The influence of El Niño Southern Oscillation (ENSO) on rainfall over the mega-dam region of South Africa

Abstract

This dissertation investigates the impact of the El Niño Southern Oscillation (ENSO) on rainfall variability over the eastern escarpment of South Africa, a key water source for surrounding regions. Using observational rainfall data from the Climate Research Unit (CRU) and ERA5 reanalysis, this study analyses annual and seasonal patterns in response to different ENSO phases, namely El Niño, La Niña, and neutral conditions, from 1981 to 2023. The results show that there is significant regional variability, particularly in the eastern escarpment which tends to record higher annual rainfall compared to other parts of the country. Seasonal analysis of CRU data identifies the December-February (DJF) and September-November (SON) seasons as the wettest periods, whereas the June-August (JJA) season experiences the least rainfall. The results show a strong correlation between ENSO and rainfall variability, particularly during DJF, when El Niño events suppress rainfall, and La Niña phases enhance rainfall. Notably, the severe El Niño events of 1982/83 and 2015/16 led to severe drought conditions. ERA5 data is found to closely align with CRU observations, successfully capturing seasonal rainfall patterns and interannual rainfall variability, though ERA5 data does slightly overestimate rainfall. Composite maps highlight spatial differences in rainfall anomalies associated with ENSO events, clearly identifying the eastern escarpment as one of the regions most impacted by ENSO. Rainfall anomalies are particularly pronounced over the eastern escarpment study site, when compared to the country as a whole, especially during DJF, with El Niño phases significantly rainfall, and La Niña phases enhancing it. This study underscores the importance of understanding how ENSO shapes rainfall variability, with implications for water resource management, agricultural planning, and drought mitigation in South Africa. Improved forecasting of ENSO events could help reduce economic losses and societal impacts linked to extreme weather climates.