Probabilistic flood forecasting

The Environment Agency provides a forecasting and warning service to people at risk from flooding. However, flood forecasts are inherently un- certain. Efforts to quantify the uncertainty based on quantile regression have failed to capture the full extent of the uncertainty associated with significant flooding events. An investigation into factors that may be correlated with the uncertainty lead to the observation that there are structural biases in the model. It is possible to remove these, and thereby reduce the mean square error of the predictions, but the benefit of this is apparent in the prediction of ’normal’ conditions, rather than in flood predictions. Additionally, a tweak to the linear fit in the quantile regression is sug- gested which is better suited to the data

Propagation of uncertainty through the hazard chain

DSTL wish to explore methods for propagating uncertainty through a succession of linked models. The Study Group have looked at the particular example of casualty estimation from airbourne dispersion and suggested two different potential solutions. If the structure of the models is sufficiently simple, and the number of degrees of freedom relatively small, a semi-analytical approach based on Bayes’ theorem can be used. In the more general case, intelligent sampling methods can be used to gradually build a picture of likely outcomes