Evaluation of the performance of Euro-CORDEX RCMs for assessing hydrological climate change impacts in Great Britain: a comparison of different spatial resolutions and quantile mapping bias correction methods

TL;DR

This study evaluates the effectiveness of Euro-CORDEX regional climate models at different resolutions and bias correction methods in simulating hydrological impacts in Great Britain, finding limited added value from higher resolution models.

Contribution

It compares the performance of 50 km and 12.5 km resolution RCMs with bias correction in hydrological impact assessments across diverse UK catchments.

Findings

High-resolution RCMs outperform in complex topography catchments but not in larger ones.

Bias correction improves monthly variability representation but not daily variability.

Higher resolution does not necessarily lead to better hydrological simulations.

Abstract

Regional Climate Models (RCMs) are an essential tool for analysing regional climate change impacts as they provide simulations with more small-scale details and expected smaller errors than global climate models. There has been much effort to increase the spatial resolution and simulation skill of RCMs, yet the extent to which this improves the projection of hydrological change is unclear. Here, we evaluate the skill of five reanalysis-driven Euro-CORDEX RCMs in simulating precipitation and temperature, and as drivers of a hydrological model to simulate river flow on four UK catchments covering different physical, climatic and hydrological characteristics. We test whether high-resolution RCMs provide added value, through analysis of two RCM resolutions, 50 km and 12.5 km, which are also bias-corrected employing the parametric quantile-mapping (QM) method, using the normal distribution for temperature, and the Gamma (GQM) and Double Gamma (DGQM) distributions for precipitation. In a small catchment with complex topography, the 12.5 km RCMs outperform their 50 km version for precipitation and temperature, but when used in combination with the hydrological model, fail to capture the observed river flow distribution. In the other (larger) catchments, only one high-resolution RCM consistently outperforms its low-resolution version, implying that in general there is no added value from using the high-resolution RCMs in those catchments. GQM decreases most of the simulation biases, except for extreme precipitation and high flows, which are further decreased by DGQM. Bias correction does not improve the representation of daily temporal variability, but it does for monthly variability, in particular when applying DGQM. Overall, an increase in RCM resolution does not imply a better simulation of hydrology and bias-correction represents an alternative to ease decision-making.