Samy A. Anwar‡, Latifa Zhouri§, Bilel Zerouali|, Yong Jie Wong¶Corresponding author: Bilel Zerouali ( b.zerouali@univ-chlef.dz ) Academic editor: Stoyan Nedkov © Samy A. Anwar, Latifa Zhouri, Bilel Zerouali, Yong Jie Wong. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Citation:
Anwar SA, Zhouri L, Zerouali B, Wong YJ (2024) Reducing uncertainty in future projections of potential evapotranspiration using a regional climate model and observational datasets: A case study of Egypt. Journal of the Bulgarian Geographical Society 51: 151-175. https://doi.org/10.3897/jbgs.e136806 |  |
This study aims to reduce uncertainty in future projections of potential evapotranspiration (PET) across Egypt by utilizing the regional climate model (RegCM4) under two distinct Representative Concentration Pathways (RCP): RCP4.5 and RCP8.5. The RegCM4 was downscaled using the medium-resolution Earth System Model from the Max Planck Institute, achieving a horizontal resolution of 20 km over Egypt. Initially, the spatial distribution of simulated PET was assessed, followed by the orrection of historical PET calculations using long-term gridded data from the Climate Research Unit (CRU) through a linear regression model (LRM) at twelve locations representing diverse climate zones in Egypt. The LRM was then applied to adjust future PET projections covering the period from 2006 to 2100. Significant spatial anomalies in PET were observed, articularly during the periods 2061–2080 and 2081–2100, with more pronounced anomalies under the RCP8.5 scenario compared to RCP4.5. Across all locations, the RegCM4 effectively captured the monthly variability of PET in relation to CRU data. Furthermore, the application of the LRM substantially improved the accuracy of simulated PET, demonstrating the ffectiveness of this approach in enhancing model performance and reducing uncertainty in future projections.