Optimization of Flood Quantiles at Lokoja River Station
Year: 2025
Type: article
Abstract: The ultimate drive of this research is to choose the best-fitted probability distribution function that optimally approximates the historical time series of the Lokoja hydrologic station in the Niger/Benue River Basin in Nigeria. The National Inland Waterways Authority (NIWA) in Lokoja provided the flood statistics. The principles of flood frequency analysis (FFA) was applied in the prediction using the annual maximum series (AMS). The observed data were fitted into eight (8) probability distribution models, involving Normal (N2), Gumbel (EV1), two-parameter log-Normal (LN2), Gamma, Pearson type III (P3), log-Pearson type III (LP3), Exponential and the Generalized Extreme Value (GEV). A MATLAB program was written to simulate and model the FFA for both stations. Hydrologic data screening was carried out, using several statistical tests at a 5-percent level of significance, to ensure that the time series attained stationary, randomness and that the random samples are independently and identically distributed. The Pre-Whitening Analysis procedures were done to eliminate the visible trend and auto-correlation from the time series record. The method of moments (MoM) was used for statistical parameter estimation. The chi-square (χ2), Kolmogorov-Smirnov (K-S), Nash–Sutcliffe Efficiency Coefficient (NSE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2) goodness of fit tests were performed at the 5-percent level of significance on the predicted data to determine the optimal distribution from the 8 frequency distributions. From the results obtained, it was found that the generalized extreme value (GEV) distribution is an optimum approximation of flood characteristics for the Lokoja hydrologic station in the Niger/Benue river basin with K-S, , NSE, RMSE, and tests statistics values of 0.871, 1.204, 0.976, 494.4, and 0.979 respectively.
Author: Egop, S.E.
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6893268532
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