<p><font face="TimesNewRomanPSMT"> <p align="left">Groundwater in arid and semi-arid areas like the Karoo region of South Africa is an important source of domestic, agricultural and industrial source of fresh water. As a scarce resource, it requires extensive quality control and protection through innovative methods and efficient strategies. The town of Beaufort West and its vicinity use groundwater as a major source of municipal and private water supply. Forty nine groundwater samples were collected from spatially referenced boreholes located in and around the town of Beaufort West and were analyzed for <font face="TimesNewRomanPSMT">EC, pH, <font face="TimesNewRomanPSMT">TDS,<font face="TimesNewRomanPSMT">TH, SAR, TA, Ca</font><font size="1" face="TimesNewRomanPSMT"><font size="1" face="TimesNewRomanPSMT">2+</font></font><font face="TimesNewRomanPSMT">, Mg</font><font size="1" face="TimesNewRomanPSMT"><font size="1" face="TimesNewRomanPSMT">2+</font></font><font face="TimesNewRomanPSMT">, Na</font><font size="1" face="TimesNewRomanPSMT"><font size="1" face="TimesNewRomanPSMT">+</font></font><font face="TimesNewRomanPSMT">, K</font><font size="1" face="TimesNewRomanPSMT"><font size="1" face="TimesNewRomanPSMT">+</font></font><font face="TimesNewRomanPSMT">, HCO</font><font size="1" face="TimesNewRomanPSMT"><font size="1" face="TimesNewRomanPSMT">3-</font></font><font size="3" face="TimesNewRomanPSMT">, Cl</font><font size="1" face="TimesNewRomanPSMT"><font size="1" face="TimesNewRomanPSMT">-</font></font><font size="3" face="TimesNewRomanPSMT">, NO</font><font size="1" face="TimesNewRomanPSMT"><font size="1" face="TimesNewRomanPSMT">3- </font></font><font size="3" face="TimesNewRomanPSMT">and SO</font><font size="1" face="TimesNewRomanPSMT"><font size="1" face="TimesNewRomanPSMT">42- </font></font><font face="TimesNewRomanPSMT"><font size="3">according to&nbsp / <font face="TimesNewRomanPSMT">SANS 241 standards and tested for ionic balance. The groundwater of the study area was characterized using WHO and South African drinking water quality standards as well as TDS and Salinity hazard classifications. These comparisons and classifications characterized the groundwater of the study area as hard to very hard, with low to medium salinity hazard. These results are in accordance with the dominance of the ions Ca</font><font size="1" face="TimesNewRomanPSMT"><font size="1" face="TimesNewRomanPSMT">2+</font></font><font face="TimesNewRomanPSMT">, Na</font><font size="1" face="TimesNewRomanPSMT"><font size="1" face="TimesNewRomanPSMT">+</font></font><font face="TimesNewRomanPSMT">, HCO</font><font size="1" face="TimesNewRomanPSMT"><font size="1" face="TimesNewRomanPSMT">3 - </font></font><font face="TimesNewRomanPSMT">and Cl</font><font size="1" face="TimesNewRomanPSMT"><font size="1" face="TimesNewRomanPSMT">- </font></font><font face="TimesNewRomanPSMT">in the groundwater samples. Linear relationships between the hydrochemical variables were analysed through correlation and multiple regression analysis to relate the groundwater quality to the underlying hydrogeochemical processes. These linear relationships explained the contribution of the measured variables towards the salinity, hardness and anthropogenic contamination of the groundwater. The groundwater of the study area was also assessed using conventional trilinear diagrams and scatter plots to interpret the water quality and determine the major ion chemistry. The conventional methods highlighted the sources of the hydrochemical variables through analysis and interpretation of rock-water interaction and evaporations processes. To supplement <font face="TimesNewRomanPSMT">these conventional methods and reveal hidden hydrogeochemical phenomenon, multivariate statistical analyses were employed. Factor analysis reduced the hydrochemical variables into three factors (Hardness, Alkalinity and Landuse) that characterize the groundwater quality in relation to the source of its hydrochemistry. Furthermore, combination of Cluster (CA) and Discriminant analyses (DA) were used to classify the groundwater in to different hydrochemical facies and determine the dominant hydrochemical variables that characterize these facies. The classification results were also compared with the trilinear diagrammatic interpretations to highlight the advantages of these multivariate statistical methods. The CA and DA