A methodological framework for extreme climate risk assessment integrating satellite and location based data sets in intelligent systems

Adaptation and resilience practitioners lack guidance on how to understand and manage extreme climate risk using the data available. We present a methodological framework to integrate the satellite as well as location based data sets to estimate extreme climate risk. The framework, in detail, has been demonstration using a study carried out to quantify extreme rainfall risks in India incorporating the influence of global (large scale oscillations) as well as local factors (population, infrastructure, economic activity) in a probabilistic model. We use nonstationary extreme value theory along with Bayesian uncertainty analysis to model the time varying influence of oscillations such as El Nino/Southern Oscillation, Indian Ocean Dipole, and North Atlantic Oscillation in augmenting high rainfall risks in 637 districts across 29 states of India. It is found that at least 50% of the districts in 8 out of 29 states are at high risk. Extreme risk is observed in 198 (similar to 31%) and 249 (similar to 39%) districts caused by heavy downpour and extremely long wet spells, respectively. This study provides a framework to identify local implications of global factors and is aimed at supporting policy makers in framing extreme rainfall-induced disaster risk reduction strategies.