Plain Language Summary
Irrigation impacts how land and atmosphere interact, both locally and
globally. Therefore, it is important to understand the effects of
irrigation practices and improve how water resources are managed.
Advanced models such as land surface models now include irrigation.
However, missing information on irrigation and other data make it
difficult to evaluate and improve irrigation in these models. This study
looks at how irrigation and crop yield are represented in the Community
Land Model (CLM5). The model was tested using observed data and its own
prediction of irrigation, and then compared to observed soil moisture
and yield in two apple orchards. The model was able to predict changes
in soil moisture caused by irrigation. Simulated irrigation was
different in timing and amount from the observed one. This could be
improved by adding more details to the irrigation routine. Next,
irrigation and crop yield were studied in the entire region. Both were
sensitive to changes in climate caused by the diverse landscape. A small
reduction in irrigation did not negatively affect yield while halving
the irrigation caused it to decrease noticeably. These findings show
that land surface models like CLM5 can be useful tools for managing
irrigation and water resources.
1 Introduction
Irrigation plays a vital role in sustaining global food production by
providing a reliable water supply to agricultural systems, especially in
semi-arid or arid regions [McLaughlin and Kinzelbach , 2015].
With a growing global population and increasing food demands, irrigation
contributes significantly to ensuring food security by enabling higher
crop yields and reducing the vulnerability of agricultural systems to
climate change [McDermid et al. , 2023; Mueller et al. ,
2012]. On the other hand, poor management of irrigation water has led
to the depletion of groundwater resources [Dangar et al. , 2021;Scanlon et al. , 2012; Wada et al. , 2010] and water use
conflicts in many regions [Cai et al. , 2003; Eshete et
al. , 2020; Gurung et al. , 2006]. Apart from quantitative and
qualitative effects on water resources [García-Garizábal et
al. , 2012; Y Zhang et al. , 2022], irrigation substantially
impacts biogeophysical and biogeochemical processes at the land surface
through alteration of the hydrological cycle or energy budget. This has
subsequent effects on climate [DeAngelis et al. , 2010;Erb et al. , 2017; Ferguson and Maxwell , 2012; Gordon
et al. , 2005; Sacks et al. , 2009]. The multidimensional role
of irrigation calls for increased efforts in effective irrigation
management and irrigation impact studies using large-scale approaches.
This is crucial not only to meet food demands and mitigate future
increases in climate change induced water stress, but also to understand
its interactions and feedback mechanisms within the Earth system
[Elliott et al. , 2014; McDermid et al. , 2023].
Modeling can be a powerful tool to simulate complex interactions in
agricultural systems, evaluate different irrigation and climate
scenarios, and provide decision support for water resources management
[Blyth et al. , 2021; Pongratz et al. , 2018]. This
necessitates comprehensive modeling frameworks that combine field-scale
representations of crop growth and irrigation with a more holistic
assessment of the impacts of irrigated agriculture on water resources
and climate at larger scale [Bin Peng et al. , 2020].
Process-based crop models include a range of crop parameterizations that
provide a unique way to study crop growth processes in response to
irrigation practices by using physical and biological principles.
However, their main purpose is to simulate yield at the field scale,
often over a single growing season, while lacking the interface with the
land surface, soil, and climate [Cheng et al. , 2020]. Land
surface models (LSMs), on the other hand, provide a more holistic
representation of the land-atmosphere interactions to capture the
feedback mechanisms between irrigation, vegetation, hydrological
processes, and climatic conditions beyond the field scale [Blyth
et al. , 2021]. Conversely, they often lack more detailed
physiological and genetic representations of crops and irrigation
management [Lombardozzi et al. , 2020; B Peng et al. ,
2018]. This limits the ability of LSMs to reliably simulate yield and
irrigation water withdrawals leading to poor model performance and
biases in related processes such as carbon, energy, and water fluxes
over intensively irrigated regions [Leng et al. , 2015;Lombardozzi et al. , 2020; Ozdogan et al. , 2010; Z
Zhang et al. , 2020].
In recognition of the important role of human land management, efforts
to advance the representation of crops and irrigation in LSMs are
ongoing [Pokhrel et al. , 2016]. Various land surface models
such as ORCHIDEE, the Community Land Model (CLM), and Noah-MP have since
added crop modules [Levis et al. , 2012; Liu et al. ,
2016; Smith et al. , 2010]. New crop representations have been
developed to improve crop growth and management processes [Boas
et al. , 2021; B Peng et al. , 2018] or to add new crop types
[Olga Dombrowski et al. , 2022; Fader et al. , 2015;Fan et al. , 2015]. Rather simple irrigation schemes are
generally incorporated based on soil moisture thresholds [de
Vrese et al. , 2016; Ozdogan et al. , 2010; Sacks et al. ,
2009], while more recent developments include the integration of
irrigation techniques [Leng et al. , 2017; Yao et al. ,
2022], irrigation water withdrawal from different sources
[Leng et al. , 2017; Xia et al. , 2022], and water
availability limitation [Yin et al. , 2020]. These studies,
however, were performed at river basin, county, or global level with
coarse spatial resolutions between 10 and 100 km. Simulated irrigation
was validated against rather uncertain statistics like total yearly
irrigation water withdrawals, without considering specific irrigation
practices. Crop and irrigation data at higher spatial (<5 km)
and temporal (e.g. daily or sub-seasonal) resolution is needed to
evaluate the representation of local irrigation schedules in LSMs and
support irrigation management decisions. However, data to reliably
constrain and further develop implemented irrigation schemes is often
lacking, e.g. irrigation amount and timing along with continuous soil
moisture (SM) observations [Lawston et al. , 2017].Lawston et al. [2017] first evaluated the sprinkler
irrigation scheme of the NASA Land Information System LSM with point and
gridded SM observations at 1 km resolution. While the model could not
capture the field scale heterogeneity and overestimated irrigation
amounts, it captured well the seasonal variability and regional average
SM dynamics. The authors did however use a prescribed crop phenology
(green vegetation fraction) and did not examine the effect of irrigation
on crop yield. A recent study examined the effect of different
irrigation setups on maize yield and two water use efficiency
definitions using the dynamic crop and irrigation scheme of the Noah-MP
LSM [Huang et al. , 2022]. They found that modeled crop yield
was sensitive to irrigation quantity and timing (in which crop growth
stage irrigation was applied) and based on these results recommended an
optimal SM threshold to trigger irrigation. While the authors lacked
data to accurately assess the irrigation amount and crop yield, their
work presents a first use of a LSM to study the effects of deficit
irrigation on crop growth, yield and water use efficiency.
