Agricultural Water Management 217 (2019) 265–281

Contents lists available at ScienceDirect

Agricultural Water Management
journal homepage: www.elsevier.com/locate/agwat

Soil salinization in very high-density olive orchards grown in southern
Portugal: Current risks and possible trends

T

Tiago B. Ramosa, , Hanaa Darouichb, Jiří Šimůnekc, Maria C. Gonçalvesd, José C. Martinsd⁎aCentro de Ciência e Tecnologia do Ambiente e do Mar (MARETEC), Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisboa, Portugal
Centro de Investigação em Agronomia, Alimentos, Ambiente e Paisagem (LEAF), Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017
Lisboa, PortugalcUniversity of California Riverside, Riverside, CA 92521, USAdInstituto Nacional de Investigação Agrária e Veterinária (INIAV), Av. República, 2780-157 Oeiras, Portugalb

ARTICLE INFOABSTRACT

Keywords:
Alentejo region
Electrical conductivity
HYDRUS-1D
Multicomponent solute transport
Sodium adsorption ratio

Deficit irrigation practices carried out in very high-density olive orchards grown in the Alentejo region ofsouthern Portugal can bring important economic benefits in terms of water savings, yields, and oils. They canalso result in serious salinization/sodification problems without proper management of soil and water resources.
The main objective of this study was to evaluate the long-term (30 years) impact of those irrigation practices on
local soil resources using a multicomponent transport modeling approach embedded in the HYDRUS-1D model.
Soil salinization and sodification risks were quantified for 160 soil profiles by considering eight different scenarios: current monitored irrigation practices (S1), using waters of variable quality (S2-S6), planting maize as analternative crop (S7), and using climate change projections for the region (S8). Despite the large observed
variability, simulations that considered current irrigation practices (S1) produced average values of the electricalconductivity of the soil solution (ECsw) at the end of the leaching seasons always below the threshold limit forcrops moderately tolerant to soil salinity. In this scenario, the average values of the sodium adsorption ratio(SAR) were also kept within the same magnitude of those determined at the beginning of the simulation period(initial conditions). Irrigations with worse quality waters (S2-S6) led to higher ECsw and SAR values. Althoughannual rainfall amounts influenced the salinity build-up, the SAR evolution depended mainly on water quality.
In maize soil profiles (S7), the simulated ECsw and SAR values were lower than in olive soil profiles, withirrigation practices contributing to salt removal during the seasons. Conversely, the climate change scenario (S8)resulted in slightly higher ECsw and SAR values than those simulated for current conditions, indicating a potentially greater risk of soil degradation in the near future. Although current irrigation practices seem to present
relatively low soil salinization/sodification risks, the variability of results and the uncertainty associated withmodel predictions indicate that close monitoring to prevent further degradation of soil and water resources in
the region should be recommended.

1. Introduction
Olive (Olea europaea L.) is a strategic crop in the Mediterraneanbasin, with a harvested area close to 10.3 million ha in 2016, whichyielded more than 18.3 million ton of fresh fruit (FAOSTAT, 2018).
Spain, Greece, Italy, Turkey, and Morocco produced over 77% of theoverall production, followed by Syria, Tunisia, Algeria, Egypt, andPortugal. In these countries, olive has provided safe economic returns tofarmers over the centuries, with trees being sparsely planted (< 100trees ha−1), without irrigation, but attaining acceptable yields even inpoor soils due to their resistance to drought, lime, and salinity.

⁎

However, production methods have been changing rapidly since theearly 1990s, with high (≥200 trees ha−1) and very high-density(≥1000 trees ha−1) olive orchards being now preferred to traditional
systems (Santos et al., 2007; Gucci et al., 2012; Gómez-del-Campo,
2013; Lorite et al., 2018). Irrigation became a critical practice due tothe higher evapotranspiration demand of the dense canopies and thelow soil volume available for each tree (Villalobos et al., 2000; Ramosand Santos, 2009; Conceição et al., 2017). However, this practice has
also limited the expansion of these production methods as suitableareas are mostly located in regions where water is scarce, and competition with other uses is great and increasing (Gómez-del-Campo, 2013).

Corresponding author at: MARETEC, Instituto Superior Técnico, Av. Rovisco Pais, 1, 1049-001 Lisboa, Portugal.
E-mail addresses: tiago_ramos@netcabo.pt, tiagobramos@tecnico.ulisboa.pt (T.B. Ramos).

https://doi.org/10.1016/j.agwat.2019.02.047
Received 12 October 2018; Received in revised form 14 February 2019; Accepted 28 February 20190378-3774/ © 2019 Elsevier B.V. All rights reserved.

Agricultural Water Management 217 (2019) 265–281

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This has so far not been the case of the Alentejo region of southern
Portugal, where high and very high-density orchards have been rapidly
expanding over the last few years, covering now more than 55,000 ha(DGADR, 2018) as new irrigation land becomes available with the Alqueva Project (EDIA, 2018).
The Alqueva Project initiated the conversion of new irrigation landin 2002, progressively adding 110,000 ha to the existing irrigated areain the region. At that time, state agencies projections included 42 crops
to be grown in the region based on their potential added value to thecountry’s economy (GPAa, 2004). Olive was only considered for tableconsumption; not for oil extraction. However, the reality followed adifferent direction when many agricultural companies started moving
into the region to seize the opportunity given by the European Commission decision 2000/406/CE to expand the Portuguese olive tree
planting quota for new orchards. This fast expansion of olive orchards
has even raised plans to further expand the irrigated land by more than
50,000 ha (EDIA, 2018) due to the lower water requirements of olive
orchards compared to other earlier projected crops.
Irrigation in high and very high-density orchards is fundamental tofulfill crop water requirements. However, irrigation practices also needto consider associated negative impacts of oversupply on olive productivity, oil quality, and the excessive crop set that may strongly affectvegetative growth and fruit production during the following season.
Thus, the system requires accurate management in terms of light interception and the use of soil nutrients and water resources. Several
studies concluded that deficit irrigation management practices, sustained or regulated, can have a positive effect on yield returns and canprovide economic benefits with no significant impact on the chemical
characteristics and the commercial value of olive oils (Tognetti et al.,2005; Ramos and Santos, 2010; Rosecrance et al., 2015; AhumadaOrellana et al., 2018; Hernández et al., 2018). However, this can trigger
a serious environmental problem as deficit irrigation practices, while
resulting in important water savings, can also potentially promote soil
salinization and sodification problems in a region where rainfall may
not always be sufficient to remove the salts accumulated in the soil
profile during irrigation. It is therefore important to closely monitor soil
and water quality in the region, to promote efficient leaching management to counteract soil salinization, and to develop necessary tools
for predicting the impact of management practices on soil and water
resources in the short and long term.
A representative example of that problem is given by Peragón et al.
(2018) for the province of Jaen (south of Spain), which holds a quarterof Spain’s orchard surface. In this region, an irrigation volume of only
150 mm y−1 is assigned by water authorities for each field, whichmeans that deficit irrigation practices are practically mandatory. Whilesoil salinization risks are high due to the low quality of irrigation waters
used in the region, promoting soil leaching may lead to a reduction in
the available water for olives, thus negatively affecting yield in the
short-term. In other regions of the Mediterranean basin, where water iseven more scarce, and freshwater resources are primarily used forurban use, marginal waters (saline waters or wastewaters) are the only
resource available for olive tree irrigation. Hence, the impact of these
low-quality waters on olive yields needs to be further minimized by
improving the decision-making process, namely by making available
appropriate tools capable of adjusting irrigation volumes and frequencies to remove salts from the root zone (Murillo et al., 2000;
Melgar et al., 2009; Aragüés et al., 2010; Ahmed et al., 2012; Ghrabet al., 2013; Tekaya et al., 2016).
Currently available modeling tools offer different solutions for
managing low-quality waters and for quantifying their impact on crop
yields and soil and water resources. Water balance models (Pereira
et al., 2007; Domínguez et al., 2011; Rosa et al., 2016) are normallylimited to the assessment of evapotranspiration and yield reductions
due to the salinity stress. These models can only consider steady-statesolutions that represent general impacts over extended time periods,
with the detailed description of the soil salinization process, including

