Abstract
Warnings for natural hazards improve societal resilience and are a good example of decision-making under uncertainty. A warning system is only useful if well defined and thus understood by stakeholders. However, most operational warning systems are heuristic: not formally or transparently defined. Bayesian decision theory provides a framework for issuing warnings under uncertainty but has not been fully exploited. Here, a decision theoretic framework is proposed for hazard warnings. The framework allows any number of warning levels and future states of nature, and a mathematical model for constructing the necessary loss functions for both generic and specific end-users is described. The approach is illustrated using one-day ahead warnings of daily severe precipitation over the UK, and compared to the current decision tool used by the UK Met Office. A probability model is proposed to predict precipitation, given ensemble forecast information, and loss functions are constructed for two generic stakeholders: an end-user and a forecaster. Results show that the Met Office tool issues fewer high-level warnings compared with our system for the generic end-user, suggesting the former may not be suitable for risk averse end-users. In addition, raw ensemble forecasts are shown to be unreliable and result in higher losses from warnings.
Highlights
Warning systems (EWSs) play a major role in reducing monetary, structural and human loss from natural hazards
This article proposes a framework for issuing hazard warnings based on Bayesian decision theory [7], which offers a strategy for optimally issuing warnings in a rational way, using probability to quantify uncertainty about the future state of nature
We suggest a simple way of constructing the necessary loss functions for both generic and specific end-users, which provides a way of interpreting the warnings from the viewpoint of the decision-maker
Summary
Warning systems (EWSs) play a major role in reducing monetary, structural and human loss from natural hazards. This article proposes a framework for issuing hazard warnings based on Bayesian decision theory [7], which offers a strategy for optimally issuing warnings in a rational way, using probability to quantify uncertainty about the future state of nature (hazard). The framework is illustrated by application to data from the UK Met Office first-guess warning system (a key component of the NSWWS) that uses predictive information in the form of ensemble forecasts (multiple predictions of potential future weather from a numerical weather model). We show how reliable probabilities for the future state of nature may be constructed from ensemble predictions and illustrate how the proposed EWS can be used to quantify the value of various probabilistic predictions, for different stakeholders.
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