Soil Moisture and Runoff Processes at Tarrawarra

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Soil Moisture and Runoff Processes at Tarrawarra

230 A Western and R Grayson

Table 9.3(c). The root mean square soil moisture simulation error (%V/V) for each run and

each soil moisture pattern

Run 1 2 3 4 5 6 7 8 9 10

14-Feb-96 3.3 3.4 3.6 3.3 3.4 3.6 3.1 5.1 4.2 3.3

23-Feb-96 2.3 2.4 2.4 2.4 2.4 2.4 2.2 4.1 2.9 2.3

28-Mar-96 2.6 2.6 2.6 2.4 2.4 2.4 2.4 2.9 2.5 2.4

13-Apr-96 3.5 3.5 3.5 3.2 3.2 3.2 3.3 4.1 3.1 3.2

22-Apr-96 5.2 5.1 5.1 5.9 5.9 5.8 5.5 6.8 5.8 5.6

3-Jul-96 6.0 5.4 4.8 6.0 5.6 5.0 5.0 5.1 5.2 5.1

2-Sep-96 3.6 3.4 3.6 3.5 3.4 3.5 3.4 3.5 3.4 3.4

20-Sep-96 4.3 4.1 3.9 4.4 4.1 3.9 4.0 3.9 4.1 4.0

25-Oct-96 7.3 6.9 6.4 7.5 7.1 6.6 5.9 6.5 7.1 6.2

10-Nov-96 7.2 7.1 7.0 7.5 7.5 7.3 5.6 7.9 7.8 6.1

29-Nov-96 4.2 4.6 5.3 4.5 4.9 5.6 3.0 7.9 6.0 3.6

20-Mar-972.6 2.6 2.6 2.6 2.6 2.6 2.72.72.6 2.6

3-Aug-975.1 5.1 5.1 3.9 3.9 3.9 3.9 4.0 3.9 3.9

these techniques invariably remove organised small-scale features such as narrow

bands of high soil moisture. Therefore, a more sophisticated approach

was adopted here. The smoothing was done in three steps. Firstly, a regression

relationship was fitted between the soil moisture and a linear combination

of the wetness index and potential radiation index (see Western et al., 1999a

for a discussion of these terrain indices). The residuals from this regression

exhibited minimal spatial organisation. Secondly, residuals from this regression

were smoothed using a thin plate spline (Hutchinson and Gessler, 1994) so

that their spatial variance was reduced by an amount equal to the nugget of

the soil moisture variogram for that pattern (Western et al., 1998a). Note that

the nugget variance is assumed to be the sum of the measurement error

variance and the variance of the small-scale (< 10 m) variability (see

Chapter 2, p. 32). Thirdly, the smoothed residuals were added back to the

estimated soil moisture obtained from the regression. By doing this we were

able to smooth the observed data without unduly distorting the moisture

pattern observed in the drainage lines. This was possible because the residuals

from the regression did not exhibit strong discontinuities around the drainage

lines, unlike the soil moisture itself.

Comparing Figures 9.10 and 9.9 indicates that the model generally captures

the seasonal trends in soil moisture. It also generally captures the existence of

topographic control. That is, when the observed pattern shows strong topographic

control, so does the model, and when the observed pattern is random

(i.e. uniform with some random variation), the model predicts a uniform pattern.

Figure 9.11 shows maps of the errors (simulated minus smoothed observed

moisture) in the simulation. The error maps are a useful way of examining the

spatial characteristics of the simulation errors. For example, they can show

whether the errors are consistently related to terrain features, as is the case on

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