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The North Indian Ocean is a region with a high coastal population and a low-lying delta, making it a high-risk region for tropical cyclone impacts. A 30-year period from 1989–2018 has been used to examine the TC landfalling exposure in the North Indian Ocean and its changes by considering 30 years of IBTrACs data, ERA5 atmospheric data, and 20 years of TRMM and DAV data. A total of 185 TCs made landfall in the NIO during the 30-year period with the majority of the TCs making landfall during the pre- and post-monsoon seasons. Rainfall associated with landfalling TCs decreased in the last 10 years of analysis (2009–2018) compared to the first 10 years of available data from 1999–2008. During the monsoon, TC activity is relatively lower compared to the post-monsoon periods, even though higher accumulated TC-associated rainfall typically occurs during the monsoon period, particularly along the eastern coastlines of the Arabian Sea and the Bay of Bengal. The TC winds impact most of the Bay of Bengal coastline, including Sri Lanka. The spatial distribution of landfalling TCs changes with the season, with most of the landfalling activity occurring during the pre- and post-monsoon periods. Interestingly, more recent TC activity has shifted to the northeast India and Bangladesh coasts, suggesting that these regions may be more vulnerable to TC impacts in the future.

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Risk assessment of climatic events and climate change is a globally challenging issue. For risk as well as vulnerability assessment, there can be a large number of socioeconomic indicators, from which it is difficult to identify the most sensitive ones. Many researchers have studied risk and vulnerability assessment through specific set of indicators. The set of selected indicators varies from expert to expert, which inherently results in a biased output. To avoid biased results in this study, the most sensitive indicators are selected through sensitivity analysis performed by applying a non-linear programming system, which is solved by Karush-Kuhn-Tucker conditions. Here, risk is assessed as a function of exposure, hazard, and vulnerability, which is defined in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5), where, exposure and vulnerability are described via socioeconomic indicators. The Kolmogorov-Smirnov statistical test is applied to select the set of indicators that are the most sensitive for the system to assess risk. The method is applied to the Bangladesh coast to determine the most sensitive socioeconomic indicators in addition to assessing different climatic and climate change hazard risks. The methodology developed in this study can be a useful tool for risk-based planning.