the salinity build-up in the soil profile being far beyond their capabilities. On the other hand, transient models (Šimůnek and Suarez,
1994; Pang and Letey, 1998; Ragab et al., 2005; van Dam et al., 2008;Šimůnek et al., 2016) have far greater capabilities, allowing for theconsideration of site-specific soil, water, and crop parameters, and accounting for time-varying field conditions that include timing andamount of irrigation, variable soil salinity conditions, and variable irrigation water quality including rainfall. Particularly, the HYDRUS
software packages (Šimůnek et al., 2016) have the capability of evaluating salt transport in the soil profile using several modeling concepts.
The standard solute transport module in HYDRUS considers the transport of one or multiple solutes, which can be either independent or
involved in sequential first-order decay reactions (Hanson et al., 2008;
Forkutsa et al., 2009; Roberts et al., 2009; Ramos et al., 2012). The
major ion chemistry module adapted from the UNSATCHEM model(Šimůnek et al., 2016) allows for the simulation of multicomponent
solute transport, describing the subsurface transport of multiple ionsthat may mutually interact, create various complex species, compete forsorption sites, and/or precipitate or dissolve (Gonçalves et al., 2006;
Ramos et al., 2011; Rasouli et al., 2013; Raij et al., 2016). Finally, theHPx module (Jacques et al., 2018), in contrast to the previous module
which is constrained to specific elements, offers users the flexibility tosimulate multicomponent solute transport while defining their own
species with particular chemical properties and reactions. However, tothe best of our knowledge, no applications related to salinity problems
have been yet reported using HPx.
Two of the approaches described above were already used to simulate solute transport in the Alentejo region, thus providing the necessary support for this study. Gonçalves et al. (2006) successfully simulated soil water contents, concentrations of major cations (Na+,Ca2+, Mg2+), the electrical conductivity of the soil solution (ECsw), thesodium adsorption ratio (SAR), and the percentage of exchangeablesodium (ESP) in 3 field lysimeters irrigated with waters of different
quality for 4 years using the major chemistry module of the HYDRUS1D model (Šimůnek et al., 2016). Later, Ramos et al. (2011, 2012)
demonstrated the fundamentals of the multicomponent transportmodeling approach in terms of model outputs, revealing that ECsw canbe adequately simulated as a nonreactive tracer in the absence ofprocesses of precipitation/dissolution in the soil profile, while themajor cations and SAR can only be successfully simulated when considering interactions between species and the competition for sorption
sites.
The overall objective of this study thus is to evaluate the long-term
impact of irrigation practices carried out in very high-density olive
orchards grown in the Alentejo region using a multicomponent transport modeling approach. This study makes use of the existing knowledge on local soil and water resources, the capability of the major
chemistry module of the HYDRUS-1D model (Šimůnek et al., 2016) toaccurately describe soil salinization and sodification in the soil profile,and the work already carried out in the region in calibrating/validating
HYDRUS-1D. The specific objectives are then (i) to define baseline levels of soil salinity/sodicity based on surveys carried out in the region
before the expansion of olive orchards and (ii) to simulate long-term(30 years) soil salinization and sodification risks in different soil profiles using eight different scenarios. The irrigation scenarios include
current monitored irrigation practices, using irrigation waters withdifferent quality, planting maize as an alternative crop, and considering
climate change projections for the region. Limitations related to themodeling approach and future research needs are also addressed. These
include the need for field measured datasets for calibrating/validating
modelling applications, namely those that use more realistic representations of the soil domain and irrigation system adopted here.
Nonetheless, this work demonstrates how the state-of-the-art knowledge can be combined without great cost to assess the long-term sustainability of production systems.
266

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T.B. Ramos, et al.

Fig. 1. Location of soil profiles and irrigation districts.

2. Material and methods
2.1. Study site description
This study was carried out in some of the main irrigated areas of the
Alentejo region, southern Portugal (Fig. 1), which included Alqueva(110,000 ha), Caia (7237 ha), Campilhas e Alto Sado (5555 ha), Lucefecit (1175 ha), Odivelas (6846 ha), Roxo (5041 ha), and Vigia(1500 ha). The climate in the region is semi-arid to dry sub-humid, with
hot dry summers and mild winters with irregular rainfall. The meanannual temperature is 15.5 °C (1979–2009), with daily surface airtemperatures at the coolest (January) and warmest (July) months
averaging 8.1 and 23.4 °C, respectively, while the minimum and maximum daily averages reach -2.9 and 33.1 °C, respectively. The meanannual rainfall is 385 mm (1979-2009), mostly occurring between
September and May, but also showing a strong annual variability withannual values ranging between 178 and 816 mm (Fig. 2). The meanannual reference evapotranspiration (ET0) is 989 mm (1979–2009),
varying between 908 and 1066 mm, while daily ET0 rates average between 0.6 mm d−1 in December and 5.6 mm d−1 in July (Fig. 2). The
soils in the region are predominantly classified as Luvisols (47.0%),
Vertisols (20.2%), Calcisols (13.4%), Fluvisols (9.5%), and Cambisols(5.7%) (IUSS Working Group WRB, 2014). For many decades, the main
irrigated crops were maize (Zea mays L.), tomato (Solanum lycopersicumL.), rice (Oryza sativa L.), and sunflower (Helianthus annuus L.). Recently, olive trees have become the main land use in the region, witholive orchards now covering 42,040 ha in Alqueva, 4149 ha in Caia,
1498 ha in Campilhas e Alto Sado, 123 ha in Lucefecit, 5033 ha in
Odivelas, 3138 ha in Roxo, and 942 ha in Vigia (DGADR, 2018).

Fig. 2. Average yearly (a) and monthly (b) values of rainfall and reference
evapotranspiration (ET0) for the period 1979–2009 (vertical bars and shaded
area represent maximum and minimum values).

alternative crop (S7), and climate change projections for the region(S8). Scenarios were run for a 30-year period and 160 soil profiles.
The current monitored irrigation practices scenario (S1) was definedaccording to Paço et al. (2014) and Darouich et al. (private communication), who quantified evapotranspiration rates and crop coefficientsfor very-high density olive orchards (1.35 m × 3.75 m; 1975 treesha−1) in commercial farms in the Alentejo region for five years. Monitored annual irrigation amounts varied between 287 and 446 mm, withwater being applied almost every day during spring and summer at an
average depth of 3 mm per event using a drip system. Hence, the S1
scenario assumed these observations as a standard irrigation practice inthe region, with total irrigation being set to 324 mm y−1, applied

2.2. Simulated scenarios
Soil salinization and sodification risks were assessed for eight different scenarios (S), which considered current irrigation practices
monitored in very-high density olive orchards grown in the Alentejo
region (S1), different qualities of irrigation waters (S2-S6), maize as an
267

Agricultural Water Management 217 (2019) 265–281

ECiw, electrical conductivity of the irrigation water; SAR, sodium adsorption ration.
avalues in brackets correspond to the standard deviation of the measured values.
bcalculated to maintain the charge balance.
c
U.S. Salinity Laboratory Staff (1954). C2, medium-salinity water (ECiw 0.25–0.75 dS m−1); C3, high-salinity water (ECiw 0.75–2.25 dS m−1); C4, very high salinity water (ECiw > 2.25 dS m−1); S1, low-sodium water(SAR limits depend on ECiw); S2, medium-sodium water (SAR limits depend on ECiw).

EDIA (2018)
Gonçalves et al. (2006)
Ramos et al. (2011)
Gonçalves et al. (2006)
Ramos et al. (2011)
Gonçalves et al. (2006)
EDIA (2018)
EDIA (2018)
C2S1
C3S1
C3S1
C3S1
C3S2
C4S2
C2S1
C2S1
1.25 (0.24)
1.51
2.44
2.99
6.00
6.04
1.25
1.25
5.17 (0.46)
8.14
10.10
16.14
16.14
32.14
5.17
5.17
1.63 (0.32)
2.50
4.16
6.52
10.21
16.70
1.63
1.63
0.14 (0.02)
0.14
0.14
0.14
0.14
0.14
0.14
0.14
1.76 (0.16)
1.85
2.80
3.16
1.93
5.10
1.76
1.76
1
2
3
4
5
6
7
8

324
324
324
324
324
324
500
324

3
3
3
3
3
3
10
3

0.52 (0.05)a
0.81
1.01
1.61
1.61
3.21
0.52
0.52

1.64 (0.07)
3.65
3.00
6.32
3.86
10.20
1.64
1.64

SAR
(mmolc L−1)0.5
Cl−b
Na+
K+
Mg2+
Ca2+
(mmolc L−1)
ECiw (dS m−1)
(mm year−1)(mm event−1)

Ionic composition of irrigation waters
Irrigation
Scenario

Table 1
Irrigation depths and ionic composition of irrigation waters applied in each scenario (values in brackets correspond to the standard deviation of measured values).

USSL classificationc

Reference

T.B. Ramos, et al.

almost every day from mid-May to mid-October, and also with anaverage depth of 3 mm per event (27 mm in May; 45 mm in June;
93 mm in July; 93 mm in August; 45 mm in September; and 21 mm in
October). These values are different from those reported by Cameira
et al. (2014) and Conceição et al. (2017) for deficit irrigated highdensity orchards (7.0 m × 4.8 m; 300 trees ha−1), where seasonal irrigation reached only 141 to 226 mm (1.3 to 1.7 mm per event). In S1,
the same irrigation scheduling was adopted throughout the years since
the objective was to quantify the long-term salt accumulation in the soil
profile and not to meet crop water requirements. The S1 scenario further considered the quality of irrigation waters monitored at 14 different locations in Alqueva between May and June 2017 (EDIA, 2018).
The mean values of the measured electrical conductivity of irrigationwaters (ECiw), concentrations of major cations, and SAR are presented
in Table 1.
The S2 to S6 scenarios considered the same standard irrigation
practice as in S1 but with lower water quality as in Gonçalves et al.
(2006) and Ramos et al. (2011) (Table 1). Water quality in the S2
through S5 scenarios was thus based on values monitored in the region
over the years (wherein ECiw has frequently reached values of
0.8–2.4 dS m−1) while S6 considered extreme conditions that are
hardly observed in Alentejo.
The S7 scenario considered maize, another traditional crop in theregion, as an alternative crop to olive. Irrigation was defined according
to Ramos et al. (2011, 2017a), with water being applied between midMay and mid-September, with a total of 500 mm per season and 10 mm
per event (30 mm in May; 120 mm in June; 150 mm in July; 150 mm inAugust; and 50 mm in September). Water quality was set as in S1(Table 1).
The S8 scenario considered climate change projections by thePortuguese Local Warming Website Project (https://portaldoclima.pt/en/) for the Alentejo region. This project made available projections ofclimate variables down to local administrative regions (NUTS3) at amonthly and seasonal scales based on simulations provided by an ensemble of state-of-the-art regional climate models forced by five global
climate models. Here, S8 adopted the Representative Concentration
Pathway 4.5 (RCP4.5) scenario for greenhouse emissions (Stocker et al.,2013), with the average surface air temperature in the region expected
to increase 0.8 °C while annual rainfall to decrease 20–30 mm for theperiod 2011–2040. RCP4.5 represents one intermediate stabilization
pathway, in which radiative forcing is stabilized at approximately
4.5 W m−2 after 2100 (assuming then constant greenhouse gases andaerosols concentrations in the atmosphere after 2150) (Stocker et al.,2013). Additional (more extreme) scenarios also available from thesame Project were not considered here as the objective was not to
conduct an extensive analysis on the impacts of climate change on soil
salinization and sodification but to provide an insight on possible
trends. Nonetheless, the climate variability in the region was considered to provide sufficient information on possible outcomes for theworse case projections. In S8, the irrigation scheduling and the qualityof irrigation waters were set as in S1 (Table 1).
2.3. Modeling approach
The HYDRUS-1D software package version 4.16 (Šimůnek et al.,2016) was used to numerically simulate one-dimensional water flow
and solute transport in variably-saturated porous media by solving the
Richards and Fickian-based convection–dispersion (CDE) equations,
respectively. The flow equation considered the analytical models proposed by Mualem (1976) and van Genuchten (1980) for describing soil
hydraulic properties, in which six parameters are required, i.e., theresidual (θr) and saturated (θs) water contents, the saturated hydraulicconductivity (Ks), the connectivity/tortuosity factor (λ), and two shapeparameters (α and η). The flow equation further incorporated a sinkterm to account for root water uptake by plants. The macroscopic approach introduced by Feddes et al. (1978) was then considered, where268

