GHIRAF: closing the gap between global models and locally-actionable information

Abstract

<p>The impact of extreme weather events on coastal areas around the world is set to increase in the future, both through sea level rise, climate change (increasing storm intensity, rainfall and droughts) and continued development and investment in hazard-prone deltaic and coastal environments. Given the changing natural and socio-economic environment, accurate predictions of current and future risk are becoming increasingly important world-wide to mitigate risks. Recent advances in computational power (e.g., cloud-computing) and data availability (e.g., growth of satellite-derived products) are enabling, for the first time, the development of global scale flood risk models for application in areas where local models are less well developed or prohibitively expensive, or for applications where a synoptic global coverage is important. Despite the increasing granularity of these global models and datasets however, they often still lack the resolution and accuracy to be “locally relevant”, especially where inundation and impact assessments are considered. While a solution to this problem is to downscale global models and datasets to the local scale, setting up local models is hampered by inconsistency between underlying datasets, and the required manual effort to generate downscaled integrated risk models inhibits their global application. To address these issues, we are developing a generalized risk assessment framework, called GHIRAF (Globally-applicable High-resolution Integrated Risk Assessment Framework), which couples data and models to quickly provide locally-actionable information on impact of historic, current- and future world-wide extreme weather events (e.g., storms, extreme rainfall, drought). The framework is designed to support world-wide efforts to reduce and mitigate risks associated with extreme weather events by aiding prevention (scenario-testing, design) and preparation (Early Warning) for extreme events, as well as support response (targeted relief efforts) and recovery (build-back-better) efforts. In this work we discuss application of the framework to study hurricane impacts on the eastern coast of the USA, as well as in data-poor, small island state environments.</p>