South African Weather Service Research Articles

Monthly rainfall prediction for different climatic zones in south Africa for 2024 using a random forest model

This study predicted 2024 rainfall in different climatic zones in South Africa using a random forest model. Previous studies have shown that random forests performed better than other models for rainfall prediction. South Africa was divided into nine using the Koppen-Geiger climate classification system, and three cities were selected for each climatic zone. Atmospheric datasets from the South African Weather Service and the National Aeronautics and Space Agency were used for this study. The datasets were trained, tested, and validated to assess the model's accuracy. With good forecast ability observed, the random forest was then used for monthly rainfall for 2024. The result of this prediction was then compared with 2022 and 2023 rainfall. The result indicated months where much rain should be expected in various cities and cities that may likely experience droughts. This result is particularly important for agriculture, water resource management, and early drought/flooding warning systems.

Maternal exposure to heat and its association with miscarriage in rural KwaZulu-Natal, South Africa: A population-based cohort study.

We sought to improve the current understanding of how climate change impacts women's reproductive health in sub-Saharan Africa. We investigated the relationship between maternal heat exposure and miscarriage (pregnancy ending before 20 weeks gestation) in a South African setting. Population-based cohort study. Our study involved data for pregnancies collected via a health and demographic surveillance system in rural KwaZulu-Natal, South Africa between 2012 and 2016. Data from the South African Weather Service were used to compute maternal exposure to heat during the following time windows for each pregnancy: during the month preceding conception (T1) and during the week preceding the study outcome (either a miscarriage or no miscarriage, T2). Heat exposure was operationalized as a continuous variable and defined as the number of days that a mother was exposed to a mean daily temperature of > 26.6°C (A "hot day," equivalent to a mean daily temperature of > 80°F) during T1 or T2. Binary logistic regression was used to investigate the relationship between maternal heat exposure and miscarriage. A total of 105/3477 pregnancies included in our analysis ended in miscarriage (3.0%). Each additional hot day during T1 was associated with a 26% higher odds of miscarriage (odds ratio: 1.26; 95% confidence interval: 1.15-1.38). No significant associations were observed between maternal heat exposure during T2 and the odds of miscarriage (odds ratio: 0.94, 95% confidence interval: 0.73-1.20). The relationship between maternal heat exposure during T1 and the odds of miscarriage was J-shaped. There is a clear relationship between maternal heat exposure during the month preceding conception and miscarriage in our sub-Saharan African setting. Given the lack of feasible strategies to reduce pregnancy loss associated with prevailing high temperatures in sub-Saharan Africa, progressive climate change will likely exacerbate existing challenges for women's reproductive health in this region.

An Analysis of the Effects of Clouds in High-Resolution Forecasting of Surface Shortwave Radiation in South Africa

Abstract We assess site-specific surface shortwave radiation forecasts from two high-resolution configurations of the South African Weather Service numerical weather prediction model, at 4 and 1.5 km. The models exhibit good skill overall in forecasting surface shortwave radiation, with zero median error for all radiation components. This information is relevant to support a growing renewable energy sector in South Africa, particularly for photovoltaics. Further model performance analysis has shown an imbalance between cloud and solar radiation forecasting errors. In addition, cloud overprediction does not necessarily equate to underestimating solar radiation. Overcast cloud regimes are predicted too often with an associated positive mean radiation bias, whereas the relative abundance of partly cloudy regimes is underpredicted by the models with mixed radiation biases. Challenges highlighted by the misrepresentation of partly cloudy regimes in solar radiation error attribution may be used to inform improvements to the numerical core, namely, the cloud and radiation schemes. Significance Statement This paper provides the first comprehensive assessment of high-resolution site-specific NWP forecasts of surface shortwave radiation in South Africa, exploring clouds as the main drivers of prediction biases. Error attribution analyses of this kind are close to none for this part of the world. Our study contributes to understanding how cloud and radiation schemes perform over South Africa, representing a step forward in the state of the art. In addition to the scientific interest, the capabilities developed through this work may benefit the second largest economy of the continent. In a country where energy security is of critical relevance, the availability of useful and usable weather information is paramount to support its industry and socioeconomic growth.