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T.B. Ramos, et al.

the potential root water uptake rate, corresponding to the potential
transpiration rate (Tp), was distributed over the root zone and reduced
due to by the presence of depth-varying water and salinity stresses(Skaggs et al., 2006; Šimůnek and Hopmans, 2009). The water stressresponse function was defined according to Feddes et al. (1978), in
which root water uptake is at the potential rate when the pressure head(h) is between h2 and h3, drops off linearly when h > h2 or h < h3,
and becomes zero when h < h4 or h > h1 (subscripts 1–4 denote
different threshold pressure heads). The salinity stress response function was defined according to Maas (1990), which is implemented in
the major ion chemistry module of HYDRUS-1D in terms of the osmotic
head (hϕ). This stress response function requires two parameters, i.e.,the osmotic head threshold value (hϕT) corresponding to the value of hϕ
above which root water uptake occurs without a reduction and theslope (s) which determines the root water uptake decline per a unit
decrease in the osmotic head below the threshold. The effects of thewater and salinity stresses were further assumed to be multiplicative(van Genuchten, 1987), enhancing those effects on root water uptake(Oster et al., 2012).
The major ion chemistry module of HYDRUS-1D took into accountthe fact that the soil liquid phase always contains a mixture of manyions that may interact, create complex species, precipitate, dissolve,
and/or compete for sorption sites on the solid phase (Šimůnek et al.,2014). The numerical model thus considered all these interactions, including aqueous complexation, precipitation/dissolution, and cationexchange. The partition of solutes between the liquid and solid phases
was described using the Gapon exchange equation (White and Zelazny,1986), further assuming that the cation exchange capacity (CEC) wasconstant, given by the sum of the exchangeable cations (Ca2+, Mg2+,
Na+, and K+), and independent of pH. Precipitation/dissolution reactions considered multicomponent kinetic expressions, which included
both forward and back reactions. The Pitzer expressions were adopted
for computing single ion activities (Šimůnek et al., 2013). The electricalconductivity of the soil solution (ECsw) was determined from individualanions and cations following McNeal et al. (1970), while the sodium
adsorption ratio (SAR) was computed from the simulated soluble Na+,
Ca2+, Mg2+ concentrations according to the U.S. Salinity Laboratory
Staff (1954) guidelines.

to 6 layers according to available observations. The physical and chemical characteristics of the deepest observed soil layer were extended tothe bottom of the soil profile when necessary. Initial soil water contents
were always set to a uniform value of 0.25 cm3 cm−3. The initial conditions for the major ion chemistry module were given for each horizon/layer in terms of concentrations of Na+, Ca2+, Mg2+, and K+ in
the liquid and solid phases. The soil hydraulic parameters of theMualem-van Genuchten model (Mualem, 1976; van Genuchten, 1980)
were taken from the PROPSOLO database, complemented with PTFs ofRamos et al. (2014b) for the characterization of deeper soil layers (24%of the data) or the hydraulic conductivity curve (Ks and λ in 42% of thedata). Solute transport parameters were estimated from PTFs proposedby Gonçalves et al. (2002). The Gapon exchange constants were takenfrom Ramos et al. (2011) and were interpreted as follows: for the Ca2+/
Mg2+ exchange (KCa/Mg = 0.35), Mg2+ was the preferred ion relative
to Ca2+ on the exchange complex. For the Ca2+/Na+ exchange (KCa/2+
was the preferred ion relative to Na+. Finally, for the
Na = 2.90), Ca
Ca2+/K+ exchange (KCa/K = 0.11), K+ was the preferred ion relative toCa2+ (Mallants et al., 2017).
Simulations were run using a climate dataset from 1979–2009. The
weather variables (rainfall (mm), average surface air temperature (ºC),
wind speed (m s−1), relative humidity (%), and solar radiation (W m-2))
were provided for the study area by the MM5 mesoscale model (http://
meteo.tecnico.ulisboa.pt), forced by the initial conditions from the
NCEP (National Centers for Environmental Prediction) Climate Forecast
System Reanalysis, at a spatial resolution of 9 km. These data were thenused to compute daily reference evapotranspiration (ET0) rates with the
FAO Penman-Monteith method (Allen et al., 1998). The S1 to S7 scenarios assumed the same climate conditions of 1979–2009, while the S8
scenario considered a 0.8 °C increase in the mean surface air temperature as well as a 30 mm decrease in the mean annual rainfall values
following projections from the Portuguese Local Warming Website
Project (https://portaldoclima.pt/en/). Hence, mean surface air temperature anomalies were projected on the 1979–2009 dataset by increasing daily temperatures by 0.8 °C, while rainfall reductions wereconsidered between April and October, also taking into account the
projected rainfall increase during the remaining months.
Crop evapotranspiration (ETc) values were computed from daily ET0
rates using the single crop coefficient (Kc) approach (Allen et al., 1998).
For olive (S1–S6, S8), Kc values between 0.53 and 0.83 were adopted
for different months of the year as reported by Paço et al. (2014) andDarouich et al. (private communication). A ground cover of 0.35 (Paçoet al., 2014) was also considered for estimating the leaf area index (LAI)of olive orchards following Allen and Pereira (2009). That value wasthen used for the partition of the ETc values into Tp and soil evaporation(Ep) rates according to Ritchie (1972). For maize (S7), Kc values of 0.30,
1.20, and 0.35 were considered for the initial, mid-season, and lateseason crop stages, respectively, with these values corresponding to thestandard Kc values for the Mediterranean region (Allen et al., 1998).
The simulated LAI curve from Ramos et al. (2017a) was then used forthe partition of maize ETc values into Tp and Ep rates (Ritchie, 1972).
Those authors calibrated/validated a crop growth model for simulating
the LAI curve and other crop state variables as a function of the heat
units, solar radiation, crop stress, and the crop development stage using
measured data collected in the Sorraia Valey region, southern Portugal.
For olive, Tp reductions due to the water stress were computed withthe following parameters: h1 = −10 cm, h2 = −25 cm, h3 = −3000 to−5000 cm, h4 = −18,000 cm. These values were taken from Egea et al.
(2016), who parameterized the Feddes et al. (1978) model for this crop
based on a literature review on the relationship between the maximum
stomatal conductance and predawn leaf water potential. Tp reductions
due to the salinity stress considered olive as a moderately tolerant crop
to soil salinity (Ayers and Westcot, 1985; Maas and Hoffman, 1977),
with the following parameters being adopted: hϕT = −2975 cm ands = 0.00021. These values were defined based on threshold values forthe electrical conductivity of the soil saturation extract (ECe), being first

2.4. Model setup
Various scenarios were run for 160 selected soil profiles for a 30year period using the major ion chemistry module of the HYDRUS-1D
software package. The upper boundary condition was determined by
the potential evaporation and transpiration rates, and the irrigation,
rainfall, and concentration fluxes (Fig. 2; Table 1). The bottom
boundary condition was specified as free drainage based on groundwater levels monitored by the Portuguese Environmental Agency in 79
piezometric stations located in the region between 1997 and 2018
(SNIRH, 2018). The groundwater table was reported to be on average at
a depth of 5.5 m while its maximum and minimum levels averaged at
depths of 7.6 and 4.2 m, respectively.
Soil information was extracted from the INFOSOLO (Ramos et al.,2017b) and PROPSOLO (Gonçalves et al., 2011) databases. The former
is a soil information system developed to compile soil data produced in
Portugal (the soil legacy database) and to support stakeholders and land
managers in the decision-making. The latter was specifically developed
to store soil hydraulic properties and for developing soil pedotransfer
functions (PTFs) that indirectly estimate them from basic soil properties(Ramos et al., 2013, 2014a,2014b). 160 soil profiles located in the
study area (Fig. 1) with a complete description of soil physical (Table 2)
and chemical (Table 3) properties of different soil horizons/layers wereselected for running the scenarios described above. The soil profiles
included a variety of Luvisols (63), Fluvisols (40), Vertisols (27), Calcisols (15), Regosols (11), and Cambisols (4) (IUSS Working Group
WRB, 2014). The soil profiles were defined to be 1.5 m deep, having 2
269

