Articles | Volume 25, issue 8
Hydrol. Earth Syst. Sci., 25, 4611–4629, 2021
Hydrol. Earth Syst. Sci., 25, 4611–4629, 2021

Research article 30 Aug 2021

Research article | 30 Aug 2021

Combining split-sample testing and hidden Markov modelling to assess the robustness of hydrological models

Etienne Guilpart et al.

Data sets

Monographie hydrologique du fleuve Sénégal. De l’origine des mesures jusqu’en 2011 J.-C. Bader, S. Cauchy, L. Duffar, and P. Saura

Grilles de pluies mensuelles IRD-HSM IRD (Institut pour la Recherche et le Développement) - HSM (Hydroscience Montpellier)

Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset ( I. Harris, T. J. Osborn, P. Jones, and D. Lister

Model code and software

Vahidesp/HMM_Classification: HMM Classifications (Version_Final) V. Espanmanesh

airGR: Suite of GR Hydrological Models for Precipitation-Runoff Modelling L. Coron, O. Delaigue, G. Thirel, D. Dorchies, C. Perrin, C. Michel, V. Andréassian, F. Bourgin, P. Brigode, N. Le Moine, T. Mathevet, S. Mouelhi, L. Oudin, R. Pushpalatha, and A. Valéry

Short summary
The stationary assumption in hydrology has become obsolete because of climate changes. In that context, it is crucial to assess the performance of a hydrologic model over a wide range of climates and their corresponding hydrologic conditions. In this paper, numerous, contrasted, climate sequences identified by a hidden Markov model (HMM) are used in a differential split-sample testing framework to assess the robustness of a hydrologic model. We illustrate the method on the Senegal River.