Nat Hazards (2009) 51:3–27 DOI 10.1007/s11069-009-9397-9 ORIGINAL PAPER

Storm surge modelling for the Bay of Bengal and Arabian Sea S. K. Dube Æ Indu Jain Æ A. D. Rao Æ T. S. Murty

Received: 30 July 2008 / Accepted: 8 April 2009 / Published online: 12 May 2009 Ó Springer Science+Business Media B.V. 2009

Abstract Most of the countries around the North Indian Ocean are threatened by storm surges associated with severe tropical cyclones. The destruction due to the storm surge flooding is a serious concern along the coastal regions of India, Bangladesh, Myanmar, Pakistan, Sri Lanka, and Oman. Storm surges cause heavy loss of lives and property damage to the coastal structures and losses of agriculture which lead to annual economic losses in these countries. About 300,000 lives were lost in one of the most severe cyclones that hit Bangladesh (then East Pakistan) in November 1970. The Andhra Cyclone devastated part of the eastern coast of India, killing about 10,000 persons in November 1977. More recently, the Chittagong cyclone of April 1991 killed 140,000 people in Bangladesh, and the Orissa coast of India was struck by a severe cyclonic storm in October 1999, killing more than 15,000 people besides enormous loss to the property in the region. These and most of the world’s greatest natural disasters associated with the tropical cyclones have been directly attributed to storm surges. The main objective of this article is to highlight the recent developments in storm surge prediction in the Bay of Bengal and the Arabian Sea. Keywords

Numerical model
Storm surges
Tropical cyclone
North Indian Ocean

1 Introduction The damage from landfalling cyclones is mainly due to three factors: rain, strong winds, and storm surges. Storm surges associated with severe tropical cyclones are by far the most damaging. Death and destruction arise directly from the intense winds that are characteristic of tropical cyclones blowing over a large surface of water. If bounded by a shallow basin, these winds cause the sea water to pile up on the coast and lead to sudden inundation and flooding of coastal regions. About 90% of the damage is due to inundation of land by S. K. Dube (&)
I. Jain
A. D. Rao Centre for Atmospheric Sciences, Indian Institute of Technology, New Delhi 110 016, India e-mail: [email protected] T. S. Murty Department of Civil Engineering, University of Ottawa, Ottawa, Canada

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sea water. In addition, flooding of the river deltas occurs from the combined effects of tides and surges from the sea, which penetrate into the rivers, because at the same time, excess water in the rivers due to heavy rains from the cyclone is trying to flow through the rivers into the sea. Almost all the loss of lives and most of the damage from a tropical cyclone are attributable to the storm surge generated by the cyclones. Thus, the real-time monitoring and warning of storm surges is of great interest. It is necessary that the problem of the storm surge be seriously addressed by the countries of the various regions through collective efforts and in an integrated manner. In this study, the cyclonic storm is the sole driving force for the dynamical processes in the sea. However, the tides and wave setup have not been included in this study. Besides contribution of tides and wave setup, Continental shelf waves, edge waves, and Topographic Rossby waves may also significantly contribute to the total water level (Morey et al. 2006). It may also be of interest to examine the inclusion of mesoscale forcing and remote forcing in the model. Observations in the Indian seas and also elsewhere have shown that mesoscale forcing could have a recognizable contribution to the storm surge amplitudes. The Indian Institute of Technology Delhi (IIT-D) storm surge model, as presently constituted, only uses synoptic scale forcing as the input. However, even if they are not as well developed as tropical cyclones, mesoscale depressions and squall lines could pack quite strong wind fields, which can then add in favorable circumstances, up to a couple of meters to the total water level envelope.

2 Storm surges in the Bay of Bengal Storm surges are an extremely serious hazard along the east coast of India, Bangladesh, Myanmar, and Sri Lanka. Although Sri Lanka is affected only occasionally by the storm surge, tropical cyclones of November 1964 and November 1978, and cyclone of November 1992 have caused extensive loss of lives and property damage in the region. Storm surges affect Myanmar to a much less extent in comparison with Bangladesh and India. Notable storm surges, which have affected Myanmar, have been during May 1967, 1968, 1970, 1975, 1982, 1992, 1994, and 2008; of which the 1982, 1994, and 2008 (Nargis) caused the heaviest loss of lives and damage. Nargis generated storm surge in excess of 4 m near Ayeyarwady deltaic region. The entire deltaic coast of Myanmar was flooded with surges ranging from 1.5 to 4.5 m. A detailed review of the problem of storm surges in the Bay of Bengal is given by Ali (1979), Rao (1982), Roy (1984), Murty (1984), Murty et al. (1986), Das (1994a, b), Go¨nnert et al. (2001), Dube et al. (1997, 2000a), and Chittibabu (1999). In this article, a brief account of the problem of storm surges in Myanmar, Bangladesh, India, Sri Lanka, Pakistan, and Oman will be given. Of all the countries surrounding the Bay of Bengal, Bangladesh suffers most from storm surges. The main factors contributing to disastrous surges in Bangladesh may be summarized as (Ali 1979) (a) (b) (c) (d) (e) (f)

shallow coastal water, convergence of the bay, high astronomical tides, thickly populated low-lying islands, favorable cyclone track, and innumerable number of inlets including world’s largest river system (Ganga– Brahmaputra–Meghna).

