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