Floods in Ghana have become a perennial challenge in the major cities and communities located in low-lying areas. Therefore, cities and communities located in these areas have been classified as potential or natural flood-prone zones. In this study, the Digital Elevation Model (DEM) of the Accra Metropolis was used to assess the drainage density and elevation patterns of the area. The annual population estimation data and flood damages were assessed to understand the damages and population trend. This research focused primarily on the elevation patterns, slope patterns, and drainage density of the Accra Metropolis. Very high drainage density values, which range between 149 and 1117 m/m², showed very high runoff converging areas. High drainage density was also found to be in the range of 1117–1702 m/m², which defined the area as a high runoff converging point. The medium and low converging points of runoff were also found to be ranging between 1702–2563 m/m² and 2563–4070 m/m², respectively. About 32% of the study area is covered by natural flood-prone zones, whereas flood-prone zones also covered 33% and frequent flood zones represent 25%. Areas in the Accra Metropolis that fall in the Accraian and Togo series rock types experience high floods. However, the lineament networks (geological structures) that dominate the Dahomeyan series imply that the geological structures in the Dahomeyan series also channel the runoffs into the low-lying areas, thereby contributing to the perennial flooding in the Accra Metropolis.

This study assessed the change in the quality of drinking water from the intake to point-of-use and the health risk to consumers of the water sources in a farming community in Ghana. Water samples were collected from five intake sources and point-of-use sources from 31 households. A quantitative microbial risk assessment (QMRA) was used to estimate the health risk. All the physicochemical parameters were found to be within the WHO guidelines except pH and water hardness. Again, none of the physicochemical parameters showed a significant difference between intake and point-of-use water sources. There were, however, significant differences in the mean total and fecal coliforms between the intake source and point-of-use source (3.63 vs 4.57 log CFU/100 mL and 1.38 vs 2.83 log CFU/100 mL, respectively). The results of the QMRA showed that the disease burden arising from exposure to river and spring water sources were above the WHO reference tolerable risk level of 1 × 10−6 Disability-Adjusted Life Years per person per year. The results of this study are expected to influence relevant stakeholders toward initiating plans that could mitigate the spread of waterborne diseases and avert the related economic implications in the study community.

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.