Hydroclimatic extremes, along with their statistics, are crucial inputs of hydrological applications, which have increasing importance in the (re)insurance industry. Among the most common applications, catastrophe models are developed to manage risk accumulation; disaster response is used to prepare (re)insurers financially after major events; Real Disaster Scenarios are built to stress-test (re)insurance exposure; parametric (or index based) solutions are developed to cover the likelihood of a loss-causing event happening, like a flood event. Additionally, connections between hydrometeorological extremes and climatic oscillations, such as NAO or ENSO, and their evolution in a changing climate, provide insights for long-term risk management, as required for regulatory purposes.
The preparedness for, and intensification of these extreme events require a synergy with research in order to estimate them accurately. In this context, recent advancements have revealed the departure of hydrometeorological processes from classical statistical models, highlighting the scaling behaviour of extremes across space and time. For instance, the estimation of the design rainfall for flood estimation not only involves determining the absolute amount for a specific return period, but also requires understanding the intra-event rainfall distribution, spatial extension, and rainfall intensities at neighbouring stations. The integration of supporting information and the application of advanced AI approaches offer as well unprecedented opportunities to enhance these estimates.
This session invites submissions, among others, on the following topics:
- Applications carried out jointly by the (re)insurance industry and research institutions.
- Coupling stochastic approaches with deterministic hydrometeorological predictions to better represent predictive uncertainty.
- Developing robust statistics under non-stationary conditions for design purposes.
- Parsimonious models of hydrometeorological extremes across various spatial and temporal scales for risk analysis and hazard prediction.
- Improving the reliable estimation of extremes with high return periods, considering physical constraints.
- Linking underlying physics and hydroclimatic indices with the stochastics of hydrometeorological extremes.
- Exploring supporting data sets for additional stochastic information and utilizing novel AI and machine learning approaches.