Due to quick population growth and urbanisation in Kumasi, Ghana, groundwater depletion is accelerating, and land cover changes reduce the rate of natural infiltration. A promising measure to combat rapid aquifer depletion is implementing Managed Aquifer Recharge (MAR), by rooftop rainwater harvesting and pumping this into wells. The objective of this paper is to delineate the (qualitative) impact of precipitation through Managed Aquifer Recharge on the groundwater level, by analyzing groundwater level changes of sites with and without MAR around Kumasi. To achieve this, multiple groundwater level and flow models have been constructed over different time periods with varying temporal resolutions to show the short- and long-term effect of precipitation on the groundwater level on sites with and without MAR. A rapid increase of groundwater level is observed during rain events, followed by a decelerating curve of infiltration towards areas with lower elevations. This dissipation is much faster in areas with high hydraulic conductivity (hours) than with low hydraulic conductivity (weeks). The groundwater level is recharged by MAR less in the dry season than in the wet seasons. MAR has a highly positive influence on the groundwater recharge. It will be most crucial to implement MAR in high elevations, where the overburden has low hydraulic conductivity, as natural recharge is limited here. The lack of soil and hydraulic head data limited the reliability of the models. Therefore, it is recommended to extend the database in these and additional research areas, aiming to differentiate the effect of MAR and the natural infiltration on the hydraulic head level.

Groundwater use has seen a significant increase in the rapidly urbanising city of Kumasi, Ghana, due to its reliability, general good quality, and low-cost development. Conversion of vegetated to urban land along with the challenges of growing groundwater abstraction has put the groundwater system at risk. This study aims to assess how the urbanisation trends in Kumasi affect the availability of groundwater resources. Sustainability is investigated by comparing (multi-)annual groundwater withdrawals to long-term average annual replenishment. Groundwater recharge is estimated in Google Earth Engine using a water-balance approach and remote-sensing datasets to bridge the hydrological data gap. Runoff is determined using the Soil Conservation Service Curve-Number (SCS-CN) method requiring soil and land-cover maps. Land-cover maps are created for 1986, 2013, and 2020 using Sentinel-2 and Landsat 8 surface reflectance products. Evapotranspiration is derived from the MODIS evapotranspiration product and precipitation is retrieved from CHIRPS. Results indicate that groundwater recharge decreased by 80% from 1986 to 2020 (124Mm3 to 28.9Mm3) attributed to a loss of permeable land (63 percentage point decrease) and intensification of urbanisation. Domestic and non-domestic groundwater consumption in 2020 is estimated to be 28.4Mm3 and 2.7Mm3 respectively. For 2020, long-term average groundwater recharge (28.9Mm3) is less than the annual abstraction (31.1Mm3) indicating current urbanisation trends are unsustainable for future groundwater availability. Under a "business as usual" scenario, population growth presents the largest challenge for the future by causing an estimated four-fold increase in groundwater consumption by 2050. Climate change and land-cover changes under the same scenario may reduce groundwater recharge by 10% and 55% respectively. This requires the implementation of policies to properly manage the groundwater resource, such as promoting low-impact development, monitoring groundwater use, and monitoring changes in the groundwater system. This study should be seen as a preliminary investigation into the components affecting groundwater sustainability. Further research is needed to assess the exact state of the groundwater system and its response to future challenges.

https://code.earthengine.google.com/b4ebac49f28c93a633eb7bd4806f409b (groundwater recharge)
https://code.earthengine.google.com/ad1a0a88240e75e3dd727a6a350edb18 (land cover changes)