Assessment of upscaling methodologies for daily crop transpiration using sap flows and two-source energy balance models in almonds under different water statuses and production systems

Hydrol. Earth Syst. Sci., 28, 4797–4818, 2024 https://doi.org/10.5194/hess-28-4797-2024 © Author(s) 2024. This work is distributed under the Creative Commons Attribution 4.0 License. Assessment of upscaling methodologies for daily crop transpiration using sap flows and two-source energy balance models in almonds under different water statuses and production systems Manuel Quintanilla-Albornoz1, Xavier Miarnau2, Ana Pelechá1, Héctor Nieto3, and Joaquim Bellvert1 1Efficient Use of Water in Agriculture Program, Institute of Agrifood Research and Technology, Fruitcentre, Parc AgroBiotech, Lleida, 25003, Spain 2Fruit Production Program, Institute of Agrifood Research and Technology, Fruitcentre, Parc AgroBiotech, Lleida, 25003, Spain 3Institute of Agricultural Sciences, ICA-CSIC, Madrid, 28006, Spain Correspondence: Manuel Quintanilla-Albornoz ([email protected]) Received: 9 January 2024 – Discussion started: 13 March 2024 Revised: 21 August 2024 – Accepted: 30 August 2024 – Published: 30 October 2024 Abstract. Daily transpiration (Td) is crucial for both irrigation water management and increasing crop water productivity. The use of the remote-sensing-based two-source energy balance model (TSEB) has proven to be robust in estimating plant transpiration and evaporation separately for various crops. However, remote sensing models provide instantaneous estimations, and so daily upscaling approaches are needed to estimate daily fluxes. Daily upscaling methodologies have not yet been examined to upscale solely transpiration in woody crops. In this regard, this study aims to evaluate the proper image acquisition time throughout the day and four methodologies used to retrieve Tdin almond trees with different production systems and water statuses. Hourly transpiration (Th) was estimated using the TSEB contextual approach (Th–TSEB) with high-resolution imagery five times during two diurnal courses. The tested methodologies were the following: the simulated evaporative fraction variable (EFsim), irradiance (Rs), reference evapotranspiration (ETo), and potential evapotranspiration (ETp). These approaches were first evaluated with in situ sap flow (T– SF) data and were then applied to the Th–TSEB. Daily T– SF showed significant differences among production systems and levels of water stress. The EFsim and ETp methods correlated better with measured T–SF and reduced the underestimation observed using the Rsand ETo methods, especially at noon in the severely water-stressed trees. However, the daily upscaling approaches applied in the TSEB (Td–TSEB) failed to detect differences between production systems. The lack of sensibility of Th–TSEB among production systems poses a challenge when estimating Tdin canopies with discontinuous architectural structures. The use of ETp as a reference variable could address this issue as it incorporates various aerodynamic and radiative properties associated with different canopy architectures that influence the daily Th–SF pattern. However, more accurate ETp estimates or more advanced ETp models are needed. 1 Introduction Almond is one of the high-value crops with the greatest water usage (Goldhamer and Fereres, 2017; López-López et al., 2018). In Spain, a paradigm change is taking place with the introduction of new intensified almond production systems with more planar designs (Iglesias and Echeverria, 2022), which may complicate the accurate estimation of evapotranspiration (ET) using remote sensing models. Thus, since the expansion of almond production is occurring in a context of increasing water scarcity, many studies have focused on quantifying its water usage in different environments and under different water regimes. From a water management point of view, there is particular interest in validating the daily ET and its components, plant transpiration (Tp) and evaporation (E), in this crop and under different production systems and Published by Copernicus Publications on behalf of the European Geosciences Union. 4798 M. Quintanilla-Albornoz et al.: Assessment of upscaling methodologies water statuses. This is relevant because almond is considered to be a drought-tolerant species able to control water loss through stomatal closure, which has been identified as a common and early event in plant responses to water deficits (Castel and Fereres, 1982; Escalona et al., 1999, Chaves et al., 2002). Romero and Botía (2006) also showed that the influence of the evaporative demand of the atmosphere on stomatal behavior was higher in well-watered almonds compared to in water-stressed almonds. The same study also demonstrated that water-stressed almonds restricted stomatal activity earlier in the morning when the atmospheric vapor pressure deficit (VPD) was still low. As a result, maximum Tpvalues occurred during this period and were significantly higher than those observed in well-watered almonds. Accurate in-field quantification of crop ET and the partitioning components Efrom soil and Tpfrom vegetation is very useful for both irrigation water management and increasing crop water productivity (Zhang et al., 2021). Consequently, several methodologies have been developed to address this objective (Evett and Tolk, 2009). Of these, remote sensing thermal-based surface energy balance models have shown their utility in retrieving ET in a wide range of environments and ecosystems (Shuttleworth and Wallace, 1985; Bastiaanssen et al., 1998; Drexler et al., 2004; Overgaard et al., 2006; Allen et al., 2007; Timmermans et al., 2007; Kalma et al., 2008; Kustas and Anderson, 2009). The advantage of using remote sensing lies in the possibility of monitoring heterogeneous surfaces over a wide range of spatial resolutions and thereby generating operational ET products (Kalma et al., 2008). One such model that calculates Tpand Eexplicitly is the two-source energy balance (TSEB), which was initially developed by Norman et al. (1995) and Kustas and Norman (1999). The separate Tpand Eoutputs provide the advantage of simultaneously evaluating canopy stress and directly quantifying plant water consumption. This information can be valuable for enhancing water use efficiency in agricultural and environmental management. Moreover, Tpis also linked to plant productivity as the exchange of both water and carbon between the atmosphere and the plant is conveyed via the leaf stoma. The TSEB approach has demonstrated its robustness in accurately estimating plant ET across diverse surface conditions and a wide range of landscapes (Kustas and Anderson, 2009; Kustas et al., 2019; Gómez-Candón et al., 2021; Gao et al., 2023; Knipper et al., 2023). To estimate Tp, the use of very-high-resolution thermal and multispectral imagery allows for the direct estimation of canopy and soil temperatures, facilitating the retrieval of ET partitioning through the use of, for example, the TSEB contextual approach (TSEB-2T) model (Nieto et al., 2019; Nassar et al., 2020; Gao et al., 2023; Quintanilla-Albornoz et al., 2023). Models for estimating ET fluxes based on remote sensing, however, can only be used to derive an instantaneous ET at the time of clear-sky satellite or aircraft overpass. Thus, the selection of a proper overpass time and the development of upscaling algorithms to extrapolate ET from instantaneous to daily scale are of special interest for the management of crop water consumption. Current thermal-infrared (TIR) polar-orbiting satellites, such as Landsat, Sentinel3, or