The work presented here builds upon previous studies to continue the
evaluation and improvement of irrigation representations in LSMs
combining local irrigation, SM, and yield observations. In particular,
this study applies CLM version 5, with a recent extension to represent
deciduous fruit trees, to model irrigation and crop growth in a
Mediterranean catchment. Specifically, we aim to: (1) evaluate the
existing irrigation scheme of CLM5 and enhance its flexibility to
account for local irrigation management practices; (2) assess whether
the model can reproduce soil moisture dynamics and crop growth in
irrigated apple orchards using the enhanced model capability; (3)
examine the potential to improve regional irrigation management by
modeling the effect of different irrigation scenarios on crop yield and
water use efficiency at the catchment scale.
2 Materials and Methods
2.1 Study Area
Located in central Greece, the Pinios Hydrologic Observatory (PHO)
covers an area of approximately 45 km2 (Figure 1). The
PHO was established in 2015 to study the Pinios catchment hydrological
processes and, ultimately, to support local authorities in the
sustainable management of water resources [V Pisinaras et al. ,
2018]. It is characterized by a Mediterranean climate with an annual
precipitation of 500 to 1200 mm, and highest precipitation amounts in
the winter months, annual potential evapotranspiration of approximately
1100 mm, and annual average air temperature of 15 °C [Bogena et
al. , 2018]. The area displays a range of altitudes from 1500 m in the
northern part down to less than 200 m in the plain. The mountainous part
of the catchment features steep slopes and is covered by forests, mixed
with shrubs and grassland, while the southern plain is primarily
characterized by agriculture and small villages. In the plain, sandy
loam soils dominate while sandy clay loam and loamy soils also occur
[V Pisinaras et al. , 2018]. The PHO is located in one of the
most productive agricultural areas in Greece owing, among other factors,
to widespread irrigation practices that account for over 85 % of the
local freshwater consumption [Panagopoulos et al. , 2018]. The
main cultivation are apple and cherry orchards (i.e.,
~78 % of agricultural area) that are irrigated between
May and October. There are a few other rainfed fruit and nut tree
orchards in the area with < 5 % coverage. Annual crops
including corn, cereal (mainly winter wheat), and potato are grown on
the remaining agricultural land. They are partially irrigated, depending
on precipitation occurrence, but cover a negligible part of the total
irrigated area. Irrigation in the orchards is typically applied through
micro sprinklers and the demand is almost entirely met by abstraction
from the alluvial groundwater system through water wells, most of which
are privately owned. Overexploitation of groundwater in the area due to
poor irrigation management practices, amongst others, has previously
been reported by Panagopoulos et al. [2018] andVassilios Pisinaras et al. [2023] resulting in the decline of
groundwater levels.
Within the PHO, irrigation management in two irrigated apple orchards,
hereafter referred to as S09 and S10, was studied (Figure 1). Both
orchards have a size of around 1.2 ha, with a mild southern slope of
<5 %. The soil texture is sandy loam and sandy clay loam with
a high gravel content (13-29 %) (Table 1) and many larger cobbles
(>64 mm according to Wentworth [1922]),
especially below 30-50 cm depth. Trees are planted in rows, oriented
North-South with 3.3 m distance between rows and an in-line distance of
1.5 m (approximately 2020 trees ha-1). The trees in
S09 and S10 were planted in 2013 and 2015 respectively, with a mixture
of 3 to 5 different varieties. Trees are pruned to a height of 3.5 m
throughout the winter season and residues are mulched back into the
soil. Bud burst typically occurs in the second half of March while fruit
development starts with the end of flowering in mid to late April.
Harvest dates range from late August to mid-November depending on the
harvested variety. Major leaf fall starts in late October and continues
until mid-November, sometimes until early December. Trees are irrigated
with a micro sprinkler system with a maximum flow rate of 60 L
hour-1 that is installed below the canopy, halfway
between the tree stems of the same row. The irrigation season typically
starts in May and continues until October. Orchards are fertilized with
80 kgN ha-1 at the end of flowering in April. Pest and
fungicide treatment is applied prior to flowering and after flowering
until late June. The grass in the alleys is generally mowed once a month
starting in March or April and mowing material is left on the ground.
During periods of intense heat, the actively growing grass cover
provides a cooling effect to protect the apples from heat damage.