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 indicate that the raw RCM are heavily biased, and lead to streamflow simulation with large biases (NSE = 0.1, R2 = 0.53, MAE = 5.91 mm/°C, and PBIAS = 0.51). Power transformation and linear scaling methods performed best in correcting the frequency-based indices, while the LS method performed best in terms of the time series-based indices (NSE = 0.87, R2 = 0.78, MAE = 3.14 mm/°C, PBIAS = 0.24) during calibration. Meanwhile, daily translation was underestimating simulated streamflow compared with observed and considered as the least performing method. Precipitation correction method has higher visual influence than temperature, and its performance in streamflow simulations was consistent and significantly considerable. Power transformation and variance scaling showed highly qualified performance compared to others with indicated time series value (NSE = 0.92, R2 = 0.88, MAE = 1.58 mm/°C and PBIAS = 0.12) during calibration and validation of streamflow. Hence, PT and VARI methods were the dominant methods which remove biasness from RCM models at Akaki River basin.
Stochastic Vertex Corrections: Linear Scaling Methods for Accurate Quasiparticle Energies
