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Developing long and short term technical solutions, mitigation measures and decision support strategies that will improve water quality in the Grootdraai Dam catchment

Dr A.R. Slaughter, Dr N.J. Griffin, Ms S. Lazar, Prof. O.N. Odume, Dr F. Akamagwunu

Sponsor: Water Research Commission
March 2022 to March 2024

The deteriorating water quality in the Grootdraai Dam catchment above Standerton has serious economic, social, and ecological implications because of its strategic importance to South Africa’s economy. On the economic front, pollution has affected the quality of the raw water, and thus the operations of industries relying on raw water. Some of these industries have had to abstract more water to fulfil their operational needs, but this is not sustainable in the medium and long term owing to water scarcity within the catchment. The poor quality of the raw water makes it more costly to treat abstracted water to standard fit for industrial use, which then contributes to variable and operational costs of these industries, which, in the long term, can lead to job losses and put in jeopardy the viability of the operations of raw water-dependent industries in the catchment.

A water quality modelling approach was adopted to explore future water quality in the catchment and its response to various interventions. The project team are engaging stakeholders in the catchment and will continue in this regard. The DWS and Rand Water quality datasets for the catchment have been obtained.

Python water resources (Pywr) was used to model yield from the catchment, calibrated against an extant water resources management plan (WRMP) yield model. This approach was taken to represent flows at a finer resolution than were contained in the WRMP yield model. To model water quality we had planned to use the Water Quality Systems Assessment Model (WQSAM), but problems with this approach led to re-coding WQSAM routines in Pywr to give Python water resources – Water Quality (Pywr-WQ), which was used for water quality modelling. A water quality model was set up in Pywr-WQ, and calibrated against DWS and Rand Water data to give the baseline condition. Models to medium- and long-term timeframes were run in Pywr-WQ where possible, and we used a multiple regression-based model when changes in land use were considered. Models were based on likely changes in the catchment as suggested by stakeholders in a workshop. Major consideration was given to the effect of climate change, expansion or reduction of coal mining in the catchment, changes in intensive agriculture, and changes in urban areas.

Climate change alone was found to have a limited impact on many water quality parameters, but certain salts and salinity increased relative to baseline with time. Combining this with a small increase in mining led to intolerable salinities in the future. Increases in cultivated land led to an increase in nitrogen nutrients, with the associated risk of algal blooms and/or ammonia toxicity. Increasing urban land in the catchment produced increased phosphate levels, which appear to be a function of increased wastewater leakage, a problem reported by stakeholders and the press. Looking at scenarios where a combination of several changes were modelled together revealed that management of the catchment can lead to a future where cleaner water can be available, even with increased demand for water. The modelled impact of mining is significant, and uncontrolled increases here are a threat to water in the catchment. However, should coal mining decrease (as is anticipated under the proposed Just Energy Transition process), future salinity threats to the catchment will be reduced.

Last Modified: Wed, 14 Aug 2024 12:41:11 SAST