Ph.D. / The toxicant dichlorodiphenyl-trichloroethane, commonly known as DDT, is a broad spectrum insecticide and is currently banned in most countries due to its toxic effects. However, in some countries restricted use of DDT has been authorized as an effective vector control within malarial control programmes. South Africa is one such country, where spraying of DDT occurs in three provinces including the Limpopo Province, KwaZulu Natal and Mpumalanga. Specifically in the Limpopo Province, spraying of DDT has been ongoing for almost 56 years within the eastern malaria belt of the province. Despite this long term spraying there is still a scarcity of data regarding DDT and its effects on indigenous aquatic organisms in South Africa. Any research regarding DDT will therefore be of the utmost value. It was in this context that the present study was initiated, which primarily aimed to assess the extent of contamination within DDT sprayed areas in South Africa and the associated effects on indigenous species, whilst identifying techniques that could be used in future monitoring of these areas. This assessment was done in the Luvuvhu River catchment at three reference sites and four exposure sites situated within the areas where indoor residual spraying of DDT is done annually. At these sites the extent of DDT contamination within the water, sediment and biota (using the bioindicator pecies C. gariepinus from only the lentic sites) in the Luvuvhu river was evaluated. The results showed that DDT concentrations were well above recommended levels in all three of the measured phases, with the highest concentrations predominantly observed at the Xikundu weir. This site was particularly impacted by DDT due to a combination of its close proximity to the DDT sprayed areas, concentration accumulation from upstream sources and environmental conditions that accentuated contamination. These elevated levels of DDT did, however, not induce significant quantifiable effects in the bioindicator C. gariepinus or in the fish and macro-invertebrate community structures. Specifically, the effects in the catfish, C. gariepinus, were assessed using a range of biomarkers specific to the endocrine disrupting effects of DDT, including indirect measures of vitellogenin (calcium, zinc, magnesium and alkali-labile phosphate (ALP) that are all present on the VTG molecule in high abundances), gonad-somatic index (GSI), condition factor (CF), analysis of covariance (ANCOVA) manipulated gonads, protein carbonyls (PC) and intersex. Although none of these biomarkers could be significantly correlated with the DDT contaminations, DDT was shown to induce a slight sub-organismal effect by slightly inducing the synthesis of ALP and Ca as well as reducing the gonad mass (shown by GSI and adjusted gonad mass biomarkers) and body condition. In contrast, the fish and macroinvertebrate communities showed no conclusive relationship with DDT contamination, using a variety of methodologies, including informal assessments, univariate diversity indices, multivariate statistics, abundance models, fish response assessment index (FRAI) as well as average score per taxon (ASPT) and Ephemeroptera, Plecoptera and Trichoptera (EPT) richness. In conclusion, it was shown that DDT concentrations within the Luvuvhu River only induced effects at the lower levels of complexity, which highlights the importance of the utilisation of biomarkers to measure more subtle long-term effects as compared to the usage of community level effects.

