3.1.1.2 Irrigation season
In 2021 and 2022, the farmer irrigated every 5-7 days starting mid-May
through October. Irrigation amounts per event varied strongly and
averaged 14 and 25 mm in S09 and S10 respectively (upper panel of Figure
3 and Figure 4). Irrigation increased SM by up to 10 vol% in the top
5 cm and about 5 vol% at 50 cm depth. To represent the observed
irrigation schedule, the CLM5 irrigation routine can be adjusted in two
ways, by (1) adapting \(\psi_{\text{target}}\) or (2) tuning the\(f_{\text{thresh}}\) parameter. Figure 5 shows the effect that
different values of these two parameters have on several aspects of the
simulated irrigation (e.g., start of irrigation period, number of
irrigation events, irrigation frequency) as well as on SM and crop
yield. In both cases, a lower parameter value results in a later onset
of irrigation, fewer irrigation events and lower total irrigation
amounts. However, the parameters have different effects on irrigation
frequency, whereby smaller values of \(f_{\text{thresh}}\) result in
less frequent irrigation events while the irrigation volume per event
increases (Figure 5). Changing\(\ \psi_{\text{target}}\), on the other
hand, has little effect on the irrigation frequency and volume. SM in
the upper 50 cm of soil increases with increasing values of both
parameters. The increase is exponential for\(\psi_{\text{target}}\ \)with values ranging between 0.195 and 0.275
cm3 cm-3 and almost linear for\(f_{\text{thresh}}\) with a somewhat smaller range. Consequently,
varying \(\psi_{\text{target}}\) has a more pronounced effect on yield
compared to \(f_{\text{thresh}}\) for the investigated range of
parameter values.
For the model run using the standard irrigation routine, we set\(f_{\text{thresh}}\) to 0.7 while leaving \(\psi_{\text{target}}\) at
its default value of -34 kPa, which resulted in approximately weekly
irrigation events of on average 26 mm per event, starting mid-May. This,
however, could only partially reproduce the observed irrigation schedule
and SM dynamics compared to using the irrigation data stream.
Nevertheless, both irrigation approaches showed fluctuations of similar
magnitude compared to the observed values in the upper soil. Less
dynamics than observed were simulated at 50 cm depth for both irrigation
approaches and both orchards. The wet bias in S10 was still persistent
throughout the profile for the simulation using the irrigation data
stream while simulated SM based on the default irrigation routine
dropped to the range of observed values (Figure 4).
Simulated and observed total yearly irrigation were similar in S09 with
the observed effective irrigation being 433 and 458 mm (75% of actual
measured irrigation) and simulated amounts being 425 and 439 mm for 2021
and 2022, respectively. In S10, observed effective irrigation amounts
were considerably higher than in S09, which could be expected
considering the lower observed SM in S10. Compared to the observed 706
and 586 mm, for 2021 and 2022, respectively, the model applied only 393
and 388 mm, which is a result of the simulated wet bias.