Articles | Volume 25, issue 8
https://doi.org/10.5194/hess-25-4611-2021
https://doi.org/10.5194/hess-25-4611-2021
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, Vahid Espanmanesh, Amaury Tilmant, and François Anctil

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Cited articles

Akintug, B. and Rasmussen, P. F.: A Markov switching model for annual hydrologic time series, Water Resour. Res., 41, 1–10, https://doi.org/10.1029/2004WR003605, 2005. a, b
Ardoin-Bardin, S.: Variabilité hydroclimatique et impacts sur les ressources en eau de grands bassins hydrographiques en zone soudano-sahélienne, PhD thesis, Université Montpellier II, https://doi.org/10.1038/ni.2208, 2004 (in French). a
Ardoin-Bardin, S., Dezetter, A., Servat, E., and Mahe, G.: Évaluation des impacts du changement climatique sur les ressources en eau d'Afrique de l'Ouest et Centrale, in: Regional Hydrological Impacts of Climatic Change – Hydroclimatic Variability, IAHS, Foz de Iguaçu, Brazil, 194–202, 2005 (in French). a
Bader, J.-C., Cauchy, S., Duffar, L., and Saura, P.: Monographie hydrologique du fleuve Sénégal. De l'origine des mesures jusqu'en 2011, IRD, Marseille (France), IRD edition, available at: https://www.documentation.ird.fr/hor/fdi:010065190 (last access: 1 July 2021), 2014 (in French). a, b, c, d, e, f, g
Bernier, J.: Etude de la stationnarité des séries hydroméléorologiques, La houille blanche, 4, 313–219, 1977 (in French). a
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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.