Hydrology Research In Press, Uncorrected Proof © IWA Publishing 2012  |  doi:10.2166/nh.2012.017

Self-organising map rainfall-runoff multivariate modelling for runoff reconstruction in inadequately gauged basins

Adebayo J. Adeloye and Rabee Rustum

School of the Built-Environment, Heriot-Watt University, Riccarton, Edinburgh, EH14 4AS, UK E-mail: a.j.adeloye@hw.ac.uk
College of Civil Engineering, Damascus University, Damascus, Syria

First received 17 January 2011; accepted in revised form 26 September 2011. Available online 8 May 2012

Water resources assessment activities in inadequately gauged basins are often significantly constrained due the insufficiency or total lack of hydro-meteorological data, resulting in huge uncertainties and ineffectual performance of water management schemes. In this study, a new methodology of rainfall-runoff modelling using the powerful clustering capability of the Self-Organising Map (SOM), unsupervised artificial neural networks, is proposed as a viable approach for harnessing the multivariate correlation between the typically long record rainfall and short record runoff in such basins. The methodology was applied to the inadequately gauged Osun basin in southwest Nigeria for the sole purpose of extending the available runoff records and, through that, reducing water resources planning uncertainty associated with the use of short runoff data records. The extended runoff records were then analysed to determine possible abstractions from the main river source at different exceedance probabilities. This study demonstrates the successful use of emerging tools to overcome practical problems in sparsely gauged basins.

Keywords: hydrological data; Nigeria; rainfall-runoff modelling; self organising map (SOM); water resources assessment

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