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Upcoming Events

Workshop on Understanding and Modeling Complex Risks in Coupled Human-Environment Systems

08/02/2022 10/02/2022

Workshops

External Event URL

Event location

Online

Losses from extreme weather events increased globally over the last decades and are expected to further increase in the future as a consequence of climatic and socio-economic changes. These losses arise from a complex interplay between human and environmental systems, whose outcomes are often hard to predict. Yet, developing plans and policies for managing risks from extreme weather and climate events is an urgent and paramount task for societies. Risk quantification and management increasingly rely on the use of model simulations of various kinds (e.g., natural catastrophe models, agent-based models, system dynamic models, etc.), and of a diverse set of techniques (e.g., extreme value analysis, scenario analysis, exploratory modeling and analysis, etc.). The value of these tools for risk quantification and management improved over the years due to increasing research, data availability and computational power. Yet, there are still significant gaps that offer many research opportunities, including – but not limited to – a better treatment of uncertainties, an improved integration of feedback mechanisms between climatic and socio-economic changes, and the need of expanding models from single to multi-hazards and -vulnerabilities.

Furthermore, systemic risks arising from the interaction of phenomena within complex and highly interconnected human-environmental systems are poorly understood, let alone modeled. Systemic risks do not often have a track record which can be used to estimate probabilities and expected losses or calibrate models, which creates methodological challenges. With these types of risks, commonly used statistical techniques may fall short and, thus, new methods to quantify and understand the systemic nature of risk are needed.

With the aim of addressing these challenges, the Risk Modeling and Insurance Working Group of the Knowledge Action Network on Emergent Risks and Extreme Events (Risk KAN) organizes a three-day workshop to bring together scientists and practitioners in the field of modeling risk in human and environmental systems.

Sessions

Day 1 – Systemic Risks

The first day will focus on systemic risks due to tipping points, incl. new modeling approaches and indicators and a review of qualitative and quantitative analysis and modeling techniques.

Invited speakers:

  • Zinta Zommers (United Nations Office for Disaster Risk Reduction)
  • TBA

 

Day 2 – Advances in Modeling Socio-Economic Impacts

The second day will focus on advances in modeling socio-economic impacts, incl. global sensitivity analysis frameworks, machine learning approaches to impact modeling, modeling of indirect impacts, modeling of climatic and socioeconomic changes including feedback mechanisms and modeling of multi-hazards and -vulnerabilities.

Invited speakers:

  • Francesca Pianosi (University of Bristol)
  • Nick Simpson (University of Cape Town)

 

Day 3 – Resilience and Adaptation

The third day will focus on resilience and adaptation, incl. topics such as insurance for ecosystem services, economics of climate adaptation, extreme events and sustainable livelihoods and reinforcing community resilience in the face of emergent risks.

Invited speakers:

  • Roger Cremades (Wageningen University & Research)
  • Tatiana Filatova (Delft University of Technology)

 

Deadlines

Abstract submission: 15 Aug 2021 – 15 October 2021
Abstract acceptance: 1 December 2021
Registration:  1 December 2021 – 31 January 2022

 

Potential outcomes

Depending on the received contributions, the workshop’s outcomes may take the form of summary reports, joint scientific papers or Policy Forum articles.
 
For more information, contact Alessio Ciullo, Institute for Environmental Decisions, ETH Zurich. Email: This email address is being protected from spambots. You need JavaScript enabled to view it.. As subject line, please use: “RISK-KAN Workshop”.
 

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Image by Tri Le.

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