Lake Volta is the world's largest man-made lake by surface area, and the fourth largest by water volume. Located completely within Ghana, it has a surface area of about 8502 km 2 (3283 square miles). Like many other lakes on the African continent, Lake Volta is a major natural resource for Ghana, storing water for the operation of the hydroelectric dam, water supply for domestic, agricultural and industrial purposes, habitat for diverse aquatic species, an avenue for recreational activities, means of navigation between the north and south parts of the country, and a climate modulator for the tropical region. The lake has experienced variable water level and surface area changes attributable to climate change and excessive water abstractions. Using histogram thresholding techniques, this study produced binary images and vector maps of the lake. Spatial extent mapping of the lake using Landsat TM 1990, ETM + 2000 and ETM + 2007 images indicated the lake experienced both increased and decreased surface area changes during the study period. The lake's surface area varied by about 197 km 2 between 1990 and 2007, with the water level fluctuating between AE7 m. Factors thought to be contributing to these changes include human factors (regulated flows, deforestation, increased water abstractions and pollution) as well as natural phenomenon (climate change, water run-off and subsequent sediment transport).
Water security has been a major challenge in the semi-arid area of West Africa including Northern Ghana, where climate change is projected to increase if appropriate measures are not taken. This study assessed rainfall and temperature projections and its impact on the water resources in the Vea catchment using an ensemble mean of four bias-corrected Regional Climate Models and Statistical Downscaling Model-Decision Centric (SDSM-DC) simulations. The ensemble mean of the bias-corrected climate simulations was used as input to an already calibrated and validated Soil and Water Assessment Tool (SWAT) model, to assess the impact of climate change on actual evapotranspiration (ET), surface runoff and water yield, relative to the baseline (1990–2017) period. The results showed that the mean annual temperature and actual ET would increase by 1.3 °C and 8.3%, respectively, for the period 2020–2049 under the medium CO2 emission (RCP4.5) scenario, indicating a trend towards a dryer climate. The surface runoff and water yield are projected to decrease by 42.7 and 38.7%, respectively. The projected decrease in water yield requires better planning and management of the water resources in the catchment.
The economic implications of extreme climate changes are found to impact sub-Saharan Africa negatively. This study aimed to analyze projected changes in length of rainy season (LRS), and rainfall extreme indices at the Vea catchment, Ghana. The analysis was performed using high-resolution simulated rainfall data from the Weather Research and Forecasting (WRF) model under moderate greenhouse gas emission scenario for the period 2020–2049 relative to the 1981–2010 period. LRS was computed from the difference between rainfall onset and cessation dates, and its trends were assessed using Mann–Kendall test and Sen's slope estimator. Annual rainfall intensity and frequency indices were computed. Results showed an increase in mean LRS from 168 to 177 days, which was at a rate of 1 day/year in the future (2020–2049). The LRS increase would be more significant at northern and south-western parts of the catchment. Rainfall intensity and frequency indices are projected to increase at spatial scale across the catchment. Projected changes in rainfall extremes could increase the frequency and intensity of drought and flood events. Thus, it is necessary to integrate suitable climate change adaptation measures such as rainwater harvesting, flood control measures, and development of early warning systems in the planning process by decision-makers at the catchment.
Accurate and current Land Use and Land Cover (LULC) maps are important for planning purposes and to monitor the alterations to the environment mostly caused by humans’ activities. The increased utilization of land resources due to population growth have led to loss of biodiversity and urban planning issues such as flooding and pollution. This study analysed the LULC changes within the Kpeshie lagoon Basin of the Greater Accra Region of Ghana and made prediction to the year 2030. Random Forest (RF) classifier was employed to classify the LULC within the study area using Landsat image for four different time-steps (1991, 2002, 2013 and 2020). LULC change analysis was performed for consecutive years (1991 – 2002, 2002 – 2013 and 2013 – 2020) and for the entire period (1991 – 2020). Subsequently, a prediction LULC was made to the year 2030 using a combination of artificial neural network (ANN) and cellular automata (CA) simulations. The LULC classification produced 92.68%, 84.35%, 84.41% and 89.93% overall accuracies and kappa statistics of 0.87, 0.87, 0.84 and 0.91 for the time-steps respectively. Over the study period, significant LULC changes were observed, as the Kpeshie Lagoon Basin which was predominantly covered by vegetation (69.33%) in 1991 had transformed into a major built-up area (50.50%) in 2020. The spatial prediction estimated built-up to cover 60.15% in 2030, followed by bare land, 32.39%, vegetation 6.97% and waterbody 0.49%. The study revealed that LULC within the Kpeshie Lagoon Basin has been hugely impacted due to urbanization and non-enforcement of regulations.
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