Tidal Analysis and Prediction for the Gangra Location, Hooghly Estuary in the Bay of Bengal

The Hooghly estuary located in the head Bay of Bengal region is a part of the highly dynamic deltaic environment. Tidal variations are pre-dominant in this estuary, and tides propagate considerable distance through a complex network of various riverine systems, inlets, bays and creeks having vital implications on water mass exchange, reworking of deltaic sediments and the mixing process. The Hooghly River houses two major ports of national importance, viz. Kolkata Dock System and Haldia Dock Complex. Tidal forcing is primarily semi-diurnal in nature and with the presence of complex riverine morphology, the tidal characteristics are substantially modified causing various tidal constituents of compound tides. The present study performs location-specific tidal analysis and prediction utilizing one-hourly tide data with SLPR2 harmonic tidal analysis tool for Gangra situated upstream of the Hooghly River. In a geomorphologic perspective, the water-level elevation at Gangra results from natural tidal flow, as well as refracted effects from cross-flow due to the presence of two natural island barriers, namely Sagar Island situated southward off Gangra and Nayachara in the east. The Hooghly channel comprises of complex bathymetric features and tidal analysis at Gangra reveals the presence of Msf (luni-solar synodic fortnightly) tidal constituent. Very few locations in India have reported on the existence of the Msf tides, and Gangra is one among them. This study also performs a comprehensive validation between the computed monthly tidal prediction from SLPR2 and measured water level at Gangra. The skill level of prediction exhibits a good match. This study also investigates the influence of atmospheric effects on sea-level pressure variations and the resultant water-level elevation from extreme weather events such as depressions and severe cyclonic storms that occurred during 2013. The study signifies the importance of tidal analysis and prediction for operational needs.