made of the problem in the Bay of Bengal region located in the North Indian Ocean. Using the contemporary numerical models, not only storm surge elevations but also coastal inundations were computed for some recent cyclones in the Bay of Bengal. These models included the interactions between storm surges, tides, and wind waves. However, it should be noted that one of the challenging issues, which still remains unsolved to a large extent, is computing the interaction between storm surges and river flooding, and the contribution of this interaction to coastal flooding and inun- dation. A dramatic example of such an interaction was during the 29 October 1999 cyclone on the coast.

17.1. INTRODUCTION­ setup/setdown. The worst possible scenario of extreme water level can occur when the storm surge coincides with Storm surge and wind waves are the manifestation of the astronomical high water. In the hinterland regions, surface winds blowing over the ocean surface, and turn the major damage and devastation can result from out quite detrimental in coastal areas during tropical extreme wind speed and coastal and inland floods due to cyclone activity. Tropical cyclones form over the warm torrential rainfall. ocean surface and are widely recognized as being among About 80 tropical cyclones form over the global ocean the natural geohazards that can result in enormous loss basins annually, and about 5–6% of this total number of life, property, and damage to infrastructure during form over the North Indian Ocean basin (Niyas et al., landfall and the postlandfall phase. During the landfall 2009). The east coast of that borders the Bay of of a tropical cyclone, the worst affected areas are the low‐ Bengal is considered the most vulnerable and susceptible lying coastal regions that directly bear the brunt resulting region in the world in the context of risk associated with from abnormal rise in water levels due to extreme winds, tropical cyclones and extreme wind waves. The frequency storm surge, and wind‐wave activity. The total water of cyclones is much higher in the Bay of Bengal as com- level elevation near the coast is a combined effect pared with the Arabian Sea with a return period of about ­resulting from the mutual nonlinear interaction between 4–5 in a year with landfall either in , Odisha, storm surges, astronomical tide, and wave‐induced Andhra Pradesh, or . Analysis of historical cyclone tracks clearly indicate that the State of Odisha 1 Department of Ocean Engineering and Naval Architecture, located on the east coast of India receives the highest fre- Indian Institute of Technology Kharagpur, West Bengal, India quency of tropical cyclone landfall followed by Andhra 2 Centre for Atmospheric Sciences, Indian Institute of Pradesh, West Bengal, and Tamil Nadu. A recent study Technology Delhi, New Delhi, India on the assessment of historical cyclone tracks for four 3 Department of Civil Engineering, University of Ottawa, decades in the Bay of Bengal clearly indicates a rising Ottawa, Ontario, Canada trend in the energy metrics such as Power Dissipation

Techniques for Disaster Risk Management and Mitigation, First Edition. Edited by Prashant K. Srivastava, Sudhir Kumar Singh, U. C. Mohanty, and Tad Murty. © 2020 John Wiley & Sons, Inc. Published 2020 by John Wiley & Sons, Inc.

239 240 TECHNIQUES FOR DISASTER RISK MANAGEMENT AND MITIGATION

Index (PDI) and the Accumulated Cyclone Energy (ACE) of wave‐induced setup/setdown from extreme wind waves for tropical cyclones that form over the Bay of Bengal in the total water level elevation. Also, at present there is region (Sahoo & Bhaskaran, 2015). The estimated PDI a growing necessity and pressing demand to improve the for tropical cyclones in the present decade is about six quality of numerical forecasts for the atmosphere and times higher as compared with the past over the Bay of ocean due to high population density and rapid growth Bengal basin, and that has direct implications on coastal of urbanization, industrialization, and infrastructure vulnerability associated with storm surges and extreme development activities, which are progressing quite rap- wind waves over this region. Another recent study on idly along the coastal belt. In the era of information tech- coastal hydrodynamics using a coupled model for cyclone nology and advancements in computational power, it is a Hudhud in the Bay of Bengal (Murty et al., 2016) clearly need of the hour that demands accurate and re­ liable indicates that the size of tropical cyclones that formed information on storm surges and extreme wind waves for over this region has also increased during the present appropriate action and timely warnings to the coastal decade. Therefore in a changing ­climate the occurrence of community. Hence, due to the complexity involved as high‐intensity tropical cyclones along with their increase well the beneficial aspects in terms of socioeconomic­ in size has a direct implication on the vulnerability of implications, a detailed study is warranted along coastal coastal belts. It means that vast expanses of coastal areas of the Indian coast and that requires substantial regions are exposed to higher wind speeds, storm surge strenuous research effort. Along with the recent envelopes, differential coastal flooding scenarios, and development and advancements in high performance impact from extreme wind waves. In their study, Murty computing (HPC) systems, it has now become possible to et al. 2016 indicate that the existing parametric wind field simulate very high‐resolution models for storm surges formulation needs to be revisited and modified accord- and wind waves with a reasonably high degree of accu- ingly considering the overall radial distance in wind field racy. The ­importance of HPC systems in atmosphere and envelope keeping in view the increased size of tropical ocean modeling studies in terms of rapid computation is cyclones over the Bay of Bengal region. A 3/5 power‐law widely recognized in operational weather centers thereby was proposed (Murty et al., 2016) that takes care of the aiding timely warnings and advisories during tropical increased tropical cyclone size over this region. Despite cyclone events. the fact that the east coast of India is highly vulnerable to In this context, some of the recent developments in the impacts from tropical cyclone landfall, there is a numerical modeling include the implementation of state‐ growing concern and urgent demand among the scientific of‐art hydrodynamic models such as the Advanced community at present to conduct a systematic and more Circulation Model (ADCIRC) and Simulating Waves focused study to address aspects of coastal vulnerability Nearshore (SWAN), and atmospheric models like in a holistic view by considering the contributions from Weather Research and Forecasting (WRF) in operational various environmental drivers leading to an overall centers to obtain realistic estimates of storm surges and assessment of risk and coastal vulnerability. Such extreme wind waves for the affected regions during the studies have long‐term implications and beneficial value impact of a tropical cyclone. At present, the role of an and therefore need to be planned in a holistic manner HPC system in computing power is quite evident and considering aspects of coastal, social, economic, and inevitable and allows dynamic coupling of atmosphere‐ environmental vulnerability having wide socioeconomic ocean models to run ensemble predictions as well run in a implications. real‐time mode providing realistic estimates of storm The study on tropical cyclone activity, storm surges, surge height, storm surge envelope, and associated wind‐ and associated extreme wind waves is a quite fascinating wave characteristics. In the Indian scenario, at present the subject having many challenges that have unequivocally coupled models (hydrodynamic model ADCIRC coupled drawn the attention of the scientific community world- with SWAN wave model) have proven efficacy in storm wide in order to provide better quality forecast in terms surge forecast. It involves a dynamic coupling between of cyclone track, intensity, and the probable landfall storm surge and wind waves through radiation stress and ­location to aid timely warnings for better emergency precisely accounts for the wave‐induced effect in the operations and evacuation measures and efficient coastal overall prediction of total water level elevation near the zone management. The archives on historical cyclone coast considering the combined mutual nonlinear inter- track records signify that each cyclone track is unique in action effects between storm surge, astronomical tides, nature, thereby posing a real challenge to the atmospheric and wind waves. Recent developments include a few case scientists and oceanographers worldwide to devise a reli- studies carried out using coupled as well stand‐alone able forecasting system to predict cyclone tracks, improve models for recent very severe cyclone cases that had land- accuracy in tropical cyclone landfall, and estimate fall along the east coast of India. The chapter provides an storm surge and associated coastal flooding and the role overview as well as discussions on the past studies carried Tropical Cyclone–Induced Storm Surges and Wind Waves in the Bay of Bengal 241 out on storm surges and extreme wind waves over the Bay intensity. Over the Bay of Bengal region, the monthly fre- of Bengal region and elucidates the recent developments quency of tropical cyclone activity portrays a bimodal dis- carried out in this field. Though significant progress in tribution, with the primary peak during November and a storm surge, wind‐wave modeling, and developments in secondary peak during the month of May. It is seen that physical parameterization has been achieved in other about 16% of tropical cyclones intensify into severe ocean basins during the past few decades, there are gap cyclones, and about 7% further intensify into very severe areas that need introspection as well as require novel and cyclonic storms. The India Meteorological Department innovative ideas in order to provide a reliable information (IMD), the nodal weather agency under the Ministry of and dissemination system that can save life and property Earth Sciences, Government of India, has developed an during a tropical cyclone event. E‐Atlas (Cyclone Warning Research Center, CWRC, India, 2011) that provides a concise picture of tropical cyclone 17.2. ­METHODOLOGY activity over the North Indian Ocean basin. The E‐Atlas is a comprehensive collection of data, framed in a Graphical The study first makes an analysis of the tropical cyclone User Interface (GUI) based interface on all the weather dis- activity over the North Indian Ocean basin covering var- turbances that led to depressions, cyclones, and severe and ious aspects on the annual frequency of cyclones, depres- very severe cyclones formation and dissipation over the sions, and severe and very severe cyclonic systems in the North Indian Ocean region. The data period spans from North Indian Ocean based on 121 years of data from the 1891 until the present, covering a total period of 127 years. India Meteorological Department (IMD). The study also The historical track details are maintained by IMD and the performs a trend analysis on tropical cyclone activity. Joint Typhoon Warning Center (JTWC) (https://www.usno. Relevant studies on tropical cyclone‐induced storm navy.mil/NOOC/nmfc-ph/RSS/jtwc/best_tracks/). The data surges over the Bay of Bengal basin are also discussed in source from JTWC is available for a period starting from detail. Thereafter, the progress and advancements made 1945 onward, whereas the IMD has a data repository avail- in storm surge modeling over the global ocean basins and able for a longer duration. There are also other sources of in particular topical studies relevant to the Bay of Bengal data, such as the International Best Track Archive for basin are reported. The role of wind waves in operational Climate Stewardship (IBTrACS) from the World sea‐state forecast and in particular their role during Meteorological Organization (WMO), which is maintained extreme weather events is highlighted. The progress in by NOAA National Centers for Environmental Information wind‐wave modeling studies both in context to global (https://www.ncdc.noaa.gov/ibtracs/). Data are provided on perspective as well in regional scale for the North Indian tropical cyclone best tracks with an objective to understand Ocean is discussed at length. Thereafter, the role of cou- their distribution, intensity, and frequency over the global pled models in an operational scenario is reported with ocean basins (Knapp et al. 2010). There are several Regional special emphasis on wave–current interaction. The Specialized Meteorological Centers (RSMCs) worldwide importance of coupled models for operational forecast and other international centers that have contributed to the and their efficacy in simulating realistic total water level development of the IBTrACS global best track tropical elevations during tropical cyclone activity is highlighted. cyclone data. The various agencies includes RSMC Miami, The role of continental shelf slope and width on the non- RSMC Honolulu, RSMC Tokyo, RSMC New Delhi, linear interaction between storm surges, tides, and wind RSMC La Reunion, RSMC Nadi, RSMC Perth, RSMC waves is discussed. Further, the results obtained from Darwin, RSMC Brisbane, RSMC Wellington, China model simulations for four severe tropical cyclones Meteorological Administration’s Shanghai Typhoon (Thane, Aila, Phailin, and Hudhud) cases are discussed in Institute (CMA/STI), Joint Typhoon Warning Center, detail. NCDC DSI‐9635, NCDC DSI‐9636, UCAR ds824.1, and the Hong Kong Observatory (HKO). The RSMC New 17.3. ­TROPICAL CYCLONE ACTIVITY OVER Delhi under IMD also contributes data on tropical cyclones THE NORTH INDIAN OCEAN for the Indian Ocean region to IBTrACS. There are some pioneering recent studies that resulted by using the IBTrACS Tropical cyclones generally form over the warm oceans v03r05 data (Knapp et al., 2010), such as the poleward shift and there are some favorable conditions that determine in the maximum intensity of tropical cyclones (Kossin their formation and sustenance. The necessary conditions et al., 2014). are sea surface temperature (SST) greater than 26°C, low In context of the Bay of Bengal region, a very recent magnitude of vertical wind shear, large low‐level vorticity, detailed study by Sahoo and Bhaskaran (2017) resulted in and higher midtroposphere relative humidity. It is well the development of a comprehensive data set on tropical ­documented that the months May–June and October– cyclone–induced storm surge and coastal inundation November are the seasons that produce cyclones of high for the east coast of India. The annual distribution in the 242 TECHNIQUES FOR DISASTER RISK MANAGEMENT AND MITIGATION frequency of depressions and cyclones in the North tropical cyclone activity, and Figure 17.1d shows the total Indian Ocean region (Sahoo & Bhaskaran, 2017) for a number of severe cyclonic storms in the Bay of Bengal. period of 125 years (1891–2015) that best fitted with a The statistics of tropical cyclone activity show that third order polynomial representing their trend is shown increased frequency of high intensity cyclones over the in Figure 17.1. Their study (Sahoo & Bhaskaran, 2017) North Indian Ocean basin is a major concern for India analyzed the past 125 years of tropical cyclone data avail- and coastal regions. Singh et al. (2000, 2001) able from IMD and mentions that a total of 1,405 cyclonic and Singh (2007) have also reported on the increasing systems developed over the North Indian Ocean region trends in frequency of intense tropical cyclone activity (Figure 17.1a), which includes a total of 775 depressions, over this region. The study by Srivastava et al. (2000) 332 cyclonic storms, and 298 severe cyclonic storms focused on the low‐energetic cyclones and concluded that (CWRC, 2011). decreasing activity is noticed over the Bay of Bengal The classification is based on the maximum sustained region in the last four decades. Other interesting studies wind speed as per the IMD norms available at http://imd. by Wang et al. (2006) and Trigo (2006) advocate that an gov.in/section/nhac/termglossary.pdf and the Dvorak increased frequency of tropical cyclones can be expected technique that used enhanced infrared and/or visible in the head Bay of Bengal region as a consequence of satellite imagery to quantify the intensity of the cyclonic northward shift in midlatitude storm tracks. A recent system. The IMD classification or the “T” classification study by Kossin et al. (2014) signifies that the recent is used to estimate quantitatively the intensity of tropical year’s location of cyclogenesis has shifted due to global cyclones based on the maximum sustained wind speed. warming with a tendency of poleward shift. Their study For example, T1.0 is used to classify a Low Pressure (Kossin et al., 2014) indicates that the poleward shift System (wind speed < 31 km h−1); T1.5 for a Depression occurred at a rate of 53 and 62 km per decade in the (wind speed between 31 and 49 km h−1); T2.0 for Deep Northern and Southern Hemispheres, respectively, how- Depression (wind speed between 50 and 61 km h−1); T2.5 ever, there is an unclear trend in the shift of cyclogenesis for Cyclonic Storm (wind speed between 62 and for the North Indian Ocean basin. In the Indian context, 88 km h−1); T3.5 for Severe Cyclonic Storm (wind speed there have been significant improvements in the opera- between 89 and 117 km h−1); T4.0 for Very Severe Cyclonic tional forecasting of tropical cyclone track, intensity, Storm (wind speed between 119 and 221 km h−1), and landfall location, storm surge and coastal flooding, and T6.5 for a Super Cyclonic Storm (wind speed > extreme wind‐waves in recent years. The joint efforts 222 km h−1). As seen from Figure 17.1 for the 125 years of from the operational weather centers like IMD and data, the postmonsoon season of October and November ESSO‐INCOIS (Indian National Centre for Ocean recorded a maximum of 238 and 204 events followed by Information Services) under the Ministry of Earth the premonsoon season of June to August with a total Sciences, Government of India were quite instrumental count of 163, 156, and 181 events, respectively. It is inter- in providing timely warnings and periodic bulletins esting that the data reveal that during the period 1921– through various modes of dissemination that resulted in 1980, the frequencies were much higher (about 18 a massive coastal evacuation effort during cyclone Phailin cyclones/year) as compared with the period from 1981 (2013). About 550,000 people from the coastal belts of until the present (Sahoo & Bhaskaran, 2017). However, Odisha and Andhra Pradesh States were evacuated to the trend in the present decade exhibits a higher fre- safer locations. quency of very severe cyclonic storms (VSCS) as com- pared with the past (Figure 17.1b). Based on analysis of 17.4. ­STUDIES ON TROPICAL CYCLONE–INDUCED the 125 years of data, the annual probability of intensifi- STORM SURGES FOR THE BAY OF BENGAL cation in terms of percentage from depression to cyclonic storm was 44.8%, and from depression to severe cyclonic One can find numerous studies in the literature that dis- storm was 21.2%, and from cyclonic storm to severe cuss the impact of tropical cyclone–induced disastrous cyclonic storm was 47.3%. The months of March‐April‐ storm surges in the Bay of Bengal. Some of the pioneer- May exhibited the highest probability of intensification ing and notable studies include Murty and Flather (1994), (71.4%, 78%, and 69.9%, respectively) for depressions Das (1994), Dube et al. (1997), Madsen and Jakobsen that eventually converted to cyclonic storms, and during (2004), Rao et al. (2007), and many others. Several factors the postmonsoon season October‐November‐December, that directly contribute to disastrous storm surge in the the respective values were 50%, 67.6%, and 59.8% (Sahoo Bay of Bengal region are discussed in these studies. Most & Bhaskaran, 2017). The annual frequency of depres- important, the convergence of the bay (funnel‐shaped), sions, cyclones, and severe cycloni, and storms for the presence of wide continental shelf encompassing the del- Bay of Bengal region is shown in Figure 17.1c. From taic environment in the head Bay of Bengal, densely pop- 1970 to the present, a decreasing trend is observed in the ulated low‐lying coastal belt, high tidal range, presence of (a) Yearly frequency of cyclones and depressions (b) Yearly frequency of very severe cyclonic storms 20 8 3 2 18 y=–1E – 05x + 0.0575x – 111.87x+72557 7 Frequency of very severe cyclonic storms 16 6 Poly. (frequency of very severe cyclonic storms) 14 5 12 10 4 8 3

