This session focuses on modeling compound events as well as multi-risk and concurrent event risk assessments. Hazards such as floods, wildfires, heatwaves and droughts often result from a combination of interacting physical processes that take place across a wide range of spatial and temporal scales. The combination of physical processes leading to an impact is referred to as a “compound event". Compound events have been identified as an important challenge by the World Climate Research Programme (WCRP) ‘Grand Challenge’ on extreme events.

These compound or concurrent events can create a chain of episodes that go beyond what governments, emergency managers, urban planners and even citizens have planned for. Climate change, apart from potentially increasing hazards’ frequency and magnitude, introduces complexities that may increase incidents of compound hazards, secondary hazards, cascades, and alter their inter-dependencies. In addition, non-stationarities in physical processes and in land use and more general in systems’ features require updated methods, techniques and statistical approaches to assess the risk due to the linking of (weather-driven) hazards. Taking a long-term perspective, adaptive strategies are needed to reduce the vulnerability of exposed elements (e.g. cities, crops, industries and critical infrastructures) with respect to these non-stationarities, usually manifesting in different spatiotemporal scales, and their complex influence on (weather-driven) hazards.

The purpose of this session is to bring together scientists and studies from a wide range of discipline areas to illustrate how including multiple perspectives improves risk assessments. Specifically, papers are sought that:

1) Highlight the physical processes and related non-stationarities characterizing hazards’ interactions, e.g., climatic drivers responsible for joint storm-flood episodes accounting for any time-lags between the event types, or, compound drivers with secondary hazards.

2) Describe new methodologies, techniques and statistical approaches (e.g., use of economic loss data to identify dependences, advances in deterministic and stochastic forecasting, clustering identification, etc.) that are useful to quantify the spatiotemporal characteristics of linked hazards.

3) Focus on socio-economic impacts (regional and/or global), caused by linked weather-driven hazards, in the past (i.e. case studies) and in the future under diverse climatic scenarios.

4) Give new insights into how vulnerability may be reduced in a long-term perspective with respect to combined hazards.