Introduction
The Mediterranean region is a transitional zone between the subtropical and
temperate zones, with high intra- and inter-annual variability (Lionello et
al., 2006). This region has been identified as one of the regional climate
change hotspots, with a high likelihood of experiencing more frequent and
more intensive heat waves, often combined with and strengthened by more
intensive and longer droughts (IPCC, 2012; Kovats et al., 2014; Diffenbaugh
and Giorgi, 2012). This will likely have adverse implications for the food
and energy producing sectors as well as for human health, tourism, labour
productivity and ecosystem services (Kovats et al., 2014; Skuras and
Psaltopoulos, 2012). However, climate change is only a part of the challenges
that the Mediterranean region will face in the near future. Environmental
degradation involving soil erosion, biodiversity loss and pollution is
negatively affecting natural and societal systems and is expected to
intensify even more in future due to urbanisation, industrialisation and
population growth (Doblas-Miranda et al., 2015; Lavorel et al., 1998;
Scarascia-Mugnozza et al., 2000; Schröter et al., 2005; Zdruli, 2014).
Most aspects of climate change and environmental degradation will affect the
Mediterranean agricultural sector directly. Agriculture plays a very
important role, not only for food security in the region itself, but also
through its economic integration in other regions, such as through the
significant export of products to the rest of Europe (Hervieu, 2006).
Agriculture therefore plays a key role for the national economies, making a
part of the rural population rely on it for their livelihood and creating
linkages with other issues and sectors, such as culture and tourism (Verner,
2012; Hervieu, 2006). Human population growth and socioeconomic developments
on the eastern and southern shores as well as the already high dependence of
the region on the international food markets will increase the need for local
food production. Additionally, potential resource allocation trade-offs,
especially for water and land, will put Mediterranean agriculture under
increased pressure, calling for more and more efficient production practices
(Verner, 2012; World Bank, 2013).
To support adaptation and mitigation efforts for climate change and
environmental degradation, Mediterranean-wide assessments of the state of
agriculture and the likely consequences of global change are required. These
would have to be complemented by analyses of the potential developments and
future difficulties of the agricultural sector and its interactions with the
environment. The large-scale character of such assessments and the necessity
of looking into possible future scenarios require the utilisation of
modelling tools that cover the essential characteristics of the dominant
agro-ecosystems in the region. At present, no suitable modelling framework
for this task exists. Given the range of conditions in the region, such a
tool should be process-based and integrate the major crop types, grasslands
and natural vegetation, taking into account the carbon cycle and hydrology of
them. Notably, the presence of perennial, woody species is a characteristic
of Mediterranean agro-ecosystems, and they deliver 45 % of agricultural
outputs (Lobianco and Esposti, 2006). Existing crop models have implemented
some tree crops, and in some cases applications in Mediterranean environments
(mostly small scale) were published. For example, the STICS crop model has
been used to simulate the growth of vineyards and apple trees (García de
Cortázar-Atauri, 2006; Nesme et al., 2006; Valdés-Gómez et al.,
2009); and in the CropSyst model, pears, apples, vineyards and peaches are
included (Marsal et al., 2013, 2014; Marsal and Stöckle, 2012). Other
modelling frameworks offer general and specific formulations for
horticultural systems that have been applied in other regions, mainly in
Anglo-Saxon countries. This is the case for the EPIC/SWAT/SWIM families
(Neitsch et al., 2004; Gerik et al., 2014) for cotton and apple, and for the
APSIM model for cotton and vineyards (Holzworth et al., 2014). In California,
another region with a Mediterranean climate, there is a dynamic modelling
community assessing climate change impacts on horticulture by process-based
(Gutiérrez et al., 2006) and empirical models (Lobell et al., 2007). At
the global scale, the GAEZ approach offers potential growing areas for
citrus, olives and cotton (IIASA/FAO, 2012). The GCWM model is probably the
most complete model in terms of perennial crops, comprising citrus, cotton,
date palm and grapes (Siebert and Döll, 2008, 2010). Other authors have
developed models for fruit trees, such as the inclusion of kiwi, vineyards
and apples in the SPASMO model (Clothier et al., 2012), walnuts in CAN-WALNUT
(Baldocchi and Wong, 2006) and date palm by Sperling (2013). A model by
Villalobos et al. (2013) focuses on transpiration of apricot, apple, citrus,
olive, peach, pistachio and walnut trees.
The goals of these applications are diverse, from simple reproduction of
experiments, via epidemiological analysis (causes and patterns of diseases),
to the simulation of phenological change and the influence of management
practices on agricultural production. Concerning the impacts of climate
change, Moriondo et al. (2015) presented a detailed review of empirical and
process-based models for olives and vineyards. They concluded that
process-based models are better suited for climate impact studies, but they
have to be completed and improved to account for the perennial nature of
these crops, the effect of higher CO2 on the atmosphere, dynamic growing
periods and the effect of management practices.
Reviewing the existing studies reveals two important points. First, there is
no single model or model family comprising all major agricultural plants of
the Mediterranean region. Second, there is no model combining dynamic
simulation of natural vegetation and agro-ecosystems for the Mediterranean
region. Some models are very advanced in some processes, like the case of
STICS for biochemical cycles. Other models have unique features, such as the
detailed consideration of hydrology in the unsaturated zone, salt and
leaching transport in the WOFOST model coupled with the SWAP and PEARL models
(Kroes and Van Dam, 2003). The CENTURY model, which focuses on soil organic
carbon computation, offers a forest module and general formulations that can
be adapted to horticulture, but only one small-scale application was
presented for the Mediterranean region (Álvaro-Fuentes et al., 2011).
Without the integrated modelling of natural vegetation and agro-ecosystems in
a comprehensive framework, there are many questions that cannot be answered.
Notably, some of them are of extreme relevance for the Mediterranean region
and concern water requirements and availability for the agricultural sector,
sustainable food production potentials under climate change, environmental
consequences of land use (including biodiversity loss), and soil carbon
sequestration patterns, including responses to land use change.
