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A New Method to Estimate Reference Crop Evapotranspiration from Geostationary Satellite Imagery: Practical Considerations

Water 2019, 11(2), 383; https://doi.org/10.3390/w11020383

Article
Crop Coefficients and Transpiration of a Super Intensive Arbequina Olive Orchard using the Dual Kc Approach and the Kcb Computation with the Fraction of Ground Cover and Height
1
Research Center on Linking Landscape, Environment, Agriculture and Food (LEAF), Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017 Lisboa, Portugal
2
Instituto Nacional de Investigação Agrária e Veterinária, I.P. (INIAV), 2565-191 Dois Portos, Portugal
3
Instituto de Ciências Agrárias e Ambientais Mediterrânicas (ICAAM), Universidade de Évora, Largo dos Colegiais, 7006-554 Évora, Portugal
*
Author to whom correspondence should be addressed.
Received: 28 January 2019 / Accepted: 19 February 2019 / Published: 22 February 2019

Abstract

:
The SIMDualKc model was used to simulate crop water requirements for a super high density olive orchard in the region of Alentejo, Portugal. This model uses the dual crop coefficient approach to estimate and partitioning the actual crop evapotranspiration (ETc act) and therefore to perform the soil water balance. The model was calibrated with 2011 tree transpiration using trunk sap flow measurements and was validated using similar data from 2012 and tested with 2013 data. Low root mean square errors (RMSE < 0.53 mm·d−1) and acceptable modelling efficiency indicators (EF > 0.25) were obtained. Further validation was performed comparing modelled ETc act with eddy covariance measurements. These indicators support the appropriateness of using SIMDualKc to guide irrigation management. The basal crop coefficient (Kcb) curves obtained with SIMDualKc for those 3 years were compared with the Kcb values computed with the Allen and Pereira approach (A&P approach) where Kcb is estimated from the fraction of ground cover and plant height considering an adjustment factor for crop stomatal control (Fr). Fr values were obtained through a trial and error procedure through comparing the Kcb estimated with this approach and with SIMDualKc. The Kcb curves obtained by both methods resulted highly correlated, which indicates that the A&P approach may be used in the irrigation management practice to estimate crop water requirements. Results of performing the soil water balance with SIMDualKc have shown that soil evaporation is a large fraction of ETc act, varying between 41% and 45% for the 3 years under study. Irrigation, applied with a drip system, represented 39 to 56% of ETc act, which shows the great importance of irrigation to achieve the water requirements of super intensive olive orchards. Nevertheless, the analysis has shown that the irrigation management adopted at the orchard produces a water deficit larger than desirable, with a ratio of ETc act to non-stressed crop evapotranspiration (ETc) varying from 70% to 94% during the mid-season, when that ratio for a eustress irrigation management could be around 90%.
Keywords:
Evapotranspiration; Irrigation; Density coefficient; Dual crop coefficients; Row crops

1. Introduction

Olive orchards consist of the dominant permanent crops in Portugal, covering approximately 50% of the total area with tree crops, namely in Alentejo, southern Portugal, where olives are mainly cropped for oil production. Super high density olive orchards, with more than 1500 trees ha−1, also known as hedgerow olive orchards, are growing fast due to their high yield and economic productivity [1,2,3]. However, they have higher water requirements than less intensive orchards [1]; it is therefore necessary to improve related knowledge about crop evapotranspiration to support appropriate irrigation management and scheduling [4,5]. Meanwhile, impacts of water deficit on olives growth and yield are relatively well known [6,7,8,9,10,11]. That knowledge about olives evapotranspiration and responses to water deficits is also essential to assess scenarios of climate change expected for the region, mainly referring to higher temperature during summer and less rainfall in winter and autumn [12,13].
Although information on crop evapotranspiration can be obtained through using various field measurements techniques [14], those involving direct measurements of ET are generally expensive, labour consuming, require appropriate skills of users and are more appropriate for research, that is, for example, measurements of soil water content [15,16], sap-flow [17,18,19] and eddy covariance [18,19,20], which are applied in this study. Differently, crop ET modelling, using commonly observed meteorological data [4], radiometric canopy temperature [21,22], web based sensors networks [23] and remote sensing information [24,25,26,27,28], including using unmanned aerial vehicles [29] might be useful irrigation management tools, namely for irrigation scheduling purposes and to generate mitigation scenarios to face drought and climate change. Example of models applications to olive orchards include WABOL [30], SIMDualKc [19] and HYDRUS 2D [31].
The most common approach to estimate potential crop evapotranspiration (ETc) is the use of the Kc-ETo approach [32], where ETo is the grass reference evapotranspiration (PM-ETo) and Kc is a crop coefficient, which relates ETc with ETo relative to various crop characteristics [4]. Kc changes throughout the crop season and a multi-stage linear approximation is commonly used to represent the Kc curve through defining their values at the initial, mid-season and end-season stages, respectively Kc ini, Kc mid and Kc end [4]. Adopting the dual Kc approach, both ETc components are considered, crop transpiration described by the basal crop coefficient Kcb and the soil evaporation described by the coefficient Ke [33]. When any crop stress occurs, Kcb is corrected through a stress coefficient, Ks, resulting that the actual Kcb (Kcb act = Ks Kcb) is smaller than the non-stress or standard one, that is, Kcb act ≤ Kcb. It results that the actual crop ET is smaller than the potential, non-stressed ET, that is, ETc act ≤ ETc. Therefore:
ETc act = (Ks Kcb + Ke) × ETo = (Kcb act + Ke) × ETo = Ks × Kc × ETo = Kc act × ETo
Field research, as the one reported in this study, is required to determine Kc and Kcb in relation with the crop characteristics and crop management. The standard or non-stressed values of Kcb and Kc are transferable to other locations—where training of olives orchards are similar—after adjusting Kc or Kcb to the prevailing climate conditions as proposed by Allen et al. [4]. The use of a well calibrated model such as SIMDualKc [34], which adopts the dual Kc approach to partitioning ETc act into actual plant transpiration and soil evaporation, using respectively Kcb act and Ke [4,33], is quite helpful to accurately computing ETc act and to derive the standard Kcb and Kc values as demonstrated in previous studies [35,36,37]. Moreover, the performance of that partition using SIMDualKc has been positively tested through comparing the model simulated transpiration against sap-flow measurements [19,38,39] or the simulated soil evaporation against micro-lysimeters’ observations [40,41,42].
In alternative to modelling, Allen and Pereira [43] proposed predicting Kcb or Kc by adopting a density coefficient (Kd) computed from the fraction of ground cover (fc) and the crop height (h), herein referred as A&P approach. It takes into consideration crop stomatal control through an adustment factor (Fr) that varies with crop characteristics and water management. The A&P approach has been used to ease the SIMDualKc modelling process applied to partial cover woody crops, such as vineyards [35], peach orchards [38] and olive orchards [19]. However, the A&P approach has not yet been used extensively for predicting Kcb throughout the crop season.
Considering the analysis above, the objectives of this study consisted of (i) calibrating and validating the model SIMDualKc using both sap-flow estimates of transpiration and ET eddy covariance observations; (ii) determining Kcb and Kc from SIMDualKc calibration; (iii) testing the A&P approach for predicting Kcb from the fraction of ground cover and crop height; and (iv) determining the terms of the soil water balance, particularly relationships between soil evaporation, actual transpiration and ETc act. The overall objective is to provide information for irrigation of olives’ orchards with reduced irrigation, so accepting a yield-water beneficial stress, the “eustress.”

2. Material and Methods

2.1. Experimental Site

The experimental site (38°24’N, 7°43’W, 143 m a.s.l.) is located in Alentejo, Southern Portugal, in a commercial super-high intensive hedgerow olive orchard farmed by “Olivais do Sul.” The orchard has a total area of 78 ha, which land has a smooth undulation. The climate is dry sub-humid of Mediterranean type, with most of the rainfall in autumn and winter; according to the Köppen-Geiger classification [44], the climate is a Csa, characterized by mild rainy winters and dry hot summers. The annual rainfall ranged between 511 and 736 mm in the experimental years, while the average monthly temperature ranged from 9.6 °C in January to 23.3 °C in July and August. Prevailing winds are from the North-West direction. Main daily weather data characterizing the experimental years is presented in Figure 1.
The soil is a Cambissol [45] having a sandy loam texture, with moderate to low infiltration. Soil water at field capacity averages 0.24 cm3·cm−3 through the soil profile down to 1.20 m, while the permanent wilting point to the same depth is 0.12 cm3·cm−3. The total available water to that depth is TAW = 160 mm. Soil water observations were performed weekly with a TDR probe (TRIME, IMKO, Ettlingen, Germany); data were used to perform the soil water balance considering a soil root zone depth Zr = 1.10 m.
The olive trees (cv. Arbequina) were hedgerow planted in 2006 adopting a super-high density (1.35 m × 3.75 m, 1975 trees ha−1). The observed crop growth stages dates along the crop seasons of 2011, 2012 and 2013 are presented in Table 1. The fraction of ground cover (fc, dimensionless) was estimated from measurements of the crown diameter along the row direction and perpendicularly to it for 51 trees. In addition, fc values estimated from remote sensing [28] were also considered. fc ranged from 0.17 to 0.38. The tree height (h, m) ranged from 3.0 to 4.0 m. Both fc and h varied through the crop season because trees were pruned during winter, by early January. However, in 2012, after a severe frost that occurred by late February, a heavy pruning was applied after, which caused larger changes in fc and h in that year. Harvesting in the study plots were performed by 16 October in 2011 and 2012 and by 10 October in 2013 (Table 1). The yield average was 14 ton·ha−1 in 2011 and 18 ton·ha−1 in 2013. A low yield of 3 ton·ha−1 was observed in 2012 due to the referred frost and heavy pruning.
Ground cover conditions affecting soil evaporation may be taken into consideration in the water balance simulations by SIMDualKc and were therefore observed. They refer to both crop residues and active ground cover [34,35,46]. In 2011, a reduced active ground cover was present from early January until May, covering 10% of the ground in the row and 5% in the inter-row. In 2012, following the frost by February, a heavy defoliation occurred and leaves formed an organic mulch from late February until early August; its ground cover fraction was estimated as 0.30, corresponding to a reduction of the soil evaporation of 30%. In 2013, an active ground cover of nearly 20% on both the row and the inter-row was observed from January until May, when it dried out and became a residues cover.
Irrigation took place nearly every day during spring and summer and was applied by the evening. A drip system was used, with emitters spaced of 0.75 m along the row. The emitters discharge was of 2.3 L h−1. The wetted fraction (fw = 0.23) was calculated from the area of a wetted ellipse circling each emitter. Irrigations were scheduled by the farmer, who adopted average daily irrigation depths close to 3 mm, however varying from 1 to 8 mm, approximately. Irrigation depths were measured with a tipping-bucket rain gauge (ARG100, Environmental Measurements Ltd., Sunderland, UK).