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3 Storm surges in the Arabian Sea Although the frequency of storm surges is less in the Arabian Sea than in the Bay of Bengal, major destructive surges can occur occasionally along the Gujarat Coast of India and Pakistan. Events of storm surges affecting the Gulf of Oman have been somewhat rare; however, there have been some cases when storm surges caused destruction along the coasts of Oman. Most recent example has been the super cyclone Gonu that struck Oman causing about $4 billion in damage (JTWC 2007) and 49 deaths.

4 Destruction potential Although the frequency of tropical cyclones in the Bay of Bengal is not high compared with northwest Pacific, the coastal regions of India, Bangladesh, and Myanmar suffer most in terms of loss of lives and property damage. The reasons, besides the inadequate prediction of storm surges accurately, are the low lands all along these coasts and considerable low-lying huge deltas, such as the Gangetic delta and the Ayeyarwady delta. These major surges usually occur, sometimes with inadequate advance notice. The number of causalities would be much lower if these surges could be predicted reasonably well in advance allowing effective warnings and evacuations in the threatened areas. The purpose of this article is to give a review of recent developments in predicting the storm surges in the Bay of Bengal and Arabian Sea. Dube et al. (1994, 1997, 2000a, 2004, 2005, 2006), Rao et al. (1997), Chittibabu (1999), Chittibabu et al. (2000, 2002), and Jain et al. (2006a, b) have developed an operational numerical storm surge prediction model which has been successfully applied in the Bay of Bengal and the Arabian Sea.

5 Data input for surge prediction models In order to achieve greater confidence in surge prediction in the Indian Seas, one should have good knowledge of the input parameters for the models. These parameters include the oceanographic and meteorological parameters (including storm characteristics), hydrological input, basin characteristics and coastal geometry, wind stress and seabed friction, and information about the astronomical tides. It has been seen that in many cases, these input parameters strongly influence the surge development. Most of the northern Bay of Bengal is very shallow and is characterized by sharp changes in seabed contours. The shallowness of water may considerably modify the surge heights in this region. Therefore, accurate bathymetry is needed for improved surge prediction. The bathymetry for the model is derived from the Earth-topography-two-minute module (ETOPO2) from the National Geophysical Data Center database (Smith and Sandwell 1997).

6 Real-time storm surge prediction system for the Bay of Bengal and the Arabian Sea In India, the study of the numerical storm surge prediction was pioneered by Das (1972). Subsequently, several workers attempted the prediction of storm surges in the Bay of Bengal (Das et al. 1974; Ghosh 1977; Johns and Ali 1980; Johns et al. 1981; Murty and Henry 1983; Dube et al. 1985a).

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Dube et al. (1994), Dube and Gaur (1995), and Chittibabu et al. (2000) developed a realtime storm surge prediction system for the coastal regions of India. Real-time storm surge prediction systems have also been developed for Bangladesh, Myanmar, Pakistan, Sri Lanka, and Oman (Dube et al. 2004; Jain et al. 2006a, b; Chittibabu et al. 2002). The National Meteorological and National Hydrological Services of many countries surrounding the North Indian Ocean have achieved some success in providing storm surge warnings and for implementing improved models through cooperative and coordinated sharing of responsibilities within the framework and overall guidance and supervision of the Tropical Cyclone Programme (TCP) of the World Meteorological Organization (WMO). The TCP of WMO supports technology transfer from the Indian Institute of Technology-Delhi/Kharagpur to run and make operational storm surge models for Bangladesh, Myanmar, Pakistan, Sri Lanka, and Oman. The forecasting system developed at IIT-D is based on the vertically integrated numerical storm surge models that were developed earlier by the group (Johns et al. 1981, 1983; Dube et al. 1985a, b). The model can be run in a few minutes on a PC in an operational office. The system is operated via a terminal menu and the output consists of the two-dimensional and three-dimensional views of peak sea surface elevations. One of the significant features of this storm surge predication system is its ability to investigate multiple forecast scenarios to be made in real time. This has an added advantage because the meteorological input needed for surge prediction can be periodically updated with the latest observations and forecast (data assimilation) from the National Weather Services. The model has been extensively tested with severe cyclonic storms.

7 Location-specific models Since the evolution of storm surges near the coast are known to be very sensitive to the coastal geometry and offshore bathymetry at the location of the landfall of the cyclone, the operational models should include these factors as accurately as possible. It is therefore felt that, besides large-scale storm surge prediction models; operational centers should use high-resolution location-specific models for accurate prediction of the surges. Keeping this in view, the above authors Rao et al. (1997), Chittibabu (1999), Chittibabu et al. (2000, 2002), Dube et al. (2000b, c, 2004), and Jain et al. (2006a, 2006b) have developed locationspecific high-resolution models for Andhra, Orissa, Tamil Nadu, and Gujarat coasts of India, and for Bangladesh, Myanmar, Pakistan, Sri Lanka, and Oman, on the lines similar to that of Dube et al. (1994). One of the important features of the model is that it uses more accurate and detailed bathymetry for the offshore waters. A simple drying scheme has also been included in the model to avoid the exposure of land near the coast to strong negative surges. Attempts have been made to test the reliability of these models by validating them for various severe cyclones which struck the coastal regions of the countries around the Bay of Bengal and the Arabian Sea. Comparison of model-simulated sea surface elevations with coarse and finer spatial resolutions suggests that the finer grid resolution near the coast is very crucial for determining the location of peak surges in addition to the local bathymetry. The model below predicts surges, and the peak surge location shifts more to the right of the landfall as the spatial resolution of the model becomes coarser (Rao et al. 2008). The storm surge models used in this study computes only peak surge amplitudes along the coast. The present models do not calculate an inland inundation associated with storm surges.

