The past few decades have seen, amongst other topical environmental issues, increased concerns regarding the imminent threat of global warming and the consequential impacts of climate change on environmental, social and economic systems. Numerous groundbreaking studies conducted independently and cooperatively have provided abundant and conclusive evidence that global climates are changing and that these changes will almost certainly impact natural and socio-economic systems. Increased global change pressures, which include, inter alia, climate change, have increased concerns over the supply of adequate quality freshwater. There is an inadequate body of knowledge pertaining to linking basic hydrological processes which drive water quality (WQ) variability with projected climate change. Incorporating such research into policy development and governance with the intention of developing adaptive WQ management strategies is also overlooked. Thus, the aim of this study was the assessment of projected climate change impacts on selected WQ constituents in the context of agricultural non-point source pollution and the development of the necessary adaptation strategies that can be incorporated into WQ management, policy development and governance. This assessment was carried out in the form of a case study in the Mkabela Catchment near Wartburg in KwaZulu-Natal, South Africa. The research involved applying climate change projections derived from seven downscaled Global Circulation Models (GCMs) used in the Fourth Intergovernmental Panel on Climate Change (IPCC) Assessment Report, in the ACRU-NPS water quality model to assess the potential impacts on selected water quality constituents (viz. sediment, nitrogen and phosphorus). Results indicated positive correlations between WQ related impacts and contaminant migration as generated from agricultural fertilizer applications. ACRU-NPS simulations indicated increases in runoff and associated changes in WQ variable generation and migration from upstream sources in response to downscaled GCM projections. However, there was limited agreement found between the simulations derived from the various downscaled GCM projections in regard to the magnitude and direction (i.e. percent changes between present and the future) of these changes in WQ variables. The rainfall distribution analyses conducted on a daily time-step resolution for each selected GCM also showed limited consistency between the GCM projections regarding rainfall changes between the present and the future. The implication was that since hydrological and climate change modelling can inform adaptation under climate change. However, adaptation to climate change in water quality management and policy development is going to require approaches that fully recognise the uncertainties presented by climate change and the associated modelling thereof. It was also considered crucial that equal attention be given to both climate change and natural variability, in order to ensure that adaptation strategies remain robust and effective under conditions of climate change and its respective uncertainties. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2013.

In recent years, surface water used for domestic, industrial, and irrigation purposes in South Africa has deteriorated due to inadequate wastewater treatment, urban and agricultural runoff, and rural settlements with deficient sanitation. Access to safe drinking water and sanitation is a basic human right, and if waterborne pathogens are present in the water environment, they compose a human health risk. With some hydrological models, e.g., Hydrological Predictions of the Environment (HYPE), it is possible to model microbial water quality and predict how land use and climate changes affect recipient water sources. In this thesis, waterborne pathogen transport in South Africa is investigated using World-Wide HYPE (WWH), to increase the understanding of the largest sources affecting pathogen concentration in surface water and processes affecting pathogen transport. Initially, a literature study was performed with emphasis on finding the most suitable pathogen to simulate. Because of the amount of available data, the indicator microorganism, E. coli, was chosen. Observed E. coli concentrations in surface water were used to evaluate the conformity of the simulated concentration, and contributions from separate sources were analysed. A sensitivity analysis was performed to increase the understanding of process parameters affecting the transport of E. coli in WWH. The findings of this project show that the largest contributions of E. coli originate from humans with unsatisfactory waste management, where wastewater is partially released directly to surface water. The largest deviation in average E. coli load per year was obtained when altering t1expdec, which denotes the half-life time of the simulated