Trends and impacts of temperature and fire regimes in South Africa’s coastal national parks: implications for tourism

Climate change poses significant risks on coastal national park-based tourism through its effect on accessibility, comfort levels, and spatiotemporal changes of attractions. Wildfires and extreme temperatures have become issues of concern. Due to the widespread occurrence of fires in national parks along coastal areas and the noticeable changes in temperature regimes, more needs to be done to deepen understanding of their trends and impacts and devise appropriate management regimes. This study analyses the trends and impacts of fires and temperature-related variables in six coastal South African national parks, namely Agulhas, Garden Route, West Coast, Table Mountain, Namaqua, and Addo Elephant National Park. The triangulation and convergence model of the mixed-methods research design was adopted as the inquiry strategy. Data on statistical weather variables from the South African Weather Services and the National Aeronautics and Space Administration were used to calculate temperature trends using Mann–Kendall trend tests and homogeneity analysis. Data collection included questionnaire surveys, key informant interviews, field observations, and document analysis. The study observed statistically significant (p < 0.05) changes in temperature-related variables in all coastal national parks. There is also an increase in the intensity and spatial spread of fires, resulting in increased fire damage over time. Despite the current efforts of the coastal parks to manage wildfires and the changing climatic patterns, the fire and temperature regimes are threatening the biophysical environment with resultant effects on tourism and the economic viability of the national parks. The need to reengineer the fire suppression mechanisms and temperature-resilient tourism facilities has been observed.

Characteristics of warm season left-moving supercells over the Highveld of South Africa

A supercell is a type of thunderstorm known to produce severe weather conditions that result in damage to property, injuries, and loss of life. Supercells were previously considered rare in South Africa, however since the implementation of the South African Weather Service's single polarisation S-band Doppler radar network in 2010, numerous cases have been identified. Despite this, there is a lack of research on supercells over South Africa, with very little information on their characteristics specific to the region. The aim of this study is to determine the characteristics of warm season left-moving supercells over the Highveld (Gauteng and Mpumalanga province) of South Africa. This was done using a radar derived database, consisting of 115 left-moving supercells, that occurred during the warm seasons (September to February) of 2010/11 to 2019/20. Left-moving supercells were found to be most common in October and November, peaking in the afternoon between 15:00 and 16:00 South African Standard Time (SAST). A shift in the seasonal distribution was observed, with the most active area over Gauteng in November, moving south-eastwards to the south-eastern parts of Mpumalanga by December. Supercell hotspots were identified over the south-western and eastern Highveld of Gauteng as well as the south-eastern parts of Mpumalanga, of which some areas of occurrence appeared to be influenced by topography. The average track direction was found to be from the south-west, with a southerly shift as the season progressed. On average supercells lasted approximately 1 h 12 min, travelled 49 km and at a speed of 41 kmh−1. Having a better understanding of the characteristics of supercells may provide a heightened awareness of preferred areas and times of occurrence of events, how they travel, how long they last and how fast and far they travel, thereby assisting in producing more accurate forecasts, nowcasts and warnings. More detailed studies are required to understand the influence of topography and the atmospheric conditions on these characteristics.

Investigation of Wind Power Potential in Mthatha, Eastern Cape Province, South Africa

South Africa is currently grappling with a national energy crisis and the high infrastructure costs associated with expanding the national grid to remote areas. Simultaneously, the government has made substantial efforts to harness renewable energy technologies, particularly wind energy. The average wind speed in a specific region significantly influences the energy yield from wind turbines. The vast open inland terrains, mountainous regions, and coastal areas in the Northern Cape, Eastern Cape, and Western Cape provinces of South Africa possess the most substantial wind potential. It is imperative to initiate wind energy projects in these provinces to cater to a significant portion of the local electricity demand, especially in remote areas disconnected from the national grid. Wind energy generation is inherently stochastic, subject to variations in both time and space. Consequently, it is essential to gain a comprehensive understanding of the local wind patterns to assess the feasibility of utilizing wind resources. In the Eastern Cape Province, the Mthatha area still lags in household electrification, presenting an opportunity to electrify some households using wind energy. This study aimed to evaluate the wind resource potential for Mthatha area, utilizing data spanning from 2018 to 2023, provided by the South African Weather Services. Two distribution models, the two-parameter Weibull and three-parameter Weibull, were employed to characterize the provided wind data. To determine the parameters associated with each distribution model, two estimation methods, the Maximum Likelihood Method (MLM) and the Method of Moments (MOM), were utilized. The performance of these distribution models was assessed using the Root Mean Square Error (RMSE) statistical indicator. The results showed that Mthatha area predominantly experiences low wind speeds, with an annual average wind speed of 3.30 m/s and an overall wind power density of approximately 48.48 W/m2. The prevailing winds predominantly originate from the south and east–southeast directions. Consequently, Mthatha is recommended for stand-alone applications, with the added suggestion of augmented wind turbines for the area.

