SCHRIFTENREIHE Institut für Pflanzenernährung und Bodenkunde ...

Soil water content (cm 3 cm -3 )
0.4
0.3
0.2
0.1
0.4
0.3
0.2
0.1
0.4
0.3
0.2
0.1
5 cm
M LDP NN Inverse LDP-NN LDP+NN
2
20 cm
4 6 8 10 12
2
40 cm
4 6 8 10 12
4-Aug 10-Aug 16-Aug 22-Aug 28-Aug
Time (days)
Fig. 5.5. Comparison of the five model parameterization procedures with measured soil
moisture in 2004 for UG 79. M: measured, LDP: laboratory-derived hydraulic parameters
NN: neural network, Inverse: inverse model, LDP-NN: simulation with Ks from NN and
measured θr, θs, α, n, and LDP-NN: simulation with θr, θs, α, n from NN and measured
Ks.
Model validation is performed with measured soil moisture and temperature
data during the growing period of 153 d from 1 May to 30 Sept. 2005 and 2006,
respectively. In general, simulated and measured water contents are
comparable during the entire simulation period (Fig. 6). The increases in water
content after rainfall at 5 and 20 cm depths are predicted reasonably well. In
contrast to this, the simulated water contents decreased quicker than the
measured values during a prolonged drought period. However, except for the
inverse model, simulated and measured water contents at 40 cm depth are not
matching well. Consistent with the model calibration, the inverse model predicts
water contents best with the lowest RMSE (Table 4). This is to be expected
because it adjusts the hydraulic parameters to fit the observed data for each soil
layer. The LDP does not yield satisfying predictions at the deeper soil mirroring
transfer gaps from laboratory data to field conditions, such as the
100