Modeling coastal vulnerability in the Indian Sundarban under possible Shared Socioeconomic Pathways (SSPs)

Abstract

Considering the dynamic and complex nature of the Indian Sundarbans, coupled with projected sea level rise (SLR), we employed an adapted methodology to identify vulnerable zones along the coastal belts of this delta. The analysis was conducted using InVEST, an open-source model developed by Stanford University. SLR is considered the primary factor in assessing flood-prone areas along the inhabited islands, particularly under cyclone-induced storm surges. A Coastal Exposure Index (CEI) was calculated for two Shared Socioeconomic Pathway (SSP) scenarios, SSP2–4.5 and SSP3–7.0 to account for the projected impact of SLR in the future. Along with SLR, key model inputs included bathymetry, continental shelf characteristics, shoreline geomorphology, wind and wave factors, and the spatial distribution of mangrove habitats. The accuracy assessment yielded a mean absolute error (MAE) of 0.0970 when compared with field measurements, demonstrating strong model reliability. The projected shift in CEI values is concerning; areas classified as low- and moderate-exposure are expected to transition to higher exposure categories by 2050 and 2100 under both SSP scenarios. By 2050, an increasing number of village blocks are projected to experience submergence during extreme precipitation events and storm surges. By 2100, the situation is likely to worsen, with an even greater number of village blocks at risk of submergence, particularly under SSP3–7.0, emphasizing the urgent need for mitigation and adaptation strategies.