Figure 11: Seasonal pattern of
monthly precipitation, irrigation, and fruit biomass averaged over all
apple orchards within the PHO and the period 2016-2022, under full
irrigation (FI) and the difference for the 75 % and the 50 % deficit
irrigation scenarios (DI75 and DI50).
4 Discussion
4.1 Evaluation of the CLM5 irrigation routine
The direct comparison of simulated SM dynamics to observed SM from a
dense sensor network in two irrigated orchards gave valuable insights
into model performance. Our findings demonstrate that the standard CLM5
irrigation routine lacks the necessary flexibility to represent specific
irrigation practices observed in the orchards. Simulated crop growth and
transpiration at the orchard scale were not sensitive to the difference
in irrigation amount and timing between the two model runs using the
standard irrigation routine and the implemented irrigation data stream
respectively. However, as differences between the simulated and actual
irrigation practices increase, the effects may become more important
especially considering runoff generation or sensible and latent heat
fluxes that were not analyzed in this study. Similarly, if the
irrigation is limited so that the crop experiences some degree of water
stress, the timing of irrigation may become more important. This could
be further tested by applying different irrigation schedules under
various amounts of irrigation using the irrigation data stream.
Prior studies using the irrigation module in CLM were limited to
calibrating the target SM or adjusting the irrigation threshold fraction
to match gross irrigation requirements reported at the country or
regional level, or performed no calibration at all [Felfelani et
al. , 2018; Leng et al. , 2015; Leng et al. , 2013;Zhu et al. , 2020]. The model, however, does not currently
consider restrictions on irrigation schedule, over irrigation, or
irrigation efficiency that significantly affect gross irrigation
requirements as our results revealed. The newly implemented irrigation
data stream can be used to overcome some of these limitations by
prescribing crop and farmer specific irrigation schedules and amounts.
This allows investigating the irrigation-induced effects on e.g., crop
yield, SM, or carbon and energy fluxes under observed irrigation
practices and can help to identify existing model biases by removing one
possible source of uncertainty. While the use of the irrigation data
stream at larger scale is currently hampered by the limited availability
of precise information on irrigation practices in most areas
[Felfelani et al. , 2018], it can serve as a valuable tool to
investigate the modeled effect of different irrigation schedules and
water availability scenarios. This can offer a basis and direction for
further developments of the irrigation routine that are necessary for a
more realistic representation of irrigation management practices
[Yao et al. , 2022].
4.2 Model uncertainties and limitations of this study
4.2.1 Parametric uncertainty
SM dynamics outside the growing season were well reproduced by CLM5,
indicating that the model was able to capture infiltration and soil
water redistribution in the studied orchards. However, the significant
SM bias in S10 suggests structural and parametric uncertainty in the
estimation of soil hydraulic properties, probably due to inappropriate
pedotransfer functions implemented in CLM5 [X. Han et al. ,
2015]. Gao et al. [2021] found that poor performance of
CLM5 in reproducing observed root zone soil moisture was mainly due to
uncertainty in porosity estimates. In addition, a high content of rock
fragments, which is typical of many Mediterranean soils [Nijland
et al. , 2010; Poesen and Lavee , 1994; Zalidis et al. ,
2002], can strongly influence the SM regime through non-linearity in
soil hydraulic conductivity and by reducing the soils’ effective
porosity [Angulo‐Jaramillo et al. , 1997]. For this reason,
most pedotransfer functions fail to correctly reproduce the hydraulic
properties of stony soils [Nasri et al. , 2015], which likely
led to biases in simulated SM in S10. Further investigation of the
results would be needed to confirm this hypothesis, e.g. data
assimilation of observed soil variables could be used to optimize soil
hydraulic parameters [Strebel et al. , 2022]. In both
orchards, the simulations showed a lower simulated SM dynamic in 50 cm
depth, which could be the result of uncertainties in the rooting
distribution and thus root water uptake within the soil profile. The
current parameterization of the vertical discretization of root fraction
results in a rather shallow profile while deeper roots may still
contribute to root water uptake in the studied orchards. Shrestha
et al. [2018] encountered a similar issue when analyzing root zone
SM on a grassland site using CLM3.5 and were able to improve simulated
SM dynamics by increasing the root fraction in deeper layers. This may
help to improve the simulated SM dynamics at 50 cm on our study sites.
