Intercomparison of joint bias correction methods for precipitation and flow from a hydrological perspective

The typical framework of the climate change impact assessment on water resources relies on plausible scenarios obtained from global climate models (GCMs) and hydrological models (HMs). Although regional climate models (RCMs) can better simulate local climate at a high-resolution grid, the direct use of model outputs from RCMs is not recommended as inputs for HMs due to systematic error. Existing studies have focused on the bias correction (BC) of climate model outputs without considering uncertainties/biases in hydrological modeling. In this regard, this study proposed an integrated framework that combines the BC of RCM precipitation and the simulated flow from the rainfall-runoff model, considering the underlying uncertainty in the parameters of the distribution function. The regional climate model, HadRM3, and the conceptual rainfall-runoff model, HYMOD, are employed. Observed daily precipitation, evapotranspiration, and discharge time series over the Thorverton catchment are compiled from the UK Meteorological Office. To examine the effectiveness of the combined strategy, four different BC approaches have been explored to reduce systematic biases in the flow simulated through the HMs using the RCM precipitation as input. Here, BCs of RCM and HM outputs have been applied under the condition that the bias-corrected ensembles should be within the range of the observed climate variability. The four BC models are considered: aathe RCM precipitation and flow are corrected by preserving their natural variabilities (Case-4). From a hydrological perspective, the Case-4 model showed the best performance among the four cases in terms of correcting the bias and the spread of the flow ensemble.

Automated summary

The study proposed an integrated framework combining bias correction of regional climate model precipitation and simulated flow from rainfall-runoff model, considering the uncertainty in distribution function parameters. Four different bias correction approaches were explored to reduce systematic biases in flow simulated by hydrological models using RCM precipitation as input. The Case-4 model, which corrects RCM precipitation and flow by preserving their natural variabilities, showed the best performance in terms of bias correction and spread of flow ensemble from a hydrological perspective.