Extreme Rainfall Modeling through the Lens of Extreme Value Theory: A Case Study of Katsina City, Nigeria

Abstract

Climate change has conveyed about strange new weather patterns, among others is changes in rainfall extremes. To make adequate inferences about extreme rainfall, it's important to invest in meteorological research. This can include the use of climate models to predict extreme rainfall events with greater accuracy. Additionally, information sharing can help ensure that countries are well-prepared for extreme weather events and can collaborate to address the global challenges posed by climate change. Monthly rainfall record of Katsina city was collected from the Nigerian Meteorological Agency (NiMet), Nigeria. In pursuit of this objective, the study employed Extreme Value Theory to model and forecast extreme rainfall of Katsina city, utilizing rainfall data spanning from 1989 to 2019. The research employed the Maximum Likelihood Estimation method to derive the model parameters. For the Generalized Extreme Value Distribution (GEVD), the block maxima approach was applied, whereas the Generalized Pareto Distribution (GPD) was fitted using the Peak Over Threshold method. The analysis revealed that the optimal model within the GEVD framework was the Frechet distribution, whereas the ordinary Pareto distribution emerged as the optimal model when considering values above the threshold. Additionally, the study included predictions for return periods of 2, 20, and 100 years based on the return level estimates, accompanied by the presentation of their respective confidence intervals. The analysis demonstrated that as the return periods lengthened, there was a proportional increase in the return levels. The study's model diagnostics, involving probability, density, quantile, and return level plots, collectively indicated that the provided models were well-suited for the dataset.