Resistance from climate extremes requires robust adaptation strategy, especially in regions that have greater density and limited resource availability. Weather extremes, such as anomalous precipitation leading to floods have now become a frequent global phenomenon. These situations could lead to unforeseen disaster if the region remains under-equipped to adapt against the vulnerabilities. Therefore, the research objective aims to explore the current status of adaptability within proposed capital region of Andhra Pradesh on backdrop of future urban growth that region may undergo. Andhra Pradesh Capital Region (APCR) has experienced severe flood events in past due to intense rainfall, overflowing Krishna river and causing inundation due to sudden releases from reservoirs upstream. An index based assessment of Mandalas were carried out by selecting relevant factors through various literature reviews. The method comprises of assigning comparative scores to the mandals based on their performance for each parameter and categorizing them on a Likert scale of 1 to 5. The selected parameters are then prioritized by adding weights through pairwise comparison techniques using Analytical Hierarchical Process. Risk Map for the region was developed using weighted sum of flood vulnerability factors and urban development scenario of 2050. The results substantiate that the regional vulnerability is cumulatively influenced by exposure and sensitivity factors. The empirical findings identify female literacy and elevation as major contributor to flood vulnerability. Requirement for immediate interventions were suggested for the mandals with higher vulnerability and greater scope of urban transformation. The proposed method will help in quick identification of susceptible mandals that may suffer higher vulnerability in future. The method proposed will also be effective for formulation policies for redirecting scarce resources in areas needing adaptation against climate disasters.
<p>Coastal landscapes are the major source of income and resources. Despite their high vulnerability to<br />coastal hazards, they are the homes of millions worldwide. Coastal floods are one of the most life-threatening<br />incidents affecting the coastal living. Bound by water on three sides, the flood sensitivity of coastal India largely<br />depends on the spatial exposure of under-equipped population groups. This spatial impact of the coastal flood<br />is likely to rise with the changing climate and exponential rise in the coastal population. The local governments<br />and stakeholders rely on spontaneous methods of coastal flood mitigation, that are temporary, and do not help<br />in long-term resilience.<br />Disaster resilience using spatial planning has been an intensely researched topic by many in this domain for<br />the past few decades. The development and availability of high-resolution remote sensing data and free and<br />open source spatial models have further facilitated the development of down-to-earth interventions for<br />resource-crunched developing nations. The research presents a comparative assessment of Business as Usual<br />Scenario (BAU) and Flood resilient scenario modelling (FResMO), emphasizing the role of spatial<br />planning in reducing coastal flood risk during cyclone YAAS (2021) on Sagar Island, West Bengal. In this<br />analysis, the flood hazard scenario of Sagar Island is developed and validated using a connected bathtub<br />model. The flood risk in the region is estimated as the product of various vulnerability and exposure<br />parameters. The vulnerability is dependent on socio-economic parameters, and exposure is related to the<br />spatial proximity of the region to coastal floods. The vulnerability and exposure parameters are ranked using<br />a multi-criteria decision using Analytical Hierarchical Process and finally integrated for estimating present<br />and future flood risk. The future flood risk scenario for 2030 is developed based on the built-up prediction<br />model &#8216;FUTURES&#8217; that integrates the temporal landuse map, demography and socio-economic factors using<br />a multi-level logistic patch growing algorithm.<br />Keywords: Coastal flood risk, Flood risk modelling, FUTURES, Spatial adaptation, Vulnerability</p>
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