PhD (Environmental Sciences) / Department of Hydrology and Water Resources / Luvuvhu River Catchment (LRC) exhibits diverse land use and land cover patterns that are influenced by seasonality and socio-cultural practices of the local communities. From 1950, the catchment has been undergoing land cover changes caused by expanding villages, new urban centres and clearing forest land for agriculture. Conversion of natural landscape for agricultural and urban purposes degraded the catchment by negatively affecting the hydrologic processes. This study was therefore conducted to evaluate the impact of land cover and land use change on the hydrology and water resources of LRC. Geographical Information Systems (GIS) and remote sensing techniques were applied to evaluate the impact of the changes on the catchment. Remotely sensed imagery was used as the primary sources of data for classification and detection of changes. Digital Elevation Models (DEMs) were used for hydrologic and geomorphic modeling in combination with information from remotely sensed imagery. Field data sets for soil and meteorology were obtained from selected sampling segments, based on the area frame sampling. The method of direct expansion was used to quantify land use classes. Flood frequency was analysed using probability distribution methods at recurrence intervals of 2, 5, 10, 20, 25, 50, 100, and 200 years. The FAO CROPWAT software based on Penman-Montheith equation was used to assess the impact of land cover changes on evapotranspiration regimes. To study the hydrological response of land cover change in the catchment, the Soil Conservation Services-Curve Number (SCS-CN) method was first used independently to simulate surface runoff and investigate the impact of land use change on runoff under historical land cover regimes. The Soil and Water Assessment Tool (SWAT) model was then applied in the Tshakhuma-Levubu subcatchment to assess the impact of land management practices on the soil and water bodies in the catchment. The results indicated that changes were having negative impacts on the hydrology of the catchment. The impact of land use and land cover change on hydrology of LRC was manifested in stream flow, surface runoff, suspended sediment and flood frequency and magnitudes. There was significant land cover and land use change from forestland, woodland and open grassland to medium size farms, subsistence agriculture and built-up land. These developments were concentrated on hillsides and hilltops in the catchment and they were of concern as they were ix impacting on the hydrological processes. Throughout the 2000’s, land use change revealed a decrease in natural forest from 32.15% to 20.67%, giving rise to agriculture which rose to 38.57% in 2010. Runoff was observed to be highly variable during the month of February with maximum runoff records of 1.63 m3 and 3.84 m3 upstream and downstream, respectively. Flood frequency results showed that an increase in the peak discharges was to be expected, especially for the discharge range corresponding to smaller and medium flood magnitudes. The use of imagery and DEMs within GIS was found to efficiently represent ground surface and allow automated extraction of features, thus bringing advantages in terms of processing efficiency, cost effectiveness, and accuracy assessments. This technique could therefore be adopted to improve land use planning, water management, and rapid identification of slopes and elevations in consideration for their functional and structural requirements. Analysis showed that the SWAT model was suitable for predicting the location and extent of pollution in the catchment. It assumed sheet and rill erosion as the dominant erosion type contributing to siltation and water pollution in rivers. The study recommends close monitoring and sustained enforcement of the rural land use regulations to prevent the conversion of land to urban land use. / NRF

Recurring drought conditions have always been an endemic feature of climate in South Africa, limiting maize development and production. However, recent projections of the future climate by the Intergovernmental Panel on Climate Change suggest that due to an increase of atmospheric greenhouse gases, the frequency and severity of droughts will increase in drought-prone areas, mostly in subtropical climates. This has raised major concern for the agricultural sector, particularly the vulnerable small-scale farmers who merely rely on rain for crop production. Farmers in the Luvuvhu River catchment are not an exception, as this area is considered economically poor, whereby a significant number of people are dependent on rain-fed farming for subsistence. This study was therefore conducted in order to improve agricultural productivity in the area and thus help in the development of measures to secure livelihoods of those vulnerable small-scale farmers. Two drought indices viz. Standardized Precipitation Evapotranspiration Index (SPEI) and Water Requirement Satisfaction Index (WRSI) were used to quantify drought. A 120-day maturing maize crop was considered and three consecutive planting dates were staggered based on the average start of the rainy season. Frequencies and probabilities during each growing stage of maize were calculated based on the results of the two indices. Temporal variations of drought