Hydro-power generation forecast in South Africa based on Machine Learning (ML) models

With the advancement of technology and the ever-growing need for electronics, electricity has become an indispensable aspect of modern life. Developing or underdeveloped nations must overcome a number of obstacles to balance the supply and demand for electricity. The difference between supply and demand for electricity has a significant impact on economic growth. Energy forecasting will be crucial in helping policymakers recognize unanticipated changes in the demand for electricity under certain circumstances in a timely manner. Energy constitutes one of the most critical components in a country's growth. To achieve this, machine learning (ML) model was created for hydropower plant electricity generation predictions. In this study, the forecasting model examined the meteorological variables in South Africa. The data included how the South African Weather Service categorized different years from 2001 to 2019 in the southern hemisphere based on climatological and social factors. The simulation allows for an experimental paradigm for the established model to explore the behaviour and performance of the components effective for prospective saving variables related to the design and applications of forecasting hydropower generation in South Africa. In addition, a variety of time-series-based models was used and supervised machine learning techniques to predict the output of energy. The precipitation model achieved regression fitting results of the highest accuracy with training R-value = 0.99976), validation (R-value = 0.99991), and testing (R-value = 0.99931). In addendum, variation in future precipitation and how hydropower plants will generate power in order to adapt to climate change were also addressed in this research. With a mean square error (MSE) of just 1.7262, our suggested model can forecast the production of hydro-electricity generation. The machine learning models created for hydroelectricity generation will minimize operating costs and maximize the energy output of hydropower generation. The suggested remedy is predicated on hydropower plant energy generation forecasts for the future, which can help decision-makers make more informed choices. Given the local weather, the suggested research project will aid in reducing the widening gap between energy output and demand.

The missing links in climate services for health and heat-health services: Examining climate-heat services in peri-urban districts in South Africa

Climate services for health can facilitate health resilience and adaptation to climate change, particularly if they are well-calibrated to promote wellness and save lives. In this study, the status of climate services for health in South Africa’s Agincourt sub-district, Mpumalanga province, was assessed. A qualitative case study methodology encompassing multiple methods of data collection was used. The results show that climate services for health in the Agincourt sub-district, albeit essential, are fragmented and underdeveloped. Scientifically informed heat-health services are non-existent. Notwithsatnding this gap, healthcare and allied professionals are aware of the importance of climate services for health. The main barrier to climate services delivery is the paucity of interagency coordination; for example, coordination to plan and respond to climate-health information between the South African Weather Services and the Departments of Health and Education is lacking. Inclusive climate services for health are essential for positive prevention and treatment outcomes. Future studies must provide an investment case for climate services for health, demonstrating the benefits of acting and the costs of inaction.

Estimation of intelligent-based quality of service in terrestrial FSO links: A case of South Africa propagation paths

