Accounting for Epistemic Uncertainty in Return Level Estimates: A Generalized Extreme Value Framework for Emergency Preparedness in Ibaji, Koton Karfe, and Lokoja, Nigeria
Author
Wash Patrick Madudu,Mba Ojong Ndoma-Egba,Chiemezie Maximillian Uzoho,Onyeche Vera Adikwu,Jehu weilum Zekpo,Ezeh Cletus Edeh, Abubakar Bello
Abstract
Reliable estimation of extreme flood return levels is fundamental to disaster risk reduction and resilient infrastructure design in flood-prone riverine communities. This study develops a Generalized Extreme Value (GEV) framework for return period analysis that explicitly accounts for epistemic uncertainty arising from limited observational records, distributional assumptions, and non-stationary climate forcing. Focusing on three contiguous communities along the lower Niger–Benue confluence-Ibaji, Koton Karfe, and Lokoja in Kogi State, Nigeria-the research integrates stationary and non-stationary GEV modeling with L-moment and maximum likelihood estimation to derive design flood quantiles for 2-, 5-, 10-, 50-, 100-, and 500-year return periods. Confidence intervals are constructed via parametric bootstrap and profile likelihood methods to quantify uncertainty in return level estimates. Non-stationary extensions incorporate inter-annual trend, temperature, and soil moisture as covariates on the location and scale parameters to evaluate temporal shifts in flood regime parameters. The framework addresses critical gaps in current Nigerian flood frequency practice by moving beyond point predictions toward probabilistic risk characterization, thereby supporting evidence-based emergency preparedness, land-use zoning, and climate-adaptive flood defense planning in data-scarce developing regions.
Keywords
generalized extreme value distribution, return period analysis, epistemic uncertainty, non-stationary flood frequency, climate covariates, Lower Niger Basin, Kogi State
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