6 2 4 1 2 Frequency of cyclones and depressions y=6E–07x3 –0.005x2 +13.098x–10709 0 0 1891 1911 1931 1951 1971 1991 2011 1891 1911 1931 1951 1971 1991 2011

(d) (c) Yearly frequency of depressions, cyclones, and severe cyclonic storms in the Yearly frequency of severe cyclonic storms in the Bay of Bengal Bay of Bengal 8 18 7 16 3 2 Annual y=–8E – 06x – 0.0466x – 90.557x+58621 14 6 12 5 10 4 8 3 6 2 4 2 1 Annual y=4E–06x3 –0.0222x2 + 46.678x–32485 0 0 1891 1901 1911 1921 1931 1941 1951 1961 1971 1981 1991 2001 2011 2021 1891 1911 1931 1951 1971 1991 2011

Figure 17.1 Annual frequency of (a) cyclones and depressions, and (b) very severe cyclones in the north Indian Ocean; (c) depressions, cyclones, and severe cyclones, and (d) very severe cyclones in the Bay of Bengal (from Sahoo & Bhaskaran, 2017). 244 TECHNIQUES FOR DISASTER RISK MANAGEMENT AND MITIGATION numerous riverine systems, tidal creeks, mudflats, coastal (Camp Dresser & McKee, 1985). Subsequent develop- geometry, complex geomorphic environment, and so on, ments and improvements in model parameterizations results in the occurrence of disastrous storm surge in the resulted in the development of the SLOSH (Sea, Lake Bay of Bengal as compared with other regions elsewhere and Overland Surges from Hurricanes) model in 1992 by in the world. The coastal inundation that results from the National Weather Service (Jelesnianski et al., 1992). storm surge during tropical cyclone landfall mainly It was a two‐dimensional, dynamic storm‐surge model depends on the storm surge height, vegetation character- that used a curvilinear polar coordinate grid structure for istics prevalent over the affected regions, and onshore spatial discretization; it was extended to elliptical and topography of the hinterland. The disastrous effects of hyperbolic grids thereafter. The National Hurricane this natural geohazard can be minimized to a large extent Center, USA, uses the SLOSH model for real‐time fore- through reliable numerical model predictions that pro- casts of storm surges. The US Army Corps of Engineers vide alerts and timely warnings to the coastal commu- (USACE) also developed a one‐dimensional numerical nities. Modeling the prevalent hydrodynamics along the model called DYNLET (Amein & Kraus, 1991). Further coastal environment during tropical cyclone activity is a research efforts have led to the development of three‐ quite challenging task due to the complex nonlinear dimensional and depth‐averaged numerical models. interaction mechanism between various environmental Parallel to the developments in the SLOSH model drivers such as tides, wind waves, currents, and storm another model called ADCIRC (Advanced Circulation surge. Model) was also developed. The ADCIRC model was a joint collaborative effort between the USACE 17.4.1. Progress of Storm‐Surge Modeling Engineering Research and Development Center, in a Global Perspective University of Notre Dame, and the University of North Carolina, USA. The present version of ADCIRC has the Studies on storm‐surge modeling started during the flexibility to run in two‐dimensional depth integrated late 1950s. During the past six decades of extensive (2DDI) and three‐dimensional (3D) modes. It is proven research and efforts, there have been tremendous devel- as one of the most robust and reliable models worldwide opments made. However, numerical modelers have been for storm surge and inundation studies, and is also used looking forward to robust, advanced techniques, and by the operational centers for real‐time forecasts. More innovative ideas to understand and predict the potential details on the governing equations and technical details variability in tropical cyclone–induced storm surges. A are available in Luettich et al. (1992) and Luettich and comprehensive overview on the various models adopted Westerink (2004). The recent developments include the by operational centers globally is available in the studies coupling of ADCIRC hydrodynamic model with SWAN by Murty (1984) and Sundermann and Lenz (1983). (Simulating Waves Nearshore) wave model available in Flather (1976) and Flather and Proctor (1983) for the Dietrich (2010). There are many case studies performed North Sea, Jelesnianski and Chen (1982) for the Gulf of and available in the studies by Hubbert et al. (1991), Mexico and Atlantic coast, and Bode and Hardy (1997) Powell and Houston (1996), Powell et al. (1998), Houston for the European coast are some of the notable studies on et al. (1999), Fleming et al. (2008), Blain et al. (2008), storm surges. Westerink et al.(2008), Dietsche et al. (2007), Peng et al. Prior studies on development of storm‐surge modeling (2004), Xie et al. (2004), and Cho et al. (2009). started with statistical analysis based on archived storm records. Some of the pioneering efforts in this context 17.4.2. Progress of Storm‐Surge Modeling that used empirical formulations include the studies by in the Bay of Bengal Basin Conner et al. (1957), Donn (1958), Bretschneider (1959), Welander (1961), Miyazaki et al. (1962), Harris (1963), The storm‐surge problem for the Indian coast started and Jelesnianski (1965). Continued efforts and improve- with the development of empirical relations and the ments in the empirical-based models resulted in the studies by Rao and Mazumdar (1966) led to generation development of the SPLASH model (Jelesnianski, 1972), of nomograms that represented the storm‐surge amplitude which estimates storm surge for a given bathymetry and as a function of storm intensity and speed. Another study approach angle of a tropical cyclone. Nomograms that by Janardhan (1967) used empirical formulations consid- were developed using this model gained popularity and ering the static wind setup and assuming a balance bet- were used for real‐time storm‐surge prediction. ween wind stress and sea‐surface slope to estimate Thereafter, in 1976 the Federal Insurance Agency devel- storm‐surge height at Sagar Islands located in the head oped the FEMA TTSURGE (Federal Emergency Bay of Bengal. There were several other studies that relied Management Agency Tetra Tech SURGE), which was on empirical models such as by Chaudhury and Ali recommended by the National Academy of Sciences (1974), Rao and Majumdar (1966), Qayyum (1983), and Tropical Cyclone–Induced Storm Surges and Wind Waves in the Bay of Bengal 245

Das et al. (1978). It was only during the early 1970s that tion. Also the inundation computation in ADCRIC uses numerical studies on storm surge were attempted. A a sophisticated drying and wetting algorithm. Studies by ­pioneering study by Das (1972) led to the development of Bhaskaran et al. (2013), Murty et al. (2014, 2016), the first numerical model for storm‐surge prediction in Gayathri et al. (2015), and Poulose et al. (2017) pioneered the Bay of Bengal. Later, Das (1980) introduced non- storm‐surge and coastal inundation modeling for the linear advective terms in the model equations and pro- Indian coast using the coupled ADCIRC + SWAN posed that inclusion of tide‐surge interaction into the model, which can handle both hydrodynamics and waves. model physics advanced the arrival time of peak surge by The most recent studies on storm surge and coastal inun- about 2 hr. It was probably the study by Murty and Henry dation using coupled ADCIRC + SWAN for various (1983) that developed for the first time a series of severe cyclonic storm surges along Indian coasts are by numerical models for tide and surge that used an irregular Bhaskaran et al. (2013) for cyclone Thane, Murty et al. rectangular grid instead of a regular rectangular grid. (2014) for cyclone Phailin, Gayathri et al. (2015) for Significant progress has been made in this subject and the cyclone Aila and Murty et al. (2016) for cyclone Hudhud. study by Johns and Ali (1980) and Johns et al. (1981) The studies could provide the total water level elevation included the Ganges–Brahmaputra–Meghna River system at the coast during a cyclonic landfall episode. At present using the depth integrated nonlinear equations of motion the coupled model is used by INCOIS (Indian National and continuity. The SPLASH model of Jelesnianski Centre for Ocean Information Services) for operational (1972) was later adopted by Ghosh (1977) for the east forecast of storm surge and inundation in the North coast of India. In another study, Johns et al. (1981) used Indian Ocean basin. the full nonlinear depth‐averaged model of Jelesnianski (1976) to investigate storm‐surge activity for the 1977 17.5. ­CHARACTERISTICS OF OCEAN cyclone Andhra. Literature review suggests that the most WIND WAVES AND THEIR ROLE DURING complex cyclone model used to model the Bay of Bengal EXTREME WEATHER EVENTS storm surge (Jarrell et al. 1982) in the 1980s was based on the US National Weather Service for the standard project The air–sea interface is a boundary between the Hurricane (Murty et al. 1986). In this study, 258 atmosphere and ocean that is quite dynamic in nature, ­simulations were analyzed generated from a total of and the exchange of momentum, heat, gas, and particles eight numerical storm‐surge models, five for the occurs across this boundary. The wind stress that acts Sri Lanka/India/Bangladesh region, two for the Burma/ over the near‐surface atmospheric boundary layer Thailand region, and one for the Andaman Islands imparts momentum, thereby generating wind waves or region. Extensive studies on storm surge were carried out surface gravity waves having wave periods ranging bet- using a finite difference model for the Bay of Bengal ween 2 to 30 seconds. Study on the characteristics of region by Dube and Gaur (1995) also popularly known as wind waves such as their generation, propagation, and IIT‐D storm‐surge model. An elaborate overview of finite dissipation mechanisms have been a subject of immense difference models is available in Dube et al. (1997). Several interest for several decades having significant practical case studies were performed using the IIT‐D model for applications and economic importance. In the recent the Indian coast. In addition, there are several studies past, there has been significant research on the study of reported on storm‐surge models by Rao et al. (1997), wind waves and their prediction due to increasing marine Chittibabu (1999), Chittibabu et al. (2000, 2002), Dube and offshore activities. A precise knowledge of the sea et al. (2000ba, 2000b, 2004), and Jain et al. (2006a, 2006b) state and its prediction is very vital for various marine‐ for the Gujarat, Andhra Pradesh, Odisha, and Tamil related operations, efficient ship routing, strategic naval Nadu coasts. operations, port and harbor development activities, Recent developments include the implementation of coastal zone management, and so on. the ADCIRC model by Rao et al. (2010) for the Nevertheless, the scientific and engineering community Kalpakkam coast located in Tamil Nadu State to eval- has a profound interest in understanding the associated uate extreme storm‐surge scenarios. The ADCIRC model kinematics and dynamics of ocean wind waves for routine uses a flexible finite element mesh that is capable to forecast and case‐based studies. The engineering resolve the complex coastline geometry as well as sophis- community working in the related disciplines of ocean ticated model physics to compute storm surge and inun- engineering, naval architecture, and civil and hydraulic dation, and hence more advantageous as compared with engineering requires precise wave‐related information to the IIT‐D storm‐surge model. Though the IIT‐D model design, operate, and manage structures or natural sys- fared reasonably well in many case studies, it could be tems in the marine environment. Ocean waves also play a used only for storm‐surge computation, unlike ADCIRC significant role in controlling coastal processes in the which has capability for both storm surge and inunda- coastal and nearshore environments. As per the existing 246 TECHNIQUES FOR DISASTER RISK MANAGEMENT AND MITIGATION knowledge, wind blowing over the ocean surface gener- of wave generation by wind and quadruplet wave–wave ates wavelets and the spectral components eventually interaction and dissipation due to white‐capping mecha- develop over time extracting energy from the wind stress. nisms. These deep‐water waves transform on reaching Through nonlinear wave–wave interaction processes, the shallow waters due to dominant physical processes like energy within a wave system gets redistributed thereby refraction, bottom friction, depth‐induced breaking, triad determining the overall wave energy at a particular loca- wave–wave interaction, wave–current interaction, tion and time, and that can be conveniently expressed in diffraction, and reflection (Holthuijsen, 2007). Hence, the form of a wave spectrum. This is the present state of choosing an appropriate wave model for the desired task is knowledge acquired despite several decades of ongoing very important considering the dominant physical research in the field of ocean wave modeling. processes relevant to the study area. The random nature of ocean waves and their complex interaction mechanism in terms of their kinematics and 17.5.1. Progress of Wind‐Wave Modeling dynamics of wave evolution was a major challenge in the in a Global Perspective past. The fundamental and classical studies on water waves with valid assumptions and developments in Broadly speaking, there have been significant improve- mathematical formulations date back to the nineteenth ments in the operational aspects of regional and global century. Table 17.1 provides an overview on the major ocean wave forecasting systems routinely used for medium advances and developments in the field of ocean wind range forecasts of ocean state variables (Tonani et al., waves during the past few decades. 2015). The GODAE (Global Ocean Data Assimilation The pioneering studies by Gelci et al. (1957) introduced Experiment) project played a key role in the collabora- the concept of energy balance equations to understand tion between national groups in the development of the phenomenon of wave evolution. Since then different global ocean forecasting systems (Smith, 2006). More categories such as first, second, and third generation wave information is available at the website https://www.godae- models have evolved. At present, the third generation oceanview.org/ on the activities related to ocean analysis wave models are used for routine wave forecasting, and and forecasting. The GODAE team has partnerships several advancements are noticed in the parameterization from various countries like the UK, France, Norway, of physical processes in a wave forecasting system. At Italy, USA, Australia, Canada, Japan, Brazil, India, and present, there has been a tremendous boost in computing China (more details on future scope of activities by power, information technology, data acquisition systems, the individual countries are available in Tonani et al., satellite remote sensing, and an increasing number of in 2015). The Marine Modeling and Analysis branch of situ observational platforms. the Environmental Modeling Center at the National Broadly speaking, the wave models can be classified Centers for Environmental Prediction (NCEP), USA, into phase‐averaging or phase‐resolving, wherein the provides information on wave forecast using the phase‐averaged models are expressed in terms of energy NOAA WAVEWATCH III (NWW3) run four times a day balance with appropriate sources and sinks used to repre- providing the hindcast and forecast information. sent the relevant physical processes. Phase-resolving The model products are available in global and regional models are based on the governing equations of fluid nested grids. The NWW3 model products for waves mechanics formulated to obtain the free surface condition. include significant wave height, wind‐sea wave height, However, the phase-averaging models have no prior primary and secondary swell wave height, wind speed restriction on the area to be modeled, whereas the phase- and direction, peak wave period, wind‐sea period, and resolving models have an inherent limitation on the spatial primary and secondary swell period. The basin‐scale dimension of the computational area. The various products cover the Atlantic, Pacific, and Indian Ocean physical processes that are accounted for in phase‐aver- regions. The regional scale simulations cover the northeast aged models include (1) wave generation by wind and northwest Atlantic, east coast of the USA, northeast accounted due to momentum transfer from atmosphere to Pacific, waters of Alaska, and Australia–Indonesia areas. ocean, (2) refraction due to water depth, (3) shoaling due The localized version of NWW3 includes location‐ to shallow water depths, (4) diffraction due to obstacles, specific areas of the waters surrounding the USA. (5) reflection due to impact with solid obstacles, (6) bot- Readers can refer to the website https://polar.ncep.noaa. tom friction due to heterogeneity of bottom materials, (7) gov/waves/for more details. In addition, there are com- wave breaking effects when steepness exceeds a critical panies such as the Ocean Weather Inc. (at http://www. level, (8) nonlinear wave–waveinteraction due to quadru- oceanweather.com/data/), which provides services to the plets and triads resulting in wave energy redistribution, coastal and ocean engineering community in the areas of and (9) wave–current interaction effects. In deep waters, marine meteorology, ocean waves and currents, ocean the physical processes can result from the combined effects engineering, and statistics of environmental data. Table 17.1 Research Advances in the Field of Ocean Surface Waves During the Past Few Decades. S.No. Advances 1940s 1950s 1960s 1970s 1980s 1990s 2000s 1 Statistical theory Theory of random Wave statistics & Mathematical Similarity form High frequency Wave number – Wave number – noise spectral developments in and work on wave spectrum frequency frequency spectra developments wave spectra – directional spectra nonlinear effects spectra 2 Nonlinear theory Nonlinear theory Nonlinear theory Wave instability Computation of Wave breaking Wave breaking Wave breaking and of regular of random waves and wave dispersion computational and energy energy dissipation waves interaction relation works dissipation studies 3 Experiments Basic studies and Observations from Advances in field Studies on Wave dynamics – Microwave Ocean observing (laboratory visual based instruments based equilibrium – use of satellite remote sensing systems and and field observations campaigns and planned ocean observations satellite based measurements) planned experiments platforms experiments 4 Air–sea interaction Sun glitter project JONSWAP field HEXOS SWADE, RASEX Coupled studies and wave experiment atmosphere–ocean projects models 5 Wave forecasting Sverdrup and SMB and PNJ wave First generation Second generation Third generation Third generation Third generation techniques Munk forecasting wave models wave models wave models wave models wave methods (WAM) with data models – ensemble assimilation modeling (WAM, WW3)

Source: Mitsuyasu (2002). 248 TECHNIQUES FOR DISASTER RISK MANAGEMENT AND MITIGATION