Free Space Optical (FSO) communication is one of the popular technologies’ dominants in the optical communication domain. Despite all the advantages, FSO links are grossly affected by severe atmospheric conditions such as fog, rain, snow, smoke, and dust or aerosol particles suspended in the air. Fog is one of the major challenges for free-space optics to achieve carrier-class availability and causes extreme attenuation. A 10-year (2010––2019) visibility data were obtained from the South African Weather Services (SAWS) with a special focus on Cape Town (–33.925° S, 18.424° E). This extracted and processed Cape Town data was used to estimate the needed fog-induced attenuation. The intelligent algorithm is rooted in the theory of Artificial Neural Networks (ANN) systems which intelligently control the sub-tropical climate conditions of the chosen region. The adopted network-based technique is capable to interact with ever-changing weather by generating appropriate improvements to the free space optical communication link quality of service for efficient services to the customers. In this study, an Intelligent Algorithm (IA) based on an Artificial Neural Network (ANN) has been utilized to improve the reliability of QoS that can satisfy customer SLAs and improve received signal over FSO communications. The results obtained by implementing the IA system indicates that the power transmitted range from 8 to 100 dB and the adjusted Signal to Noise Ratio (SNR) between 390–––420 dB at 650 nm optical wavelength, whereas the same trend could be observed when the optical wavelength of 1550 nm was applied. Subsequently, the model effectively estimated the automatic transmit power control for enhancement in SNR and resulting in better QoS at the receiving end, regardless of the related weather changes along with the transmission link.

A new flood chronology for KwaZulu-Natal (1836–2022): the April 2022 Durban floods in historical context

ABSTRACT In April 2022, flooding and associated geohazards caused major loss of life and extensive damage in the greater Durban region and large areas of the KwaZulu-Natal (KZN) coastal zone. Heavy rainfall that triggered the flooding and mass movement events was reported in national and international media as having ‘smashed weather records’. However, no systematic and up-to-date flood record exists for KZN to allow the April 2022 floods to be viewed within their full historical context. This study presents an historical geographic account of flooding in KZN, with a particular focus on the greater Durban region. The flood record expands upon available databases held by the South African Weather Service, drawing on missionary accounts, newspapers and personal diaries to identify all significant flood events in KZN since the mid-nineteenth century. We document 53 significant flood events from 1850-1899 (average ~1.1 per annum) and 210 from 1900-2022 (average ~1.7 per annum). Within the limits of our data, we suggest that the frequency of flooding in Durban has likely doubled over the last century. Our research confirms that the April 2022 floods were likely the most catastrophic natural disaster yet recorded in KZN, in collective terms of lives lost and overall economic impact.

Heatwave Variability and Structure in South Africa during Summer Drought

Pronounced subsidence leading to summer drought over southern Africa causes warmer than average surface air temperatures or even heatwave (HW) conditions. We investigated the occurrence of HWs during the summer drought over South Africa based on station data and the ECMWF ERA5 reanalyses. Temperature observations from the South African Weather Service were analyzed for seasonality and long-term trends (1981–2020) as background to the occurrence and variability of HWs. We focused on three severe El Niño Southern Oscillation (ENSO)-induced drought seasons, i.e., 1982/83, 1991/92, and 2015/16, to investigate HW characteristics. While 1997/98 was among the strongest El Niño seasons, the impacts were not as severe because it coincided with an intense Angola low, which allowed for rain-bearing cloud bands to form. Results showed that the hottest months were spread across the austral summer season from December to February. Regions experiencing high mean maximum temperatures and high HW frequencies exhibited a strong ENSO signal, with record HWs occurring during 2015/16. The establishment and persistence of a middle-level high-pressure system over Botswana/Namibia (Botswana High) appears to trigger the longest-lasting HWs during drought seasons. The Botswana high is usually coupled with a near-surface continental heat low and/or tropical warm air advection towards the affected region. It was also found that intense ENSO-induced drought events coincided with high HW frequency over South Africa, such as during 1982/83, 1991/92, and the recent 2015/16 events. The results of this study contribute to understanding drought and heat wave dynamics in a region experiencing rapid warming as a result of climate change.

