Abstract
Abstract. In climate change impact research, the assessment of future river runoff as well as the catchment-scale water balance is impeded by different sources of modeling uncertainty. Some research has already been done in order to quantify the uncertainty of climate projections originating from the climate models and the downscaling techniques, as well as from the internal variability evaluated from climate model member ensembles. Yet, the use of hydrological models adds another layer of uncertainty. Within the QBic3 project (Québec–Bavarian International Collaboration on Climate Change), the relative contributions to the overall uncertainty from the whole model chain (from global climate models to water management models) are investigated using an ensemble of multiple climate and hydrological models. Although there are many options to downscale global climate projections to the regional scale, recent impact studies tend to use regional climate models (RCMs). One reason for that is that the physical coherence between atmospheric and land-surface variables is preserved. The coherence between temperature and precipitation is of particular interest in hydrology. However, the regional climate model outputs often are biased compared to the observed climatology of a given region. Therefore, biases in those outputs are often corrected to facilitate the reproduction of historic runoff conditions when used in hydrological models, even if those corrections alter the relationship between temperature and precipitation. So, as bias correction may affect the consistency between RCM output variables, the use of correction techniques and even the use of (biased) climate model data itself is sometimes disputed among scientists. For these reasons, the effect of bias correction on simulated runoff regimes and the relative change in selected runoff indicators is explored. If it affects the conclusion of climate change analysis in hydrology, we should consider it as a source of uncertainty. If not, the application of bias correction methods is either unnecessary to obtain the change signal in hydro-climatic projections, or safe to use for the production of present and future river runoff scenarios as it does not alter the change signal. The results of the present paper highlight the analysis of daily runoff simulated with four different hydrological models in two natural-flow catchments, driven by different regional climate models for a reference and a future period. As expected, bias correction of climate model outputs is important for the reproduction of the runoff regime of the past, regardless of the hydrological model used. Then again, its impact on the relative change of flow indicators between reference and future periods is weak for most indicators, with the exception of the timing of the spring flood peak. Still, our results indicate that the impact of bias correction on runoff indicators increases with bias in the climate simulations.
Highlights
In the recent past, the availability of regional climate model (RCM) simulations, especially over Europe and North America, has considerably increased, while the understandingPublished by Copernicus Publications on behalf of the European Geosciences Union.M
We investigate the impact of bias correction of precipitation and near-surface air temperature on the simulations from four different hydrological models in two natural flow catchments in southern Germany and southern Quebec when driven by multiple global climate models (GCMs)–RCM data sets for both a reference (1971–2000) and a future period (2041– 2070)
To compare the effect of bias correction with the uncertainty range introduced by climate and hydrological models and the natural variability of climate, two ensembles per catchment are constructed from the models presented before: 1. At Saumon four HyMs are combined with either the direct (BC0) or bias corrected (BC1) meteorological data sets of five members of CRCM driven by CGCM for 20 members per ensemble. (This ensemble allows the estimation of the natural variability of climate over southern Quebec.)
Summary
The availability of regional climate model (RCM) simulations, especially over Europe and North America, has considerably increased, while the understanding. Bias corrections of RCM outputs typically make use of one of two general approaches: extracting deltas (differences between a future and a reference period) to be applied on observed meteorological data in order to construct future time series, or deriving scaling parameters to adjust both past and future RCM outputs to more closely fit observed climatic conditions (Teutschbein and Seibert, 2010). The effect of bias correction on the projected change signal and its contribution to the overall uncertainty, in relation to the actual biases of the regional climate simulations, is explored This evaluates the relevance of applying time consuming bias correction methods in the scope of hydrological climate change impact assessment
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