2.2. Eddy Covariance Measurements

The eddy covariance (EC) micrometeorological technique was used to measure evapotranspiration during short periods of the irrigation season (July–August in 2011 and June–August in 2012) as a strategy to calibrate the sap flow measurements and obtaining accurate transpiration estimates. This strategy has been successfully used in previous studies [38,47].
The EC system comprised a three-dimensional sonic anemometer and a krypton hygrometer (respectively Models CSAT3 and KH20, Campbell Scientific, Inc., Logan, UT, USA) mounted at a height of 4.8 m on a metallic tower, with a path separation of 0.1 m. The fetch were 470 m, 353 m, 455 m and 504 m for the north, west, south and east directions, respectively (Figure 2). EC raw data (H—sensible heat flux density and LE—latent heat flux density) were collected at a 10 Hz frequency to a datalogger (Model CR1000, Campbell Scientific, Inc., Logan, UT, USA) and afterwards analysed with the Software package TK3 [48] for correction and calculation of 30 min-averages. Data corrections were performed according to Foken et al. [49]. They included: despiking of the raw data [50]; coordinate rotation using the Double Rotation method [51] to account for the non-flat terrain conditions; corrections for oxygen cross-sensitivity of the krypton hygrometer [52] and spectral loss [53]; conversion of buoyancy flux to sensible heat flux [54]; and the WPL correction for density fluctuations [55]. To evaluate the surface energy balance, soil heat flux (G) and net radiation (Rn) were measured using eight soil heat flux plates (calibrated Peltier modules sealed 20 V, 4.4 A, 40 × 40 × 3.9 mm, RS Components, Madrid, Spain) and a net radiometer (Model NR-LITE, Campbell Scientific, Inc., Logan, Utah, USA), respectively. The soil heat flux plates were placed in the tree row and in the inter-rows at a depth of 2 cm. The daily energy balance equation error closure was determined by linear regression forced to origin.
The footprint analysis [56], performed to access the representativeness of the EC measurements, showed that over 90% of the fluxes sensed, determined as cumulative normalized flux (CNF), came from the region of interest, regarding the four main cardinal points. The fetch (distance to the edge of the plot) in the main directions, varying approximately between 350 and 500 m, was then considered adequate (Figure 3). Measurements were mainly affected by fluxes coming from an upwind area at a distance of 15 m from the tower (maximum of the one-dimensional footprint function, which provides the relative contribution to the vertical flux: Qf = (1/Qo) dQ/dx, for a given height z, being Qo the latent heat flux density measured at point x = (0, z)).
Measurements in days with prevailing winds from the North-East direction were discarded given the vicinity of a building approximately at 200 m in that direction. Prevailing winds, calculated as average frequency for each of the EC measurement periods, were in agreement with historical climatic data, which indicates the North-West direction (Figure 4). Wind direction frequency was further analysed in detail for individual days and EC data screened accordingly for specific modelling purposes.
The energy balance equation closure error (Figure 5) was determined with a linear regression analysis forced to the origin using daily values of the measured fluxes (LE, H, Rn and G). The error, below 10% (H + LE = 0.91 (Rn − G), R² = 0.87), is similar to that found by other authors using this technique in orchards [57,58,59].

2.3. Transpiration from Sap Flow Measurements with the Granier Method

Plant transpiration was measured with thermal dissipation Granier probes [60] between DOY (day of year) 134 in 2011 and DOY 194 in 2013 (continuous half-hourly records). Six trees, chosen according to trunk diameter class frequency determined in a large sample of the orchard, were equipped with 1 cm length sensors (UP GmbH, Cottbus, Germany). Average data were stored each thirty minutes in a datalogger (Model CR1000, Campbell Scientific, Inc., Logan, UT, USA). Natural temperature gradients in the tree trunk were accounted for using data from non-heated sap flow sensors calculated during long periods. Daily transpiration was calculated from the half-hourly data. For flux calculation, the sapwood area in the cross section of the trunk was evaluated from core samples taken with an increment borer. Observation of the samples allowed considering the whole area conductive except the bark.
Sap flow methods are often referred as underestimating transpiration [19,61,62,63,64,65]. Although the original calibration of this method, which allows converting temperature differences between the two probes into crop transpiration, was initially considered universal, more recently, evidences show that it is advisable to verify its accuracy. Raw sap flow observations (SF, mm·d−1) were therefore corrected to obtain calibrated sap-flow transpiration (TSF, mm·d−1), as referred in previous applications to tree crops [38,47,61]. This was done by mathematically relating sap-flow, SF, with transpiration computed from EC observations (TEC = ETEC–Esim). TEC represents the difference between ET obtained from EC measurements (ETEC, mm·d−1) and the soil evaporation (Esim, mm·d−1) simulated with the two phases Ritchie model [66], the latter computed following Allen et al. [4]. The computation of Esim based upon the water balance of the soil evaporation layer using soil moisture observations. Parameters characterizing that soil layer, with a depth Ze = 0.10 m, consisted of: total and readily evaporable water, respectively TEW = 18 mm and REW = 9 mm. Further information on Esim data was provided by Paço et al. [19]. This procedure allows enlarging the available field information to the entire simulation period [67,68].
The best mathematical relationships between TEC and SF (Figure 6) were of exponential form:
TEC = KSF eαSF
with KSF = 0.34 and α = 1.40 (R2 = 0.55; n = 13) for 2011 and KSF = 0.34 and α = 1.72 (R2 = 0.61; n = 28) for 2012, which was extended for 2013. The adjustment of SF was performed with Equation 2 assuming that the transpiration derived from sap-flow measurements (TSF) equals that estimated from EC observations as referred above (TEC). The approach used may be affected by uncertainties in computing Esim as well as measurements errors affecting both TEC and SF. Nevertheless, The TSF values resulting from the application of Equation 2, which varied between 0.36 and 3.54 mm·d−1, are comparable to transpiration values reported for other studies on olive orchards [18,59,69].
TSF data were screened for outliers taking into consideration: (a) the range of values used to establish the sap flow calibration equation and the respective Kcb values (Kcb SF = TSF/ETo), which should be kept within an acceptable range. The common definition of outliers by Tukey [70] was used, thus considering as outliers the Kcb SF values higher than 1.5 times the interquartile range, below the first quartile or above the third quartile. The application of these rules led to discarding a total of 177 TSF values, with outliers representing 3.5% and 7% of observations in 2011 and 2012, respectively, contrasting with 56% of values computed for 2013. That high number of discarded values was most likely due to the fact that the SF equation used for 2013 was not purposefully calibrated. It was assumed that discarding the outliers decreased the uncertainties of observations and computations.

2.4. Basal Crop Coefficients Derived from Crop Height and Fraction of Ground Cover

To take into account fraction of ground cover by the crop (fc) and the crop height (h), a density coefficient (Kd) is used with the A&P approach to estimate Kcb [43]. Kd (dimensionless) is defined as the minimum value among the effective ground cover corrected for the density of shading or corrected for the crop shading depending upon the crop height. Thus, Kd is given by:
Kd = min(1, ML fc eff, fc eff(1/(1+h)))
where fc eff is the effective fraction of ground covered or shaded by vegetation near solar noon (dimensionless), ML is a multiplier on fc eff describing the effect of canopy density on shading the ground, thus on maximum relative ET per fraction of ground shaded and h (m) is the mean height of the crop vegetation.
Kcb is estimated from Kd as
Kcb = Kc min + Kd (Kcb full − Kc min)
when the inter-row is bare soil or, when an active ground cover is present, as
K cb = K cb   cover +   K d ( max [ K cb   full   K cb   cover ,   K cb   full K cb   cover 2 ] )
where Kc min is the minimum Kc for bare soil (≈ 0.15 for typical agricultural conditions), Kcb cover is the Kcb of the ground cover in the absence of tree foliage and reflects the density and vigour of the active ground cover crop and Kcb full is the estimated basal Kcb during the peak plant growth for conditions having nearly full ground cover. The second term of the max function allows taking into consideration the impacts of shading of the canopy over the ground cover crop. Further information is provided by Allen and Pereira [43].
The Kcb full represents a general upper limit on Kcb mid for tall vegetation having full ground cover and LAI > 3 under full water supply. Kcb full takes into consideration crop stomatal control and is approximated as a function of the mean plant height and adjusted for climate [43],
K cb   full =   F r ( min ( 1.0 + 0.1 h ,   1.20 ) + [ 0.04 ( u 2 2 ) 0.004 ( RH min 45 ) ] ( h 3 ) 0.3 )
with Fr (0–1) is an adjustment factor relative to crop stomatal control, the min function assumes that 1.20 is the upper bound for Kcb full prior to adjustment for climate. h (m) is the average maximum crop height, u2 (m s−1) is the average wind speed at 2 m height, RHmin (%) is the average minimum relative humidity during mid-season.
The parameter Fr applies a downward adjustment when the vegetation exhibits more stomatal control on transpiration than it is typical of most annual agricultural crops. Fr < 1.0 for various tree crops and natural vegetation. Standard values for Fr for various tree crops are provided by Allen and Pereira [43] but Fr values may be adjusted experimentally, namely through comparing Kcb obtained with the A&P approach with those derived through a different but accurate procedure, namely through the use of models. Fr may also be computed through comparing the leaf resistances for the crop (rl, s m−1) with that of grass (100 s m−1) considering the climate conditions [43], thus:
F r Δ +   γ   ( 1 + 0.34   u 2 ) Δ +   γ   ( 1 + 0.34   u 2   r l 100 )
where rl (s·m−1) is mean leaf resistance for the vegetation in question (s·m−1) Δ is the slope of the saturation vapour pressure curve (kPa·°C−1), γ is the psychometric constant (kPa·°C−1) and u2 is the average wind speed during the relevant crop stage measured at 2 m height (m·s−1). rl values for various crops are tabulated by Allen e Pereira [43], who also referred that for most annual agricultural crops rl is often not far from 100 s·m−1.

