Return period estimates of extreme sea level along the east coast of India from numerical simulations

Abstract

Estimates of return periods of extreme sea level events along the coast are useful for impact assessment. In this study, a vertically integrated 2D model was developed for the simulation of storm surges in the Bay of Bengal. The bathymetry for the model was derived from an improved ETOPO-5 dataset, which was prepared in our earlier work. The meteorological forcing for the model was obtained from the cyclone model of Holland using the data available for 136 low pressure systems (LPS) that occurred during 1974-2000 in the Bay of Bengal. The simulated total sea level and the surge component were obtained for each event. The simulated peak levels showed good agreement with the observations available at few stations. The annual maxima of sea levels, extracted from the simulations, were fitted with Gumbel distribution using r-largest annual maxima method to estimate the 5-year and 50-year return periods of extreme events at 26 stations along the east coast of India. The return periods estimated from simulated sea levels showed good agreement with those obtained from observations. The 5-year and 50-year return levels of total sea level along the east coast of India show a considerable increase from south to north, with the 50year return total sea levels being as high as 6.9 m and 8.7 m at stations along the north eastern coast such as Sagar Island and Chandipur, respectively. The high return levels are expected at these stations as the cyclones developed in the Bay of Bengal generally move north or north-west, producing extreme events in the northern part and moreover, these stations are characterized by high tidal ranges. However, at some regions in the southern part such as Surya Lanka and Machilipatnam, though 50-year return levels of total sea-level are not very high (2.98 m and 2.97 m, respectively) because of the relatively lower tidal ranges, high return levels of surges (0.84 m and 0.57 m, respectively) are found. In addition to the role of shallow bathymetry (-5.0 m and 6.1 m, respectively) at the two stations, the high return levels of surges are attributed to the effect of geometrical configuration at Surya Lanka and width and orientation of continental shelf (100 km) at Machilipatnam.

Characteristics of Tides in the Bay of Bengal

“…The model used in the present study has been described in our earlier studies (Unnikrishnan et al 1999;Sindhu 2012;Sindhu and Unnikrishnan 2012). The model consists of a system of vertically integrated continuity equation and the equations of momentum.…”

Bay of Bengal cyclone extreme water level estimate uncertainty

“…Furthermore, future work should investigate flood risk uncertainty due to wave set-up and tidal contributions (see Jain et al, 2010b;Sindhu and Unnikrishnan, 2011), inundation modelling uncertainties (e.g. roughness and DEM uncertainty; see Lewis et al, 2012), and projected future changes to the extreme water-level climate (see Karim and Mimura, 2008).…”

“…[4] Each of these parameters is subject to natural variability even for storms of the same recurrence interval. For example, the estimated extreme pressure drop (ΔP) of the "1 in 50 year" cyclone has varied widely in three recent Bay of Bengal extreme water-level estimation studies: (1) 66 hPa, based on analysis of cyclones in a small region of interest (Rao et al, 2010); (2) between 66 hPa and 94 hPa, dependent upon the region of interest (Jain et al, 2010a); (3) 68.7 hPa, based on the analysis of cyclones throughout the Bay of Bengal (Sindhu and Unnikrishnan, 2011). However, the impact of such natural variability in cyclone parameters on flood hazard has yet to be quantified.…”

Integrating hydrodynamic model and Monte Carlo simulation for predicting extreme water levels in a river system

“…These simulation methods, such as the joint probability method (Pugh and Vassie 1979;Tawn and Vassie 1989) and the Monte Carlo simulation (Svensson et al 2013;Zhong et al 2013), can be used to accurately predict extreme water levels. The second approach is a frequency analysis of annual maximum water levels that results from the combination of several forcing factors (Gumbel 1985;Sindhu and Unnikrishnan 2012). This approach can be used when reasonably long observation data (greater than 60 years) are available (Huang et al 2008;Xu and Huang 2008).…”