270

33(10)
45(11)
88(15)

26(8)50(12)
97(36)

22(11)
57(19)
106(35)27(7)55(14)
113(34)22(9)52(20)
154(26)30(9)62(13)
130(19)0(0)28(11)
63(12)0(0)28(8)
66(30)

4(5)30(12)
62(23)0(0)27(7)
77(29)0(0)24(11)
87(17)0(0)28(6)
82(24)

Layer bottom(cm)

17.2(9.1)
14.0(6.3)
34.2(25.8)

27.8(7.0)
25.3(10.0)
27.1(3.4)

33.3(10.6)
29.5(11.4)
37.0(17.9)

10.1(7.0)
15.8(12.0)
13.7(10.5)

41.5(7.2)
35.4(7.5)
41.5(8.8)

20.9(7.4)
16.5(7.0)
26.2(3.2)

25.8(5.8)
24.7(6.2)
22.8(8.4)

29.0(6.5)
28.1(4.7)
18.3(4.4)

35.4(8.9)
29.6(7.6)
25.8(8.7)

50.5(5.9)
40.3(9.1)
39.1(9.4)

35.0(6.5)
31.3(8.5)
27.0(3.9)

25.5(5.6)
17.2(1.8)
12.4(1.1)

Bulk density (g cm−1)

1.52(0.10)
1.56(0.12)
1.64(0.14)

18.7(9.7)
1.62(0.12)
28.3(11.3) 1.66(0.12)
24.9(10.5) 1.68(0.11)

13.2(1.4)
18.2(6.4)
21.1(7.4)

14.3(2.4)
1.62(0.07)
23.5(11.9) 1.67(0.07)
23.2(6.6)
1.70(0.10)

38.0(10.3) 1.40(0.13)
48.9(6.7)
1.36(0.04)
49.8(0.7)
1.38(0.04)

Clay (%)

17.3(5.1)
16.9(3.4)
12.2(8.8)

39.7(10.0) 1.46(0.18)
44.4(9.3)
1.52(0.15)
30.8(18.5) 1.57(0.09)

27.4(9.4)
15.8(6.6)
1.52(0.10)
29.1(10.8) 17.5(8.1)
1.60(0.07)
21.5(5.6)
33.2(13.4) 1.56(0.01)

12.6(4.6)
12.7(4.5)
12.3(6.6)

26.1(2.7)
25.7(5.2)
26.2(4.6)

9.2(1.5)
9.8(1.9)
8.3(2.6)

15.5(4.8)
17.4(9.7)
11.7(2.5)

Coarse sand (%) Fine sand (%) Silt (%)

0.088(0.049)
0.122(0.051)
0.076(0.031)

0.049(0.028)
0.058(0.032)
0.085(0.024)

0.054(0.035)
0.077(0.038)
0.070(0.026)

0.038(0.021)
0.053(0.037)
0.061(0.034)

0.041(0.026)
0.031(0.007)
0.059(0.026)

0.095(0.068)
0.074(0.032)
0.074(0.019)

0.498(0.059)
0.483(0.060)
0.437(0.050)

0.412(0.059)
0.388(0.036)
0.438(0.014)

0.392(0.050)
0.387(0.059)
0.406(0.033)

0.390(0.031)
0.386(0.032)
0.389(0.042)

0.411(0.029)
0.398(0.061)
0.377(0.027)

0.530(0.051)
0.496(0.010)
0.416(0.08)

η (-)

Ks (cm d−1)Λ (-)

DL (cm)0.196(0.161) 1.136(0.063) 90.9(76.2)
0.085(0.062) 1.150(0.057) 44.6(40.3)
0.091(0.031) 1.249(0.180) 108.2(86.3)

0.190(0.383) 1.253(0.103) 68.6(63.0)
0.037(0.044) 1.307(0.129) 30.7(23.8)
0.068(0.030) 1.148(0.048) 43.3(0.4)

0.122(0.121) 1.240(0.129) 78.4(86.1)
0.098(0.103) 1.169(0.078) 40.0(51.1)
0.076(0.026) 1.221(0.093) 61.8(47.6)

0.023(0.031) 1.278(0.056) 33.4(45.7)
0.054(0.045) 1.230(0.071) 59.1(53.1)
0.077(0.067) 1.199(0.071) 78.2(90.2)

0.066(0.022) 1.246(0.059) 36.1(13.5)
0.087(0.096) 1.157(0.034) 16.9(5.8)
0.105(0.066) 1.195(0.057) 41.3(21.3)−7.31(1.98) 7.6(6.4)−6.99(2.94) 4.4(3.1)−5.18(2.27) 7.7(2.3)−6.25(2.70) 10.9(18.9)−5.26(1.21) 3.8(2.1)−7.09(0.59) 6.7(3.2)−4.16(2.21) 5.4(5.4)−5.81(2.57) 6.2(5.8)−5.02(1.84) 6.8(2.9)−4.60(1.13) 2.7(0.9)−5.11(1.81) 5.3(4.5)−6.11(1.90) 6.1(5.3)−4.16(1.24) 5.3(1.9)−6.38(1.83) 7.1(6.8)−5.03(1.63) 10.5(6.1)

0.176(0.120) 1.188(0.082) 156.8(172.2) −5.77(2.38) 4.7(3.7)
0.060(0.035) 1.119(0.023) 66.1(13.3)−7.64(0.46) 3.3(1.9)
0.083(0.014) 1.756(0.301) 95.0(24.9)−0.86(3.04) 7.9(0.5)θr (cm3 cm−3) θs (cm3 cm−3) α (cm−1)

θr, residual water content; θs, saturated water content; α and η, empirical shape parameters; λ, pore connectivity/tortuosity parameter; Ks, saturated hydraulic conductivity; DL, longitudinal dispersivity.

Calcisols
12≥3
Cambisols
12≥3
Fluvisols
12≥3
Luvisols
1
2≥3
Regosols
12≥3
Vertisols
1
2≥3

Soil/layer Layer top(cm)

Table 2Main physical soil characteristics (values in brackets correspond to the standard deviation of measured values).

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271

8.44(0.16)
8.43(0.20)
8.68(0.07)

7.28(0.27)
7.47(0.48)
8.83(0.33)

6.28(0.87)
6.45(0.99)
6.98(1.06)

6.81(0.93)
7.16(0.68)
7.65(0.89)

6.52(1.29)
6.69(0.86)
7.84(1.00)

7.82(0.56)
7.81(0.58)
8.38(0.47)

0.7(0.2)
0.5(0.1)
0.2(0.2)

1.9(0.8)
0.6(0.2)
0.5(0.4)

1.3(0.6)
0.5(0.4)
0.5(0.4)

1.6(0.6)
1.1(0.4)
0.4(0.2)

1.6(0.6)
1.3(0.6)
0.5(0.3)

pH (-)

1.9(0.5)
1.2(0.1)
0.6(0.1)

OM (%)

0.58(0.42)
0.54(0.56)
0.52(0.27)

0.77(0.32)
1.04(0.00)
0.54(0.11)

0.73(0.94)
0.67(1.09)
1.25(1.33)

0.70(0.98)
0.74(0.73)
0.94(1.03)

0.54(0.64)
0.47(0.52)
1.69(0.56)

0.33(0.09)
0.29(0.01)
0.29(0.05)

ECe (dS m−1)

2.04(2.33)
1.82(1.82)
1.09(0.90)

1.22(0.51)
2.55(0.00)
1.31(0.52)

0.86(1.08)
0.85(1.23)
0.93(0.93)

1.66(2.71)
0.83(0.50)
1.30(1.20)

0.42(0.44)
0.26(0.23)
0.84(0.43)

1.36(0.44)
1.53(0.33)
1.33(0.27)

Ca2+

0.36(0.29)
0.52(0.75)
0.46(0.28)

0.77(0.18)
0.76(0.00)
0.72(0.05)

0.42(0.56)
0.44(0.74)
0.76(0.93)

1.10(1.66)
0.78(0.50)
1.41(1.34)

0.29(0.27)
0.20(0.16)
0.72(0.27)

0.09(0.04)
0.11(0.03)
0.10(0.03)

Mg2+

Soluble ions (mmolc L−1)

0.02(0.02)
0.02(0.01)
0.01(0.01)

0.02(0.01)
0.02(0.00)
0.04(0.03)

0.08(0.15)
0.02(0.03)
0.02(0.03)

0.06(0.02)
0.02(0.01)
0.02(0.01)

0.03(0.02)
0.01(0.00)
0.01(0.00)

0.05(0.05)
0.03(0.03)
0.04(0.01)

K+

0.58(0.63)
0.37(0.28)
0.93(1.30)

0.94(0.61)
1.47(0.00)
0.67(0.35)

0.90(1.12)
1.39(1.80)
3.54(3.75)

1.81(1.92)
2.84(2.41)
4.67(4.23)

0.68(0.80)
0.94(0.82)
4.52(1.75)

0.21(0.26)
0.13(0.01)
0.15(0.01)

Na+

3.00(10.36)
2.73(4.13)
2.49(21.25)

2.94(1.29)
4.80(0.00)
2.73(0.10)

2.25(2.60)
2.71(3.49)
5.25(5.29)