2.5. The SIMDualKc Model

The SIMDualKc water balance model [34] uses a daily time step to perform the soil water balance at the field scale through computing crop ET with the dual Kc approach [4,33,43]. The SIMDualKc model adopts the approach described with Equation 1, thus assuming that ETc act = ETc when the soil water depletion fraction does not exceeds the depletion fraction for no stress, p (dimensionless).
The model computes the soil water depletion at the end of every day:
D r ,   i   =   D r ,   i 1   ( P e RO ) i I i   CR i + ET c act ,   i + DP i
where Dr,i and Dr,i−1 are the root zone depletion at the end of respectively day i and day i−1 (mm), Pe is precipitation (mm), RO is runoff (mm), I is the net irrigation depth that infiltrates the soil (mm), CR is capillary rise from the groundwater table (mm), ETc act is actual crop evapotranspiration (mm) and DP is deep percolation through the bottom of the root zone (mm), all referring to day i. CR and DP are calculated with parametric equations described by Liu et al. [71] and RO is estimated using the curve number approach [72]. Because the groundwater table is quite deep in the region, CR was assumed to be null.
The model computes the actual ET (ETc act, mm) as a function of the available soil water in the root zone using a water stress coefficient (Ks, 0–1). Ks is computed daily as a linear function of the depletion Dr in the effective root zone [4,33]:
K s =   TAW   D r TAW RAW = TAW   D r ( 1 p )   TAW for   D r   >   RAW
K s = 1 for   D r     RAW
where TAW and RAW are, respectively, the total and readily available soil water (mm) relative to the root zone depth Zr and p is the soil water depletion fraction for no stress, therefore with RAW = p TAW. Thus, ETc act = ETc when Ks = 1.0; otherwise, when water and/or salinity stress occurs, ETc act < ETc and Ks < 1.0. Then, because Kcb act = Ks Kcb, it results Kcb act < Kcb. Similarly, it results that actual transpiration will be smaller than its potential value, thus Tc act < ETc when Ks < 1.0.
The evaporation from the soil surface (Es, mm·d−1) is limited by the amount of energy available at that surface in conjunction with the energy consumed as crop transpiration [4,72]. The model computes the evaporation coefficient (Ke) performing a daily water balance of the evaporation soil layer, which is characterized by its depth (Ze, m), the total evaporable water (TEW, mm) and the readily evaporable water (REW, mm). TEW is the maximum depth of water that can be evaporated from the evaporation soil layer when that layer has been fully wetted and REW is the depth of water that can be evaporated without water availability restrictions. Es is maximum when the topsoil is fully wetted by rain or irrigation and the soil surface shadowed by the crop is minimum. Differently, Es is minimum when the crop fully shadows the soil and energy available for evaporation is minimum [4,34,72].
The maximum value of Ke is attained when the soil is wet. That value is limited by the energy available at the soil surface, which corresponds to the difference between Kc max, representing maximum ET when the crop would fully cover the soil and Kcb, representing the effective transpiration of the crop. As the topsoil dries, less water is available for evaporation and Es is reduced. This reduction in Es is proportional to the amount of water remaining in the surface soil layer. Ke is then computed considering an evaporation reduction coefficient (Kr ≤ 1.0) as
K e =   K r   ( K c max   K cb ) with   K e     f ew   K c max
where Kc max is the maximum value of Kc (i.e., Kcb + Ke) following a wetting event by rain or irrigation, generally 1.20 and few is the fraction of the soil that is both exposed to radiation and wetted by rain or irrigation, that is, the fraction of soil surface from which most evaporation occurs. Thus, few depends upon the fraction of ground covered by the crop (fc) and of the fraction of soil wetted by irrigation (fw), thus few = min(1−fc, fw). Kr is calculated using the 2-stage drying cycle approach [4,66], where the first stage is the energy limiting stage and the second is the water limited stage or falling rate stage, where evaporation decreases as evaporable water decreases in the evaporation soil layer beyond the readily evaporable water (REW):
K r = 1 for   D e , i 1     REW
K r =   TEW D e ,   i 1 TEW REW   for   D e , i 1   >   REW
Further descriptions of the model and auxiliary equations are given by Rosa et al. [34].
The input data required by the model include:
  • Daily climatic data: reference evapotranspiration (ЕТo, mm), precipitation (P, mm), minimum relative humidity (RHmin, %) and wind speed at 2 m height (u2, m·s−1).
  • Soil data for a multi-layered soil: number of layers and related depths d (m); the respective soil water content at field capacity and at the wilting point (θFC and θWP, m3·m−3) or the total available water (TAW, mm); the characteristics of the soil evaporation layer (Ze, REW and TEW); and the soil water content at planting in both the root zone and the evaporation layer expressed as a % of depletion of TAW and TEW, respectively.
  • Crop data: dates of the crop growth stages (non-growing, initial, crop development, mid-season and end season); the basal crop coefficients for the non-growing, initial, mid-season and end season (Kcb non-growing, Kcb ini, Kcb mid and Kcb end); the soil water depletion fractions for no stress at the same stages (рnon-growing, pini, pmid and pend); root depths (Zr, m), plant height (h, m) and the fraction of ground cover by the crop (fc, %) throughout the crop season.
  • Irrigation scheduling data: dates and depths of observed irrigation events. In addition, the model requires data relative to the fraction of soil wetted by irrigation (fw). In this application, fw = 0.23 because the soil was wetted by micro-irrigation.
  • Parameters required to compute capillary rise and deep percolation when using the parametric equations proposed by Liu et al [71]; in the present application only the parameters aD and bD characterizing DP were used.
  • Base data to compute surface runoff using the Curve Number method [72].
  • Information characterizing the active ground cover, soil residues and mulches and related effects on Es [34].
  • soil and water salinity information, however not used in the present application.

2.6. Model Calibration and Validation and Goodness of Fit Indicators

So far, the calibration and validation of the SIMDualKc model have been performed either through comparing computed actual transpiration with sap-flow measurements [38,39], simulated ETc act with EC observed evapotranspiration [73,74] or, more commonly, simulated with observed soil water content [35,37,41,75,76]. In this study, model calibration was considered as the process of adjusting influential model parameters and inputs within their reasonable ranges so that the simulated Tc act values were in agreement with sap flow derived data (TSF).
The calibration procedure consisted of adjusting the crop parameters—Kcb and p values relative to the non-growing, initial-, mid- and end-season growth stages-, the soil evaporation parameters—TEW, REW and Ze-, the deep percolation parameters aD and bD and the CN of the runoff curve number algorithm, by minimizing the differences between TSF and simulated Tc act. Daily data of 2011 was used. An initial set of parameters was selected: crop parameters (Kcb and p) from Allen et al. [4] and Paço et al [19]; soil evaporation parameters from Paço et al. [19], CN from Allen et al. [72] and DP parameters from Paço et al. [19]. The initial soil water conditions in 2011 were observed in the field and consisted of the initial depletion of the evaporation layer, that was 40% of TEW and the initial depletion in the entire root zone, that was 40% of TAW. A trial and error procedure was then developed for selecting first the Kcb values. In the following, the trial and error procedure was applied to the p values, then to the referred soil evaporation, deep percolation and, lastly, to the CN parameters.
The validation of SIMDualKc consisted of using the previously calibrated parameters (Kcb non-growing, Kcb ini, Kcb mid, Kcb end, p non-growing, p ini, p mid, p end, TEW, REW, Ze, aD, bD and CN) with daily data of 2012. TSF data relative to 2013 was used for model testing with the same calibrated parameters. The process of calibration and validation was considered satisfactory when the goodness-of-fit indicators relative to the validation were within 20% of variation relative to the calibration.
A set of goodness-of-fit indicators were used to assess the model accuracy, as in prior studies with SIMDualKc. These indicators are fully described by Pereira et al. [37]. They included a linear regression coefficient (b0) of the regression forced through the origin between observed TSF and predicted Tc act and the determination coefficient R2 of the ordinary least squares regression. A regression coefficient b0 close to 1.0 indicates that the predicted values are statistically close to the observed ones and a determination coefficient R2 near 1.0 indicates that most of the variance of the observed values is explained by the model estimates. Errors of estimation were assessed using the root mean square error (RMSE) and the average absolute error (AAE) as indicators. Targeted values for error indicators are 0.0 which corresponds to a perfect match between simulated and observed values. In addition, to assess any bias tendency of the estimations, the percent bias (PBIAS, %) was used. The PBIAS measures the average tendency of predictions to be larger or smaller than the corresponding observations, with positive values indicating an over-estimation bias and negative values indicating an under-estimation bias. To assess the modelling quality, the Nash and Sutcliff [77] modelling efficiency (EF, dimensionless) was used. The EF is a normalized statistic that determines the relative magnitude of the residual variance compared to the measured data variance or how well observations versus simulations fit the 1:1 line [78]. EF values close to 1.0 indicate that the variance of residuals is much smaller than the variance of observations, thus that model performance is excellent. Contrarily, when EF is negative this means that there is no gain in using the model, thus that the mean of observations is as good predictor as the model.