the moderate-resolution imaging spectroradiometer (MODIS) on board Terra, have an overpass time close to 10:00 UTC (mean locator solar time). However, several studies suggest that the best accuracies in ET retrievals would be captured better in the early afternoon (Delogu et al., 2012; Anderson et al., 2021). Bellvert et al. (2014) also showed that early afternoon was the most appropriate moment to detect maximum differences in canopy temperature between wellwatered and water-stressed crops. For this reason, in coming years, new TIR satellite missions including TRISHNA (Thermal infraRed Imaging Satellite for High Resolution Natural resource Assessment) (Lagouarde et al., 2018), SBG (Surface Biology and Geology) (Basilio et al., 2022), or LSTM (Land Surface Temperature Monitoring) (Koetz et al., 2018) are planned at an overpass time around 13:00 UTC (GMT time). Daily upscaling of ET fluxes is commonly performed by assuming a constant relationship over the course of the day between instantaneous ET and a reference meteorological forcing that can be computed at hourly and daily time steps (Crago and Brutsaert, 1996; Van Niel et al., 2011; Cammalleri et al., 2014). This hypothesis is generally known as self-preservation (Crago and Brutsaert, 1996). Generally, the most commonly used methods for upscaling ET are the evaporative fraction (EF) method, the solar radiation (Rs) method, the stress factor method, and the canopy resistance method (Hoedjes et al., 2008; Delogu et al., 2012; Cammalleri et al., 2014; Jiang et al., 2021; Nassar et al., 2021). The EF method assumes that the ratio of latent heat flux (LE) to surface available energy (AE) remains relatively stable throughout the day under clear-sky conditions. However, studies have shown that the EF method can lead to systematic underestimation for estimating daily evapotranspiration fluxes under wet conditions, as the ratio between LE and AE tends to be stable around midday but significantly higher during the early morning and late afternoon (Shuttleworth et al., 1989; Brutsaert, 1992; Crago and Brutsaert, 1996; Lhomme and Elguero, 1999; Gentine et al., 2007). To address this challenge, Hoedjes et al. (2008) introduced a parameterization of the diurnal EF pattern based on the primary atmospheric forcing parameters: Rsand relative humidity (RH). Implementing this approach, known as EFsim, Delogu et al. (2012) successfully reduced the overestimation associated with the EF method from 15.8 % to 6.5%. Additionally, while estimating the instantaneous AE at a specific time can be relatively straightforward using thermal imagery and meteorological data, determining daily AE needs daily course measurements or estimates of net radiation (Rn) and soil heat flux (G), which can be challenging. Given that the diurnal pattern of AE is primarily influenced by Rs, it has become a common practice to use Rsas a reference variable for the estimation of daily ET fluxes from inHydrol. Earth Syst. Sci., 28, 4797–4818, 2024 https://doi.org/10.5194/hess-28-4797-2024 M. Quintanilla-Albornoz et al.: Assessment of upscaling methodologies 4799 stantaneous measurements (Jackson et al., 1983; Zhang and Lemeur, 1995). The use of Rsin the context of remote sensing applications has fewer requirements than the EF method, with the latter needing auxiliary information, such as Rn, that can be complex to measure and may further limit operational utility. When using the Rsupscaling method, both Cammalleri et al. (2014) and Nassar et al. (2021) improved daily ET compared to when using EF methods. Another upscaling method that has been proposed is the stress factor method. This approach employs the reference evapotranspiration (ETo) or potential evapotranspiration (ETp) as a reference variable, which inherently accounts for the key meteorological factors influencing the evaporative process (Trezza, 2002; Delogu et al., 2012). Trezza (2002) found a constant ratio between ET and ETo during the daytime and employed it to estimate daily ET using remote sensing estimations, achieving better results compared to EF upscaling methods. However, Cammalleri et al. (2014) obtained similar results when using both the EF method and the ETo to estimate daily ET in sites without stress conditions. For their part, Delogu et al. (2012) evaluated the use of ETp as a reference variable and obtained worse results compared to the EF method for a dataset with stress events. This was attributed to the fact that the AE followed both stressed and unstressed ET patterns, whereas ETp often increased independently of the water stress levels. Nevertheless, the aforementioned upscaling methods reported in the literature for agricultural ecosystems have only been validated against daily ET, usually over sites with eddy covariance flux towers, with a footprint with mixed information on the spatial variability (Cammalleri et al., 2014; Xu et al., 2018; Jiang et al., 2021). Therefore, to the best of our knowledge, the use of upscaling methodologies to estimate daily Tp(Td) based on instantaneous Tpvalues has not been previously examined. Furthermore, the diurnal pattern of Tphas a different response between well-watered and water-stressed crops (Poni et al., 2009; Tuzet et al., 2003), and this different response would also depend on the stomatal control of each species in relation to soil water and vapor pressure deficits. Thus, the hypothesis of this study is that upscaling methods may have different responses for waterstressed and well-watered almond trees (Sánchez et al., 2021; Jofreˇ Cekalovi´ c et al., 2022; Iglesias and Echeverria, 2022; Peddinti and Kisekka, 2022; Knipper et al., 2023). Therefore, the main purpose of this study is to evaluate different Tdupscaling methodologies in almond trees under different production systems and water regimes using sap flow measurements. This study aims to contribute to our understanding and to establish a reference for upscaling remote sensing canopy Tpin woody crops, which is crucial in mapping daily ET partitioning from field to global scales. 2 Materials and methods 2.1 Trial location and design This study was conducted in an almond orchard situated at the experimental station of the IRTA (Institute of Agrifood Research and Technology) in Les Borges Blanques, Spain (41°30031.8900 N, 0°51010.7000 E; 323m elevation) (Fig. 1a). The almond orchard was planted in June 2009, with “Marinada” used as the scion cultivar onto an INRA GF-677 rootstock. Additionally, the orchard was planted with different planting distances and was subjected to different pruning techniques. The combination of planting distance and pruning techniques will be referred to as the “production system”. Three almond production systems were evaluated: open vase with minimal pruning (MP) spaced at 5.5×3.5 m, central axis spaced at 5 ×3 m, and hedgerow spaced at 4.5×3 m (Fig. 1b and c). The orchard was situated on a clay-loamtextured soil, with a depth ranging from 1.6 to 2m. The study site has a Mediterranean climate, with an average annual rainfall of 364mm and an average annual evapotranspiration of 1088 mm. Two different dates were selected to assess the diurnal course of Tp: 29 June and 19 August 2022. Figure 2 displayed the meteorological conditions during the campaign. The orchard was irrigated using a drip irrigation system. In the open-vase (MP) system, two lateral pipes were positioned on each side of the tree at 40cm, with a dripper placed every 70cm and a water discharge rate of 2.2 L h−1. The central axis and hedgerow systems had a lateral pipe along the row line, with drippers placed at 60cm intervals with a water discharge