Abstract:
Selecting a suitable bias correction method is important to provide reliable inputs for evaluation of climate change impact. Their influence
was studied by comparing three discharge outputs from the SWAT model. The result after calibration with original RCM indicates that the
raw RCM are heavily biased, and lead to streamflow simulation with large biases (NSE ¼ 0.1, R2 ¼ 0.53, MAE ¼ 5.91 mm/°C, and PBIAS ¼
0.51). Power transformation and linear scaling methods performed best in correcting the frequency-based indices, while the LS method per formed best in terms of the time series-based indices (NSE ¼ 0.87, R2 ¼ 0.78, MAE ¼ 3.14 mm/°C, PBIAS ¼ 0.24) during calibration.
Meanwhile, daily translation was underestimating simulated streamflow compared with observed and was considered as the least perform ing method. The precipitation correction method has higher visual influence than temperature, and its performance in streamflow
simulations was consistent and considerable. Power transformation and variance scaling showed highly qualified performance compared
to others with indicated time series values (NSE ¼ 0.92, R2 ¼ 0.88, MAE ¼ 1.58 mm/°C and PBIAS ¼ 0.12) during calibration and validation
of streamflow. Hence, PT and VARI were the dominant methods to remove bias from RCM models at Akaki River basin.