To better assess the potential responses of Mediterranean agro-ecosystems to
these forcings, we have extended the representation of Mediterranean
agriculture in the Lund-Potsdam-Jena managed Land (LPJmL) model. LPJmL
computes the dynamics of natural vegetation, annual crops and natural
grasslands by considering carbon pools and fluxes, hydrological variables,
and coupled photosynthesis and transpiration (Sitch et al., 2003; Gerten et
al., 2004; Bondeau et al., 2007). The model has undergone major developments
with respect to the hydrology, including a river routing and irrigation
scheme (Rost et al., 2008), the management of dams and reservoirs (Biemans et
al., 2011), and a five-soil-layer hydrology (Schaphoff et al., 2013). The
representation of agricultural systems has also been improved by including
bioenergy systems (tree and grass bioenergy plantations, jatropha, sugar
cane; Beringer and Lucht, 2008; Lapola et al., 2009). An agricultural
management module has been added, representing the combined influence of
management practices on plant and stand development (e.g. fertiliser inputs,
mechanisation, use of high-yielding varieties, weed and pest control, etc.;
Fader et al., 2010), and better representation of sowing dates and multiple
cropping systems (Waha et al., 2012, 2013). LPJmL is widely recognised as a
state-of-the-art agro-ecosystem and hydrology model and has undergone the
broadest possible range of validation efforts against experimental and
observational data. In a recent intercomparison of global hydrological models
and global gridded crop models for the assessment of future irrigation water
availability, Elliot et al. (2014) indicated that LPJmL is unique in that it
performs well in both categories. LPJmL is intensively used at the global and
macro-regional scales in various research fields, particularly for questions
related to future food security, land use change, and adaptation to climate
change (Gerten et al., 2008; Rost et al., 2009; Lapola et al., 2010; Fader et
al., 2010, 2013; Waha et al., 2013; Müller et al., 2014).
Since Mediterranean-specific crops have been lacking in LPJmL, we present
here an extension that includes 10 new crop functional types that are
especially important in the region. Most of these may be called
“agricultural trees”: nut trees, date palms, citrus trees, orchards, olive
trees, grapes, cotton, potatoes, vegetables and fodder grasses. Their
inclusion made it possible to account for ∼ 88 % of irrigated areas
of the Mediterranean instead of ∼ 50 %.
The following section outlines the methodology applied, including the
compilation of a new input data set with land use patterns which was needed
for the validation and application of the model, the description of the
modelling approach and parametrisation of the new crops, as well as the
computation of irrigation requirements and soil carbon densities. The results
section details exemplarily the performance of the model in simulating
yields, soil carbon and irrigation water requirements. Finally, the paper is
closed by a discussion on perspectives for future developments, potential
applications and further refinements.
Methods
As a function of climatic conditions and agricultural management, LPJmL
simulates, spatially explicitly and at a daily to yearly temporal resolution,
growing periods (sowing and harvest dates), net and gross primary
productivity, carbon sequestration in plants' compartments and soil,
heterotrophic and autotrophic respiration, agricultural production, as well
as a number of hydrological variables, such as runoff, soil evaporation,
plant transpiration, plants' interception, percolation, infiltration, river
discharge, irrigation water requirements, water stress and soil water
content. Required inputs are (a) gridded, monthly climate variables
(temperature, cloudiness interpolated to daily values and precipitation and
rainy days converted through a weather generator in daily values);
(b) atmospheric CO2 concentrations; (c) gridded soil texture as
described in Schaphoff et al. (2013); and (d) a gridded data set of land use
patterns prescribing which crops are grown where and whether they are
irrigated or rain-fed.
For the present study, we used climate inputs at 30 arcmin spatial
resolution and global CO2 concentrations derived from the CRU 3.10 data
sets (Harris et al., 2014). The land use patterns for the crops in LPJmL had
to be compiled from different sources, as explained in the following section.
The model was spun up for 5000 years with dynamic natural vegetation in order
to bring the carbon pools into equilibrium and additionally for 390 years
with natural and agricultural vegetation. The spun-up simulations were
followed by a transient run from 1901 to 2010 using the land use patterns
described in the next section.
Land use patterns for use in LPJmL
LPJmL needs irrigated and rain-fed physical (as opposed to harvested) areas
for each simulated crop. Physical and harvested areas differ through multiple
cropping practices; that is, when one area is used twice in a year, the
harvested area is twice as large as the physical area. For the present model
development a new land use data set had to be compiled from different
sources: Portmann et al. (2011), hereafter MIRCA, Monfreda et al. (2008),
hereafter MON, Klein Goldewijk et al. (2011), hereafter HYDE, and
Ramankutty et al. (2008), hereafter RAM.
The first step for the compilation of the land use data set was determining
the harvested areas of all LPJmL classes, including the new Mediterranean
crops, for the present time. For crops present in MIRCA, which
differentiate between irrigated and rain-fed areas, all values in that study
were used directly. For the missing crops, MON corresponding classes (see
Table S1 in the Supplement) were compared at grid-cell level with MIRCA
classes “other perennial” and “other annual”, thereby splitting the
harvested areas into the rain-fed and irrigated parts. This procedure was
done for olives, non-citrus orchards, nut trees and vegetables. For the first
three groups, MIRCA class “other perennial” was used for the
grid-cell-specific splitting between rain-fed and irrigated areas. For
vegetables, rain-fed and irrigated areas were derived through comparison with
the “other annual” class.
Large inconsistences were found at the grid-cell level between MIRCA and
MON, for example, cases where a single crop area in MON was larger than
the sum of the rain-fed and irrigated corresponding “other” classes in
MIRCA, and the absence or small extent of the class irrigated “other
perennial” in areas that intuitively may be assumed to be irrigated, like in
the case of orchards and olives in Egypt. The author of MIRCA (F. Portmann,
personal communication, 2013) assumes that most of these inconsistences are
due to scale, the grid-cell differences being potentially large but
presenting a good agreement at the administrative level. Grasslands,
representing meadows, were taken directly from RAM and assumed to always be
rain-fed.
After deriving harvested areas for around the year 2000, the calculation
followed the flowchart shown in Fig. S1 in the Supplement. Harvested areas
were compared with cropland and grassland areas from RAM in order to
exclude multiple cropping and derive physical cultivation areas. Decadal
cropland data from HYDE were interpolated to derive annual values and then
used for extrapolating the land use patterns of ∼ 2000 to the past,
until 1700 (see below, Eqs. 1 to 5). Historical irrigation fractions were
determined as explained in Fader et al. (2010).
Due to inconsistences between HYDE and the data set combining MIRCA, MON
and RAM (hereafter MMR), proportionally changing cell fractions using
the HYDE historical trend would have given an unrealistic overall trend of
cropland areas. For this reason, crop-specific bias corrections had to be
performed in between, as follows.
First, the global (g) area difference (D) was calculated:
D2000=HYDEg,2000-MMRg,2000.