rate of 3.8 L h−1per dripper. Daily irrigation was scheduled on a weekly basis to complement potential crop evapotranspiration (ETc) using ETc =(ETo ×Kc) −effective rainfall, as described by Allen et al. (1998). ETo was obtained from a meteorological station within Catalonia’s official network of meteorological stations (SMC, https://ruralcat.gencat.cat/web/guest/agrometeo, last access: 20 December 2023), situated 500m away from the study site. The ETo is estimated using the FAO-56 Penman– Monteith method (Allen et al., 1998). Kc refers to the crop coefficient. The Kc was assigned based on different phenological stages following Goldhamer and Girona (2012). The assigned Kc values were as follows: Kc1=0.70 (April), Kc2=0.95 (May), Kc3=1.09 (June), Kc4=1.15 (July), Kc5=1.17 (August), and Kc6=1.12 (September). Effective rainfall was determined following the method outlined by Olivo et al. (2009), which considers half of the precipitation for days with a single event exceeding 10mm; otherwise, it is considered to be zero. Three irrigation treatments were implemented for each production system during the 2021 and 2022 growing seasons: (i) full irrigation, where irrigation matches ET requirements (100% ETc); (ii) mild stress, irrigated at 50% ETc; and (iii) severe stress, irrigated at 20% https://doi.org/10.5194/hess-28-4797-2024 Hydrol. Earth Syst. Sci., 28, 4797–4818, 2024 4800 M. Quintanilla-Albornoz et al.: Assessment of upscaling methodologies Figure 1. (a) Location of the almond orchard in Les Borges Blanques; (b) experimental design of the orchard, showing in different colors the three production systems and the three irrigation treatments; and (c) photographs from summer 2022 of production systems. ETc. The water applied was quantified using digital water meters (CZ2000-3M, Contazara, Zaragoza, Spain). 2.2 Sap flow measurement Sap flow sensors offer substantial advantages, enabling the continuous and automated measurement of sap movements for each plant with a high temporal resolution (Smith and Allen, 1996; Forster, 2017; Fernandez et al., 2001). When properly calibrated, these sensors can measure the Tpfor the entire plant (López-Bernal et al., 2010; Forster, 2017; Noun et al., 2022). Among the sap-flow-measuring methods available, the compensation heat pulse (CHP) has been suggested as a tool for detecting water stress and for irrigationscheduling purposes (Fernandez, 2001; Alarcón et al., 2005). Therefore, the CHP sap flow method combined with the calibrated average gradient technique was employed to estimate the Tp. The sap flow system consists of a 2mm diameter 4.8W stainless-steel heater and two temperature sensors positioned 10 and 5 mm downstream and upstream of the heater, respectively. Each temperature sensor is embedded with two E-type thermocouples (chrome-constantan wire) spaced 10 mm apart along the needle. The heat pulse velocity at 5 and 15 mm below the cambium is used to calculate the sap flow density across the trunk radius. The sap flow system was developed by the IAS-CSIC laboratory. For further specifications, refer to Villalobos et al. (2009). Sap flow data were collected every 15min and stored in a CR1000 data logger (Campbell Scientific Inc., Logan, UT, USA). Sap flow sensors were installed in each production system, monitoring two trees from the full-irrigation and severestress treatments and one tree from the mild-stress treatments, as shown in Fig. 1b. They were installed at 0.5m above the ground. Each sap flow transpiration (T–SF) underwent correction for wound and azimuthal effects (LópezBernal et al., 2010) using actual Tpmeasured by a water balance method (Twb) on 13 July 2022. The Twb was calculated using Eq. (1): Twb =P+IR−1SWC −DP −E, (1) Hydrol. Earth Syst. Sci., 28, 4797–4818, 2024 https://doi.org/10.5194/hess-28-4797-2024 M. Quintanilla-Albornoz et al.: Assessment of upscaling methodologies 4801 Figure 2. Meteorological conditions at an hourly scale during the flight campaign: reference evapotranspiration (ETo), air temperature (Ta), relative humidity (RH), solar radiation (Rs), wind speed at 10m (u10), and vapor pressure deficit (VPD). The xaxis represents the time in UTC+0, with solar noon occurring around 12:00 UTC at the experimental site. where Pis precipitation, IRis the amount of water applied through irrigation, 1SWC is the difference in soil water content (SWC) between 2 consecutive days, DP is deep percolation, and Ecorresponds to evaporation. P, DP, and IRwere considered to be zero because the water balance was calculated for days without Pand IRapplied. Additionally, the soil was covered with plastic sheeting during these days to prevent Efluxes (E≈0). Differences between Twb and T– SF measurements were assumed to remain constant throughout the season, as demonstrated by Espadafor et al. (2015). The calibrated T–SF was used to calculate both the accumulated hourly Tp(Th–SF) and the accumulated daily Tp(Td– SF). The SWC was measured using a neutron probe at intervals of 20cm down to a depth of 180cm (Campbell Pacific Nuclear Scientific, Model 503). The tubes used for SWC measurements were installed to cover one-quarter of the planting area. In each tree, two groups of three tubes were installed in parallel, positioned below the emitter, at a quarter of the inter-row distance and at half of the inter-row distance. Soil samples were taken at the time of tube installation to estimate the volumetric moisture content (cm3of watercm−3of soil). This measurement was then used to calibrate the neutron probe readings. 2.3 Field measurement 2.3.1 Stem water potential, stomatal conductance, leaf transpiration, and leaf area index Stem water potential (9s), stomatal conductance (gs), and leaf transpiration (Eleaf) were measured at 07:00, 09:00, 12:00, 14:00, and 16:00UTC solar time during the UAV flight campaign and in the same trees where sap flow sensors were installed. The measurement of 9sfollowed the protocol outlined by McCutchan and Shackel (1992). The 9swas determined by measuring three shaded leaves from each tree. Prior to measurement, each leaf was enclosed in a plastic bar covered with aluminum foil for 1 h to equalize the water potential between the leaf, stem, and branches. A pressure chamber (Plant Water Status Console, Model 3500; SoilMoisture Equipment Corp., Santa Barbara, CA) was utilized to obtain the 9sin all measurements within 1 h. The gsand Eleaf were measured using the LI-600 porometer/fluorometer (LI-COR Inc., Lincoln, NE, USA). Three sunny leaves were measured in each tree concomitant to image acquisition. The leaf area index (LAI) was determined for trees equipped with sap flow sensors using the LAI-2200 Plant Canopy Analyzer (PCA) (LI-COR Inc., Lincoln, NE, USA). The LAI was measured for each flight date around midday. The LAI measurement procedure involved one measurement being taken above the tree and four being taken below the tree. The incident radiation above the tree was recorded in an https://doi.org/10.5194/hess-28-4797-2024 Hydrol. Earth Syst. Sci., 28, 4797–4818, 2024 4802 M. Quintanilla-Albornoz et al.: Assessment of upscaling methodologies open area using five sensor rings. A single measurement was taken in each cardinal direction (N, S, E, and W) beneath the tree. The LAI was subsequently estimated from the vertical profile of the crown using the FV2200 v.2.1.1 software. The accuracy of LAI estimations was 0.57m2m−2(QuintanillaAlbornoz et al., 2023). 