Apparent Temperature Modifies the Effects of Air Pollution on Cardiovascular Disease Mortality in Cape Town, South Africa

Cardiovascular disease (CVD) is the top cause of mortality and a main contributor to disability globally. The evidence so far is varied on whether cold or heat modifies the CVD effects of air pollution. Weather conditions and air pollution sources and levels are different in different countries. Studies in low-and middle-income countries are lacking. Mortality data were obtained from Statistics South Africa. Air pollution and meteorological data were obtained from the South African Weather Service. A time-stratified case–crossover epidemiological design was applied. The association between air pollutants (PM10, NO2 and SO2) and CVD mortality was investigated using conditional logistic regression models. Susceptibility by sex and age groups was investigated. In total, 54,356 CVD deaths were included in the 10-year study. The daily PM10, NO2 and SO2 levels exceeded the daily WHO guidelines on 463, 421 and 8 days of the 3652 days, respectively. Higher air pollution risks were observed in this study compared to those reported in meta-analyses. In general, the elderly and females seemed to be vulnerable to air pollutants, especially at high and moderate apparent temperature levels. Harvesting effects were observed at longer lags. The results can be used to develop an early warning system for the city.

Health Risks of Temperature Variability on Hospital Admissions in Cape Town, 2011-2016.

Epidemiological studies have provided compelling evidence of associations between temperature variability (TV) and health outcomes. However, such studies are limited in developing countries. This study aimed to investigate the relationship between TV and hospital admissions for cause-specific diseases in South Africa. Hospital admission data for cardiovascular diseases (CVD) and respiratory diseases (RD) were obtained from seven private hospitals in Cape Town from 1 January 2011 to 31 October 2016. Meteorological data were obtained from the South African Weather Service (SAWS). A quasi-Poisson regression model was used to investigate the association between TV and health outcomes after controlling for potential effect modifiers. A positive and statistically significant association between TV and hospital admissions for both diseases was observed, even after controlling for the non-linear and delayed effects of daily mean temperature and relative humidity. TV showed the greatest effect on the entire study group when using short lags, 0-2 days for CVD and 0-1 days for RD hospitalisations. However, the elderly were more sensitive to RD hospitalisation and the 15-64 year age group was more sensitive to CVD hospitalisations. Men were more susceptible to hospitalisation than females. The results indicate that more attention should be paid to the effects of temperature variability and change on human health. Furthermore, different weather and climate metrics, such as TV, should be considered in understanding the climate component of the epidemiology of these (and other diseases), especially in light of climate change, where a wider range and extreme climate events are expected to occur in future.

Risk Factors and Occurrence of Bovine Brucellosis in the Limpopo Province, South Africa

Background: Bovine brucellosis causes substantial loses in livestock production systems through abortions and infertility. The aim of this study was to determine whether the occurrence of bovine brucellosis is influenced by climatic variabilities. Methods: Data on confirmed cases of bovine brucellosis from the year 2013 to 2018 was retrieved from the Limpopo Department of Agriculture Land Reform and Rural Development. Temperature and rainfall data was retrieved from the South African Weather Services. A semi structured questionnaire was used to gather information on management related risk factors from 60 purposively selected farmers. Data were analysed using microsoft excel, simple linear regression, Pearson’s correlation coefficient and Mann-Kendall test. Result: The average occurrence rate of bovine brucellosis was 3.96%. Simple linear regression coefficient analysis indicated a non-significant (p greater than 0.05) effect of temperature and rainfall variabilities on occurrence of bovine brucellosis. Seventy seven per cent of cattle farmers were aware of bovine brucellosis and 83% experienced cases of abortion in their herds.

Meteorological Drought Variability and Its Impact on Wheat Yields across South Africa

Drought is one of the natural hazards that have negatively affected the agricultural sector worldwide. The aims of this study were to track drought characteristics (duration (DD), severity (DS), and frequency (DF)) in South Africa between 2002 and 2021 and to evaluate its impact on wheat production. Climate data were collected from the South African Weather Service (SAWS) along with wheat yield data from the Department of Agriculture, Forestry and Fisheries (2002-2021). The standard precipitation index (SPI) was calculated on 3-, 6-, 9-, and 12-month time scales, and the trend was then tracked using the Mann-Kendall (MK) test. To signify the climatic effects on crop yield, the standardized yield residual series (SYRS) was computed along with the crop-drought resilience factor (CR) on a provincial scale (2002-2021). The output of the SPI analysis for 32 stations covering all of South Africa indicates a drought tendency across the country. On a regional scale, western coastal provinces (WES-C and NR-C) have been more vulnerable to meteorological droughts over the past 20 years. Positive correlation results between SYRS and wheat yield indicate that the WES-C province was highly influenced by drought during all stages of wheat growth (Apr-Nov). Historical drought spells in 2003, 2009, and 2010 with low CR = 0.64 caused the province to be highly impacted by the negative impacts of droughts on yield loss. Overall, drought events have historically impacted the western part of the country and dominated in the coastal area. Thus, mitigation plans should be commenced, and priority should be given to this region. These findings can assist policymakers in budgeting for irrigation demand in rainfed agricultural regions.