Bias correction of HYDE global values was performed by
HYDEg,y,bias_corrected=HYDEg,y-D2000,
where y represents years from 1700 to 2000.
Cell (c) bias correction was done by
HYDEcorrected,c,y=HYDEc,yHYDEg,y×HYDEg,y,bias_corrected.
Proportional temporal change of MMR cell values was done from 2000
backwards by
MMRy,c,proportional=HYDEcorrected,c,yHYDEcorrected,c,y+1×MMRy,c.
And final cell values (LULPJmL) were calculated by
LULPJmL=HYDEcorrected,g,yMIRCAproportional,g,y×MMRy,c,proportional.
Cell fractions from 2001 to 2010 follow the trend between 1950 and 2000.
This procedure yields a gridded global data set at 30 arcmin spatial
resolution of the cultivation areas of 24 crops from 1700 to 2010. Table 1
shows the resulting areas for each crop class of LPJmL. Expanding LPJmL for
modelling Mediterranean crops made it possible to account for ∼ 88 %
of irrigated areas of the Mediterranean instead of ∼ 50 %. For
rain-fed areas, the improvement was from ∼ 21 to 73 %. The remaining
areas are mostly fallow land and were included in the “others” model class
which is parametrised as grasslands.
Areas of LPJmL crops in the Mediterranean region. The stars indicate
crops that are implemented in this study.
Rain-fed
Irrigated
Total
106 ha
%
106 ha
%
106 ha
%
Temp. cer.
50.11
12.0
6.38
24.8
56.49
12.7
Maize
13.84
3.3
3.37
13.1
17.21
3.9
Fodder grass*
9.90
2.4
0.48
1.8
10.38
2.3
Trop. cer.
7.64
1.8
0.25
1.0
7.89
1.8
Pulses
6.03
1.4
0.68
2.6
6.71
1.5
Olives*
4.86
1.2
1.61
6.3
6.47
1.5
Vegetables*
4.24
1.0
1.60
6.2
5.84
1.3
Orchards*
4.27
1.0
1.26
4.9
5.53
1.2
Sunflower
4.40
1.1
0.43
1.7
4.82
1.1
Grapes*
4.09
1.0
0.59
2.3
4.68
1.1
Potatoes*
1.40
0.3
0.66
2.6
2.06
0.5
Cotton*
0.34
0.1
1.67
6.5
2.01
0.5
Nuts*
1.45
0.3
0.52
2.0
1.97
0.4
Temp. roots
1.17
0.3
0.74
2.9
1.91
0.4
Rapeseed
1.72
0.4
0.05
0.2
1.77
0.4
Groundnuts
1.56
0.4
0.11
0.4
1.67
0.4
Rice
0.14
0.0
0.88
3.4
1.02
0.2
Citrus*
0.12
0.0
0.86
3.3
0.98
0.2
Soybeans
0.63
0.2
0.17
0.7
0.80
0.2
Trop. roots
0.59
0.1
0.00
0.0
0.59
0.1
Date palm*
0.03
0.0
0.24
0.9
0.27
0.1
Sugar cane
0.16
0.0
0.09
0.3
0.24
0.1
Others
43.83
10.5
3.11
12.1
46.94
10.6
Grasslands
254.86
61.1
0.00
0.0
254.86
57.5
Total
417.36
100.0
25.74
100.0
443.10
100.0
Total without grasslands
162.50
25.74
188.25
Crop area considered before/after development (%)
21/73
51/88
23/75
There is a general lack of information about the planting areas for
agricultural trees at national and subnational level. Nevertheless, it was
possible to make two comparisons. First, EUROSTAT offers information about
olive tree areas at country level for eight countries of the northern
Mediterranean. Our results are in good agreement with their numbers, with a
mean absolute percent error of 26 % (MAPE, calculated as the sum of
percentage differences between their values and our values, divided by their
values, and finally multiplied by 1 over the sample size). Second, national
harvested areas as reported by FAOSTAT for dates, olives, cotton seed, grapes
and potatoes as an average of 2000 to 2009 could be compared with our data
set. The agreement is high (MAPE < 30 %) for all classes except for
olives and dates (MAPE 47 and 46 %, respectively) where our data set has
mainly smaller areas. This is due to the fact that MIRCA, RAM and MON
have been compiled for the year 2000 and the FAO shows strongly an
accelerated expansion of areas from 2000 to 2010, for example 54 % for
olives in Morocco and 45 % for dates in Egypt and Turkey. Overall, the
input data set compiled here has no better alternatives and appears to be
broadly suitable for applications until newer versions of the land use data
used as sources are released.
Implementation, calibration and parametrisation of Mediterranean
agricultural trees and crops
Twelve crops were already present in LPJmL (temperate cereals, rice, tropical
cereals, maize, temperate roots, tropical roots, pulses, rapeseed, soybeans,
sunflower, sugar cane, others). In this study we included nut trees, date
palms, citrus trees, orchards, olive trees, grapes, potatoes, cotton,
vegetables and fodder grasses.
Key parameters of agricultural trees and
potatoes. R: representative tree/plant for parametrisation;
K_est: tree density range; HI: harvest
ratio/index; Tb: base temperature;
GDD: growing degree day requirements to grow full leaf coverage (in
deciduous trees) or to reach ripeness of fruit (in evergreen trees);
Tlim: lower and upper coldest monthly mean
temperature; Phopt: lower and upper temperature
optimum for photosynthesis; HE: maximal height of tree;
Phu_1/2: fraction of potential heat units
accumulated in the first (1) or second (2) inflexion point of the optimal
leaf area curve; Lmax_1/2: fraction of maximal leaf
area index accumulated in the first (1) or second (2) inflexion point of the
optimal leaf area curve; Phusen: fraction of growing season at
which senescence becomes the dominant process; Lai_ha: fraction of leaf area index still present at harvest; PHU:
potential heat units for ripeness; WCF: water content factor for
conversion from dry to fresh matter.