classifications resulted in to six different hydrochemical facies that are characterized by NO</font><font size="1" face="TimesNewRomanPSMT"><font size="1" face="TimesNewRomanPSMT">3 -</font></font><font face="TimesNewRomanPSMT">, Na</font><font size="1" face="TimesNewRomanPSMT"><font size="1" face="TimesNewRomanPSMT">+ </font></font><font face="TimesNewRomanPSMT">and pH. These three hydrochemical variables explain 93.9% of the differences between the water types and highlight the influence of natural hydrogeochemical and anthropogenic processes on the groundwater quality. All the univariate, bivariate, multivariate statistical and conventional hydrogeochemical analyses results were analyzed spatially using ArcGIS 10.0. The spatial analysis employed the Inverse Distance Weighted (IDW) interpolation method to predict spatial distribution of unmeasured areas and reclassification of the interpolation results for classification purposes. The results of the different analyses methods employed in the thesis illustrate that the groundwater in the study area is generally hard but permissible in the absence of better alternative water source and useful for irrigation.</font></font></font></font></font></font></p> </font></p>

<p><font face="TimesNewRomanPSMT"> <p align="left">Groundwater in arid and semi-arid areas like the Karoo region of South Africa is an important source of domestic, agricultural and industrial source of fresh water. As a scarce resource, it requires extensive quality control and protection through innovative methods and efficient strategies. The town of Beaufort West and its vicinity use groundwater as a major source of municipal and private water supply. Forty nine groundwater samples were collected from spatially referenced boreholes located in and around the town of Beaufort West and were analyzed for <font face="TimesNewRomanPSMT">EC, pH, <font face="TimesNewRomanPSMT">TDS,<font face="TimesNewRomanPSMT">TH, SAR, TA, Ca</font><font size="1" face="TimesNewRomanPSMT"><font size="1" face="TimesNewRomanPSMT">2+</font></font><font face="TimesNewRomanPSMT">, Mg</font><font size="1" face="TimesNewRomanPSMT"><font size="1" face="TimesNewRomanPSMT">2+</font></font><font face="TimesNewRomanPSMT">, Na</font><font size="1" face="TimesNewRomanPSMT"><font size="1" face="TimesNewRomanPSMT">+</font></font><font face="TimesNewRomanPSMT">, K</font><font size="1" face="TimesNewRomanPSMT"><font size="1" face="TimesNewRomanPSMT">+</font></font><font face="TimesNewRomanPSMT">, HCO</font><font size="1" face="TimesNewRomanPSMT"><font size="1" face="TimesNewRomanPSMT">3-</font></font><font size="3" face="TimesNewRomanPSMT">, Cl</font><font size="1" face="TimesNewRomanPSMT"><font size="1" face="TimesNewRomanPSMT">-</font></font><font size="3" face="TimesNewRomanPSMT">, NO</font><font size="1" face="TimesNewRomanPSMT"><font size="1" face="TimesNewRomanPSMT">3- </font></font><font size="3" face="TimesNewRomanPSMT">and SO</font><font size="1" face="TimesNewRomanPSMT"><font size="1" face="TimesNewRomanPSMT">42- </font></font><font face="TimesNewRomanPSMT"><font size="3">according to&nbsp / <font face="TimesNewRomanPSMT">SANS 241 standards and tested for ionic balance. The groundwater of the study area was characterized using WHO and South African drinking water quality standards as well as TDS and Salinity hazard classifications. These comparisons and classifications characterized the groundwater of the study area as hard to very hard, with low to medium salinity hazard. These results are in accordance with the dominance of the ions Ca</font><font size="1" face="TimesNewRomanPSMT"><font size="1" face="TimesNewRomanPSMT">2+</font></font><font face="TimesNewRomanPSMT">, Na</font><font size="1" face="TimesNewRomanPSMT"><font size="1" face="TimesNewRomanPSMT">+</font></font><font face="TimesNewRomanPSMT">, HCO</font><font size="1" face="TimesNewRomanPSMT"><font size="1" face="TimesNewRomanPSMT">3 - </font></font><font face="TimesNewRomanPSMT">and Cl</font><font size="1" face="TimesNewRomanPSMT"><font size="1" face="TimesNewRomanPSMT">- </font></font><font face="TimesNewRomanPSMT">in the groundwater samples. Linear relationships between the hydrochemical variables were analysed through correlation and multiple regression analysis to relate the groundwater quality to the underlying hydrogeochemical processes. These linear relationships explained the contribution of the