Delineation of management regions for South African rivers based on water chemistry

Abstract

The rivers of South Africa differ in water chemistry because of differences in geology and climate, and in the nature of the terrestrial vegetation. Thus the riverine biotas also differ in their water quality requirements. This paper describes the use of multivariate analytical techniques on a large inorganic chemical database as a means of dividing the country's rivers into regions of like water chemistry for the purposes of water quality management of aquatic ecosystems. Data were used from about 500 usable sites for the three earliest hydrological years (October 1980-September 1983 inclusive) for which information is available for most of the sites. In some cases separate winter (May-September) and summer (November-March) analyses were performed. Those observations in which orthophosphate-phosphorus (PO 3 -P) was greater than 0.1 mg 1-1 and/or nitrate-plus nitrite-nitrogen (NO2+3-N) was greater than 0.5 mg 1 i and/or conductivity > 500 mS m -~, were excluded. Some of the analyses were run on the subset of records for which the reading of weir height (as a surrogate for discharge) was within 10% of the mean. Chemical variables comprised conductivity and pH, and the concentrations of chloride, total alkalinity (TAL), sodium, calcium, potassium, magnesium, chloride, sulphate, fluoride, silicate, nitrate-plus nitrite-nitrogen, ammoniumnitrogen and orthophosphate-phosphorus, as well as the ratios C1 :(Cl-+ TAL), CI-:(CI + SO42+) and Na+:(Na + + Ca2+). Multivariate techniques included principal components analysis, detrended correspondence analysis and cluster analysis. Eigenvalues are low ( < 0.05) for detrended correspondence analyses but high ( > 0.7) for principal components analyses. In all cases, [silicate] correlates most strongly with the first axis, and conductivity, [K ÷] and/or [SO 2 ] with the second. Alkalinity correlates inversely with [H+], while [Ca 2+] and [Mg 2+] are closely correlated with each other and with alkalinity. [Na +] and [CI-] are correlated with each other and weakly with conductivity. Cluster analysis of secondary drainage regions is used together with biogeographic and physiographic information to produce a map of South Africa divided into five major regions for the management of water quality for riverine ecosystems.

Using multivariate statistical analysis to assess changes in water chemistry in sections of the Vaal Dam catchment between 1991 and 2008

“…The spatial differences between the sites, highlighted by the output from the ANCOVA analysis, confirm the variability between rivers within the catchment (Day et al, 1998), and, together with the CCA, suggest that additional environmental variables, including land use, point-source pollution and diffuse inputs from agricultural sources, are stronger influences on the quality of water in the Vaal Dam catchment when compared with S and N deposition. Inclusion of more sites and more environmental variables could be used with CCA analysis to explain more of the variation in the data set than was found in this investigation.…”

The genetic legacy of lower sea levels: does the confluence of rivers during the last glacial maximum explain the contemporary distribution of a primary freshwater fish (Pseudobarbus burchelli, Cyprinidae) across isolated river systems?

“…Pseudobarbus burchelli populations in the Breede, Duiwenhoks and Goukou river systems are presently isolated in upland tributaries that originate almost entirely in Table Mountain sandstones. These clear and oligotrophic mountain streams have low mineral and suspended materials (Day et al, 1998), and are the typical habitat for most of the redfin species. In contrast, the Heuningnes River system flows over gentle gradient, its water is eutrophic and carries high mineral and suspended materials, because it drains over Bokkeveld marine sediments and Sandveld sands (Keyser, 1998).…”

Water Quality Assessment of Gufu River in Three Gorges Reservoir (China) Using Multivariable Statistical Methods

“…The nitrogen which is a leading factor of the water quality change for the UR with little anthropogenic interference could attribute to 'geological' nitrogen (Holloway et al, 1998). The T-Hard and T-Alk might arise from dissolution of limestone and gypsum soils (Vega et al, 1998), which can be thus explained as a mineral component of the surface river water, and the characteristics of water quality distribution also accord with mountain river water quality characteristics (Day et al, 1998). EC in the UR was related mainly to T-Hard and T-Alk, whereas it would also due to anthropogenic influences, such as land use (Walker and Pan, 2006), hydropower exploitation (Zhang et al, 2010), and so on.…”