Crop
R
Seasonality
K_est (trees ha-1)
HI (frac)
Tb (∘C)
GDD (acc. ∘C)
Phopt (∘ C)
Tlim (∘C)
HE (m)
WCF (% of DM)
Citrus
Orange tree
Evergreen broadleaved
500–4600g
0.5h
18a
1000i
23 to 30g
-10 to 40g
10j
13k
Olive trees
Olive trees
Evergreen broadleaved
75–690n,o
0.45f
15l
1000p
15 to 30n,m
-10 to 15n,m
15m
30q
Date palm
Date palm
Evergreen broadleaved
100–920r
0.5u
14s
1700u
25 to 35s
-10 to 40s
20s
70s,t
Orchards
Apple tree
Deciduous broadleaved
325–2990aa
0.49v
7a
400x
15 to 25a
-15 to 15z
13y
16k
Nut trees
Almond tree
Deciduous broadleaved
100-920ac
0.6w
7ab
300ad
20 to 25ab
-10 to 15ab
10ae
90af
Grapes
Vine plants
Deciduous broadleaved
2000–15 000ak
0.6al
10ai
300ah
17 to 20ag
10 to 15ag
2aj
20k
Cotton
Cotton plants
Deciduous broadleaved
20 000–184 000aq
0.19am
15ao
300ao
16 to 22ao
-10 to 40ao
3ap
91an
Crop
R
LAI curve parameters (frac)
Tb
PHU
Phopt
WCF
Phu_1
Lmax_1
Phu_2
Lmax_2
Phusen
Lai_ha
(∘C)
(acc. ∘C)
(∘C)
(% of DM)
Potatoes
Potatoes
0.15a
0.01a
0.5a
0.95a
0.9a
0.0b
0.0c
1500–2400a
16 to 25a,d,e
2011
a Neitsch et al. (2004) (SWAT model). For
Tb of citrus, the Tb of poplar and oak was taken as a
proxy. b Gordon et al. (1997). c Haverkort and
MacKerron (1995) compiled different base temperatures in different studies
and showed that a base temperature of 0.0 ∘C describes the
tuberisation rate well in temperate zones. d FAO (2008).
e Ku et al. (1977). f Morales Sierra (2012).
g FAO (2013a). For Tlim of citrus, it was assumed that
the rest can be induced by water deficit, not only by low temperatures.
h Cannell (1985). i Based on FAO (2013a) information that
indicates that citrus need 7 to 14 months from flowering to maturity.
j Orwa et al. (2009). k Bastin and Henken (1997).
l Aguilera et al. (2014). m California Rare Fruit
Growers (1997). n FAO (2013b). o Roussos (2007).
p Orlandi et al. (2014). q Kailis and Harris (2007).
r Al-Khayri and Niblet (2012). s FAO (2002).
t Elshibli (2009). u Large variations and lack of data
lead to estimation of these parameters assuming relatively small,
high-yielding varieties of palms, flowering around 1 month, developing fruits
in around 4 months, with 1000 kg weight and yields of 500 kg per palm.
v Zanotelli et al. (2013). w Toky et al. (1989).
x Wünsche and Lakso (2000). y Parametrised as a
standard apple tree and not as a dwarf. z Perry (2011). Apple, pear
and cherry trees can survive lower winter temperatures, but other fruit trees
(i.e. fig, peach and apricot trees) cannot. aa FAO (2011).
ab Pontificia Universidad Católica de Chile (2008).
ac Netafim (2013). ad Janick and Paull (2008).
ae Duke (1983). af Alasalvar and Shahidi (2008).
ag FAO (2013c). ah Meier (2001).
ai Strik (2011). aj Ministry of agriculture, food and
rural affairs (2013). ak Slovak Wine Academy Pezinok (2009).
al Zamski and Schaffer (1996). am Sakin (2012).
an Yan et al. (2007). ao Tsiros et al. (2009).
ap Kiranga (2013). In wild conditions, cotton can be up to 5 m
high, in crops, up to 1.5 m. aq Wright et al. (2004), Paytas and
Tarrago (2011). Planting density varies largely.
For each new crop, a representative species was selected for which the
parameterisation was performed (see Table 2 for details on the parameters
described in the following sentences). Potatoes were introduced as an annual
crop following the approaches as described in Bondeau et al. (2007) for other
annual crops. Potatoes are planted in early spring in cooler climates and
late winter in warmer regions (FAO, 2008). In LPJmL they are sown each year
in the areas indicated by the land use input, taking into account the
seasonality of rainfall and temperature and the experience of farmers (see
Waha et al., 2012). In the case of no water stress, leaf area index (LAI)
development follows a prescribed curve (as in SWAT) with inflexion points
according to the parameters shown in Table 2 (Phu_1/2, Lmax_1/2, Phusen),
but LAI is reduced in the case of water stress by scaling it to the
difference between atmospheric demand and water supply. Phenology and
maturity are modelled after the heat unit theory: when the accumulated
difference between daily temperatures and base temperature reaches a
prescribed total growing degree amount (called hereafter PHU for potential
heat units), then the potatoes are ripe and are harvested. Absorbed
photosynthetically active radiation drives assimilation. Carbon allocation to
different parts of the plant is a function of PHU development. The PHU
parameter used depends on the mean temperature for spring varieties and on
the sowing date for the winter/autumn varieties, and ranges from 1500 to
2400∘ Cd (with lower PHU in cooler climates).
Agricultural trees – including grapes and cotton which are modelled in the
present study as small trees – are planted as samplings with 2.3 g carbon
in sapwood and a LAI of 1.6 in the growing areas indicated by the land use
input. Each agricultural tree has a country- and tree-specific planting
density, and a tree-specific parameter determines the number of years that
are needed for trees to grow before the first harvest. The latter parameter
depends not only on the varieties used and on the biophysical situation, but
also on management, especially on the usage of fertilisers and irrigation.
There are insufficient quantitative data on this issue, and we therefore
assumed this parameter to be 4 years for all agricultural trees. After these
years, a plant-specific portion (HI, the “harvest ratio” or “harvest
index”) of the net primary productivity (NPP) of the tree is harvested every
year. Thus, fruit growth is represented by a carbon accumulation that equals
the multiplication of HI and NPP. An additional tree-specific parameter
determines the replanting cycles of trees. Since there are no data available
on this, we assumed that plantations are renewed (replanted) after 40 years.
Most agricultural trees have chilling requirements; that is, they need a
period of low temperatures before flowering. This is modelled using the
parameter Tlim shown in Table 2. A 20-year running average of the
coldest month maximum and minimum temperatures is compared with these values
and defines the bioclimatic limits of each species. Hence, temperature
warming above these limits would inhibit the establishment and survival of
the perennial crops.
For deciduous trees, the active phase starts when the daily temperature is
higher than the base temperature, and it is assumed that fruit growth occurs
in the second half of the active phase of the year, i.e. when the
phenological scalar (fraction of the maximum leaf coverage) is > 0.5 and
before leaf senescence starts. Leaf senescence occurs when daily temperatures
fall below the base temperature.
