Browsing by Author "Johnson, Katelyn Ann."

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Beach water quality: a comprehensive analysis of the pathogenic pollution of the Durban coastline.
Johnson, Katelyn Ann.; Stretch, Derek Dewey.
South Africa's beaches have many local and international visitors. Various recreational activities occur along the Durban coastline, especially during the holiday seasons. Beach water quality is negatively affected by pathogenic pollution which enters coastal water via stormwater and river discharges. Poor water quality jeopardises public health and has an adverse effect on tourism and the economy. The focus of this research is pathogenic pollution of Durban‟s coastal waters. In an attempt to understand the changes and establish any trends in pathogenic water quality conditions over the past decade, a critical assessment and statistical review of the historical water quality conditions of the Durban beaches has been done. This involves a general statistical analysis and water quality classification according to the new South African Water Quality Guidelines. Statistical parameters considered include arithmetic mean, standard deviation, geometric mean, and percentiles. A total of 42 beaches were analysed. Beaches were grouped into 4 sections: Northern, City, Bluff, and Southern. Water quality data for E.coli and Enterococcus were analysed from 2003 to 2013. The highest concentrations of both bacteria occurred in summer and autumn most often. Generally the average levels of both bacteria have either remained consistent or increased. Large standard deviations noted indicate variability in pollution as they represent a large spread of data from the average pollution values. Geometric mean comparisons show that Enterococcus levels were generally higher than E.coli, but both bacteria follow same patterns. Classification of water quality conditions shows that water quality has deteriorated as the frequency of poor water quality has increased. Water quality is classified as poor more frequently based on Enterococcus when compared to E.coli. However, higher levels of E.coli are allowed than Enterococcus as per the guidelines. Beaches located near rivers and stormwater outfalls are adversely affected and are shown to exhibit poorer water quality conditions. A case study was completed involving the analysis of the beach water quality data for 2009 to 2013 to determine the possible eligibility of Durban‟s beaches to receive the Blue Flag Award. Based on the microbiological water quality, it is unlikely that Durban will be a “Blue Flag coastline” in the immediate future. Most beaches have not managed to consistently meet the criteria for both E.coli and Enterococcus. As of October 2014, 7 beaches had pilot status.
Detecting and assessing the impacts of outlier events and data availability on design rainfall and flood estimation in South Africa.
(2021) Singh, Keanu Reeve.; Smithers, Jeffrey Colin.; Johnson, Katelyn Ann.
Accurate Design Rainfall Estimation (DRE) and Design Flood Estimation (DFE) require long periods of quality-controlled data for the planning, design, operation, and improved flood risk assessment of hydraulic structures. However, observed hydrological data frequently include outlier events and there is a decline of hydrological monitoring in South Africa which may impact DRE and DFE. It is therefore necessary to assess the impact of outlier events and reduced data availability on DRE and DFE. The aims of this study were to: (a) assess the impact of outlier events on DRE and DFE in South Africa, (b) assess the performance of outlier detection methods under South African conditions, and (c) assess the impact of reduced data availability on DRE and DFE in South Africa. The impact of synthetic Low Outlier (LO) and High Outlier (HO) events on DRE and DFE from observed and synthetically generated data series were assessed. The performance of the BoxPlot, Modified Z-Score (MSZ) and Multiple Grubbs-Beck Test (MGBT) outlier detection methods were assessed. Record length and network density were reduced to assess the impact of reduced data availability on DRE and DFE. Results from the analysis of observed data show that design rainfall is impacted by up to 22% and design floods by up to 45% in the presence of LOs. Design rainfall is impacted by up to 16% and design floods by up to 46% in the presence of HOs. For synthetically generated data series, design rainfall and floods are impacted by up to 2% and 1% respectively in the presence of LOs and by up to 13% in the presence of HOs. At best, LOs in observed rainfall and streamflow data are under-detected by up to 6% and 30% respectively by the MGBT method, whereas HOs are over-detected up to 50% and 150% respectively by the MZS method. Design rainfall and flood events are impacted by up to 4% and 24% respectively by reduced record lengths, and by up to 4.5% and 60% respectively from a reduced gauged network. This study indicates that outlier detection be adopted as regular practice in South Africa and that additional national resources must be directed towards maintaining and improving the hydrological monitoring networks in South Africa.