17.5.2. Progress of Wind‐Wave Modeling in Context and SWAN (0.002° × 0.002°). For general ocean of Indian Seas circulation, the ROMS model configured at a resolution of 0.125° × 0.125° is used. The General NOAA Oil Spill The ocean state forecast information for the Indian Modeling Environment (GNOME) Oil Spill model simu- subcontinent is quite vital, having diverse application and lates the oil spill trajectories. The forecasted winds societal benefits amongst the user community. The fore- obtained from atmospheric models from different meteo- casts have inherent economic advantages varying from rological agencies such as NCMRWF and ECMWF force traditional fishing to offshore‐related activities. Besides, the ocean models. To improve the nearshore and coastal with the numerous major and minor ports located along forecasts, models configured using nested grids are used the Indian coastline, the environmental information on and run in high-performance computers (HPC). The ser- sea state related to wind waves, swell activity, currents, vices provided by INCOIS include location specific fore- and tides is critical for efficient port operations. The casts; forecast for coastal, deep sea, and island areas; port movement of vessel traffic and operational activities and harbor forecast; and web map services. In addition, inside a port requires prior knowledge of environmental emergency services by ESSO–INCOIS also cover oil spill factors that aids port operations. Offshore activities such advisories, search and rescue operations, and high wave as mooring operations and loading and off‐loading of alerts for coastal regions. The forecast services for port liquid and gas products to facilities located in the hinter- and harbor includes 75 locations along the Indian coast land also requires accurate, timely sea‐state information. and island locations. These location‐specific forecasts are Recreational activities at selected coastal locations also subdivided into two zones, one up to 20 km and the other need appropriate information of sea state for smooth ranging from 20 to 50 km. In addition, ocean wave fore- operations. Many applications in the ocean environment casts are provided to neighboring countries like the require precise information of sea state; some of them are Maldives. The advisories for oil spill cover a forecast optimum shipping routes, erection of marine systems, period of three days updated at an interval of every 3–6 search and rescue operations in the sea, defense‐related hours based on requirement. Similarly, the services for activities, oil spills, and so on. During extreme weather high wave alert to coastal regions cover a forecast of 1–2 events, sea‐state information is imperative for offshore oil days updated every three hours. The value added service platforms and planning and evacuation measures for the covers information on the inland vessel limits in forecast coastal population. mode for one day updated every three hours. Validation of The Earth System Science Organization (ESSO)–Indian these location‐specific forecasts in near real‐time is based National Centre for Ocean Information Services (INCOIS) on the availability of satellite passes over the Indian Ocean established the Integrated Indian Ocean Forecasting region. ESSO–INCOIS disseminates the information in System (INDOFOS) for medium‐range prediction of the the vernacular by various modes such as e‐mail, mobile surface and subsurface characteristics of the Indian phone, TV, radio, and electronic display boards to the Ocean. The predictions have a lead time of 5–7 days at stakeholders. For areas that have no electricity supply, the present. The activities under the INDOFOS cover a broad dissemination mode is through manual display boards. gamut such as surface wave forecast cov­ ering aspects of Readers can refer to the we­ bsite http://www.incois.gov.in/ wave height, period, and direction for both wind waves portal/osf/osf.jsp for more details. and swells; sea‐surface currents; sea‐surface temperature; The Meteorological and Oceanographic Satellite mixed layer depth; depth of the 20° isotherm; astronomical Data Archival Centre (MOSDAC) under the Space tides; wind speed and direction; and oil spill trajectory Applications Centre (SAC), Indian Space Research modeling. The forecast provided by ESSO–INCOIS is Organization (ISRO) provides the forecast map of wind‐ widely used by the user community such as fishermen, waves using the WAM model every six hours extended to Indian Navy, Indian Coast Guard, shipping corporations, 120 hr. The WAM‐computed parameters such as wave offshore oil and gas exploration companies, and the height, period, and direction, swell height, and wind scientific community at large. The research activities under speed form an integral part of the forecast system cov- INDOFOS have expanded and, at present, location‐ ering the geographical domain extending from the zero specific forecasts are available for selected areas covering meridian to 160°E longitude, and from 70°N to 70°S in the Arabian Sea, Bay of Bengal, North Indian Ocean, the zonal direction. The classification of sea states from South Indian Ocean, Red Sea, Persian Gulf, and South the WAM computed wave heights covers five broad cate- China Sea. Besides, detailed forecasts are also available for gories: (1) slight, (2) moderate, (3) rough, (4) very rough, potential fishing zones, union territories, and island and (5) high. In addition to WAM forecasts, MOSDAC regions of India. The operational wave models and their also provides wave forecasts from the SWAN (Simulating resolutions used at ESSO–INCOIS include the MIKE‐21 Waves Nearshore) model at a six-hour interval extended SW (1° to 0.07°), NOAA WAVEWATCH III (1° to 0.05°), to 120 hr. The domain of SWAN runs covers 60°E–90°E Tropical Cyclone–Induced Storm Surges and Wind Waves in the Bay of Bengal 249 and 21°N–11°S. In addition to these wave forecasts, other increase in wave heights. In a following current, the products such as mixed layer depth (MLD), sea level opposite occurs, where the wavelength increases, the anomaly (SLA), sea surface current, temperature, and group velocity increases, and the wave heights are salinity using Princeton Ocean Model (POM) also form reduced. Wave period will be longer in following currents activities of MOSDAC. and shorter in opposing currents. Thus, the Doppler shift plays an important role in affecting the wave characteris- 17.6. ­COUPLED WAVE‐HYDRODYNAMIC MODELS tics. The modulation of absolute frequency by unsteady currents and modulation of intrinsic frequency by propa- Prior studies have used wave and hydrodynamic models gation over spatial gradients of current can also occur. as separate entities to simulate the flow and wave condi- Various empirical theories for wave–current interaction tions over a region; most of them are case‐based studies. in the bottom­ boundary layer suggest that the friction Coupling of wave currents as a single modeling system coefficient experienced by waves in a current regime will has been long recognized and their interaction controls be larger than in no current. This also applies to the effec- the momentum and energy exchange between the tive current friction factor in the presence of waves. atmosphere and the ocean that needs to be better resolved. Another effect is the vertical wind shear on wave breaking The coupling of these models can be achieved at various (Wolf et al., 1988). levels of complexity. One can find a complete review on The topic of wave–current interaction is found in the wave–current interaction mechanisms in the study by studies by Ardhuin et al. (2009), Mellor (2003, 2011), Jonsson (1990) and more recently in Cavaleri et al. (2007). Mellor et al. (2008), Kumar et al. (2012), and Zodiatis The effects from waves that are considered in the coupled et al. (2015). Bolanos et al. (2011, 2014) advocated the modeling system are due to the radiation stress and importance of the wave–current interaction in a tidal Stokes drift. Another study by Babanin (2011) also shows dominated estuary and showed that inclusion of wave that interaction of turbulence and bottom stress is also effects through radiation stress improved the velocity important. structure. The wave‐induced surface and bottom stress, and radiation stress are the mechanisms through which 17.6.1. Role of Wave–Current Interaction waves interact with currents. Surface waves may also affect currents in other ways, such as through the wave‐induced In a broad sense the wave–current interaction can be Stokes’ drift and the Coriolis wave stress (Huang, 1979; defined as the interaction mechanism between surface Jenkins, 1987). Wave‐induced wind stress increases the waves and the mean flow. The effect from currents that magnitude of currents both at the surface and near the includes tidal and wind-driven currents, river currents, seabed. On the other hand, wave‐induced bottom stress and so on, contributes to the mean flow. The process of weakens the currents both at the sea surface and near the wave–current interaction leads to transfer of energy seabed. Near the sea bottom, there exist enhanced levels of thereby affecting both waves as well the mean flow. In turbulence due to wind–wave interaction (Grant & shallow water depths, the propagation of wind waves is Madsen, 1979; Mathisen & Madsen, 1996, 1999). In highly dependent on the bathymetric profile and coastal particular, the short‐period oscillatory nature of wave hydrodynamics. When waves encounter currents in tidal orbital velocity leads to a thin boundary layer above the inlets, at river mouths, or nearshore zones, the wave bottom. In this boundary layer, the fluid velocity changes dynamics will be affected based on the speed and direction from its free stream value to zero at the bottom, where of the interacting current. The waves affect the currents no‐slip condition applies. The high shear velocity within mainly through the exchange of momentum flux from the wave bottom boundary layer produces high levels of waves to currents. In turn, the currents can also affect the turbulence intensity and large bottom shear stress. In shal- waves in different ways. It can affect the effective wind, low coastal waters, the near‐bottom flow consists of waves and the fetch that in turn affects the wave generation. The and slowly varying currents. The strong turbulence inten- effect of depth refraction and current refraction can sity within the thin wave bottom boundary layer therefore cause changes in the wave parameters. Strong currents can have an impact on the currents, especially in causing can have a significant influence on wave propagation the currents to experience an increased bottom resistance characteristics. In the presence of an opposing current, in the presence of waves. Using the wave–current interac- the wavelength will tend to shorten, thereby causing the tion model proposed by Grant and Madsen (1979), group velocity to decrease. To maintain conservation of Ningsih et al. (2000) and Xie et al. (2001) have shown that energy flux, the wave energy increases, resulting in a the surface waves could significantly affect the currents by ­localized increase of wave height. In addition, opposing modifying the bottom drag coefficient. The waves affect currents will refract the waves such that they are focused the wind stress by increasing the surface roughness upon the area of strongest flow, which will cause a further length. The significance of wave–current interaction 250 TECHNIQUES FOR DISASTER RISK MANAGEMENT AND MITIGATION depends mainly on the water depth. The current experi- In the nearshore areas, the effect of radiation stress ences an increased bottom resistance in the presence of also contributes to wave setup. As the waves approach waves in shallow waters with high wind‐wave activity. The breaking point, there will be a small progressive setdown wind waves modify the coastal circulation through of the mean water level below the still water level. This enhancement of bottom stress. setdown is caused by an increase in the radiation stress owing to decreased water depth as the waves propagate 17.6.2. Role of Coupled Models in Operational Forecast toward the shore. The setdown is maximum just seaward of the breaking point. In the surf zone, there is a decrease The ocean state is quite complex due to the mutual in radiation stress as wave energy is dissipated. This effect nonlinear interaction between the winds, currents, and is stronger than the radiation stress increase owing to waves. During extreme events like tropical cyclones, these continued decrease in the water depth. The result is a pro- interactions are significant as the energy associated is gressive increase or setup of mean water level above the quite high. The nonlinear wave–current interaction mech- still water level in the direction of the shore. The surf anisms during an extreme event results in radiation stress. zone setup typically is significantly larger than the set- Radiation stress, a term coined by Longuet‐Higgins and down that occurs seaward of the breaking point. The Stewart (1964), causes the lowering (setdown) and raising wave setup is of particular concern during storm events, (setup) of the mean water level that is induced by when higher wind waves resulting from the storm can waves as they propagate into the nearshore regions. This increase the mean sea level. Hence, the radiation stress is of immense importance in operational prediction plays a major role in coastal regions. of the storm‐surge heights. Radiation stress can be Under certain conditions, it will become very impor- defined as the depth‐integrated and phase‐averaged tant to take the interaction effects into consideration for excess momentum flux caused by the presence of surface an accurate prediction of nearshore waves and currents, gravity waves exerted on the mean flow. The radiation and understand aspects related to resultant sediment stress describes the additional forcing due to the pre­ transport and beach change. The storm events increase sence of waves that change the mean depth‐integrated the water level and enhance the risk on the coastal struc- horizontal momentum in the fluid layer. As a result, the tures. The mutual interaction between currents and waves varying radiation stress thereby induces changes in the through radiation stress thereby play an important role in mean surface elevation and the mean flow. In a practical the coastal environment. Hence, a proper understanding sense, the nearshore waves induce currents through radi- and quantification of the nonlinear interaction mecha- ation stress, and resultant currents conversely affect the nism is crucial. To achieve reliable estimates of this wave field, thus wave–current interaction always takes mutual interaction mechanism, it is mandatory to have place to a greater or lesser extent. The radiation stress very high resolution spatial grids coupled to both wave changes as a wave propagates through water of changing and hydrodynamic models. depth. Considering the wave–current interaction during cyclones, the radiation stress is modified by both chang- 17.6.3. Effect of Continental Shelf on the Nonlinear ing water depth and external force. During a cyclonic Interaction Mechanism event, there is a significant radiation stress being gener- ated. It is clear that waves always interact with currents The basin characteristics, which include the coastline by means of radiation stress. The current field is formu- geometry, relief features of the bottom such as width and lated with depth‐averaged shallow water equations. The slope of the continental shelf, also play an important role shallow water equations include the stress term, which in the overall development of storm surges. In the context incorporates the radiation stress. The wave‐induced stress of the Indian coastline, the west coast of India has a thereby influences the mean water surface elevation and larger continental shelf area compared to the east coast. the depth‐averaged currents. This in turn can affect the In general, the shelf width is about 60 km in Kerala State wave characteristic that modifies the radiation stress (off Kochi), and that gradually increases to about 330 km ­generated by the waves. The energy of a surface wave is south of Gujarat (off Daman). The shelf break occurs dependent on the mean water surface elevation. Therefore, along the entire coastline at water depths of about 130 m. the radiation stress from waves affects the current, and Poulose et al. (2017) performed an idealized experiment hence the mean water surface elevation and the depth‐ representing the west coast of India to understand the averaged velocity. These variations can affect the wave role of the continental shelf on the nonlinear interaction parameter, which again result in a modified radiation mechanism between storm surges, tides, and wind waves. stress. Hence, the nonlinear interaction mechanism, In this context, the tidal range also increases from south focused on effect of radiation stress on currents and its to north, and a maximum of about 11 m is attained over countereffect on waves, can be explained. regions in the Gulf of Khambhat. Owing to high tidal Tropical Cyclone–Induced Storm Surges and Wind Waves in the Bay of Bengal 251 range in the northern regions, one can expect a higher formulation was used along each track considering a degree of nonlinear interaction between storm surges, constant pressure drop of 40 mb with 30 km as the radius wind waves, and tides over this region during tropical of maximum winds. The forward motion was maintained cyclone activity. The bathymetry for the west coast of as 10 km h−1, which is the average speed of a tropical India highlighting the continental shelf region is shown cyclone. Model simulations were performed to compute in Figure 17.2a and the corresponding idealized bathym- storm surge in stand‐alone mode as well in a coupled etry is shown in Figure 17.2b. mode to investigate the nonlinear interaction between The computational mesh for the study region was gener- surge–wave, surge–tide, surge–tide–wave scenarios inde- ated using the Surface Modeling System (SMS), which was pendently (Poulose et al., 2017). The results obtained in maintained to 100 m near the coast and relaxed to 15 km at the stand‐alone mode of ADCIRC simulated storm the open ocean boundary (Figure 17.3) located approxi- surges for the 13 different locations (in Figure 17.3) are mately 900 km away from the coast. Major tidal constitu- shown in Figure 17.4. ents such as S2, M2, K2, T2, N2, K1, O1, P1, and Q1 are The amplification of peak surge varied from about provided as forcing at the open boundary obtained from 1.5 m in the south to about 4.5 m in the north when the the FES 2004 tide model. A minimum drag coefficient for continental shelf width increased from 45 to 300 km, bottom friction was specified as 0.005 with an explicit respectively. It indicates that the rate of amplification is scheme in time discretization at a time step of one second. about 12 cm for a linear increment of 10 km in the Numerical experiments were carried out using the continental shelf width. The spatial distribution of cur- ­computational mesh (Figure 17.3) considering 13 ideal- rents showed that the magnitude was about 0.5 m s−1 at ized cyclone tracks making landfall perpendicular to the the shelf break and that increased while approaching the coast at different locations separated by a horizontal dis- coast (Poulose et al., 2017). The nonlinear interaction tance of approximately 100 km. The Jelesnianski wind between various components is shown in Figure 17.5,

(a)

Gulf of Kutch Mahi River GUJARAT Narmada River Gulf of Tapi River Khambhat

Approx. 330 Km continental shelf width MAHARASHTRA

Approx. 90 Km Zuari River

KARNATAKA

ARABIAN SEA

Approx. 60 Km KERALA

Figure 17.2 (a) Bathymetry for the west coast of India and (b) idealized bathymetry for the west coast of India. 252 TECHNIQUES FOR DISASTER RISK MANAGEMENT AND MITIGATION

(b)

Depth (m) 330 km Shelf depth: 3–130 m 5590 5383 5176 4969 4762 4555 4348 4142 3935 3728 3521 3314 3107 2900 2693 2486 Shelf break 2279 2072 1865 1658 1451 1245 1038 65 Km 831 624 417 210 3

Figure 17.2 (Continued) and that varied with the phase of the tide and also was ­methodology adopted to model the storm surge. The dependent on the tidal range at a particular location. inputs are categorized into three sets: location specific Simulated results signify that the maximum interaction inputs, meteorological, and oceanographic inputs. occurs during the low tide and it varied between 20 and Features such as the topography and coastal orientation 45%, while it was minimum during the flood tide (bet- are the location‐specific inputs, whereas the oceano- ween 15 and 30%) from track 1 to 13. The interaction graphic inputs include the bathymetry and tidal ampli- effect varied between 15 and 40% for both high and ebb tudes. The meteorological input refers to the atmospheric tide conditions as the shelf width increased from south to condition during the cyclonic events. north. The study brings to light that by removing the tidal This section discusses the performance of the stand‐ effect from surge–tide–wave interaction, the surge levels alone ADCIRC model for the Thane event. Cyclone are still modified with the nonlinear interaction and the Thane made its landfall between Cuddalore and differences noticed from surge‐wave case alone depict the Puducherry in the morning hours of 30 December 2011 fact that continental shelf width plays an important role and was one of most severe cyclones the Tamil Nadu coast in generating the nonlinearity in surge–tide–wave had ever experienced. The coastline of Tamil Nadu has a interaction. length of about 1,076 km constituting about 15% of the total coastal length of India. The coastline is bounded on 17.7. ­STORM SURGE AND INUNDATION the north by Pulicat Lake and the south by Kanyakumari, MODELING FOR CYCLONE THANE which stretches over 13 ­districts. The coastal orientation of Tamil Nadu is straight and narrow without many This section provides the methodology developed to indentations except at . Fringing and patch model the storm surge and the inundation associated reefs are present near Rameswaram. The Gulf of Mannar, with severe cyclone Thane, which made landfall in Tamil Pitchavaram, Vedaranyam, and Point Calimere have well‐ Nadu located in the east coast of India. Figure 17.6 developed ­mangrove systems. There are many industries is the flowchart that provides an overview on the along the coastal region. There are two power plants at Track 13

Track 12

Track 11

Track 10

Track 9

Track 8

Track 7 Coast line 66 hour Track 6

Track 5

Track 4 6 hour

Track 3

Track 2

Track 1

Open boundary

Figure 17.3 Grid structure along with the synthetic cyclone tracks.

