Browsing by Author "Aremu, A. S."

Now showing 1 - 3 of 3
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    Groundwater Suitability for Domestic Use in an Industrial Area of Ilorin, Kwara State, Nigeria
    (Association for the Advancement of Modelling and Simulation Techniques in Enterprises, France, 2017) Ayanshola, A. M.; Mandal, K.; Bilewu, S. O; Salami, A. W.; Aremu, A. S.; Kolade, S. O.
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    Impact of climate change on surface water resources of Ilorin, Nigeria.
    (University of Ilorin, Nigeria,, 2010-07-26) Makanjuola O. R.; Salami A. W.; Ayanshola, A.M.; Aremu, A. S.; Yusuf, K.O.
    This paper presents the impact of climate change on surface water resources of Ilorin. The study involved the collection of data on meteorological and hydrological variables. The hydrometeorological variables were subjected to statistical, trend, and reduction pattern analysis. The statistical analysis was used to determine statistical parameters while Mann-Kendall and regression analyses were used to detect the significance of the trend in each variable. Reduction pattern analysis was used to depict the fluctuation of the variables over time. Based on the analyses, it was discovered that there is tendency for an increase in rainfall while there is tendency for decrease in evaporation. It was also discovered that there will be no significant change in min. temperature, max. temperature, Oyun streamflow, and Asa streamflow.
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    Probabilistic Analysis of Peak Daily Rainfall for Prediction Purposes in Selected Areas of Northern Nigeria
    (Nigerian Journal of Technological Research (NJTR), Federal University of Technology, Minna, 2016-06-05) Salami, A. W.; Aremu, A. S.; Ayanshola, A. M.; Abdulkadir, T. S.; Garba, M. K.
    In this study, probability analysis was performed on peak daily rainfall data in order to predict rainfall interval values and to determine the best fit functions in some parts of Nigeria. The selected towns are Kaduna, Kano, Yola, Jos, Damaturu and Maiduguri. The obtained peak daily rainfall values were subjected to Gumbel, Log-Gumbel, Normal, Log-Normal, Pearson and Log-Pearson probability distributions. Mathematical equation for probability distribution functions were established for each town and used to predict peak rainfall. The predicted values were subjected to goodness of fit tests such as Chi-square, Correlation Coefficient, Coefficient of Determination and Errors of Estimates to determine how best the fits are. The model that satisfies the tests adequately was selected as the best fit model. The study revealed that the peak rainfall at Kaduna, Jos, Kano, Yola and Damaturu are best fitted by log-Gumbel, while log- Pearson distribution is suitable for predicting peak rainfall in Maiduguri. The result also shows that the occurrences of peak daily rainfall depth of 100 mm and above are rare in the selected areas.