Can physically-based models represent changes in hydrological processes expected during megafloods?

Recent years have seen floods of unprecedented intensity, leading to large loss of life, including in Spain (October 2024), Libya (September 2023) and Germany, Belgium, Luxembourg and the Netherlands (July 2021). The potential for conventional methods to underestimate extreme events was vividly and tragically illustrated in the devastating flooding of the Ahrtal associated with the latter event.

As well as the intensification of rainfall resulting from global heating, the anomalous behaviour of extreme floods may result from changes in hydrological processes, such as a transition to infiltration excess overland flow (Mushtaq et al, 2023). There is evidence of a large reduction in response time as rainfall intensity increases, for some catchments (Faulkner and Benn, 2019).

Empirical methods of flood frequency estimation have potential for estimating extreme floods (Bertola et al.,2023; Merz et al., 2022). However, they have limited ability to deal with changes in the physical processes of flood generation. Similarly, conceptual hydrological models can struggle to represent the impact of heterogeneous, nonlinear or otherwise complicated processes.

We investigate and benchmark the ability of a physically-based model (SHETRAN) to simulate extreme events beyond the range of observed conditions, examining how it represents changes in hydrological processes as the rainfall becomes more extreme, up to the probable maximum precipitation. Using a case study in the headwaters of the River Wye in Wales, UK, we find that the model represents the expected acceleration and intensification of runoff-generating processes. It does so partly by routing more runoff over the ground surface.

The findings are important for testing the resilience of society to extreme hazards, in a time of rapid environmental change.

 

Bertola, M., Blöschl, G., Bohac, M. et al. (2023). Megafloods in Europe can be anticipated from observations in hydrologically similar catchments. Nat. Geosci. 16, 982–988. https://doi.org/10.1038/s41561-023-01300-5.

Faulkner, D. and Benn, J. (2019). Reservoir flood estimation: the way ahead. Dams and Reservoirs, https://doi.org/10.1680/jdare.19.00028.

Merz, B., Basso, S., Fischer, S., Lun, D., Blöschl, G., Merz, R., et al. (2022). Understanding heavy tails of flood peak distributions, Water Resources Research, 58, e2021WR030506.

Mushtaq, S., Miniussi, A., Merz, R., Tarasova, L., Marra, F., & Basso, S. (2023). Prediction of extraordinarily high floods emerging from heterogeneous flow generation processes. Geophysical Research Letters, 50, e2023GL105429. https://doi.org/10.1029/2023GL105429.