3. Results and Discussion

3.1. Calibration and Validation of SIMDualKc with Sap Flow Data

The initial and calibrated values of the parameters relative to the SIMDualKc model are presented in Table 2 and they refer to the conditions observed in field. The calibrated Kcb parameters are smaller than the initial ones, mainly those relative to the non-growing season and the initial periods (Kcb non-growing and Kcb ini). Differences for Kcb mid resulted relatively small. However, Kcb mid adjusted to the climate are slightly smaller than reported in Table 2 (0.44 to 0.47) because local climate is dry and RHmin is small, while Kcb end are slightly increased (0.44 to 0.48) since RHmin is also increased by the end-season. The calibrated p parameters closely follow those proposed by FAO56 [4]. Runoff CN changed little and deep percolation parameters did not change because these parameters were adjusted in a previous study [19].
Most studies in literature report on actual Kcb values, that is, when the crop was affected by water stress since deficit irrigation is mostly used in olive orchards. In addition, most studies focus on the irrigation season only and data relative to other crop growth stages are quite scarce, namely for the non-growing period and initial crop stage. Moreover, Kcb values reported in literature are not adjusted to the climate. Therefore, it results difficult to compare our results with those relative to other studies. Kcb mid values of this study are similar to those reported by Santos et al. [26] when using remote sensing observations of an olive orchard under water stress conditions. Slightly lower Kcb mid values were reported by Conceição et al. [59]. Differently, López-Olivari et al [69] reported much lower Kcb mid values, averaging 0.28, which is likely due to water stress as commonly imposed by farmers. Higher Kcb mid values (0.58 ± 0.14) were reported by Cammalleri et al. [18] for an intensive olive orchard with fc = 0.35. Er-Raki et al. [20] also reported a higher Kcb mid of 0.54. Much higher Kcb values were proposed by Allen and Pereira [43]. The p values are similar to those reported by Er-Raki et al. [20] and by Rallo et al. [79].
Results in Figure 7, comparing model simulated Tc act with transpiration derived from sap-flow measurements (TSF), show a good agreement between Tc act and TSF along the three years of study. The dynamics of both Tc act and TSF are coherently described for all three years but fitting is less good for 2012 due to the heavy frost and subsequent pruning that occurred by that winter. Apparently, the trees responded well to pruning and that exceptional condition could have justified using specific Kcb and p values but that option was not adopted because validating the model requires using the calibrated parameters. However, despite the peculiar condition occurring in 2012, as well as the uncertainties associated with deriving TSF, model predicted Tc act described reasonably well the dynamics of transpiration of the olive orchard throughout the three experimental seasons.
The goodness-of-fit indicators (Table 3) show a slight tendency for the model to over-estimate TSF, with b0 = 1.04 and PBIAS = 3.9% in the calibration year; the same trend for over-estimation was observed in 2012 and 2013, with b0 of 1.03 and 1.17 and PBIAS of 5.2% and 13.3%. The determination coefficients are high for the first and third years, with respectively R2 = 0.75 and 0.78; due to the problems identified to 2012, it resulted R2 = 0.60, however reasonably high. These R2 values indicate, therefore, that a large fraction of the variance of TSF is explained by the model. Estimation errors are reasonably low, with RMSE ranging from 0.37 to 0.53 mm·d−1 and AAE ranging from 0.25 to 0.42 mm·d−1. The modelling efficiency is reasonably high for the calibration year, with EF = 0.57 and acceptable for the validation and testing years. Overall, results for the goodness-of-fit indicators show that the predicted Tc act values were statistically close to the sap-flow observed ones and that most of the variation of the observed values is explained by the model. Higher errors of estimation were reported by Rallo et al. [79] when using a modification of the FAO56 approach for an intensive olive orchard, with RMSE ranging from 0.30 to 0.78 mm·d−1. Er-Raki et al. [20], also reported higher Tc act estimation errors, with RMSE = 0.59 mm·d−1.
The dynamics of the actual crop evapotranspiration (ETc act) simulated along 2011 and 2012, which was computed with the calibrated model parameters of Table 2, is presented in Figure 8. ETc act are compared with evapotranspiration values observed with the eddy covariance system (ETEC). Despite ETEC observations were in reduced number, they could be used for further validate the model since results show that the model was able to adequately estimate the dynamics of ETc act during the mid-season stage when ETEC was measured. The average ETEC value during this stage was 2.5 mm·d−1 (±0.41, n = 28). Goodness-of-fit indicators for 2012 show a tendency for the SIMDualKc model to under-estimate ETEC (b0 = 0.94 and PBIAS = −6.0%), thus contrary to the tendency observed when Tc act was compared with TSF. However, given the reduced number of EC observations, it is not possible to draw a conclusion about model trending. An acceptably high R2 = 0.64 was obtained, which indicates that a large fraction of the variance of ETEC observations is explained by the model. In addition, acceptable estimation errors of ETc act were obtained with RMSE = 0.42 mm·d−1, AAE = 0.35 mm·d−1 and, consequently, a reasonably good EF value of 0.45 was obtained. Higher estimation errors for ETc act, ranging from 0.54 to 0.71 mm·d−1, were reported by Er-Raki et al. [20].

3.2. Dual and Single Crop Coefficients

The standard and actual Kcb curves, the Ke curves and the standard and actual single Kc curves derived for the 3-year observed hedgerow olive orchard are shown in Figure 9. Precipitation and irrigation events are also depicted to easily interpreting the behaviour of the Ke, Kcb and Kc curves. The represented Kcb values were adjusted for climate as a function of wind speed, RHmin and crop height as proposed in FAO56 [4], so resulting slightly different from those given in Table 2 as referred before. The time-averaged standard Kc relative to the various crop stages was computed by summing the standard Kcb of the same crop stages with the daily Ke values computed for the same periods, that is, Kc = Kcb + Ke. Differently, Kc act = Kcb act + Ke, thus varying daily.
The Kcb and Kcb act curves (Figure 9) are coincident during winter and most of autumn and spring periods when rainfall was enough to avoid water stress, that is, when Kcb act = Kcb as discussed about Equation 1. Differently, high water stress occurred during most of the mid-season stage, particularly during 2012 and 2013, when those curves indicated Kcb act < Kcb. This water stress occurred because the orchard was under-irrigated following the common practice of deficit irrigation in olive orchards, in particular because irrigations were started too late in those years. To achieve a eustress irrigation management it would be desirable that irrigation would start earlier in both 2012 and 2013, eventually adopting larger irrigation depths applied every two or three days instead of 3 mm depths every day. However, further studies are required to better defining the best eustress management with consideration of impacts of transpiration deficits on yields.
Soil evaporation Ke curves show to react daily to the soil wetting events, mainly with various peaks occurring in response to rainfall events (Figure 9). During the rainfall season, from early autumn to early spring, Ke is often high because the inter-row is wide and solar energy is then available at the soil surface. During mid-season soil evaporation decreases and Ke drops because soil dries and residues of the ground cover vegetation act as an organic mulch to limit evaporation. Ke peaks are much small during most of the mid-season stage because irrigation was applied along the row of trees, so under crop shadow, thus resulting in limited energy available for evaporating the irrigation water.
Differently of the time-averaged standard Kc curves, the Kc act curves show numerous peaks in response to the rainfall events (Figure 9) since these curves represent Kc act = Kcb act + Ke as referred above. The Kc and Kc act curves are coincident in the periods without water stress, thus during the rainy season, roughly from October to May. The standard and actual Kc values during the non-growing periods and the initial stage are highly dependent upon the precipitation amounts. Differently, the Kc act curves lay bellow the Kc curve under water stress conditions during mid-season.
Figure 9 clearly shows the contrasting behaviour of standard Kc and Kcb. The above described dynamics of Kc curves, particularly the dependence of Kc values from soil evaporation, mainly due to soil wettings by precipitation, explains why the time-averaged standard Kc mid is smaller than Kc non-growing and Kc ini, that refer to the rainy season, while the higher standard Kcb values are for the mid-season, Kcb mid, thus when transpiration is higher under irrigation.
The time averaged Kc are presented in Table 4 for all crop stages of 2011, 2012 and 2013. Kc ini values, ranging 0.70 to 0.96, are in the range of values reported for intensive olive orchards [43,80,81]. The Kc mid values ranged 0.70 to 0.78, which are comparable with values reported by various authors [18,43,81,82]. Kc values for the late season (0.79 to 0.90) increased relative to the mid-season due to the occurrence of various precipitation events. High Kc end values found in the present study are comparable with those reported by Testi et al. [80].

3.3. Kcb Predicted with the A&P Approach vs. Kcb Obtained with SIMDualKc

The A&P approach (Section 2.4, Allen & Pereira [43]) was applied with fc and h data observed along the three seasons. The dynamics of fc and h are presented in Figure 10a in conjunction with the corresponding computed values for Kcb (Kcb A&P), which are compared with the standard Kcb obtained with SIMDualKc for the same days (Kcb SIMDualKc), resulting a close match (Figure 10b) except for the cases that follow the heavy frost with great defoliation and severe pruning in the winter of 2012.
When computing the density coefficient Kd (Equation (3)) the value ML = 1.5 proposed by Allen and Pereira [43] was used. Nevertheless, because insufficient information on shadowing was available, it was assumed fc = fc eff·Kcb full (Equation (5)) was computed with observed weather data for the various crop stages and considering the observed crop height. The Fr values were obtained after applying a simple trial and error procedure against the values obtained by the model. That trial and error procedure was initiated with the value Fr = 0.48 proposed by Allen and Pereira [43] for the entire season and olive orchards without ground cover but just with residues of old leaves. The resulting value for the non-growing and the initial stages was Fr = 0.49, thus about the same as the proposed one, while values for both the mid- and late-season were respectively 0.55 and 0.53. These slightly higher values of Fr were likely due to the fact that less stomatal control was required by Arbequina olives under irrigation. Nevertheless, the estimated Fr = 0.48 proposed by Allen and Pereira [43] would also provide for quite good Kcb estimations.
For the period from November to April, Kcb were computed from Kd with Equation 4b relative to the case when active ground cover exists. A variable Kcb cover, from 0.004 to 0.01, was used. For May to October, the Equation 4a was used with Kc min = 0.15
Results in Figure 10b show that Kcb computed with the A&P approach match quite well the Kcb SIMDualKc except for the cases that follow the heavy frost that occurred in the winter of 2012, which produced a great defoliation and required a severe pruning. Consequently, Kcb A&P were then smaller than the Kcb non-growing obtained with SIMDualKc. For all other cases, Kcb A&P values were close to the Kcb SIMDualKc estimates.
The regression coefficient relating both Kcb sets (Figure 11) results close to the unit (b0 = 0.97), thus indicating a good overall match, while the determination coefficient is high (R2 = 0.77). The estimation of Kcb A&P approach has a small RMSE of 0.05 relative to the modelled Kcb SIMDualKc values. Comparing the mean square error of Kcb A&P with the variance of the Kcb SIMDualKc values it resulted a high EF of 0.76. In conclusion, results of comparing both sets of Kcb values indicate that the A&P approach is adequate for estimating Kcb values to be used when assessing crop water requirements for olives and to support irrigation management decision making.