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8 Basic features of the model 8.1 Dynamic storm model In the present model, surge is generated by a cyclone, tracking across the analysis area. In view of the strong associated winds and consequent high values of wind stress, the forcing due to barometric changes has been neglected. Thus, the surface wind field associated with tropical cyclone is the primary requirement for modelling storm surges. The wind field at the sea surface is derived by using a dynamic storm model developed by Jelesnianski and Taylor (1973). This storm model uses the radius of maximum wind and the pressure drop as inputs. The main component of the storm model is a trajectory model and a wind speed profile approximation scheme. The trajectory model represents a balance among pressure gradient, centrifugal, Coriolis, and surface frictional forces for a stationary storm. A variable pressure deficit, forward speed, and radius of maximum winds are used to compute the wind stress at model grid points to drive the surge model. The storm strength is reduced after the cyclone crosses the coast. 8.2 Storm surge model A vertically integrated numerical storm surge prediction model of IIT-D (Dube et al. 1994) has been adopted for the surge prediction, which may be used as a menu-driven stand-alone system. The details of the model and the numerical solution procedure are described in Das et al. (1983) and Dube et al. (1985a, b). Only a brief description of the specific features will be presented here. The model is fully non-linear and is forced by wind stress and quadratic bottom friction. The applied surface wind-stress is determined from a bulk quadratic law (Johns et al. 1985), in which wind speed and direction are specified. It is found that the non-linear advection terms have a significant effect on the final results, especially in the shallow coastal waters of the head Bay of Bengal. Therefore, for operational applications, the non-linear terms cannot be left out. 8.3 Integration procedure A conditionally stable semi-explicit finite difference scheme with staggered grid is used for the numerical solution of the model equations. The staggered grid consists of three distinct types of computational points on which the sea surface elevations and the zonal and meridional components of depth-averaged currents are computed. Following Sielecki (1968), the computational stability is achieved by satisfying the CFL (Courant–Friedrich– Lewy) criterion. With a fine resolution grid specification of 3.7 km 9 3.7 km, it is found that computational stability is achieved with a time step of 80 s. 8.4 Bottom stress The bottom stress is computed from the depth-integrated current using conventional quadratic law with a constant coefficient of 0.0026. This value has been arrived at performing several numerical experiments (Johns et al. 1983). 8.5 Boundary and initial conditions The coastal boundaries are taken as vertical sidewalls across which the normal transport vanishes. The model has also the option to include a continuously deforming shoreline;

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however, it is not included in the present operational system due to the unavailability of detailed onshore topographic data. The normal currents across the open sea boundaries are prescribed by a radiation condition given by Heaps (1973). As usual it is assumed that the motion in the sea is generated from an initial state of rest. 8.6 Bathymetry The bathymetry for the model is derived from the ETOPO2 from the National Geophysical Data Center database and is interpolated at the model grid points by using cubic spline interpolation scheme. With this procedure, sufficiently accurate and realistic bathymetry is generated. A simple drying scheme has also been included in the model to avoid the exposure of land near the coast due to strong negative surges.

9 Validation experiments In order to validate the models, several simulation experiments have been performed by using the data of severe cyclonic storms hitting the coastal regions of the Bay of Bengal and the Arabian Sea. In this article, an attempt has been made to compare the simulated sea surface elevations with available observations from local tide gauges where ever possible, with post storm survey estimates of Meteorological Departments of India, Bangladesh, Myanmar, Oman, Pakistan, and Sri Lanka. Unisys Weather Information Services estimate the intensity of tropical cyclones and the associated range of surge heights based on the post-analysis of all available satellite images, surface data, upper air data, and radar data. This information has also been used for validation. In this article, results of the numerical experiments carried out using location-specific models to simulate the surges generated by 1999 Orissa (India), 1991 Chittagong and 2007 Sidr (Bangladesh), 1998 Kandla (India), 1964 Rameswaram (Srilanka), 1999 Karachi (Pakistan), 1982 Gwa, 2006 Mala and 2008 Nargis (Myanmar), and 2007 Gonu (Oman), which struck the coasts along the North Indian Ocean, are reported. The model computed surges are in good agreement with the available observational estimates. The storm surge model requires the wind stress forcing as the basic input to the model. For this purpose, the wind stress is computed by using a dynamic storm model of (Jelesnianski and Taylor 1973). In order to obtain a dynamic wind profile in the storm model, initially a stationary symmetric model wind profile is taken, and then correction is applied to approximate the asymmetry due to the storm motion. The storm model represents a balance among pressure gradient, centrifugal, Coriolis, and surface frictional forces for a stationary storm. The input parameters for the above-mentioned storms are given in the Table 1. 9.1 Myanmar: Gwa cyclone (May 1982) A well-marked depression formed over the southwest Bay on April 30, 1982 at 0600 UTC. Moving northward, it intensified into a deep depression on May 1 at 0000 UTC. The system developed into a cyclonic storm by 1200 UTC of May 1 and moved northeastward. It intensified into a severe cyclonic storm by May 2 at 1200 UTC and moved eastward. Thereafter, it further intensified into a very severe cyclonic storm on the same day after six hours. The system gained further strength till May 4 at 1200 UTC with maximum wind speed of 62 ms-1. Afterward, it slightly weakened at 1800 UTC of the same day and

