Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 4.256 IF 4.256
  • IF 5-year value: 4.819 IF 5-year
    4.819
  • CiteScore value: 4.10 CiteScore
    4.10
  • SNIP value: 1.412 SNIP 1.412
  • SJR value: 2.023 SJR 2.023
  • IPP value: 3.97 IPP 3.97
  • h5-index value: 58 h5-index 58
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 99 Scimago H
    index 99
Discussion papers
https://doi.org/10.5194/hess-2019-37
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-37
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 10 May 2019

Research article | 10 May 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

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

Joel Fiddes1,2, Kristoffer Aalstad1, and Sebastian Westermann1 Joel Fiddes et al.
  • 1University of Oslo
  • 2WSL Institute for Snow and Avalanche Research SLF

Abstract. Spatial variability in high-relief landscapes is immense, and grid-based models cannot be run at spatial resolutions to explicitly represent important physical processes. This hampers the assessment of the current and future evolution of important issues such as water availability or mass movement hazards. Here, we present a new processing chain that couples an efficient subgrid method with a downscaling tool and data assimilation method with the purpose to improve numerical simulation of surface processes at multiple spatial and temporal scales in ungauged basins. The novelty of the approach is that while we add 1–2 orders of magnitude of computational cost by ensemble simulations, we save 4–5 orders of magnitude over explicitly simulating a high-resolution grid. This approach makes data assimilation at large spatio-temporal scales feasible. In addition, this approach utilises only freely available global datasets and is therefore able to run globally. We demonstrate marked improvements in estimating snow height and snow water equivalent at various experimental scales using this approach. We propose this as a suitable method for a wide variety of operational and research applications where surface models need to be run at large scales with sparse to non-existent ground observations and with the flexibility to assimilate diverse variables retrieved by EO missions.

Joel Fiddes et al.
Interactive discussion
Status: open (until 05 Jul 2019)
Status: open (until 05 Jul 2019)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement
Joel Fiddes et al.
Joel Fiddes et al.
Viewed  
Total article views: 177 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
121 55 1 177 2 1
  • HTML: 121
  • PDF: 55
  • XML: 1
  • Total: 177
  • BibTeX: 2
  • EndNote: 1
Views and downloads (calculated since 10 May 2019)
Cumulative views and downloads (calculated since 10 May 2019)
Viewed (geographical distribution)  
Total article views: 130 (including HTML, PDF, and XML) Thereof 124 with geography defined and 6 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited  
Saved  
No saved metrics found.
Discussed  
No discussed metrics found.
Latest update: 22 May 2019
Publications Copernicus
Download
Short summary
In our 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 snowcover into a modelling framework. Importantly (and 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.
In our paper we address one of the big challenges in snow hydrology, namely the accurate...
Citation