Translating the concept of climate risk into an assessment framework to inform adaptation planning: Insights from a pilot study of flood risk in Himachal Pradesh, Northern India

2018 
Abstract Climate risk assessments provide the basis for identifying those areas and people that have been, or potentially will be, most affected by the adverse impacts of climate change. They allow hot-spots to be identified, and serve as input for the prioritization and design of adaptation actions. Over recent years, at the level of international climate science and policy, there has been a shift in the conceptualization of vulnerability toward emergence of ‘climate risk’ as a central concept. Despite this shift, few studies have operationalized these latest concepts to deliver assessment results at local, national, or regional scales, and clarity is lacking. Drawing from a pilot study conducted in the Indian Himalayas we demonstrate how core components of hazard, vulnerability, and exposure have been integrated to assess flood risk at two different scales, and critically discuss how these results have fed into adaptation planning. Firstly, within a state-wide assessment of glacial lake outburst flood risk, proxy indicators of exposure and vulnerability were combined with worst-case scenario modelling of the outburst hazard. At this scale, first-order assessment results are coarse, but have guided the design of monitoring strategies and other low-regret adaptation actions. Secondly, an assessment of seasonal monsoon and cloudburst-related flood risk was undertaken for individual mapped elements exposed along the main river valleys of Kullu district, drawing on innovative techniques using dendrogeomorphology to reconstruct potential flood magnitudes. Results at this scale have allowed specific adaptation strategies to be targeted towards hot-spots of risk. A comprehensive risk assessment must integrate across disciplines of physical and social science, to provide the necessary robust foundation for adaptation planning.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    36
    References
    23
    Citations
    NaN
    KQI
    []