The study investigated the spatial pattern of decadal variations in annual rainfall amounts in the Sokoto-Rima River Basin, Northwestern Nigeria. Rainfall dataset which is available on high-resolution (0.5 x 0.5 degree) grids resolution from the Climatic Research Unit CRU TS 3.21 of the University of East Anglia, Norwich, United AFRREV VOL. 11 (4), S/NO 48, SEPTEMBER, 2017 56Copyright © International Association of African Researchers and Reviewers, 2006-2017: www.afrrevjo.net. Indexed African Journals Online: www.ajol.info Kingdom was used for the period 1943-2012 for Bunza, Dakindari southwest of the study area, Gulma, Augi, northwest of the Sokoto-Rima basin, Goronyo, Galadi, northeast Maje and Dan-Dume stations southeast of the basin. Correlation of CRU TS dataset was performed with measured rainfall data (Yelwa climatic station) from Nigerian Meteorological Agency, using the Pearson Product Moment Correlation statistic at 0.05 significant levels. In general, the study found that periods of downward fluctuations in annual rainfall below mean values corresponded with period of rise in global temperature occasioned by anthropogenic greenhouse gases emission. The study revealed a significant decrease in the annual rainfall from late 1980 onwards.

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In this study, a method for estimating the exponent “n” values of the catchment-area equations of four sub-basins within the poorly gauged Benin-Owena River Basin Development Authority (BORBDA) in Nigeria is presented to enable the estimation of flows at ungauged sites within the basin and the determination of small hydropower (SHP) potential at different locations in each sub-basin and the entire basin. Optimal prediction of streamflow characteristics in poorly gauged basin requires developing a methodology for extrapolation of data from gauged to ungauged sites within the basin. Four sub-catchments of BORBDA, a poorly gauged basin in Nigeria, were investigated using Remote Sensing (RS), Geographic Information System (GIS), statistical techniques, and Natural Resources Conservation Service-Curve Number (NRCS-CN) hydrological model. Discharge values at gauged sites (Qg) were obtained from recorded discharge values collected for 12 months at an established gauging station in each sub-basin. RS and GIS techniques were used to develop classification maps and obtain crucial data like curve number (CN), elevation, Hydrologic Soil Group (HSG), rainfall intensity, slope, area of gauged and ungauged required for evaluating spatial discharge (ungauged) utilizing NRCS-CN model. From the established model for each sub-basin, exponent “n” in the relationship between discharge and catchment area was obtained to be 0.23, 0.41, 0.71, and 0.74. Using the lumped modeling approach, which considers a watershed as a single unit for computation, where watershed parameters and variables were to be averaged produced “n” = 0.52 for BORBDA area, which is within the range of 0.5–0.85 suggested by previous researchers. Obtained BORBDA exponent “n” was validated for use in the entire basin through soil homogeneity test by generating BORBDA soil map which confirms the four sub-basins investigated share similar HSG A, B, and D with BORBDA. The exponent “n” value is useful for predicting flows in ungauged parts of the basin. The exponent “n” value obtained for the basin is helpful in the assessment of discharge and determination of SHP potential at different locations within the poorly gauged BORBDA basin, and the dissemination of the research findings will find practical use and guide to practicing hydrologists in Nigeria and locations around the world with similar challenges of poorly gauged basins particularly Africa and other developing countries.

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A substantial amount of renewable energy (RE)-based electrical power is generated over the last ten years due to global warming issues. Solar photovoltaic (PV) is being incredibly utilized because of its boundless quality. However, the inherent intermittency of PV power production at high penetration level to the grid leads to complications related grid reliability, stability and transportable unit of electric power. A viable approach to addressing this problem is to develop a reliable power forecast model for the short-term horizon related to scheduling and transmission. Based on an existing forecast model built on genetic algorithm (GA)-optimized hidden Markov model (HMM), this paper implements the model validation process using more recent input dataset. Model evaluation is based on the computation of normalized root mean square error (nRMSE). As the validation result, HMM+GA is sufficient to accurately forecast PV Po under clear sky day (CSD) condition. Contrariwise, for cloudy days (CDs) presenting instantaneous changes in solar irradiance (Gs) between some hours of the day, HMM+GA adapted with a correction factor (x); articulated as HMM+GA+x; is adequate to forecast the Po more precisely when the average change in the absolute value of Gs ( ) in the morning ( ) is greater than 128% and/or when in the evening ( ) exceeds 90%. Particularly, the average nRMSE of 2.63% showed that HMM+GA with or without x are suitable techniques for forecasting PV Po on an hourly basis. Therefore, the validation results are in harmony with those of the baseline models.