4.63(6.25)
4.47(3.35)
7.38(6.67)

1.41(1.51)
1.41(1.18)
6.09(2.32)

1.70(0.67)
1.80(0.40)
1.62(0.07)

Cl−

0.89(0.98)
0.54(0.36)
1.63(2.02)

1.29(0.62)
1.49(0.00)
0.85(0.49)

2.09(1.58)
2.69(2.13)
5.53(4.27)

3.07(2.71)
4.22(2.72)
5.09(3.19)

1.59(1.35)
2.72(1.84)
7.52(1.36)

0.34(0.37)
0.17(0.01)
0.23(0.02)

SAR (mmolc L−1)0.5

270.4(85.9)
250.5(76.0)
193.7(70.0)

88.5(76.7)
74.7(78.5)
156.0(84.2)

83.4(56.0)
139.3(69.1)
132.5(70.8)

53.1(21.0)
63.1(26.4)
102.4(59.5)

103.7(44.8)
122.6(30.0)
113.8(25.8)

304.1(99.7)
282.2(8.4)
183.3(31.4)

Ca2+

41.7(25.1)
50.7(27.7)
72.5(48.5)

26.8(18.7)
24.7(11.0)
69.6(36.0)

30.7(32.7)
62.4(42.5)
63.7(35.6)

23.9(8.7)
39.0(23.6)
51.9(31.2)

35.9(16.6)
65.2(41.3)
68.8(39.6)

8.0(3.2)
8.9(0.3)
9.6(5.7)

Mg2+

1.7(1.5)
1.3(1.2)
0.9(0.6)

3.9(4.2)
0.9(0.2)
0.8(0.3)

1.8(2.3)
1.3(1.2)
0.9(0.6)

2.3(1.6)
1.5(0.7)
0.9(0.5)

1.1(0.6)
0.9(0.4)
1.1(0.3)

2.5(2.1)
2.1(1.5)
2.5(0.6)

K+

Exchangeable cations (mmolc kg−1)

2.5(1.5)
3.1(2.9)
5.3(7.5)

4.4(2.8)
2.9(0.5)
2.5(0.8)

2.6(3.1)
5.8(7.1)
11.0(10.0)

4.9(3.9)
8.4(7.5)
15.4(12.9)

1.2(0.7)
3.9(3.6)
14.2(7.1)

0.7(0.4)
0.4(0.1)
0.7(0.3)

Na+

OM, organic matter content; ECe, electrical conductivity of the saturation extract; SAR, sodium adsorption ratio; CEC, cation exchange capacity; ESP, exchangeable sodium percentage.

Calcisols
12≥3
Cambisols
12≥3
Fluvisols
12≥3
Luvisols
1
2≥3
Regosols
12≥3
Vertisols
1
2≥3

Soil/layer

Table 3Main chemical soil characteristics (values in brackets correspond to the standard deviation of measured values).

316.3(89.3)
305.6(83.8)
272.4(98.9)

123.7(93.2)
103.1(88.7)
228.8(109.5)

120.9(74.0)
211.4(98.2)
213.4(85.5)

88.8(29.6)
116.5(49.8)
178.0(98.1)

141.9(58.3)
192.6(70.0)
197.8(68.0)

315.3(100.8)
293.6(6.6)
196.1(36.7)

CEC (mmolc kg−1)

0.81(0.61)
1.09(1.24)
1.81(2.51)

5.03(3.18)
3.44(1.58)
1.52(1.05)

2.05(3.36)
2.80(3.79)
4.96(4.31)

4.14(3.88)
4.59(3.63)
6.38(4.44)

0.60(0.36)
1.42(0.97)
5.92(2.10)

0.21(0.15)
0.14(0.03)
0.38(0.10)

ESP (%)

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T.B. Ramos, et al.

converted into ECsw using the ECsw/ECe = 2 ratio, and later into hϕusing a linear relationship found by U.S. Salinity Laboratory Staff(1954). For maize, the water stress was computed with the following
parameters: h1 = −15 cm, h2 = −30 cm, h3 = −325 to −600 cm,
h4 = −8000 cm (Wesseling et al., 1991). Tp reductions due to thesalinity stress were obtained by setting hϕT = −1244.88 cm ands = 0.000157456 (Ayers and Westcot, 1985; Ramos et al., 2011).
In Olive, the root depth was set to 1.2 m in all soil profiles (Paçoet al., 2014; Darouich et al., private communication), except for Calcisols where it was constrained by the depth of the calcaric layer located below 0.31–1.08 m. In maize, the root depth was set to 0.6 m(Ramos et al., 2011), with the same constraints for Calcisols.

much larger ECe and SAR values than the other soil profiles. Althoughthese higher ECe values could still be viable for growing crops moderately tolerant to soil salinity (< 6.0 dS m−1; Ayers and Westcot, 1985),
these relatively high measured SAR values revealed serious management problems that should be addressed.
Luvisols (63 soil profiles) were well represented in the study area,
being located in Caia, Lucefecit, Vigia, Odivelas, Roxo, and Alqueva(Odivelas Fase II, Monte Novo, Alvito-Pisão, Brinches, and BrinchesEnxoé). At the time, olive represented 12.3% of the characterized area,with maize, wheat, pasture, sugar beet, and melon (Cucumis melo L.)
being also present. The measured ECe values of the topsoil layers
averaged 0.73 dS m−1 (Table 3), ranging from 0.10 to 5.08 dS m−1. For
the bottom layers and at different depths (> 25 cm), averages reached0.67–1.25 dS m−1, ranging from 0.09 to 5.93 dS m−1. ECe values werethus relatively low in most soil profiles, with some notable exceptions.
Two soil profiles located in Roxo showed remarkably higher salinity
values than all other profiles, with ECe values reaching
2.15–5.93 dS m−1 at different depths. Also, six soil profiles in Alqueva(Odivelas Fase II) showed evidence of salt accumulation, with somehaving ECe values of 1.41–2.05 dS m−1 at shallower depths (< 25 cm)
while others registering values of 2.54–5.07 dS m−1 at deeper depths(> 50 cm). Hence, the measured ECe values in the Luvisols referencegroup revealed large variability, reaching in some locations values that
could affect the crop production of less tolerant crops to soil salinity. In
these locations, the observed salinity build-up seemed again to bemostly associated with irrigation and fertilization management practices carried out throughout previous years, which may also have led tothe degradation of soil characteristics as reflected by measured SAR
values. For the topsoil layers, the measured SAR values averaged 2.09
(mmolc L-1)0.5, ranging from 0.36 to 6.14 (mmolc L-1)0.5. For the bottom
layers, the measured SAR values averaged 2.69 (mmolc L-1)0.5 at depthsof 20–60 cm (ranging from 0.5 to 7.97 (mmolc L-1)0.5), and 5.53 (mmolc
L-1)0.5 at depths below 60 cm (ranging from 0.42 to 16.09 (mmolc L1 0.5) ). The highest values were again found in Alqueva (Odivelas FaseII) and Roxo, but also in Odivelas and Alqueva (Monte Novo). In theselocations, the measured SAR values showed an increasing trend withsoil depth, increasing from 2.35 to 6.14 (mmolc L-1)0.5 in the topsoil
layers to 9.55–16.09 (mmolc L-1)0.5 in the deeper layers.
Regosols (11 soil profiles) were found in Alqueva (Odivelas Fase II),
Roxo, and Lucefecit. At the time, maize and pasture were the main land
uses. The measured ECe values were always lower than 1.3 dS m−1(Table 3), constituting no risk for the crop production. Also, the SAR
values were relatively low (< 1.91 (mmolc L-1)0.5), showing no risk ofsoil sodification.
Vertisols (27 soil profiles) were studied in Caia, Vigia, Lucefecit, andAlqueva (Odivelas Fase II, Brinches-Enxoé, Serpa, and Beringel-Serpa).
Olive was the main land use in 14.8% of the sampled locations, withmaize, wheat, sugar beet, and fallow representing the remaining areas.
The measured ECe values were again found to be relatively small,averaging between 0.52-0.58 dS m−1 at different depths (Table 3). Only
one profile showed a notably higher value (2.15 dS m−1) at lower
depths. The measured SAR values were also relatively low, withaverages varying between 0.54–1.63 (mmolc L-1)0.5 at different depths(Table 3). However, two profiles located in Alqueva (Odivelas Fase II)
showed SAR values higher than 5.0 (mmolc L-1)0.5 at depths below
100 cm.

