Multi-model approach in a variable spatial framework for streamflow simulation

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: what are the contributions of multi-model approaches in a variable spatial framework for the simulation of streamflow over a large sample of catchments?

To this end, a large set of 121 uninfluenced catchments in France was assembled, with precipitation, evapotranspiration and streamflow 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 13 hydrological structures, three calibration options and two spatial frameworks, for a total of 78 distinct modelling options. A simple average method was used to combine the streamflow at the outlet of the catchments and sub-catchments. In this work, the benchmark considered is the most efficient lumped model considered individually on each catchment.

The semi-distributed multi-model approach generates better performance at the time-series scale than the lumped models. The gain is mainly brought by the multi-model aspect while the spatial framework gives a more occasional benefit. This study also highlight the advantages of using a large set of models in a semi-distributed multi-model framework to simulate streamflow in a large sample hydrology context.