The sensitivity experiments performed using two parameters of the CLM5
irrigation routine (Figure 5) and the results from the irrigation
scenarios revealed relatively low sensitivity of crop yield to reduced
irrigation (Figure 9). The new plant hydraulics introduced byKennedy et al. [2019] advanced the physical basis for
hydraulic stress in the model, but there is large uncertainty in its
parameterization and in capturing the relationship of plant water stress
and SM deficit for different crops. To better quantify the model
performance and find the most suitable parameters for apple orchards,
comparison of simulations to observations from stressed and non-stressed
crops would be necessary. Additionally, sensitivity analysis of plant
hydraulic parameters, which was out of the scope for this paper, could
help to better constrain these model parameters.
4.2.2 Crop representation
The PHO catchment is characterized by a diversity of small-scale farm
holders resulting in considerable heterogeneity in management practices,
which cannot be fully captured by the model. While simulated yield was
close to observations during a “good” year for the point-scale
simulations, according to Mattas et al. [2019] average Greek
apple production in 2016 was only ~23 t
ha-1. This suggests a great variability in orchard
productivity, apple cultivars, or type of end product (e.g. apples for
direct consumption or for juice) which would necessitate the inclusion
of additional crop types and management practices in CLM5. In striving
for global applicability, CLM5 and other LSMs face constraints in
computational resources and often insufficient observational data to
parameterize additional crop types, which results in biases in certain
regions, while others are more accurately represented
[Lombardozzi et al. , 2020]. In our case, the model
demonstrated a strong correlation of yield and irrigation with the
climatic gradient induced by the topography in the PHO, indicating a
high sensitivity to model forcing data. The large simulated differences
in yield between orchards in the plain (~50 ton
ha-1) vs. the higher altitudes (as low as 16 ton
ha-1) may however be exacerbated, as CLM5 employs a
single set of parameters for a given crop across diverse geographies and
climates. In reality, various cultivars of the same crop type, along
with plant physiological adaptations to their environments, can lead to
comparable productivity levels despite variations in climatic
conditions. This phenomenon is evident in the cultivation of numerous
crops, including apples, across climates on a global scale
[Sherman and Beckman , 2002]. The issue has been addressed byLombardozzi et al. [2020] who recommended further
developments in CLM5 to improve phenological triggers and agricultural
management, and to include different cultivars. In the future, the
incorporation of additional satellite-derived crop data, advanced
parameterizations, or the use of crop calendars to constrain these
models may help reduce some of the biases [Pongratz et al. ,
2018; Yao et al. , 2022; Z Zhang et al. , 2020].
At the orchard scale, we found discrepancies between observed and
simulated SM during the growing season that suggest limitations specific
to the current representation of orchards. As CLM5 does not allow
intercropping, the actively growing grass cover in the orchard alleys is
not included in the CLM5-FruitTree sub-model [Olga Dombrowski et
al. , 2022]. Consequently, our simulations do not account for the
additional root water uptake and transpiration as well as interception
of the irrigation water from the grasses. The former may explain the
smaller simulated decline in SM early in the season compared to the
observations, while we considered the latter to some extent by assuming
a reduced irrigation efficiency. In doing so, we did however neglect the
additional ET flux. Yao et al. [2022] developed and tested
different irrigation techniques in CLM5 and found an increase in canopy
evaporation through increased interception for their implementation of
sprinkler irrigation. However, the overall impact on ET and total
applied irrigation remained small compared to the control run using the
standard CLM5 irrigation. More importantly, accounting for conveyance
and application losses would increase the simulated irrigation amount
and could lead to more realistic irrigation values [Yao et al. ,
2022].
Despite these limitations, and though we could not validate the
simulation of crop yield and irrigation requirements in the PHO
catchment due to the lack of observational data, the reasonable modeling
results at the orchard scale give some confidence in the robustness of
the regional simulations.