4.5 Surge 1 4 Surge 2 Surge 3 Surge 4 3.5 Surge 5 Surge 6 3 Surge 7 Surge 8 Surge 9 2.5 Surge 10 Surge 11 Surge 12 2 Surge 13 Landfall time

Elevation (m) Elevation 1. 5

1

0.5

0

–0.5 30 35 40 45 50 55 60 Time (h)

Figure 17.4 Storm surge evolution along various locations on the west coast of India. 254 TECHNIQUES FOR DISASTER RISK MANAGEMENT AND MITIGATION

(a) (b) 1. 2 1. 2 NLT (Track 1) NLT (Track 1) NLT (Track 5) NLT (Track 5) 0.8 NLT (Track 9) 0.8 NLT (Track 9) NLT (Track 13) NLT (Track 13) Landfall time Landfall time 0.4 0.4

0 0 Elevation (m) Elevation Elevation (m) Elevation –0.4 –0.4

–0.8 –0.8

High tide Low tide –1.2 –1.2 30 35 40 45 50 55 60 30 35 40 45 50 55 60 Time (h) Time (h)

(c) (d) 1. 2 1. 2 NLT (Track 1) NLT (Track 1) NLT (Track 5) NLT (Track 5) 0.8 NLT (Track 9) 0.8 NLT (Track 13) NLT (Track 13) NLT (Track 9) Landfall time Landfall time 0.4 0.4

0 0 Elevation (m) Elevation Elevation (m) Elevation –0.4 –0.4

–0.8 –0.8

Midflood tide Midebb tide –1.2 –1.2 30 35 40 45 50 55 60 30 35 40 45 50 55 60 Time (h) Time (h)

Figure 17.5 Time variation of nonlinear term (NLT) for tracks 1, 5, 9, and 13 (in Figure 17.3) during (a) high tide, (b) low tide, (c) midflood tide, and (d) midebb tide.

Ennore: the Ennore Thermal Power Plant with a produc- tion capacity of 200 MW and the North Thermal Meterological Power Plant with a production capacity of 600 MW. The inputs Location-specific Oceanographic cyclone‐induced peak surge and also the horizontal extent inputs inputs of inland flooding was computed using the ADCIRC

Dynamic storm model model in a stand‐alone mode. The wetting and drying algorithm in the ADCIRC model enables the estimation of inland penetration of water from the storm surge. The Storm surge model‐computed water level elevation and inundation re model equations validated against the tide gauge observations and the post- cyclone survey conducted by the ICMAM‐PD, Chennai. Numerical solution 17.7.1. Details of Cyclone Thane

Sea surface elevation Cyclone Thane, during 25–31 December 2011, was coastal inundation classified as the strongest tropical cyclone of 2011 in the North Indian Ocean region (Figure 17.3). The India Figure 17.6 Flow chart for the stand‐alone modeling system. Meteorological Department (IMD) classified Thane as Tropical Cyclone–Induced Storm Surges and Wind Waves in the Bay of Bengal 255

Bay of Bengal (BOB) 05 and the Joint Typhoon Warning Centre (JTWC) as 06B, that developed as a tropical dis- turbance in the west of Indonesia. Further, on 25 December 2011 the system was designated as a “Depression” as it continued to move in the northwest. On the next day it was named Thane and it continued its movement in the westward direction. After a slack period of almost three days, the system intensified into a very severe cyclonic storm on 28 December, and finally made landfall on 30 December on the north Tamil Nadu coast. It weakened rapidly and dissipated over the neighboring north Kerala in the morning of 31 December 2011. During the initial phase of Thane on 25 December, the wind speed near the center reached 65 kmh−1. On 28 December, the JTWC reported Thane as a Category‐1 Hurricane on the Saffir–Simpson wind scale. The maximum sustained wind was in the order of 120 km h−1. Thereafter, it continued to intensify with winds reaching about 165 km h−1 on 29 December, 2011. Figure 17.7 shows the satellite imagery of cyclone Thane as of 29 December 2011 (0735 Z). The devastation asso- ciated with this cyclone was quite severe. There are reports Figure 17.7 Satellite imagery of cyclone Thane. that Thane left at least 46 dead in Tamil Nadu and Puducherry (Punithavathi et al., 2012). The coastal belts and the Puducherry coast (Medha et al., 2015). of Cuddalore and Puducherry were the worst affected Figure 17.8 shows the track of cyclone Thane and the during this extreme event. affected areas on the Tamil Nadu coast. The IMD post- The postcyclone survey conducted by the IMD (Medha cyclone survey (Medha et al., 2015) also reported that the & Sunitha Devi., 2015) reported the lowest observed associated storm surge was about 1 m, which inundated mean sea level pressure as 969 mb recorded at Cuddalore, low‐lying areas of Cuddalore, Puducherry, and with a maximum estimated wind speed of 139 km h−1. At Villupuram districts at the time of landfall. The observa- Puducherry the maximum recorded wind was about tions made by satellite and Doppler Weather Radar 125 km h−1 at the time of landfall. Gale wind speeds bet- (DWR) reported the maximum intensity from 0300 UTC ween 120 and 140 kmh−1 prevailed over north Tamil Nadu of 29 December 2011 until 0000 UTC of 30 December.

20 N

Track of very severe cyclonic storm, Thane based on 1200 UTC of 28 December 2011 Thane cyclone affected areas in Tamil Nadu 2011

15 N

D DD CS SCS VSCS VSCS VSCS CS CS CS CS 28/18 31/12 31/00 30/12 30/00 29/12 29/06 28/12 27/18 27/12 CS 27/06 CS 27/00 CS 26/18 CS Legend 26/12 DD High 26/06 DD 10 N DD Medium 26/00 D Low D DATE/TIME: IN UTC 25/18 Scale IST=UTC+0530 HRS 25/12 Cone of uncertainty in 030 60 120 track forecast Kilometers D: Depression DD: Deep depression CS: Cyclonic storm SCS: Severe cyclonic storm VSCS:; Very severe cyclonic storm 70E 75E 80E 85E 90E 5 N

Figure 17.8 Track of cyclone Thane in the Bay of Bengal (left) and cyclone affected areas in Tamil Nadu (right). 256 TECHNIQUES FOR DISASTER RISK MANAGEMENT AND MITIGATION

The radar bulletins­ issued by DWR Chennai were discon- for the domain used in the present study. A enlarged sec- tinued beyond 0600 UTC on 30 December 2011, as the tion of Figure 17.10a near the coast is shown in cyclonic system started weakening thereafter. Figure 17.10b; it clearly depicts the grid structure relax- ing from the coastline toward both the inland and off- 17.7.2. Data and Methodology shore boundaries. In order to study the inland inundation due to a storm surge, the onshore grid boundary is fixed In order to achieve the best possible topography of the at +10 m contour assuming that the inland penetration of study region, a blended product of the GEBCO and seawater never exceeds this contour limit. It is reasonable SRTM was used. The GEBCO (General Bathymetric as in most of the cases the +10 m contour is more than Chart of the Oceans) is a global digital database gener- 5 km inland from the coast. In the offshore region, a res- ated through a combination of quality controlled depth olution of about 20 km is used and the grid size refines soundings survey data blended with satellite‐derived to 100 m when approaching the nearshore region. gravity data. Even though the GEBCO data cover the The grid comprises 68,896 nodes and 136,809 elements. A topography for both oceans and land, the resolution is rectangular open ocean boundary is used for the present coarse and may not serve the purpose for realistic esti- study. The rectangular shape is computationally more mates of onshore inundation over the land area. Hence, intensive (due to larger node numbers) compared with a for the study area, the high-resolution SRTM (Shuttle semicircular shape. The advantage is it avoids the problem Radar Topography Mission) data are utilized for genera- of computational instability at corner node points sepa- tion of the onshore topography. The high‐resolution rating the mainland from the offshore boundaries. SRTM data are blended with the GEBCO offshore The landward boundary of the grid is fixed from the topography leading to a hybrid topographic database for coast (zero meter contour line) to the +10 m topographic the study area (Figure 17.9). The GEBCO bathymetric elevation contour presuming that the surge would never data with a resolution of 30 arc second are the best avail- exceed 10 m in this region. The flexible grid refines near able high‐resolution data for ocean, and they were the shoreline (zero contour line) and relaxes both in the blended with SRTM data having a horizontal resolution onshore and offshore direction away from the shoreline. of 90 m for the onshore areas of Tamil Nadu. The wind field used to simulate coastal inundation for the The SMS (Surface Modeling System) package is used Thane event is the dynamic Holland wind field model to generate the finite element mesh for the study area. that utilizes the best track record files from the JTWC Figure 17.10a shows the finite element unstructured mesh (Joint Typhoon Warning Centre). The Holland model

Mesh module elevation 4450.0 3900.0 3350.0 2800.0 2250.0 1700.0 1150.0 600.0 50.0 –500.0

Figure 17.9 Blended topography of the study domain. Tropical Cyclone–Induced Storm Surges and Wind Waves in the Bay of Bengal 257

(a) (b)

Pondicherry

10 m topo line

Coastline

Figure 17.10 (a) The study domain and the finite element grid structure, and (b) same as in (a) and zoomed near the coast.

Mesh module wind stress or velocity (74) mag 4 08:24:00 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0

Figure 17.11 The Holland wind field over the study domain at time of landfall. calculates the wind field and provides information on sea‐ accordance with the IMD report of 38.55 m s−1 level pressure distribution and gradient winds within a (Figure 17.11). tropical cyclone. The wind speed in terms of surface The ADCIRC model in the present study uses the stress is then specified to the ADCIRC model based on spherical coordinate system, and simulations are exe- the relation proposed by Garratt (1977). The computed cuted from a cold start. The hybrid bottom frictional for- maximum wind speed was 40.3 m s−1, which is in close mulation is used in the present study. It has a definite 258 TECHNIQUES FOR DISASTER RISK MANAGEMENT AND MITIGATION

(a) 1 Land fall time Observed 0.8 Predicted 0.6 Model 0.4 0.2 0 –0.0 0.00 0.00 0.00 0.00 0.00 0.00 12.00 –0.4 0.00 12.00 12.00 11 11 11 11 11 11 11 11

–0.6 11 11 –0.8 12/27/20 12/27/20 12/29/20 12/28/20 12/30/20 12/30/20 12/28/20 12/26/20 12/29/20 12/30/20

(b) 0.6 Observed 0.5 Land fall time Model 0.4 Predicted tide 0.3 0.2 0.1 0 –0.1 0.00 –0.2 0.00 0.00 0.00 12.00 12.00 12.00 12.00 11 11 11 11 11 11 –0.3 11 11 –0.4 –0.5 2/27/20 2/30/20 12/28/20 2/28/20 12/29/20 2/29/20 12/30/20 12/31/20 1 1 1 1

Figure 17.12 The comparison of observed, predicted, and model‐simulated water levels at (a) Ennore and (b) . advantage over a constant and quadratic bottom fric- comparison of the observed water level to the predicted tional formulation especially for nearshore areas where and model simulations indicates a good match bathymetric gradients are rapid. The hybrid formulation (Figure 17.12). The predicted tide (Figure 17.12) for the treats bottom stress as proportional to the local water two locations was performed using the SLPR2 (Sea Level depth, permitting realistic estimation of onshore inunda- Processing Software) model, and model simulations were tion using the wetting and drying algorithm. The weight- made using the ADCIRC model. The observed water ing factor that relates the relative contribution of level is the combined effect of the sea level, tides, and primitive and wave portions in the GWCE (Generalized meteorological residue. The contribution from the storm‐ Wave Continuity Equation) is set to 0.01, which is a rec- surge component is extracted from the observed and ommended value. The ramp function, which is the spin- model simulations by subtracting the predicted tide. up time, is set as one day for a five-day total simulation Figure 17.13 shows the residual component (contribution length spanning 25–30 December 2011. The model time of storm surge) at these two locations. step and eddy viscosity were set to 2.0 s and 5.0 m2 s−1, respectively. For grid nodes in the open ocean boundary, 17.7.3. Results and Discussions six tidal constituents are prescribed: K1, M2, N2, O1, P1, and S2 in the ADCIRC model. The combination of these Model computed total water level elevations show that tidal constituents depicts the true tidal field that exists in the entire Tamil Nadu coast was affected by storm surges the Bay of Bengal region. The amplitude and phase of with varying magnitudes. The computed maximum water these six tidal constituents at the open ocean boundary level elevation was about 1.2 m north of the landfall synchronizes with the model simulation start time (12 Z ­location and extends to the Mamallapuram coast of 25 December 2011). The progressive tidal field propa- (Figure 17.14). The occurrence of peak surge is noticed gates from the open boundary marching forward with toward the right side of the storm track with a spatial time into the nearshore areas. extent of about 200 km along the coast. The model results The model simulations are validated with two tide show a lower magnitude of about 0.7 m toward the gauge stations located near the landfall point, one located southern parts of Tamil Nadu such as Nagapattinam. to the north and another to the south of the landfall The simulated maximum water level elevation matches point. The tide gauge station at Ennore (located north) is very well with the observations reported by IMD. The about 180 km from landfall point, whereas the one at horizontal distance the seawater traveled crossing the Nagapattinam (located south) is about 100 km away. A zero meter contour line is treated as the extent of inland Tropical Cyclone–Induced Storm Surges and Wind Waves in the Bay of Bengal 259

(a) 1 Land fall time Obs_res 0.8 Mod_res 0.6 0.4 0.2 0 –0.2 0.00 0.00 0.00 –0.4 0.00 0.00 12.00 12.00 12.00 12.00 12.00 11 11 11 11 11 11 11 11 11 –0.6 11 –0.8 –1 12/29/20 12/31/20 12/27/20 12/28/20 12/30/20 12/28/20 12/27/20 12/30/20 12/29/20 12/26/20

(b) 1 0.8 Land fall time Obs_res 0.6 Mod_res 0.4 0.2 0 –0.2 –0.4 0.00 0.00 0.00 0.00 12.00 12.00 12.00 12.00

–0.6 11 11 11 11 –0.8 11 11 11 11 –1 2/27/20 12/28/20 2/28/20 12/29/20 2/29/20 12/30/20 2/30/20 12/31/20 1 1 1 1

Figure 17.13 The residual at (a) Ennore and (b) Nagapattinam.

Max. water level elevation 1. 16 town, which had close proximity to the landfall of cyclone 1.02 0.88 Pulicat Lake Thane. Table 17.2 provides more details on the inunda- 0.74 0.6 tion extent along with the respective distances from the 0.46 0.32 Chennai coastline. The inland intrusion of seawater was greater in 0.18 0.04 the south compared with the northern part of coastal –0.1 Tamil Nadu. This is due to the low‐lying flat terrain fea- Puducherry tures prominent over the south Tamil Nadu coast. Cuddalore The study signifies that the maximum inundation occurred toward the right of the storm track, which had a strong dependence on the beach slope. The beach

Nagapattinam slope and shoreline characteristics for the Tamil Nadu coast from the field measurements conducted by ICMAM‐PD are shown in Table 17.3. It is evident from Table 17.3 that Chennai has a very wide and flat beach with a slope of 1:190, whereas some locations in the south of Tamil Nadu such as Silver Beach south of Cuddalore have a slope of 1:220 (Figure 17.16a). The Figure 17.14 Maximum water surface elevation computed by study also performs a comparison between the model‐ ADCIRC and the magnified view near to Mamallapuram show- computed inland inundations against the observations ing inundation. collected from field campaign (Figure 17.16b). The model‐computed inundation matches fairly well with inundation. The inland inundation is very much influ- observations. It is found that for locations having mild enced by the inland topography. Even for small surge slopes, such as the Marine Beach in Chennai, amplitudes, a larger inundation extent can be expected Periyakuppam in Kancheepuram, Veeranampattinam depending on the topography of the location. in Puducherry, and Silver Beach in Cuddalore, the The study performed an extensive analysis on the extent model‐computed inundation is slightly lower as com- of inland intrusion at 40 different locations along the pared with the field measurements (Figure 17.16). This coast estimated from the model simulations (Figure 17.15). difference can be attributed to the GEBCO bathymetric The extent of onshore inundation varied from 2 to 349 m data used in the present study. The study signifies that with an average inundation distance of 47.5 m. The the overall performance of ADCIRC‐computed peak maximum value of 349 m occurred at Cuddalore old surge and coastal inundation is satisfactory. 260 TECHNIQUES FOR DISASTER RISK MANAGEMENT AND MITIGATION

Figure 17.15 Estimated inundation along the selected 40 coastal locations.