3.4. Water Balance and Respective Components

The terms of the soil water balance computed with the SIMDualKc model are presented in Table 5. There are appreciable differences among years which mainly steam from the differences in precipitation (P, mm). This is particularly the case of deep percolation and runoff (DP and RO, mm), which are much higher in the wet year of 2013, representing 47% of precipitation, which contrast with smaller DP and RO values in 2011 that represent 34% of the precipitation total. Those percent values indicate that most of precipitation, 53 to 66%, was used by the crop. As it could be expected, the larger amount of DP and RO occurred during the non-growing, initial, crop development and late season crop stages, that is, during the rainy season, which coincides with the period when crop water demand, evapotranspiration, is smaller as analysed relative to Figure 7 and Figure 9. However, the distribution of DP and RO throughout the year varied enormously as a consequence of the inter- and intra-annual variability of the precipitation, as it may be noticed in Figure 1. DP and RO were very small during the mid-season and their occurrence refers only to a few rainfall events. The use of small drip irrigation applied depths makes that DP and RO are not originated by irrigation; moreover, the deficit irrigation management adopted also, likely, did not influence DP or RO occurrences.
The soil water storage (ΔSW, mm) at end of each crop stage and of the year greatly varied inter and intra-annually, mostly due to the referred variability of precipitation and likely less influenced by irrigation management as indicated by similar ΔSW values at end of the late season. This fact agrees with the conclusion that the adopted deficit irrigation management did not contribute to operational water losses.
Because rainfall was very small during the periods of large crop water demand, irrigation represented 39%, 56% and 50% of ETc act respectively in 2011, 2012 and 2013. Overall, irrigation occurred during the mid- and late season, mainly the former. These results show the great contribution of irrigation to achieve successful olives growth and yields, particularly in case of super-intensive olives orchards. Naturally, most of evapotranspiration occurred during the mid-season and, however less important, during the late-season. Soil evaporation exceeded transpiration during the non-growing and initial crop stages because Es mostly originated from the inter-row when wetted by rainfall. The large values of Es estimated for the mid-season were mostly due to rainfall events that occurred early in that season; similarly, most of Es estimated for the late season also were due to rainfall events wetting the exposed inter-row. Es originated from irrigation was reduced because energy available under the canopy shadow was little.
The consumptive use of water is analysed in Table 6. The ratios of Es/ETc act show that Es represents 45 to 67% of ETc act during the non-growing stage and 40 to 70% during the initial crop stage. These periods are those when more precipitation occurs, so where the soil is frequently wetted by rain and soil cover is less, particularly in the inter-row. Thus, soil evaporation is the main consumptive water use during those stages. During the crop development, mid-season and late season stages, transpiration becomes the most important, corresponding to 52 to 67% of ETc act. The variability of the ratio Es/ETc act is then quite small, depending upon the occurrence of precipitation, which is the main source of soil wetting in the inter-row and of the subsequent evaporation. Es could likely be reduce if less frequent but larger irrigation amounts would be used instead of daily irrigation events averaging 3 mm per event. Es/ETc act ratios smaller than those computed in this study were reported by various researchers but referring to the irrigation season only [18,20,69]. Less frequent irrigations are also referred in these studies.
Results relative to the ratio ETc act/ETc (Table 6) show that ETc act was smaller by 3%, 18% and 14% relative to the potential ETc in 2011, 2012 and 2013, respectively. During the mid-season, that ratio ETc act/ETc was small for the years of 2012 and 2013, when ETc act was smaller by respectively 30% and 22% comparatively to the potential ETc. Those decreases are likely excessive as analysed before (Figure 9) and may have affected yields, namely that of 2012. Adopting eustress irrigation, the ratio ETc act/ETc should likely not decrease below 90%. However, this threshold needs to be better assessed in terms of yield and economic impacts.

4. Conclusions

The calibration, validation and testing of the SIMDualKc model for a super high density Arbequina olive orchard was successfully performed using measurements of sap flow plant transpiration and eddy covariance evapotranspiration. Three years of field studies, from 2011 to 2013, were used, which made it possible to partitioning olive orchard ET into actual transpiration and soil evaporation. The results showed a good accuracy of the model to simulate transpiration during both the growing and non-growing seasons. Goodness of fit indicators were less good for 2012 when the orchard suffered an intense defoliation following a great frost and, later, a heavy pruning. However, the model could well represent the dynamics of evapotranspiration, transpiration and soil evaporation throughout the three years.
The calibration of the model and its validation and testing provided for updated basal and single crop coefficient curves for potential and actual crop water use, as well as soil evaporation coefficients. The standard Kcb were close to 0.30 during the non-growing and initial crop stages and 0.48 during the mid-season. Differently, the standard Kc varied from 0.70 to 0.96 during the non-growing and initial crop stages and decreased to 0.66 to 0.78 during the mid-season. The variability of standard Kc values results from the fact that, contrarily to Kcb, the Kc values depend upon soil evaporation, consequently also upon precipitation. It may be concluded that for perennial crops, as olive orchards and other woody crops, it is important to focus on using the dual instead of single Kc, thus in using basal Kcb.
The A&P approach for estimating Kcb values from the fraction of ground cover and crop height was tested against the Kcb values obtained with the model SIMDualKc after calibration. The Kcb obtained with A&P matched well the Kcb values obtained from SIMDualKc. Thus, results allow assuming that the A&P approach is useful to accurately estimating the Kcb values required to assess crop water requirements and deciding irrigation management practices of olives orchards. Likely, that conclusion may be extended to other crops under condition of properly parameterizing Kcb full used for computing Kcb from fc and h.
For the three years, deep percolation and runoff totalized 34% to 47% of the precipitation, thus with most of precipitation being used as crop evapotranspiration. Those percent values highly depend upon the annual distribution of precipitation and were not depending upon irrigation because frequent and small application depths were used. Soil water storage also revealed to vary along the year with precipitation. Soil evaporation ranged from 42% to 45% of annual olives ET, with Es > Tc act during the rainy season, the contrary occurring during the irrigation period. This fact also relates with the above referred irrigation management practiced. Irrigation amounts for 39% to 56% of the actual crop ET, which confirms the great importance of irrigation for super intensive olive orchards. However, those amounts correspond to deficit irrigation, with ETc act smaller than potential ETc by 30% and 22% in 2012 and 2013, respectively. Those deficits are apparently excessive relative to a desirable eustress management that could lead to high yields and save water. Further research is required along these lines.

Author Contributions

T.A.P. and L.S.P. conceptualization, T.A.P., P.P. and L.S.P. methodology, P.P. calibration and validation of the model, T.A.P., P.P., J.S. and L.S.P. formal data analysis, J.S. and F.L.S. resources, T.A.P. and P.P. original draft preparation, L.S.P. review and editing, F.L.S. project supervision.