severity from 1975 to 2015 were evaluated and trends were analyzed using the non-parametric Spearman’s Rank Correlation test at α (0.05) significance level. For assessing climate change impact on droughts, SPEI and WRSI were computed using an output from downscaled projections of CSIRO Mark3.5 under the SRES A2 emission scenario for the period 1980/81 – 2099/100. The frequency of drought was calculated and the difference of SPEI and WRSI means between future climate periods and the base period were assessed using the independent t-test at α (0.10) significance level in STATISTICA software. The study revealed that planting a 120-day maturing maize crop in December would pose a high risk of frequent severe-extreme droughts during the flowering to the grain-filling stage at Levubu, Lwamondo, Thohoyandou, and Tshiombo; while planting in October could place crops at a lower risk of reduced yield and even total crop failure. In contrast, stations located in the low-lying plains of the catchment (Punda Maria, Sigonde, and Pafuri) were exposed to frequent moderate droughts following planting in October, with favorable conditions noted following the December planting date. Further analysis on the performance of the crop under various drought conditions revealed that WRSI values corresponding to more intense drought conditions were detected during the December planting date for all stations. Moreover, at Punda Maria, Sigonde and Pafuri, it was observed that extreme drought (WRSI <50) occurred once in five seasons, regardless of the planting date. Temporal analysis on historical droughts in the area indicated that there had been eight agricultural seasons subjected to extreme widespread droughts resulting in total crop failure i.e. 1983/84, 1988/89, 1991/92, 1993/94, 2001/02, 2002/03, 2004/05 and 2014/15. Results of Spearman’s rank correlation test revealed weak increasing drought trends at Thohoyandou (ρ = of 0.5 for WRSI) and at Levubu and Lwamondo (ρ = of 0.4 for SPEI), with no significant trends at the other stations. The study further revealed that climate change would enhance the severity of drought across the catchment. This was statistically significant (at 10% significance level) for the near-future and intermediate-future climates, relative to the base period. Drought remains a threat to rain-fed maize production in the Luvuvhu River catchment area of South Africa. In order to mitigate the possible effects of droughts under climate change, optimal planting dates were recommended for each region. The use of seasonal forecasts during drought seasons would also be useful for local rain-fed maize growers especially in regions where moisture is available for a short period during the growing season. It was further recommended that the Government ensure proper support such as effective early warning systems and inputs to the farmers. Moreover, essential communication between scientists, decision makers, and the farmers can help in planning and decision making ahead of and during the occurrence of droughts. / Agriculture, Animal Health and Human Ecology / M. Sc. (Agriculture)

Thesis (M.S.)--University of Wyoming, 2007. / Title from PDF title page (viewed on Nov. 21, 2008). Includes bibliographical references.

MESHWR / Department of Hydrology and Water Resources / This study was aimed at investigating the long term seasonal and annual changes in rainfall duration and magnitude at Luvuvhu River Catchment (LRC). Rainfall in this catchment is highly variable and is characterised of extreme events which shift runoff process, affect the timing and magnitude of floods and drought, and alter groundwater recharge. This study was motivated by the year to year changes of rainfall which have some effects on the availability of water resources. Computed long term total seasonal, annual rainfall and total number of seasonal rainy days were used to identify trends for the period of 51 years (1965- 2015), using Mann Kendal (MK), linear regression (LR) and quantile regression methods. The MK, LR and quantile regression methods have indicated dominance of decreasing trends of the annual, seasonal rainfall and duration of seasonal rainfall although they were not statistically significant. However, statistical significant decreasing trends in duration of seasonal rainfall were identified by MK and LR at Matiwa, Palmaryville, Levubu, and Entabeni Bos stations only. Quantile regression identified the statistically significant decreasing trends on 0.2, 0.5 and 0.7 quantiles only in the Palmaryville, Levubu and Entabeni Bos, respectively. Stations with non-statistically significant decreasing trends of annual and seasonal rainfall had magnitude of change ranging from 0.12 to 12.31 and 0.54 to 6.72 mm, respectively. Stations with non-statistically increasing trends of annual and seasonal rainfall magnitude had positive magnitude of change ranging from 1.51 to 6.78 and 2.05 to 6.51 mm, respectively. The Study recommended further studies using other approaches to determine the duration of rainfall to improve, update and compare the results obtained in the current study. Continuous monitoring and installation of rain gauges are recommended on the lower reaches of the catchment for the findings to be of complete picture for the whole catchment and to also minimize the rainfall gaps in the stations. Water resources should be used in a sustainable way to avoid water crisis risk in the next generations. / NRF