Klerksdorp Tornado on March 4, 2007, in South Africa: A Synoptic Overview

This research delves into the factors contributing to tornado formation in South Africa, with a specific focus on the Klerksdorp tornado that occurred on March 4, 2007, in Northwest Province. Despite their recurrent occurrence and significant potential for damage, tornadoes have received relatively little attention. Between 1990 and 2014, these weather phenomena incurred estimated costs exceeding half a billion American dollars in addition to other weather-related disasters. In this study, data from multiple observation systems, including the National Oceanic and Atmospheric Administration (NOAA), the National Centers for Environmental Prediction (NCEP), the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) in Germany, and the South African Weather Service (SAWS), were thoroughly analyzed. The findings reveal that the Klerksdorp tornado was linked to a cold front and a cut-off low, which were the dominant weather systems on the day of the tornado. This case study enhances our comprehension of tornado dynamics in South Africa, aiding in improving short-term forecasts and potentially early warning systems. Future research should concentrate on the recurring nature of tornadoes in association with tropical weather systems and locally-driven factors.

The Impact of Climate Change on Gender Inequality in the Labour Market: A Case Study of South Africa

South Africa has been exposed to climate change and has been experiencing associated extreme climatic events such as droughts, floods, and heat waves. These have impacted water and fuel sources, habitats, human health, and economic productivity. Poorer populations and particularly females are more affected. The main objective of this study is therefore to assess gender inequalities in employment resultant from the effects of climate change and extreme climatic events. The study employs binary, ordered, and multinomial logistic models to analyse the effects on employment, intensity of employment and the effects in economic sectors, respectively. The study computes temperature deviations from its long-run mean as climate change variable and uses the Keetch–Byram Drought Index and number of heatwave days per year as proxies for extreme climatic events. Data for the work are from the South African Weather Services database and the National Income Dynamic Survey. The findings suggest that climate change reduces the probability of being employed more for males than females, but extreme events have more negative effects on female employment than males. We suggest that while climate change mitigations and adaptation measures geared towards the labour market should take priority in general, when extreme climate events occur, labour market support measures should weigh more towards females.

Measurement of indoor and outdoor SO2 and NO2 and indoor BTEX/VOC concentrations at pre-schools in Mabopane Township, Tshwane Metropolitan Municipality, Gauteng Province, South Africa

Introduction: Children spend approximately 7 hours a day at pre-schools, mostly indoors. The aim of the study was to assess indoor and outdoor SO2 and NO2 and indoor BTEX/VOC at pre-schools situated in Mabopane, Tshwane Metropolitan Municipality, Gauteng Province, South Africa Methodology: Six pre-schools in the study area were purposively selected to participate in the study. Indoor and outdoor levels of SO2 and NO2, and indoor BTEX/VOC were assessed over 24hrs for two weeks using Radiello passive samplers. Indoor temperature and relative humidity were also monitored using temperature data loggers. The outdoor temperature and relative humidity data was provided by South African Weather Services. Results: The indoor and outdoor SO2 median levels were 0.22 &amp;#x3bc;g.m-3 and 1.12 &amp;#x3bc;g.m-3 The indoor and outdoor NO2 median levels were 21.10 &amp;#x3bc;g.m-3 and 17.22 &amp;#x3bc;g.m-3, respectively. The indoor/outdoor ratios for NO2 and SO2 were 0.20 and 1.07, respectively. The infiltration factor parameter estimates of NO2 and SO2 were 0.48 and 0.01. The indoor and outdoor temperature and relative humidity levels were 2.11 &amp;#xb0;C and 22.4 &amp;#xb0;C, and 43.14% and 57%. The median concentrations of BTEX ranged from 0.5µg/m3 to 43.19µg/m3. The Kruskal-Wallis test showed that the level of benzene in the six schools differed significantly - χ^2=13.87,p-value=0.016. The levels of toluene at the six school did not differ significantly - χ^2=5.96,p-value=0.310. There was no statistically significant difference between the six schools in terms of ethylbenzene - χ^2=6.30,p-value=0.2710. The p-value for Xylene showed there was no statistically significant difference between the six schools - χ^2=6.01,p-value=0.2967.. Conclusion: The levels of outdoor NO2, SO2 and BTEX/VOC were below the South African National Ambient Air Quality Standards and WHO Air Quality Guidelines. However, they still pose a health risk for children attending pre-school in the area. Keywords: Air pollution; SO2, NO2, BTEX/VOC, Temperature; Relative humidity; South African