3. Results and discussion
3.1. Baseline salinity and sodicity levels
Calcisols (15 soil profiles) selected for this study were mostly located in Alqueva (Brinches, Brinches-Enxoé, Orada-Amoreira, andErvidel irrigation blocks) and Vigia. Olive orchards already covered
80% of the area when the soil profiles were characterized. The measured ECe values of the topsoil layers averaged 0.33 dS m−1 (Table 3),
ranging from 0.23 to 0.52 dS m−1, while the bottom layers averaged
0.29 dS m−1, varying from 0.25 to 0.36 dS m−1. As such, the measured
salinity levels were relatively low and smaller than the threshold limit(< 1.3 dS m−1) defined by Ayers and Westcot (1985) for crops sensitive
to soil salinity. Likewise, the SAR values computed from Na+, Ca2+,and Mg2+ concentrations measured in the soil solution (hereafter denoted as measured SAR values) were also relatively low, averaging 0.34
(mmolc L-1)0.5 in the topsoil layer (ranging from 0.11 to 1.33 (mmolc L1 0.5) ) and decreasing then with depth.
Cambisols (4 soil profiles), being less represented in the study area,
were located in Alqueva (Ervidel, Serpa, and Monte Novo). Oliveorchards were the dominant land use at all sites. The measured ECevalues of the topsoil layers averaged 0.54 dS m−1 (Table 3), showing
relatively large variations (0.10–1.64 dS m−1) that were also visible atdeeper depths. The same variability was further noticed in the measured SAR values, with the topsoil layer average reaching 1.59 (mmolc
L-1)0.5, while the maximum and minimum values were 3.90 and 0.62(mmolc L-1)0.5, respectively. Two soil profiles showed evidence of salt
accumulation in the deeper layers, with measured ECe values reaching
0.91–1.52 dS m−1 below the 50 cm depth (SAR between 5.73–7.80(mmolc L-1)0.5) and 1.87–2.47 dS m−1 below the 100 cm depth (SAR
> 9.0 (mmolc L-1)0.5). These values may well have resulted from irrigation and fertilization management practices or the characteristics ofthe parent material (igneous rocks and consolidated clastic sedimentaryrocks) since no detailed information regarding historical management
practices or the characteristics of the parent material existed. Nonetheless, the measured salinity levels (< 3.0 dS m−1) found in these locations were well tolerable by crops moderately sensitive to soil salinity(Ayers and Westcot, 1985).
Fluvisols (40 soil profiles) were mainly distributed in Campilhas e
Alto Sado, but also in Alqueva (Odivelas Fase II). The main land uses
were maize, wheat (Triticum aestivum L.), sugar beet (Beta vulgaris L.),pasture, and fallow. For the topsoil layers, the measured ECe and SAR
values averaged 0.33 dS m−1 and 3.07 (mmolc L-1)0.5 (Table 3), respectively, ranging from 0.20 to 3.59 dS m−1 and from 0.76 to 5.96(mmolc L-1)0.5, respectively. For the bottom layers and at different
depths, the measured ECe values reached averages of 0.74-0.94 dS m−1(ranging from 0.23 to 3.99 dS m−1) while the measured SAR values
averaged 4.22–5.09 (mmolc L-1)0.5 (ranging from 1.26 to 9.13 (mmolc L1 0.5) ). Most measured ECe and SAR values were relatively low, posing
no risk for crop production or indicating any risk of soil degradation.
However, two profiles located in Alqueva (Odivelas Fase II), one withwheat (1.36–3.59 dS m−1; 3.76–9.13 (mmolc L-1)0.5) and another withsugar beet (0.96–3.99 dS m−1; 5.71–8.78 (mmolc L-1)0.5), registered3.2. Soil salinization risks from current irrigation practices
Fig. 3 shows the simulated average ECsw values at depths of 10, 50,
90, and 130 cm, as well as the range between the corresponding
minimum and maximum values. Results are related to the S1 scenario,
which considers irrigation practices currently carried out in very highdensity olive orchards grown in the region. Also, results were groupedby soil reference groups (IUSS Working Group WRB, 2014) to reduce
the large variability observed in model simulations, assuming thus that272

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Fig. 3. (a) Average simulated values of the electrical conductivity of the soil solution (ECsw) at 10, 50, 90, and 130 cm depth in Calcisols (15 profiles), Cambisols (4profiles), and Fluvisols (40 profiles) (shaded areas represent range between maximum and minimum values). (b) Average simulated values of the electrical conductivity of the soil solution (ECsw) at 10, 50, 90, and 130 cm depth in Luvisols (63 profiles), Regosols (11 profiles), and Vertisols (27 profiles) (shaded areas represent
range between maximum and minimum values).

Fig. 3. (continued)273

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soil profiles in these classification groups possess, to some extent, similar physical and chemical characteristics, particularly in terms of soil
horizons/layers, particle size distributions, soil structure, soil water
retention, soil infiltration, and solute composition.
The ECsw values showed a similar behavior in all topsoil layers(10 cm), increasing during the dry season due to irrigation (May to
October) and then decreasing during the rainfall season (November toApril) due to leaching, similarly as in Gonçalves et al. (2006) andRamos et al. (2011, 2012). In some Calcisols and Fluvisols profiles, theECsw values regularly reached values higher than 12 dS m−1(ECe = 6 dS m−1) at the end of each irrigation season (Fig. 3a), thus
exceeding the threshold limit for crops moderately tolerant to soil
salinity (Ayers and Westcot, 1985), including the one set here for olive(hϕT = −2975 cm; ECsw = 8 dS m−1; ECe = 4 dS m−1).
All soil reference groups showed evidence of salt accumulation at
deeper depths (≥ 50 cm), particularly during the years when annual
rainfall amounts were smaller than the average. This happened in years
2–8 (1980–1986), 12–17 (1990–1995), 20–21 (1998-1998), and 26–27(2004–2005) (Fig. 2), reflecting the large interannual variability ofrainfall observed in Portugal and the frequent occurrence of droughtspells in the region (Trigo and DaCamara, 2000; Paulo et al., 2012). In
Calcisols (Fig. 3a), salt accumulation was more pronounced at a depthof 50 cm, reaching in some profiles values higher than 10 dS m−1. At
deeper depths, the higher permeability of the calcaric layers favoredsalt leaching. In Fluvisols (Fig. 3a), Luvisols (Fig. 3b), and Regosols(Fig. 3b), salt accumulation was more evident at depths below 50 cm,
with some soil profiles, i.e., those with worse drainage conditions,
showing ECsw values above the threshold limit at depths of 90 cm.
Cambisols (Fig. 3a) showed similar average results as soils in previous
soil reference groups, with the analysis regarding variability being
limited by the number of soil profiles available. In Vertisols (Fig. 3b),
salt accumulation also occurred at depths below 50 cm during drier

years, but to a lesser extent.
The SAR values were more constant throughout the years (Fig. 4),
not showing the same seasonal and interannual variability observed for
ECsw nor the same dependence on rainfall, similarly as in Gonçalves
et al. (2006) and Ramos et al. (2011). After the 30-year simulation
period, most soil reference groups continued exhibiting similar average
SAR values as those determined from the initial soluble Na+, Ca2+, andMg2+concentrations. The exceptions were Calcisols and Cambisols reference groups, which showed an increasing trend at different depths,
although simulated averages always remained below 3.2 (mmolc
L−1)0.5. On the other hand, for the worst-case Fluvisols (Fig. 4a) andLuvisols (Fig. 4b), i.e., the soil profiles presenting initial higher SAR
values, results showed the SAR values decreasing at different depths
during the first 15 years of the simulation period. Explanations for thiswere surely related to the improved water quality considered in S1(Table 1) compared to the one used in these locations. However, arguments related to the limitations in the model structure by not considering fertilization practices or some important biogeochemical process that would explain those higher SAR values might also be valid.
To summarize, the S1 results showed that despite the large variability observed in model simulations, particularly in Fluvisols, Luvisols,
and Regosols, the average ECsw values in all soil depths always remained below the threshold limit. No salt precipitation was ever observed, with the reported pIAP values (the negative logarithm of the ion
activity product) indicating that conditions remained undersaturated
with respect to calcite and gypsum (Truesdell and Jones, 1974). Also,
apart from Calcisols and Cambisols, all other soil reference groups
presented the average SAR values always within the same magnitude as
the initial soil conditions. Based on these results, soil salinization andsodification risks resulting from current irrigation practices carried out
in very high-density olive orchards can be considered low. Nonetheless,the variability of results necessitates the recommendation to closely

Fig. 4. (a) Average simulated values of the sodium adsorption ration (SAR) at 10, 50, 90, and 130 cm depth in Calcisols (15 profiles), Cambisols (4 profiles), andFluvisols (40 profiles) (shaded areas represent range between maximum and minimum values). (b)Average simulated values of the sodium adsorption ration (SAR) at
10, 50, 90, and 130 cm depth in Luvisols (63 profiles), Regosols (11 profiles), and Vertisols (27 profiles) (shaded areas represent range between maximum andminimum values).
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Fig. 4. (continued)

monitor the soil profiles most susceptible to soil salinization to prevent
further degradation of soil resources in the region.

simulated ECsw values was frequently higher than the threshold limit at
depths ≥ 90 cm.
Scenario S6 with the worse irrigation water quality, hardly observedin the region, produced the highest ECsw values. Fluvisols (Fig. 5a)again registered the worst predictions, with the average ECsw values
often increasing above 15 dS m−1 at different depths.
The crop scenario S7, which considered maize as an alternative crop
to olive, differed considerably from previous predictions, with the ECsw
values always remaining below those in S1 throughout the simulation
period. The results thus confirmed the contribution of deficit irrigation
practices to salt accumulation in the soil profile. For maize, where full
irrigation practices are usually adopted (Cameira et al., 2003; Paredes
et al., 2014; González et al., 2015; Ramos et al., 2017), soil salinity
levels remained within the same magnitude as those determined at the
beginning of the simulation period (i.e., the initial conditions) due tothe higher irrigation amounts applied per year and per event, thus
promoting to some extent salt leaching.
The climate change scenario S8, despite being relatively moderateby considering only a 0.8 °C increase in the average surface air temperature and a 30 mm decrease in the annual rainfall values, resulted inslightly higher ECsw predictions than in S1. In most soil profiles, thesimulated ECsw values also remained always below the threshold limit
for crops moderately tolerant to soil salinity. Nonetheless, the ECsw
values were significantly higher than in S1 following the rainy years 10
and 30. Reductions in annual rainfall amounts had thus an obvious
negative effect on salt leaching, leading to greater soil salinization risks
in the long term. Note that projected reductions in annual rainfall values were only considered between April and October, with rainfall
increasing during winter months, in line with the Portuguese Local
Warming Website Project (https://portaldoclima.pt/en/). However,this increase did not translate in larger percolation to promote soilleaching but, in most cases, in higher runoff.
Additionally, scenario S8 considered the same irrigation inputs as in
S1. Although olive irrigation water requirements would be expected toincrease only modestly due to higher ETc rates (Tanasijevic et al., 2014;