PhD / An important component of fish health is an optimally functioning reproductive system. The Luvuvhu River Catchment in the Limpopo Province, South Africa, is a tropical, high-risk malaria area where 1,1,1-trichloro-2,2-bis(p-chlorophenyl)ethane (DDT), an endocrine disrupting chemical (EDC), has been used annually since 1945 as a malaria vector control. DDT is known to affect testes morphology and motility of fish sperm. As such, testicular histology and sperm motility (kinematic) parameters were studied as reproductive indicators of the reproductive capacity for two wild, indigenous fish species (Oreochromis mossambicus and Clarias gariepinus) from the currently DDT sprayed area. Three field studies were carried out over two years (2007 – 2008), including two high flow (HF) periods and one low flow (LF) period [HF 1 (March 07), LF (October 07), HF 2 (February 08)]. Both species were sampled from three sites on the Luvuvhu River for testicular histology and computer assisted sperm analysis (CASA), during all three field studies. The sites included a reference site outside the DDT sprayed area, Albasini Dam (AD), and two exposed sites within the DDT sprayed area, Xikundu Weir (XW) and Nandoni Dam (ND). CASA, based on open-source software, was used for the first time in South Africa to assess sperm kinematic parameters of indigenous fish species in field conditions. These included percent motile sperm (% MOT), curvilinear velocity (VCL μm s-1), velocity of an average path (VAP μm s-1), straight line velocity (VSL μm s-1), linearity (LIN %), progression (PROG μm), and average efficiency (AVE. EFF.). Water and sediment samples were collected during all field studies from the three sites for metal and EDC analysis. Controlled laboratory studies were also carried out on the sperm of both species, externally sourced from aquaculture farms equipped to breed and raise fish in toxicant free water. The laboratory studies involved in vitro exposure of spermatozoa to two different, but environmentally relevant, concentrations of both DDT (DDT 1: 0.27 μg L-1; DDT 2: 0.5 μg L-1) and 1,1-dihloro-2,2-bis(p-chlorophenyl)ethylene (DDE) (DDE 1: 0.11 μg L-1; DDE 2: 1.0 μg L-1) with the aim to provide data to support the possible outcomes found in the field studies using CASA. Furthermore, peroxidation of sperm lipids was assayed by production of malondialdehyde (MDA) after in vitro exposure of spermatozoa to DDT and DDE. DDT and its metabolites were found in varying concentrations in the water from all three sites (0.1 μg L-1 – 1.2 μg L-1). Levels of dieldrin (3.5 μg L-1) and lindane (9.4 μg L-1) residues were also found at XW in HF 2. The histological results revealed alterations to testis tissue of both species at all three sites. The testes were assessed through the identification of alterations and an organ index was calculated: Testes Index (IT). The index is indicative of the histological response in the respective tissue type. O. mossambicus at XW had the highest mean IT value during LF (7.45 ± 5.73) and for all field studies combined (5.47 ± 4.63), primarily due to the occurrence of testicular oocytes (intersex), where the frequency of prevalence was 72.73% and 58.82% respectively. These results were statistically higher than the laboratory control (C) group. The CASA results showed statistical differences primarily for O. mossambicus, where motility parameters were lower at XW when compared to AD. Laboratory exposures found a decrease in sperm motility (% MOT) between the control (C) group and the DDT 1, DDE 1 and DDE 2 exposed groups for C. gariepinus. No significant differences were seen for lipid peroxidation (MDA). On the other hand, no significant differences were seen in CASA parameters between the control and exposed laboratory groups for O. mossambicus, but there was an increase in MDA production from the control to the DDT 1 exposure group.

PhDH / Department of Hydrology and Water Resources / Demand for water resources has been on the increase and is compounded by population growth and related development demands. Thus, numerous sectors are affected by water scarcity and therefore effective management of drought-induced water deficit is of importance. Luvuvhu River Catchment (LRC), a tributary of the Limpopo River Basin in South Africa has witnessed an increasing frequency of drought events over the recent decades. Drought impacts negatively on communities’ livelihoods, development, economy, water resources, and agricultural yields. Drought assessment in terms of frequency and severity using Drought Indices (DI) in different parts of the world has been reported. However, the forecasting and prediction component which is significant in drought preparedness and setting up early warning systems is still inadequate in several regions of the world. This study aimed at characterising, assessing, and predicting drought conditions using DI as a drought quantifying parameter in the LRC. This was achieved through the application of hybrid statistical and machine learning models including predictions via a combination of hybrid models. Rainfall and temperature data were obtained from South African Weather Service, evapotranspiration, streamflow, and reservoir storage data were obtained from the