Lack of robustness of hydrological models: A large-sample diagnosis and an attempt to identify the hydrological and climatic drivers

Abstract. The transferability of hydrological models over contrasted climate conditions, also identified as model robustness, has been the subject of much research in last decades. The occasional lack of robustness identified in such models is not only an operational challenge – since it affects the confidence that can be placed in projections of climate change impact – but it also hints at possible deficiencies in the structure of these models. This paper presents a large-scale application of the robustness assessment test (RAT) for three hydrological models with different levels of complexity: GR6J, HYPE and MIKE SHE. The dataset comprises 352 catchments located in Denmark, France and Sweden. Our aim is to evaluate how robustness varies over the dataset and between models and whether the lack of robustness can be linked to some hydrological and/or climatic characteristics of the catchments (thus providing a clue on where to focus model improvement efforts). We show that although the tested models are very different, they encounter similar robustness issues over the dataset. However, models do not necessarily lack robustness on the same catchments and are not sensitive to the same hydrological characteristics. This work highlights the applicability of the RAT regardless of model type and its ability to provide a detailed diagnostic evaluation of model robustness issues.