microorganism. The half-life time was also the process parameter with the most significant effect on the simulated concentration. In addition, when the parameter that specifies the fraction of E. coli that is released directly to surface water was altered, which affects one of the largest E. coli sources, a large deviation in average E. coli load per year was observed. This finding shows the importance of estimating the load from contamination sources accurately. The conformity of simulated and observed E.coli load was acceptable, but the simulated discharge needs to be improved to achieve better conformity of the E. coli concentration in surface water. WWH has great potential to simulate waterborne pathogens, but further developments to improve the simulated discharge are encouraged to obtain more reliable results. / Under de senaste åren har kvaliteten av ytvattnet i Sydafrika försämrats på grund av bristfälliga vattenreningsverk, avrinning från urbana miljöer och åkermark och områden med undermålig sanitet. Att ha tillgång till rent vatten och fungerande sanitet är en grundläggande mänsklig rättighet och om patogener är närvarande utgör detta en hälsorisk för människor som kommer i kontakt med dessa smittoämnen. Det är möjligt att modellera vattens kvalitet med avseende på mikroorganismer och att förutse hur markanvändning påverkar kvaliteten i recipienten. I detta arbete har transporten av vattenburna patogener i Sydafrika undersökts genom World-Wide HYPE (WWH) med syftet att öka förståelsen av de största källorna som bidrar till ökande koncentrationer av patogener i ytvatten, samt att öka förståelsen av processerna som påverkar transporten. En litteraturstudie utfördes för att hitta en passande patogen att simulera, och på grund av mängd tillgängliga data valdes indikatororganismen E. coli. Uppmätt koncentration av E. coli i ytvatten i Sydafrika användes för att utvärdera överrensstämmelsen med simulerad koncentration, och bidrag från olika källor av E. coli analyserades. En kompletterande känslighetsanalys utfördes för att öka förståelsen om transportprocesserna i WWH. Resultatet visade att de största bidragskällorna av E. coli till ytvatten i modellen är människor med otillräcklig hantering av mänskligt avfall där genererat avloppsvatten delvis släpps ut direkt till ytvattnet. Från känslighetsanalysen visade det sig att den mest känsliga modellparametern var t1expdec som beskriver mikroorganismenshalveringstid. Det var också den processparameter som också hade störst påverkan på den simulerade E. coli koncentrationen. När parametern som bestämmer andelen av E.coli som släpps ut direkt till ytvatten varierades, som påverkar en av de största källorna, resulterade det också i stor förändring i genomsnittlig belastning av E. coli per år. Detta indikerar att det är viktigt att estimera bidragskällorna korrekt. Överrensstämmelsen mellan simulerad och uppmätt belastning av E. coli per dag var acceptabel men det simulerade vattenflödet bör förbättras för att uppnå en bättre överrensstämmelse mellan simulerade och uppmätta koncentrationer av E. coli. WWH har stor potential att modellera vattenburna patogener, men vidareutveckling av simulerade vattenflöden behöver utföras att få mer tillförlitliga resultat.

Der großräumige Abbau von Braunkohle in der Lausitz führte in der Vergangenheit zu einer extremen Beeinflussung des Wasserhaushaltes im Einzugsgebiet der Spree. Mit dem Beginn der Sanierung und Flutung der Tagebaue kommt es nun langfristig zu einer verstärkten Nutzung der existierenden Oberflächengewässer und der Einbindung der entstehenden Tagebaurestseen in das Fließgewässernetz. Die Kopplung von Mengenbewirtschaftungsmodellen mit Gütemodellen berücksichtigt die Verfügbarkeit und Verteilung der begrenzten Ressource Wasser im Einzugsgebiet und der aus der Bewirtschaftung resultierenden Gewässergüte. Dies entspricht auch dem Leitbild der EU-WRRL (2000) für ein integriertes Flussgebietsmanagement, was eine einzugsgebietsbezogene Betrachtung der vorhandenen Ressourcen unter Berücksichtigung aller beeinflussten und beeinflussenden Kriterien fordert. Werden Modelle, die unterschiedlich sensitive und komplexe Systeme abbilden, miteinander gekoppelt, erfordert dies eine Anpassung der Datenstruktur und der zeitlichen Skalen. Schwerpunkt dieser Arbeit war die Entwicklung einfacher, robuster Simulationswerkzeuge für die Prognose der Gewässergüte in den Talsperren Bautzen und Quitzdorf. Als Basis diente das komplexe Standgewässergütemodell SALMO. Das Modell wurde zunächst um einfache Algorithmen ergänzt, so dass es trotz einer angepassten, stark reduzierten Datengrundlage, plausible Ergebnisse simulierte. Stochastisch erzeugte Bewirtschaftungsszenarien und die komplex simulierten Modellergebnisse bezüglich der resultierenden Gewässergüte, wurden als Trainingsdaten für ein Künstliches Neuronales Netz (ANN) genutzt. Die für beide Talsperren