Rainfall projection using CIMP6 models of extreme area in Johor

• Main Authors: Hamid, H, Rahmat, S N, Kasmin, H, Tukimat, N N
• Format: Conference or Workshop Item
• Language: English
• Published: 2023
• Subjects: T Technology (General)
• Online Access: http://eprints.uthm.edu.my/11436/1/P16842_f029a6cad6f3d22db395b706d642b25a%203.pdf; http://eprints.uthm.edu.my/11436/; http://10.1088/1755-1315/1347/1/012013
• Institution: Universiti Tun Hussein Onn Malaysia

This paper explores the impact of climate change on rainfall patterns, particularly extreme intensity, in Johor, Malaysia. The study focuses on addressing uncertainties in climate change projections by selecting suitable Global Climate Models (GCMs) based on location and topography. Four CMIP6 models (GFDL-ESM4, IPSL-CM6A-LR, MIROC6, and MRI-ESM2- 0) were chosen for analysis. The research employs statistical downscaling, using historical observed data (1988-2020) and GCM output data, with a bias correction through linear scaling. The performance of the GCMs is assessed using various metrics including Root Mean Square Error (RMSE), Coefficient of Determination (R2), Percentage of Bias (Pbias), and Nash- Sutcliffe Efficiency (NSE). The IPSL-CM6A model is identified as the most suitable for rainfall projection in Johor. Under the severe climate scenario (SSP5-8.5), the analysis indicates increasing rainfall intensity in January from 2025 to 2054, notably at the Pusat Pertanian Endau station with a significant 50% increment. However, for the projected period 2055 to 2084, most stations experience a decrease in rainfall from January to June, with the Ladang Sg. Plentong station showing the largest reduction of about 40% in January. Conversely, the latter half of the year shows increased rainfall for all stations. The Mann-Kendall Test method highlights a significant decreasing trend in rainfall across all stations from 2025 to 2084 under the SSP5-8.5 scenario. This suggests that without mitigation efforts, the area will likely experience decreasing rainfall intensity due to the effects of climate change