measured variables towards the salinity, hardness and anthropogenic contamination of the groundwater. The groundwater of the study area was also assessed using conventional trilinear diagrams and scatter plots to interpret the water quality and determine the major ion chemistry. The conventional methods highlighted the sources of the hydrochemical variables through analysis and interpretation of rock-water interaction and evaporations processes. To supplement <font face="TimesNewRomanPSMT">these conventional methods and reveal hidden hydrogeochemical phenomenon, multivariate statistical analyses were employed. Factor analysis reduced the hydrochemical variables into three factors (Hardness, Alkalinity and Landuse) that characterize the groundwater quality in relation to the source of its hydrochemistry. Furthermore, combination of Cluster (CA) and Discriminant analyses (DA) were used to classify the groundwater in to different hydrochemical facies and determine the dominant hydrochemical variables that characterize these facies. The classification results were also compared with the trilinear diagrammatic interpretations to highlight the advantages of these multivariate statistical methods. The CA and DA classifications resulted in to six different hydrochemical facies that are characterized by NO</font><font size="1" face="TimesNewRomanPSMT"><font size="1" face="TimesNewRomanPSMT">3 -</font></font><font face="TimesNewRomanPSMT">, Na</font><font size="1" face="TimesNewRomanPSMT"><font size="1" face="TimesNewRomanPSMT">+ </font></font><font face="TimesNewRomanPSMT">and pH. These three hydrochemical variables explain 93.9% of the differences between the water types and highlight the influence of natural hydrogeochemical and anthropogenic processes on the groundwater quality. All the univariate, bivariate, multivariate statistical and conventional hydrogeochemical analyses results were analyzed spatially using ArcGIS 10.0. The spatial analysis employed the Inverse Distance Weighted (IDW) interpolation method to predict spatial distribution of unmeasured areas and reclassification of the interpolation results for classification purposes. The results of the different analyses methods employed in the thesis illustrate that the groundwater in the study area is generally hard but permissible in the absence of better alternative water source and useful for irrigation.</font></font></font></font></font></font></p> </font></p>

Magister Scientiae - MSc (Environ & Water Science) / Groundwater in arid and semi-arid areas like the Karoo region of South Africa is an important source of domestic, agricultural and industrial source of fresh water. As a scarce resource, it requires extensive quality control and protection through innovative methods and efficient strategies. The town of Beaufort West and its vicinity use groundwater as a major source of municipal and private water supply. Forty nine groundwater samples were collected from spatially referenced boreholes located in and around the town of Beaufort West and were analyzed for EC, pH, TDS,TH, SAR, TA, Ca2+, Mg2+, Na+, K+, HCO3-, Cl-, NO3- and SO42- according to SANS 241 standards and tested for ionic balance. The groundwater of the study area was characterized using WHO and South African drinking water quality standards as well as TDS and Salinity hazard classifications. These comparisons and classifications characterized the groundwater of the study area as hard to very hard, with low to medium salinity hazard. These results are in accordance with the dominance of the ions Ca2+, Na+, HCO3 - and Cl- in the groundwater samples. Linear relationships between the hydrochemical variables were analysed through correlation and multiple regression analysis to relate the groundwater quality to the underlying hydrogeochemical processes. These linear relationships explained the contribution of the measured variables towards the salinity, hardness and anthropogenic contamination of the groundwater. The groundwater of the study area was also assessed using conventional trilinear diagrams and scatter plots to interpret the water quality and determine the major ion chemistry. The conventional methods highlighted the sources of the hydrochemical variables through analysis and interpretation of rock-water interaction and evaporations processes. To supplement these conventional methods and reveal hidden hydrogeochemical