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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 22, issue 11 | Copyright

Special issue: Integration of Earth observations and models for global water...

Hydrol. Earth Syst. Sci., 22, 5711-5734, 2018
https://doi.org/10.5194/hess-22-5711-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 05 Nov 2018

Research article | 05 Nov 2018

Assimilation of passive microwave AMSR-2 satellite observations in a snowpack evolution model over northeastern Canada

Fanny Larue1,2,3, Alain Royer1,2, Danielle De Sève3, Alexandre Roy1,2,4,a, and Emmanuel Cosme5 Fanny Larue et al.
  • 1CARTEL, Université de Sherbrooke, Québec, Canada
  • 2Centre d'Études Nordiques, Québec, Canada
  • 3IREQ, Hydro-Québec, Québec, Canada
  • 4Département de Géographie, Université de Montréal, Québec, Canada
  • 5Institut des Géosciences de l'Environnement, IGE, UGA-CNRS, Grenoble, France
  • anow at: Université du Québec à Trois-Rivière, Québec, Canada

Abstract. Over northeastern Canada, the amount of water stored in a snowpack, estimated by its snow water equivalent (SWE) amount, is a key variable for hydrological applications. The limited number of weather stations driving snowpack models over large and remote northern areas generates great uncertainty in SWE evolution. A data assimilation (DA) scheme was developed to improve SWE estimates by updating meteorological forcing data and snowpack states with passive microwave (PMW) satellite observations and without using any surface-based data. In this DA experiment, a particle filter with a Sequential Importance Resampling algorithm (SIR) was applied and an inflation technique of the observation error matrix was developed to avoid ensemble degeneracy. Advanced Microwave Scanning Radiometer 2 (AMSR-2) brightness temperature (TB) observations were assimilated into a chain of models composed of the Crocus multilayer snowpack model and radiative transfer models. The microwave snow emission model (Dense Media Radiative Transfer – Multi-Layer model, DMRT-ML), the vegetation transmissivity model (ω-τopt), and atmospheric and soil radiative transfer models were calibrated to simulate the contributions from the snowpack, the vegetation, and the soil, respectively, at the top of the atmosphere. DA experiments were performed for 12 stations where daily continuous SWE measurements were acquired over 4 winters (2012–2016). Best SWE estimates are obtained with the assimilation of the TBs at 11, 19, and 37GHz in vertical polarizations. The overall SWE bias is reduced by 68% compared to the original SWE simulations, from 23.7kgm−2 without assimilation to 7.5kgm−2 with the assimilation of the three frequencies. The overall SWE relative percentage of error (RPE) is 14.1% (19% without assimilation) for sites with a fraction of forest cover below 75%, which is in the range of accuracy needed for hydrological applications. This research opens the way for global applications to improve SWE estimates over large and remote areas, even when vegetation contributions are up to 50% of the PMW signal.

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A data assimilation scheme was developed to improve snow water equivalent (SWE) simulations by updating meteorological forcings and snowpack states using passive microwave satellite observations. A chain of models was first calibrated to simulate satellite observations over northeastern Canada. The assimilation was then validated over 12 stations where daily SWE measurements were acquired during 4 winters (2012–2016). The overall SWE bias is reduced by 68 % compared to original SWE simulations.
A data assimilation scheme was developed to improve snow water equivalent (SWE) simulations by...
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