Evaluation of four bias correction methods and random forest model for climate change projection in the Mara River Basin, East Africa. Issue 4 (2nd March 2022)

• Record Type: Journal Article
• Authors: das, Priyanko; Zhang, Zhenke; Ren, Hang
• Is Part Of: Journal of water and climate change. Volume 13:Issue 4(2022)
• Page Start: 1900
• Page End: 1919
• Publication Date: 2022-03-02
• Subjects: BIAS correction -- climate change -- CORDEX Africa -- RCMs -- RF; Water -- Periodicals; Hydrology -- Periodicals; Climatic changes -- Periodicals; Climatic changes; Hydrology; Water; Electronic journals; Periodicals; 333.9116
• DOI: 10.2166/wcc.2022.299
• Languages: English
• ISSNs: 2040-2244

Abstract

Abstract: This study evaluates the performance of four bias correction methods based on CORDEX (coordinated regional climate downscaling experiment) domain six regional climate models (RCMs) at the Mara River Basin. A suitable bias correction method was considered to develop the future climate scenario. The performance of bias correction methods was evaluated by various statistical metrics based on the historical period and revealed that the distribution mapping (DM) techniques have strong performance under the different climatic conditions. The effectiveness of the DM method is found to be better at capturing the coefficient of variation and standard deviation of observed rainfall and temperature. Therefore, this study considers the future climate (2026–2095) from bias-corrected RCMs output using DM techniques. The results from bias-adjusted RCMs show an increase of rainfall (+118.3%) and temperature (+2.91) in the future climate under Representative Concentration Pathways (RCPs) 2.6, 4.5 and 8.5. In addition, this study tested the random forest (RF) method to determine the capacity of each bias-corrected RCMs for reproducing the future rainfall and temperature under the RCP 4.5 and RCP 8.5 scenario. The results demonstrate that the RF can reproduce the climate variable with its average correlation (R2) of 0.93 for rainfall and 0.95 for temperature. HIGHLIGHTS: The performance of four bias correction methods and climate simulation of six CORDEX Africa regional climate models (RCMs) was evaluated. The statistical matrix showed that the distribution mapping (DM) method results were better when compared to the other methods. The random forest (RF) model was performed for reproducing the climate scenario from the RCMs. It was found that the ensemble of all RCMs showed better results for the future climate scenario. Graphical Abstract