Regional climate model (RCMs) simulations are used in hydrological (climate-change) impact assessment studies. However, RCMs exhibit noticeable deviation from observation, and can show large variation in ensemble projections (biases). The objective of this study is twofold, first to assess the robustness of two high-skill bias-correction methods; empirical quantile mapping (QM), and scaled distribution mapping (SDM) in improving the hydrological modelling of the Rhine River. The second is to assess the potential impacts of climate change on low flows at Lobith based on RCP8.5 scenario. The two correction methods are applied to correct the systematic bias from five climate datasets from the Coordinated Downscaling Experiment in Europe (EURO-CORDEX) covering the Rhine domain, using high resolution gridded datasets (1 km2) spanning from 1979 to 2019.
The bias-corrected simulations from the hydrological model provided more accurate discharge estimates than the wet biased simulations, with an average error of less than 100 m³/s at Lobith. The correction methods are also capable of correcting unprecedented temperature and precipitation values, making them useful in climate assessment studies in the Rhine river. However, it appears that the accuracy of the bias correction depends on the parent GCM, performance of the raw RCM and the skill of the hydrological model in estimating discharges at the point of interest. In addition to that, the drizzling effect could not be reduced using these methods.
Noticeable climate change impacts at Lobith are found using the bias-corrected projections. These projections suggest that low flows are going to be more frequent and longer in the coming 38 years. Unprecedented discharges (< 700 m³/s) are projected to occur at least 50 times between 2020 - 2060. This is coupled by a decrease in the long-term mean annual flow by 100 m³/s and a slight shift in the seasonality of low flows (2 weeks shift).
The general hydrograph at Lobith is set to change due to climate change for the period (2020 – 2060), with relatively higher discharges from early June to end of August followed by relatively lower discharges in the last four months of the year. Water levels are projected to decline in average by 20 cm (early June to the end of August) and increase in average by 30 cm (end of August till to end of December). The study recommends the need of combining bias correction, the feedbacks in the climate system (land use changes) and climate adaptation strategies to study these effects further.