17.8. STORM­ SURGE AND INUNDATION population of Bangladesh (close to 50 million) resides in MODELING FOR CYCLONE AILA this region. The high population density leads to higher risk and economic loss from storm impact. Another The head bay region located in the north Bay of notable feature of this deltaic environment is the presence Bengal is a low‐lying area and highly vulnerable to of numerous tidal creeks and the river inlet systems. The impact from tropical cyclones. This region features the tidal creeks and the riverine systems allow the free prop- world’s largest delta system, the Ganges–Brahmaputra agation of storm surges over a long distance upstream, delta, and severe cyclones such as the 1970 Bhola cyclone, resulting in the inundation of low‐lying inland areas. the 1991 Bangladesh cyclone, and others, have had Another factor that contributes to the disastrous surge significant impact on this low‐lying deltaic environment. effect is the tidal amplitude of this region. Therefore, The coastal processes over this region are highly dynamic an inundation map for this region is highly necessary to with time. The impact from a storm can lead to significant understand the impact of storm surges. This section dis- changes in the coastal geomorphology over this region, cusses the storm surge and inundation scenario for the thereby making it more vulnerable to future disasters. severe cyclonic storm Aila that had landfall over the The region is thickly populated and extends for about head Bay region. The ADCIRC model is employed to 350 km across the states of West Bengal in India and understand the extent of inland inundation that resulted Bangladesh. Nearly more than one‐third of the total from this cyclonic event. The model simulation was also Tropical Cyclone–Induced Storm Surges and Wind Waves in the Bay of Bengal 261

Table 17.2 Model Computed Inland Inundation Along the Tamil Nadu Coast From Cyclone Thane. S.No. Location Geographic coordinates Distance from the coastline Inland intrusion (in meters) 1 Pulicat 13° 24′36″ N; 1.4 km 29.09 80° 11′36″ E 2 Kattupalli Village 13° 18′18″ N; 1.82 km 49.10 80° 19′30″ E 3 Minjur 13° 16′12″ N; 8.2 km 46.36 80° 16′12″ E 4 Ennore 13° 12′18″ N; 0.47 km 24.58 80° 18′54″ E 5 Tondiarpet 13° 07′30″ N; 0.45 km 9.09 80° 17′42″ E 6 Chennai 13° 09′00″ N; 4.15 km 27.61 80° 15′00″ E 7 Old Washermanpet 13° 06′20″ N; 1.36 km 26.14 80° 17′24″ E 8 Parrys 13° 05′12″ N; 1.37 km 12.28 80° 17′06″ E 9 Adyar 12° 59′36″ N; 1.86 km 62.68 80° 15′00″ E 10 Muttukal 12° 48′36″ N; 0.84 km 59.68 80° 13′48″ E 11 Covelong 12° 47′54″ N; 0.51 km 16.51 80° 15′00″ E 12 Perur 12° 42′54″ N; 0.57 km 62.74 80° 13′30″ E 13 Pattipulam 12° 40′30″ N; 0.54 km 50.55 80° 12′54″ E 14 Mamallapuram 12° 37′48″ N; 0.78 km 18.61 80° 11′24″ E 15 Kalpakkam 12° 30′54″ N; 0.71 km 38.92 80° 09′18″ E 16 Thenpattinam 12° 24′36″ N; 1.62 km 45.85 80° 06′36″ E 17 Odiyur 12° 18′56″ N; 1.27 km 38.85 80° 01′48″ E 18 Thazhankadu 12° 18′56″ N; 0.96 km 35.88 80° 01′48″ E 19 Kazhikupam 12° 18′56″ N; 1.28 km 14.47 80° 01′48″ E 20 Kuilapalayam 12° 18′56″ N; 1.36 km 31.31 80° 01′48″ E 21 Thazhamkuda 12° 18′56″ N; 0.23 km 24.17 80° 01′48″ E 22 Cuddalore 12° 18′56″ N; 4.2 km 15.23 80° 01′48″ E 23 Devanampattinam 12° 18′56″ N; 0.49 km 14.29 80° 01′48″ E 24 Cuddalore Old Town 12° 18′56″ N; 1.34 km 349.83 80° 01′48″ E 25 Sangoli Kuppam 12° 18′56″ N; 2.16 km 17.21 80° 01′48″ E (Continued ) 262 TECHNIQUES FOR DISASTER RISK MANAGEMENT AND MITIGATION

Table 17.2 (Continued) S.No. Location Geographic coordinates Distance from the coastline Inland intrusion (in meters) 26 Parangipettai 12° 18′56″ N; 2.2 km 163.55 80° 01′48″ E 27 Poompuhar 12° 18′56″ N; 1.48 km 14.91 80° 01′48″ E 28 Karaikal 12° 18′56″ N; 1.61 km 11.60 80° 01′48″ E 29 Thethi Nagar 12° 18′56″ N; 0.24 km 1.89 80° 01′48″ E 30 Nagore 10° 48′28″ N; 1.29 km 47.58 79° 49′52″ E 31 Nagapattinam 10° 46′12″ N; 2.36 km 21.05 79° 49′48″ E 32 North Poiganallur 10° 46′12″ N; 0.94 km 68.37 79° 49′48″ E 33 South Poiganallur 10° 46′12″ N; 0.74 km 79.50 79° 49′48″ E 34 Velankanni 10° 46′12″ N; 1.11 km 60.37 79° 49′48″ E 35 Vettaikaraniruppu 10° 46′12″ N; 2.42 km 48.54 79° 49′48″ E 36 Kovilpathu 10° 46′12″ N; 1.41 km 32.78 79° 49′48″ E 37 Pushpavanam 10° 46′12″ N; 1.96 km 75.88 79° 49′48″ E 38 Thopputhurai 10° 46′12″ N; 2.13 km 25.87 79° 49′48″ E 39 Vedaranyam 10° 46′12″ N; 2.11 km 64.31 79° 49′48″ E 40 Kodikarai 10° 46′12″ N; 1.02 km 60.88 79° 49′48″ E

Table 17.3 Beach Slope and Shoreline Characteristics for the Tamil Nadu Coast. Distance from the S.No. City landfall point Beach slope Shoreline characteristics 1 Pulicat (Thiruvallur) 196 km north 1:55 Wide steep beach 2 Near Marina beach (Chennai) 154 km north 1:190 Very wide flat beach 3 Panayur (Thiruvallur) 135 km north 1:40 Steep beach with resorts 4 Nemil (Thiruvallur) 944 km north 1:25 Steep beach, fisherman villages and desalination plant 5 Kalpakkam (Kanchepuram) 85 km north 1:15 Industrial township 6 Periyakuppam (Kanchepuram) 46 km north 1:135 Fisherman village 7 Alapakkkam (Kanchepuram) 30 km north 1:25 Sand dunes shrimp pond 8 Chinamudaliyar kupam 20 km north 1:10 Rocky shore settlements near to the coast (Villipuram) 9 Tantriyan kupam (Vilipuram) 14 km north 1:30 Steep beach with sea wall 10 Veeranampattinam (Puducherry) 9 km north 1:105 Long flat sandy beach, green belt 11 Nallavadu (Puducherry) 4 km north 1:34 Sandy beach settlements close to beach 12 Narambai (Puducherry) Near landfall 1:17 Steep beach 13 South of Silver beach (Cuddalore) 4 km south 1:220 Flat sandy beach with tourist establishments 14 Rajapettai (Cuddalore) 11 km south 1:30 Steep beach, coconut tress 15 Chittripettai (Cuddalore) 20 km south 1:20 Steep beach, village establishments (a)

(b)

250 Model computed Field measurement 200

150

100

50 Onshore inundation distance (m) Onshore 0 ) ) ) i i ta ta ) du va licat nayur Nemili Pu Pa Rajapet Chittripet yakuppam Nalla kuppam (Cuddalore) (Cuddalore) Narambhai ri (Thiruvalluru) (Thiruvalluru) (Thiruvalluru) ry (Pondicher ry (Pondicher (Villupuram) Kalpakkam Alapakkam (Chennai) (Villupuram) Pe Chinamudaliyar South of sliver (Kancheepuram) (Kancheepuram) (Kancheepuram) ry (Pondicher Tantriyan kuppam Tantriyan Beach (Cuddalo re Near marina beach Veeranaampattinam Coastal stations

Figure 17.16 (a) Beach slope along the Tamil Nadu coast, and (b) validation of ADCIRC computed inland inundation (in meters) with field‐based measurements conducted by ICMAM‐PD along the Tamil Nadu coast. 264 TECHNIQUES FOR DISASTER RISK MANAGEMENT AND MITIGATION compared with the best possible data available for this Bengal coast during the premonsoon season. The extreme event. In addition, a few locations that were sig- IMD report states that Aila resulted in a storm surge nificantly affected by storm surge and inundation are exceeding 2 m along on the Indian coast. also tabulated. The surge level was about 3 m for the Bangladesh region. The astronomical tide during the time of landfall ranged 17.8.1. Details of Cyclone Aila between 4 and 5 m, and the cumulative effects from storm surge resulted in a total water level elevation exceeding A low‐pressure system that formed during the morning 4 m, which severely inundated the onshore regions. The hours of 22 May 2009 over the southeastern region in the trajectory of Aila followed a northward direction Bay of Bengal coincided with the leading edge of the (Figure 17.17) toward West Bengal, which is the usual advancing monsoon current. It intensified into a depres- premonsoon ­climatology track for cyclones that have sion almost 30 hours later, and the India Meteorological cyclogenesis in the southeast Bay of Bengal. Department (IMD) stated its geographical location cen- tered around 16.5°N, 88.0°E at 0600 UTC on 23 May 17.8.2. Data and Methodology 2009. The IMD periodically monitored this system from satellite imagery, and Doppler Weather Radar (DWR) The topographic features of Sundarbans include low‐ located at monitored its trajectory, which lying alluvial flat plains, numerous river channels, and approached toward the West Bengal coast. The system tidal inlet creeks. Accurate modeling of water level eleva- crossed the coast near Diamond Harbor (located at tion and inundation demands proper specification of these 21.5°N, 88.0°E) and further dissipated over the northern tidal creeks, river drainage systems, and shallow mudflats. region of West Bengal after 0600 UTC on 26 May 2009. Therefore, a high‐resolution grid that essentially captures It attained the intensity of a Deep Depression with wind these topographic variations and complex coastline geom- speed exceeding 14 m s−1 on 24 May 2009 (0300 UTC) etry is an essential prerequisite. High‐resolution bathy- transforming into a Cyclonic Storm (wind speed metric data from the GEBCO (offshore) blended with exceeding 17 m s−1) during 1200 UTC on the same day. SRTM data (onshore) of 90 m grid resolution for the The IMD named this system Aila. Thereafter, it intensi- study. It is believed that the hybrid data set essentially han- fied into a severe cyclonic storm (SCS) with wind speed dles better representation of the inland surge penetration exceeding 25 m s−1 at 0600 UTC on 25 May 2009 just and coastal inundation envelope. The study area extends before its landfall on the West Bengal coast. The system from Paradip in Odisha State, India, up to Chittagong in experienced rapid intensification while approaching Bangladesh. The topography of the study region is shown the coast, retaining its intensity level of SCS for more in Figure 17.18. The model domain comprises unstruc- than 12 hours after the landfall. It is notable that Aila was tured grid elements constructed using the grid generation the only cyclone in the past two decades to cross the West tool Surface Modeling System (SMS). The resultant mesh

30N

28N

26N

24N

22N

20N

18N

16N

14N

12N 75E 78E 81E 84E 87E 90E 93E 96E 99E 102E 105E

Figure 17.17 Track of cyclone Aila (IMD report). Tropical Cyclone–Induced Storm Surges and Wind Waves in the Bay of Bengal 265

Mesh module elevation 3000.95 2747.05 2493.15 2239.25 1985.35 1731.45 1477.55 1223.65 969.75 715.85 461.95 208.05 –45.85

Figure 17.18 The topography of the study region (offshore from GEBCO and onshore from SRTM) and the finite element mesh generated for the study.

Figure 17.19 The enlarged view of mesh near to . comprises 627,191 nodes and 317,589 triangular elements inland. There is only a remote possibility that the water- specified with a rectangular shape offshore boundary. line due to seawater intrusion reaches this distance This shape is preferred over a semicircular arc onshore. The grid design is in accordance with the boundary so as to avoid computational instability at the GEBCO bathymetry data having a resolution of about corner nodes. The onshore model domain extends up to 20 km in the offshore region, refining to horizontal reso- +10 m contour line over land and that provides a real- lution of about 250 m in coastal and nearshore regions istic scenario of the onshore inundation. The elevation (Figure 17.19). The grid resolution of 250 m is sufficient of +10 m topography is a valid assumption, as in to represent well a better picture of the peak storm surge geographical space this contour line lies almost 4–5 km near the coast. In addition, the high‐resolution grid in 266 TECHNIQUES FOR DISASTER RISK MANAGEMENT AND MITIGATION

the ADCIRC model. The ADCIRC computation pro- vides the maximum water surface elevation during the entire episode of cyclone Aila. The estimate of inunda- tion is obtained by defining a coastline boundary and determining the horizontal extent the water has moved inland. The coastline is set as zero meter elevation from the blended GEBCO + SRTM data set. The software ArcGIS is used to obtain the inundation envelope and to visualize the inundation along the coastal stretch. From the model result, the horizontal extent of inundation along various locations is determined to identify the high risk and most vulnerable areas.

17.8.3. Results and Discussions

Model simulations clearly indicate that the entire head Bay region including West Bengal and Bangladesh were severely affected by the impact of storm surge from cyclone Aila. The maximum surge height attained an amplitude of about 4 m along the regions of Dongajara and Sundarbans region. According to the media reports and the IMD doc- uments, the storm surge reported was 3 m along the west- ern parts of Bangladesh, which submerged several villages. Figure 17.20 The actual and modified track of cyclone Aila. It also mentions that the resultant storm surge over Sundarbans in West Bengal exceeded 2 m. coastal areas essentially takes care of the numerous river The presence of numerous river drainage systems along systems and tidal creeks in the study area. The choice of with several tidal creeks in the head Bay region provides free grid resolution used in this study is supported by Blain allowances for storm‐surge propagation into the river sys- et al. (1998) and Rao et al. (2009). tems. Model simulations show that surge propagated into The wind field for cyclone Aila was constructed using most of the river systems. The surge amplitude in rivers, the Jelesnianski parametric formulation based on the best‐ such as the Matla, Bidyadhari, and Garal, reached nearly 4 track data from the Joint Typhoon Warning Centre m. Model computation also indicates that surge propagated (JTWC). The computed wind speed in terms of wind stress up to 40 km upstream in these rivers through the numerous is provided to the ADCIRC model using the relation pro- tidal inlets; this resulted in inundation of banks far away posed by Garratt (1977). The actual track is close to the from the coast. The worst affected areas with highest surge Hooghly River and the eye of the cyclone passed close to amplitude were the Sundarbans along the India and the river stretch. Hence, the wind field over the river Bangladesh sector. Figure 17.21 shows the maximum storm domain was comparatively weak. In this study, a hypothet- surge observed along the Sundarbans region. The results ical experiment was performed in order to study the effect clearly indicate that maximum surge occurred not only of strong winds in the river stretch, and t­herefore the along the coast adjoining the mainland, but also inside the actual track was shifted toward the left by a distance of river systems. The black line with an arrow head approximately 50 km such that the radius of the maximum (Figure 17.21) indicates the actual track of cyclone Aila. winds covers the domain of the river stretch (Figure 17.20). In order to investigate the interaction between tide and The storm‐surge model setup was executed using surge, a comparison of the tide and surge amplitude at 60 days hot‐start open boundary tidal forcing as the nine different locations in Bangladesh coast was carried initial condition covering a period of five days (from 22 out. The markings in Figure 17.22 show the locations. May 2009 18:00 h until 26 May 2009 06:00 h) with a pre- The comparison signifies that at most locations scribed time step of 10 min. The hybrid bottom friction the storm tide and astronomical tide matched their coefficient, which considers the local water depth, is phase with varying amplitudes. Observational data are used in model simulation. The hybrid bottom friction an essential prerequisite to verify numerical models. coefficient provides a better description as compared Unfortunately there are no observational data av­ ailable with linear and quadratic bottom friction formulations during the Aila event to compare with the numerical and is more accurate in shallow waters when alternate ­simulations. Therefore, the results presented in this wetting and drying condition occurs. The computation study are a hypothetical scenario on the development of of inundation used the wetting and drying algorithm in storm‐surge height and associated coastal inundation. Tropical Cyclone–Induced Storm Surges and Wind Waves in the Bay of Bengal 267

Storm tide (m) 3.9 3.4 3.0 2.6 2.1 1. 7 1. 3 0.9 0.4 0.0

Figure 17.21 The peak storm surge observed along the Sundarbans region.

Figure 17.22 Representative locations along the Bangladesh coast used for the comparison of water levels.