Following Sitch et al. (2003), evergreen trees are assumed to have constant
leaf coverage and leaf longevities of more than 1 year. In this case, the
accumulation of carbohydrates in the fruits occurs on days where temperature
is above a tree-specific base temperature until a tree-specific threshold is
reached (GDD).
Grass grows in the same areas of agricultural trees, except for cotton and
grape plantations and orchards. For these three classes we assumed that
grasses and weeds do not grow, thereby avoiding competition with the crops,
implying that any ground cover is eradicated through some sort of weed
control. This is the dominant practice in reality, although exceptions to
this rule are gaining in importance.
Categories “vegetables” and “fodder grass” are modelled following the
modelling approach of C3 grass described in Sitch et al. (2003). This is very
appropriate for fodder grasses in the Mediterranean region since these are
mainly alfalfa and clover. For vegetables, this parametrisation accounts for
the very large physiological and allometric heterogeneity of vegetables, and
also for multiple harvests per year, a fact that is well represented by a
constant cover of the areas. Following the implementation of temperate
herbaceous plant functional types (PFTs) in Sitch et al. (2003), the
photosynthesis in vegetables and fodder grasses is assumed to be optimal
between 10 and 30 ∘C. Vegetables and fodder grasses are harvested
once their phenology is complete (i.e. the growing degree day accumulation
determined by a parameter was reached) and the biomass increment is equal to
or greater than 200 g C m-2 since the last harvest event. At that
time, 50 % of the aboveground biomass is transferred to the harvest
compartment. This assumption may be rather low for some vegetables such as
lettuce, and rather high for others, such as beans. For the conversion from
dry to fresh matter it was assumed that vegetables have an average water
content of 40 %, which, again, is rather low for some cases, e.g.
cucumbers, but rather high for e.g. garlic. The moisture content of fodder
grass varies approximately between 10 and 75 %, depending on whether it is
reported for hay, silage or fresh fodder. Here we represent fodder grass for
hay production and assume thus a moisture content of 10 %.
The standard calibration process for agricultural management in LPJmL crops
was extended in order to include agricultural trees. For annual crops this
procedure consists in performing a set of runs with systematically modified
management parameters representing the heterogeneity of fields, high-yielding
varieties and the maximal achievable LAI (see more details in Fader et al.,
2010). Similarly, 10 runs systematically modifying the tree- and
country-specific plantation density parameter were performed to calibrate the
management of agricultural tree plantations. Planting densities range from 25
to 230 % of the standard values, which were derived from literature
research (see Table 2). For grapes the range was prescribed between 2000 and
15 000 vines per hectare. The tree density for each country was then chosen
based on the best matching with reported FAO yields.
Irrigation water requirements and soil carbon
The computation of net and gross irrigation requirements (NIR and GIR,
respectively; see below) in LPJmL is explained in detail in Rost et
al. (2008) and Rohwer et al. (2006). The functioning of soil decomposition,
soil biochemistry and soil hydrology, including soil organic carbon (SOC), is
explained in Schaphoff et al. (2013) and Sitch et al. (2003). In the
following paragraphs a short and simplified summary of these procedures will
be given.
Irrigation is triggered in irrigated areas when soil water content is lower
than 90 % of the field capacity in the upper 50 cm of the soil (here
“irrigated layer”). The plants' NIR is modelled in LPJmL as the amount of
water that plants need, taking into account the water holding capacity of the
irrigated layer and the relative soil moisture (Rost et al., 2008):
NIRmmd-1=min1fRilDSy-wr,1-wilWHC,
where D (mm d-1) is the atmospheric demand, which depends on
potential evapotranspiration and canopy conductance. Sy (mm d-1) is
the soil water supply, which equals a crop's specific maximum transpirational
rate if the soil is saturated or declines linearly with soil moisture.
fRil (fraction) is the proportion of roots in the irrigated layer.
wil (fraction) is the water content in the irrigated layer.
wr (fraction) is the water content weighted with the root density
for the soil column. WHC (mm) is the field capacity of the irrigated layer
(water holding capacity). GIR, also called water withdrawal or extraction,
is obtained by dividing NIR by the project efficiencies (EP):
GIRmmd-1=NIREP.
EP is a country-specific parameter calculated for LPJmL by Rohwer et al. (2006) after the approach described in the FAO irrigation manual (Savva and
Frenken, 2002). It takes into account reported data on conveyance efficiency
(EC), field application efficiency (EA) and a management factor (MF):
EP0to<1=EC×EA×MF.
EA represents the water use efficiency in the fields and its increase from
surface irrigation systems, via sprinkler systems, to drip irrigation
systems. EC represents the water use efficiency in the distribution systems
and is assumed to be linked to irrigation systems (lower for open channels
than for pressurised pipelines). MF varies between 0.9 and 1 and is higher
in pressurised and small-scale systems under the assumption that large-scale
systems are more difficult to manage (see more details in Rohwer et al.,
2006).
The soil column in LPJmL has five hydrologically and thermally active layers
(20, 30, 50 cm, 1 and 1 m thickness) where roots have access to water.
Infiltration depends on the soil water content of the first layer (water that
does not infiltrate runs off), and percolation between the layers was
simulated following the storage routine technique (see Schaphoff et al.,
2013, for more details). Excess water over the saturation level is assumed to
feed subsurface runoff. LPJmL has two soil carbon pools, with intermediate
and fast turnover (0.001 and 0.03 rate of turnover per year at
10 ∘C). The maximum decomposition rate is reached around field
capacity and decreases afterwards due to decreased soil oxygen content. A
simple energy balance model is used for the thermal soil module. It includes
a one-dimensional heat conduction equation, convection of latent heat,
thawing and sensible heat (see Schaphoff et al., 2013, for more details).
Results
The performance of the improved LPJmL version was tested by simulating
agricultural yields, irrigation water requirements and soil carbon density,
and comparing the results to published observations.
Agricultural yields
Scatter plot of LPJmL-simulated yields (averages over 2000–2009)
versus reported yields from FAOSTAT for Mediterranean countries. The line
represents the 1:1 line. FM stands for fresh matter.
The radius of the bubbles indicates the relative size of the harvested area in the respective country.
