No abstract

Over the last decades, water quality at the Mthatha River Catchment (MRC) within the Eastern Cape Province of South Africa has been threatened by various pollutants. The continuous effluent concentration discharges from the Mthatha Prison and the Efata School for the Blind and Deaf have caused ineffable damage to the Mthatha River's water quality. Thus, the time series-measured data between 2012 and 2020 were analysed to determine the trends and enable forecasting of selected water quality parameters using the Thomas–Fiering (T–F) stochastic model. The Kendall's τ test trends show an increase in the coefficient of variation of 0.54 and 0.67 at the Mthatha Prison and Efata School, respectively, for abrupt changes, whereas the mean monthly T–F forecasted model shows a good correlation value range from 0.79 to 0.87 for the various predicted variables. The simulated predicted models and trends could serve as a measure to forecast selected water quality parameters' occurrence and a likelihood period where the river pollutants could be controlled. Water managers and researchers would find usefulness in the employed tools for an effective control planning of the river pollutants.

Mthatha town of Eastern Cape Province, South Africa has been challenged to address the pollutant issues that are coming from rampant densification and effluent concentration discharge from the Mthatha Correctional Services Centre and the Efata School for the Blind and Deaf which have caused ineffable impaired damage to the Mthatha River Catchment (MRC). This paper is aimed at identifying drivers of poor water quality in the catchment and classified the River’s water quality into different cluster groups for proper pollutant source control measures. Water quality parameters data comprising of pH; conductivity; Phosphorus; Ammonia (NH4-N); Feacals; and E-coli covering 95 percent and 105 percent of the upstream and downstream sections of the River were available at ten monitored sites of the river catchment. These datasets covering eight years 2012-2020 were analysed in this study. Factor analysis as a choice of principal component analysis (PCA) and Agglomerative Hierarchical Clustering (AHC) was used to deduce inferences for the pollutants’ subsequent classification. The results classified the catchment into three different clusters of lower pollutant (LP), medium pollutant (MP), and high pollutant (HP) areas, with PC1 accounting for 84.54% of the total variance from the three components classification. Adaptive catchment managers would find usefulness in the employed statistical tools in ensuring real-time measures for river non-point pollutants sources control that could offer additional benefits in maintaining a safe life above and below water in the preservation of their public values benefit. The study recommends the issuance of compliance notices and non-point pollutant source control measures to improve the water quality (WQ) parameters.

Estimation of crop water requirements is of paramount importance towards the management of agricultural water resources, which is a major mitigating strategy against the effects of climate change on food security. South Africa water shortage poses a threat on agricultural efficiency. Since irrigation uses about 60% of the fresh water available, it therefore becomes important to optimise the use of irrigation water in order to maximize crop yield at the farm level in order to avoid wastage. In this study, combined application of an artificial neural network (ANN) and a crop -growth simulation model for the estimation of crop irrigation water requirements and the irrigation scheduling of potatoes at Winterton irrigation scheme, South Africa was investigated. The crop-water demand from planting to harvest date, when to irrigate, the optimum stage in the drying cycle when to apply water and the amount of irrigation water to be applied per time, were estimated in this study. Five feed -forward backward propagation artificial neural network predictive models were developed with varied number of neurons and hidden layers and evaluated. The optimal ANN model, which has 5 inputs, 5 neurons, 1 hidden layer and 1 output was used to predict monthly reference evapotranspiration (ETo) in the Winterton area. The optimal ANN model produced a root-mean-square error (RMSE) of 0.67, Pearson correlation coefficient (r) of 0.97 and coefficient of determination (R 2 ) of 0.94. The validation of the model between the measured and predicted ETo shows a r value of 0.9048. The predicted ETo was one of the input variables into a crop growth simulation model, called CROPWAT. The results indicated that the total crop water requirement was 1259.2 mm/decade and net irrigation water requirement was 1276.9 mm/decade, spread over a 5-day irrigation time during the entire 140 days of cropping season for potatoes. A combination of the artificial neural networks and the crop growth simulation models have proved to be a robust technique for estimating crop irrigation water requirements in the face of limited or no daily meteorological datasets.

Effective planning, design and management of irrigation water resources requires the estimation of reference evapotranspiration (ET₀). Standard Pen man -Monteith (PM) equation, also called FAO -56 method, was approved by the United Nations for estimating ET₀. However, in many developing countries, such as South Africa, a major limitation to the successful use of this FAO -56 method, is the non-availability or limited data sets of the required input variables. It is imperative to develop alternative methods for estimating ET o . This study models weather and meteorological parameters considered in the estimation of ETₒ by performing multivariate analysis of the correlated variables, using adaptive neuro-fuzzy inference systems (ANFIS). Weather and Meteorological data between 2001 and 2020 for Winterton irrigation scheme (WIS) in South Africa were used in this study. Average monthly data of minimum and maximum temperature (°C), rainfall (mm), relative humidity (%), and wind-speed (m/s) were inputs to the ANFIS model, with ETₒ as output. ANFIS indicated that temperature gradients and wind-speed have the highest impact on ETₒ while rainfall and relative humidity have lower significance on ETₒ. The correlation of temperature and wind speed with ETₒ was presented using input -output surface viewer. This study improves ETₒ estimation.