Estimation of areai reduction factors using daily rainfall data and a geographically centred approach

Abstract

This paper presents the estimation of geographically centred and probabilistically correct areal reduction factors (ARFs) from daily rainfall data to explain the unique relationship between average design point rainfall and average areal design rainfall estimates at a catchment level in the C5 secondary drainage region in South Africa as a pilot case study. The methodology adopted is based on a modified version of Bell's geographically centred approach. The sample ARF values estimated varied with catchment area, storm duration and return period, hence confirming the probabilistic nature. The derived algorithms also provided improved probabilistic ARF estimates in comparison to the geographically and storm-centred methods currently used in South Africa. At a national level, it is envisaged that the implementation and expansion of the methodology will ultimately contribute towards improved ARF estimations at a catchment level in South Africa. Consequently, the improved ARF estimations will also result in improved design flood estimations.