phenomenon, multivariate statistical analyses were employed. Factor analysis reduced the hydrochemical variables into three factors (Hardness, Alkalinity and Landuse) that characterize the groundwater quality in relation to the source of its hydrochemistry. Furthermore, combination of Cluster (CA) and Discriminant analyses (DA) were used to classify the groundwater in to different hydrochemical facies and determine the dominant hydrochemical variables that characterize these facies. The classification results were also compared with the trilinear diagrammatic interpretations to highlight the advantages of these multivariate statistical methods. The CA and DA classifications resulted in to six different hydrochemical facies that are characterized by NO3 -, Na+ and pH. These three hydrochemical variables explain 93.9% of the differences between the water types and highlight the influence of natural hydrogeochemical and anthropogenic processes on the groundwater quality. All the univariate, bivariate, multivariate statistical and conventional hydrogeochemical analyses results were analyzed spatially using ArcGIS 10.0. The spatial analysis employed the Inverse Distance Weighted (IDW) interpolation method to predict spatial distribution of unmeasured areas and reclassification of the interpolation results for classification purposes. The results of the different analyses methods employed in the thesis illustrate that the groundwater in the study area is generally hard but permissible in the absence of better alternative water source and useful for irrigation.

The Permo-Triassic lower Beaufort Group fluvial deposits extend over 100s of kilometres within the Karoo Basin, South Africa. A detailed study of the depositional architecture and stacking patterns of sand bodies within a 900 m thick succession has enabled interpretation of the controls on ancient river channel and overbank processes. Facies include very fine- to medium-grained sandstone, intra-formational conglomerate, mudstone and palaeosols. Channel-belts are dominated by upper flow regime structures, consistent with a flashy to ephemeral fluvial system. The overbank deposits comprise splays interbedded with purple, green and grey mudstone; these floodplain colour changes signify water table fluctuations. A hierarchy of channel-related elements has been established that recognises beds, bedsets, storeys, channel-belts, complexes and complex sets. Each channel-belt may be single- or multi-storey, whereby one storey represents the complete cut and fill cycle of a single migrating river, comprising bar accretion elements and channel-abandonment fill. The abandonment fill elements often consist of heterolithic plugs of climbing ripple-laminated very fine-grained sandstone, or interbedded claystone with siltstone. The Beaufort channel-belts preserve either lateral- or downstream-accretion patterns, or a combination. Each belt has either a lenticular or tabular geometry, recognisable by an erosional base overlain by intra-formational conglomerate lag and barform deposits. Genetically related channel-belts cluster to form complexes, of which two broad styles have been identified: Type A) laterally and vertically stacked channel-belts, and Type B) sub-vertically stacked channel-belts. There is evidence of localised clustering of sub-vertically stacked channel-belts adjacent to extensive overbank mudstone deposits. The apparent lack of a well-defined ‘container’ surface with mappable margins, suggests that this stacked channel-belt architecture represents an avulsion complex rather than a palaeovalley-fill. The lateral and stratigraphic variability in fluvial-overbank architecture is interpreted as the interplay of several controls. Allogenic forcing factors include, tectonic subsidence that influences accommodation, sediment supply, and high frequency climate cycles associated with the flashy discharge regime and expressed in the mudrock colour changes and distribution of palaeosols. The depositional river style, variability in channel-belt stacking patterns and compensational stacking of some channel-belt/splay complexes is interpreted to be the result of autogenic channel avulsion, supported by an absence of significant erosion. The relative merits of basin-axial trunk river and distributive fluvial system (DFS) models are assessed from detailed architectural and stratigraphic outcrop studies.