2.3.2 Image acquisition campaign A total of 10 flights were conducted on 29 June and 29 August 2022 with UAV DRONEHEXA XL (DroneTools, Seville, Spain). On each day, five flights were conducted at 07:00, 09:00, 12:00, 14:00, and 16:00 UTC (GMT). The UAV was outfitted with a MicaSense RedEdge-MX multispectral camera (MicaSense, Northlake Way, Seattle, USA) and a FLIR SC655 thermal camera (FLIR Systems, Wilsonville, OR, United States). MicaSense RedEdge-MX captures images in five spectral bands at wavelengths of 475 ±20, 560 ±20, 668 ±10, 717 ±10, and 840 ±40 nm. FLIR SC655 has a spectral response in the range of 7.5– 13 µm. The flights were carried out at a height of 50m above ground level to capture multispectral and thermal images with spatial resolutions of 0.03 and 0.06 m, respectively. All images were subjected to radiometric, atmospheric, and geometric correction. The FieldSpec 4 Standard-Res Spectroradiometer (Malvern Panalytical, Inc., United Kingdom) was used to acquire in situ spectral measurements of various ground targets simultaneously with the image acquisition for radiometric calibration. The FieldSpec 4 StandardRes Spectroradiometer has an optical resolution of 3–10 mm and a wavelength response between 350 and 2500 nm. Before conducting spectral measurements on the ground targets, the spectroradiometer was calibrated using a white reference panel (white-color Spectralon) and a dark reference. The thermal sensor underwent radiometric calibration in the laboratory using a blackbody (model P80P, Land Instruments, Dronfield, United Kingdom). Additionally, in situ temperature measurements were acquired using an SI-111SS Apogee infrared radiometer connected to an Apogee AT-100 microCache Bluetooth micrologger (Apogee Instruments Inc., Logan, UT, USA). The mosaicking process and the generation of the digital elevation model (DEM) and the digital surface model (DSM) were performed using Agisoft Metashape Professional software (Agisoft LLC., St. Petersburg, Russia). Geometric and radiometric corrections was conducted using QGIS 3.4 (QGIS 3.4.15). 2.4 TSEB model description The TSEB scheme, initially introduced by Norman et al. (1995) and further refined by Kustas and Anderson (2009), was utilized to estimate Tpemploying highresolution images. The TSEB is an energy balance model that assumes that net surface radiation (Rn) is primarily distributed among sensible heat flux (H), latent heat flux (LE), and soil heat flux (G). Consequently, the LE (W m−2) is calculated as the residual of the surface energy equation using Eqs. (2a), (2b), and (2c): LE ≈Rn−H−G, (2a) LEs≈Rn,s−Hs−G, (2b) LEc≈Rn,c−Hs,(2c) where the subscripts c and s refer to the energy fluxes of the canopy and soil, respectively. The Campbell and Norman (1998) canopy transfer model, considering a rectangular clumping index, was employed to estimate Rn,sand Rn,c, as described by Parry et al. (2019) and Quintanilla-Albornoz et al. (2023). Gwas assumed to be a constant fraction of Rn,sof around 0.35. A series resistance scheme was utilized, dividing Hinto soil (Hs) and canopy (Hc), as shown in Eqs. (3a), (3b), and (3c): Hs=ρCp Ts−Tac rs,(3a) Hc=ρCp Tc−Tac rx ,(3b) Hs+Hc=ρCp Tac −Ta ra,(3c) where ρis the air density; Cpis the specific heat of the air; Tsis the soil temperature; Tcis the canopy temperature; Ta is the air temperature; Tac is the temperature in the canopy air space, equivalent to the aerodynamic temperature; rsis the resistance to heat flow in the boundary layer immediately above the soil surface; rxis the total boundary layer resistance of the complete canopy leaves; and rais the aerodynamic resistance to turbulent heat transport between the air canopy layer and the overlying air layer. The resistances were derived according to Kustas and Norman (1999) and Norman et al. (1995). The contextual approach of the TSEB model (TSEB– 2T) was evaluated in this study and is available online at https://doi.org/10.5281/zenodo.8134956 (Nieto et al., 2023). The TSEB–2T was applied with direct measurements of Tc and Tsfrom high-resolution thermal images. Tcand Tswere obtained with a supervised image classification based on use of the DSM and the soil-adjusted vegetation index (SAVI). SAVI was chosen due to its ability to reduce the impact of ground brightness in the nearand shortwave-infrared wavelengths, which enhances the contrast between vegetation and the ground surface (Qi et al., 1994). Pixels were classified as canopy if they exhibited a DSM greater than 1.5m and a SAVI greater than 0.2. Pixels that did not meet these conditions were classified as pure soil. These layers were employed to retrieve the Tcand Tsfrom thermal images. Finally, the hourly Tpin millimeters (Th-TSEB) was estimated using 1000 ×3600 ×LEc/(ρwλ), where ρwis the density of water (assumed to be 1000 kg m−3), and λis the latent heat of vaporization (J kg−1): λ=1×106×(2.501 – 0.002361 Ta). Hydrol. Earth Syst. Sci., 28, 4797–4818, 2024 https://doi.org/10.5194/hess-28-4797-2024 M. Quintanilla-Albornoz et al.: Assessment of upscaling methodologies 4803 All biophysical traits required for TSEB models, namely the fractional canopy cover (fc), canopy height (hc), and canopy width (wc), were obtained using the multispectral and DSM high-resolution images. For additional details on the biophysical traits’ procedures, refer to Quintanilla-Albornoz et al. (2023). 2.5 Models evaluated to upscale daily transpiration The self-conservation method is the most commonly used approach to upscale ET fluxes from instantaneous measurements. This assumes a constant relationship between the instantaneous ET and some meteorological variables over time under certain conditions. According to Cammalleri et al. (2014), the relationship between instantaneous measurement of ET fluxes and a reference variable can be illustrated using Eq. (4): ETd=β1 λ λLEt XtXd,(4) where λLEtis the instantaneous latent heat flux at the acquisition time t, Xtand Xdare the values of the reference variable at the acquisition time t and during the day d, and β represents a correction factor to account for potential biases or nighttime ET. This paper evaluates four self-preservation approaches, elucidated below, along with their implications for estimating Tdin almond crops. 2.5.1 Simulated evaporative fraction variable (EFsim) method The EFsim is based on the evaporative fraction (EF) method. The EF method assumes that the ratio between LE and AE is relatively constant during the day. Following Eqs. (5a), (5b), and (5c), we can obtain the daily LE fluxes: EF =LE AE ,(5a) AE =Rn−G, (5b) LEd=AEd×EF,(5c) where LEdand AEdcorrespond to daily accumulated LE and AE, respectively. Rncan be determined from remote sensing data using Eq. (6): Rn=(1−α)·Rs+ε·Ratm −ε·σ·Trad,(6) where αcorresponds to the albedo, εcorresponds to the surface emissivity, Ratm corresponds to the atmospheric longwave radiation, σcorresponds to the Stefan–Boltzmann constant, and Trad corresponds to the radiometric temperature. To avoid daily measurement of Rnand G, the AE can be extrapolated from instantaneous AE estimated through thermal imagery and Rsfollowing the methods proposed by Jackson et al. (1983) and Delogu et al. (2012), as expressed in Eq. (7): AEd=Rs,dAEt Rs,t,(7) where AEtrepresents the instantaneous AE estimated through thermal imagery, Rs,drepresents the daily Rs, and Rs,tis the Rsat the measurement time. According to Hoedjes et al. (2008), the daily pattern of EF can be simulated as a function of Rsand RH, as in Eq. (8a). However, EFsim is a theoretical curve and must be adjusted using real EF values with Eq. (8b): EFsim =1.2−0.4Rs 1000 +0.5RH 100,(8a) EFadj =EFsim EFt,obs EFt,sim ,(8b) where Rsis in W m−2, and RH is in percentage. Additionally, EFt,obs represents actual EF values estimated using remote sensing imagery based on Eq. (5a), and EFt,sim is the EFsim at the time of EFt,obs. Finally, the EFsim method employs Eq. (5c) with an EF estimated using Eq. (8b) and an AEdestimated using Eq. (7) to estimate LEd. 2.5.2 Incoming shortwave solar radiation (Rs) approach An alternative strategy consists of replacing AE as a reference variable with the Rs. This method is founded on the principle that Rsis the primary radiation flux during the day, resulting in a strong correlation and associated variations between actual ET and Rs(Jackson et al., 1983; Delogu et al., 2012; Nassar et al., 2021). Thus, LEdcan be estimated with Eq. (9): LEd=Rs,dLEt Rs,t,(9) where Rs,dcorresponds to daily Rs, and Rs,tis the Rsat the time that LE was estimated. 