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HYPROP-FIT filters the unreliable
K(h)
data pairs near saturation depending on
θ
the measuring accuracy of the tensio shafts (Peters and Durner, 2008). After
θ(h)
this parametric functions
(h
)
and
K
(h
)
are adjusted to the measuring points
i
i
i
i
and
K(h)
gained by non-linear optimization. In HYPROP-FIT the user can
select the type of function; all usual models can be found (van Genuchten,
1980; Brooks and Corey, 1964; Kosugi, 1996; Fredlung-Xing, 1994) in uni- and
bi-modal form as well as in a more sophisticated modelling as Peters-Durner-
Iden (PDI) variant (Peters, 2013; Iden und Durner, 2014). You can find a com-
plete description of the evaluation procedure as well as the models and the
curve fitting in the HYPROP-FIT manual (http://www.umsmuc.de/static/Ma-
nual_HYPROP.pdf) and in Peters et al., (2015).
θ(h)
Parameter optimization
The
and
K(h)
functions are adapted simultaneously to the data points.
This is essential as distinct parameters (i. e
and
n
) at van Genuchten/Mua-
lem) influence the shape of both functions.
The adaption is accomplished by a non-linear regression under minimization
of the sum of all assessed squares of the distance between data points and
model forecast. However, the assumption the water content is spread out
linear over the column is not always fulfilled in coarse, pored or structured soil.
Therefore, the so called "integral fit" is applied for the adaption of the retenti-
on function to avoid a systematic error (Peters and Durner, 2006). For details of
the fitting procedure and data assessment please refer to Peters and Durner
(2007, 2008) and Peters et al., (2015).
81 | Theory
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