Articles | Volume 22, issue 10
Hydrol. Earth Syst. Sci., 22, 5081–5095, 2018
https://doi.org/10.5194/hess-22-5081-2018
Hydrol. Earth Syst. Sci., 22, 5081–5095, 2018
https://doi.org/10.5194/hess-22-5081-2018

Cutting-edge case studies 02 Oct 2018

Cutting-edge case studies | 02 Oct 2018

Rainfall-runoff modelling using river-stage time series in the absence of reliable discharge information: a case study in the semi-arid Mara River basin

Petra Hulsman et al.

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Cited articles

Alvisi, S., Mascellani, G., Franchini, M., and Bárdossy, A.: Water level forecasting through fuzzy logic and artificial neural network approaches, Hydrol. Earth Syst. Sci., 10, 1–17, https://doi.org/10.5194/hess-10-1-2006, 2006. 
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Short summary
In many river basins, the development of hydrological models is challenged by poor discharge data availability and quality. In contrast, water level data are more reliable, as these are direct measurements and are unprocessed. In this study, an alternative calibration method is presented using water-level time series and the Strickler–Manning formula instead of discharge. This is applied to a semi-distributed rainfall-runoff model for the semi-arid, poorly gauged Mara River basin in Kenya.