Funding

This study was supported by the projects PTDC/AGR-PRO/111717/2009 and EXPL/AGR-PRO/1559/2012 and the research unit LEAF (UID/AGR/04129/2013) funded by “Fundação para a Ciência e a Tecnologia,” Portugal. The second author also acknowledges the same institution for a Post-Doc research grant (SFRH/BPD/79767/2011).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Connor, D.J.; Gómez-del-Campo, M.; Rousseaux, M.C.; Searles, P.S. Structure, management and productivity of hedgerow olive orchards: A review. Sci. Hortic. 2014, 169, 71–93. [Google Scholar] [CrossRef]
  2. Trentacoste, E.R.; Puertas, C.M.; Sadras, V.O. Effect of irrigation and tree density on vegetative growth, oil yield and water use efficiency in young olive orchard under arid conditions in Mendoza, Argentina. Irrig. Sci. 2015, 33, 429–440. [Google Scholar] [CrossRef]
  3. Ahumada-Orellana, L.E.; Ortega-Farías, S.; Searles, P.S. Olive oil quality response to irrigation cut-off strategies in a super-high density orchard. Agric. Water Manag. 2018, 202, 81–88. [Google Scholar] [CrossRef]
  4. Allen, R.G.; Pereira, L.S.; Raes, D.; Smith, M. Crop Evapotranspiration Guidelines for Computing Crop Water Requirements; FAO: Rome, Italy, 1998; p. 300. [Google Scholar]
  5. Pereira, L.S.; Oweis, T.; Zairi, A. Irrigation management under water scarcity. Agric. Water Manag. 2002, 57, 175–206. [Google Scholar] [CrossRef]
  6. Palese, A.M.; Nuzzo, V.; Favati, F.; Pietrafesa, A.; Celano, G.; Xiloyannisa, C. Effects of water deficit on the vegetative response, yield and oil quality of olive trees (Olea europaea L., cv Coratina) grown under intensive cultivation. Sci. Hortic. 2010, 125, 222–229. [Google Scholar] [CrossRef]
  7. Rallo, G.; Provenzano, G. Modelling eco-physiological response of table olive trees (Olea europaea L.) to soil water deficit conditions. Agric. Water Manag. 2013, 120, 79–88. [Google Scholar] [CrossRef]
  8. Fernández, J.E.; Perez-Martin, A.; Torres-Ruiz, J.M.; Cuevas, M.V.; Rodriguez-Dominguez, C.M.; Elsayed-Farag, S.; Morales-Sillero, A.; García, J.M.; Hernandez-Santana, V.; Diaz-Espejo, A. A regulated deficit irrigation strategy for hedgerow olive orchards with high plant density. Plant Soil 2013, 372, 279–295. [Google Scholar] [CrossRef][Green Version]
  9. Gómez-del-Campo, M. Summer deficit-irrigation strategies in a hedgerow olive orchard cv. ‘Arbequina’: Effect on fruit characteristics and yield. Irrig. Sci. 2013, 31, 259–269. [Google Scholar] [CrossRef]
  10. Padilla-Díaz, C.M.; Rodriguez-Dominguez, C.M.; Hernandez-Santana, V.; Perez-Martin, A.; Fernández, J.E. Scheduling regulated deficit irrigation in a hedgerow olive orchard from leaf turgor pressure related measurements. Agric. Water Manag. 2016, 164, 28–37. [Google Scholar] [CrossRef][Green Version]
  11. Padilla-Díaz, C.M.; Rodriguez-Dominguez, C.M.; Hernandez-Santana, V.; Perez-Martin, A.; Fernandes, R.D.M.; Montero, A.; García, J.M.; Fernández, J.E. Water status, gas exchange and crop performance in a super high density olive orchard under deficit irrigation scheduled from leaf turgor measurements. Agric. Water Manag. 2018, 202, 241–252. [Google Scholar] [CrossRef][Green Version]
  12. Tanasijevic, L.; Todorovic, M.; Pizzigalli, C.; Lionello, P.; Pereira, L.S. Impacts of climate change on olive crop evapotranspiration and irrigation requirements in the Mediterranean region. Agric. Water Manag. 2014, 144, 54–68. [Google Scholar] [CrossRef]
  13. Haworth, M.; Marino, G.; Brunetti, C.; Killi, D.; De Carlo, A.; Centritto, M. The Impact of Heat Stress and Water Deficit on the Photosynthetic and Stomatal Physiology of Olive (Olea europaea L.)—A Case Study of the 2017 HeatWave. Plants 2018, 7, 76. [Google Scholar] [CrossRef] [PubMed]
  14. Allen, R.G.; Pereira, L.S.; Howell, T.A.; Jensen, M.E. Evapotranspiration information reporting: I. Factors governing measurement accuracy. Agric. Water Manag. 2011, 98, 899–920. [Google Scholar] [CrossRef]
  15. Egea, G.; Diaz-Espejo, A.; Fernández, J.E. Soil moisture dynamics in a hedgerow olive orchard under well-watered and deficit irrigation regimes: Assessment, prediction and scenario analysis. Agric. Water Manag. 2016, 164, 197–211. [Google Scholar] [CrossRef][Green Version]
  16. Rallo, G.; Provenzano, G.; Castellini, M.; Puig-Sirera, A. Application of EMI and FDR sensors to assess the fraction of transpirable soil water over an olive grove. Water 2018, 10, 168. [Google Scholar] [CrossRef]
  17. Ramos, A.F.; Santos, F.L. Water use, transpiration and crop coefficients for olives (cv. Cordovil), grown in orchards in Southern Portugal. Biosyst. Eng. 2009, 102, 321–333. [Google Scholar] [CrossRef]
  18. Cammalleri, C.; Rallo, G.; Agnese, C.; Ciraolo, G.; Minacapilli, M.; Provenzano, G. Combined use of eddy covariance and sap flow techniques for partition of ET fluxes and water stress assessment in an irrigated olive orchard. Agric. Water Manag. 2013, 120, 89–97. [Google Scholar] [CrossRef][Green Version]
  19. Paço, T.A.; Pocas, I.; Cunha, M.; Silvestre, J.C.; Santos, F.L.; Paredes, P.; Pereira, L.S. Evapotranspiration and crop coefficients for a super intensive olive orchard. An application of SIMDualKc and METRIC models using ground and satellite observations. J. Hydrol. 2014, 519, 2067–2080. [Google Scholar] [CrossRef][Green Version]
  20. Er-Raki, S.; Chehbouni, A.; Boulet, G.; Williams, D.G. Using the dual approach of FAO-56 for partitioning ET into soil and plant components for olive orchards in a semi-arid region. Agric. Water Manag. 2010, 97, 1769–1778. [Google Scholar] [CrossRef][Green Version]
  21. Sepulcre-Cantó, G.; Zarco-Tejada, P.J.; Jiménez-Muños, J.C.; Sobrino, J.A.; De Miguel, E.; Villalobos, F.J. Detection of water stress in olive orchard with thermal remote sensing imagery. Agric. For. Meteorol. 2006, 136, 31–44. [Google Scholar] [CrossRef]
  22. Santos, F.L. Assessing olive evapotranspiration partitioning from soil water balance and radiometric soil and canopy temperatures. Agronomy 2018, 8, 43. [Google Scholar] [CrossRef]
  23. Capraro, F.; Tosetti, S.; Rossomando, F.; Mut, V.; Serman, F.V. Web-Based System for the Remote Monitoring and Management of Precision Irrigation: A Case Study in an Arid Region of Argentina. Sensors 2018, 18, 3847. [Google Scholar] [CrossRef] [PubMed]
  24. Hoedjes, J.C.B.; Chehbouni, A.; Jacob, F.; Ezzahar, J.; Boulet, G. Deriving daily evapotranspiration from remotely sensed instantaneous evaporative fraction over olive orchard in semi-arid Morocco. J. Hydrol. 2008, 354, 53–64. [Google Scholar] [CrossRef][Green Version]
  25. Minacapilli, M.; Agnese, C.; Blanda, F.; Cammalleri, C.; Ciraolo, G.; D’Urso, G.; Iovino, M.; Pumo, D.; Provenzano, G.; Rallo, G. Estimation of actual evapotranspiration of Mediterranean perennial crops by means of remote-sensing based surface energy balance models. Hydrol. Earth Syst. Sci. 2009, 13, 1061–1074. [Google Scholar] [CrossRef][Green Version]
  26. Santos, C.; Lorite, I.J.; Allen, R.G.; Tasumi, M. Aerodynamic parameterization of the satellite-based energy balance (METRIC) model for ET estimation in rainfed olive orchards of Andalusia, Spain. Water Resour. Manag. 2012, 26, 3267–3283. [Google Scholar] [CrossRef]
  27. Pôças, I.; Paço, T.A.; Cunha, M.; Andrade, J.A.; Silvestre, J.; Sousa, A.; Santos, F.L.; Pereira, L.S.; Allen, R.G. Satellite based evapotranspiration of a super-intensive olive orchard: Application of METRIC algorithm. Biosyst. Eng. 2014, 128, 69–81. [Google Scholar] [CrossRef]
  28. Pôças, I.; Paço, T.A.; Paredes, P.; Cunha, M.; Pereira, L.S. Estimation of actual crop coefficients using remotely sensed vegetation indices and soil water balance modelled data. Remote Sens. 2015, 7, 2373–2400. [Google Scholar] [CrossRef]
  29. Ortega-Farías, S.; Ortega-Salazar, S.; Poblete, T.; Kilic, A.; Allen, R.G.; Poblete-Echeverría, C.; Ahumada-Orellana, L.; Zuñiga, M.; Sepúlveda, D. Estimation of energy balance components over a drip-irrigated olive orchard using thermal and multispectral cameras placed on a helicopter-based unmanned aerial vehicle (UAV). Remote Sens. 2016, 8, 638. [Google Scholar] [CrossRef]
  30. Abazi, U.; Lorite, I.J.; Cárceles, B.; Martínez Raya, A.; Durán, V.H.; Francia, J.R.; Gómez, J.A. WABOL: A conceptual water balance model for analyzing rainfall water use in olive orchards under different soil and cover crop management strategies. Comput. Electron. Agric. 2013, 91, 35–48. [Google Scholar] [CrossRef][Green Version]
  31. Autovino, D.; Rallo, G.; Provenzano, G. Predicting soil and plant water status dynamic in olive orchards under different irrigation systems with Hydrus-2D: Model performance and scenario analysis. Agric. Water Manag. 2018, 203, 225–235. [Google Scholar] [CrossRef][Green Version]
  32. Pereira, L.S.; Allen, R.G.; Smith, M.; Raes, D. Crop evapotranspiration estimation with FAO56: Past and future. Agric. Water Manag. 2015, 147, 4–20. [Google Scholar] [CrossRef]
  33. Allen, R.G.; Pereira, L.S.; Smith, M.; Raes, D.; Wright, J. FAO-56 Dual Crop Coefficient Method for Estimating Evaporation from Soil and Application Extensions. J. Irrig. Drain. Eng. 2005, 131, 2–13. [Google Scholar] [CrossRef][Green Version]
  34. Rosa, R.D.; Paredes, P.; Rodrigues, G.C.; Alves, I.; Fernando, R.M.; Pereira, L.S.; Allen, R.G. Implementing the dual crop coefficient approach in interactive software. 1. Background and computational strategy. Agric. Water Manag. 2012, 103, 8–24. [Google Scholar] [CrossRef]
  35. Fandiño, M.; Cancela, J.J.; Rey, B.J.; Martínez, E.M.; Rosa, R.G.; Pereira, L.S. Using the dual-Kc approach to model evapotranspiration of albariño vineyards (Vitis vinifera L. cv. albariño) with consideration of active ground cover. Agric. Water Manag. 2012, 112, 75–87. [Google Scholar] [CrossRef]
  36. Paredes, P.; Rodrigues, G.C.; Alves, I.; Pereira, L.S. Partitioning evapotranspiration, yield prediction and economic returns of maize under various irrigation management strategies. Agric. Water Manag. 2014, 135, 27–39. [Google Scholar] [CrossRef]
  37. Pereira, L.S.; Paredes, P.; Rodrigues, G.C.; Neves, M. Modeling malt barley water use and evapotranspiration partitioning in two contrasting rainfall years. Assessing AquaCrop and SIMDualKc models. Agric. Water Manag. 2015, 159, 239–254, Corrigendum in 2015, 163, 408. [Google Scholar] [CrossRef]
