IAHS2022-490, updated on 31 Mar 2023
IAHS-AISH Scientific Assembly 2022
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Combining multiple hydrological model structures in a semi-distributed modelling environment.

Cyril Thébault1, Charles Perrin1, Vazken Andréassian1, Guillaume Thirel1, and Sébastien Legrand2
Cyril Thébault et al.
  • 1Université Paris-Saclay, INRAE, HYCAR, Antony, France
  • 2Compagnie Nationale du Rhône, DIGP, Lyon, France

Accounting for the variability of processes and climate conditions between catchments and within catchments remains a challenge in hydrological modelling. To address this issue, various approaches were developed over the past decades. Among them, multi-model approaches provide a way to quantify and reduce the uncertainty linked to the choice of model structure, and semi-distributed approaches propose a good compromise to account for spatial variability of the processes by dividing the catchment in sub-catchments while maintaining a limited level of complexity. However, these two approaches were barely applied together. The aim of this work is to answer the following question: can we improve the efficiency of hydrological models by implementing a multi-model approach within a semi-distributed framework? In this work, the benchmark considered is a lumped model with a fixed structure.

To this end, a large set of 147 catchments in France was assembled, with precipitation, evapotranspiration and flow data at an hourly time step over the 1998-2018 period. The semi-distribution set-up was kept simple by considering a single intermediate catchment between a downstream station and one or more upstream catchments. The multi-model approach was implemented with two versions of the GR model (namely GR4H and GR5H). Within a semi-distributed framework, the two models were either used individually, i.e. applied on all sub-catchments (called GR4H-SD and GR5H-SD respectively), or combined using a simple and a weighted mean.

The first step of this work was to check whether past conclusions published in the scientific literature, obtained with lumped multi-models, were the same in a semi-distributed framework. In other words, does the multi-model approach generate better performance than individual models in a semi-distributed context?

Another possible combination of the semi-distributed and the multi-model approaches would be to make different choices of model structures or combinations on each sub-catchment. Intuitively, it makes sense to propagate the flow simulated by the best model from upstream to downstream. The second analysis therefore focuses on the following question: is the best upstream model always the most useful downstream?

The results and the operational implications of this work will be analyzed in the case of the Rhône basin.

How to cite: Thébault, C., Perrin, C., Andréassian, V., Thirel, G., and Legrand, S.: Combining multiple hydrological model structures in a semi-distributed modelling environment., IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-490, https://doi.org/10.5194/iahs2022-490, 2022.