Using reanalysis‐driven regional climate model outputs for hydrology modelling

Abstract

Bias correction methods are usually applied to climate model outputs before using these outputs for hydrological climate change impact studies. However, the use of a bias correction procedure is debatable, due to the lack of physical basis and the bias nonstationarity of climate model outputs between future and historical periods. The direct use of climate model outputs for impact studies has therefore been recommended in a few studies. This study investigates the possibility of using reanalysis‐driven regional climate model (RCM) outputs directly for hydrological modelling by comparing the performance of bias‐corrected and nonbias‐corrected climate simulations in hydrological simulations over 246 watersheds in the Province of Québec, Canada. When using RCM outputs directly, the hydrological model is specifically calibrated using RCM simulations. Two evaluation metrics (Nash–Sutcliffe efficiency [NSE] and transformed root mean square error [TRMSE]) and three hydrological indicators (mean, high, and low flows) are used as criteria for this comparison. Two reanalysis‐driven RCMs with resolutions of 45 km and 15 km are used to investigate the scale effect of climate model simulations and bias correction approaches on hydrology modelling. The results show that nonbias‐corrected simulations perform better than bias‐corrected simulations for the reproduction of the observed streamflows when using NSE and TRMSE as criteria. The nonbias‐corrected simulations are also better than or comparable with the bias‐corrected simulations in terms of reproducing the three hydrological indicators. These results imply that the raw RCM outputs driven by reanalysis can be used directly for hydrological modelling with a specific calibration of hydrological models using these datasets when gauged observations are scarce or unavailable. The nonbias‐corrected simulations (at a minimum) should be provided to end users, along with the bias‐corrected ones, especially for studying the uncertainty of hydrological climate change impacts. This is especially true when using an RCM with a high resolution, since the scale effect is observed when the RCM resolution increases from a 45‐km to a 15‐km scale.

Bias nonstationarity of global climate model outputs: The role of internal climate variability and climate model sensitivity

“…This study indicates that the bias nonstationarity of climate model outputs plays a significant role in future precipitation projections, which implies that the bias nonstationarity should be specifically considered when bias correcting climate model precipitation for impacts studies. In special cases, when biases of a climate model simulation for a future period are less than half that for a reference period, or when the bias direction (positive or negative) changes between the reference and future periods, a bias correction method would deteriorate the original climate model simulation (Maraun, ; Chen et al ., , ). For example, Chen et al .…”

“…For example, Chen et al . () showed that, depending on different RCM simulations, a bias correction method can deteriorate the original reanalysis driven RCM simulations for 23 to 33% watersheds over a total of 246 watersheds in the Province of Québec, Canada, because of the bias nonstationarity between the calibration and validation periods. Thus, the bias correction uncertainty should be paid particular attention in climate change impact studies.…”

Using raw regional climate model outputs for quantifying climate change impacts on hydrology

“…However, all bias correction methods are based on the assumption that the biases of GCM/RCM outputs are constant over time. This assumption, however, is often questionable (Chen & Brissette, ; Chen et al, ; Chen, Brissette, & Lucas‐Picher, ; Christensen, Boberg, Christensen, & Lucas‐Picher, ; Maraun, ; Maraun, ), especially when applying a bias correction to unconditional climatological distributions and disregarding the fact that the bias magnitude may depend on the weather types (Maraun, ). For example, Christensen et al () pointed out that model biases have the potential to grow alongside global warming conditions.…”

“…It is well known that the observed data also suffer from biases and, for various reasons, such as low gauge density, instrument problems, and station displacement. Previous studies (e.g., Chen & Brissette, ; Essou, Brissette, & Lucas‐Picher, ) have shown that global and regional reanalyses can be used as proxies of gauged precipitation for hydrological modelling in regions with a sparse network coverage. The performance of reanalysis is even better than gauged observations.…”

“…Chen and Brissette () investigated the direct use of reanalysis‐driven RCM outputs for hydrological modelling by comparing bias‐corrected and nonbias‐corrected RCM simulations representing hydrological regimes over 246 watersheds in the Province of Quebec and found that raw simulations can be used directly for climate change impact studies. Bias correction delivered minor advantages when using reanalysis‐driven RCM for hydrological modelling over northern watersheds.…”