The difference in surge amplitude was evident as Aila water level elev­ ation was about 0.6 m prior to landfall. approached its landfall location. For the locations Hiron The buildup of water level is evident increasing to about Point, Tiger Point, and Basakhali, the difference between 1.0 m, as obtained from model simula­ tions ­during land- tide and storm tide was more than 1.25 m (Figure 17.23) fall. In particular, the station Char Jabar is located in a as the cyclonic system approached toward the coast. The very shallow water depth environment and is sheltered by rise in storm surge is evident at all the eight stations dur- several shallow shoals and island barriers (Figure 17.22), ing landfall, and found to be weaker when Aila was far unlike the case with the remaining stations. The shallow away from the coast. For locations such as Kuakata, Char nature of this location along with island barriers itself pro- Chenga, Chittagong, and Sandwip located far off from vides a complex pattern of tidal propagation. The difference Aila’s track, the surge amplitude was about 0.65 m. in water level compared with the remaining stations is due Another notable feature is the phase difference in resur- to the choice of the grid resolution. It deciphers the fact gence time at most of the locations. The water levels were that there is a demand for high‐resolution grids in these constantly higher for storm‐tide simulation almost four areas to model shoals of smaller dimensions. days prior to landfall at Char Jabar location. The relative The comparison of storm‐tide amplitude (Figure 17.24) water level heights were lower as compared with the indicates a significant phase difference in the storm‐surge remaining stations (Figure 17.23). At Char Jabar, the characteristics. The locations Basakhali and Hiron Point (a) (b) 3.5 Basakhali: tide 3.5 Hiron point: tide 3 Basakhali: storm tide 3 Hiron point: storm tide 2.5 2.5 2 2 1. 5 1. 5 1 1

ter level (m) level ter 0.5 (m) level ter 0.5 0 0 Wa Wa –0.5 5/22/2009 12:005/23/2009 0:005/23/2009 12:005/24/2009 0:005/24/2009 12:005/25/2009 0:005/25/2009 12:005/26/2009 0:005/26/2009 12:00 –0.5 5/22/2009 12:005/23/2009 0:005/23/2009 12:005/24/2009 0:005/24/2009 12:005/25/2009 0:005/25/2009 12:005/26/2009 0:005/26/2009 1 –1 –1 –1.5 –1.5 2:00

Time (UTC) Time (UTC)

(c) (d) 3 Tiger point: tide 2.5 Kuakata: tide Tiger point: storm tide Kuakata: storm tide 2.5 2 2 1. 5 1. 5 1 1

ter level (m) level ter (m) level ter 0.5 0.5 Wa Wa 0 0 5/23/2009 0:005/23/2009 12:005/24/2009 0:00 5/25/2009 0:00 5/26/2009 0:005/26/2009 1 5/22/2009 12:005/23/2009 0:005/23/2009 12:005/24/2009 0:005/24/2009 12:00 5/25/2009 12:00 5/26/2009 12:00 5/22/2009 12:00 5/24/2009 12:00 5/25/2009 12:00 –0.5 5/25/2009 0:00 5/26/2009 0:00 –0.5

–1 –1 2:00

Time (UTC) Time (UTC)

Figure 17.23 The comparison of tide and storm tide at selected locations. (e) (f) 1. 2 Char jabbar: tide 2 Char chenga: tide Char chenga: storm tide 1 Char jabbar: storm tide 1. 5 0.8 1 0.6

0.5 (m) level ter 0.4 ter level (m) level ter 0 Wa 0.2 Wa 5/23/2009 0:005/23/2009 12:005/24/2009 0:005/24/2009 12:00 5/25/2009 12:00 5/26/2009 12:00 5/22/2009 12:00 5/25/2009 0:00 5/26/2009 0:00 0 –0.5 5/22/2009 12:005/23/2009 0:005/23/2009 12:005/24/2009 0:005/24/2009 12:005/25/2009 0:005/25/2009 12:005/26/2009 0:005/26/2009 12:00 –0.2 –1

Time (UTC) Time (UTC)

(g) (h)

2 Chittagong: tide 2 Sandwip: tide Chittagong: storm tide Sandwip: storm tide 1. 5 1. 5

1 1

0.5 0.5 ter level (m) level ter ter level (m) level ter 0 0 Wa

Wa 5/22/2009 12:00 5/24/2009 0:00 5/25/2009 0:005/25/2009 12:00 5/22/2009 12:005/23/2009 0:005/23/2009 12:005/24/2009 0:005/24/2009 12:005/25/2009 0:005/25/2009 12:005/26/2009 0:005/26/2009 12:00 5/23/2009 0:005/23/2009 12:00 5/24/2009 12:00 5/26/2009 0:005/26/2009 1 –0.5 –0.5

–1 –1 2:00

Time (UTC) Time (UTC)

Figure 17.23 (Continued) 270 TECHNIQUES FOR DISASTER RISK MANAGEMENT AND MITIGATION

3.5

3 Basakhali 2.5 Hiron point Tiger point 2 Kuakata Char chenga 1. 5 Char jabbar Sandwip 1 Chittagong

0.5 Storm tide (m)

0 5/22/2009 12:00 5/23/2009 0:00 5/23/2009 12:00 5/24/2009 0:00 5/24/2009 12:00 5/25/2009 0:00 5/25/2009 12:00 5/26/2009 0:00 5/26/2009 12:00 –0.5

–1

–1.5 Time (UTC)

Figure 17.24 Model computed amplitude and phase difference of the storm tide component at various locations.

1. 6

1. 4 Hiron point Basakhali 1. 2 Tigerpoint Kuakata 1 Char madras Char chenga 0.8 Char jabbar Sandwip Surge (m) Surge 0.6 Chittagong

0.4

0.2

0 5/22/200912:00 5/23/2009 0:00 5/23/2009 12:00 5/24/2009 0:00 5/24/2009 12:00 5/25/2009 0:00 5/25/2009 12:00 5/26/2009 0:00 5/26/2009 1

2:00

Time (UTC)

Figure 17.25 The surge alone component at different coastal locations from the time of storm genesis to dissipation.

(Figure 17.24) exhibited a good match in both surge and Kuakata) ­experienced higher surges, compared with amplitude and phase. In addition, the surge amplitude the remaining five stations located within the riverine envi- decreased as one progresses from west to east, proportional ronment. The model could represent the time lag in occur- to the relative distance of respective locations from the rence of peak surge (Figure 17.24) at these four stations. track of cyclone Aila. The phase difference in surge char- The most devastating effect of storm surges is the inun- acteristics (Figure 17.24) is evident as one progresses east dation along the coast. The inundation volume depends from Aila’s track. During the landfall time, the surge mostly on the surge amplitude, but the slope of the beach amplitude was 2.75 m at Hiron point and that reduces to is also a determinant of the inundation extent. The 1.1 m at Char Chenga. Figure 17.25 provides the time importance of beach slope on coastal inundation was series information on the potential storm surge at all the ­discussed by Bhaskaran et al. (2014). The complex nine locations marked in Figure 17.22. The four stations to­pography and geometry of the head Bay region is a real located nearshore (Basakhali, Hiron Point, Tiger Point, challenge in the overall estimation of inundation. Most Tropical Cyclone–Induced Storm Surges and Wind Waves in the Bay of Bengal 271 of this area comprises numerous river creeks and tidal The results from this study indicate that the Sundarbans systems. In order to determine the extent of inundation, region experienced high inundation with seawater progress- the horizontal distance that the water moved inland from ing up to half a kilometer inland. The inundation obtained the coastline needs to be estimated, and to obtain the from the model results shows that the entire coastal stretch realistic estimates of inland inundation it is an essential including India and Bangladesh are inundated. The severe pre‐requisite to represent accurately the coastal geometry inundation was observed at the location of maximum of small island bodies. The most complex task is to define surge. The low‐lying topography of this region and the the coastline from where the inundation needs to be esti- propagation of storm surge through the rivers intensified mated. The study defined the coastline as the zero meter the inundation scenario. Figure 17.26 shows the inundation contour obtained from blended SRTM‐GEBCO data. of the Sundarbans region. The patches in the nearshore

(a)

(b)

Figure 17.26 (a) Model computed onshore inundation for the entire head Bay, (b) inundation envelope for the Sundarbans region marked as a rectangle in (a). 272 TECHNIQUES FOR DISASTER RISK MANAGEMENT AND MITIGATION

(a)

(b)

Figure 17.27 Comparison of inundation scenario (a) MODIS imagery of onshore inundation, (b) ADCIRC computed inundation for cyclone Aila. regions and immediate vicinity seen in Figure 17.26a indi- The patches in both the images indicate the inundation cate indicate the scenario of inland inundation over area. The MODIS observation indicates the flooding to Sundarbans and the nearby regions. The inundation over be intense; however, the imagery is four days after Aila’s this region is quite high owing to low land elevation. passage. The imagery also represents the flooding that Brakenridge et al. (2013) investigated the flooding for resulted from heavy rainfall, which the current modeling cyclone Aila using satellite mapping techniques. Their study does not account for. The inundation extent for study used eight MODIS images to obtain the flooding certain locations along the head Bay region is identified map of the Ganges–Brahmaputra delta. The inundation to assess the impact of inland inundation. Flooding map as reported by Brakenridge et al. (2013) is shown in occurred mostly along the eastern regions of West Bengal Figure 17.27a. The study also performed a comparison of and the western parts of Bangladesh, mainly the inundation from the model computed result (Figure 17.27b) Sundarbans area. The inundation over eastern regions of with the satellite imageries (shown in Figure 17.27a). Bangladesh was very minimal. Tropical Cyclone–Induced Storm Surges and Wind Waves in the Bay of Bengal 273

> than 600 m

500–600m 400–500m 300–400m 200–300m 100–200m

Figure 17.28 Storm‐surge affected areas and associated onshore inundation range.

The computed horizontal extent of inundation shows 17.9. ­COUPLED MODELING SYSTEM that locations cover mostly the regions in the Indian coast FOR CYCLONE PHAILIN and Sundarbans region of Bangladesh. The Sundarbans area is a region of high biodiversity and of ecological Along the east coast of India, Odisha State experi- importance, and for cyclone Aila it is the region that ences the highest impact of land‐falling tropical cyclones experienced the highest inundation extent. The places in and associated storm surges. During the period 1897– the Sundarbans area named SNP (Sundarbans National 2007, it was reported by Jain et al. (2010) that about 70 Park Location) as human settlements are not available. cyclones struck the coast of Odisha. In addition to the The region is considered for analysis owing to its low‐ highest number of land‐falling cyclones, the Coastal lying topography and rich biodiversity. The average Vulnerability Index (CVI) for the Odisha coast attrib- horizontal extent of the flooding in the identified loca- uted to other factors such as the rate of shoreline change, tions reached nearly up to 350 m. For locations such as rate of sea‐level change, coastal slope, significant wave Bakkali, Bindapadmapur, Brojaballabpur, and Lothian height, and tidal range also remains high. A study by Island, the inundation extent exceeded half a kilometer. Sreenivasa Kumar et al. (2010) analyzed the vulnera- The inundation extent over Sundarbans was higher bility for this coast based on all the above mentioned ­compared with other locations. Figure 17.28 identifies parameters. The CVI based on these factors suggests a the locations inundated between 200 and 700 m. It can be low vulnerability level along the coastal stretches of seen that most of the locations are inundated to an extent Ganjam, Chilka, and Southern Puri. The vulnerability is of 200–400 m range. higher for a stretch of 107 km extending from northern The distance that water propagates inland depends not Puri to Balasore. Another study by Rao (1968) also sup- only on elevation of the land, but also on the vegetation ports the fact of high vulnerability levels for the coastal characteristics of that region. An accurate estimate of inun- stretch from Puri to Balasore in respect of storm surges. dation is always challenging as the data do not account for The 480 km coastline of Odisha spreads over six coastal the type of vegetation and the friction it offers to the water districts that include Ganjam, Puri, Jagatsinghpur, flow. At some locations, the presence of buildings or some Kendrapara, Bhadrak, and Balasore. thick vegetation can divert the water to a region that is not In terms of cyclone intensity, the very severe cyclonic vulnerable. Proper modeling of inundation requires accurate storm Phailin that made landfall over the Odisha coast dur- data of the coastal topography, the river systems, and the ing October 2013 was next to the 1999 super cyclone. Even tidal amplitude in addition to storm‐surge characteristics. though Phailin resulted in severe destruction during and 274 TECHNIQUES FOR DISASTER RISK MANAGEMENT AND MITIGATION after landfall, the extent of destruction caused due to loss of life was averted due to better operational prediction skills Start and effective evacuation management measures. Timely warnings and alerts issued by the Indian Meteorological Department (IMD) and Indian National Centre for Initialization Tight coupling and Ocean Information Services (INCOIS) helped the disaster of MPI & dynamic interaction arrays management team to evacuate the human population along between ADCIRC + SWAN the coastal belt to safer locations. It was a major evacuation effort witnessed for the Indian coast and about 500,000 Read the input people were relocated to safer locations. Keeping in view parameters the vulnerability and risk associated with the Odisha coast Message due to the high frequency of cyclonic storms, there is a need passing Coupler to develop a location‐specific operational forecasting between models system for such extreme events. This section demonstrates a coupled hydrodynamic‐wave modeling system to predict Compute storm surge associated with cyclone Phailin. wave Extreme events like cyclones are always associated with parameters strong winds. It drags the water toward the coast resulting in storm surge and associated inundation. In addition, the Computation of Compute tides and radiation associated storm strong winds also result in extreme waves. Wave breaking stress effects along the nearshore regions will transfer the radia- surge tion stress to the underlying water column. Hence, the waves and the currents interact nonlinearly with each End other by transferring momentum flux. High radiation stress produced during such extreme events can result in Parallel Parallel an increased water level elevation near the coast. The ADCIRC Unstructured stand‐alone storm‐surge models fail to capture this inter- model SWAN model action, and that can result in the modification of storm‐ surge amplitude. Hence, a coupled wave‐hydrodynamic Figure 17.29 Flow chart of the modeling system. model is a necessity to understand the mutual nonlinear interaction effects due to combined wave–current interac- tion mechanism. A methodology was developed to couple the hydrodynamic and wave model by including the radi- scribed coupling time interval uses the radiation stress from ation stress component. The tight coupling approach was SWAN to extrapolate forward the wave forcing in time. adopted to achieve the best quality results. A tight cou- After the completion of a coupling interval time step, the pled approach refers to the use of the same computa- ADCIRC model passes on the wind velocity, water levels, tional grid for both wave and hydrodynamic model. It currents, and roughness length information to the SWAN reduces the error accounting due to the interpolation of model. The gradient at each element is then projected to the data between two different grid systems. Moreover, the vertices taking the area‐weighted average of gradients on online coupling procedure could efficiently transfer the elements adjacent to each vertex. The information on water information between these two models in a prescribed levels and ambient currents thus computed by ADCIRC is time step. This implies that the calculation at each time shared with SWAN, thereby updating the time‐varying step considers the information from both the hydrody- water depth and related wave processes such as wave propa- namic and wave model simultaneously. The study gation and depth‐induced breaking. employed the coupled ADCIRC‐SWAN model for the In other words, the ADCIRC model is codriven by simulation of storm surge associated with cyclone Phailin. ­gradients of radiation stress (τs) computed from the Figure 17.29 provides the flow chart of the coupled mod- SWAN model, and mathematically expressed in the form: eling system used in this study. Sxx Sxy The SWAN model is driven by wind field, water level, and sx,wave surface currents, all generated by the ADCIRC simulation­ x y output. The ADCIRC model interpolates the spatial wind Sxy Syy sy,wave fields to the computational vertices, and passes this x y information to the SWAN model. In a tight coupling mode of ADCIRC + SWAN, the ADCIRC model in the pre- where, Sxx, Sxy, and Syy are the wave radiation stresses. Tropical Cyclone–Induced Storm Surges and Wind Waves in the Bay of Bengal 275

Both the ADCIRC and SWAN models run on the same during 10 and 11 October with an increase in wind speed local submesh and cluster core. The two models march from 83–204 km h−1. On 12 October, the maximum wind ahead with time and each forced with mutual exchange speed reached nearly 213 km h−1 and the system made its of information. The SWAN model uses the sweeping landfall with the same intensity. The system later weak- method to update wave information at the computational ened into a well‐marked low over southwest Bihar. vertices, which makes it possible to handle larger time Figure 17.30b shows the track of cyclone Phailin. steps ­compared with the ADCIRC model, which is The IMD report mentions that the system resulted in limited by diffusion and Courant number time step, due heavy rainfall over Odisha State leading to floods and the to semiexplicit formulation, and also for implementing its strong gale winds resulting in damage to the structures wetting and drying algorithm. Therefore, at each cou- and buildings. A maximum storm surge of around 2–2.5 m pling interval, ADCIRC runs first assuming that along above astronomical tide was reported along the low‐lying nearshore and shallow coastal regions the wave prop- areas of Ganjam district. The storm resulted in a major erties are more dependent on circulation. evacuation effort by the National Disaster Management Authority for the coastal states of Andhra Pradesh and 17.9.1. Details of Cyclone Phailin Odisha.