2.5.3 Stress factor approach The stress factor approach involves upscaling the instantaneous ET using either the reference (ETo) or potential evapotranspiration (ETp), as depicted in Eq. (10): ETd=SF ·ETo(ETp).(10) The stress factor is defined as the ratio between ET and instantaneous ETo or ETp (SF =ET /ETo (or ETp)). The ETo was obtained using the FAO-56 method (Allen et al., 1998). ETp was estimated using the Penman– Monteith one-source energy balance model and by forcing it with meteorological data and the actual LAI (Allen et al., 1998). The ETp obtained from the Penman–Monteith model is available in the Python programming language at https://doi.org/10.5281/zenodo.8134956 (Nieto et al., 2023). The minimum bulk canopy resistance for the ETp model was determined through a method that parameterizes the relationship between gsand VPD, as described by Kustas https://doi.org/10.5194/hess-28-4797-2024 Hydrol. Earth Syst. Sci., 28, 4797–4818, 2024 4804 M. Quintanilla-Albornoz et al.: Assessment of upscaling methodologies et al. (2022). Meteorological data were obtained from the weather station of the Meteorological Service of Catalonia located near the experimental orchard. The EFsim,Rs, ETo, and ETp upscaling methods were used to estimate Tdfrom Th–SF measurements and from Th–TSEB estimations. The Tdobtained using the EFsim,Rs, ETo, and ETp upscaling methods from Th–SF measurements was called Td–SF–ETsim,Td–SF–Rs,Td–SF–ETo, and Td– SF–ETp, while the Tdestimated from Th-TSEB estimations was called Td–TSEB–ETsim,Td–TSEB–Rs,Td–TSEB–ETo, and Td–TSEB–ETp, respectively. 3 Results 3.1 Biophysical traits and physiological measurements Table 1 shows an analysis of variance (ANOVA) of the main biophysical traits, and Table 2 shows the average of each biophysical variable for each production system and irrigation treatment. The fractional canopy cover (fc) significantly varied between production systems, with open vase (MP) and hedgerows presenting the highest and lowest values, respectively. The average fcfor each production system was 0.56, 0.50, and 0.47 for open vase (MP), central axis, and hedgerow, respectively. Canopy height (hc) varied significantly between production systems, irrigation treatments, and their interaction. Overall, taller trees were observed in the open-vase (MP) system. However, the open-vase (MP) and hedgerow systems led to smaller trees in the severe-stress treatment, whereas the central axis had the smallest trees in the mild-stress treatment. The measured LAI did not show significant differences among production systems or irrigation treatments. 3.1.1 Stem water potential, stomatal conductance, and leaf transpiration The diurnal patterns of 9s,gs, and Eleaf exhibited variations primarily attributed to the irrigation treatment (Fig. 3). These variations led to significant differences in tree daily average 9s,gs, and Eleaf among the different irrigation treatments (Table 1). Moreover, the interaction between production system and irrigation treatment (PS×TRT) had a significant impact, primarily attributable to the central axis subjected to the mild-stress treatment. The central axis under the mild-stress treatment exhibited values comparable to those observed in the severe-stress treatment. The daily pattern of 9sexhibited significant differences between irrigation treatments as early as 07:00 UTC. In contrast, discernible significant differences between irrigation treatments for gsand Eleaf were evident as early as 09:00UTC. Differences in 9s,gs, and Eleaf between irrigation treatments remained evident until 16:00 UTC. The peak disparities in 9s,gs, and Eleaf among irrigation treatments were observed around 12:00 UTC. During this time, 9shad its most reduced values, with an average of −1.35 MPa in the full-irrigation treatment, −1.86 MPa in the mild-stress treatment, and −2.30 MPa in the severe-stress treatment. Simultaneously, gsattained its maximum values with an average of 0.41, 0.25, and 0.12mol m−2s−1for the full-irrigation, mild-stress, and severe-stress treatments, respectively. The most pronounced variations in Eleaf among irrigation treatments occurred at 12:00UTC, and the highest Eleaf values were recorded at 14:00UTC, with, respectively, averaged values of 10.61, 6.96, and 5.24mmol m−2s−1for the full-irrigation, mild-stress, and severe-stress treatments. Finally, on average, the tree daily mean 9sfor the fully irrigated treatment was −1.18MPa, while the mild-stress and severe-stress treatments showed values of −1.65 and −1.99 MPa, respectively. Similarly, the tree daily averaged values of gswere 0.32, 0.21, and 0.13mol m−2s−1for the full-irrigation, mild-stress, and severe-stress treatments, respectively. Additionally, the tree daily Eleaf values were 7.74, 5.77, and 4.12 mol m−2s−1for the full-irrigation, mildstress, and severe-stress treatments, respectively. 3.1.2 Sap flow transpiration The Td–SF showed significant differences among production systems, irrigation treatments, PS×TRT, and dates (Table 1). The open-vase (MP) system transpired significantly higher, with an average of 3.13mm d−1compared to 2.64 mm d−1for the central-axis system and 2.46mmd−1for the hedgerow system. Notably, in hedgerow, the mild-stress treatment showed higher Td–SF values compared to the fullirrigation treatment, although the difference was not statistically significant (Table 2). Figure 4 shows the daily patterns of Th–SF. The Th–SF patterns exhibited variations based on production system, irrigation treatment, and date. The daily pattern may vary between days due to differences in the main weather forcing parameters (see Fig. 2), as well as an irrigation scheduling error that occurred on 29 June at 12:00UTC, coinciding with the ongoing measurements. The error in the irrigation schedule resulted in significant pattern variations, particularly in the severe-stress treatment. In this treatment, Th–SF exhibited a notable increase at 13:00 UTC, reaching its peak at 14:00 and 15:00 UTC on 29 June in all production systems. The maximum Th–SF rates recorded in the severe-stress treatment on 29 June were 0.14, 0.20, and 0.23mm h−1for the openvase (MP), central-axis, and hedgerow systems, respectively. Conversely, the maximum Th–SF rates in the severe-stress treatment on 29 August were observed between 10:00 and 12:00 UTC, with 0.10, 0.12, and 0.07 mm h−1for the openvase (MP), central-axis, and hedgerow systems, respectively. In the full-irrigation treatment, the maximum Th–SF rates varied depending on the day and the production system, occurring between 12:00 and 14:00 UTC. In the openvase (MP) system, the highest Th–SF values, averaging 0.45 mm h−1, were recorded at 14:00UTC. In the centralaxis system under full irrigation, the maximum Th–SF ocHydrol. Earth Syst. Sci., 28, 4797–4818, 2024 https://doi.org/10.5194/hess-28-4797-2024 M. Quintanilla-Albornoz et al.: Assessment of upscaling methodologies 4805 Table 1. Analysis of variance (three-way