- Title
- Modelling water quality : complexity versus simplicity
- Creator
- Jacobs, Haden
- ThesisAdvisor
- Hughes, Dennis
- ThesisAdvisor
- Slaughter, Andrew
- Subject
- Water quality management -- Mathematical models
- Subject
- Water quality -- Measurement
- Subject
- Water quality biological assessment
- Date
- 2017
- Type
- Thesis
- Type
- Masters
- Type
- MSc
- Identifier
- http://hdl.handle.net/10962/4754
- Identifier
- vital:20721
- Description
- Water quality management makes use of water quality models as decision making tools. Water quality management decisions need to be informed by information that is as reliable as possible. There are many situations where observational data are limited and therefore models or simulation methods have a significant role to play in providing some information that can be used to guide management decisions. Water quality modelling is the use of mathematical equations and statistics to represent the processes affecting water quality in the natural environment. Water quality data are expensive and difficult to obtain. Nutrient sampling requires a technician to obtain ‘grab samples’ which need to be kept at low temperatures and analysed in a laboratory. The laboratory analyses of nutrients is expensive and time consuming. The data required by water quality models are seldom available as complete datasets of sufficient length. This is especially true for ungauged regions, either in small rural catchments or even major rivers in developing countries. Water quality modelling requires simulated or observed water quantity data as water quality is affected by water quantity. Both the water quality modelling and water quantity modelling require data to simulate the required processes. Data are necessary for both model structure as well as model set up for calibration and validation. This study aimed to investigate the simulation of water quality in a low order stream with limited observed data using a relatively complex as well as a much simpler water quality model, represented by QUAL2K and an in-house developed Mass Balance Nutrient (MBN) model, respectively. The two models differ greatly in the approach adopted for water quality modelling, with QUAL2K being an instream water quality fate model and the MBN model being a catchment scale model that links water quantity and quality. The MBN model uses hydrological routines to simulate those components of the hydrological cycle that are expected to differ with respect to their water quality signatures (low flows, high flows, etc.). Incremental flows are broken down into flow fractions, and nutrient signatures are assigned to fractions to represent catchment nutrient load input. A linear regression linked to an urban runoff model was used to simulate water quality entering the river system from failing municipal infrastructure, which was found to be a highly variable source of nutrients within the system. A simple algal model was adapted from CE-QUAL-W2 to simulate nutrient assimilation by benthic algae. QUAL2K, an instream water quality fate model, proved unsuitable for modelling diffuse sources for a wide range of conditions and was data intensive when compared to the data requirements of the MBN model. QUAL2K did not simulate water quality accurately over a wide range of flow conditions and was found to be more suitable to simulating point sources. The MBN model did not provide accurate results in terms of the simulation of individual daily water quality values; however, the general trends and frequency characteristics of the simulations were satisfactory. Despite some uncertainties, the MBN model remains useful for extending data for catchments with limited observed water quality data. The MBN model was found to be more suitable for South African conditions than QUAL2K, given the data requirements of each model and water quality and flow data available from the Department of Water and Sanitation. The MBN model was found to be particularly useful by providing frequency distributions of water quality loads or concentrations using minimal data that can be related to the risks of exceeding management thresholds.
- Format
- 114 pages, pdf
- Publisher
- Rhodes University, Faculty of Science, Institute for Water Research
- Language
- English
- Rights
- Jacobs, Haden
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Collections
RU Institute for Water Research, including the Unilever Centre for Environmental Water Quality (IWR)
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