Vulnerability assessment of ecosystem bestows an idea about the ecosystem health and its ability to resist environmental stress. Indian Sundarban region situated at the southwest part of Ganga-Brahmaputra-Meghna delta has been under constant threat of frequent climate hazards and long-term climate change. Various attempts have been made for vulnerability assessment of this mangrove ecosystem focusing only on static nontemporal variables. The present work hypothesises that mangrove ecosystems are highly adaptive and respond to the changing environment by various natural resilience strategies at the ecosystem level. So, a better understanding of the dynamics of mangrove ecosystem will provide an idea about the state of vulnerability for this ecosystem. The present study uses 16 parameters to create exposure, sensitivity and adaptive capacity risk indices and constructs the vulnerability status for the Indian Sundarban. The results showed that the sea-level rise will happen between 0.7 m to 0.9 m under baseline climate conditions. The CMIP6 multi-model ensemble showed that the future minimum temperature for the region will go up to 29.48 degrees C, thereby reducing the max and min temperature difference for the region. The fuzzy AHP-based vulnerability assessment revealed that the western Sundarban is more prone to climate vulnerability. The island-like Surendranagar, Lothian, and WestAjmalmari are extremely vulnerable. The proximity to human habitat will play an important role in climate change sensitivity to the Sundarban region. The time series analysis of mangrove forests showed the MannKendall '& tau;' value varies between 0.82 to -0.83. The central Sundarban forests area shows a varying degree of forest health deterioration. The assessments from the present study and the maps will help the environmental and risk-managers to identify the regions needing more climate change adaptation strategy.
Sardar, P; Samadder, SR
Long-term ecological vulnerability assessment of Indian Sundarban region under present and future climatic conditions under CMIP6 model
Ecological Informatics