Assessing robustness in global hydrological predictions by comparing modelling and Earth observations

Rafael Pimentel (1, 2) , Louise Crochemore (3, 2, 4, 5, 6, 7, 8, 9) , Jafet C M Andersson (2) , Berit Arheimer (2)

1 Universidad de Córdoba = University of Córdoba [Córdoba]
2 SMHI - Swedish Meteorological and Hydrological Institute
3 Swedish Meteorological and Hydrological Institute (SMHI), Hydrology Research, Norrköping
4 UR HBAN - Hydrosystèmes et Bioprocédés
5 Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
6 RiverLy - RiverLy - Fonctionnement des hydrosystèmes
7 UGA - Université Grenoble Alpes
8 IGE - Institut des Géosciences de l’Environnement
9 UR HBAN - Hydrosystèmes et Bioprocédés

Hydrological modelling to support hypotheses on Earth system boundaries or the accelerating water crisis is nowadays done at the global scale, with difficulties associated to model uncertainties. Here we bring a robustness analysis of internal model variables as an additional tool for model evaluation using data from six Earth observation products and the global catchment model World-Wide HYPE in a comparative study. The assessment shows that: (i) variables have high agreement in mid-latitude temperate regions; (ii) the variables with higher agreement, and associated with good model performance in streamflow, were actual evapotranspiration, fractional snow cover and snow water equivalent; and (iii) changes in total water storage showed very poor agreement, probably due to an insufficient number of aquifers in the model set-up. We propose this procedure as a standard complementary method in global hydrological modelling, highlighting the importance of justifying models before using them for scenario analysis or water accounting.

Mots clés: global hydrological modelling evaluation; water cycle; model-based robustness analysis; multi-variable estimation; Earth observations; UPH 16; global hydrological modelling evaluation water cycle model-based robustness analysis multivariable estimation Earth observations UPH; multivariable estimation; UPH

Domaines: Sciences de l'environnement

Rafael Pimentel, Louise Crochemore, Jafet C M Andersson, Berit Arheimer. Assessing robustness in global hydrological predictions by comparing modelling and Earth observations. Hydrological Sciences Journal, 2023, 68 (16), pp.2357-2372. ⟨10.1080/02626667.2023.2267544⟩. ⟨hal-04521937⟩