Effective communication of uncertainty around modelling in hazard and risk models

Model-based decision support is common in many disciplinary fields including disaster risk management. There are multiple risk and hazard models to support decision-making during different disaster risk management phases - response, recovery, reduction, and readiness. However, in these modelling exercises, uncertainties are manifested starting from data that goes into the model, the assumptions made to categorise and analyse these data in those models, and outputs that are generated from the model. Hence, communicating these model uncertainties is helpful in many ways. Decision-makers will be able to make a separation of the object, source, and ‘owner’ of the uncertainty and make informed decisions, scientists will get better acknowledgment for their work in modelling, and policy makers will have better insights on making adaptive policy frameworks. 

There has been good research in the last two decades in understanding model analytics and model uncertainties but we still do not have enough knowledge on what the actual uncertainties are helpful for decision-makers while making these decisions. This research will engage decision-makers to identify model-uncertainties need, while also understanding the current practice of model uncertainty communication  from modellers.