PE EIE[R-Rg RESEARCH ON - HJ Andrews Experimental Forest

Parameter Estimation
In order to estimate conductivity (K) and
diffusivity (D), both of which are functions of
soil water content (0), we used standar d
laboratory and computational procedures
(Richards 1948, Green and Corey 1971) . The
so-called moisture release curve, a plot of ,1i
against 0, was also obtained in the laboratory .
Curves for K, D, and as functions of 0 are
shown in figure 1 .
Our Cedar River core samples of the
Everett gravelly sandy loam were biased to an
unknown degree by the removal of the large
stone fraction before the laboratory analysis .
These stones constitute perhaps up to 20 per -
cent of the volume of the Everett soil series .
Because these stones tend to impede the flo w
of water in unsaturated soils-the path aroun d
them is longer than it would be for smaller
obstructions-we believe our estimates of K
may be somewhat too large . This decrease in
conductivity associated with the presence o f
large stones is especially noteworthy if th e
soil is highly unsaturated because water
strongly held by matric forces flows only
along capillary paths around the stones an d
not directly through the interstices of aggregations
of large stones and cobbles .
Since conductivity of water in unsaturate d
soils is extremely difficult to measure directly,
it has become common practice to estimate
this parameter from pore-size distribution
data, or equivalently, from the soil
moisture release curve. Green and Corey
(1971) have reviewed three methods fo r
calculating K based on pore-size distribution
and found that all give good results whe n
compared with measured data . Our curve for
K as a function of 0 is based on laboratory
measurements of K for the saturated Everett
soil and adjusted for unsaturated soils by us e
of the Marshall pore-interaction relationship
with a matching factor (Marshall 1958) .
The shape of the moisture release curve i s
also affected by sampling procedures. Removal
of large stones clearly biases the result s
in the direction of a soil with smaller pores .
Accordingly, the correct relationship is some -
what to the left of the one determined in the
laboratory . This implies that the slope aO i s
different from that indicated by our laboratory
data and that our estimate of the dif -
fusivity D = -K is biased .
ao
Computational Procedures
We used the Remson computer program ,
which provides an approximation to the solution
of (3). As we have already noted, it is
possible to calculate at the same time a n
approximation to the volume flux of water ,
v = -D ae, at any depth in the soil. For thi s
calculation initial moisture content must b e
specified at a number of equally space d
points, called nodes, along the vertical soil
profile. Our nodes were set at depths of 1, 3 ,
5, 7, 9, and 11 cm. Initial moisture content
was measured at approximately 5 .5 cm by
tensiometer. The lysimeter plate at 11 cm
depth provided another point of known
moisture content . Because suction at the
lysimeter plate was maintained at a constant
level, the moisture content at that point coul d
be obtained directly from the empirically
determined moisture release curve. Estimate s
of initial values at other nodes were obtaine d
by linear extrapolation . Moisture content at
the upper boundary (the soil surface) varie d
with incoming precipitation .
Results and Discussion
Observed flow of water through the lysimeter
plate is compared to that predicted by
the flow equation in figures 2, 3, and 4 which
summarize the outcomes of three field experiments,
and the corresponding computer runs .
The results follow a consistent pattern . Predicted
and observed flows begin, peak, an d
subside at about the same time . Maximu m
predicted flow is about 25 percent greate r
than observed. At lower levels of flow th e
correspondence is closer .
We conclude that the Richards flow equa -
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