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Comparative Case Study of Rainfall - Runoff Models over the Nyando River Basin

https://doi.org/10.20987/jmrs.08.2012.604

Volume 6:4
Stephen K. Rwigi , Alfred O Opere and Francis M. Mutua
Department of Meteorology, University of Nairobi
CORRESPONDING AUTHOR
Stephen K. Rwigi Department of Meteorology,
University of Nairobi P.O. Box 30197-00100, Nairobi, Kenya
(Manuscript received 9 June 2011, in final form 10 July 2012)

Abstract

The performance of some existing rainfall-runoff linear systems models; the Simple Linear Model (SLM), the Linear Perturbation Model (LPM), the Linearly Varying Gain Factor Model (LVGFM) and a conceptual model (the Soil Moisture Accounting and Routing (SMAR) are tested on data from the Nyando catchment. The linear systems models were applied to the Nyando catchment in both non-parametric and also under the constraint of the gamma function impulse responses. There was no meaningful loss of generality associated with the constraint of the gamma function. The data used in this study are: daily rainfall, evaporation and runoff. Arithmetic mean method was used to convert point rainfall measurements to areal average rainfall. Runoff and evaporation data were tested for consistency before application to the models. It was found that daily rainfall, runoff, and evaporation data over the Nyando catchment were of good quality. The results obtained from the application of the models to the catchment indicated that among the four models, the conceptual SMAR model had the best performance for Nyando. It was followed by the LVGFM, the LPM and the SLM in that order. Compared with the linear systems models, the SMAR model performed consistently better in both calibration and verification periods; an indication that the model is apparently superior compared to the linear systems models. We therefore concluded that among the models considered, the SMAR model is the best model suited for the Nyando catchment..Among the linear systems models, the LVGFM performed better than the other two. Hence in the absence of the SMAR model, LVGFM, which does almost as well as the SMAR model, may be applied.
Key words: Linear systems model, conceptual models, simple linear model, linear perturbation model, linearly varying gain factor model, Nyando catchment, calibration period, verification period, rainfall, evaporation, runoff
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