- Title
- Modelling water quality dynamics by integrating PYWR, climate change, and land-cover scenarios: a case study in the Grootdraai Dam Catchment, Upper Vaal, South Africa
- Creator
- Lazar, Sofia
- ThesisAdvisor
- Griffin, N.
- ThesisAdvisor
- Akamagwuna, F.
- ThesisAdvisor
- Slaughter, A.R.
- Subject
- Uncatalogued
- Date
- 2024-10-11
- Type
- Academic theses
- Type
- Master's theses
- Type
- text
- Identifier
- http://hdl.handle.net/10962/465000
- Identifier
- vital:76564
- Description
- Water resource management faces global challenges in allocation, quality, and sustainability. Despite extensive focus on quantity, water quality remains neglected, especially in developing nations, owing to data scarcity and funding issues. Water quantity modelling is more advanced, leaving water quality modelling lagging, as it requires finer spatiotemporal scales. Global water quality models, including those used in South Africa, encounter complexity and data requirements, and some proprietary models limit access. In South Africa, a water quality model is integrated with the less accessible Water Resources Yield Model (WRYM). However, WRYM's spatial lumping may not suffice for water quality assessment, emphasising the need for improvement. This study aims to address the gap in water quality modelling by transitioning from lumped, proprietary, and monthly time-step models applied in South Africa to more spatially distributed, user-friendly, transparent, fast models and daily time-step models, using the Grootdraai Dam Catchment in the Upper Vaal as a study region. The study examines providing water quality simulation for various variables under different tested scenarios, including (i) land-use scenarios (e.g., urbanisation, industrialisation, population growth and expansion in agricultural areas); (ii) mixed scenarios (e.g., climate change, mine closure, and demand increase). The study proposed a framework shifting from the WRYM to a Python water resources (Pywr) model, linked with the Water Quality Systems Assessment Model (WQSAM) in the Grootdraai Dam Catchment. This integration, the Python water resources-Water Quality (Pywr-WQ) model, was developed by the Water Research centre (WRc) in the United Kingdom. The study employed multiple regression models to develop land-use models, the outcomes of which were integrated into the Pywr-WQ model for medium and long term land-use scenario predictions. The study resulted in the following findings: (1) significant patterns emerge concerning the impacts of urbanisation, mining, and agricultural expansion on water quality; (2) urban areas exhibit elevated levels of nitrate plus nitrite and ammonium over the long term associated with human activities and infrastructure development; (3) increased cultivation leads to heightened phosphate levels, indicative of agricultural runoff and potential high fertiliser usage, while the expansion of mining activities results in elevated concentrations of sulphate and Total Dissolved Solids (TDS), attributed to the discharge of mine effluents; (4) noticeable declines in the concentrations of TDS and sulphate are evident in the medium to long term when compared to the baseline simulations. However, the worst-case scenario (i.e., a 70% abstraction increase) exhibits elevated peaks and concentrations compared to scenarios with more probable demand increases (e.g., a 5% increase).
- Description
- Thesis (MSc) -- Faculty of Science, Institute for Water Research, 2024
- Format
- computer, online resource, application/pdf, 1 online resource (221 pages), pdf
- Publisher
- Rhodes University, Faculty of Science, Institute for Water Research
- Language
- English
- Rights
- Lazar, Sofia
- Rights
- Use of this resource is governed by the terms and conditions of the Creative Commons "Attribution-NonCommercial-ShareAlike" License (http://creativecommons.org/licenses/by-nc-sa/2.0/)
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Collections
RU Institute for Water Research, including the Unilever Centre for Environmental Water Quality (IWR)
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | LAZAR-MSC-TR24-200.pdf | 5 MB | Adobe Acrobat PDF | View Details Download |