Articles | Volume 23, issue 11
https://doi.org/10.5194/hess-23-4717-2019
https://doi.org/10.5194/hess-23-4717-2019
Research article
 | 
19 Nov 2019
Research article |  | 19 Nov 2019

Hyper-resolution ensemble-based snow reanalysis in mountain regions using clustering

Joel Fiddes, Kristoffer Aalstad, and Sebastian Westermann

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Cited articles

Aalstad, K., Westermann, S., Schuler, T. V., Boike, J., and Bertino, L.: Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites, The Cryosphere, 12, 247–270, https://doi.org/10.5194/tc-12-247-2018, 2018. a, b, c, d, e, f, g, h, i, j
Baldo, E. and Margulis, S. A.: Assessment of a multiresolution snow reanalysis framework: a multidecadal reanalysis case over the upper Yampa River basin, Colorado, Hydrol. Earth Syst. Sci., 22, 3575–3587, https://doi.org/10.5194/hess-22-3575-2018, 2018. a, b
Bertoldi, G., Rigon, R., and Over, T. M.: Impact of watershed geomorphic characteristics on the energy and water budgets, J. Hydrometeorol., 7, 389–403, https://doi.org/10.1175/JHM500.1, 2006. a
Beven, K. and Binley, A.: The Future of Distributed Models: Model Calibration and Uncertainty Prediction, Hydrol. Process., 6, 279–298, https://doi.org/10.1002/hyp.3360060305, 1992. a
Beven, K. J. and Kirkby, M. J.: A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d'appel variable de l'hydrologie du bassin versant, Hydrol. Sci. B., 24, 43–69, 1979. a
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Short summary
In this paper we address one of the big challenges in snow hydrology, namely the accurate simulation of the seasonal snowpack in ungauged regions. We do this by assimilating satellite observations of snow cover into a modelling framework. Importantly (and a novelty of the paper), we include a clustering approach that permits highly efficient ensemble simulations. Efficiency gains and dependency on purely global datasets, means that this method can be applied over large areas anywhere on Earth.