4.3 Implications for irrigation management
We studied the relationship between crop yield and water use efficiency,
and irrigation at the regional scale, as it is determinant for a
reasonable allocation of irrigation water according to crop needs. For
most part of the PHO, CWUE and yield were little affected when
irrigation was reduced to 75 %, suggesting that this scenario lies
closer to the optimal irrigation that maximizes yield while minimizing
water consumption as opposed to the FI scenario. These results are
similar to a study by Li et al. [2018] who used CLM to
schedule irrigation in a citrus orchard in Spain which resulted in 24 %
less irrigation compared to the farmers’ practices. This could indicate
that farmers irrigate too much when water is available and water prices
are low [Latinopoulos , 2005]. Simulated apple yield was
sensitive to a reduction of 50 % of the applied irrigation water
causing up to 30 % decline in yields. The effect, however, varied with
different meteorological conditions and soil types within the PHO. At
higher altitudes, cooler temperatures and lower incoming radiation
rather than water scarcity limited crop growth. Irrigation in these
orchards could thus be greatly reduced without negatively affecting
yield. Moreover, under the same climatic conditions, orchards growing on
soils with a higher percentage of clay (southeastern part of the
catchment) could maintain similar yield and CWUE under 50 % reduction
in irrigation water because of the greater water holding capacity of the
soil. This will make orchards growing on these soils less prone to
experience to water stress. The effect of deficit irrigation on fruit
growth and yield varied between years and throughout the growing season.
Years with high productivity and greater dependence on irrigation (due
to low rainfall) showed greater yield loss under deficit irrigation
(Figure 10). At the seasonal scale, fruit growth showed the highest
reduction in August followed by July and September (Figure 11). This was
mainly an effect of higher temperature, little rainfall, and larger leaf
area that resulted in high irrigation requirements in this month.
Apples, similarly to other crops, show different susceptibility to
drought stress depending on their growth stages whereby flowering and
fruit set as well as fruit development and maturation are highly
susceptible to drought. The latter stage falls within the period
July-September where the model showed the largest reduction in fruit
growth. While the simulated plant water stress is currently linked to
environmental conditions rather than capturing plant physiological
differences in this stage of growth, it suggests that under limited
water conditions, irrigation should be prioritized during these months
to maintain reasonable yields.
4.4 Perspectives for further application and model development
The analysis performed in this study displays the current ability and
potential way forward of applying CLM5 for irrigation and water
resources management at various scales. Prospectively, future
applications and research studies should focus on the improvement of
input datasets, crop and irrigation parameterizations, and process
representation. Input related improvements include the creation of
high-resolution climate and land use information, especially crop types
and the extent and type of irrigation. Our results clearly showed how
climate and environmental heterogeneity (e.g. topography, landuse, soil
properties) can greatly affect total crop water requirements,
emphasizing the need for spatially explicit modeling for large-scale
applications. Model investigation at the orchard scale revealed the
importance of soil and crop-specific parameterization to correctly
represent soil moisture and phenology dynamics, and harvest time.
Extending simulations to larger scales will thus require further
improving soil hydraulic parameterization through improved pedotransfer
functions [Vereecken et al. , 2022] or parametrization of soil
hydraulic properties through data assimilation approaches [Xujun
Han et al. , 2014]. Furthermore, information on crop management and
improved differentiation between different crop varieties and cultivars
(e.g. different growing seasons and harvest of cherry compared to apple
trees) is necessary, as these can result in distinct irrigation seasons
and amounts. Concerning irrigation, this could include either
crop-specific or spatially explicit values for irrigation parameters
that are currently the same for all irrigated crops, hence not
reflecting different management strategies or susceptibilities to water
stress. Lastly, some processes could be refined or added to represent
irrigation requirements more realistically. These include a
parameterization of irrigation efficiency, water availability
considerations and more flexible irrigation schedules that can be
tailored to represent typical field practices. Conducting parallel
testing and assessment of future developments covering greater spatial
and temporal scales (e.g., in the form of long-term observatories) will
be crucial, especially as more accurate irrigation data becomes
available.
5 Conclusions
This study assessed the ability of the CLM5-FruitTree sub-model to
represent irrigation practices in fruit orchards in a small
Mediterranean catchment and explored the effects of different irrigation
scenarios on simulated yield and CWUE. The standard CLM5 irrigation
routine could not accurately reproduce observed irrigation practices,
which motivated the implementation of an irrigation data stream that
directly prescribes measured irrigation data. Using this irrigation data
stream, observed SM dynamics in the two studied apple orchards were well
captured by the model. We did however find some discrepancies between
observed and simulated SM, transpiration, and yield that were related to
uncertainties in soil hydraulic parameters and limitations in the crop
representation, which does, for instance, not account for the active
grass cover growing in the alleys.