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Nat Hazards (2009) 51:3–27 Table 1 Central pressure drop and radius of maximum winds for some selected cyclones

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Name of the cyclone

DP (hPa)

Rmax (km)

1982 Gwa cyclone

55

30

2006 Mala cyclone

48

30

2008 Nargis cyclone

65

25

1991 Chittagong cyclone

70

40

2007 Sidr cyclone

68

25

1999 Orissa cyclone

98

40

1964 Rameswaram cyclone

60

30

1998 Kandla cyclone

40

30

1999 Pakistan cyclone

40

30

2007 Gonu cyclone

98

40

Fig. 1 Surge contours (m) associated with 1982 Gwa cyclone (Jain et al. 2006a)

finally crossed the coast around the midnight of May 4 near Gwa. The track of the cyclone (Fig. 1) and the relevant data are taken from the Unisys Weather Information Services (1982).

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The model is integrated with a pressure drop of 55 hPa and radius of maximum winds of 30 km. The model-computed surge contours along the Myanmar coast are shown in Fig. 1. A maximum surge of 4 m is predicted to the right of the landfall point near Gwa (Jain et al. 2006a). Unisys Weather Information Services estimate the intensity of tropical cyclones and the associated range of surge heights based on the post-analysis of all available satellite images, surface data, upper air data, and radar data. During this cyclone, the surge of the order of 4 m was reported along the Deltaic coast of Myanmar near Gwa (IOC-UNESCO 1999). This is in good agreement with our simulated sea level elevations. 9.2 Myanmar: Mala cyclone (May 2006) A depression developed over the southeast Bay of Bengal at 0000 UTC of April 24, concentrated into a deep depression near 10° N and 89.6° E at 0600 UTC on April 25. It moved northwestward and became a cyclonic storm at 0000 UTC on April 26, intensified into a severe cyclonic storm ‘‘Mala’’ over the central Bay of Bengal at 1530 UTC on April 26 and intensified further into a severe cyclonic storm with a core of hurricane winds at 1200 UTC on April 27 centered over southeast bay and adjoining east-central Bay of Bengal. It remained practically stationary and lay centered at 1500 UTC near 13° N and 90.5° E, about 270 km northwest of Port Blair. The system moved slowly in a northnortheasterly direction and further rapidly intensified into a very severe cyclonic storm over east-central Bay of Bengal and lay centered at 0300 UTC of April 28. The system moved further in a northeasterly direction toward the Arakan coast and rapidly weakened over land after peaking in intensity, and struck near Gwa around 0900 UTC of April 29 and quickly dissipated over Myanmar. The Department of Meteorology and Hydrology, Yangon, estimated the lowest central pressure of the cyclone to be 957 hPa and the maximum sustained surface winds of 67 ms-1. The model is integrated with a pressure drop of 48 hPa and radius of maximum winds of 40 km. The model-computed surge contours along the Myanmar coast are shown in Fig. 2. Peak storm surge of about 4 m was reported for the May 2006 Mala cyclone on the Myanmar deltaic coast near Gwa by the Department of Meteorology and Hydrology, Yangon. The simulated surge contours (Fig. 2) show a maximum surge of 3.6 m to the right of the landfall point near Gwa, and the peak surge at Pathein is about 2 m. The coast from Ye to Moulmein also was affected by surges of 1–2 m. 9.3 Myanmar: Nargis cyclone (May 2008) In the last week of April, an area of low pressure was detected over the Bay of Bengal about 1,150 km east–southeast of Chennai, India. At 0300 UTC on April 27, Indian Meteorological Department (IMD) classified the system as a depression, and nine hours later, the system intensified into a deep depression. At 0000 UTC on April 28, the system became a cyclonic Storm Nargis when it was located about 550 km east of Chennai. On April 28, the motion of Nargis became nearly stationary when located between ridges to its northwest and southeast. The system gained further strength to become a severe cyclonic storm by May 28 at 0600 UTC. On May 1, after turning nearly due eastward, the system continued to gain strength and attained a maximum wind speed of 59 ms-1 on May 2 at 0600 UTC, as it approached the coast of Burma. Around 1200 UTC on May 2, cyclone Nargis made landfall in the Ayeyarwady Division of Myanmar. Early on May 3, it quickly weakened after turning to the northeast toward the rugged terrain near the Myanmar– Thailand border.

