Comparative Analyses of Stationary and Non-Stationary IDF Rainfall Models for Umuahia

This study aimed to develop IDF models and compare rainfall intensity obtained from the stationary and non-stationary IDF models for Umuahia in South-Eastern Nigeria. The research used a long-term rainfall dataset spanning three decades (1992-2022) sourced from the Nigerian Meteorological Agency. The daily rainfall data recorded over 24 hours was downscaled to shorter periods using the Indian Meteorological Department model (IMD). For determining the best distribution fitting for the rainfall data, the Kolmogorov-Smirnov (K-S) test was utilised. The result from the K-S test revealed that Gumbel EVT-1 was the best-fitting distribution for creating the stationary IDF models. The GEVt-I model, which includes a time-dependent location parameter, proved the most effective for non-stationary models. The comparative analysis showed that non-stationary models forecasted greater rainfall intensities for shorter return periods (2-10 years), with variations between 4.93 and 16.16% for the 2-year return period. In contrast, for longer return periods (25-100 years), stationary models yielded higher intensity predictions, with differences ranging from -0.29 -13.21%. These results have important implications for infrastructure design and flood risk management in Umuahia, indicating that existing drainage systems based on stationary assumptions may be undersized by 5-16%, which could elevate the risk of flooding during typical rainfall events.