  38. Paço, T.A.; Ferreira, M.I.; Rosa, R.G.; Paredes, P.; Rodrigues, G.; Conceição, N.; Pacheco, C.; Pereira, L.S. The dual crop coefficient approach using a density factor to simulate the evapotranspiration of a peach orchard: SIMDualKc model versus eddy covariance measurements. Irrig. Sci. 2012, 30, 115–126. [Google Scholar] [CrossRef]
  39. Ran, H.; Kang, S.; Li, F.; Tong, L.; Ding, R.; Du, T.; Li, S.; Zhang, X. Performance of AquaCrop and SIMDualKc models in evapotranspiration partitioning on full and deficit irrigated maize for seed production under plastic film-mulch in an arid region of China. Agric. Syst. 2017, 151, 20–32. [Google Scholar] [CrossRef]
  40. Zhao, N.; Liu, Y.; Cai, J.; Paredes, P.; Rosa, R.D.; Pereira, L.S. Dual crop coefficient modelling applied to the winter wheat-summer maize crop sequence in North China Plain: Basal crop coefficients and soil evaporation component. Agric. Water Manag. 2013, 117, 93–105. [Google Scholar] [CrossRef]
  41. Gao, Y.; Yang, L.; Shen, X.; Li, X.; Sun, J.; Duan, A.; Wu, L. Winter wheat with subsurface drip irrigation (SDI): Crop coefficients, water-use estimates and effects of SDI on grain yield and water use efficiency. Agric. Water Manag. 2014, 146, 1–10. [Google Scholar] [CrossRef]
  42. Wei, Z.; Paredes, P.; Liu, Y.; Chi, W.-W.; Pereira, L.S. Modelling transpiration, soil evaporation and yield prediction of soybean in North China Plain. Agric. Water Manag. 2015, 147, 43–53. [Google Scholar] [CrossRef]
  43. Allen, R.G.; Pereira, L.S. Estimating crop coefficients from fraction of ground cover and height. Irrig. Sci. 2009, 28, 17–34. [Google Scholar] [CrossRef]
  44. Kottek, M.; Grieser, J.; Beck, C.; Rudolf, B.; Rubel, F. World Map of the Köppen-Geiger climate classification updated. Meteorol. Z. 2006, 15, 259–263. [Google Scholar] [CrossRef]
  45. IUSS; Working Group WRB. World Reference Base for Soil Resources 2014, Update 2015. In International Soil Classification System for Naming Soils and Creating Legends for Soil Maps; World Soil Resources Reports No. 106; FAO: Rome, Italy, 2015; 192p. [Google Scholar]
  46. Fandiño, M.; Olmedo, J.L.; Martínez, E.M.; Valladares, J.; Paredes, P.; Rey, B.J.; Mota, M.; Cancela, J.J.; Pereira, L.S. Assessing and modelling water use and the partition of evapotranspiration of irrigated hop (Humulus lupulus) and relations of transpiration with hops yield and alpha-acids. Ind. Crop Prod. 2015, 77, 204–217. [Google Scholar] [CrossRef]
  47. Ferreira, M.I.; Paço, T.A.; Silvestre, J. Combining techniques to study evapotranspiration in woody crops: Application to small areas—Two case studies. Acta Hortic. ISHS 2004, 664, 225–232. [Google Scholar] [CrossRef]
  48. Mauder, M.; Foken, T. Documentation and Instruction Manual of the Eddy-Covariance Software Package TK3 (Update). Arbeitsergebnisse Nr. 62; 2015 Univ Bayreuth, Abt Mikrometeorol. Available online: https://epub.uni-bayreuth.de/2130/1/ARBERG062.pdf (accessed on 5 July 2017).
  49. Foken, T.; Leuning, R.; Oncley, S.P.; Mauder, M.; Aubinet, M. Corrections and data quality. In Eddy Covariance: A Practical Guide to Measurement and Data Analysis; Aubinet, M., Vesala, T., Papale, D., Eds.; Springer: Berlin/Heidelberg, Germany, 2011. [Google Scholar]
  50. Vickers, D.; Mahrt, L. Quality control and flux sampling problems for tower and aircraft data. J. Atmos. Ocean. Technol. 1997, 14, 512–526. [Google Scholar] [CrossRef]
  51. Kaimal, J.C.; Finnigan, J.J. Atmospheric Boundary Layer Flows: Their Structure and Measurement; Oxford University Press: New York, NY, USA, 1994. [Google Scholar]
  52. Tanner, B.D.; Swiatek, E.; Greene, J.P. Density fluctuations and use of the krypton hygrometer in surface flux measurements. In Management of Irrigation and Drainage Systems: Integrated Perspectives; Allen, R.G., Ed.; American Society of Civil Engineers: New York, NY, USA, 1993; pp. 945–952. [Google Scholar]
  53. Moore, C.J. Frequency-response corrections for eddy-correlation systems. Bound.-Layer Meteorol. 1986, 37, 17–35. [Google Scholar] [CrossRef]
  54. Schotanus, P.; Nieuwstadt, F.T.M.; Debruin, H.A.R. Temperature-measurement with a sonic anemometer and its application to heat and moisture fluxes. Bound.-Layer Meteorol. 1983, 26, 81–93. [Google Scholar] [CrossRef]
  55. Webb, E.K.; Pearman, G.I.; Leuning, R. Correction of flux measurements for density effects due to heat and water-vapor transfer. Q. J. R. Meteorol. Soc. 1980, 106, 85–100. [Google Scholar] [CrossRef]
  56. Schuepp, P.H.; Leclerc, M.Y.; Macpherson, J.I.; Desjardins, R.L. Footprint prediction of scalar fluxes from analytical solutions of the diffusion equation. Bound.-Layer Meteorol. 1990, 50, 355–373. [Google Scholar] [CrossRef]
  57. Testi, L.; Villalobos, F.J.; Orgaz, F. Evapotranspiration of a young irrigated olive orchard in southern Spain. Agric. For. Meteorol. 2004, 121, 1–18. [Google Scholar] [CrossRef]
  58. Teixeira, A.H.D.C.; Bastiaanssen, W.G.M.; Moura, M.S.B.; Soares, J.M.; Ahmad, M.D.; Bos, M.G. Energy and water balance measurements for water productivity analysis in irrigated mango trees, Northeast Brazil. Agric. For. Meteorol. 2008, 148, 1524–1537. [Google Scholar] [CrossRef]
  59. Conceição, N.; Tezza, L.; Häusler, M.; Lourenço, S.; Pacheco, C.A.; Ferreira, M.I. Three years of monitoring evapotranspiration components and crop and stress coefficients in a deficit irrigated intensive olive orchard. Agric. Water Manag. 2017, 191, 138–152. [Google Scholar] [CrossRef]
  60. Granier, A. Une nouvelle méthode pour la mesure du flux de sève brute dans le tronc des arbres. Ann. Sci. For. 1985, 42, 193–200. [Google Scholar] [CrossRef][Green Version]
  61. Silva, R.M.; Paço, T.A.; Ferreira, M.I.; Oliveira, M. Transpiration of a kiwifruit orchard estimated using the Granier sap flow method calibrated under field conditions. Acta Hortic. ISHS 2008, 792, 593–600. [Google Scholar] [CrossRef]
  62. Steppe, K.; De Pauw, D.J.W.; Doody, T.M.; Teskey, R.O. A comparison of sap flux density using thermal dissipation, heat pulse velocity and heat field deformation methods. Agric. For. Meteorol. 2010, 150, 1046–1056. [Google Scholar] [CrossRef]
  63. Bush, S.E.; Hultine, K.R.; Sperry, J.S.; Ehleringer, J.R. Calibration of thermal dissipation sap flow probes for ring- and diffuse-porous trees. Tree Physiol. 2010, 30, 1545–1554. [Google Scholar] [CrossRef][Green Version]
  64. Hölttä, T.; Linkosalo, T.; Riikonen, A.; Sevanto, S.; Nikinmaa, E. An analysis of Granier sap flow method, its sensitivity to heat storage and a new approach to improve its time dynamics. Agric. For. Meteorol. 2015, 211–212, 2–12. [Google Scholar] [CrossRef]
  65. Marañón-Jiménez, S.; Van den Bulcke, J.; Piayda, A.; Van Acker, J.; Cuntz, M.; Rebmann, C.; Steppe, K. X-ray computed microtomography characterizes the wound effect that causes sap flow underestimation by thermal dissipation sensors. Tree Physiol. 2017, 38, 288–302. [Google Scholar] [CrossRef]
  66. Ritchie, J.T. Model for predicting evaporation from a row crop with incomplete cover. Water Resour. Res. 1972, 8, 1204–1213. [Google Scholar] [CrossRef]
  67. Paço, T.A.; Conceição, N.; Ferreira, M.I. Measurements and estimates of peach orchard evapotranspiration in Mediterranean conditions. Acta Hortic. 2004, 664, 505–512. [Google Scholar] [CrossRef]
  68. Paço, T.A.; Conceição, N.; Ferreira, M.I. Peach orchard evapotranspiration in a sandy soil: Comparison between eddy covariance measurements and estimates by the FAO 56 approach. Agric. Water Manag. 2006, 85, 305–313. [Google Scholar] [CrossRef]
  69. López-Olivari, R.; Ortega-Farías, S.; Poblete-Echeverría, C. Partitioning of net radiation and evapotranspiration over a superintensive drip-irrigated olive orchard. Irrig. Sci. 2016, 34, 17–31. [Google Scholar] [CrossRef]
  70. Tukey, J.W. Exploratory Data Analysis; Addison-Wesley: Reading, MA, USA, 1977. [Google Scholar]
  71. Liu, Y.; Pereira, L.S.; Fernando, R.M. Fluxes through the bottom boundary of the root zone in silty soils: Parametric approaches to estimate groundwater contribution and percolation. Agric. Water Manag. 2006, 84, 27–40. [Google Scholar] [CrossRef]
  72. Allen, R.G.; Wright, J.L.; Pruitt, W.O.; Pereira, L.S.; Jensen, M.E. Water requirements. In Design and Operation of Farm Irrigation Systems, 2nd ed.; Hoffman, G.J., Evans, R.G., Jensen, M.E., Martin, D.L., Elliot, R.L., Eds.; ASABE: St. Joseph, MI, USA, 2007; pp. 208–288. [Google Scholar]
  73. Zhang, B.; Liu, Y.; Xu, D.; Zhao, N.; Lei, B.; Rosa, R.D.; Paredes, P.; Paco, T.A.; Pereira, L.S. The dual crop coefficient approach to estimate and partitioning evapotranspiration of the winter wheat-summer maize crop sequence in North China Plain. Irrig. Sci. 2013, 31, 1303–1316. [Google Scholar] [CrossRef]
  74. Tian, F.; Yang, P.; Hu, H.; Dai, C. Partitioning of cotton field evapotranspiration under mulched drip irrigation based on a dual crop coefficient model. Water 2016, 8, 72. [Google Scholar] [CrossRef]
  75. Rosa, R.D.; Paredes, P.; Rodrigues, G.C.; Fernando, R.M.; Alves, I.; Pereira, L.S.; Allen, R.G. Implementing the dual crop coefficient approach in interactive software: 2. Model testing. Agric. Water Manag. 2012, 103, 62–77. [Google Scholar] [CrossRef]
  76. Zhang, H.; Huang, G.; Xu, X.; Xiong, Y.; Huang, Q. Estimating evapotranspiration of processing tomato under plastic mulch using the SIMDualKc model. Water 2018, 10, 1088. [Google Scholar] [CrossRef]
  77. Nash, J.E.; Sutcliffe, J.V. River flow forecasting through conceptual models: Part 1. A discussion of principles. J. Hydrol. 1970, 10, 282–290. [Google Scholar] [CrossRef]
  78. Moriasi, D.N.; Arnold, J.G.; Van Liew, M.W.; Bingner, R.L.; Harmel, R.D.; Veith, T.L. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE 2007, 50, 885–900. [Google Scholar] [CrossRef]
  79. Rallo, G.; Baiamonte, G.; Manzano Juárez, J.; Provenzano, G. Improvement of FAO-56 model to estimate transpiration fluxes of drought tolerant crops under soil water deficit: Application for olive groves. J. Irrig. Drain. Eng. 2014, 140, A4014001. [Google Scholar] [CrossRef]