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Fig. 2 Simulated peak surge contours (m) for 2006 Mala cyclone

The model is integrated with a pressure drop of 65 hPa and radius of maximum winds of 25 km. The model-computed surge contours along the coast of Myanmar are shown in Fig. 3. It may be seen that a maximum surge of 4.5 m is occurred close to the landfall point. The Deltaic region of Ayeyarwady is affected by surges between 2.5 and 4 m. The Myanmar coast from Pyapon to Yangon is flooded with a surge of more than 2 m. The computed surge values at Pegu and Moulmein are 2.5 m and 1.5 m, respectively. During this cyclone, the surge of the order of 4 m was reported by Unisys (2008). The Department of Meteorology and Hydrology, Yangon also reported the surge of about 4 m at the Deltaic region of Ayeyarwady. This is in good agreement with our simulated sea level elevations. 9.4 Bangladesh: Chittagong cyclone (April 1991) Detected as a low-pressure area over the southeast bay and adjoining Andaman Sea on the morning of April 23, it developed into a depression near 10° N and 89° E at 0300 UTC on April 25. It became a cyclonic storm near 12.5° N and 87.5° E at 0300 UTC on April 27, intensified into a severe cyclonic storm near 14.5° N and 87.5° E at 1800 UTC on the same

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Fig. 3 Simulated peak surge contours (m) for 2008 Nargis cyclone

day. It further intensified into a severe cyclonic storm with a core of hurricane winds near 15.5° N and 87.5° E at 0300 UTC on April 28. The cyclone crossed Bangladesh coast a little north of Chittagong at 2000 UTC on April 29. This cyclone is one of the worst killer cyclones in human history. The estimated maximum wind speed is 65 ms-1. A surge height of 6–7.6 m is reported. As per media reports, tidal waves of 6 m in height swept a coastal stretch of nearly 240 km in Bangladesh. Numerical experiments are carried out with a pressure drop of 70 hPa and radius of maximum winds of 40 km. It can be seen from Fig. 4 that there is a maximum surge of about 7 m to the south of Chittagong. The computed surge values at Chittagong and Cox’s Bazar are 5.8 m and 3.8 m, respectively (Dube et al. 2004). According to Roy et al. (1999), the astronomical tide at Chittagong at the time of landfall is about 1.5 m. Thus, the total water level at Chittagong is about 7.3 m, which compares well with the available reports. 9.5 Bangladesh: Sidr cyclone (November 2007) The depression formed over southeast Bay of Bengal and adjoining Andaman Sea intensified into a deep depression and moved northwestward. It lay centered at 1800 UTC of November 11, 2007 near 10.5° N and 91.5° E, about 200 Km southwest of Port Blair. The system moved slightly westward and intensified into a cyclonic storm ‘‘Sidr’’ and lay centered at 0300 UTC of November 12 over southeast Bay of Bengal near 10.5° N and 91.0° E. It moved northwestward and further intensified into a severe cyclonic storm and lay centered at 1200 UTC of November 12 over southeast Bay of Bengal near 11.5° N, 90.0° E, about 300 km west of Port Blair. The severe cyclonic storm ‘‘Sidr’’ over southeast

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Fig. 4 Surge contours (m) associated with 1991 Chittagong cyclone (Dube et al. 2004)

Bay of Bengal remained practically stationary and intensified into a very severe cyclonic storm, lay centered at 1800 UTC of November 12 over southeast Bay of Bengal near 11.5° N and 90° E. The very severe cyclonic storm ‘‘Sidr’’ over southeast Bay of Bengal moved northwestward in the early hours of November 13 near 12° N and 89.5° E and further moved slightly northward and lay centered at 0900 UTC of November 13 near 13° N 89.5° E. The very severe cyclonic storm ‘‘Sidr’’ over east-central and adjoining west-central Bay of Bengal moved further northward and lay centered at 2100 UTC of November 14 over eastcentral and adjoining west-central Bay of Bengal near 17° N and 89° E, about 650 Km south of Kolkata. The ‘‘sidr’’ over north Bay of Bengal moved northeastward and crossed west Bangladesh coast near 89.8° E around 1700 UTC of the November 15 and lay centered at 1800 UTC over Bangladesh near 22.5° N and 90.5° E, about 100 km south of Dhaka. The system further moved in a north-northeasterly direction and weakens gradually. The Unisys (2007) estimated the lowest central pressure of the cyclone to be 944 hPa and the maximum sustained winds of 60–69 ms-1. Numerical experiments are carried with a pressure drop of 68 hPa and with a radius of maximum wind of 25 km. The landfall surge contours computed by the model for the November 2007 SIDR cyclone are shown in Fig. 5. The maximum surge of about 5.8 m is simulated to be near Mongla Port, and a surge of about 1.5 m is computed at Kuakata. The maximum computed surge height of 5.8 m is in agreement with the highest value in the range of surge estimates provided by Unisys.

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Fig. 5 Simulated peak surge contours (m) for the 2007 Sidr cyclone

9.6 East coast of India: Orissa cyclone (October 1999) A depression formed in the Bay of Bengal near 12° N and 98.5° E at 0600 UTC of October 25, 1999. It became a cyclonic storm in the early hours of October 26 and located at 13.5° N and 96.5° E. The cyclone moved further in a northwesterly direction and lay centered at 16° N and 92° E. It became a severe cyclonic storm with a core of hurricane winds on October 27 at 0300 UTC. The cyclone further intensified into a very severe cyclonic storm and lay centered at 17.5° N and 89.5° E on October 28. Cyclone crossed the Orissa coast of India close to Paradip between 0430 and 0630 UTC of 29 October. For the track land falling near Paradip, the pressure drop is 98 hPa and radius of maximum winds is 40 km. For this experiment, the tentative best track of the cyclone is taken from the IMD the cyclone crossed few kilometers south of Paradip. The model is integrated with 98 hPa and 40 km radius of maximum winds. Figure 6 shows the model-computed peak surge envelope along the Orissa coast. It can be seen that a maximum surge of about 7.8 m occurred close to the landfall point (Dube et al. 2000b). The coastal regions between Konark and Chandbali are affected by a surge of more than 5 m. Post-storm survey reports also show that the surge is more than 7 m, near Paradip.

