HEALTH AND SANITATION
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Mitchel (2007) used a 6-min time step over a
50-year simulation period, and concluded that the
selection of either the YAS or YBS operating rule
has negligible impact on the estimation of yield
and volumetric reliability. There are however other
algorithms rather than YAS and YBS; Ghisi (2010)
estimated rainwater tank sizing and potential for
potable water savings using an algorithm of the
Neptune computer program which is neither YAS
nor YBS.
MASS CURVE METHOD
The mass curve methods use a mass balance of
the worst drought recorded, thereby assuming that
a more severe drought will not occur in the future
(Ndiritu et al., 2014). The method further assumes
that the tank is initially full at the beginning of the
flow record and hence will be full at the start of
the critical period (McMahon and Adeyole, 2005).
Restriction control curves that are a function of
storage content cannot be handled by the method
(McMahon and Adeloye, 2005) and no estimate of
the expected reliability is provided (Ndiritu et al.,
2014). Moreover, the algorithm does not account for
storage losses.
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PROCEDURES BASED ON STOCHASTIC
DATA GENERATION
Storage estimates are made based on stochastic or
synthetic streamflow data in conjunction with one of the
critical period methods (McMahon and Adeyole, 2005).
One of the methods based on this procedure is the
behavioural analysis method discussed earlier.
PROBABILITY MATRIX METHODS
The probability matrix method is based on analytical
derivation of probability distribution functions of
design parameters; probabilistic modelling of the
storage process is possible without the need of
continuous simulations (Raimondi and Becciu, 2014).
The method considers a maximum of two isolated
rainfall events rather than the entire time series and
it assumes that the tank is full at the end of the first
rainfall event (Raimondi and Becciu, 2014).
The limitation in this method is that each year of the record
is simulated separately, thereby ignoring serial correlation of
the hydrological variables involved (Ndiritu et al., 2011a). PA
Next month we take an in-depth look at
RWH models.
November 2018 Volume 24 I Number 9
33