Investigating statistical methods for modelling natural hazards in a changing climate

World Futures Lab is pleased to announce that Arun Raveendran has been awarded a Research Seed Grant under its Grants for Impact initiative. The grant will support preliminary research into statistical point processes and their applications in modelling natural hazards under changing climatic conditions.

About the Research

Natural hazards such as extreme rainfall and flooding can exhibit changing occurrence rates and temporal clustering. Climate change adds further complexity by altering the frequency and patterns of extreme events, creating challenges for conventional statistical models used in hazard analysis.

Arun's research will investigate two classes of statistical point processes: inhomogeneous Poisson processes and Hawkes processes. Inhomogeneous Poisson processes provide a framework for modelling event occurrences with time-varying rates, while Hawkes processes can represent temporal clustering, in which one event may increase the likelihood of subsequent events.

The study will assess the capabilities, assumptions, and limitations of these approaches in natural hazard modelling. It aims to explore their potential to improve statistical methods for investigating climate-related risks.

“Coming from Keralam, where extreme rainfall and flooding affect communities and livelihoods, I hope to explore how statistical methods can improve natural hazard modelling and, in the long term, contribute to more effective early warning systems, both locally and globally. ” — Arun Raveendran