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Nigeria's Jebba sub-basins are synonymous to frequent flooding, high rate of erosion, depletion of soil nutrients and unsustainable water use. The uncontrolled flooding may be a result of numerous factors related to topography, geology, climate and human activity. The present work was an attempt to describe the application of Geographical Information System (GIS) and Digital Elevation Model (DEM) for the estimation of morphometric characteristics of eight sub-basins in the upstream watershed of Jebba reservoir, Nigeria. Morphometric characteristics such as topographic, areal, relief and network were determined. Soil Conservation Service (SCS) technique was applied to estimate hydrographs. The study revealed that sub-basin number 3 had the lowest time of concentration and maximum depth of runoff while sub-basin number 2 had maximum ratio of circulation of 1.8 and it is tagged as the area that is highly prone to flood. The peak runoff in the sub-basins ranged between 330.10 and 924.86 m3/s (25-year return period) and for 100-year intervals ranged between 502.69 to 1408.40 m3/s. The estimated peak runoffs can be adopted for designing and constructing erosion control structures in the catchment area.

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.

This study utilized Spatial Information Technology (SIT) such as Remote Sensing (RS), a Geographical Information System (GIS), the Global Positioning System (GPS) and a high-resolution Digital Elevation Model (DEM) for a morphometrical analysis of five sub-basins within the Lower Niger River Basin, Nigeria. Morpho-metrical parameters, such as the total relief, relative relief, relief ratio, ruggedness number, texture ratio, elongation ratio, circularity ratio, form factor ratio, drainage density, stream frequency, sinuosity factor and bifurcation ratio, have been computed and analyzed. The study revealed that the contribution of the morphometric parameters to flooding suggest catchment No. 1 has the least concentration time and the highest runoff depth. Catchment No. 4 has the highest circularity ratio (0.35) as the most hazardous site where floods could reach a great volume over a small area.

This paper presents the evaluation of the impact of climate change on water resources and yield capacity of Asa and Kampe reservoirs. Trend analysis of mean temperature, runoff, rainfall and evapotranspiration was carried out using Mann Kendall and Sen’s slope, while runoff was modeled as a function of temperature, rainfall and evapotranspiration using Artificial Neural Networks (ANN). Rainfall and runoff exhibited positive trends at the two dam sites and their upstream while forecasted ten-year runoff displayed increasing positive trend which indicates high reservoir inflow. The reservoir yield capacity estimated with the ANN forecasted runoff was higher by about 38% and 17% compared to that obtained with historical runoff at Asa and Kampe respectively. This is an indication that there is tendency for water resources of the reservoir to increase and thus more water will be available for water supply and irrigation to ensure food security.

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

This paper presents the evaluation of methods of determination of peak runoff for an ungauged catchment. The present study made use of Geographical Information System (GIS) for the morphometric analysis of four sub-catchments within Jere sub-basin in Gurara Basin, Nigeria. Various morphometric parameters were computed and analyzed as sub-basin characteristics are important factors in determining the runoff. Three methods of peak runoff estimation were used to determine peak runoff of the study area for four return periods. The results of the peak runoff estimation based on rational method for the four sub-catchments for 25-year, 50-year, 75-year and 100-year return periods varied between 224m3/s and 1036m3/s, while the results for the SCS method varied between 105.3m3/s and 162.05m3/s and the results from the Cypress Creek method varied between 22.8m3/s and 86.55m3/s. The results obtained from the three methods showed that there are significant differences in the peak runoff results and also there are significant differences in the peak runoff results for the different return periods.

Efficient management of hydropower reservoir can only be realized when there is sufficient understanding of interactions existing between reservoir variables and energy generation. Reservoir inflow, storage, reservoir elevation, turbine release, net generating had, plant use coefficient, tail race level and evaporation losses are the major hydropower reservoir variables affecting the energy generation. Thus, this paper presents the modelling of reservoir variables of two hydropower dams along the River Niger (Kainji and Jebba dams) in Nigeria for energy generation using multilayer perceptron neural network. Total monthly historical data of Kainji and Jebba hydropower reservoirs’ variables and energy generated were collected from Power Holding Company of Nigeria respectively for a period of (1970-2011) and (1984-2011) for the network training. These data were divided into training, testing and holdout data set. The neural network summary yielded a good forecast for Kainji and Jebba hydropower reservoirs with correlation coefficients of 0.89 and 0.77 respectively. These values of the correlation coefficient showed that the networks are reliable for modeling energy generation as a function of reservoir variables for future energy prediction. Key words: Hydropower, reservoir variables, neural network, energy generation, coefficient of correlation

Over more than five decades, the energy sector in Nigeria, particularly the rural energy sector, is characterized by lack of access, low purchasing power and over-dependence on traditional fuels for meeting basic energy needs. In an attempt by the government to solving this challenge, the hydropower scheme came on stream as the forerunner in 1968, 1986 and 1990 at Kainji, Jebba and Shiroro respectively. The objective was to improve access to reliable, secure, affordable, climate friendly and sustainable energy services and to boost investment in energy in Nigeria. However, this solution seems to be characterized with some challenges at the downstream sector of the hydropower dams. The communities in the flood plains experience annual flooding when the authorities of Power Holding Company of Nigeria (PHCN) open the gates of the dams to let off water at the peak of the rains. The floods have caused damages and untold hardships to lives and properties. The occurrence of flood has great effect on communities and farming activities downstream of Jebba and Shiroro dams.

The stream flow at the three hydropower reservoirs in Nigeria were modeled using hydro-meteorological parameters and Artificial Neural Network (ANN). The model revealed positive relationship between the observed and the modeled reservoir inflow with values of correlation coefficient of 0.57, 0.84 and 0.92 for Kainji, Jebba and Shiroro hydropower reservoir respectively. The established model was used to predict 20 years stream-flow for each of the hydropower reservoirs which were found to have similar statistics with the observed values. The predicted reservoir inflow were subjected to trend analysis which revealed an upward trend with percentage increase of 4.58%, 6.34% and 5.42% for Kainji, Jebba and Shiroro hydropower reservoirs respectively. The upward trend is an indication of increase in water availability for hydropower generation at the three stations given other constraints are brought under control. http://dx.doi.org/10.4314/njt.v34i1.4