Tropical cyclones are the most devastating weather events which still needs to understand much due to the uniqueness in their paths and intensification locations. One such recent very severe cyclonic storm, Titli, is perfectly fit for the individual case study. The cyclone Titli originated from a low-pressure area formed over the southeast Bay of Bengal (BoB) and adjoining north of the Andaman Sea on 7th October 2018 and intensified into a very severe cyclonic storm on 10th over a region of low saline water pool in the western Bay of Bengal. The addition of low salinity water in this region is from the lower Mahanadi basin and surroundings that received excess rainfall in September 2018. The low saline waters developed high stratification and suppressed the upwelling at the cold-core eddy center and restricted to 50 m. Persistent high heat content and high internal energy are the primary sources of intensification of cyclone Titli. Generally, intense cyclones enrich biomass's enhancement in the ocean's surface layer after their passage. But in the case of Titli, biomass enrichment did not happen due to persistent high stratification, which is about 3–4. Even the 80 knots (150 km/h) winds cannot break up the upper layer stratification, suppressing the cyclone-induced upwelling. Observations show only a 0.2–0.4 mg/m$^{3}$ rise in chlorophyll on the surface and it may be due to the land drove nutrients and lightning. Data analyzed from the high-resolution global NEMO model also shows the intrusion of low saline waters off India's east coast. Though the model is slightly overestimating the upwelling in the cold core eddy's surface layers, it represents the barrier caused by stratified waters. Both the observations and model datasets are well correlating, daily analyzed model data is well capturing the seasonal stratification caused by the low saline waters in the northern Bay of Bengal. These high-resolution global ocean model datasets are useful and essential for the accurate forecast of cyclones over the Bay of Bengal.

Extraction of shorelines using satellite imagery is an effective method because customary digitization is a longand hectic process. This study focuses on extracting and detecting shoreline changes from Landsat-8 imageries ofthe Visakhapatnam–Kakinada coast along the east coast of India using an object-based approach. An object-based approach for the automatic detection of coastline from Landsat imagery using the Feature Extraction Workflow by Maximum Likelihood is implemented by the maximum classification method (MLC). The resulting vector polyline is smoothened for every 100 m using ArcGIS software. Delineation of multi-temporal satellite images was performed by visual interpretation from 2014 to 2019 to detect the shoreline changes. Different available techniques and methods are employed to observe shoreline changes. In addition to this, the shoreline information simulated by satellite remote sensing is in fair agreement with RTK GPS observations. The observed and remote sensing shoreline changes help to identify the areas of accretion and eroding zones overthe long term. During this study, erosion and deposition changes were observed along RK beach, Rushikonda beach, Uppada beach, and Kakinada beach. The spatial variation rates were calculated using the statistical methods of the Digital Shoreline Analysis System (DSAS) during specific periods. The maximum observed shoreline accretion and erosion rates at Kakinada are 5.3 and –4.35 m/year indicates slight accretion. The maximum observed accretion and erosion rates at Uppada beach are 3.8 and –6.78 m/year, respectively indicatingerosion. Similarly, at RK Beach the maximum observed shoreline accretion and erosion rates are 3.68 and –3.68 m/year, respectively indicating the beach is in a stable state. At Rushikonda beach, the maximum observed shoreline accretion and erosion rates are 2.24 and –3.04 m/year, respectively indicating erosion.