Department of Water and Sanitation while root zone soil moisture data was derived from the NASA earth data Giovanni repository. The Standardised Precipitation Index (SPI), Standardised Precipitation Evapotranspiration Index (SPEI), Standardised Streamflow Index (SSI), and Nonlinear Aggregated Drought Index (NADI) were selected to assess and characterise drought conditions in the LRC. SPI is precipitation based, SPEI is precipitation and evapotranspiration based, SSI is based on streamflow while NADI is a multivariate index based on rainfall, potential evapotranspiration, streamflow, and storage reservoir volume. All indices detected major historical drought events that have occurred and reported over the study area, although the precipitation based indices were the only indices that categorised the 1991/1992 drought as extreme at 6- and 12- month timescales while the streamflow index and multivariate NADI underestimated the event. The most recent 2014/16 drought was also categorised to be extreme by the standardised indices. The study found that the multivariate index underestimates most historical drought events in the catchment. The indices further showed that the most prevalent drought events in the LRC were mild drought. Extreme drought events were the least found at 6.42%, 1.08%, 1.56%, and 4.4% for SPI, SPEI, SSI, and NADI, respectively. Standardised indices and NADI showed negative trends and positive upward trends, respectively. The positive trend showed by NADI depicts a decreased drought severity over the study period. Drought events were characterised based on duration, intensity, severity, and frequency of drought events for each decade of the 30 years considered in this study i.e. between 1986 – 1996, 1996 – 2006, 2006 – 2016. This was done to get finer details of how drought characteristics behaved at a 10-year interval over the study period. An increased drought duration was observed between 1986 - 1996 while the shortest duration was observed between 1996 - 2006 followed by 2006 - 2016. NADI showed an overall lowest catchment duration at 1- month timescale compared to the standardised indices. The relationship between drought severity and duration revealed a strong linear relationship across all indices at all timescales (i.e. an R2 of between 0.6353 and 0.9714, 0.6353 and 0.973, 0.2725 and 0.976 at 1-, 6- and 12- month timescales, respectively). In assessing the overall utilisation of an index, the five decision criteria (robustness, tractability, transparency, sophistication, and extendibility) were assigned a raw score of between one and five. The sum of the weighted scores (i.e. raw scores multiplied by the relative importance factor) was the total for each index. SPEI ranked the highest with a total weight score of 129 followed by the SSI with a score of 125 and then the SPI with a score of 106 while NADI scored the lowest with a weight of 84. Since SPEI ranked the highest of all the four indices evaluated, it is regarded as an index that best describes drought conditions in the LRC and was therefore used in drought prediction. Statistical (GAM-Generalised Additive Models) and machine learning (LSTM-Long Short Term Memory) based techniques were used for drought prediction. The dependent variables were decomposed using Ensemble Empirical Mode Decomposition (EEMD). Model inputs were determined using the gradient boosting, and all variables showing some relative off importance were considered to influence the target values. Rain, temperature, non-linear trend, SPEI at lag1, and 2 were found to be important in predicting SPEI and the IMFs (Intrinsic Mode Functions) at 1, 6- and 12- month timescales. Seven models were applied based on the different learning techniques using the SPEI time series at all timescales. Prediction combinations of GAM performed better at 1- and 6- month timescales while at 12- month, an undecomposed GAM outperformed the decomposition and the combination of predictions with a correlation coefficient of 0.9591. The study also found that the correlation between target values, LSTM, and LSTM-fQRA was the same at 0.9997 at 1- month and 0.9996 at 6- and 12- month timescales. Further statistical evaluations showed that LSTM-fQRA was the better model compared to an undecomposed LSTM (i.e. RMSE of 0.0199 for LSTM-fQRA over 0.0241 for LSTM). The best performing GAM and LSTM based models were used to conduct uncertainty analysis, which was based on the prediction interval. The PICP and PINAW results indicated that LSTM-fQRA was the best model to predict SPEI timeseries at all timescales. The conclusions drawn from drought predictions conducted in this study are that machine learning neural networks are better suited to predict drought conditions in the LRC, while for improved model accuracy, time series decomposition and prediction combinations are also implementable. The applied hybrid machine learning models can be used for operational drought forecasting and further be incorporated into existing early warning systems for drought risk assessment and management in the LRC for better water resources management. Keywords: Decomposition, drought, drought indices, early warning system, frequency, machine learning, prediction intervals, severity, water resources. / NRF

MESHWR / Department of Hydrology and Water Resources / See the attached abstract below