Background: There is limited data on the association between diarrhoea among children aged under five years (U5D) and water use, sanitation, hygiene, and socio-economics factors in low-income communities. The study investigated U5D and the associated risk factors in the Zeekoe catchment in Cape Town, South Africa. Methods: A cross-sectional study was conducted in 707 households in six informal settlements (IS) two formal settlements (FS) (March–June 2017). Results: Most IS households used public taps (74.4%) and shared toilets (93.0%), while FS households used piped water on premises (89.6%) and private toilets (98.3%). IS respondents had higher average hand-washing scores than those of FS (0.04 vs. −0.14, p = 0.02). The overall U5D prevalence was 15.3% (range: 8.6%–24.2%) and was higher in FS than in IS (21.2% vs. 13.4%, respectively, p = 0.01). Water storage >12 h was associated with increasing U5D (OR = 1.88, 95% CI 1.00–3.55, p = 0.05). Water treatment (OR = 0.57, 95%CI 0.34–0.97, p = 0.04), good hand-washing practices (OR = 0.59, 95%CI 0.42–0.82, p = 0.002) and Hepatitis A vaccination (OR = 0.51, 95%CI 0.28–0.9, p = 0.02) had significant preventing effects on U5D. Conclusions: The study highlights that good hygiene practice is a key intervention against U5D in informal settlements. The promotion of hand-washing, proper water storage, and hygienic breastfeeding is highly recommended.

The need to develop new and computationally efficient artificial intelligence models that accurately simulate river flows in data-scarce regions, considering not only current but also projected future climate change conditions is vital. In this study, a hybrid artificial neural network (ANN) model that combines HEC-HMS and the feed-forward neural network (FFNN) was developed in the Python programming language and applied to simulate future stream flows in the River Mayanja catchment in Central Uganda. The study results suggest that the performance of the validated hybrid HEC-HMS-ANN model during calibration and validation (NSE and R2 > 0.99) was more superior to the corresponding performance obtained using individual HEC-HMS (NSE and R2 > 0.50), MIKE HYDRO (NSE and R2 > 0.42), and ANN models (NSE and R2 > 0.56). Using the developed hybrid ANN model, future average daily stream flows are projected to increase by up to 17.3% [2.2–39.5%] and 18.5% [0.8–42.7%] considering the SSP2-4.5 and SSP5-8.5 future climate change scenarios. The study demonstrates that well-trained hybrid ANN models could provide more computationally efficient models for the simulation of future stream flow and for undertaking water resource assessments in catchments with limited in situ observed data.

In the period 2015–2017, the City of Cape Town, South Africa, faced the possibility of taps running dry due to a prolonged drought. To mitigate the impacts of water scarcity, many households installed rainwater tanks to harvest water to use for non-potable purposes such as toilet flushing and washing. The installation of the rainwater tanks was mainly arbitrary, in response to perceived impact of water scarcity rather than a systematic needs assessment. This study was thus undertaken to determine the available opportunity to optimise the use of these rainwater tanks using real-time control (RTC) techniques. Many studies have demonstrated the potential of rainwater harvesting (RWH) systems to supplement potable water supply and minimize stormwater flows to downstream drainage networks. RTC technology can be used to enhance the performance of RWH systems in achieving these two objectives, by receiving a rainfall forecast and initiating pre-storm release in real time. In this study, RTC was applied on the RWH system at the New Engineering Building, University of Cape Town (UCT) to enhance water supply and increase rainwater retention period. The performance with RTC was compared with the conventional management of the RWH system. It was determined that RWH with RTC technology was generally superior in simultaneously achieving water supply and rainwater retention benefits compared to the conventional management approach. RTC provides an active operation which optimizes the performance of the system across varying conditions but requires an assiduous management process designed to meet set objectives. It was concluded that the active release mechanism employing RTC exhibited great potential; the system opens up the possibility of delivering a more robust and reliable system due to its ability to provide failure detection and centralised control. The system can readily be adapted to variation of local climatic conditions in the short and long term.

In early 2018, Cape Town, South Africa, experienced severe water shortages during the worst drought in nearly a century (2015–2017), underscoring the need to diversify water resources, including groundwater. This study evaluated infiltration rates and hydraulic properties of three representative stormwater ponds in the Zeekoe Catchment, Cape Town, to assess their feasibility as recharge basins for transferring detained stormwater runoff into the underlying aquifer. Field infiltration data were analysed to estimate hydraulic properties, while laboratory permeability tests and material classification on 36 soil samples provided inputs for numerical modelling using HYDRUS 2-D software. Simulations estimated recharge rates and indicated wetting front movement from pond surfaces to the water table (~5.5 m depth) ranged between 15 and 140 h. The results revealed field hydraulic conductivity values of 0.3 to 19.9 cm/h, with laboratory estimates up to 103% higher due to controlled conditions. Simulated infiltration rates were 67–182% higher than field measurements, attributed to idealised assumptions. Despite these variations, ponds in the central catchment exhibited the highest infiltration rates, indicating suitability for artificial recharge. Explicit recognition of pond-specific infiltration variability significantly contributes to informed urban water security planning, enabling targeted interventions to optimise groundwater recharge initiatives.

The sustainable management of groundwater resources in developing countries is often challenging due to limited measurement and monitoring infrastructure to collect data necessary for decision support. To make a contribution towards addressing these challenges, this study investigated the use of Internet of Things (IoT) technology and low-cost sensors to collect the required groundwater-level data and develop a model to map the recharge potential with stormwater. The study focused on two stormwater ponds located in a highly urbanised area in Cape Town, South Africa. A combination of Geographic Information System and analytic hierarchy process was integrated to generate a groundwater recharge potential zone map of the study area. The IoT-based data were used to develop and calibrate a numerical groundwater model in MODFLOW. The study determined that retrofitted stormwater ponds are potential groundwater augmentation zones and can provide opportunity for stormwater recharge in urban areas. Overall, this study highlights the potential of IoT to collect hydrogeological data with low-cost sensors. Data can be collected at high temporal resolution, and the spatial scale can be increased due to availability of low-cost sensors.

A stormwater detention pond in a low-income residential area in Cape Town, South Africa, was retrofitted to enhance its infiltration capacity and support, among other things, Managed Aquifer Recharge (MAR) in the Cape Flats Aquifer (CFA). Continuous field monitoring was not feasible owing to theft and vandalism risks, leading to the development of a calibrated and validated hydrological model. This model, which integrated the surface and subsurface interactions, evaluated the pond’s performance under six scenarios. The scenarios included: pre- and post-retrofit (Scenarios 1 and 2); potential MAR coupled with the planned abstraction and additional MAR by the City of Cape Town in the CFA (Scenario 3); the impact of an increased infiltration area coupled with a lowered water table (Scenario 4); and climate change impacts on MAR (Scenarios 5 and 6). The study found that retrofitting increased recharge by 118% even with a high water table—i.e., 1.2 m below ground level—(Scenario 2). Scenario 3 indicated groundwater abstraction could increase MAR by up to 290% as the water table is lowered. These findings demonstrate the potential hydrological benefits of retrofitted ponds in enhancing MAR while maintaining their detention functions.