Cost assessments of the overall economic impacts of natural hazards, costs of prevention and responses to natural hazards supply crucial information for decision support and policy development in the fields of natural hazard management and climate change adaptation planning. However, the significant diversity in methods that are currently applied and terminology used in costs assessments of different natural hazards and impacted sectors make it difficult to establish comprehensive, robust and reliable cost figures, and also hinder comparison of costs across countries, across hazards and across impacted sectors.
This session is supported by the EU FP7 project ConHaz – Costs of natural Hazards (http://conhaz.org/) and aims to review current knowledge on methods for assessing individual cost types (such as direct damages to housing, indirect losses in the macro-economy, as well as costs of risk reduction) and aims to show how these methods are used in the context of various natural hazards (e.g. floods, droughts, alpine hazards, coastal hazards). We welcome submissions in the areas of assessing these various types of damage induced by natural hazards. Also, we are interested in contributions that focus on the cost-effectiveness or efficiency of risk reduction to natural hazards and adaptation to increasing weather risks that are due to climate change. Presentations are welcome for instance on data collection, model development, validation, uncertainty analysis, risk assessment frameworks as well as presentations about the application of damage models in case studies. Also, presentations are welcome on the experiences in the assessment of direct and indirect costs of projects aimed at reduction of natural hazard risks, including climate adaptation measures.
Abstracts are sought from those involved in both the theoretical and practical aspects of the above mentioned or related topics
It is foreseen to publish a special issue of selected papers with the journal NHESS (Natural Hazards and Earth System Sciences) of the EGU.