To examine the potential to improve regional irrigation management using
CLM5, we simulated different irrigation scenarios and analyzed their
effect on crop yield and CWUE. The model showed distinct effects of
deficit irrigation on yield and CWUE for scenarios with 25 % and 50 %
reduction in irrigation (DI75 and DI50, respectively) that were tested
using the irrigation data stream. While DI75 had negligible negative
effect on yield and CWUE, DI50 notably reduced both yield and CWUE.
Based on the modeling results, this would suggest substantial water
savings of up to 125 mm year-1 with little to no
effect on apple yields and up to 250 mm year-1 when
accepting up to 30 % reduction in yield (although potential effects of
fruit quality need to be considered as well). These effects varied
depending on climatic conditions, soil type, and timing of irrigation.
Hence, under limited water availability, irrigation should primarily
focus on the summer months July to September and on sandy soils with
lower water holding capacity.
The outcomes of this study demonstrate the potential use of CLM5 in
irrigation and water resources management research and applications.
Future research efforts should focus on improving soil and crop
parameterizations, and as well as process representation. Finally, we
anticipate that implementing more realistic irrigation schedules in land
surface models such as CLM5 will allow for better water resource
management at the local and regional level.
Acknowledgments
This research received support from the ATLAS project, funded through
the EU’s Horizon 2020 research and innovation program, under grant
agreement No. 857125. Further support was received from the German
Research Foundation (DFG) through the project 357874777, which is part
of the research unit FOR 2694 Cosmic Sense as well as through the
PhenoRob project as part of Germany’s Excellence Strategy - EXC
2070–390732324. Additional support was received from TERENO
(Terrestrial Environmental Observatories) of the Helmholtz Association
of National Research Centers (HGF), Germany. Furthermore, this work was
in part supported by the National Center for Atmospheric Research
(NCAR), which is a major facility sponsored by the NSF under Cooperative
Agreement 1852977. Simulations were conducted using computing resources
from the supercomputers JURECA and JUWELS from Jülich Supercomputing
Centre (JSC).
The authors would like to thank Nikos and Stamatis Oikonomou for
granting access and use of their orchards, providing information, and
supporting instrument maintenance. We further thank Bernd Schilling and
Ansgar Weuthen for instrument preparation and monitoring of the data
transmission, the team at SWRI including Vassilios Pisinaras, Andreas
Panagopoulos, Anna Chatzi, and Konstantinos Babakos for their great
support in instrument installation and maintenance, and the members of
the Soil, Water and Plant Tissue Analysis Laboratory of SWRI for
conducting the soil analysis.
Open Research
The CLM5-FruitTree sub-model used in this work is freely available via
Zenodo at https://doi.org/10.5281/zenodo.8154390 [O.
Dombrowski , 2022] and on Github at
https://github.com/odombro/CTSM.git under the branch
release-clm5.0_FruitTree. The irrigation data stream implementation is
available on Zenodo at https://doi.org/10.5281/zenodo.8290143
[Swenson , 2023] and on Github at
https://github.com/swensosc/ctsm.git under the branch
irrigation_streams. Climate data from climate stations CS1, CS2, and
CS3 from the TERENO sites Agia (TERENO ID: AGIA_K_001, AGIA_K_002,
AGIA_CK_003) are freely available via the TERENO data portal
[TERENO , 2023]. Data collected from the two apple orchards
S09 and S10 is available via the TEODOOR database at
https://doi.tereno.net/landingpage/doi/10.34731/e1ss-pc69
[O. Dombrowski and Bogena , 2023]. Data analysis was performed
in Python version 3.10.4 [PythonSoftwareFoundation , 2023]
available at https://www.python.org/downloads/ and figures were
made with Matplotlib version 3.5.2 [Caswell et al. , 2022],
available under the Matplotlib license at https://matplotlib.org/.
The map overview was created with QGIS version 3.12.3 [Dawson et
al. , 2022] available at https://qgis.org/.