scholarly journals REVIEW OF WATER RESOURCES AND MANAGEMENT IN SOUTH AFRICA

2020 ◽  
Author(s):  
ELKINGTON SIBUSISO MNGUNI
Keyword(s):  
South Africa ◽  
Water Resources

Climate change impacts on water resources in a rural community in Limpopo province, South Africa: a community-based adaptation to water insecurity

2020 ◽  
Vol 12 (5) ◽  
pp. 587-598
Author(s):  
Sejabaledi Agnes Rankoana
Keyword(s):  
Climate Change ◽  
South Africa ◽  
Water Resources ◽  
Water Supply ◽  
Rural Community ◽  
Limpopo Province ◽  
Community Based ◽  
Content Type ◽  
Adaptation Practices ◽  
Community Based Adaptation

Purpose The study explored the impacts of climate change on water resources, and the community-based adaptation practices adopted to ensure water security in a rural community in Limpopo Province, South Africa. Design/methodology/approach The study was conducted in Limpopo Province, South Africa. The participatory approach was used to allow community members to share their challenges of water scarcity, and the measures they have developed to cope with inconsistent water supply. Findings The study results show that the community obtains water for household consumption from the reticulation system supplied by Mutale River and the community borehole. These resources are negatively impacted by drought, change in the frequency and distribution of rainfall, and increased temperature patterns. The water levels in the river and borehole have declined, resulting in unsustainable water supply. The community-based adaptation practices facilitated by the water committee include observance of restrictions and regulations on the water resources use. Others involve securing water from neighbouring resources. Originality/value This type of community-based action in response to climate change could be used as part of rural water management strategies under climate change.

scholarly journals Inter basin water transfers

1985 ◽  
Vol 4 (3) ◽  
pp. 93-98 ◽  
Author(s):  
T. P. C. Van Robbroeck
Keyword(s):  
South Africa ◽  
Economic Development ◽  
Water Resources ◽  
National Interest ◽  
Transfer Scheme ◽  
Water Transfers ◽  
Vaal River ◽  
Run Off ◽  
Basin Water ◽  
Reserve Storage

Most economic development in South Africa has taken place on the dry plateau of the interior following the discovery of minerals. The much smaller wetter parts get most of the run-off and have generally limited possibilities for advantageous use of their water resources. Consequently, it is logical to transfer water between basins with a surplus to ones experiencing shortages. There is a body of opinion that such transfers should be stopped in the interest of the decentralistion policy, but such action is not considered in the national interest. The intricate system of inter-basin transfer from the Komati, the Usutu and the Vaal River to supply the Eastern Transvaal coalfields is described. This system has shown its flexibility during the recent drought. The most important inter-basin transfer scheme is the Tugeia- Vaal Project which is described in broad terms. The advantages of the principle of reserve storage and of co-operation with Escom are dealt with. Possible further projects to augment the Vaal River supplies are mentioned. Other inter-basin transfer schemes implemented by the Department of Water Affairs such as the Orange River Project, the Riviersonderend-Berg River Project and others of lesser importance are dealth with.

scholarly journals Modelling streamflow response to hydro-climatic variables in the Upper Mkomazi River, South Africa

2014 ◽  
Author(s):  
◽  
Oluwaseun Kunle Oyebode
Keyword(s):  
South Africa ◽  
Water Resources ◽  
Genetic Programming ◽  
Predictive Models ◽  
Model Development ◽  
De Algorithm ◽  
Planning And Management ◽  
Ann Models ◽  
Artificial Neural

Streamflow modelling remains crucial to decision-making especially when it concerns planning and management of water resources systems in water-stressed regions. This study proposes a suitable method for streamflow modelling irrespective of the limited availability of historical datasets. Two data-driven modelling techniques were applied comparatively so as to achieve this aim. Genetic programming (GP), an evolutionary algorithm approach and a differential evolution (DE)-trained artificial neural network (ANN) were used for streamflow prediction in the upper Mkomazi River, South Africa. Historical records of streamflow and meteorological variables for a 19-year period (1994- 2012) were used for model development and also in the selection of predictor variables into the input vector space of the models. In both approaches, individual monthly predictive models were developed for each month of the year using a 1-year lead time. Two case studies were considered in development of the ANN models. Case study 1 involved the use of correlation analysis in selecting input variables as employed during GP model development, while the DE algorithm was used for training and optimizing the model parameters. However in case study 2, genetic programming was incorporated as a screening tool for determining the dimensionality of the ANN models, while the learning process was further fine-tuned by subjecting the DE algorithm to sensitivity analysis. Altogether, the performance of the three sets of predictive models were evaluated comparatively using three statistical measures namely, Mean Absolute Percent Error (MAPE), Root Mean-Squared Error (RMSE) and coefficient of determination (R2). Results showed better predictive performance by the GP models both during the training and validation phases when compared with the ANNs. Although the ANN models developed in case study 1 gave satisfactory results during the training phase, they were unable to extensively replicate those results during the validation phase. It was found that results from case study 1 were considerably influenced by the problems of overfitting and memorization, which are typical of ANNs when subjected to small amount of datasets. However, results from case study 2 showed great improvement across the three evaluation criteria, as the overfitting and memorization problems were significantly minimized, thus leading to improved accuracy in the predictions of the ANN models. It was concluded that the conjunctive use of the two evolutionary computation methods (GP and DE) can be used to improve the performance of artificial neural networks models, especially when availability of datasets is limited. In addition, the GP models can be deployed as predictive tools for the purpose of planning and management of water resources within the Mkomazi region and KwaZulu-Natal province as a whole.

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