On the need for bias correction in regional climate scenarios to assess climate change impacts on river runoff
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.