Investigating integrated catchment management using a simple water quantity and quality model : a case study of the Crocodile River Catchment, South Africa

Internationally, water resources are facing increasing pressure due to over-exploitation and pollution. Integrated Water Resource Management (IWRM) has been accepted internationally as a paradigm for integrative and sustainable management of water resources. However, in practice, the implementation and success of IWRM policies has been hampered by the lack of availability of integrative decision support tools, especially within the context of limited resources and observed data. This is true for the Crocodile River Catchment (CRC), located within the Mpumalanga Province of South Africa. The catchment has been experiencing a decline in water quality as a result of the point source input of a cocktail of pollutants, which are discharged from industrial and municipal wastewater treatment plants, as well as diffuse source runoff and return flows from the extensive areas of irrigated agriculture and mining sites. The decline in water quality has profound implications for a range of stakeholders across the catchment including increased treatment costs and reduced crop yields. The combination of deteriorating water quality and the lack of understanding of the relationships between water quantity and quality for determining compliance/non-compliance in the CRC have resulted in collaboration between stakeholders, willing to work in a participatory and transparent manner to create an Integrated Water Quality Management Plan (IWQMP). This project aimed to model water quality, (combined water quality and quantity), to facilitate the IWQMP aiding in the understanding of the relationship between water quantity and quality in the CRC. A relatively simple water quality model (WQSAM) was used that receives inputs from established water quantity systems models, and was designed to be a water quality decision support tool for South African catchments. The model was applied to the CRC, achieving acceptable simulations of total dissolved solids (used as a surrogate for salinity) and nutrients (including orthophosphates, nitrates +nitrites and ammonium) for historical conditions. Validation results revealed that there is little consistency within the catchment, attributed to the non-stationary nature of water quality at many of the sites in the CRC. The analyses of the results using a number of representations including, seasonal load distributions, load duration curves and load flow plots, confirmed that the WQSAM model was able to capture the variability of relationships between water quantity and quality, provided that simulated hydrology was sufficiently accurate. The outputs produced by WQSAM was seen as useful for the CRC, with the Inkomati-Usuthu Catchment Management Agency (IUCMA) planning to operationalise the model in 2015. The ability of WQSAM to simulate water quality in data scarce catchments, with constituents that are appropriate for the needs of water resource management within South Africa, is highly beneficial

Modelling water temperature in the lower Olifants River and the implications for climate change

Freshwater systems in southern Africa are under threat of climate change, not only from altered flow regimes as rainfall patterns change, but also from biologically significant increases in water temperature. Statistical models can predict water temperatures from air temperatures, and air temperatures may rise by up to 7 °C by 2100. Statistical water temperature models require less data input than physical models, which is particularly useful in data deficient regions. We validated a statistical water temperature model in the lower Olifants River, South Africa, and verified its spatial applicability in the upper Klaserie River. Monthly and daily temporal scale calibrations and validations were conducted. The results show that simulated water temperatures in all cases closely mimicked those of the observed data for both temporal resolutions and across sites (NSE>0.75 for the Olifants River and NSE>0.8 for the Klaserie). Overall, the model performed better at a monthly than a daily scale, while generally underestimating from the observed (indicated by negative percentage bias values). The statistical models can be used to predict water temperature variance using air temperature and this use can have implications for future climate projections and the effects climate change will have on aquatic species. Significance:• Statistical modelling can be used to simulate water temperature variance from observed air temperature, which has implications for future projections and climate change scenarios.• While there are many other factors affecting water temperature, air temperature accounts for up to 95% of water temperature variance.• The model used can successfully simulate water temperature variance for different rivers

Modelling water temperature in the lower Olifants River and the implications for climate change

