Search

Extreme rainfall and temperatures are climate variables that threaten global water supplies and surface water quality (SWQ). To understand how rainfall and temperature interact with surface water quality and water treatment costs, this study, unlike previous ones, uses time series analysis (TSA) and water quality index (WQI) modelling to fill significant research gaps. The study uses data from the Barekese Water Treatment Headworks, Ghana. Water quality data from 2000 to 2019 for the Barekese Headworks were obtained from the Ghana Water Company Limited. Rainfall data for the catchment was obtained from the Ghana Meteorological Agency. The Mann-Kendall statistical test for trend and the Canadian Council of Ministers of the Environment (CCME) water quality index was applied to data sets. The Mann-Kendall trend test showed no significant change in annual temperatures. An increasing trend for annual rainfall was observed, but this was not statistically significant (Z = 0.21). Sen's slope estimator (Q) showed that rainfall increases at 3.03 mm annually. pH correlated negatively with rainfall (r = - 0.15). Correlations were observed between rainfall and temperature and dissolved oxygen (DO), turbidity, Total Dissolved Solids (TDS), Nitrate (NO3−), Phosphate (PO43−), and Manganese (Mn). Rainfall was observed to increase the cost of liming, coagulation, and disinfection. A 20.26% deterioration in SWQ was observed from 2009 to 2019. The SWQ over the period under study and according to the CCME water quality index was 80% marginal, 10% fair and 10% poor. The findings reveal that the concurrent use of TSA and WQI modelling can help elucidate how rainfall and temperature interact with SWQ and water treatment costs. It further contributes knowledge to attaining the Africa Union Agenda 2063 on climate resilience and the Sustainable Development Goals (SDG) 6 and 6.3 on universal water access and quality improvement.