Which bias correction methods are suited to represent hydrologic extremes in the Alps?

Abstract

Projections from regional climate models are traditionally bias-corrected with ground observations before being used for hydrological modelling in order to improve the representation of local climate features. The choice of correction method affects hydrological projections, especially in mountain regions where the relationship between precipitation and temperature is a key property of the hydrological cycle as it controls the partitioning between solid and liquid precipitation. While several studies have investigated the sensitivity of hydrological projections (i.e., projections generated by feeding a hydrological model with climate projections) to the choice of bias correction technique, none have focused on single-model initial-condition large ensembles (SMILEs), which are a suitable tool for disentangling the response of hydrological extremes to climate change from their natural variability. In addition, the interaction between hydrological model choice and bias-correction method needs to be investigated in order to obtain more reliable hydrological projections. The objective of this work is to identify the most appropriate statistical techniques to adjust climate SMILEs for studying changes in hydrological extremes in mountainous terrain. For this purpose, we bias-corrected the climate projections of two high-resolution SMILEs (0.11°) under the Representative Concentration Pathway 8.5 for the domain of Switzerland. Specifically, we used a 2 km gridded reanalysis derived from ground observations and three techniques to correct the precipitation and temperature time series: (1) univariate quantile mapping, (2) trend-preserving univariate quantile mapping, and (3) trend-preserving multivariate quantile mapping. Then, we used the bias-corrected time series as inputs to an ensemble of 11 hydrological models to simulate streamflow at the outlet of 93 near-natural Swiss catchments for the period 1955 - 2099. We compared the performance of the three bias-correction techniques with respect to their ability to simulate historical floods and droughts. In addition, we determined the most fit-for-purpose method by examining both the robustness of the corrections (e.g. towards model choice, transferability in time) and the sensitivity of future hydrological projections to the choice of bias correction technique.