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

Abstract

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 10 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 15 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 20 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 25 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. 30Hydrol. Earth Syst. Sci. Discuss., https://doi

Multivariate Bias‐Correction of High‐Resolution Regional Climate Change Simulations for West Africa: Performance and Climate Change Implications

“…Meyer et al. (2019) used MBCn to investigate the bias‐correction effect in hydrological impact studies. They showed that MBCn‐corrected GCM‐RCM data caused more precipitation to fall as snow.…”

Reply on RC2

“…The N-dimension multivariate bias correction (MBCn) by Cannon (2018) was selected in this study to correct biases of hourly precipitation and temperature. MBCn was chosen because it is arguably the most advanced quantile-based multivariate bias correction method available (Meyer et al, 2019;Chen et al, 2018).…”

Impacts of Using State‐of‐the‐Art Multivariate Bias Correction Methods on Hydrological Modeling Over North America

“…The interdependence of key climate variables, such as precipitation ( P ) and temperature ( T ) dependence, may be crucial for modeling hydrological processes in impact studies. For example, the P ‐ T correlation can influence the transition between rainfall and snowfall, and also the snowmelt process (Chen et al, 2018; Meyer et al, 2019). With further development of bias correction methods, recent studies have put more effort into correcting or reconstructing the intervariable correlations of climate model outputs.…”

“…Seo et al (2019) investigated the impacts of biased P ‐ T correlation on hydrological variables over two watersheds and found that the impacts of P ‐ T correlation are more evident on low flow and subsurface hydrological variables while less remarkable to flow variables with high variability. More recently, Meyer et al (2019) compared univariate quantile mapping and MBCn in simulating hydrological variables over two alpine catchments. They found that the snow water equivalents, glacier volumes, and streamflow regime simulated using MBCn‐corrected data are consistently better than those simulated using univariate quantile mapping corrected data.…”