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Volume 22, issue 9 | Copyright

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

Hydrol. Earth Syst. Sci., 22, 4685-4697, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 07 Sep 2018

Research article | 07 Sep 2018

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, Hydroinformatics Chair Group, P.O. Box 3015, 2601 DA, Delft, the Netherlands
  • 2Deltares, Operational Water Management, P.O. Box 177, 2600 MH, Delft, the Netherlands
  • 3Wageningen University and Research, Hydrology and Quantitative Water Management group, P.O. Box 47, 6700 AA, Wageningen, the Netherlands
  • 4Delft University of Technology, Water Resources Section, P.O. Box 5048, 2600 GA, Delft, the Netherlands

Abstract. Hydrological modelling in the Canadian sub-Arctic is hindered by sparse meteorological and snowpack data. The 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 to 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 study was unable to demonstrate statistically significant correlations (p< 0.05) among the measured snowpack, global hydrological model and GlobSnow-2 SWE compared to snowmelt runoff volume or peak discharge. The GlobSnow-2 product is found to under-predict late-season snowpacks 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, for example the relationship between passive-microwave-measured SWE to snowmelt runoff volume.

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In high-latitude (> 60° N) watersheds, measuring the snowpack and predicting of snowmelt runoff are uncertain due to the lack of data and complex physical processes. 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, though they still have clear limitations in remote boreal forest and tundra environments.
In high-latitude ( 60° N) watersheds, measuring the snowpack and predicting of snowmelt runoff...