Multi-scenario flood risk assessment: A case of Sagar Island, West Bengal, India

Coastal landscapes are the major source of income and resources. Despite their high vulnerability to
coastal hazards, they are the homes of millions worldwide. Coastal floods are one of the most life-threatening
incidents affecting the coastal living. Bound by water on three sides, the flood sensitivity of coastal India largely
depends on the spatial exposure of under-equipped population groups. This spatial impact of the coastal flood
is likely to rise with the changing climate and exponential rise in the coastal population. The local governments
and stakeholders rely on spontaneous methods of coastal flood mitigation, that are temporary, and do not help
in long-term resilience.
Disaster resilience using spatial planning has been an intensely researched topic by many in this domain for
the past few decades. The development and availability of high-resolution remote sensing data and free and
open source spatial models have further facilitated the development of down-to-earth interventions for
resource-crunched developing nations. The research presents a comparative assessment of Business as Usual
Scenario (BAU) and Flood resilient scenario modelling (FResMO), emphasizing the role of spatial
planning in reducing coastal flood risk during cyclone YAAS (2021) on Sagar Island, West Bengal. In this
analysis, the flood hazard scenario of Sagar Island is developed and validated using a connected bathtub
model. The flood risk in the region is estimated as the product of various vulnerability and exposure
parameters. The vulnerability is dependent on socio-economic parameters, and exposure is related to the
spatial proximity of the region to coastal floods. The vulnerability and exposure parameters are ranked using
a multi-criteria decision using Analytical Hierarchical Process and finally integrated for estimating present
and future flood risk. The future flood risk scenario for 2030 is developed based on the built-up prediction
model ‘FUTURES’ that integrates the temporal landuse map, demography and socio-economic factors using
a multi-level logistic patch growing algorithm.
Keywords: Coastal flood risk, Flood risk modelling, FUTURES, Spatial adaptation, Vulnerability