Quantifying monthly areal rainfall uncertainty using a data-based stochastic rainfall generator
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Date
2015-04-28
Authors
Mkhize, Nhlakanipho
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Abstract
This study aims to establish whether a data-based model of incorporating uncertainty in daily
areal rainfall estimates can be adapted to a coarser monthly time scale and still provide
reasonable uncertainty estimates for water resource modelling applications. The daily
generator was formulated as a simple, efficient and robust model of stochastically generating
sequences of uncertainty-impacted areal rainfall estimates from point rainfall measurements.
The data - based model is tested on 8 catchments spread across South Africa. It is found
that the selected rain gauge combinations have an impact on one of the parameters of the
model (the scaling factor) and the degree of bias on the standard deviation and skewness
values of the generated stochastic sequences. A correlation-based rain gauge selection
approach is proposed to minimise this bias.
Statistical analysis of the generated stochastic areal rainfalls shows that the data-based
model provides realistic uncertainty estimates. However the actual bias on the low rainfalls
(< 20th percentile) is between 0.9 – 46.8%. The importance of these rainfalls in water
resource modelling applications though is low.