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Fig. 6 Peak surge contours (m) associated with 1999 Orissa cyclone (Dube et al. 2000b)

9.7 Sri Lanka: Rameswaram cyclone (December 1964) Rameswaram cyclone was one of the severest storms, which affected Sri Lanka and the southern Indian peninsula. The track of the storm is shown in Fig. 7. It may be seen from Fig. 7 that the storm crossed the Sri Lankan coast about 50 km north of Trincomalee on December 22 at about 0600 UTC. Then, it further moved northwestward and struck the Indian coast about 30 km south of Tondi on December 23 at about 0600 UTC. The reported maximum wind speed associated with the storm during the period was 60 ms-1. The model is integrated ahead in time up to 72 h with a pressure drop of 60 hPa and a radius of maximum wind of 30 km. The peak surge contours are shown in Fig. 7. A maximum surge of about 5.4 m is computed near Tondi, which is shown in the figure (Chittibabu et al. 2002). Heavy inland flooding in the region, with an estimated peak elevation of about 5 m, was reported by Rao and Mazumdar (1966). The predicted and reported maximum sea surface elevations are in good agreement. It can also be seen from the figure that the coastal region to the north of Mannar Island along the west coast of Sri Lanka is flooded with a surge of more than 4 m. 9.8 West coast of India: Kandla cyclone (June 1998) A depression was formed over the southeast Arabian Sea near 10.5° N and 69° E on 1200 UTC of June 4, 1998. Moving northwestward, it intensified into a deep depression on June 5 at 0900 UTC and lay centered at 11.5° N and 70° E. The depression intensified into a cyclonic storm at 0600 UTC of the same day. Moving northwestward, it intensified further

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Fig. 7 Surge contours (m) associated with 1964 Rameswaram cyclone (Chittibabu et al. 2002)

into a severe cyclonic storm on June 6th evening when it was located about 580 km southwest of Goa. Moving northward, it intensified into a very severe cyclonic storm on June 7th afternoon and was located about 700 km southwest of Mumbai. It continued to move northward and was located about 500 km west-southwest of Mumbai at 0600 UTC of June 8. This storm was located about 350 km southwest of Porbandar on the evening of June 8. It crossed Gujarat coast near Porbandar between 0100 h and 0200 UTC of June 9 and lay centered at 0300 UTC about 50 km north of Porbandar. The system maintained its intensity as a very severe cyclonic storm even after crossing the coast till noon when it lay over the Gulf of Kutch, about 50 km southwest of Kandla port. It was located near Kandla at 0900 h UTC of June 9. Thereafter, it continued to move northeastward and weakened into a severe cyclonic storm about 100 km south of Barmer in southwest Rajasthan by the midnight of the same day. The movement of the cyclone is shown in the Fig. 8. As per IMD, the lowest estimated pressure was about 961 hPa. It was reported that at Kandla, the storm surge height was of the order of 2–3 m over and above the astronomical tide of 6.6 m (Chittibabu et al. 2000; IMD Report, Feb. 1999). The astronomical tide coupled with storm surge and very strong winds resulted in a phenomenal fury of the cyclone at the Kandla port, which has never been experienced before. Using reports from the IMD, we have taken pressure drop as 40 hPa and radius of maximum winds as 30 km. The model is integrated ahead in time up to 48 h. Fig. 5 shows the model-computed peak surge contours. A maximum surge of about 5 m is predicted to the south of the landfall point. It may also be seen that at Porbandar and Dwaraka, the computed surges are about 3.5 m and 2 m, respectively.

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Fig. 8 Surge contours (m) associated with 1998 Kandla cyclone (Chittibabu et al. 2000)

The maximum computed surge at Kandla was 2.1 m, which is in agreement with the reported surge (IMD Report, Feb. 1999). A surge of 2 m is also computed near Mandvi. 9.9 Pakistan: Karachi cyclone (May 1999) A depression developed over the eastern Arabian Sea on May 15, 1999 at 0000 UTC near 12° N, 72.5° E and continued until 1800 UTC of the same day. It intensified into a deep depression by May 16 at 0000 UTC and further intensified into a cyclonic storm by 1200 UTC. The system gained further strength to become a severe cyclonic storm by May 17 at 0600 UTC. The system continued to gain strength and attained a maximum wind speed of 57 ms-1 on May 19 at 0000 UTC, which it maintained through to the early hours of May 20. The cyclone made landfall at 0600 UTC on May 20 near Wari creek at about 23.6° N and 68.2° E. In this experiment, the cyclone track (Fig. 9) and the relevant data are taken from the Unisys Weather Information Services (1999). Using these data, numerical experiments are carried out with a pressure drop (the difference between the central and ambient pressures) of 40 hPa and radius of maximum winds of 30 km. The surge contours computed by the model at the time of landfall are shown in Fig. 9. It may be seen that a maximum surge of 3.8 m is predicted near the Bhitiaro creek, and the surges at Wari creek and southern Kori creek are 3.4 m and 2 m, respectively (Jain et al. 2006b). The surge of about 0.5 m can be seen near the southern part of Karachi. Unisys Weather Information Services estimate the intensity of tropical cyclones and the associated range of surge heights based on the postanalysis of all available satellite images, surface data, upper air data, and radar data. It is found that the maximum computed surge height of 3.8 m is comparable with the highest value in the range of surge estimates provided by Unisys (1999).

