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

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 use a comprehensive range of evaluation indices for aspects of the distribution such as means and extremes, as well as for the structure of time series. We test whether high-resolution RCMs provide added value, through analysis of two RCM resolutions, 0.44° (50 km) and 0.11° (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. The performance of these is considered for a range of meteorological variables and for the skill in simulating hydrological impacts.In a small catchment with complex topography, the 0.11° RCMs outperform their 0.44° 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, which reduces most of the simulation biases. Overall, an increase in RCM resolution does not imply a better simulation of hydrology and bias-correction represents an alternative to ease decision-making.An important driver for increasing RCM resolution is the need to improve the analysis of climate change impacts for decision-making (e.g. Macadam et al., 2016;Qian et al., 2015). For hydrology, the standard analysis of climate change impacts generally involves coupling uncorrected or bias-corrected GCM or RCM precipitation and temperature outputs with hydrological models to simulate river flow scenarios (e.g.

An improved statistical bias correction method that also corrects dry climate models

“…Like other univariate algorithms, it does not improve spatiotemporal statistics, i.e. the distribution of precipitation fields on specific days in the model or the persistency of weather patterns (Pastén-Zapata et al, 2020;Potter et al, 2020;Charles et al, 2020). Multivariate methods have already been introduced but suffer from disadvantages such as very high computational demands or a limited measure of the full multivariate dependence of structure (e.g.…”

Impacts of climate change and rising atmospheric CO2 on future projected reference evapotranspiration in Emilia-Romagna (Italy)

“…has become increasingly popular and widely used because of its efficiency and low computational cost (Fang et al 2015;Sun et al 2019;Pastén-Zapata et al 2020;Torma et al 2020). For this purpose, the full calibration period 1981-2005 is considered.…”

Evaluation and selection of CORDEX-SA datasets and bias correction methods for a hydrological impact study in a humid tropical river basin, Kerala

“…The reliability of the BC methods to represent observed climate variables was evaluated using the indices mentioned in Table 4 (Sillmann et al 2013;Pastén-Zapata et al 2020) for the baseline period. Based on their ability to simulate all indices relative to other BC methods and uncorrected GCM-RCM data, the BC methods and raw GCM-RCM were then ranked for each GCM-RCM pair.…”

“…Regional climate models (RCMs) are well recognised for providing a better understanding of relevant regional/local climate phenomena, their variability and changes. They are widely used to downscale the coarse resolution GCMs and are expected to deliver more precise and reliable climate change forecasts on finer spatial scales for hydrological impact studies (Fowler et al 2007;Mendez et al 2020;Pastén-Zapata et al 2020). Future projections of precipitation from RCMs have been widely used to evaluate the effect of climate change on different systems of water management and hydrological modelling (Mendez et al 2020).…”