where \(\theta_{wilt,i}\) is the volumetric SM value at wilting point in
a given soil layer. \(\theta_{\text{target}}\) and\(\theta_{\text{wilt}}\)
are calculated by inverting the equation for soil matric potential (SMP)
(Eq. 7.53 in D Lawrence et al. [2018]) at the respective
depth. By default, the SMP parameters \(\psi_{\text{target}}\) and\(\psi_{\text{wilt}}\) are set to -34 and -1500 kPa, considered field
capacity and permanent wilting point, respectively.
In addition to \(w_{\text{target}}\), \(w_{\text{wilt}}\),\(f_{\text{thresh}}\), and \(z_{\text{irrig}}\), the user can define the
irrigation duration (𝑇𝑖𝑟𝑟𝑖𝑔). Irrigation is applied
directly to the ground surface at an intensity equal to\(\frac{D_{\text{irrig}}}{T_{\text{irrig}}}\). Irrigation parameters are
not spatially distributed but are defined globally for a given model
domain independent of geographic location or crop type.
2.2.4 Irrigation data stream implementation
To study and evaluate the modeling outcomes under specific observed
irrigation practices, an irrigation data stream was implemented in CLM5
to enable continuous prescription of irrigation parameters, i.e.,
irrigation rate, duration, and start time. These parameters are defined
separately for one or multiple crop types and for each grid cell. This
allows to account for differences in irrigation management depending on
crop type and location to accurately reproduce local management
practices. In addition, using the data stream, the applied irrigation
amount can be easily adjusted, thus creating different irrigation
scenarios while maintaining the same irrigation schedule. As irrigation
is prescribed, the irrigation SM threshold that is calculated in the
standard irrigation routine is not needed for this implementation.
2.3 Model Implementation
2.3.1 Orchard scale simulations
For the simulations of S09 and S10, CLM5-FruitTree was run in single
point mode and forced with hourly meteorological data from the two
orchards. Fertilizer amount and soil texture were adjusted according to
information provided by the farmer and soil samples. The default
parameter file was adapted to account for the local climate and orchards
characteristics. Crop parameters such as the different phenological
stages were adjusted according to observations from the phenocam
pictures, harvest information, and communication with the farmer. In the
absence of observed bud break dates, parameters for the bud break
prediction model were calibrated such that bud break would occur around
the estimated date of 15th of March using the
available local climate data. The modified crop parameters are listed in
Table 3. Additionally, the observed irrigation time series was used as
input to the irrigation data stream.
In order to balance ecosystem carbon and nitrogen pools and total water
storage in CLM5 [D Lawrence et al. , 2018], a 200 years model
spin-up was performed. For this, the CRUNCEPv7 atmospheric forcing data
set from 1986 to 2016 [Viovy , 2018] and the parameterized
apple plant functional type were used. Using the model state at the end
of the spin-up, simulations were then re-initiated from planting in 2013
(S09) and 2015 (S10) using meteorological data from climate station CS1
(2016-2020) and data from the Atmos41 sensors installed in the orchards
for the years 2021 and 2022.
Table 3: Local crop parameters
for the apple plant functional type.