Cyclone Phailin was one of the strongest tropical 17.9.2. Data and Methodology cyclones, which affected the Odisha coast in October 2013. It was a very severe cyclonic storm that made land- The domain for the modeling study extends from fall near Gopalpur with wind speed reaching up to Paradeep in the north to Puducherry in the south covering 215 km h−1 and a central pressure of about 940 mb nearly 1,000 km of the coastline. The bathymetric data for (Figure 17.30a). The system intensified into a deep the study domain were extracted from the General depression while moving in the west‐northwestward Bathymetric Charts of the Ocean (GEBCO) data down- direction on the morning of 9 October. On the evening of loaded from the British Oceanographic Data Centre the same day, the system further strengthened into a (BODC). The surface water modeling system (SMS), soft- cyclonic storm. Thereafter, the system rapidly intensified ware by Aquaveo, was used to generate the finite element from a cyclonic storm to a very severe cyclonic storm mesh for the study region (Figure 17.31). The construction

(a) (b)

10/14/2013 0000 UTC

10/13/2013 0600 UTC Bhadrak Paradeep Ganjam Gopalpur 10/12/2013 1200 UTC Visakhapattanam

10/11/2012 1200 UTC

10/11/2013 0000 UTC 10/10/2013 1200 UTC

Nellore 10/10/2013 0000 UTC

10/9/2013 1200 UTC Chennai 10/9/2013 0000 UTC

Puducherry 10/8/2013 1200 UTC

Figure 17.30 The satellite imagery of cyclone Phailin. 276 TECHNIQUES FOR DISASTER RISK MANAGEMENT AND MITIGATION

Bathymetry(m)

3954

0

Figure 17.31 The finite element mesh of the study region. of mesh has a very fine resolution near to coastal region study is optimized considering the constraints in computa- relaxing to coarser resolution in the offshore boundary. tional time and therefore can also be used effectively for Along the coastal regions, a resolution less than 1 km was operational purposes. chosen and that gradually increased to about 30 km in the The coupling of hydrodynamic and wave model takes offshore region. This unstructured fine resolution mesh into account the wave radiation stress and the corresponding has the capability to resolve the sharp gradients in bathym- wave setup in the water column. The stand‐alone version etry along the nearshore regions. The rectangular of the ADCIRC model fails to include the contribution boundary covers an offshore distance of approximately from the waves. In order to understand the contribution 750 km (Figure 17.31). A recent study by Bhaskaran et al. from wave setup, a comparative study with and without (2013) suggests that a high‐resolution flexible mesh in wave setup was attempted. Hence, two model runs were nearshore areas could essentially resolve the complex made, one with a stand‐alone ADCIRC model, and the bathymetry, and thereby provide a better resolution for other with ADCIRC and SWAN coupled. Both the models wave transformation. The criteria in fixing grid resolution were run for a period of 162 hours starting from 0600 UTC of 1 km nearshore is justified based on the study supported of 6 October 2013 up to the landfall and weakening of the by Rao et al. (2009), which highlights that a grid resolution cyclone. The models were forced in the open ocean of 1 km is sufficient and good enough for precise computa- boundary using the water level data obtained from the Le tion of storm‐surge height along the east coast of India. Provost tidal database using 13 tidal harmonic constitu-

The capability of the unstructured grid over a rectangular ents: K1, M1, N2, O1, P1, S2, K2, L2, 2N2, MU2, NU2, Q1, domain was understood from earlier case studies of and T2. The Jelesnianski parametric wind formulation was cyclones Thane (section 17.7) and Aila (section 17.8). used to generate the cyclonic wind field. The Garratt (1977) Hence, for this coupled model run, the same design criteria formulation was used to provide necessary wind stress to were adopted. The computational time of model simula- the ADCIRC model. The bottom friction coefficient used tion is significantly affected by the number of nodal points in the coupled model was 0.0028 with 10 s as time step. The in the unstructured mesh. A high‐resolution mesh demands coupling interval between the models was determined more computational time and can serve as a limitation for based on the SWAN time step, as SWAN is an uncondi- operational studies. The grid structure used in the present tionally stable model allowing higher time step. In this Tropical Cyclone–Induced Storm Surges and Wind Waves in the Bay of Bengal 277

Wind speed (m s–1) 61.7 54.9 48.1 41.3 34.5 27.7 20.9 14.1 7. 2 0.4

Wind velocity (m s–1) 61.70 m s–1 0.44 m s–1

Figure 17.32 Phailin wind field generated using the Jelesnianski formulation. study, the SWAN time step and coupling time interval was The first set of experiments was performed using set to 600 s. The study by Bhaskaran et al. (2013) supports ADCIRC in a stand‐alone mode including the effects that the coupling time interval of 600 s performed fairly of astronomical and meteorological forcing. In the sec- well in capturing the nonlinear interaction. The SWAN ond experiment, a coupled run was performed including model was implemented in a nonstationary mode pre- the wave radiation stress from SWAN tightly coupled scribed with 36 directional and 34 frequency bins. The into the ADCIRC model. The model‐computed logarithmic frequency bins ranged from 0.04 to 1.0 Hz, significant wave height and water level elevations are whereas the wave directions have an angular resolution of compared with the available field observations. During 10°. The quadruplet nonlinear wave–wave interaction was this extreme event, the observational data for waves computed using the discrete interaction approximation were available only from the wave rider buoy off (DIA) technique. The formulation of Madsen et al. (1989) Gopalpur coast. The available observational data cor- was used to describe the bottom friction formulation in responds to the time when the storm intensified from a SWAN that permits spatially varying roughness length, severe cyclonic storm to a very severe cyclonic storm. and thereby the bottom friction coefficient. The value of In case of water level elevation, the in situ data were 0.05 was used as the bottom roughness length scale. available from the tide gauge located off Paradeep. These data were used to validate the model‐simulated 17.9.3. Results and Discussions water level elevations. Even though the wave characteristic at a location is sig- The coupled ADCIRC‐SWAN model estimates the nificantly governed by the wind effects, a contribution wind‐wave characteristics in addition to the surface eleva- from distant swells can also be expected. In the Bay of tion and currents. The model simulation estimated a Bengal region, the presence of distant swells from a syn- maximum wind speed of 60 m s−1. The report by IMD optic storm in the Southern Ocean was reported in studies was in accordance with the model simulations. IMD by Nayak et al. (2013) and Sandhya et al. (2014). The reported a wind speed of 58 m s−1 during the landfall time influence from distant swells can significantly impact the with a pressure drop of 66 mb. Wind speed in excess of locally‐generated wind waves. However, in the present 25 m s−1 was experienced over the entire coastal stretch of simulation, the effects of distant swells are neglected in Odisha. Even the northern districts of Andhra Pradesh the study domain. The study signifies that the significant experienced strong winds. The wind field for the study wave height simulated by the model is in close conjunction domain during the landfall time of cyclone Phailin is with the in situ buoy observation off Gopalpur coast shown in Figure 17.32. (Figure 17.33). The gradual increase in wave height as the 278 TECHNIQUES FOR DISASTER RISK MANAGEMENT AND MITIGATION

8

7

Without swells 6 Observation 5

4

3

Significant wave height (m) 2

1 10/11/2013 0:00 10/10/2013 6:0010/10/2013 12:0010/10/2013 18:00 10/11/2013 6:0010/11/2013 12:0010/11/2013 18:0010/12/2013 0:0010/12/2013 6:0010/12/2013 12:00

Time (UTC)

Figure 17.33 Comparison of model computed significant wave height against buoy observations.

3.4 Time of landfall 2.9 2.4 Res-adc Res-adcswan 1. 9

1. 4 0.9

Residual water level (m) 0.4

–0.1 10/11/2013 0:00 10/11/2013 12:00 10/12/2013 0:00 10/12/2013 12:00 10/13/2013 0:00 10/13/2013 1

2:00

Time (UTC)

Figure 17.34 The water level at Ganjam for coupled and uncoupled run. storm approaches close to the coast is evident from the a maximum of 2.9 m at the landfall time. These results model simulation as well in the observations. indicate the fact that wave‐induced setup contributed The coupled model computed water surface elevation about 23% to the peak storm surge. Unfortunately, there at Ganjam for the simulation time period as compared were no wave and water level observations available at against the stand‐alone ADCIRC water surface elevation Ganjam location to verify the model computation. From (Figure 17.34). The IMD reported that the location the network of observations, Paradeep was the only near- Ganjam, situated toward the right side of the track, expe- est available location to verify the water levels and is rienced severe storm surge. The residual water level from located approximately 220 km north of Gopalpur. the stand‐alone ADCIRC run with only forcing from The model‐computed surge residual for the Paradeep tides and wind stress estimates the residual water level up location is shown in Figure 17.35. The surge amplitude at to 2.4 m during the landfall time. The effect of the wave Paradeep is reported to be about 0.8 m. The model results radiation stress and the mutual interaction between the from the coupled run estimated surge amplitude of about waves and current increased the water level at the landfall 0.75 m. The figure clearly shows an increase in water level time by about 0.5 m. The coupled model showed a slightly for the coupled model runs. It is clear from the figure that higher water level throughout the simulation period with the surge residual from the coupled model run is closer to Tropical Cyclone–Induced Storm Surges and Wind Waves in the Bay of Bengal 279

0.8 Time of land fall 0.7 Residual (ADCIRC alone) Residual (coupled model) 0.6 Residual (observed) 0.5 0.4 ter level (m) level ter 0.3 0.2 sidual wa

Re 0.1 0 10/10/2013 0:00 10/10/2013 12:00 10/11/2013 0:00 10/11/2013 12:00 10/12/2013 12:00 10/12/2013 0:00 10/13/2013 12:00 10/13/2013 –0.1

0:00

Time (UTC)

Figure 17.35 Model-computed storm surge for the Paradeep location. the observation than the stand‐alone model run. During (a) the time of landfall, the stand‐alone model run underes- timated the surge level by 0.3 m, whereas for the coupled run the difference between observation and model is less than 0.08 m. The contribution from the wave radiation stress amplified the surge level by 0.25 m at Paradeep dur- ing the time of landfall. It is estimated that the wave‐ induced setup contributed to about 36% of the total water level elevation. A slight underestimation of the peak surge is observed even for the coupled model run. This marginal error can be related to the resolution of the Surge(m) coastal bathymetry as well as the meteorological forcing. 2.3 The coupled model highlighted the contribution from the 0 wave‐induced setup. The water level data at Ganjam and Paradeep indicate a contribution of about 23% and 36% from wave setup, respectively. (b) Figure 17.36 shows the spatial distribution of storm surge with uncoupled and coupled runs. It is very clear from the surge distribution that a major portion of the Odisha coast is influenced by storm surge. The peak surge occurred toward the right side of the storm track. Figure 17.35a showed a peak surge of about 2.3 m at Ganjam using the uncoupled model run. For the cou- pled model run, the computed peak surge value attains up to 2.8 m. It is also noticed that the spatial alongshore Surge(m) extent of surge levels was higher in the coupled model 2.8 run as compared with the ADCIRC simulation using a stand‐alone mode. Even the difference of 0.5 m is 0 significant in context to the inland inundation extent. Higher surge amplitude and stronger waves could further push the extent of inundation. The role of wave setup in Figure 17.36 Peak storm surge computed at Ganjam from (a) affecting the water level during extreme events is clear uncoupled and (b) coupled model runs. from the model simulations. The study thereby empha- sizes the fact that realistic estimates of storm surge are bution of the wave setup is governed by several other possible when wave models are coupled with hydrody- factors like coastal geomorphology, translational speed namic models. It also needs to be noted that the contri- of cyclones, and so on. 280 TECHNIQUES FOR DISASTER RISK MANAGEMENT AND MITIGATION

Evolution of wave setup induced water levels along the coast from Gopalpur to Paradeep 0.6

10UTC of Oct12

0.5 12UTC of Oct12 13UTC of Oct12

14UTC of Oct12 0.4 15UTC of Oct12

ter level (m) level ter (Time of Iandfall) 16UTC of Oct12

0.3 tup induced wa

ve 0.2 Wa

0.1 050100 150 200 250

Gopalpur Distance (km) Paradeep

Figure 17.37 Alongshore time evolution of wave induced setup between Gopalpur and Paradeep.

A detailed analysis on the time evolution of wave setup 17.10. ­COUPLED MODELING SYSTEM along a stretch of 250 km between Paradeep and FOR CYCLONE HUDHUD Gopalpur is investigated to understand the nature of wave setup. Over the period of model simulation, the The coupled model discussed in section 17.6 performed temporal modification of wave setup as a function of dis- exceptionally well for cyclone Phailin that had landfall on tance from the landfall point is discussed. the Odisha coast. However, the capability and short‐come Figure 17.37 shows the variations in water levels due of the coupled model can only be understood with more to wave setup that extend from Gopalpur to Paradeep detailed studies. Also the wave‐induced setup could along the Odisha coast. It can be seen that the water exhibit spatial variability depending upon the wind stress level is much higher at Gopalpur compared with and the coastal characteristics. It results from radiation Paradeep, with a peak near Ganjam. Ganjam was the stress owing to the wave transformation along the coastal closest location to landfall and higher wave‐induced and nearshore waters. The wave transformation along setup is observed at this location. The wave setup this region is governed by the coastal bathymetry as well increased from 0.4 to 0.58 m at landfall time. Also at the geomorphologic features. Therefore, more location‐ Paradeep, the time evolution of the wave set is evident. specific studies along the coastal region are warranted for The wave setup‐induced water level increased from 0.2 operational forecasts of such extreme events. Therefore, to 0.35 m. Up to a distance of 140 km, the effect of wave this section explores the performance of the coupled setup is prominent, and beyond it the effect reduced modeling system for cyclone Hudhud, which made land- considerably. Figure 17.38 provides the spatial distribu- fall on the Andhra Pradesh coast. tion of wave‐induced setup along the coastal belt. The The coastal state of Andhra Pradesh is the second most distribution of wave‐induced setup near the coast is evi- vulnerable state after Odisha in regard to the number of dent from Vizianagaram to Bhubaneswar. From this tropical cyclones that make landfall. The coastal belt of figure the setdown process is also evident off the Tekkali Andhra Pradesh has major industrial establishments with coast extending far north off the Puri coast. This sug- a total of 10 ports, including the major and minor ones. gests that the effect of waves can either create a setup or In addition, coastal Andhra Pradesh is quite fertile with setdown along the coast. Overall, the study highlighted agricultural farmlands spread over several hectares and the role of radiation stress in modifying the surge small‐scale industries. The two major river systems, the amplitude and, hence, the need of a coupled modeling Godavari and , make the land highly fertile. In systems to study the surge levels in a realistic manner. the context of cyclonic events, these river systems can Tropical Cyclone–Induced Storm Surges and Wind Waves in the Bay of Bengal 281

Wave setup induced water level(m) 0.6 0.5 0.4 0.3 0.2 0.1 0.0 –0.1 –0.2 –0.4

Figure 17.38 Spatial distribution of the wave induced setup along the coast. play a role in modifying the surges. Moreover, the deltaic changing climate by Emmanuel (2005) proposed that the environment along the coastal stretch makes it highly vul- PDI (Power Dissipation Index) is directly linked to nerable to inundation. The geomorphologic features of strength of tropical cyclones. Also, Sahoo and Bhaskaran coastal Andhra Pradesh are highly diverse. The average (2016) indicated a manifold increase in PDI for the North shelf width over this region is about 43 km. One can find Indian Ocean basin. The cyclonic systems that developed beach ridges, mudflats, mangrove forests, spits, lagoons, over the North Indian Ocean basin clearly show that the barriers, estuaries, and tidal inlets along the coast. In recent cyclonic storms are highly intense with a higher addition, at some locations rocky headland fringed cliffs, radius of maximum winds, and strong winds in the outer wave‐cut benches, and other erosional landforms are core. It brings to light that that earlier parametric wind prominent. The recent cyclonic storm named Hudhud formulations such as those studied by Jelesnianski and affected the entire coastal stretch from Visakhapatnam to Taylor (1973) and Holland (1980), when applied for the Bheemunipatnam. present cyclonic systems, can underestimate wind speed Climate change is a major concern for the coastal as well as storm‐surge characteristics, and hence there is a community owing to the rise in sea level. This is a long‐ need to revisit these formulations. The study by Murty term change and there are certain other factors that indi- et al. (2016) provides a detailed description of the need to rectly influence the coastal community more often. The modify the existing parametric wind formulations, and a recent study by Sahoo and Bhaskaran (2016) indicates modified formulation was proposed. For compact that there is an increase in the frequency of intense cyclones, the original Jelesnianski formulation was good cyclonic storms in the Bay of Bengal basin. Emmanuel enough. As mentioned, the cyclones in the present decade (2005) also pointed out that there is paradigm shift in the have a larger size, and therefore higher winds are spread frequency of intense tropical cyclones and hurricanes. over a larger area highlighting the necessity for a better Detailed analysis on the effect of cyclone intensity in a wind formulation. 282 TECHNIQUES FOR DISASTER RISK MANAGEMENT AND MITIGATION

17.10.1. Details of Cyclone Hudhud

A low‐pressure system that formed over Tenasserim coast adjoining the Andaman Sea in the early hours on 6 October 2014 upgraded to a depression on the next day, as documented by the India Meteorological Department (IMD). It intensified into a deep depres- sion under favorable conditions moving in the west‐ northwestward direction. The Joint Typhoon Warning Centre (JTWC) and IMD issued timely advisories. On 8 October 2014, the system further intensified, and the IMD named it Hudhud. On entry to the southeast Bay of Bengal basin, the system continued to move in the west‐northwest direction. Figure 17.39 shows the satellite image of cyclone Hudhud at the time of land- fall and the track details are shown in Figure 17.40. In the morning hours of 9 October, the system remained as SCS and further intensified into a very severe cyclonic storm (VSCS) in the following hours on 10 October. As per the report on 10 October by the JTWC, Hudhud was classified a Category‐1 tropical cyclone with advisory upgrading it to a Category‐2 later the same day. On 11 October 2014, the system underwent very rapid intensification reaching its peak intensity with a minimum central pressure of 950 mb, and with an average wind speed of 185 km h−1. The landfall occurred near Figure 17.39 Satellite image of cyclone Hudhud at the time of Visakhapatnam in Andhra Pradesh during the noon landfall.

