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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 7
Hydrol. Earth Syst. Sci., 22, 3575-3587, 2018
https://doi.org/10.5194/hess-22-3575-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Hydrol. Earth Syst. Sci., 22, 3575-3587, 2018
https://doi.org/10.5194/hess-22-3575-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 02 Jul 2018

Research article | 02 Jul 2018

Assessment of a multiresolution snow reanalysis framework: a multidecadal reanalysis case over the upper Yampa River basin, Colorado

Elisabeth Baldo and Steven A. Margulis Elisabeth Baldo and Steven A. Margulis
  • Department of Civil and Environmental Engineering, University of California, Los Angeles, California, USA

Abstract. A multiresolution (MR) approach was successfully implemented in the context of a data assimilation (DA) framework to efficiently estimate snow water equivalent (SWE) over a large head water catchment in the Colorado River basin (CRB), while decreasing computational constraints by 60%. A total of 31 years of fractional snow cover area (fSCA) images derived from Landsat TM, ETM+, and OLI sensor measurements were assimilated to generate two SWE reanalysis datasets, a baseline case at a uniform 90m spatial resolution and another using the MR approach. A comparison of the two showed negligible differences in terms of snow accumulation, melt, and timing for the posterior estimates (in terms of both ensemble median and coefficient of variation). The MR approach underestimated the baseline peak SWE by less than 2% and underestimated day of peak and duration of the accumulation season by a day on average. The largest differences were, by construct, limited primarily to areas of low complexity, where shallow snowpacks tend to exist. The MR approach should allow for more computationally efficient implementations of snow data assimilation applications over large-scale mountain ranges, with accuracies similar to those that would be obtained using  ∼ 100m simulations. Such uniform resolution applications are generally infeasible due to the computationally expensive nature of ensemble-based DA frameworks.

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Montane snowpacks are extremely complex to represent and usually require assimilating remote sensing images at very fine spatial resolutions, which is computationally expensive. Adapting the grid size of the terrain to its complexity was shown to cut runtime and storage needs by half while preserving the accuracy of ~ 100 m snow estimates. This novel approach will facilitate the large-scale implementation of high-resolution remote sensing data assimilation over snow-dominated montane ranges.
Montane snowpacks are extremely complex to represent and usually require assimilating remote...
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