![]()
Figure 1 shows LPJmL-simulated yields in metric tonnes fresh matter per
hectare for the calibrated run for all new crops where FAOSTAT had data in
the Mediterranean region, averaged for the period 2000–2009. LPJmL simulates
all nuts, olives, fruits and potatoes in very good agreement with reported
values, showing Willmott coefficients of ≥ 0.6 in all cases. Only two cases with large planting areas and significant
differences are visible, both in Turkey, for grapes and nut trees. The latter
is due to the chosen representative tree for the parametrisation of this
group (almonds), which does not represent the majority of nut plantations in
Turkey. In 2010 almost 70 % of nut trees in Turkey were hazelnuts and only
3 % almonds (FAO, 2015a). The underestimation of grape yield in Turkey
might be related to more than one factor, including the fact that the wine
sector has been very dynamic in recent years there, with increases in
production but decreases in area harvested (FAO, 2015a). FAO calculates
yields by dividing national production by harvested area and calculates,
thus, an increase in yields and a higher average over the years analysed. Our
input data set shows a slight increase in grape areas with relative constant
production; hence, we calculated a lower yield average over the years. Also,
the general parametrisation for European grapes probably cannot represent the
special character of local Turkish varieties that are well adapted to sandy
soils and high altitudes.
Validating subnational patterns of yields is very difficult due to a general
lack of data on this and important differences with other estimations in
terms of scale, methods and time frames. However, we included in Fig. S2 a
comparison with the yields from Monfreda et al. (2008) for the new crops
where this study offers subnational data (note that their estimates are for
the period of time around the year 2000 and at the administration level).
LPJmL reproduces correctly a number of spatial patterns, such as some
high-yielding regions: olives in Greece, vineyards in Israel, Lebanon,
southern Spain, the Po Valley and the Italian regions of Emilia-Romagna and
Lazio, potatoes in Turkey, Greece, Egypt, Morocco, Israel, Lebanon and
Algeria, as well as cotton yields in southern Spain, Greece, Turkey, Egypt,
Israel and Lebanon. Also, some low-yielding zones are in good agreement, as
is the case for potatoes in the Balkans, Portugal and Tunisia, olives in
Morocco, Algeria and Tunisia, and cotton in Tunisia. However, a few patterns
shown by Monfreda et al. (2008) are not shown in LPJmL simulations, including
the north–south pattern of olives in Spain, the high-yielding zone of olives
in southern Italy, and the grape and olive yields in Egypt. The first case is
due to the extremely high management intensity in southern Spain which is not
captured by the national calibration of planting densities (Scheidel and
Krausmann, 2011). The latter case is originated by differences between the
MON and MIRCA land use data sets that produce a lack of irrigated areas
of grapes and olives in Egypt in our data set (see Sect. 2.2 for more
details). The same is the case for the gaps in potatoes, e.g. in France.
Overall there is a large agreement with no systematic differences between the
spatial patterns shown by Monfreda et al. (2008) and the ones computed in the
present study.
Irrigation water requirements
LPJmL-simulated net irrigation water requirements (NIR),
as average over the period 2000–2009.
![]()
Figure 2 shows LPJmL-simulated NIR per hectare, which presents a clear
north–south pattern that follows the climate-driven patterns of potential
evapotranspiration. Konzmann et al. (2013) presented simulated irrigation
requirements globally for around 10 crop functional types with a former
version of the LPJmL model where tree plantations were represented as mowed
grasslands. Their Fig. 1 shows a grid-cell pattern broadly similar to ours,
but our more detailed representation of Mediterranean crops leads to higher
values in various regions, including Algeria, Tunisia, Israel, Lebanon,
Greece and the Iberian Peninsula. This is in good agreement with a general
tendency of trees to absorb and transpire more water than grasslands
(Belluscio, 2009).
Comparison of irrigation consumptive water use from Siebert et
al. (2010) and net irrigation water requirements computed in this study as a
percentage of the Siebert et al. (2010) values. Negative (positive) values
indicate higher (lower) values in their study.
![]()
Siebert et al. (2010) computed present irrigation consumptive water use for
subnational administrative units by means of the GCWM model that compares
well with our NIR estimates (Fig. 3). However, they estimated higher values
in Egypt, Libya, Greece and Portugal, and lower values in the Po Valley and
southern France. These differences are mainly linked to disparities in the
land use data set used as inputs: Siebert et al. (2010) areas equipped for
irrigation are larger than the ones used in the present study in the first
group of countries, and smaller in the second group. The comparison in
absolute terms (Fig. S3) shows a similar pattern but with additional high
differences in the Spanish province of Andalucía. These differences may
be linked to the model approach, for example, the difference in the crops
considered (olives, orchards, and nuts are only considered in the present
study by LPJmL), different methods to model evapotranspiration
(Penman–Monteith versus Priestley–Taylor) and differences in growing
periods (e.g. dynamic versus static sowing dates for annual crops). Since
Andalucía is a region strongly characterised by horticulture, and taking
into account that we parametrised vegetables as C3 grasses, it is worthwhile
looking in more detail into this class, also because neither Siebert et
al. (2010) nor the present study account for cultivation of vegetables in
greenhouses. We computed independent irrigation water requirements of
2357 m3 ha-1 based on the values presented in Table 2 from
Gallardo and Thompson (2013) that concerns various vegetables and water melon
grown in greenhouses. Based on the crop cycles described in their
publication, we assumed the possibility of planting three types of vegetables
per year using the same area (multiple cropping). Vegetables are planted on
around 1.6 Mha in the Mediterranean region. This yields a total water
consumption of 11.3 km3. The present study computed 9.7 km3, that
is, a very similar value.
In total, the agricultural sector in the Mediterranean was simulated to
withdraw approx. 223 km3 of water per year for irrigation (average
2000–2009), with GIR being especially high in the Nile Delta, the eastern
Mediterranean and in some Spanish regions (not shown). Our national GIR
values are in good agreement with AQUASTAT data (FAO, 2015b) (Fig. 4,
squares), with some differences. It is difficult to evaluate the quality of
the AQUASTAT data. For example, the values of three countries with large
differences from our estimates (Algeria, Lebanon and Jordan) are in fact not
reported data but modelled data. Assuming that the modelling was performed by
the FAO's CROPWAT model, our estimates might be more accurate, since we
perform a validated process-based calculation of transpiration instead of
prescribing crop water coefficients. Another example of uncertainty is shown
in the case of Egypt. In Fig. 4 (red symbols) it is evident that the
estimates for Egypt vary largely; for example, Rayan and Djebedjian (2004)
presented much lower estimates than AQUASTAT.