3.3. Scenario analysis
3.3.1. Soil salinization risks
Fig. 5 displays the box-plots of simulated ECsw values for the S1-S8
scenarios at depths of 10, 50, 90, and 130 cm at the beginning of thesimulation period (initial conditions) and at years 5, 10, 20, and 30. The
results refer only to the salinity build-up at the end of a particular year’s
leaching season, just before the beginning of the next irrigation cycle.
The analysis thus focuses only on comparing the final salinity levels fordifferent scenarios over the years, without considering the ECsw values
during irrigation seasons, which may reach higher values than those
discussed here.
Scenarios S2 and S3 with lower irrigation water quality produced
slightly worse results than S1 (Table 1), with increasing ECsw values dueto the salinity build-up in the soil profile during dry years (years 2–8,
12–17, 20–21, and 26–27) and decreasing ECsw values as a result of
increased soil leaching during rainy years. As a result, although ECsw
were always higher than in S1, there was no gradual increase of ECsw
over the years. Even so, in most soil reference groups the ECsw values
remained below the threshold limit for crops moderately tolerant to soil
salinity (ECsw < 12 dS m−1), except during years 5 and 30 in all soil
groups. In Fluvisols (Fig. 5b), the threshold limit was additionally also
breached by the third quartile of simulation data at a depth of 90 cm
during year 5 and a depth of 130 cm during years 10 and 30.
Scenarios S4 and S5 with even lower water quality, not commonly
observed in the region except in smaller water reservoirs or during
periods of water scarcity, showed the third quartile of the simulated
ECsw values regularly breaching the threshold limit for crops moderately tolerant to soil salinity at different depths in most soil referencegroups. In Cambisols (Fig. 5a), this limit was breached even by the
median of simulations at a depth of 90 cm during year 5. The worse
predictions were obtained for Fluvisols (Fig. 5a), where the median of275

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L−1)0.5), Regosols (5.87 (mmolc L−1)0.5), Luvisols (6.00 (mmolc
L−1)0.5), and Vertisols (5.09 (mmolc L−1)0.5), the average maximum
SAR values were again registered at depths of 50–90 cm. The same wasobserved for the water quality scenarios S5 and S6 but with the maximum average SAR values ranging from 10 to 15 (mmolc L−1)0.5).
Scenario S5 thus produced similar soil sodification levels as S6 due tothe greater unbalance between Na+ and Ca2++Mg2+ in their irrigation waters (Table 1). The main difference was mostly in the time required to reach these maxima, with simulated averages being highest in
S6 after 10 and 20 years, indicating that the sodification process occurred at a faster pace in S6 than in S5. Note that the SAR values at theend of simulations for scenarios S3-S6 were within the same order ofmagnitude as those found in some of the worse-case profiles in theregion, indicating the need to improve future irrigation practices in theregion to prevent further degradation of soil resources. Also, despite thehigher SAR values in S5 and S6, salt precipitation never became a relevant process during the analysis.
In contrast to ECsw, the crop scenario S7 produced an increase in the
SAR values throughout the years in all soil reference groups.
Nevertheless, the average SAR values remained always lower than 2.31
(mmolc L−1)0.5, i.e., slightly below S1 predictions. Increased soilleaching due to different maize irrigation practices also ended up
contributing to the removal of Na+ from the root zone (Qadir et al.,2003), decreasing soil sodification risks. On the other hand, the climatechange scenario S8 produced slightly higher SAR values in all soil reference groups than those reported for S1, remaining still below 3.91
(mmolc L−1)0.5. Consequently, the S8 results suggested that climatechange may contribute to the further degradation of soil resources in
the region if management practices are not adjusted.

Valverde et al., 2015), additional irrigation amounts would inevitably
lead to an increased salinity build-up as the leaching fraction would not
be met to wash away accumulated salts from the root zone. S8 results
further showed that larger rainfall reductions, as those considered in
more extreme projections, would likely lead to an enhancement in thesalinity build-up to levels that would undoubtedly affect olive development, particularly during extended drier periods. However, thatwould also be dependent on the rain distribution during the rainyseason.
3.3.2. Soil sodification risks
Scenarios S1 and S2 produced comparable SAR results in all soil
reference groups (Fig. 6), with S2 values generally increasing
throughout the simulation period. For Calcisols (Fig. 6a) and Vertisols(Fig. 6b), the average SAR values reached a maximum of 3.10 and 2.53(mmolc L−1)0.5), respectively, at a depth of 50 cm. For Cambisols(Fig. 6a), Luvisols (Fig. 6b), and Regosols (Fig. 6b), the average SAR
values reached a maximum of 3.72, 3.13, and 3.13 (mmolc L−1)0.5)),
respectively, at a depth of 90 cm. The S2 results further showed thesame SAR reduction trend for the worse-case profiles. However, theremaining profiles revealed increasing soil sodification levels in all soil
depths. Only Fluvisols (Fig. 6a) showed a decrease in the average SAR
values in the topsoil layers (≥50 cm), with the bottom layers maintaining similar results as those determined at the beginning of the simulation period (the initial condition).
The water quality scenarios S3 and S4 also produced comparable
results, with all soil reference groups showing an increasing SAR trend
until the end of the simulation period. In Calcisols (6.00 (mmolc
L−1)0.5), Cambisols (6.87 (mmolc L−1)0.5), Fluvisols (7.70 (mmolc

Fig. 5. (a) Box-plot of the evolution of the electrical conductivity of the soil solution (ECsw) at 10, 50, 90, and 130 cm depth in Calcisols (15 profiles), Cambisols (4profiles), and Fluvisols (40 profiles) and in different scenarios (S) after 5, 10, 20, 30 years (y). (b) Box-plot of the evolution of the electrical conductivity of the soil
solution (ECsw) at 10, 50, 90, and 130 cm depth in Luvisols (63 profiles), Regosols (11 profiles), and Vertisols (27 profiles) and in different scenarios (S) after 5, 10,
20, 30 years (y).
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Fig. 5. (continued)

from 0.035-0.090 cm3 cm−3).
This study further relied on inputs from available data sources on
measured soil hydraulic properties for the soil profiles included in thisstudy (Gonçalves et al., 2011; Ramos et al., 2017). These parameters
were determined in the laboratory on soil samples of a certain size,
being likely less representative of actual flow conditions, transport, andreaction processes occurring at the field scale due to limitations in the
porous media continuum. As a result, simulations errors, as those given
earlier, are usually relatively larger than when soil hydraulic parameters are calibrated in situ by minimizing deviations between model
simulations and field observations (González et al., 2015; Ramos et al.,2017; Karandish and Šimůnek, 2018). This is even more relevant forsoils with a high clay content, where the role of macropores on soil
water dynamics can only be accurately described using dual-porosity/dual-permeability models (e.g., Šimůnek and van Genuchten, 2008).
Additionally, soil hydraulic data were also complemented with information estimated from regional PTFs using the particle size distribution as inputs (Ramos et al., 2014b), which are known to produce
RMSEs ranging from 0.038-0.058 cm3 cm−3 for water retention at different matric heads and 0.588 for log(Ks). However, these PTFs havenever been evaluated for simulating water flow under field conditions,bringing additional uncertainty to model predictions. Likewise, solutetransport simulations were indirectly estimated using soil PTFs from
Gonçalves et al. (2002) that were developed based on a very limited
number of samples (24).
Limitations related to model structure errors should also be addressed while analyzing model results. First, the more accurate axisymmetric or three-dimensional representations of flow and solutetransport under drip irrigation (Lazarovitch et al., 2005; Hanson et al.,2008; Kandelous et al., 2011; Ramos et al., 2012; Egea et al., 2016;
Autovino et al., 2018) were not adopted in this study. In drip irrigation,
irrigation volumes and salts are not uniformly applied over the soil

