Performance assessment of six bias correction methods using observed and RCM data at upper Awash basin, Oromia, Ethiopia

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 = 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 was considered as the least performing 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 = 0.92, R2 = 0.88, MAE = 1.58 mm/°C and PBIAS = 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. HIGHLIGHTS This paper clearly identifies that bias correction methods' performances are watershed-dependent.; Bias correction methods should be separately applied to each watershed.; Also SWAT Model clearly identifies that bias correction methods have not been adjusted for all uncertainty related to the models.; Bias of RCM should be removed with caution.; Power Transformation and Variance scaling methods performed best.;

Keywords

• akaki river
• bias correction methods
• climate change
• performance assessment