Bangladesh N

Myanmar

India Paradip Gopalpur 20°0 ′ 0 ″ N Srikakulam Visakhapatnam Kakinada

5°0 ′ 0 ″ N Nellore

Chennai Bay of Bengal Puducherry 0°0 ′ 0 ″ N1

Sri lanka 5°0 ′ 0 ″ N1 80°0′0″E 85°0′0″E 90°0′0″E 95°0′0″E

Figure 17.40 The track of cyclone Hudhud based on the IMD report. Tropical Cyclone–Induced Storm Surges and Wind Waves in the Bay of Bengal 283 hours of 12 October 2014. The recorded maximum wind 17.10.2. Data and Methodology gust by the Cyclone Warning Centre in Visakhapatnam was about 260 km h−1. The Doppler Weather Radar The GEBCO data provide a spatial resolution of 30 arc (DWR) at Visakhapatnam reported the eye diameter as second, which is the best freely available data set for 66 km. Hudhud resulted in a trail of destruction after its the ocean bathymetry. Using this data set (Figure17.41), landfall. It continued over land for quite some time and the finite element mesh was generated using the Surface finally weakened into a low‐pressure system over eastern Modeling System (SMS). Instead of using a location‐ Uttar Pradesh before its final dissipation. As per the specific domain, as was used in previous studies, the media reports, the catastrophe due to this extreme entire Bay of Bengal is included in the present study weather event resulted in a loss of more than 1,000 crores domain. The finite element mesh is designed in such a in Andhra Pradesh state. It was the first postmonsoon way as to have fine resolution in the nearshore and coastal cyclone to cross Visakhapatnam since 1985, and interest- areas and a coarser resolution in the open ocean. The ingly landfall occurred on the same day as cyclone Phailin coastal resolution is less than 500 m, which gradually in 2013. The operational weather agency used numerical increases toward the open ocean relaxing to about 30 km. weather prediction (NWP) models, including dynamic The resolution of the finite element mesh is in accordance statistical models during the cyclone Hudhud to predict to the criteria set by the previous studies by Bhaskaran genesis, track, and intensity. The operational forecasts et al. (2013) and Rao et al. (2009). The finite element were issued to the national and state level disaster author- mesh comprises 123,594 vertices and 235,952 elements ities with hourly updates on its movement and intensity (Figure 17.42). The best possible effort was made to opti- on the day of landfall for emergency preparedness. The mize the grids in terms of the computational time. The IMD issued warning disseminations to local people in the present grid can be used for an operational scenario for affected states of India. The Indian National Centre for any coastal state to simulate storm‐surge scenario associ- Ocean Information Services (INCOIS) at Hyderabad ated with a cyclonic system for the east coast of India as issued warnings through text messaging and Electronic it covers the entire Bay of Bengal. Moreover, the grid Display Boards (EDB) to coastal populations, especially could resolve the complex coastal features and the wave meant for the fishermen. The INCOIS bulletins also transformation owing to the optimum resolution. incorporated the cyclone warnings issued by the IMD. Figure 17.43 shows the zoomed image of the coastal belt

Depth (m) 4500.0 4000.0 3500.0 3000.0 PARADEEP 2500.0 2000.0 1500.0 VISAKHAPATNAM 1000.0 KAKINADA 500.0 0.0

ENNORE CHENNAI

NAGAPAT TINAM

TUTICORN

Figure 17.41 The bathymetry of the study region. 284 TECHNIQUES FOR DISASTER RISK MANAGEMENT AND MITIGATION

0.0028. This bottom friction coefficient is found suitable to capture the sandy bottom characteristics off the Andhra Pradesh coast. Moreover, the selected friction coefficient proved to be an optimum configuration with both the ADCIRC and the SWAN models (Murty et al., 2014). The ADCIRC model was forced with the tidal constituents from Le Provost tidal database along the open ocean boundary with a time step of about 10 s. The spectral distribution of the wave energy propagation and its evolution over space and time in the SWAN model is achieved using 36 directional bins and 35 frequency bins. The prescription of wave frequency used logarithmic frequency bins ranging from 0.04 to 1.0 Hz, with an angular resolution of 10°. The physical process for ­nonlinear wave–wave interaction activates using the quadruplet Discrete Interaction Approximation (DIA) technique. The bottom friction formulation of Madsen Figure 17.42 The finite element mesh of the study region. et al. (1989) takes care of the bottom resistance for spa- tially varying roughness length in nearshore regions. This study used the Madsen formulation with 0.05 as the that provides the location of the wave rider buoy and tide bottom roughness length scale. The source/sink functions gauge measurement. used in the SWAN run for the wind input and white cap- The coupled ADCIRC‐SWAN model was run for a ping dissipation used the Komen et al. (1984) formula- period of 120 hours with a ramp function of one day tion. The coupling interval between the ADCIRC and using the parallel High Performance Computing (HPC) SWAN models is set as 600 s. system at INCOIS, Hyderabad, using 320 processors. The ADCIRC model was forced using the modified The model was run with a bottom friction coefficient of parametric wind formulation of Jelesnianski and Taylor

Paradeep

Odisha Location of datawell wave rider buoy Bhubaneshwar and tide gauge observation Berhampur

Andhra Pradesh Srikakulam Bheemunipatnam Visakhapatnam

Kakinada

Yanam

Ongole

Figure 17.43 The zoomed image of the coastal region. Tropical Cyclone–Induced Storm Surges and Wind Waves in the Bay of Bengal 285 as discussed in Murty et al. (2016). The existing The in situ observations during cyclone Hudhud are Jelesnianski and Taylor wind formulation was used to obtained from the directional wave rider buoy located off calculate the maximum wind speed (V(r)) and expressed Gangavaram at a depth of 20 m, and the tide gauge at in the form: Visakhapatnam. The wave rider buoy measured the direc- tional displacements from horizontal motion and vertical 2Rrm V rVr (17.1) motion using the onboard compass. Two‐dimensional direc- Rr22 m tional wave energy spectra representing the distribution of wave energy over different frequencies and directions were The modified version of the Jelesnianski formulation generated from the displacements. Finally, the wave param- that considers increased cyclone size for the present eters such as the significant wave height, maximum wave decade cyclones (Murty et al., 2016) that developed height, peak wave period, and mean wave period are derived over the North Indian Ocean region can be expressed in from the wave spectrum. The wave rider buoy measures the the form: wave height and wave periods ranging between 1.6 and 30 s with an accuracy of 0.5% of measured value. The data from q r these two locations obtained from the INCOIS were used to 2Rrm V rVr (17.2) validate the ­coupled model performance. Rr22 m 17.10.3. Results and Discussions In the above equations, V(r) is the value of the maximum wind speed and Rm is the radial distance from The modified and unmodified wind formulation resulted the storm center, where the maximum wind speed is con- in an increase in the radial distance from the cyclone centrated. Murty et al. (2016) proposed an optimum center. Strong outer core winds are observed for the mod- value of qr = 3/5 based on several numerical experiments. ified wind formulation. Figure 17.44 clearly shows the The modified formulation was also validated for several effect of the modified wind formulation as a function of case studies. The study indicates that the proposed value radial distance from the cyclone center. The wind stress of qr modified the radial wind profile from the center to computed using the modified Jelesnianski formulation the outer core of the cyclone. The study applied the mod- indicated an increase in the area of the cyclonic storm. ified formulation for five very severe cyclonic storms that Figure 17.45a and 17.45b clearly demonstrates the developed over the Bay of Bengal basin. difference in the extent of minimum wind speed of 5 m s−1

50

45

40 Modified 35

) Unmodified –1 30

25

20

Wind speed (m s Wind 15

10

5

0 –100 0100 200 300 400 500 600 Radial distance from the cyclone centre (km)

Figure 17.44 Comparison of the radial profile of the cyclonic wind speeds with the original and modified version of the Jelesnianski parametric wind model. 286 TECHNIQUES FOR DISASTER RISK MANAGEMENT AND MITIGATION

(a) Wind speed (m s–1) Wind speed (m s–1) 24.0 30.0 21.9 27.2 19.8 09 October, 2014 24.4 17.7 10 October, 2014 (00 h) 21.7 15.6 (00 h) 18.9 13.4 16.1 11.3 Visakhapatnam 13.3 Visakhapatnam 9.2 Kakinada 10.6 Kakinada 7.1 7.8 5.0 Nellore 5.0 Nellore –1 Wind velocity (m s ) Chennai –1 24.00 m s–1 Wind velocity (m s ) Chennai 5.00m s–1 30.00 m s–1 5.00 m s–1

Wind speed (m s–1) Wind speed (m s–1) 35.0 46.0 31.7 41.4 28.3 36.9 25.0 10 October, 2014 11 October, 2014 32.3 (18 h) (06 h) 21.7 27.8 18.3 23.2 15.0 Visakhapatnam 18.7 Visakhapatnam 11.7 Kakinada Kakinada 14.1 8.3 9.6 5.0 5.0 Nellore Nellore –1 Wind velocity (m s ) Chennai –1 Chennai –1 Wind velocity (m s ) 35.00 m s 46.00 m s–1 5.00m s–1 5.00 m s–1

Figure 17.45 (a) Time series plot of the wind envelope from original Jelesnianski parametric wind formulation; (b) time series plot of the wind envelope from modified Jelesnianski parametric wind formulation. with unmodified and modified winds. An increase in the ­experienced higher wave height. The time evolution of radius of maximum wind is also evident. The modified for- the significant wave height at Gangavaram, south of mulation provided a better estimate of the wind field on a Visakhapatnam, shows an increase in wave height as the spatial scale. It is obvious from the wind field that the mod- cyclone approached its landfall. A comparison of wave ified wind formulation will serve better for the simulation height with in situ observation off Visakhapatnam of storm surge and associated inundation. The maximum exhibits a very good match (Figure 17.47). The study sig- wind velocity computed by the model simulations is nearly nifies that the coupled model has proven its efficacy in 46 m s−1. As seen from the modified wind formulation, estimating realistically the hydrodynamic conditions for close to the landfall time, the entire Bay of Bengal experi- cyclone Hudhud. enced a minimum wind speed of nearly 5 m s−1. The surge residual computed from the coupled and Figure 17.46 shows the model computed significant stand‐alone model simulations for Bheemunipatnam wave height for the study region. Higher significant wave location are shown in Figure 17.48. The surge residual heights are noticed on the right of the storm track in the from the coupled model takes into account the wave‐ nearshore region. In the coastal region, wave heights in induced setup and wind setup. The maximum computed excess of 8 m are observed north of Visakhapatnam, storm surge at Bheemunipatnam was about 2.3 m. The whereas along the southern side of the cyclone track difference in the surge residual between the stand‐alone the wave heights were less than 4 m. The right side of the and coupled run is about 0.5 m. The in situ observation storm track experiences stronger winds owing to the of the water level was available only at Visakhapatnam to cyclonic circulation and hence the northern sector verify the model performance. (b) Wind speed (m s–1) Wind speed (m s–1) 24.0 30.0 21.9 27.2 19.8 09 October, 2014 24.4 17.7 (00 h) 21.7 10 October, 2014 15.6 18.9 (00 h) 13.4 16.1 11.3 Visakhapatnam 13.3 Visakhapatnam 9.2 Kakinada 10.6 Kakinada 7.1 7.8 5.0 5.0 Nellore Nellore –1 Chennai Wind velocity (m s–1) Chennai Wind velocity (m s ) –1 24.00 m s–1 30.00 m s 5.00m s–1 5.00 m s–1

Wind speed (m s–1) Wind speed (m s–1) 46.0 35.0 41.4 31.7 36.9 11 October, 2014 28.3 32.3 (06 h) 25.0 10 October, 2014 27.8 21.7 (18 h) 23.2 18.3 18.7 Visakhapatnam 15.0 Visakhapatnam 14.1 Kakinada Kakinada 11.7 9.6 8.3 5.0 5.0 Nellore Nellore Wind velocity (m s–1) Chennai Wind velocity (m s–1) Chennai 46.00 m s–1 5.00m s–1 35.00 m s–1 5.00m s–1

Figure 17.45 (Continued)

Hs(m) Gopalpur 17.0 9°0 ʹ 0 ʺ N 15.1 Palasa at Kasibugga 13.2 11.3 Srikakulam Vijayanagaram 9.4 8°0 ʹ 0 ʺ N1 7. 6 Visakhapatnam 5.7 3.8 1. 9 Kakinada 7°0 ʹ 0 ʺ N1 Bay of Bengal 0.0

Machilipattanam 6°0 ʹ 0 ʺ N1

Ongole ʹ 0 ʺ N1 15°0

Nellore

80°0ʹ0ʺE 81°0ʹ0ʺE 82°0ʹ0ʺE 83°0ʹ0ʺE 84°0ʹ0ʺE 85°0ʹ0ʺE 86°0ʹ0ʺE 87°0ʹ0ʺE 88°0ʹ0ʺE 89°0ʹ0ʺE

Figure 17.46 Model‐computed significant wave height. 288 TECHNIQUES FOR DISASTER RISK MANAGEMENT AND MITIGATION

10 9 8 Wave rider buoy observation 7 Significant wave height from coupled model

height (m) 6 5 4 3 2

Significant wave Significant 1 0 08/10/14 12:0009/10/14 00:0009/10/14 12:0010/10/14 00:0010/10/14 12:0011/10/14 00:0011/10/14 12:0012/10/14 00:0012/10/14 12:0013/10/14 00:0013/10/14 1

2:00

Time (UTC)

Figure 17.47 Validation of significant wave height with buoy observations off Visakhapatnam coast.

2.50 Near Bheemunipatnam

2.00

1.50 ADCIRC+SWAN

1.00 ADCIRC Surge (m) Surge

0.50

0.00 10/10/2014 10/11/2014 10/11/2014 10/12/2014 10/12/2014 10/13/2014 10/13/2014 12:00 00:00 12:00 00:00 12:00 00:00 12:00

Time (UTC)

1. 3 Near Visakhapatnam 1. 1

0.9 Observed ADCIRC 0.7 ADCIRC+SWAN

0.5 Surge (m) Surge

0.3

0.1

–0.110/11/2014 10/11/2014 10/11/2014 10/11/2014 10/12/2014 10/12/2014 10/12/2014 10/12/2014 10/12/2014 10/13/2014 10/13/2014 06:00 10:48 15:36 20:24 01:12 06:00 10:48 15:36 20:24 01:12 06:00

Time (UTC)

Figure 17.48 Time series plot of the surge residual (in meters) at Bheemunipatnam and Visakhapatnam. Tropical Cyclone–Induced Storm Surges and Wind Waves in the Bay of Bengal 289

0.50

0.40 Visakhapatnam 0.30 Bheemunipatnam 0.20

0.10 induced setup (m)

ve 0.00 10/10/2014 10/10/2014 10/11/2014 10/11/2014 10/12/2014 10/12/2014 10/13/2014 10/13/2014 Wa 00:00 12:00 00:00 12:00 00:00 12:00 00:00 12:00 –0.10 –0.20 Time (UTC)

Figure 17.49 Comparison of wave‐induced setup along Visakhapatnam and Bheemunipatnam.

(a) (b)

81° 0 83° 0 Baruva 81° 0 83° 0 Baruva ′ ′ ′ ′ 1.46 Signi’cant wave heights (m) Amalapadu Slope (%) 1. 06 Kalingapatnam 0.21 1. 03 Kalingapatnam < 0.3 1. 03 0.27 1. 06 Koyyam 0.3 – 0.6 Bhimunipatnam 0.42 1. 08 1. 15 0.6 – 0.9 Visakhapatnam 0.82 1. 11 Visakhapatnam 1. 11 0.9 – 1. 2 0.92 1. 15 Pudimadaka >1.2 0.99 1. 20 1. 03 Tetagunta 1. 01 1. 34 1. 01 Vulnerability Level 17° Rajahmundry 17° 17° 17° G Kakinada Rajahmundry Godavari Kakinada 0′ o 0′ 0′ 0′ d 0.21 Krishna a Krishna v Kolleru Lake a Bay of Bengal ri Kolleru Lake Vodalarevu Vijayawada Vijayawada 1. 34 Machilipatnam Gollapalem Wave station 0.82 0.92 (Wave ranges in meters) Chirala Bay of Bengal 0.27 Lankavanidibba Kottapatnam 0.42 l Vulnerability rank 0.91 Vulnerability rank ve 1 (Very low) 1 (Very low) 15° 15° 15° 15° 2 (Low) Kavali 2 (Low) 0 0 0 0 ′ 3 (Moderate) ′ ′ 0.99 3 (Moderate) ′ Penner 4 (High) 4 (High) Koduru Penner 5 (Very high) Vulnerability Le Vulnerability 1. 08 5 (Very high) Durgarajpattanam 1. 08 50 km Pulicat 50 km 81° 0 83° 0 Pulicat Lake 81° 83° ′ ′ Pulicat Lake 1. 2 0′ 0′

Figure 17.50 Coastal vulnerability rank based on (a) coastal slopes and (b) significant wave height during fair weather conditions for the Andhra Pradesh coast (Nageswara Rao et al., 2008).

A comparison of the surge residual computed from the ­predominant offshore winds at the location. On the other stand‐alone and coupled model against observation hand, the wave setup remained almost invariant at showed a slight overestimation from the model runs. Also, Visakhapatnam, and that was followed by setdown during the stand‐alone and the coupled model runs showed no the landfall event (Figure 17.49). significant difference in the surge amplitude. From the A detailed study on the vulnerability of the Andhra model simulations, it is found that Bheemunipatnam expe- Pradesh coast, especially the stretch from Bheemunipatnam rienced a wave setup; whereas a setdown is observed at to Visakhapatnam, was carried by Nageswara Rao et al. Visakhapatnam. As the cyclone approached landfall, an (2008). The study used remote sensing techniques to increase of wave setup along Bheemunipatnam is evident. assess sea level rise and coastal vulnerability. Figure 17.50 Until the time of landfall, the wave setup is found to shows the vulnerability rank along this stretch based on increase, and after the time of landfall the setdown the coastal slope and the significant wave height during is noticed. This setdown could be attributed to the fair weather conditions. 290 TECHNIQUES FOR DISASTER RISK MANAGEMENT AND MITIGATION

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