Water quality indices (WQIs) are customarily associated with heavy data input demand, making them more rigorous and bulky. Such burdensome attributes are too taxing, time-consuming, and command a significant amount of resources to implement, which discourages their application and directly influences water resource monitoring. It is then imperative to focus on developing compatible, simpler, and less-demanding WQI tools, but with equally matching computational ability. Surrogate models are the best fitting, conforming to the prescribed features and scope. Therefore, this study attempts to provide a surrogate WQI as an alternative water quality monitoring tool that requires fewer inputs, minimal effort, and marginal resources to function. Accordingly, multivariate statistical techniques which include principal component analysis (PCA), hierarchical clustering analysis (HCA) and multiple linear regression (MLR) are applied primarily to determine four proxy variables and establish relevant model coefficients. As a result, chlorophyll-a, electrical conductivity, pondus Hydrogenium and turbidity are the final four proxy variables retained. A vital feature of the proposed surrogate index is that the input parameters qualify for inclusion into remote monitoring systems; henceforth, the model can be applied in remote monitoring programs. Reflecting on the model validation results, the proposed surrogate WQI is considered scientifically stable, with a minimum magnitude of divergence from the ideal water quality values. More importantly, the model displayed a predictive pattern identical to the ideal graph, matching on both index scores and classification values. The established surrogate model is an important milestone with the potential of promoting water resource monitoring and assisting in capturing of spatial and temporal changes in South African river catchments. This paper aims at outlining the methods used in developing the surrogate water quality index and document the results achieved.

Water quality indices (WQIs) are necessary for simplifying the reporting of complex and technical water quality information. They are scientifically based communication models that are capable of converting multi-variable water quality data to produce a single unit less digit score that describes overall water quality. This in turn is important for providing a structured platform to evaluate and compare water quality of various water resources [1, 2]. Water quality indices are not aimed at replacing detailed water quality analysis, rather they are tools aimed at providing a quick guide to assist water quality experts, policymakers and the public by communicating water quality data in a more consistent and ongoing manner [2, 3]. Following the studies by Poonam, et al. [2], Lumb et al. [4], Sutadian et al. [5], and Paun et al. [6], it has been noted that most WQIs are designed for a particular region and are source-specific, thus creating a gap and ample scope to develop a universally acceptable WQI. However, it is extremely difficult to develop a water quality model that is globally acceptable, hence

The assessment of water quality has turned to be an ultimate goal for most water resource and environmental stakeholders, with ever-increasing global consideration. Against this backdrop, various tools and water quality guidelines have been adopted worldwide to govern water quality deterioration and institute the sustainable use of water resources. Water quality impairment is mainly associated with a sudden increase in population and related proceedings, which include urbanization, industrialization and agricultural production, among others. Such socio-economic activities accelerate water contamination and cause pollution stress to the aquatic environment. Scientifically based water quality index (WQI) models are then essentially important to measure the degree of contamination and advise whether specific water resources require restoration and to what extent. Such comprehensive evaluations reflect the integrated impact of adverse parameter concentrations and assist in the prioritization of remedial actions. WQI is a simple, yet intelligible and systematically structured, indexing scale beneficial for communicating water quality data to non-technical individuals, policymakers and, more importantly, water scientists. The index number is normally presented as a relative scale ranging from zero (worst quality) to one hundred (best quality). WQIs simplify and streamline what would otherwise be impractical assignments, thus justifying the efforts of developing water quality indices (WQIs). Generally, WQIs are not designed for broad applications; they are customarily developed for specific watersheds and/or regions, unless different basins share similar attributes and test a comparable range of water quality parameters. Their design and formation are governed by their intended use together with the degree of accuracy required, and such technicalities ultimately define the application boundaries of WQIs. This is perhaps the most demanding scientific need—that is, to establish a universal water quality index (UWQI) that can function in most, if not all, the catchments in South Africa. In cognizance of such a need, this study attempts to provide an index that is not limited to certain application boundaries, with a contribution that is significant not only to the authors, but also to the nation at large. The proposed WQI is based on the weighted arithmetic sum method, with parameters, weight coefficients and sub-index rating curves established through expert opinion in the form of the participation-based Rand Corporation’s Delphi Technique and extracts from the literature. UWQI functions with thirteen explanatory variables, which are NH3, Ca, Cl, Chl-a, EC, F, CaCO3, Mg, Mn, NO3, pH, SO4 and turbidity (NTU). Based on the model validation analysis, UWQI is considered robust and technically stable, with negligible variation from the ideal values. Moreover, the prediction pattern corresponds to the ideal graph with comparable index scores and identical classification grades, which signifies the readiness of the model to appraise water quality status across South African watersheds. The research article intends to substantiate the methods used and document the results achieved.

Although access to clean and potable water is a requirement for healthy living, the constant release of non-point source pollutants into water bodies has resulted in water quality degradation. In a bid to curb this situation, water quality models are used as a tool. This study reviews 10 non-point source models, namely: AGNPS, ANSWERS, CREAMS, SWRRB, HSPF, SWAT, EPD RIV1, DMA, CMBA, and MA, giving consideration to their nature, components, area of use, strengths, and limitations. Our review indicated that hydrological processes and mechanisms involved in the movement of non-point source pollutants have not been completely developed in these models. However, HSPF and EPD RIV1 models (which have in-stream process components) are limited due to limitations in their operations and computational difficulties. Further research would seek to develop a non-point source pollutant model that would not only adequately and effectively simulate non-point source pollutants in water bodies, but would also be easy to assess, user-friendly, and time-efficient.

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