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Discussion papers | Copyright
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 28 Jun 2018

Research article | 28 Jun 2018

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

Effects of univariate and multivariate bias correction on hydrological impact projections in alpine catchments

Judith Meyer1, Irene Kohn1, Kerstin Stahl1, Kirsti Hakala2, Jan Seibert2, and Alex J. Cannon3 Judith Meyer et al.
  • 1Faculty of Environment and Natural Resources, University of Freiburg, Freiburg, 79098, Germany
  • 2Department of Geography, University of Zurich, Zurich, 8057, Switzerland
  • 3Climate Research Division, Environment and Climate Change Canada, BC V8W 2Y2, Victoria, Canada

Abstract. Alpine catchments show a high sensitivity to climate variation as they include the elevation range of the snow line. Therefore, the correct representation of climate variables and their interdependence is crucial when describing or predicting hydrological processes. When using climate model simulations in hydrological impact studies, forcing meteorological data are usually downscaled and bias corrected, most often by univariate approaches such as quantile mapping of individual variables. However, univariate correction neglects the relationships that exist between climate variables. In this study glacio-hydrological simulations were performed for two partly glacierized alpine catchments using a recently developed multivariate bias correction method to post-process EURO-CORDEX regional climate model outputs between 1976 and 2100. These simulations were compared to those obtained by using the common univariate quantile mapping for bias correction. As both methods correct each climate variable’s distribution in the same way, the marginal distributions of the individual variables show no differences. Yet, regarding the interdependence of precipitation and air temperature, clear differences are notable in the studied catchments. Simultaneous correction based on the multivariate approach lead to more precipitation below air temperatures of 0°C and therefore more simulated snowfall than with the data of the univariate approach. This difference translated to considerable consequences for the hydrological responses of the catchments. The multivariate bias correction forced simulations showed distinctly different results for projected snow cover characteristics, snowmelt-driven streamflow components, and expected glacier disappearance dates in the future. For the historical period the fraction of precipitation above and below 0°C, the simulated snow water equivalents, glacier volumes, and the streamflow regime resulting from the multivariate-corrected data corresponded better with reference data than the results of univariate bias correction. Differences in simulated total streamflow due to the different bias correction approaches may be considered negligible given the generally large spread of the projections, but systematic differences in the seasonally delayed streamflow components from snowmelt in particular will matter from a planning perspective. While this study does not allow concluding definitively that multivariate bias correction approaches are generally preferable, it clearly demonstrates that incorporating or ignoring inter-variable relationships between air temperature and precipitation data can impact the conclusions drawn in hydrological climate change impact studies.

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Short summary
Several multivariate bias correction methods have been developed recently, but only few studies have tested the effect of multivariate bias correction on hydrological impact projections. This study shows that incorporating or ignoring inter-variable relations between air temperature and precipitation can have a notable effect on the projected snowfall fraction. The effect translated to considerable consequences for the glacio-hydrological responses and streamflow components of the catchments.
Several multivariate bias correction methods have been developed recently, but only few studies...