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Fig. 9 Surge contours (m) associated with 1999 Pakistan cyclone (Jain et al. 2006b)

9.10 Oman: Gonu cyclone (June 2007) A depression was formed over the eastern Arabian Sea in the late hours of June 1, 2007. Moving westward, it steadily intensified into a deep depression on the early hours of June 2. The system intensified into a cyclonic storm ‘‘Gonu’’ near 15.1° N and 67.7° E on the same day when it was located 760 km southwest of Mumbai, India. Moving northwestward, it rapidly intensified into a severe cyclonic storm on June 3 and lay centered at 16.8° N and 67.4° E at 0600 UTC. The system further intensified into a very severe cyclonic storm on late hours of June 3. It became a very severe cyclonic storm with a core of hurricane winds near 19.2° N and 64.9° E at 600 UTC on June 4. Gonu strengthened further and became a super cyclonic storm at 1800 UTC on June 4 with sustained winds reaching up to 240 km h-1 (150 mph) and pressure of 920 hPa. The super cyclonic storm Gonu weakened into very severe cyclonic storm and lay centered at 2100 UTC of June 4 over northwest Arabian sea and adjoining central and northeast Arabian Sea near 20.5° N and 63.5° E, about 600 km southwest of Dwarka (India). The cyclone gradually weakened as it continued tracking northwestward, and over a period of 24 hour, the intensity decreased by 95 km h-1. On June 6, the cyclone turned to the north-northwest to severe cyclonic storm status and later to cyclonic status early on June 7, and Gonu crossed the Makran coast in Iran. The track of the cyclone (Fig. 10) and the relevant data are taken from the Unisys Weather Information Services (2007). Numerical experiments are carried out with a pressure drop of 98 hPa and radius of maximum wind as 40 km. The surge contours computed by the model are shown in Fig. 10. A maximum surge of 2.6 m is simulated near the eastern tip of Oman due to the close proximity of the storm track. A surge of about

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Fig. 10 Surge contours (m) associated with 2007 Gonu cyclone

2.6 m and 2.5 m are simulated at Sur and Nazwa Fins, respectively. The peak surge value at Muscat is around 2 m. Also, it is seen that a coastal stretch of about 100 km to the northeastern Oman is flooded with surges of about 1 m. The computed maximum surge value of 2.6 m is comparable with the estimated range of surge heights provided the by Unisys Weather Information Services (2007).

10 Integration of socioeconomic and physical vulnerability (PV) data In recent years, there has been considerable concern regarding the vulnerability of coasts due to cyclones and associated surges in view of projected global warming and sea level rise. In this section, we have undertaken as a case study the development of disaster management plan (DMP) for cyclones and associated storm surges for mitigation in the nine coastal districts of the state of Andhra Pradesh (AP), India. Based on historical cyclone data, through a simple statistical analysis, Delta P (Pressure deficit) was determined for cyclones making landfall on the AP coast, for return periods of 2, 5, 10, 25, and 50 years. The storm surge model developed by IIT-D was applied with the 50-year Delta P value for a set of synthetic tracks, which were developed by compositing

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Fig. 11 Synthesized cyclone tracks for Andhra Pradesh

actual tracks as well as from theoretical ones, ensuring that each coastal district was covered. These are depicted in Fig. 11. The results of the computer simulations, calibrated with observed surge data for each region of the coast, provided maximum probable surge amplitudes at the mandal level (mandal is the geographical unit immediately below the district level, and is made up of several villages and may be towns). The water levels can be projected onto the adjacent coastal area making use of the onshore topography to demarcate the horizontal extent of inundation. A generally accepted procedure in determining the extent of land inundation by a storm surge is to assume that a water level of 5 m at the coastline would have an impact up to the 5-m land contour, a 10-m water level would impact up to the 10-m contour, and so on. This is a conservative approach and may somewhat over-estimate the extent of inundation, but is an acceptable approach for coastal zone storm mitigation planning purposes. In summary, the approach for determination of the PV is as follows: (a)

A database of tropical cyclone (TC)-generated storm surges (SS) impacting the AP Coast was drawn from the IMD and from several other National and International sources. (b) Due to climate change, projections into future were limited to 50 years. (c) All the available cyclone tracks for AP were synthesized into composite tracks to cover each of the coastal districts of AP. (d) Making use of the projected pressure drop, the IIT-D storm surge model was applied using the synthetic tracks to determine the maximum possible storm surge amplitude (during a 50-year period) at various locations along the AP coast.

