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Discussion papers
https://doi.org/10.5194/essd-2019-103
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2019-103
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: data description paper 31 Jul 2019

Submitted as: data description paper | 31 Jul 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Earth System Science Data (ESSD).

High-Resolution Meteorological Forcing Data for Hydrological Modelling and Climate Change Impact Analysis in Mackenzie River Basin

Zilefac Elvis Asong1, Mohamed Ezzat Elshamy1, Daniel Princz1, Howard Simon Wheater1, John W. Pomeroy1,2, Alain Pietroniro1,2,3, and Alex Cannon4 Zilefac Elvis Asong et al.
  • 1Global Institute for Water Security, University of Saskatchewan, Innovation Blvd, Saskatoon, SK, Canada S7N 3H5
  • 2Centre for Hydrology, University of Saskatchewan, 121 Research Drive, Saskatoon, SK, Canada S7N 1K2
  • 3Environment and Climate Change Canada, 11 Innovation Blvd, Saskatoon, SK, Canada S7N 3H5
  • 4Climate Research Division, Environment and Climate Change Canada, BC V8W 2Y2, Victoria, Canada

Abstract. Cold regions hydrology is very sensitive to the impacts of climate warming. Impacts of warming over recent decades in western Canada include glacier retreat, permafrost thaw and changing patterns of precipitation, with increased proportion of winter precipitation falling as rainfall and shorter durations of snowcover, and consequent changes in flow regimes. Future warming is expected to continue these trends. Physically realistic and sophisticated hydrological models driven by reliable climate forcing can provide the capability to assess hydrological responses to climate change. However, the provision of reliable forcing data remains problematic. Hydrological processes in cold regions involve complex phase changes and so are very sensitive to small biases in the driving meteorology, particularly in temperature and precipitation, including precipitation phase. Cold regions often have sparse surface observations, particularly at high elevations that generate a large amount of runoff. This paper aims to provide an improved set of forcing data for large scale hydrological models for climate change impact assessment. The best available gridded data in Canada is from the high resolution forecasts of the Global Environmental Multiscale (GEM) atmospheric model and outputs of the Canadian Precipitation Analysis (CaPA) but these datasets have a short historical record. The EU WATCH ERA-Interim reanalysis (WFDEI) has a longer historical record, but has often been found to be biased relative to observations over Canada. The aim of this study, therefore, is to blend the strengths of both datasets (GEM-CaPA and WFDEI) to produce a less-biased long record product (WFDEI-GEM-CaPA) for hydrological modelling and climate change impacts assessment over the Mackenzie River Basin. First, a multivariate generalization of the quantile mapping technique was implemented to bias-correct WFDEI against GEM-CaPA at 3 h × 0.125° resolution during the 2005–2016 overlap period, followed by a hindcast of WFDEI-GEM-CaPA from 1979. The derived WFDEI-GEM-CaPA data are validated against station observations as a preliminary step to assess its added value. This product is then used to bias-correct climate projections from the Canadian Centre for Climate Modelling and Analysis Canadian Regional Climate Model (CanRCM4) between 1950–2100 under RCP8.5, and an analysis of the datasets shows the biases in the original WFDEI product have been removed and the climate change signals in CanRCM4 are preserved. The resulting bias-corrected datasets are a consistent set of historical and climate projection data suitable for large-scale modelling and future climate scenario analysis. The final product (WFDEI-GEM-CaPA, 1979–2016) is freely available at the Federated Research Data Repository at http://dx.doi.org/10.20383/101.0111 (Asong et al., 2018) while the original and corrected CanRCM4 data are available at https://doi.org/10.20383/101.0162 (Asong et al., 2019).

Zilefac Elvis Asong et al.
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Status: open (until 25 Sep 2019)
Status: open (until 25 Sep 2019)
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Zilefac Elvis Asong et al.
Data sets

A Bias-Corrected 3-hourly 0.125 Gridded Meteorological Forcing Data Set (1979 – 2016) for Land Surface Modeling in North America Z. E. Asong, H. Wheater, J. Pomeroy, A. Pietroniro, and M. Elshamy https://doi.org/10.20383/101.0111

High-Resolution Meteorological Forcing Data for Hydrological Modelling and Climate Change Impact Analysis in Mackenzie River Basin Z. E. Asong, M. E. Elshamy, D. Princz, H. S. Wheater, J. W. Pomeroy, A. Pietroniro, and A. Cannon https://doi.org/10.20383/101.0162

Zilefac Elvis Asong et al.
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