ANOVA) testing the effect of date, production system (PS), irrigation treatment (TRT), and their interactions on fractional canopy cover (fc), canopy height (hc), leaf area index (LAI), stem water potential (9s), and hourly (Th–SF) and daily transpiration (Td–SF) measured by sap flow sensors. The pvalues less than 0.05 were considered to be statistically significant, while “ns” represents a statistically non-significant interaction. Variable Date PS TRT PS×date TRT×date PS×TRT PS×TRT×date fcns 0.008 ns ns ns ns ns hc0.0004 <0.0001 <0.0001 ns ns <0.0001 ns LAI ns ns ns ns ns ns ns 9sns 0.0059 <0.0001 ns 0.0044 0.0398 ns gsns ns <0.0001 ns 0.0046 0.0152 ns Eleaf 0.0003 0.0098 <0.0001 ns 0.0321 0.0188 ns Td–SF 0.0001 0.0033 <0.0001 ns ns 0.0111 ns Th–SFmorning 0.0003 <0.0001 <0.0001 ns ns 0.015 ns Th–SFmidday ns ns <0.0001 ns ns ns ns Th–SFafternoon ns 0.005 <0.0001 ns 0.011 0.001 ns Table 2. Comparison of fractional canopy cover (fc), canopy height (hc), leaf area index (LAI), and daily transpiration (Td–SF) measured during the flight campaign. Different letters mean significant differences at p < 0.05 using Tukey’s honest significant difference test considering the interaction between production system and irrigation treatment. Production system Irrigation treatment fchcLAI Td–SF Open vase Full irrigation 0.61a 5.82a 3.12 4.61a Mild stress 0.57a 5.42b 2.8 3.8ab Severe stress 0.51a 5.01c 2.96 1.3c Central axis Full irrigation 0.53ab 4.11d 3.08 3.75b Mild stress 0.5ab 4.07d 3.27 2.6b Severe stress 0.48ab 3.5e 3.16 1.54c Hedgerow Full irrigation 0.44b 4.02d 2.61 3.37b Mild stress 0.5b 4.78c 3.7 3.59b Severe stress 0.49b 4.18d 3.65 0.99c curred at 12:00 UTC on 29 June and at 14:00 UTC on 19 August, with a Th–SF rate of 0.43 mm h−1for both dates. In the hedgerow system, the full-irrigation treatment yielded a maximum Th–SF of 0.37 mm h−1on both days, observed at 12:00 UTC on 29 June and at 14:00UTC on 19 August. Similarly to the full-irrigation treatment, in the mild-stress treatment, the timing of maximum Th–SF depended on the day and the production system. In the mild-stress treatment for the open vase (MP), the maximum Th–SF was recorded at 12:00UTC, corresponding to 0.45mm h−1on 29 June and to 0.39 mm h−1on 19 August. In contrast, the mild-stress treatment for the central-axis system reached its peak at 14:00 UTC on 19 June and at 12:00UTC on 19 August, with Th–SF rates of 0.26 and 0.30 mm h−1, respectively. In the hedgerow system, under the mild-stress treatment, the maximum Th–SF rates of approximately 0.38 and 0.34mmh−1 were observed at 14:00UTC on 29 June and at 12:00 UTC on 19 August, respectively. The Th–SF exhibited significant differences between 06:00 and 21:00UTC, attributed to the irrigation treatments. Th– SF for the severe-stress treatment was systematically lower than that for the other two treatments. These differences were more evident during daytime hours. Thus, the maximum differences between the full-irrigation and severe-stress treatments were observed at 12:00UTC, reflecting an averaged difference of 0.28 mm h−1. Furthermore, nocturnal fluxes, which accounted for approximately 5% of the total Td–SF, were observed, with the exception of one tree in the openvase (MP) system and one tree in the hedgerow system (both under the severe-stress treatment), where nocturnal Th–SF contributed to 21.3% and 10.6 % of the total Td–SF, respectively. The statistical analysis showed that Th–SF during the morning (6:00 to 10:00UTC) and afternoon (14:00 to 18:00 UTC) showed significant differences among production systems and PS×TRT (Table 1). During those daytime intervals, the open-vase (MP) production system demonstrated significantly higher Tpcompared to the other production systems. The significance of PS ×TRT is explained by the fact that the hedgerow, under the mild-stress treatment, exhibited higher Th–SF values than the full-irrigation treatment in both time periods. Notably, although there was no https://doi.org/10.5194/hess-28-4797-2024 Hydrol. Earth Syst. Sci., 28, 4797–4818, 2024 4812 M. Quintanilla-Albornoz et al.: Assessment of upscaling methodologies Table 6. Analysis of variance (three-way ANOVA) for the evaluation of the effects of date, production system (PS), and irrigation treatment (TRT) on Tdestimated with TSEB–2T using flights conducted at 14:00 UTC and EFsim,Rs, ETo, and ETp upscaling methods. The pvalues less than 0.05 were considered to be statistically significant, while “ns” refers to a statistically non-significant interaction. Model Date PS TRT PS×date TRT×date PS×TRT PS×TRT×date Td–TSEB–EFsim 0.0002 ns <0.0001 ns ns ns ns Td–TSEB–Rs0.0007 ns <0.0001 ns ns ns ns Td–TSEB–ETo 0.0003 ns <0.0001 ns ns ns ns Td–TSEB–ETp 0.0252 ns <0.0001 ns ns ns ns Table 7. Analysis of variance (three-way ANOVA) evaluating the effects of date, production system (PS), and irrigation treatment (TRT) on the root mean squared error (RMSE) of Tdestimated with TSEB–2T using flights conducted at 14:00 UTC and EFsim,Rs, ETo, and ETp upscaling methods. The pvalues less than 0.05 were considered to be statistically significant, while “ns” refers to a statistically nonsignificant interaction. Model Date PS TRT PS×date TRT×date PS×TRT PS×TRT×date Td–TSEB–EFsim ns ns 0.0071 ns ns ns 0.0375 Td–TSEB–Rsns ns 0.0094 ns ns ns ns Td–TSEB–ETo ns ns 0.0183 ns ns ns ns Td–TSEB–ETp ns ns 0.0420 ns ns ns 0.0416 Table 8. Root mean squared error (RMSE, mm d−1) of the estimated Tdusing the TSEB–2T and EFsim,Rs, ETo, and ETp upscaling approaches by irrigation treatment. Different letters mean significant differences at p < 0.05 using Tukey’s honest significant difference test considering the irrigation treatments. Irrigation treatment EFsim RsETo ETp Full irrigation 0.55b 0.43b 0.44b 0.66ab Mild stress 0.32b 0.32b 0.37b 0.42b Severe stress 0.97a 0.85a 0.83a 0.92a results and were able to detect greater differences between irrigation treatments, while the irrigation treatment did not significantly affect the RMSE (Tables 3 and 4). This is in line with the findings of Anderson et al. (2021), who showed how earlier overpasses often created uniform maps of ET without differentiating between crop water demand. Additionally, Bellvert et al. (2014) showed that the optimal time for capturing high-resolution thermal images to minimize shade effects and to monitor leaf water potential and canopy temperature is around solar noon. The higher overestimations in Th–TSEB estimated using images obtained at 07:00, 09:00, and 16:00 UTC could be explained by the shadow that covered the thermal images. The thermal images captured at 07:00, 09:00, and 16:00 UTC were more susceptible to thermal radiation directionality (TDR) and shadow effects resulting from the higher zenith angle of the sun. Moreover, the significant contrast between inter-row soils and canopies leads to considerable directional variability in the thermal images (Mwangi et al., 2023). Although TSEB–2T accounts for radiation directionality when estimating H(Norman et al., 1995) and shortwave transmittance (Parry et al., 2019), it may still be susceptible to shadow effects because it does not distinguish between sunlit and shaded sources. To address this issue, Mwangi et al. (2022) proposed a four-component scheme (SPARSE4) as an option to account for sunlit vs. shaded soil and/or vegetation energy sources. This scheme couples a dual-source energy balance (SPARSE) model with the physically based unified four-component radiative transfer (UFR97) model. However, The Th–TSEB model demonstrates effective differentiation between irrigation treatments at all hours, particularly around solar noon, where