The Effect of Rainfall on Escherichia coli and Chemical Oxygen Demand in the Effluent Discharge from the Crocodile River Wastewater Treatment; South Africa

The declining state of municipal wastewater treatment is one of the major contributors to the many pollution challenges faced in most parts of South Africa. Escherichia coli and Chemical Oxygen Demand are used as indicators for the performance of wastewater treatment plants. Wastewater treatment plant (WWTP) efficiency challenges are associated with susceptibility to seasonal variations that alter microbial density in wastewater. This study sought to investigate the effect of rainfall on E. coli and COD in the effluent wastewater discharged from the Crocodile River, Mpumalanga Province, South Africa. To cover the spatial distribution of the pollutant in the Crocodile River, water samples were collected from 2016 to 2021 at three strategic sites. The rainfall data was acquired from the South African Weather Services from 2016 to 2021, which contains daily rainfall measurements for each sampling site. Data analysis was carried out using Microsoft Excel 2019, Seaborn package, and Python Spyder (version 3.8). The White River, which is located on the upper stream, recorded the highest COD levels of 97.941 mg/L and 120.588 mg/L in autumn and spring, respectively. Matsulu WWTP was found to have the highest E. coli concentration per milliliter (72.47 cfu/100 mL) in the spring compared to any other location or time of year. The results also indicated that each of the sampling sites recorded above 60 (cfu)/100 mL of E. coli in Kanyamazane (spring), Matsulu (summer), and White River (winter). It was noted that the rainfall is a significant predictor (p &lt; 0.004) of E. coli. Additionally, it was discovered during the data analysis that the rainfall parameter did not significantly affect COD prediction (p &gt; 0.634), implying that rain was not a reliable predictor of COD.

Analysis of trends, recurrences, severity and frequency of droughts using standardised precipitation index: Case of OR Tambo District Municipality, Eastern Cape, South Africa.

South Africa is susceptible to droughts. However, little documentation exists on drought occurrence in South Africa at national, provincial and municipal administrative boundaries. This study profiles hydrological drought in OR Tambo District Municipality from 1998 to 2018, computing frequency, severity and intensity in order to show areas of high vulnerability. Data used were obtained from South African Weather Services. Standardised precipitation index (SPI) was calculated using the Meteorological Drought Monitor (MDM) software. Results showed a wide variation in monthly precipitation throughout the year. Coastal areas receive higher rainfall than inland municipalities. The study revealed that Nyandeni experienced the highest drought frequency of 62%, Mhlontlo (58%), King Sabatha Dalindyebo Municipality (57%), Ngquza Hill (55%) and Port St Johns Municipality showing the least at 52%. Hydrological drought severity frequency and duration varied between seven days and nine weeks. Drought intensity class exposed the annual average intensity for the five local municipalities represented as follows: KSDM (–0.71), PSJM (–0.99), Ngquza Hill (–0.81), Nyandeni (–0.71) and Mhlontlo (–0.62). The longest drought duration across OR Tambo was experienced in 2014 with durations varying from 3 to 11 weeks across the municipalities. OR Tambo District Municipality is susceptible to hydrological droughts and the extent varies across local municipalities. Results could be used for both adaptation planning and mitigating the impacts of future droughts. In addition, they could assist in guiding allocation of drought relief resources in ways that prioritise drought prone and vulnerable municipality.