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Optimization models have been developed to maximize annual energy generation from the Doma dam, subject to the constraint of releases for irrigation, ecological purposes, the water supply, the maximum yield from the reservoir and reservoir storage. The model was solved with LINGO software for various mean annual inflow exceedence probabilities. Two scenarios of hydropower retrofitting were considered. Scenario 1, with the reservoir inflows at 50%, 75%, and 90% probabilities of exceedence, gives the total annual hydropower as 0.531 MW, 0.450 MW and 0.291 MW, respectively. The corresponding values for scenario 2 were 0.615 MW, 0.507 MW, and 0.346 MW respectively. The study also considered increasing the reservoir’s live storage to 32.63Mm3 by taking part of the flood storage so that the maximum draft increases to 7 Mm3. With this upper limit of storage and draft with reservoir inflows of 50%, 75% and 90% probabilities of exceedence, the hydropower generated increased to 0.609 MW, 0.540 MW, and 0.347 MW respectively for the scenario 1 arrangement, while those of scenario 2 increased to 0.699 MW, 0.579MW and 0.406 MW respectively. The results indicate that the Doma Dam is suitable for the production of hydroelectric power and that its generation potential is between 0.61 MW and 0.70 MW.

Hydrological models are very useful for predictions in many ungauged basins across the world. There are many hydrological models available for discharge data generation with different complexities and varied input parameter requirements. Studies have shown that models with many input parameters do not necessarily perform better than those with few input parameters. Basin morphometric parameters play significant roles in the conversion of rainfall to runoff and obtaining good estimates of these parameters for use in runoff models is sometime challenging as Inaccurate input into models can propagate errors and make the models to perform poorly. This study employs the method of principal component analysis to reduce the number of morphometric parameters required to run a runoff model without losing any major information. Parameters for five selected study basins in central Nigeria were measured and analysed. The result shows that three morphometric parameters (Fitness Ratio, Ruggedness Number and Watershed Eccentricity) can adequately represent other parameters as an input into a runoff model for the basins. This reduces significantly the time and effort needed to compute all the parameters which in actual fact may not improve the quality or efficiency of the runoff model.

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. Keywords: Peak rainfall, Probability distribution models, Return period, Probability interval and goodness of fit tests