Scatter plot of LPJmL-simulated gross irrigation water requirements
(hm3, averages over 2000–2009) and the estimates presented by other
studies. The line represents the 1:1 line. Egypt and Spain have been
coloured in red and green, respectively, to visualise the disparities between
different studies. (Note that Cánovas Cuenca, 2013, assumes a fixed
requirement of 6176 m3 ha-1.)
![]()
Döll and Siebert (2002) were probably the first authors who quantified
irrigation water requirements at the global level while distinguishing two
crop classes (rice and non-rice). Their Table 5 shows GIR for Egypt, Israel
and Spain according to independent data and their calculations (using
irrigation areas from 1995 and climate from 1961 to 1990). Despite the
difference in the period of time analysed and the methodology, Fig. 4 shows
that our results agree well for Israel and Spain for the independent data
(from the water commissioner of Israel and the executive secretary of the
International Commission of Irrigation and Drainage, respectively), while
they found their values to be overestimated for both countries. For Egypt,
independent data deliver 30 % higher GIR than our calculations and 27 %
lower than the values of Döll and Siebert (2002). However, the
reliability of the reported water use data cannot be established, especially
in the case of Egypt, where these numbers are relevant for negotiations on
water allocation treaties with upstream countries of the Nile River.
A report by Cánovas Cuenca (2013), quoting an unpublished paper by
Cánovas and del Campo (2006), shows in its Table 17 irrigation water
requirements for Mediterranean countries assuming that every hectare
agriculture needs 6176 m3 of water per year. Our analysis shows that
while this number delivers a fair estimate of irrigation requirements for
some northern Mediterranean countries, it strongly underestimates irrigation
requirements of dry Mediterranean countries (Fig. 4, dots). This confirms
that the environmental and climate diversity of the Mediterranean region
requires spatially explicit modelling approaches.
To summarise, LPJmL computes Mediterranean irrigation water requirements in
the range of former studies, even if comparisons are a challenge due to
inconsistent model inputs, differences in modelling approaches, and due to
the fact that to our best knowledge, this is the first study with a complete
representation of Mediterranean crops.
Soil carbon density
As mentioned in the introduction, some assessments can only be performed
with a model that includes natural and agricultural ecosystems with a fair
detail in the hydrological cycle. This is the case for carbon sequestration
by soils in a unit of area that has both natural and agricultural vegetation
– a very important variable for the climate debate and the ecosystem service
research domain.
Soils under forest, grasslands, and cropland show different carbon densities
depending on climate, vegetation, soil structure, and management. Generally
speaking, forests have higher proportions of soil organic carbon (SOC)
compared to mowed grasslands, and they, in turn, have higher values compared
to cropland planted with annual crops (Jobbágy and Jackson, 2000; Eclesia
et al., 2012; Werth et al., 2005). Also, evergreen broadleaved forests and
plantations have usually higher SOC than deciduous forests and plantations in
semi-arid climates (Doblas-Miranda et al., 2013). Agricultural tree
plantations have a lower tree density than forest, generally a regular
distribution of trees that increase soil evaporation, and they are subject to
removal of biomass (harvest). These factors lead to lower SOC values in tree
plantations compared to forest. However, management of tree plantations,
including irrigation input, planting density, presence or eradication of
grass strips and mulching can strongly increase or decrease SOC. Putting all
this information together and assuming that management and environmental
factors are comparable, the SOC of agricultural tree plantations is expected
to be generally higher than the SOC of mowed grasslands and generally lower
than the SOC of natural forests and native grasslands. This is especially
true for evergreen tree plantations and managed grasslands with
high-frequency mowing. Hence, the implementation of agricultural trees in the
LPJmL should produce higher SOC over the entire soil profile in many
Mediterranean areas. This is because before the implementation of
agricultural trees, the areas corresponding to these agro-ecosystems were
simulated as mowed grasslands.
Difference in LPJmL-simulated soil organic carbon (average
2000–2009) before and after the implementation of agricultural trees
described in this study. Negative (positive) values indicate that the
development decreased (increased) soil organic content.
![]()
As expected, Fig. 5 shows that implementing agricultural trees in LPJmL
increased the carbon stock in soils in the whole Mediterranean except France.
This exception is due to the fact that there are only two new implemented
crops in France with significant areas: non-citrus orchards and grapes. Both
are deciduous trees, with high soil evaporation at the beginning and end of
the active period affecting carbon decomposition. Also, orchards have a
relatively low planting density in France (1300 trees per hectare), which
reduces shadow effects and litter input that, in turn, results in lower soil
carbon compared to high-density mowed grass.
Validation of the new SOC patterns is very challenging since SOC measurements
are spatially discontinuous as well as dependent on local conditions,
sampling method and small-scale drainage conditions. Comparison with
empirically based or process-based modelled SOC is also difficult due to
differences in approaches, parameters, processes considered and issues of
scale. Nevertheless, we compared our SOC estimates before
(LPJmLOld) and after (LPJmLNew) the implementation of
agricultural trees with the organic carbon density from the HWSD database
(Hiederer and Köchy, 2012). These data are produced by establishing
functions between SOC and soil type, topography, climate variables and land
use situation. For this comparison we calculated the difference of absolute
differences (/LPJmLOld - HWSD/-/LPJmLNew - HWSD/,
Fig. 6). Considering significant differences (results <> 1 t ha-1), the number of grid cells with decreased differences to
the HWSD estimates almost doubles the number of grid cells with increased
differences (767 versus 460 grid cells). This means that the development
presented in this study moved LPJmL's results for SOC closer to HWSD values.
Difference between the HWSD soil carbon density and
LPJmL-simulated soil carbon density before and after the model development
presented here (/LPJmLOld - HWSD/-/LPJmLNew - HWSD/). Positive
values indicate an improvement in simulation of soil carbon stock due to the
implementation of Mediterranean crops described in the present study.
![]()
As mentioned before, options for comparison with other SOC estimates are
limited. The documentation of HWSD estimates (Hiederer and Köchy, 2012)
offers an impressive effort in comparing their estimates with other
assessments. They found large, spatially diverse differences from other
estimates; some of them could be associated with differences in approaches.
When comparing LPJmL results with HWSD estimates, it is necessary to bear in
mind that HWSD offers a more detailed spatial scale and representation of
processes linked to soil types, while LPJmL has a more detailed influence of
land use history, seasonality of temperature and types of crops in SOC
formation.