trainierten ANN sind als effektive Black-Box-Module in der Lage, das komplexe Systemverhalten des deterministischen Modells SALMO widerzuspiegeln. Durch eine Kopplung der entwickelten ANN mit dem Bewirtschaftungsmodell WBalMo ist es möglich, Bewirtschaftungsalternativen hinsichtlich ihrer Konsequenzen für die Gewässergüte zu bewerten. ANN sind systemgebundene Modelle, die nicht auf andere Gewässersysteme übertragen werden können. Allerdings stellt die hier erarbeitete Methodik einen fundierten Ansatz dar, der für die Entwicklung weiterer aggregierter Gütemodule im Rahmen integrierter Bewirtschaftungsmodelle angewendet werden kann. / The large-scale extraction of lignite in Lusatia in the past had an extreme impact on the water balance of the Spree river catchment. The restoration and flooding of the opencast pits put heavy demand on existing surface waters for a long time period. The resulting artificial lakes have to be integrated in the riverine network. The coupling of management models and water quality models allows to consider both availability and distribution of limited water resources in the catchment and resulting water quality. This is corresponding to the principles of the EU-WFD for integrated river basin management, which is a basin-related consideration of available resources taking into account all influencing and influenced characteristics. Adjustment of data structure and time scale is necessary if models describing unequally sensitive and complex systems are to be coupled. Main focus of this task was to develop simple and robust simulation tools for the prediction of water quality in the reservoirs Bautzen and Quitzdorf. The complex water quality model SALMO served as a basis. In a first step, simple algorithms had to be amended in order to generate plausible simulation results despite of an adapted reduced data base. Stochastically generated management scenarios and complex simulated model results regarding the resulting water quality were employed as training data for an Artificial Neuronal Network (ANN). The trained ANN’s are efficient black box modules. As such they are able to mirror complex system behaviour of the deterministic model SALMO. By coupling the developed ANN with the management model WBalMo it is possible to evaluate management strategies in terms of their impact on the quality of the water bodies. ANN’s are system-linked models. A transfer to other aquatic systems is not possible. However, the methodology developed here represents an in-depth approach which is applicable to the development of further aggregated water quality models in the framework of integrated management models.

The Water Framework Directive requires reduced environmental impacts from human activities and for the assessment of the non-market benefits of pollution remediation schemes. This policy shift has exacerbated the research problems surrounding the physical, social and economic consequences of the relationship between land use and water quality. This research seeks to quantify the major socio-economic and environmental benefits for people which may arise as riverine pollution is reduced. To achieve these aims this research integrates primary data analyses combining choice experiment techniques with geographical information system based analyses of secondary data concerning the spatial distributions of riverine pollution. Current knowledge on the microbial quality of river water, measured by faecal indicator organism (FIO) concentrations and assessed at catchment scale, is inadequate. This research develops generic regression models to predict base- and high-flow faecal coliform (FC) and enterococci (EN) concentrations, using land cover and population (human and livestock) variables. The resulting models are then used both to predict FIO concentrations in unmonitored watercourses and to evaluate the likely impacts of different land use scenarios, enabling insights into the optimal locations and cost-effective mix of implementation strategies. Valuation experiments frequently conflate respondents’ preferences for different aspects of water quality. This analysis uses stated preference techniques to disaggregate the values of recreation and ecological attributes of water quality, thereby allowing decision makers to better understand the consequences of adopting alternative investment strategies which favour either ecological, recreational or a mix of benefits. The results reveal heterogeneous preferences across society; specifically, latent class analysis identifies three distinct groups, holding significantly different preferences for water quality. From a methodological perspective this research greatly enhances the ongoing synthesis of geographic and economic social sciences and addresses important policy questions which are of interest to a variety of stakeholders, including government departments and the water industry.