2.3.2 Regional case simulations
A regional model domain, encompassing the entire PHO, was set up at a
spatial resolution of 1 ha. This resolution was a compromise between
accounting for the diverse, patchy landscape with small field and
orchard sizes (from a few 100 m2 to some hectares) and
a reasonable computational effort. For the land use information, the
database of agricultural fields and orchards was combined with the
remaining land uses digitized from satellite imagery. Since CLM5 allows
to define fractional land use in a single grid cell, the overall area of
individual land use classes was still accurately represented.
The slope of the terrain was derived from the EU-DEM. Furthermore, the
surface parameter defining the depth to bedrock was adjusted based on
the minimum (0.27 m) and maximum (1.3 m) depths available to roots from
the ESDB, which were linearly scaled by the slope. In the plain area,
the value was set between 10 and 20 m to represent the thick alluvial
deposits and prevailing free drainage conditions. Lastly, the maximum
fractional saturated area (fmax) that controls runoff
generation was set to zero for all grid cells containing crops due to
the deep groundwater table, gentle sloping in the plain, and assuming
that there are no large saturated areas in the fields and orchards.
fmax was set to 0.16 in the remaining areas of the
catchment as extracted from the global dataset. The adjusted parameters
for apple were used as described in section 2.3.1 while a separate
parameter set was used for cherries to account for the earlier start of
the growing season and harvest, and lower productivity as compared to
apples. For the sake of consistency, parameters for winter wheat and
potato were also modified based on Boas et al. [2021] with
minor adjustments to growing seasons to account for the local climate
[Dercas et al. , 2022; FAO , 2023].
For the model spin-up, the available global GSWP3 v1 atmospheric forcing
data set providing data from 1901 to 2010 at a 3-hourly temporal and
0.5° spatial resolution was used [Lange and Büchner , 2020].
The model was spun-up for 720 years until equilibrium for soil carbon
and nitrogen pools, soil water storage, and other ecosystem variables
was reached for all land uses in the catchment. For the remaining
simulations, the model was forced with a 7-year time series obtained
from the observational data of meteorological stations CS1, CS2
(2016-2022), and CS3 (2018-2022) in the study area as well as from the
two Atmos41 stations in orchard S09 and S10 (2021-2022) (Figure 1). The
data was spatially interpolated to the same resolution as the surface
data using inverse distance weighting. The interpolation of
precipitation and temperature included a weighting factor for elevation
variation using a linear correlation between station elevation and mean
annual station precipitation and temperature, respectively, as described
in Panagoulia [1995]. Another short spin-up period of 3 years
was performed as the orchards had just reached their maximum lifespan
before orchard rotation is initiated and new seedlings need a couple of
years to reach the full productivity level [Olga Dombrowski et
al. , 2022].
2.4 Simulation scenarios
To assess how well CLM5-FruitTree can represent soil moisture dynamics
and crop growth in the study area, 1D simulations were first performed
in orchards S09 and S10 for the growing seasons 2021 and 2022. Two model
set-ups were tested: the first used the default CLM5 irrigation routine
with adapted parameterization to approximate the observed irrigation
schedule, while the second was prescribed with the observed irrigation
through the irrigation data stream. By directly applying irrigation
water to the ground surface, CLM5 assumes an irrigation efficiency of
100 % which is hardly achieved in sprinkler irrigation [Gilley
and Watts , 1977]. For the irrigation data stream, we thus assumed
that only 75 % of the water volume measured by the hydrometers is
reaching the ground surface while the rest is lost through evaporation
from leaf surfaces, transpiration of the grass cover in the orchard
alleys, and leakages in the piping system. Modeling results were
compared to observed SM and tree transpiration at a daily time step as
well as crop yield and development. Pearson’s r (r), the root mean
square error (RMSE) and the percent bias (%bias) were calculated for
statistical model evaluation.
For the regional case, we conducted three simulation experiments to test
different irrigation scenarios. Regional data on irrigation outside the
instrumented orchards S09 and S10 was not available. Thus, the model was
run using the default CLM5 irrigation routine with the same
parameterization that was used for the point scale simulations, in the
following considered the full irrigation scenario (FI). Based on this
scenario, two deficit irrigation scenarios were created for both apple
and cherry orchards with 75 and 50 % of full irrigation (DI75 and DI50,
respectively) using the irrigation data stream. All scenarios were run
over the same 7-year period (2016-2022). To investigate the differences
between irrigation scenarios, multi-year averages and seasonal dynamics
of irrigation, SM, crop growth or yield, and crop water use efficiency
(CWUE) were calculated and compared. In this study, CWUE was defined as
the amount of yield produced per unit volume of water consumed
[Ibragimov et al. , 2007]: