Climate Extremes in Port Harcourt: Modeling Rainfall Anomalies using GCMs and RCMs.

Abstract

The study analyzes long-term rainfall trends, identifies seasonal variability and extreme weather events, assesses rainfall anomalies and drought frequency, and compares the accuracy of Global Climate Models (GCMs) and Regional Climate Models (RCMs) in predicting localized conditions for more reliable regional climate adaptation planning. The study utilized monthly rainfall data from NIMET, categorized into meteorological seasons (DJF, MAM, JJA, SON). Descriptive statistics and anomalies were computed to analyze long-term rainfall trends. Droughts and extreme rainfall events were identified, and Global Climate Models (GCMs) and Regional Climate Models (RCMs) were compared to observed data. The study revealed a slight annual decrease in rainfall of approximately 13.57 mm per year, with a moderate p-value (0.0858) and low R squared (0.0983), indicating high variability. Peak rainfall occurred between May and September, while drier months were December and January. Rainfall anomalies showed maximum positive and negative values of 281.02 mm (August 2002) and 243.29 mm (September 2019), respectively. Extreme rainfall events increased slightly by 0.04 per year, with 21 events surpassing the 95th percentile (2002.90 mm). The longest droughts lasted two months, notably from January to February 2016 and 2018. Regional Climate Models (RCMs) closely matched observed data, while Global Climate Models (GCMs) exhibited greater variability. The study identified increased extreme rainfall events and recurring drought periods, emphasizing the need for climate adaptation strategies. The study recommends regional climate models (RCMs) for future planning and implementing Water Sensitive Urban Design (WSUD) to mitigate flooding and extreme weather impacts in coastal cities like Port Harcourt.

Suggested citation

Enyinnaya Okoro Okoro, Opeminola Nicholas Digha (2025). Climate Extremes in Port Harcourt: Modeling Rainfall Anomalies using GCMs and RCMs.Nature and Trends in Science and Technology, 1(1). https://doi.org/10.69798/71328586

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