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.

The Response of Sensitive LULC Changes to Runoff and Sediment Yield in a Semihumid Urban Watershed of the Upper Awash Subbasin Using the SWAT+ Model, Oromia, Ethiopia

“…Te Akaki watershed is dominated by six soil types, which are expected to cause soil erosion and runof. As shown in Table 2, Pellic vertisols dominate this watershed, covering 55% of the area [22].…”

Impact of Climate and Land Use/Cover Changes on Streamflow in Yadot Watershed, Genale Dawa Basin, Ethiopia

“…This research used power transformation for rainfall and variance scaling methods for temperature as the preferable bias correction methods. These methods have been widely applied for bias adjustment (Fang et al, 2015; Tumsa, 2022). In regional climate models, the power transformation technique was applied to address geographic distributional biases in rainfall outputs.…”

Title: Assessing Impact of Climate Variability and Change on Maize Yield in Gamo Zone, Southern Ethiopia: A Modelling Perspective.

“…These methods were used because of their best performance than others by applying all bias-correction methods in the tool and comparing their result in correcting the biasness. The corrected GCMs climate data has been used for APSIM crop model input for future maize crop system simulation and impact (Dubey et al, 2021;Mendez et al, 2020;Nyunt et al, 2013;Tumsa, 2022) and others.…”