differences between all irrigation treatments are evident. Conversely, it is noteworthy that Th–TSEB fails to exhibit significant differences between production systems around solar noon (Table 3). This poses a challenge when estimating Tdusing upscaling methods as no model detected variations in Tdby production system, as evidenced by Td–SF (Table 6). This limitation is especially critical when estimating Tdfrom Th– TSEB in canopies with diverse architectural structures. While the comparison between actual Td–SF and Td– TSEB–EFsim,Td–TSEB–Rs,Td–TSEB–ETo, and Td–TSEB– ETp showed similar results (Fig. 9), Td–TSEB–Rsand Td– TSEB–ETo enhanced the accuracy of Tdestimates, which is reflected in the reduced RMSE values of 0.62 and 0.61 mm d−1, respectively. It should be noted that both Cammalleri et al. (2014) and Nassar et al. (2021) reported the Rsmethod as yielding the best results when used as an upscaling parameter to estimate daily ET. However, our results suggest that the superior performance of Rsand ETo in estimating Tdcan be attributed to their capacity to rectify the overestimation observed in Th–TSEB estimates rather than their inherent alignment with the Th–SF pattern. The unHydrol. Earth Syst. Sci., 28, 4797–4818, 2024 https://doi.org/10.5194/hess-28-4797-2024 M. Quintanilla-Albornoz et al.: Assessment of upscaling methodologies 4813 derestimation is clarified by the %Rsat 14:00 UTC, which ranged from −2.47 % to −14.47%, while the %ETo ranged from −4.71 % to −8.52 % (Fig. 6). Related to our results, Van Niel et al. (2012) and Cammalleri et al. (2014) observed a systematic underestimation of estimated daily ET values using the Rsapproach for a wide range of ecosystems and weather conditions. In this regard, Anderson et al. (1997) proposed a correction factor of 1.1 to compensate for systematic bias, increasing by 10 % the daily ET estimations. Among the advantages of the Rsmethod, Cammalleri et al. (2014) highlighted its uniform bias around the acquisition time and throughout the season, which is in contrast to using the EF method, ETo, and the top-of-atmosphere radiance (RTOA) as reference variables. Nassar et al. (2021) also found that the Rsmethod exhibits less sensitivity to seasonal and climate variations compared to the EF approach and the use of the ratio of net radiation to solar radiation (Rn/Rs). It is important to clarify that both the above-cited studies evaluated the EF approach with actual AE measurements using eddy covariance towers as validation data sources. However, our findings indicate that the correction factor proposed by Anderson et al. (1997) of 1.1 for Tdshould be determined by water stress; otherwise, it may be deemed inadequate. In addition, the timing of the overpass is crucial in determining a correction factor for using the Rsand ETo approaches, particularly for trees experiencing water stress. For instance, although the minimum RRMSE of Td–SF–Rsof 10 % might be achievable at 15:00 UTC, the RRMSE of Td– SF–Rscould reach 25 %–30% around midday in trees under waters stress. Given the variations in the RRMSE when estimating Td–SF–Rsthroughout the day and between days, establishing an appropriate correction factor for water-stressed trees presents a challenge. These findings complement those of Cammalleri et al. (2014) and Nassar et al. (2021), who concluded that the Rsmethod is minimally influenced by the timing of the daytime overpass in unstressed vegetation. The errors associated with the Rsand ETo approaches could be attributable to fluctuations in the daily patterns of Rs, ETo, and the physiological condition of the tree throughout the day. Rsand ETo exhibited an almost perfect concave shape, with their maximum values occurring at 13:00UTC (Fig. 2), which was the local solar noon at our study site. Conversely, in the full-irrigation treatment, while stomatal closure was observed at 12:00UTC, both Eleaf and Th–SF either remained steady or even increased until 14:00UTC (Figs. 3 and 4). The maintenance or increase in Eleaf and Th– SF during the early afternoon can be attributed to the rise in air temperature and the decrease in RH, consequently leading to an increase in VPD during the afternoon (Fig. 2). The variation in patterns between Rs, ETo, and Th–SF resulted in lower hourly values of Th–SF / Rs(or Th–SF /ETo) at midday compared to those observed during the early afternoon (15:00–16:00 UTC). On the other hand, Th–SF / Rs(or Th– SF /ETo) in the early afternoon exhibited more representative values for estimating Tdin the fully irrigated treatment from Th–SF (Fig. 6). In contrast, the early water stress, as indicated by the 9s, resulted in stomatal closure being detected at 09:00 UTC in the severe-stress treatment. The impact of stomatal closure can be observed in Fig. 4, where the maximum Th– SF was achieved before noon in the severe-stress treatment, specifically between 10:00 and 12:00 UTC. Despite the increase in Rsand ETo, the maximum Th–SF in the severestress treatment remained nearly constant between 10:00 and 16:00 UTC on 19 August. On 29 June, Th–SF in the severestress treatment increased rapidly, starting from 12:00 UTC and reaching its maximum value at 14:00UTC, similarly to the full-irrigation treatment. As a result, the disparities between Th–SF and Rs(or ETo) were most pronounced at midday (Fig. 6), leading to significant potential underestimations when using midday measurements. While Rsand ETo decreased, Th–SF remained at its maximum until 16:00 UTC. Consequently, the relationship between Th–SF and Rs(or ETo) started to become more representative of Td–SF at 15:00 UTC on both dates. Therefore, it appears to be the case that the optimal time to estimate instantaneous Tpfor daily estimations would fall in the early afternoon, specifically at 15:00 UTC, for both the Rsand ETo approaches in all irrigation treatments. Regarding the ETo method, in line with the findings of Cammalleri et al. (2014), using ETo as a reference variable produced results similar to those of the Rsmethod, indicating that it does not represent an improvement. Moreover, the %ETo pattern varied from one date to another (Fig. 6), introducing uncertainty into the potential upscaling adjustments. Differences in the %ETo patterns between days may be attributable to variations in the aerodynamic properties of the canopy between the reference vegetation and the almond canopy. For instance, Colaizzi et al. (2006) obtained good results when applying the ETo method to alfalfa and irrigated cotton but poor results for bare soil in drying conditions. It should also be noted that the microclimatic conditions at the location of the weather station may differ from those of the study site, introducing uncertainty into estimation of the actual ETo of the orchard under study. These two issues may be a possible limitation when using ETo as a reference variable to estimate Tdfluxes (Cammalleri et al., 2014). The EFsim and ETp methods appear to enhance the Td, considering the better and more consistent RRMSE observed throughout the day for Td–SF–EFsim and Td–SF–ETp in contrast to Td–SF–Rsand Td–SF–ETo. This improvement was also noted in the RRMSE of Td–TSEB–EFsim and Td–TSEB– ETp, which yielded more similar RRMSE compared to Th– TSEB. This suggests that the EFsim and ETp methods induced fewer modifications to the error associated with TSEB itself compared to the Rsand ETo methods. The improvement in the EFsim method aligns with the daily EF curve observed in previous studies, which does not remain constant but instead exhibits an upward concave shape, especially in https://doi.org/10.5194/hess-28-4797-2024 Hydrol. Earth Syst. Sci., 