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Discussion papers | Copyright
https://doi.org/10.5194/hess-2018-82
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 02 May 2018

Research article | 02 May 2018

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

Global re-analysis datasets to improve hydrological assessment and snow water equivalent estimation in a Sub-Arctic watershed

David R. Casson1,2, Micha Werner1,2, Albrecht Weerts2,3, and Dimitri Solomatine1,4 David R. Casson et al.
  • 1IHE Delft Institute of Water Education, PO Box 3015, 2601 DA, Delft, Netherlands
  • 2Deltares, P.O. Box 177, 2600 MH Delft, Netherlands
  • 3Wageningen University and Research, P.O. box 47, 6700 AA, Wageningen, Netherlands
  • 4Delft University of Technology, P.O. Box 5048, 2600 GA, Delft, Netherlands

Abstract. Hydrological modelling in the Canadian Sub-Arctic is hindered by sparse meteorological and snowpack data. Snow Water Equivalent (SWE) of the winter snowpack is a key predictor and driver of spring flow, but the use of SWE data in hydrological applications is limited due to high uncertainty. Global re-analysis datasets that provide gridded meteorological and SWE data may be well suited to improve hydrological assessment and snowpack simulation. To investigate representation of hydrological processes and SWE for application in hydropower operations, global re-analysis datasets covering 1979–2014 from the European Union FP7 eartH2Observe project are applied to global and local conceptual hydrological models. The recently developed Multi-Source Weighted-Ensemble Precipitation (MSWEP) and the Watch Forcing Data applied to ERA-Interim data (WFDEI) are used to simulate snowpack accumulation, spring snowmelt volume and annual streamflow. The GlobSnow-2 SWE product funded by the European Space Agency with daily coverage from 1979–2014 is evaluated against in-situ SWE measurement over the local watershed. Results demonstrate the successful application of global datasets for streamflow prediction, snowpack accumulation and snowmelt timing in a snowmelt driven Sub-Arctic watershed. The GlobSnow-2 product is found to under-predict late season snowpack over the study area, and shows a premature decline of SWE prior to the true onset of the snowmelt. Of the datasets tested, the MSWEP precipitation results in annual SWE estimates that are better predictors of snowmelt volume and peak discharge than the WFDEI or GlobSnow-2. This study demonstrates the operational and scientific utility of the global re-analysis datasets in the Sub-Arctic, although knowledge gaps remain in global satellite based datasets for snowpack representation.

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In high-latitude (> 60 N) watersheds, the estimation of snowpack and prediction of snowmelt runoff are uncertain due to the lack of measurement data. This provides challenges for hydrological assessment and operational water management. Global re-analysis datasets have great potential to aid in snowpack representation and snowmelt prediction when combined with a distributed hydrological model, however still have clear limitations in remote boreal and tundra environments.
In high-latitude (> 60 N) watersheds, the estimation of snowpack and prediction of snowmelt...
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