3.4. About model predictions
The ECsw and SAR predictions presented above were associated witha large uncertainty as the modeling approach was not calibrated/validated in situ for each soil profile. Models generally require extensive
calibration to assess if surface boundary conditions (i.e., potential
evapotranspiration rates) are properly defined, water flow and solutetransport are accurately described, and the problem at hand is suitably
addressed by the model structure (Forkutsa et al., 2009; Rasouli et al.,2013; Xu et al., 2013; Cameira et al., 2014; González et al., 2015; Raijet al., 2016; Ramos et al., 2017). This is often accomplished by evaluating deviations between field observations and model predictions and,if necessary, adjusting model parameters to minimize these deviations.
Validation then follows, again by comparing model predictions with anindependent observation field dataset. Hence, model calibration/validation is a time-consuming and laborious process that could not be
replicated for all studied locations.
Instead, the modeling approach adopted in this study followed
closely Gonçalves et al. (2006) and Ramos et al. (2011, 2012), who
extensively applied the HYDRUS software package (Šimůnek et al.,2016) for simulating multicomponent solute transport in a maize field
located in the Alentejo region. In these studies, all input variables (i.e.,the soil hydraulic properties, solute transport parameters, atmosphericdemand, Gapon constants, physical and chemical characteristics of thesoil, LAI, and root depth) were measured independently, resulting inmodel predictions reaching the root mean square errors (RMSE) of
0.03-0.04 cm3 cm−3 for soil water contents, 0.85–2.35 dS m-1 for ECsw,
and 3.91–6.27 (mmolc L-1)0.5 for SAR. While these studies were conducted in maize fields, the HYDRUS software package has also beensuccessfully tested for simulating soil water dynamics in high and very
high-density olive orchards (Egea et al., 2016; Autovino et al., 2018),
providing further background for this study (the RMSE values ranging
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surface as considered in the one-dimensional modeling approach carried out here. Salts tend to accumulate below the dripper, from where
they are then transported downwards and sideways depending on irrigation volumes and frequency, rainfall, evapotranspiration rates, andsoil hydraulic properties. Thus, such simplification can lead to inaccurate results in the short term, with the salinity build-up and thesalinity stress in the root zone being most likely overestimated (Hanson
et al., 2008). However, the relevance of this simplification was assumed
to be reduced in this long-term study (30 years), mostly because results
were considered only at the end of the leaching season when salts could
be assumed to be more evenly distributed in the soil profile, and theiraccumulation extends farther than just below the drip emitter, approximating then a 1D solution as in Ramos et al. (2012). Nonetheless,future studies should overcome this limitation, namely by developing
field datasets capable of validating modelling applications that use
more complex representations of the soil domain. Even if salts redistribute horizontally with time their distribution along the soil horizon
in a real three-dimensional soil system will never be as uniform as theone considered in a 1D solution. To demonstrate this point, we used
HYDRUS (2D/3D) (Šimůnek et al., 2016) to simulate the developmentof the salinity profile in an axisymmetrical three-dimensional soil profile with a radius of 67.5 and a depth of 150 cm for the same irrigationand climate conditions (S1 scenario) as used in one-dimensional simulations (Fig. 7). Fig. 7 shows that while (due to winter rainfalls) soil
salinities are in the long term relatively uniform in the top soil layer,
salts tend to accumulate more under trees than inter-row in deeper soil
layers.
Second, this study considered similarly as Gonçalves et al. (2006);
Ramos et al. (2011), and Raij et al. (2016) the feasibility of the multicomponent transport modeling approach embedded in the HYDRUS

software package (Šimůnek et al., 2016) for quantifying soil salinizationand sodification risks from irrigation practices. Cation exchange reactions were considered to be well described by the Gapon exchange
equations. Also, precipitation/dissolution reactions were assumed to bereasonably well accounted for using the available multicomponent kinetic expressions.
The effects of the salinity build-up on the unsaturated soil hydraulicproperties were also not considered in this study. This factor has so far
been considered only in a limited number of studies (e.g., Gonçalves
et al., 2006; Mallants et al., 2017). The HYDRUS software package
accounts for the adverse effects of salt accumulation on the soil hydraulic conductivity by using reduction functions developed by McNeal(1968) that depend on the salinity and SAR levels. Mallants et al.
(2017) applied this approach to hypothetical scenarios in which nonconventional irrigation waters with high concentrations of total dissolved solids and high SAR values were used, but results were nevervalidated using field data. Gonçalves et al. (2006) also applied thisapproach in their lysimeters studies but the simulation period was too
short and concentrations too low to provide an insight on the effect ofsoil salinity on soil hydraulic properties. Consequently, the approach
used in HYDRUS for considering the effect of salt accumulation on soil
hydraulic properties remains to be validated, and for that purpose, it
was not adopted here. Nevertheless, the relevance of this process for theobjectives of this study is not disputed. Salt accumulation (particularly,
Na+ and K+) often leads to clay dispersion, swelling, flocculation, andoverall degradation of soil physicomechanical properties, reducing, as aresult, the soil hydraulic conductivity, infiltration rate, and soil water
retention. As such, ECsw and SAR predictions would likely be aggravated if the effects of salt accumulation on soil hydraulic properties
were considered. Likewise, fertilization practices were not included inFig. 6. (a) Box-plot of the evolution of the sodium adsorption ration (SAR) at 10, 50, 90, and 130 cm depth in Calcisols (15 profiles), Cambisols (4 profiles), andFluvisols (40 profiles) and in different scenarios (S) after 5, 10, 20, 30 years (y). (b) Box-plot of the evolution of the sodium adsorption ration (SAR) at 10, 50, 90, and130 cm depth in Luvisols (63 profiles), Regosols (11 profiles), and Vertisols (27 profiles) and in different scenarios (S) after 5, 10, 20, 30 years (y).
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Fig. 6. (continued)

include new data sources, management practices, and tools to assess
further the impact of deficit irrigation practices carried out in very
high-density olive orchards (≥1000 trees ha−1) grown in the Alentejo
region on local soil resources. There is also the need for extending thisresearch to high-density olive orchards (≥200 trees ha−1), where theimpact of the intensive management practices on soil resources also
deserves a more detailed analysis. With the intensification of agriculture, it is thus further critical to extend this study to other Mediterranean crops such as vine grapes, fruit trees, horticultural and protein crops, for which irrigation introduced substantial changes in theirproduction systems or made possible their implementation in the region. This research should focus not only on soil salinization/sodification, but also on other soil degradation processes which likewise
threaten the sustainability of agricultural production in the region(Masselink et al., 2017; Cerdà et al., 2018; Di Prima et al., 2018;Rodrigo-Comino et al., 2018a, 2018b). Only then the sustainability of
the Mediterranean agriculture systems can be achieved, and the United
Nations Sustainable Development Goals can be met (Keesstra et al.,2016; UN, 2018).

Fig. 7. Spatial distribution of the electrical conductivity of the soil solution in a
Fluvisol (axisymmetric representation of the soil profile with a radius of
67.5 cm and a depth of 150 cm; the wetted radius ≈10 cm located in the top left
corner of each salinity profile; the root distribution with the maximum rooting
radius and depth of 67 and 120 cm, respectively).

model simulations, which, if considered, would likely also increase
ECsw values in all scenarios.
Despite all limitations, the adopted modeling approach provided acertain degree of confidence in ECsw and SAR predictions, being basedon modeling concepts that have been the focus of extensive researchover the last few decades (Šimůnek et al., 2014, 2016), with most being
successfully tested in the Alentejo region (Gonçalves et al., 2006; Ramos
et al., 2011,2012). Anyhow, these limitations discussed above only
emphasize the need for regular monitoring of soil salinization/sodification risks in the Alentejo region since even the most advance modeling approach will always be a mere simplification of the processes anddetails involved, and of nature’s variability. There is thus the need to

4. Conclusions
The Alentejo region in southern Portugal has recently experienced amajor land use change, with the area with high and very-high density
olive orchards expanding rapidly over the last few years. This has also
led to great changes in the management of soil and water resources,
with deficit irrigation practices being now often adopted to maximize
yields and the quality of olive oils. As a result, soil salinization andsodification problems can potentially increase in a region where rainfall
may not be sufficient to leach the accumulated salts during irrigation.
Before the expansion of olive orchards, different soil survey studies
revealed relatively low soil salinization and sodification levels in the279

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region, with several exceptions being found, particularly in Fluvisols
and Luvisols, as a result of erroneous management practices.
The scenario based on current irrigation practices in very highdensity olive orchards (S1) showed reduced risks of soil salinization,with simulated average ECsw values at the end of the leaching seasons(i.e., at the end of the irrigation cycles) always remaining below thesalinity threshold limit for olive and for most crops moderately tolerant
to soil salinity. Likewise, soil sodification risks were considered to begenerally quite low, with the simulated average SAR values being also
kept within the same magnitude of those determined at the beginningof the simulation period.
The scenarios based on the expected deterioration of the quality ofirrigation waters (S2-S6) produced higher salinization/sodification levels, with some closely matching the worse ECsw and SAR values foundfor baseline conditions (i.e., before the establishment of olive orchards).
Olive deficit irrigation practices (S1) also resulted in worse indicators
than those obtained when considering maize as an alternative crop(S7). The full irrigation practice, commonly used for maize, with larger
water depths per event and season, helped in reducing the salinity
build-up in the soil profile. Climate change (S8) also contributed to theincrease in soil salinization/sodification levels, with ECsw and SAR
values being always slightly higher than those simulated for current
conditions (S1). Climate change projections considered in this study
were very modest, involving only a 0.8 °C increase in the average valueof the surface air temperature and a 30 mm decrease in the corresponding annual rainfall amounts. For more extreme projections, thedependency of ECsw on annual rainfall showed that soil salinizationlevels would likely increase if larger rainfall reductions were considered. On the other hand, SAR predictions would not be much affected.
Finally, the results displayed a relatively large variability in all soil
reference groups and scenarios. Model predictions were also associatedwith large uncertainties as the HYDRUS-1D model was not calibrated/
validated in situ for each soil profile, relying on existing datasets,
auxiliary tools (PTFs), and on past research carried out in the region.
Nonetheless, model predictions presented here should not be taken
lightly as the risk of soil salinization/sodification is real. There is thus
the need to closely monitor the problem at the local scale to prevent
further degradation of soil and water resources in the region.

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Acknowledgments
This research was performed within the Project SOIL4EVER(Increasing water productivity through the sustainable use of soils,
PTDC/ASP-SOL/28796/2017) of the Fundação para a Ciência eTecnologia (FCT). MARETEC acknowledges the national funds from
FCT (Project UID/EEA/50009/2019). T. B. Ramos was supported by the
FCT grant SFRH/BPD/110655/2015.
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