  80. Testi, L.; Orgaz, F.; Villalobos, F.J. Variations in bulk canopy conductance of an irrigated olive (Olea europaea L.) orchard. Environ. Exp. Bot. 2006, 55, 15–28. [Google Scholar] [CrossRef]
  81. Martínez-Cob, A.; Faci, J.M. Evapotranspiration of an hedge-pruned olive orchard in a semiarid area of NE Spain. Agric. Water Manag. 2010, 97, 410–418. [Google Scholar] [CrossRef][Green Version]
  82. Fernández, J.E.; Moreno, F. Water Use by the Olive Tree. J. Crop Prod. 2000, 2, 101–162. [Google Scholar] [CrossRef][Green Version]
Figure 1. Daily weather data of Viana do Alentejo relative to (a) maximum (Tmax) and minimum (Tmin) temperatures, (b) minimum relative humidity (RHmin), wind speed at 2 m height (u2) and (c) precipitation and reference evapotranspiration (ETo) for the period 2011–2013.
Figure 1. Daily weather data of Viana do Alentejo relative to (a) maximum (Tmax) and minimum (Tmin) temperatures, (b) minimum relative humidity (RHmin), wind speed at 2 m height (u2) and (c) precipitation and reference evapotranspiration (ETo) for the period 2011–2013.
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Figure 2. Olive orchard and location of the eddy covariance tower (black triangle, 38°24′46.3″N, 7°43′39.8″W).
Figure 2. Olive orchard and location of the eddy covariance tower (black triangle, 38°24′46.3″N, 7°43′39.8″W).
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Figure 3. Cumulative normalized flux (CNF, dashed line) and relative contribution to the vertical flux for a given height z, (Qf, continuous line) according to distance from the measurement point (EC tower) to the plot limit (x), obtained with footprint analysis; marked values for the fetch and CNF according to the cardinal directions; x is the distance between the observation point and the fluxes source region.
Figure 3. Cumulative normalized flux (CNF, dashed line) and relative contribution to the vertical flux for a given height z, (Qf, continuous line) according to distance from the measurement point (EC tower) to the plot limit (x), obtained with footprint analysis; marked values for the fetch and CNF according to the cardinal directions; x is the distance between the observation point and the fluxes source region.
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DirectionFetch (m)CNF
East5040.96
West3530.94
North4700.95
South4550.95
Figure 4. Wind prevailing directions during periods of eddy covariance measurements and resultant vector, (a) July–August 2011, (b) June–August 2012 (deg.: degrees).
Figure 4. Wind prevailing directions during periods of eddy covariance measurements and resultant vector, (a) July–August 2011, (b) June–August 2012 (deg.: degrees).
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Figure 5. Surface energy balance equation for flux density daily values of LE (latent heat flux density), H (sensible heat flux density), G (soil heat flux density) and Rn (net radiation).
Figure 5. Surface energy balance equation for flux density daily values of LE (latent heat flux density), H (sensible heat flux density), G (soil heat flux density) and Rn (net radiation).
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Figure 6. Relationships between the raw transpiration flux observed with the Granier sap-flow method (SF) and the transpiration rate computed from the eddy covariance observations (TEC) relative to 2011 (squares) and 2012 (circles).
Figure 6. Relationships between the raw transpiration flux observed with the Granier sap-flow method (SF) and the transpiration rate computed from the eddy covariance observations (TEC) relative to 2011 (squares) and 2012 (circles).
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Figure 7. Simulated transpiration dynamics (Tc act, ) compared with sap flow adjusted transpiration (TSF, •) in the (a) calibration (2011), (b) validation (2012) and (c) testing (2013).
Figure 7. Simulated transpiration dynamics (Tc act, ) compared with sap flow adjusted transpiration (TSF, •) in the (a) calibration (2011), (b) validation (2012) and (c) testing (2013).
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Figure 8. Actual crop evapotranspiration dynamics measured with Eddy covariance (ETEC, Δ) and simulated with SIMDualKc (ETc act, ) for (a) 2011 and (b) 2012.
Figure 8. Actual crop evapotranspiration dynamics measured with Eddy covariance (ETEC, Δ) and simulated with SIMDualKc (ETc act, ) for (a) 2011 and (b) 2012.
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Figure 9. Standard and actual basal crop coefficients (Kcb, Kcb act), evaporation coefficient (Ke) and standard and actual single crop coefficients (Kc, Kc act) for a super-intensive olive orchard for: (a) 2011, (b) 2012 and (c) 2013, with depicting precipitation and irrigation events. Kcb () Kcb act (- - -) Ke () Kc mean () Kc (―) Kc act (…) precipitation (׀), irrigation (׀).
Figure 9. Standard and actual basal crop coefficients (Kcb, Kcb act), evaporation coefficient (Ke) and standard and actual single crop coefficients (Kc, Kc act) for a super-intensive olive orchard for: (a) 2011, (b) 2012 and (c) 2013, with depicting precipitation and irrigation events. Kcb () Kcb act (- - -) Ke () Kc mean () Kc (―) Kc act (…) precipitation (׀), irrigation (׀).
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Figure 10. (a) Fraction of ground cover (fc, x) measured and/or derived from SAVI and olives height (h, Δ) observed and (b) Kcb estimates using the A&P approach (○) compared with the Kcb curve computed with the SIMDualKc model (―) for the period 2011 to 2013.
Figure 10. (a) Fraction of ground cover (fc, x) measured and/or derived from SAVI and olives height (h, Δ) observed and (b) Kcb estimates using the A&P approach (○) compared with the Kcb curve computed with the SIMDualKc model (―) for the period 2011 to 2013.
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Figure 11. Linear regression forced to the origin of the Kcb values estimated with the A&P approach compared with the Kcb values obtained from the SIMDualKc model; also depicted the 1:1 line (- - -).
Figure 11. Linear regression forced to the origin of the Kcb values estimated with the A&P approach compared with the Kcb values obtained from the SIMDualKc model; also depicted the 1:1 line (- - -).
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Table 1. Crop growth stages of the olive orchard under study.
Table 1. Crop growth stages of the olive orchard under study.
YearCrop Growth States
Non-GrowingInitiationCrop DevelopmentMid-SeasonLate-SeasonNon-Growing
20111/1–28/21/3–15/316/3–30/41/5–13/914/9–11/1112/11–31/12
20121/1–20/321/3–11/412/4–14/515/5–10/911/9–23/1124/11–31/12
20131/1–2/33/3–17/318/3–20/421/4–19/920/9–15/1116/11–31/12
Table 2. Initial and calibrated parameters used in SIMDualKc model.
Table 2. Initial and calibrated parameters used in SIMDualKc model.
Parameters InitialCalibrated
CropKcb non-growing0.500.30
Kcb ini0.500.30
Kcb mid0.550.48
Kcb end0.500.43
pnon-growing 0.650.65
pini0.650.65
pmid 0.650.60
pend0.650.65
Soil evaporationTEW1818
REW99
Ze0.100.10
RunoffCN7275
Deep percolationaD246246
bD−0.02−0.02
Kcb non-growing—basal crop coefficient for the non-growing stage; Kcb ini—basal crop coefficient for the initial crop development stage, Kcb mid—basal crop coefficient for the mid stage, Kcb end—basal crop coefficient for the late-season stage, p—depletion fraction, ML parameter, TEW—total evaporable water, REW—readily evaporable water, Ze—thickness of the evaporation layer, CN—curve number, aD and bD—deep percolation parameters.
Table 3. Goodness-of-fit indicators relative to the SIMDualKc simulated transpiration (Tc act) when compared with transpiration obtained from sap flow (TSF).
Table 3. Goodness-of-fit indicators relative to the SIMDualKc simulated transpiration (Tc act) when compared with transpiration obtained from sap flow (TSF).
nb0R2PBIAS (%)RMSE (mm·d−1)AAE (mm·d−1)EF
Calibration, 20112011.040.752.00.530.420.57
Validation, 20123381.040.603.00.470.360.25
Testing, 20131111.170.7813.30.370.250.35
n = number of observations, b0 = regression coefficient, R2 = determination coefficient, PBIAS = percent bias; RMSE = root mean square error, ARE = average relative error, AAE = average absolute error, EF = modelling efficiency.
Table 4. Time-averaged crop coefficients (Kc) for the different crop growth stages.
Table 4. Time-averaged crop coefficients (Kc) for the different crop growth stages.
Crop Growth Stages201120122013
Non-growing *0.910.770.75
Initial0.960.700.96
Crop development0.96–0.780.70–0.660.96–0.68
Mid-season0.780.660.68
Late season0.78–0.790.66–0.900.68–0.83
End season0.790.900.83
* average value relative to both non-growing periods, by the end and the beginning of the year.
Table 5. Simulated soil water balance components (all variables in mm).
Table 5. Simulated soil water balance components (all variables in mm).
Crop Growth StagesYearPIΔSWRODPEsTc actETc act
Non-growing 20111890−2011547034104
201276035030374582
20132240−2012856343106
Initial2011370−160311718
2012365800203050
20131060−15554231033
Development2011950151811275481
20123341400213051
20131590403189384179
Mid-season201120326063128186311497
201213299−400117191308
2013293585400173269442
Late season201114159−771024863111
201235342−89701026074134
201321834−5827455562117
Full year2011665319−354098342469811
2012511350−3670132255370625
2013736392175273352425777
P = precipitation, I = net irrigation depths, ΔSW = variation in stored soil water, RO = runoff, DP = deep percolation, Es = soil evaporation, Tc act = actual crop transpiration, ETc act = actual crop evapotranspiration.
Table 6. Ratios of soil evaporation to actual evapotranspiration (Es/ETc act, %) and of actual to potential evapotranspiration (ETc act/ETc %).
Table 6. Ratios of soil evaporation to actual evapotranspiration (Es/ETc act, %) and of actual to potential evapotranspiration (ETc act/ETc %).
YearsNon-Growing PeriodsInitialDevelopmentMid-SeasonLate-SeasonWhole Year
Es/ETc act (%)2011676133374342
2012454041384541
2013597048394745
ETc act/ETc (%)2011100100100948897
201210010098709982
20131001001007810086

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