Assessment of the water balance of the Barekese reservoir in Kumasi, Ghana

The Barekese Reservoir constructed across the Offin River provides 80% of the total public pipe borne water supplied to the Kumasi metropolis and its environs. The reservoir was designed to produce both potable water and hydropower, however, the hydropower component has not been implemented since its construction in 1971.There is also reported land cover degradation in the catchment area which has the propensity to alter the hydrologic cycle and hence runoff into the reservoir. A 10 year water balance has been assessed for the Barekese Reservoir using an integrated Remote Sensing and GIS approach for estimation of surface runoff based on Soil Conservation Service Curve Number (SCS-CN). The SCS-CN model was calibrated against observed discharges recorded at Offinso located 10.3km upstream from Barekese and the result of the calibration used to simulate runoff into the reservoir. The SCS-CN model produced an R2 value of 0.84 and an efficiency of 82.68%. Monthly observed reservoir levels were used for the calibration and validation of the water balance model. The water balance model produced an R2 value of 0.84 and an efficiency of 81.9%. The monthly water budget revealed that total catchment runoff and direct precipitation respectively constituted 94.32% and 5.68% of the inflows while spilled water, water withdrawal and evaporation respectively amounted to 72.19%, 20.85% and 6.96% of the outflows. This result reveals that the reservoir is being underutilized. The current average production of treated water is 109,000m3day but the reservoir can safely yield the design capacity of 220,000m3day and an additional average hydropower of 368.6kW in six months during the rainy season provided the economic analysis for the hydropower generation is found to be justifiable.Keywords: Water balance, Barekese Reservoir, SCS-CN model, Offinso, Hydropowe

Machine learning based groundwater prediction in a data-scarce basin of Ghana

Groundwater (GW) is a key source of drinking water and irrigation to combat growing food insecurity and for improved water access in rural sub-Saharan Africa. However, there are limited studies due to data scarcity in the region. New modeling techniques such as Machine learning (ML) are found robust and promising tools to assess GW recharge with less expensive data. The study utilized ML technique in GW recharge prediction for selected locations to assess sustainability of GW resources in Ghana. Two artificial neural networks (ANN) models namely Feedforward Neural Network with Multilayer Perceptron (FNN-MLP) and Extreme Learning Machine (FNN-ELM) were used for the prediction of GW using 58 years (1960–2018) of GW data. Model evaluation between FNN-MLP and FNN-ELM showed that the former approach was better in predicting GW with R2 ranging from 0.97 to 0.99 while the latter has an R2 between 0.42 to 0.68. The overall performance of both models was acceptable and suggests that ANN is a useful forecasting tool for GW assessment. The outcomes from this study will add value to the current methods of GW assessment and development, which is one of the pillars of the sustainable development goals (SDG 6)

Projections and impact assessment of the local climate change conditions of the Black Volta Basin of Ghana based on the Statistical DownScaling Model

The uncertainties and biases associated with Global Climate Models (GCMs) ascend from global to regional and local scales which delimits the applicability and suitability of GCMs in site-specific impact assessment research. The study downscaled two GCMs to evaluate effects of climate change (CC) in the Black Volta Basin (BVB) using Statistical DownScaling Model (SDSM) and 40-year ground station data. The study employed Taylor diagrams, dimensionless, dimensioned, and goodness of fit statistics to evaluate model performance. SDSM produced good performance in downscaling daily precipitation, maximum and minimum temperature in the basin. Future projections of precipitation by HadCM3 and CanESM2 indicated decreasing trend as revealed by the delta statistics and ITA plots. Both models projected near- to far-future increases in temperature and decreases in precipitation by 2.05-23.89, 5.41–46.35, and 5.84–35.33% in the near, mid, and far future respectively. Therefore, BVB is expected to become hotter and drier by 2100. As such, climate actions to combat detrimental effects on the BVB must be revamped since the basin hosts one of the largest hydropower dams in Ghana. The study is expected to support the integration of CC mitigation into local, national, and international policies, and support knowledge and capacity building to meet CC challenges