Using the information on rainfall intensities to represent the variability in time of dominant processes in a hydrological model

High-intensity precipitation events activate heterogeneous catchment processes and trigger flood events for which hydrological models are known to have limited predictive efficiency. The goal of this study is to find a way to inform the structure of a hydrological model on the time variability of dominant processes by using precipitation intensities as a surrogate for the activation of fast heterogeneous runoff processes. Accurately simulating flood generated by high-intensity rainfall events occurring on dry soil-moisture conditions, during or after the summer period, is a particular challenge (Astagneau et al., 2021), typically in southern France in a Mediterranean context.

Starting from the concept developed by Peredo et al. (2021), we made three modelling hypotheses on the explicit dependence of the storages and fluxes of a conceptual hydrological model on rainfall intensity rates. The modifications introduced increasingly modify model functioning as rainfall intensity increases, and are neutral in case of low rainfall intensity. We evaluated these hypotheses over a large set of 229 catchments in France representing a large variety of conditions, and we used a selection of 10,652 flood events to evaluate model performance. We used the GR5H model at the hourly time step as structural basis for model improvement. The relevance of our approach was assessed against several event and catchment characteristics (precipitation intensity and spatial variability, antecedent wetness conditions…) characterising very different flood generating processes.

Results showed significant improvements in the simulation of summer floods resulting from high-intensity rainfall events in several catchments of the Mediterranean area, while maintaining a limited level of complexity of the model structure.

References

Astagneau, P. C., et al., (2021). When does a parsimonious model fail to simulate floods? Learning from the seasonality of model bias. Hydrological Sciences Journal, 66(8), 1288–1305, https://doi.org/10.1080/02626667.2021.1923720

Peredo, D., et al., (2021). Investigating hydrological model versatility to simulate extreme flood events. Hydrological Sciences Journal, In Review.