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Relationships Among Surface Water Resources in the WR90, WR2005 and WR2012 Datasets of South Africa Using Mean Annual Runoff of Quaternary Catchments

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Abstract

The study used mainly regression models as predictive tools among mean annual runoff (MAR) pertaining to hydrological dataset a of surface water resources (WR) of South Africa; i.e. WR90, WR2005 and WR2012. MAR is the hydrological catchment response. It was found that linear models were generally suitable to correlate any pair of datasets. The level of linearity was measured by the coefficient of determination (R2), hence correlation coefficient. The relatively high values of R2 of the models suggested that no drastic climatic variability occurred in the South African hydrology, i.e. with regard to the quaternary catchment (QC) of the different water management areas (WMAs). Hence, linear mathematical relationships could well describe the temporal evolution of surface water resources in South Africa, in terms of MAR as hydrological response, and indirectly could give an indication of the climate variability between the different datasets.

Keywords

  • Regression models
  • Mean annual runoff
  • Quaternary catchment
  • Climate variability

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References

  • Bai Y, Chen Z, Xie J, Li C (2016) Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models. J Hydrol 532:193–206

    CrossRef  Google Scholar 

  • Besaw LE, Rizzo DM, Bieman PR, Hackett WR (2010) Advances in ungauged streamflow prediction using artificial neural networks. J Hydrol 386:27–37

    CrossRef  Google Scholar 

  • Gupta PK, Chauhan S, Oza MP (2016) Modelling surface run-off and trends analysis over India. J Earth Syst Sci 125(6):1089–1102

    Google Scholar 

  • Meshgi A, Schmitter P, Chui TFM, Babovic V (2015) Development of a modular streamflow model to quantify runoff contributions from different land uses in tropical urban environments using genetic programming. J Hydrol 525:711–723

    CrossRef  Google Scholar 

  • Middleton BJ, Bailey AK. Water resources of South Africa, 2005 Study (WR2005). User’s guide. Water research commission report no TT 513/11, Version 2, 2011. Pretoria, RSA

    Google Scholar 

  • Molina-Sanchis I, Lázaro R, Arnau-Rosalén E, Calvo-Cases A (2016) Rainfall timing and runoff: the influence of the criterion for rain event separation. J Hydrol Hydromech 64(3):226–236

    CrossRef  Google Scholar 

  • Naeem UA, Nisar H, Ejaz N (2012) Development of empirical equations for the peak flood of the Chenab River using GIS. Arab J Sci Eng 37:945–954

    CrossRef  Google Scholar 

  • Onyari E, Ilunga F. Application of MLP neural network and M5P model tree in predicting streamflow: a case study of Luvuvhu catchment, South Africa. In: International conference on information and multimedia technology (ICMT 2010), Hong Kong, China, pp V3-156–160

    Google Scholar 

  • Qaderi M, Khaleqi MR, Dastorani MT, Chenari KS (2014) A comparative study of the efficiency of artificial neural network and multivariate regression in prioritizing climate factors affecting runoff generation in research plots: a case study of Sangane Station, Khorasan Razavi. Int Bull Water Resour Dev (IBWRD) (II)(04)-S.N. (07):XL1–LIII

    Google Scholar 

  • Rajsekhar D, Singh VP, Mishra AK (2015) Multivariate drought index: an information theory based approach for integrated drought assessment. J Hydrol 526:164–182

    CrossRef  Google Scholar 

  • Samuel J, Coulibaly P, Metcalfe R (2011) Estimation of continuous streamflow in Ontario ungauged basins: comparison of regionalisation methods. J Hydrol Eng ASCE, 447–459

    Google Scholar 

  • Singh VP, Woolhiser DA (2002) Mathematical modeling of watershed hydrology. J Hydrol Eng 7(4):270–292

    CrossRef  Google Scholar 

  • Tayfur G, Brocca L (2015) Fuzzy logic for rainfall-runoff modelling considering soil moisture. Water Resour Manag 29:519–3533

    CrossRef  Google Scholar 

  • Water Resources of South Africa, 2012 Study (WR2012). http://waterresourceswr2012.co.za/. Accessed on 29 Sept 2016

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Correspondence to Masengo Ilunga .

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Ilunga, M. (2020). Relationships Among Surface Water Resources in the WR90, WR2005 and WR2012 Datasets of South Africa Using Mean Annual Runoff of Quaternary Catchments. In: Matondo, J.I., Alemaw, B.F., Sandwidi, W.J.P. (eds) Climate Variability and Change in Africa . Sustainable Development Goals Series. Springer, Cham. https://doi.org/10.1007/978-3-030-31543-6_9

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