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(e)

The total water level envelope (TWLE) was determined by superimposing the tidal amplitudes and wind-wave setup on the surge amplitudes. (f) These water levels were then projected onto the coastal land using onshore topography data to demarcate the horizontal extent of inundation. (g) This conservative approach may slightly over-estimate the extent of inundation, but is desirable for hazard mitigation and for coastal zone management, and is widely used around the world. (h) Maps of regions subjected to possible wind damage from cyclones also were prepared. 10.1 PV maps for the coastal districts Inundation by storm surge and regions subjected to wind damage were mapped for the districts of Prakasham (Fig. 12) and Guntur (Fig. 13) of coastal AP. The PV maps were prepared for four scenarios: (a) frequent (10% annual recurrence interval), (b) infrequent (2% annual recurrence interval), (c) a future climate scenario resulting in an intensification of the pressure field by 5%, and (d) a more extreme case of intensification of 7%. The three large rivers in AP, Godavari, Krishna, and Pennar, are subject to storm surge penetration. The storm surge penetration into these rivers was determined by projecting the surge water levels into the rivers. It was assumed that for a river with many meanders, the storm surge would penetrate 10% farther than on land. If the river had few meanders, then the increased penetration was 15%. PV maps for storm surge penetration above the rivers were then prepared (Fig. 14). 10.2 Social vulnerability (SV) Social vulnerability was developed for physically vulnerable mandals. By using the available population and other data, along with the PV maps, overall cyclone vulnerability index maps were developed. Figure 15 shows the map for one of the districts of coastal AP.

11 Future perspectives 1. Storm surge predictions are affected by the error in tropical cyclone predictions in terms of both their tracks and intensities. Taking this into account, ensemble and probabilistic methods and outputs should be considered for use in operational storm surge forecast. 2. As mesoscale NWP models with high resolution are having ability to solve tropical cyclone wind fields, use of the results from these models in tropical storm surge modeling should be investigated. 3. Total water level is the combined effect of storm surge, wave set-up, and high tide; therefore, accurate prediction of wind waves and tidal height together with their nonlinear interaction with the storm surge in the model is essential. Johns et al. (1985), and more recently, Sinha et al. (1996, 2008) have shown that the non-linear interaction of surge and tide may significantly modify the evolution of surges. However, prediction methods for wave setup are not well established yet. Therefore, further studies of wave setup prediction method are needed.

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(a)

Latitude

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15 79

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80

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Longitude Fig. 12 a Land inundation map affected by storm surge and b regions affected by cyclonic winds for Prakasham District, Andhra Pradesh, India

4. While accurate prediction of surge is important, it is also necessary to have an estimate of the coastal stretch likely to be inundated for effective evacuations. In order to achieve this, development of real-time ocean–river coupled models are required for the regions where they do not exist. For the Bay of Bengal region, this aspect is very important as one of the world’s largest river systems (Ganga–Brahmputra–Meghana) joins the head Bay of Bengal. Dube et al. (1986, 2005) have studied in detail about the penetration of surge through rivers and its interaction with river runoff. It would also be necessary to develop very high-resolution coastal inundation models to estimate flooding of the low-lying coastal regions in the event of storm surges. Currently, efforts are being made to use FE (finite element) models for the purpose.

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(a) 16.8

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Longitude Fig. 13 a Land inundation map affected by storm surge and b regions affected by cyclonic winds for Guntur District, Andhra Pradesh, India

5. Another area that requires attention is the impact of climate change and a possible sealevel rise and changes in the frequency and intensity of storms. These factors may change flooding risks from storm surges, especially in the low-lying regions of the North Indian Ocean such as Bangladesh and Maldives. 6. Detailed time histories and data dossiers of individual storm surges should be prepared by the concerned countries, which will enable calibration of storm surge models and improvement of prediction techniques. These data are also helpful to assess the potential and susceptibility of the coastlines. Estimates of storm surge potential from historic records are also valuable for efficient administration of cyclone mitigation plans to determine the safety and economics of coastal construction and installations. 7. It would also be appropriate to store all the pertinent data when a given storm affects an area, i.e., inundation maps, high-water marks, etc. It is also important to mention

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Latitude

17

16.5

16

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80.5

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Longitude Fig. 14 Storm surge penetration through the Krishna River System Overal Cyclone Vulenerability Index Map of Prakasham District

Latitude

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Longitude Fig. 15 Overall cyclone vulnerability map for Prakasham District, Andhra Pradesh, India

that now GIS (geographical information system) work is a common tool for most researchers, the design and creation of a GIS that contains precipitation, stream flow, and cyclone track data would be very valuable.

12 Conclusions Location-Specific high-resolution (1 km 9 1 km) storm surge models have been developed for the regions of North Indian Ocean prone to cyclonic storms. The models have been validated with the available data of storm surges generated by severe cyclonic storms. The models are able to simulate surge heights, which are in broad agreement with the

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estimated values provided by Unisys and Meteorological Services of different countries. The results emphasize the suitability of a fine resolution location-specific model for an operational prediction of surges in the countries of the North Indian Ocean. The TCP of WMO supported technology transfer to run and make operational the IIT-D storm surge models for Bangladesh, Myanmar, Sri Lanka, Pakistan, and Oman. In the present models, the cyclonic storm is the sole driving force for the dynamical processes in the sea. However, the tides and wave setup have not been included in the present study. Therefore, the non-linear interaction of surge and the tide has not been studied. Such an interaction may be significant if the occurrence of the surge coincides with that of the high tide (Johns et al. 1985; Sinha et al. 1996, 2008). In general, if there is a break in the coast, such as a river, it provides an additional path for the water to escape into the river, instead of getting piled up. The numerical models used in this study do not take into account the effect of rivers that communicate with the Arabian Sea and Bay of Bengal. However, the discharge of the fresh water carried by the river may modify the surge height along the coasts. The heavy precipitation associated with cyclone as well as seaward flow of river is expected to modify, to some extent, the evolution of the surge. However, this study does not include these effects.

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