How process knowledge can improve flood forecasting: A stochastic and deterministic perspective (Invited)

How process knowledge can improve flood forecasting: A stochastic and deterministic perspective (Invited)

Svenja Fischer

Hydrology and Environmental Hydraulics, Wageningen University & Research, Wageningen, the Netherlands

 

Flood prediction remains challenging. With changing climate and environment, predictions are becoming more important because the impacts are increasing, while at the same time, they are becoming more challenging because flood-generating mechanisms change. Floods can be triggered by different processes, such as heavy rain, long-duration rain or melting snow. With changing climate, these processes are expected to change in frequency and magnitude. However, in current flood prediction models, the different flood-generating mechanisms are not explicitly considered and all flood events are treated equally. While in stochastic hydrology, process knowledge has been shown to be able to improve flood estimation and reduce uncertainty, this is less well studied for physical models. This can introduce uncertainty into the estimation.

The first step is to identify the relationship between atmospheric and catchment characteristics, flood-generation processes and the flood hydrograph. The identified relations are then integrated in the hydrological models by directly tailoring the physical relations to each flood type. In combination with an dynamic weighting approach, this enables a non-stationary and flexible flood prediction that can capture the changing frequency and magnitude of flood types and provide different flood scenarios with assigned probabilities. This approach does not only reduce the error in flood peak prediction but also improves the link of the model parameters to physical processes and thus increases our understanding of flood processes. Moreover, the uncertainty of the considered process can be directly quantified by a probability-based evaluation.