Browsing by Author "Jovanovic, Nebo"

Now showing 1 - 6 of 6
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    Assessing the Effects of Land Use on Surface Water Quality in the Lower uMfolozi Floodplain System, South Africa
    (International Journal of Environmental Research and Public Health, 2021-01-11) Chirima, George; Dlamini, Mandla; Jovanovic, Nebo; Adam, Elhadi
    This study investigated the impacts of cultivation on water and soil quality in the lower uMfolozi floodplain system in KwaZulu-Natal province, South Africa. We did this by assessing seasonal variations in purposefully selected water and soil properties in these two land-use systems. The observed values were statistically analysed by performing Student’s paired t-tests to determine seasonal trends in these variables. Results revealed significant seasonal differences in chloride and sodium concentrations and electrical conductivity (EC) and the sodium adsorption ratio (SAR) with cultivated sites exhibiting higher values. Most of the analyzed chemical parameters were within acceptable limits specified by the South African agricultural-water-quality (SAWQ) water quality guidelines for irrigation except for sodium adsorption ratio (SAR), chloride, sodium and EC. EC, pH and nitrate content which were higher than the specified SAWQ limits in cultivated sites. Quantities of glyphosate, ametryn and imidacloprid could not be measured because they were below detectable limits. The study concludes that most water quality parameters met SAWQ’s standards. These results argue for concerted efforts to systematically monitor water and soil quality characteristics in this environment to enhance sustainability by providing timely information for management purposes.
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    Assessment of the spatiotemporal dynamics of the hydrological state of non-perennial river systems and identification of flow-contributing areas
    (South African Water Research Commission, 2024) Maswanganye, Sagwati E; Dube, Timothy; Jovanovic, Nebo; Kapangaziwiri, Evison; Mazvimavi, Dominic
    Non-perennial rivers (NPRs) have three hydrological states; each state has its importance, function and implication for water resource management. The dynamics of these states have been inadequately assessed and understood. Hence, this study sought to determine the spatiotemporal variations in the hydrological conditions of NPRs, focusing on the Touws river–karoo drylands and Molototsi river within the semi-arid region of the Limpopo province of South Africa. Additionally, the study aimed to delineate and characterize the primary areas contributing to runoff in these two river systems. Sentinel-1 and sentinel-2 satellite data sources were employed in this study. Specifically, the modified normalized difference water index (MNDWI) derived from sentinel-2 was utilized to delineate water surface areas along the two rivers. Subsequently, these derived datasets were utilized to assess the hydrological states over a 32-month period (2019–2022). Based on the presence of water, the river’s state was classified as flowing, pooled, or dry. The results showed that remote sensing can be used to determine the hydrological state of the two river systems with ~90% overall accuracy. However, there is about a 30% chance that a flow event can be missed using Sentinel-2 due to clouds and temporal resolution. Some of these gaps can be filled using synthetic aperture radar (SAR) data (Sentinel-1), as demonstrated with the Molototsi river. In the Molototsi catchment, the upper catchment contributes the majority of flows. For the Touws river, the southwestern part of the catchment was determined as the major contributing area for the observed flows. This suggests that the chosen observation site might not be representative of upper catchment dynamics; therefore, a monitoring site in the upper catchment is required. This study provided hydrological information and an approach that can be used to monitor the hydrological states for better understanding and management of NPRs and catchments
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    A comparison of ensemble and deep learning algorithms to model groundwater levels in a data-scarce aquifer of Southern Africa
    (MDPI, 2022) Gaffoor, Zaheed; Pietersen, Kevin; Jovanovic, Nebo
    Machine learning and deep learning have demonstrated usefulness in modelling various groundwater phenomena. However, these techniques require large amounts of data to develop reliable models. In the Southern African Development Community, groundwater datasets are generally poorly developed. Hence, the question arises as to whether machine learning can be a reliable tool to support groundwater management in the data-scarce environments of Southern Africa. This study tests two machine learning algorithms, a gradient-boosted decision tree (GBDT) and a long short-term memory neural network (LSTM-NN), to model groundwater level (GWL) changes in the Shire Valley Alluvial Aquifer.
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    Investigation of water use and trends in South Africa: a case study for the Mzimvubu to Tsitsikamma Water Management Area 7 (WMA7)
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025) Jovanovic, Nebo; Mulangaphuma, Lawrence
    This paper investigated sectoral water use and trends in the Mzimvubu to TsitsikammaWater Management Area 7 (WMA7). The investigation considered the Water Authorisation and Registration Management System (WARMS) database and field surveys as a source of water use information. The study was able to successfully make use of time series statistical analysis to show water use trends for identified priority sectors over a 5-year period by sourcing historical water use data of the study area. Further, the groundwater stress index and streamflow impact were applied to assess water use impacts on the surface and groundwater. The WARMS database and field survey results identified major sectoral water users such as agriculture (irrigation), municipal water services, dam storage, afforestation, power generation, recreation, mining, and industries. Study findings revealed that the agricultural sector is a major water user, with an estimated 60% of the total waterrequirement over a 5-year period (2018 to 2022). The application of the groundwater stress index revealed that the majority of the Quaternary catchments have surplus groundwater available. The application of streamflow impact revealed that the majority of catchments have low flow or no flow. The rise of water use clearly indicates a lack of water use compliance and enforcement. An increase in total water use could put water resources under stress, including an impact on the aquatic ecosystem, reduced water quality, and economic and social consequences. Therefore, the study recommends that a follow-up on compliance of surface water and groundwater use licenses be regularly conducted.
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    Use of multi-source remotely sensed data in monitoring the spatial distribution of pools and pool dynamics along non-perennial rivers in semi-arid environments, South Africa
    (Taylor and Francis Group, 2022) Maswanganye, Sagwati Eugene; Dube, Timothy; Jovanovic, Nebo
    This study explored the use of multi-source remotely sensed data in monitoring the spatial distribution of pools and pool dynamics in two distinct semi-arid sites in South Africa. The factors that control the pool dynamics were also examined. Three water extraction indices were used, these included Normalised Difference Water Index (NDWI), Modified NDWI and Normalised Difference Vegetation Index. In addition, random forest classifier and Sentinel-1 SAR data were used in mapping pools and pools dynamics for both sites.
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    Using the water balance approach to understand pool dynamics along non-perennial rivers in the semi-arid areas of South Africa
    (Elsevier, 2022) Maswanganye, Sagwati E.; Dube, Timothy; Jovanovic, Nebo
    The Touws River in the Klein Karoo region of South Africa Study focus: This study sought to improve the understanding of pool dynamics along non-perennial rivers (NPRs) by utilising the water balance approach to assess the water fluxes that influence pool dynamics in the Touws River. The water balance model made use of various in-situ and satellite-derived data. New hydrological insights: The analysis of the water losses from the pool showed that most of the water was lost through evaporation. The interaction between the pool and groundwater is dependent on the water levels, as the pool loses water to the subsurface up to a certain depth then it starts gaining. When the Wolverfontein 2 pool is full, it can retained water for approximately 258 days without having a surface water inflow.