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Environmental Engineering

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    Water quality modeling study for Umhlangane River, South Africa.
    (2016) Macholo, Thabo Chadwick.; Kumarasamy, Muthukrishna Vellaisamy.
    Over the past few decades, river water quality has been a critical issue in many parts of the world due to various domestic, industrial and agricultural pollutants. The challenge lies in developing mechanisms and tools, that will assist us to mitigate, prevent or possibly reverse deteriorating river water quality. Water quality models are the most useful tools in describing river ecological conditions, assessing effects of water pollution and assisting decision makers for water quality management. They can be used to predict the changes of the water quality parameters like Dissolved Oxygen (DO), Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), etc. They also contribute in reducing the cost of labour and time needed to conduct field studies or experiments to some degree. One of the well-known water quality models is the Hydrologic Engineering Centre River Analysis System (HEC-RAS). This study aimed to assess pollutant transport characteristics of Umhlangane River north of Durban using the HEC-RAS model. Hydraulic outputs were produced by executing the hydraulic model for each defined point in time. The water quality simulation was obtained from the HECRAS model with modelled hydraulic data as inputs. The Hybrid Cells in Series (HCIS) model is a conceptual mixing cells based water quality model that has an advantage over the Fickian based advection dispersion equation model (ADE). An impulse response of the HCIS model matches with the same of the ADE, when the Peclet number is more than four. The HCIS model produced reasonable results in terms of percentage error when compared with actual recorded data. The simulation results of BOD and COD tend not to vary with time unlike the observed results due to average constant input of pollutants. A main advantage with this model is that it deals with first order ordinary differential equation and which can accommodate any reaction kinetics without any complexity in model equation unlike the ADE model. Thus this study aimed to derive a model component for the HCIS and investigated its ability to simulate water quality parameters such as BOD, COD and DO under predefined condition. The proposed model in this study yielded positive outcome at the upper reach of Umhlangane River with an average agreement between simulation results and the observed data. The work is concluded by rendering a future potential scope of the HCIS to incorporate nutrient dynamics and non-point source pollution.
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    The impacts of degraded vegetation on water flows: a case study in the Mzimvubu catchment.
    (2019) Sutcliffe, Roanne Ruth.; Toucher, Michele Lynn.
    The Mzimvubu River is the largest undeveloped river course in South Africa, with the Mzimvubu catchment set to undergo high levels of both social and economic development. A study was undertaken for the catchment with the aim being to determine the impacts of different land use management scenarios on the catchment water flows through the use of the ACRU model. The verification stage of the study involved the modelling of the baseline scenarios of two preselected catchments, viz. T35C and T32A/B/C, in order to perform statistical comparisons of both simulated and observed streamflow. Whilst a number of the desired statistics were out of the ±15% confidence range, the differences between observed and simulated variances and standard deviations were well within the range and the R2 and Nash-Sutcliffe Efficiency Index (Ef) factors, though not exceeding 0.7, were deemed acceptable. The verification of the two Mzimvubu catchments was not ideal, and it was hypothesised that this may have been due, in part, to the parameterisation of degraded areas in the ACRU model configuration Degradation of vegetation can be considered in a number of different ways (from loss of cover through to bush encroachment and poor burning practice), although in ACRU it has only been modelled as a pure loss of vegetative cover. A methodology for determining vegetation parameters was thus determined from Leaf Area Index (LAI) data for 2008-2017 for sites within degraded areas and pristine veld areas within protected sites, and included calculation of crop coefficient, interception and percentage surface cover parameters that were then used within ACRU as the degraded vegetation parameters. These parameters were then input into the model, with simulations being run for both study catchments using both the Kristensen and FAO dual crop coefficients, as well as a set of simulations using degraded parameters that were calculated by using a percentage change (between 10 and 15 % difference) on the existing Acocks veld parameters within the model. This percentage change yielded very minor changes to the initial verification simulations; however, the two other sets of runs using the different crop coefficients both made significant changes to the verification simulations. The T32A/B/C simulation improved by almost 20 % and was only just outside the range of ±15% for the Kristensen set of runs. The T35C simulation, on the other hand, worsened although a challenge existed insofar as only the natural and degraded vegetation Hydrological Response Units (HRUs) had updated parameters – the large amount of commercial forestry,a known streamflow reduction activity (SFRA), within the catchment could have played a role in the under simulation of all the catchment’s model runs. Lastly, land use change scenarios were then modelled by changing both vegetative parameters and the area of different HRUs within both the T35C and T32A/B/C catchments. The scenarios modelled considered land degradation in its many forms, from the degradation of natural vegetation and subsequent rehabilitation, the increase in bush encroachment, differing severities and timing of burning, changes in areas under irrigated and dryland agriculture, and the conversion of traditional dryland crops to biofuel crops. These different scenarios proved to have different sensitivities to change, although all scenarios showed a lessening in the sensitivity as the area under change increased. Given the problems with both rainfall and streamflow records, further research on remote sensing and satellite imagery could provide another source of data for both climatic and land use. Further to this, the methodology used to determine the degraded vegetation parameters using remotely sensed data was shown to be an explicit and repeatable method and can be extended to incorporate the calculation of the parameters of other land uses, such as forestry and agricultural practices. This could be done in conjunction with in situ studies to test whether the methodology works for all types of land use.