Freshwater systems in southern Africa are under threat of climate change, not only from altered flow regimes as rainfall patterns change, but also from biologically significant increases in water temperature. Statistical models can predict water temperatures from air temperatures, and air temperatures may rise by up to 7 °C by 2100. Statistical water temperature models require less data input than physical models, which is particularly useful in data deficient regions. We validated a statistical water temperature model in the lower Olifants River, South Africa, and verified its spatial applicability in the upper Klaserie River. Monthly and daily temporal scale calibrations and validations were conducted. The results show that simulated water temperatures in all cases closely mimicked those of the observed data for both temporal resolutions and across sites (NSE>0.75 for the Olifants River and NSE>0.8 for the Klaserie). Overall, the model performed better at a monthly than a daily scale, while generally underestimating from the observed (indicated by negative percentage bias values). The statistical models can be used to predict water temperature variance using air temperature and this use can have implications for future climate projections and the effects climate change will have on aquatic species. SIGNIFICANCE : • Statistical modelling can be used to simulate water temperature variance from observed air temperature, which has implications for future projections and climate change scenarios. • While there are many other factors affecting water temperature, air temperature accounts for up to 95% of water temperature variance. • The model used can successfully simulate water temperature variance for different rivers.South African National Research Foundation, iThemba LABS, Oppenheimer Generations.http://www.sajs.co.zahj2022Mammal Research InstituteZoology and Entomolog

Research related to clinical dentistry (1967 - 1983)

Philosophiae Doctor - PhDFour spectrophotometric procedures for the analysis of phosphorus were evaluated (1F1). The phosphorus concentrations in saliva and dentine were determined. All four analytical procedures were accurate but the one method was the most sensitive. An acid etch enamel biopsy procedure which was originally developed in Switzerland was modified in our laboratory (#2). It was subsequently shown by other investigators that the modified technique was much more accurate for the determination of the fluoride concentration in enamel than the original procedure

Modelling water temperature in the lower Olifants River and the implications for climate change

Freshwater systems in southern Africa are under threat of climate change, not only from altered flow regimes as rainfall patterns change, but also from biologically significant increases in water temperature. Statistical models can predict water temperatures from air temperatures, and air temperatures may rise by up to 7 °C by 2100. Statistical water temperature models require less data input than physical models, which is particularly useful in data deficient regions. We validated a statistical water temperature model in the lower Olifants River, South Africa, and verified its spatial applicability in the upper Klaserie River. Monthly and daily temporal scale calibrations and validations were conducted. The results show that simulated water temperatures in all cases closely mimicked those of the observed data for both temporal resolutions and across sites (NSE>0.75 for the Olifants River and NSE>0.8 for the Klaserie). Overall, the model performed better at a monthly than a daily scale, while generally underestimating from the observed (indicated by negative percentage bias values). The statistical models can be used to predict water temperature variance using air temperature and this use can have implications for future climate projections and the effects climate change will have on aquatic species. Significance: Statistical modelling can be used to simulate water temperature variance from observed air temperature, which has implications for future projections and climate change scenarios. While there are many other factors affecting water temperature, air temperature accounts for up to 95% of water temperature variance. The model used can successfully simulate water temperature variance for different rivers

Evaluating herbivore management outcomes and associated vegetation impacts

African savannas are characterised by temporal and spatial fluxes that are linked to fluxes in herbivore populations and vegetation structure and composition. We need to be concerned about these fluxes only when management actions cause the system to shift towards a less desired state. Large herbivores are a key attribute of African savannas and are important for tourism and biodiversity. Large protected areas such as the Kruger National Park (KNP) manage for high biodiversity as the desired state, whilst private protected areas, such as those adjacent to the KNP, generally manage for high income. Biodiversity, sustainability and economic indicators are thus required to flag thresholds of potential concern (TPCs) that may result in a particular set of objectives not being achieved. In large conservation areas such as the KNP, vegetation changes that result from herbivore impact, or lack thereof, affect biodiversity and TPCs are used to indicate unacceptable change leading to a possible loss of biodiversity; in private protected areas the loss of large herbivores is seen as an important indicator of economic loss. Therefore, the first-level indicators aim to evaluate the forage available to sustain grazers without deleteriously affecting the vegetation composition, structure and basal cover. Various approaches to monitoring for these indicators were considered and the importance of the selection of sites that are representative of the intensity of herbivore use is emphasised. The most crucial step in the adaptive management process is the feedback of information to inform management decisions and enable learning. Feedback loops tend to be more efficient where the organisation’s vision is focused on, for example, economic gain, than in larger protected areas, such as the KNP, where the vision to conserve biodiversity is broader and more complex. Conservation implications: In rangeland, optimising herbivore numbers to achieve the management objectives without causing unacceptable or irreversible change in the vegetation is challenging. This manuscript explores different avenues to evaluate herbivore impact and the outcomes of management approaches that may affect vegetation