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.

Selection of relative weights for different indicators is a critical step during assessment of composite hazards, vulnerability, and risk. While assigning weight to an indicator, it is important to consider the influence of an individual indicator on a particular composite index. In general, the larger the weight of the indicator, the higher the importance of that indicator compared to other indicators. In this study, a new matrix based statistical framework (MSF) for weight assignment is developed that can be considered as the simplest and most accurate method for assigning weights for a large number of indicators. This method (MSF) is based on the valuation of the correlation matrix and Eigenvector associated with Eigenvalue. Relying on the inter build up methodology, MSF can fulfill some built-in gaps among other weightage methods. It can also directly give the ‘decision’ to select the relative weights that are found from the Eigenvector corresponding to the largest Eigenvalue. The new method is applied by assigning weights to 15 socio-economic indicators and assessed vulnerability and risk in the Bangladesh coast. While comparing with other weight methods, it is found that MSF gives the most acceptable physical explanation about the relative values of weights of indicators. In terms of accuracy, MSF is found to be most accurate compared to other weight methods. When large numbers of indicators are involved in an application, MSF is found to be relatively simple and easy to apply compared to other methods.

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.