Discussion
Advances through consistent carbon–water–agriculture modelling
Environmental degradation, climate change, and population growth will put
Mediterranean agriculture and natural ecosystems under pressure (IPCC, 2012;
Kovats et al., 2014; Diffenbaugh and Giorgi, 2012; Skuras and Psaltopoulos,
2012; Doblas-Miranda et al., 2015). Timely and appropriate coping with the
combination of these challenges will need collaboration between the
Mediterranean countries and local communities in a number of issues,
including advanced development of adaptation options, common plans on energy
transition, environmental policy and best-practice rules in nature
conservation and agricultural management. Collaboration will have to be
designed in a framework that allows for taking into account of the larger
European and global picture in terms of environmental change, dynamics of
ecological systems, foreign investments and migration movements. This calls
for new tools that are able to be applied at a large scale and can account
for the interlinkages between agricultural systems, the carbon cycle, and
water resources.
With the model development presented in the current study, LPJmL is now
suitable for supporting Mediterranean decision makers. The inclusion of
Mediterranean crops in LPJmL not only increased substantially the proportion
of agricultural areas for which quantitative assessments are possible, but it
also improved the potential for computation of irrigation requirements and
soil carbon. The outcome is a model with a comprehensive representation of
ecophysiological processes for all vegetation types (natural and
agricultural) in a consistent and validated framework that produces estimates
of carbon, agricultural and hydrological variables for the entire
Mediterranean basin. As such, LPJmL is especially suitable for analyses of
water issues. Taking into account the projected water scarcity due to climate
change in the Mediterranean area, the continuously dropping groundwater
tables due to overexploitation, and the projected increases in irrigation
water demand (Fischer et al., 2007; Konzmann et al., 2013; Wada et al., 2010;
Arnell, 2004), this constitutes a promising area for future model
applications and further development. A first application of this model
development was presented by Fader et al. (2015), pointing out that
irrigation water needs of perennial crops in the Mediterranean region might
increase significantly under climate change, and some countries may face
constraints to meet the higher water demands.
Potential applications and perspective for further research
The inclusion of perennial crops in LPJmL presented in the current study
opens up the possibility for a number of large-scale applications and
research studies that cannot be performed with empirical and/or
input-intensive agronomic models. These include assessments of climate change
impacts on hydrological variables, agricultural production and carbon
sequestration, as well as evaluation of consequences of land use change
(including expansion of irrigated areas). Some of these applications could
also be performed by land surface models, but LPJmL now has the advantage of
considering perennial crops in detail, which allows more precise
quantifications not only in the Mediterranean region, but also in other
macro-regions where agriculture is partially dominated by tree crops, such as
Australia, South Africa, Chile, western Argentina and California. Moreover,
having a more accurate representation of perennial crops also allows studies
on shifts in suitable growing areas for agricultural trees, diversity of
diets, resilience of agricultural systems, needs for climate change
adaptation and implications for food security, as well as assessments of
ecosystem services provided by perennial cultures, for example habitat
provision for avifauna.
Further improvements and refinements of LPJmL can be envisaged for
applications in the Mediterranean area. We can divide these potential
improvements into three groups, enumerated from least to most complex and
work-intensive: (a) input-related, (b) parameter-related, and (c) inclusion
of new processes.
The most important input-related improvement concerns national and
subnational studies and is related to the need to increase the spatial
resolution in all inputs used by LPJmL. The limited availability of climate
data and scenarios in a higher spatial resolution for the whole basin as well
as missing detailed flow direction maps, especially for North Africa, have
constrained this refinement until now. Nevertheless, work on data
interpolation and downscaling is ongoing to bring the model to the 15 arcmin
resolution. Another input-related issue is the need for scenarios of future
crop patterns as a consequence of climatic and socio-economic change. With
climate change very likely affecting the potential growing areas of
agricultural trees and their profitability, studies aiming at a future
quantification of agricultural, biochemical and hydrological variables would
profit from coupling between models like LPJmL and land use models.
Small-scale application aiming at comparing and analysing single crops may
require parameter-related changes such as re-parametrisation allowing
differentiation of harvesting times after uses and varieties (e.g. varieties
of grapes, difference between table olives and olives for oil),
grid-cell-specific planting densities and its differentiation between
irrigated and rain-fed conditions, as well as crop-specific setting up of
fruits, which at present depends on the phenological development but is not
differentiated for different crops. For national-scale studies,
re-parametrisation with different representative plants for the groups (e.g.
using hazelnuts instead of almonds for nut trees in Turkey) is possible
without any difficulty. For this group of improvements, data on management
are essential, including harvesting times, post-harvest uses, planting
densities, planted varieties, etc. Another benefit of more precise management
information would be the possibility of differentiating parameters that are
assumed to be static and equal for all plants in the present study, such as
the number of years that a perennial plantation stays in production before
being renewed and the period of time from the planting until the first
harvest (see Sect. 2.2).
Some refinements in modelled processes should be undertaken for studies
aiming at detecting year-to-year phenological changes or sub-yearly patterns
of carbon allocation in agricultural trees. These may include improved
representation of chilling requirements, for example, implementing the
chilling units approach (Byrne and Bacon, 2015), the variable harvest index
depending on the special conditions of the year, implementation of dwarf
trees, and daily update of carbon partitioning. Also, including a more
differentiated approach to agricultural management, such as discretising
practices, typology and processes affected, may be necessary for assessments
of climate change adaptation and soil carbon. Also connected to soil carbon,
the inclusion of erosion and salinisation would be essential since this
process plays an important role in the semi-arid, hilly and terraced
landscapes of the Mediterranean area (García-Orenes et al., 2012;
Poessen and Hooke, 1997).
Finally, the inclusion of horticulture in LPJmL opens the door to further
large developments aiming at the assessment of alternative agro-ecosystem
managements and their environmental performance. One clear example of this
would be the link between agroforestry systems and biodiversity conservation.
Code availability and technicalities
LPJmL is written in the C programming language and is run mainly under
UNIX-like systems. Inputs and outputs are in binary format. Depending on the
size of the region analysed and on the desired spatial resolution, it may
require a high-performing computational structure. The version used as the
basis for the present development was 3.5.003 and the revision number 2133
from 17 May 2014.
The main site for downloads of different model versions can be found under
https://www.pik-potsdam.de/research/projects/activities/biosphere-water-modelling/lpjml/versions.
There, downloads are free of charge and possible after registration.
However, the latest model version that includes the agricultural module and
the Mediterranean development is not available yet since the different
working groups are still compiling a complete technical documentation and
merging the last developments into one unique model version. Please contact
the first author of this publication if you plan an application of the model
and envisage longer-term scientific collaboration.