28, 4797–4818, 2024 4814 M. Quintanilla-Albornoz et al.: Assessment of upscaling methodologies non-stressed vegetation (Hoedjes et al., 2008; Delogu et al., 2012; Lhomme and Elguero, 1999; Brutsaert, 1992). Delogu et al. (2012) showed an improvement in the reconstruction of daily ET for various sites and under different climatic conditions, including low water stress, using the EFsim methodology. This is in line with our findings if we consider the fact that their “low-water-stress” conditions align with the mild-stress treatment. However, it should be noted that the RRMSE of Td–SF–EFsim may increase when using AE estimations during the afternoon for trees under water stress. This is why the RMSE values are significantly higher in Td– TSEB–EFsim when using the TSEB–2T at 14:00 UTC (Table 7). The actual EF shape under water stress during the day differs from the EFsim shape, presenting a flatter profile (Hoedjes et al., 2008; Lhomme and Elguero, 1999). The larger differences between actual EF and EFsim during the afternoon could potentially lead to an underestimation of Td when using the EFsim method. However, EFsim was able to reduce the RRMSE of Td–SF–EFsim at noon in the severestress treatment by up to 15.35% and 17.61% compared to Td–SF–ETo and Td–SF–Rs, respectively. Additionally, the Td–SF–EFsim exhibited 5% less RRMSE than Td–SF–ETo and Td–SF–Rsin the full-irrigation and mild-stress treatments. This indicates that the EFsim method might perform well under certain conditions but may have limitations, especially when applied to severely water-stressed trees using afternoon measurements. The remarkable similarity in patterns between ETp and Th–SF is particularly surprising, considering the concerns raised by Delogu et al. (2012) regarding the applicability of the ETp method under stress conditions. Delogu et al. (2012) suggested that actual ET and ETp might exhibit different daily patterns due to stomatal closure, potentially causing a negative bias when using ETp as a daily upscaling parameter. However, the daily curve of the ratio between Th–SF and hourly ETp (%ETp) mitigates the sinusoidal shape of %Rsand %ETo, which otherwise increases exponentially from noon to 18:00UTC. Furthermore, the RRMSE of Td–SF–ETp improves compared to the other methodologies and shows less variability among irrigation treatments and hours compared to using Rsand ETo as the adjustment variables. This improvement was particularly evident at 12:00UTC, where Td– SF–ETp showed approximately 5% less RRMSE in the fullirrigation and mild-stress treatments while reducing RRMSE at noon by 10.6% and 12.92 % in the severe-stress treatment compared to Td–SF–ETo and Td–SF–Rs, respectively. Moreover, the ETp model may hold an advantage due to its incorporation of distinct aerodynamic and radiative properties associated with various canopy architectures, which influence the Th–SF pattern. The variation in RMSE in the estimation of ETp among production systems likely impacted the sensitivity of the ETp model fit to each specific production system. The absence of significant differences in LAI among production systems could affect the accuracy of ETp. Quintanilla-Albornoz et al. (2023) already showed a discrepancy between measured LAI and the fraction of intercepted photosynthetically active radiation (fIPAR) at the study site, where the hedgerow presented higher LAI values but low fIPAR levels. Considering the fact that fIPAR represents 45% of the absorbed-light spectrum (Campbell and Norman, 1998), these results reinforce the idea of improving the shortwave transmittance model for estimating ET fluxes. Indeed, among the complexities, estimating parameters such as LAI, albedo, and the potential single-leaf stomatal resistance is considered to be challenging and can pose difficulties in making ETp estimations suitable for operational purposes (Delogu et al., 2012; Gao et al., 2022). However, enhanced ETp models and the refinement of crucial inputs like LAI and albedo can streamline and enhance ETp estimations, further enhancing their utility as a parameter for Td estimation in trees with different canopy architecture. For instance, implementing a more intricate model to estimate ETp, like the Shuttleworth and Wallace two-source model (Shuttleworth and Wallace, 1985), could enhance the daily upscaling method. This study was conducted over 2 measurement days with meteorological forcing conditions representative of typical summer days at the study site (Fig. 2). Additional measurement days would allow for the consideration of a wider range of meteorological forcing conditions and vegetative stages in almond trees for a more robust assessment of daily Tppatterns. However, the data collected effectively represent trees under varying levels of water stress (Fig. 3), consistently with the conditions necessary to address the hypothesis of this work. 5 Conclusion This study evaluates four methodologies to estimate Tdfrom instantaneous measurements. The daily upscaling methods were evaluated using sap flow measurements in almond trees under three different production systems and three irrigation treatments. Additionally, this study analyzed the daily pattern of physiological parameters, such as 9s,gs,Eleaf, and Th–SF, to determine the best moment to estimate both Thand Td. The Th–TSEB model effectively distinguished between irrigation treatments, especially at 12:00 and 14:00UTC, when differences between the three irrigation treatments were apparent. However, the Th–TSEB did not show significant differences between the production systems at that time. Therefore, of the evaluated upscaling methods, none of the models could discern the significant differences in Tdestimates across production systems, as observed in Td–SF. In addition, the upscaling methodologies were less accurate in severely stressed trees. Especially when using Rsand ETo as reference variables, the levels of underestimation exhibited significant variations between irrigation treatments and across different hours. Underestimation was as high as 30 % around noon for trees under water stress using the Rsand ETo methods. Therefore, it is advisable to carefully choose an apHydrol. Earth Syst. Sci., 28, 4797–4818, 2024 https://doi.org/10.5194/hess-28-4797-2024 M. Quintanilla-Albornoz et al.: Assessment of upscaling methodologies 4815 propriate time schedule. In this context, the EFsim and ETp methods demonstrated more consistent relationships with Th–SF and mitigated the underestimation observed in all irrigation treatments when using the other methods. For instance, both the ETsim and ETp models reduced the RRMSE by 5 % in the full-irrigation and mild-stress treatments using measurements at 12:00 UTC. In the severe-stress treatment, EFsim reduced the RRMSE by 17.61 % and 15.25 % at noon compared to the Rsand ETo methods, respectively, while ETp reduced it by 10.6% and 12.92% at noon compared to the Rsand ETo methods, respectively. Moreover, ETp has the advantage of incorporating different aerodynamic and radiative properties associated with production systems. In this sense, the ETp method may be an option to better characterize the Tdin trees with different canopy architectures. In this study, similar LAI estimates between production systems could affect the ETp model, where the hedgerow system showed a significantly higher error. This situation could impact the sensitivity of the ETp model with regard to differentiating Tdbetween production systems. One approach to enhance Tdestimations could involve refining the Penman–Monteith ETp model and improving the estimations of parameters such as the LAI, albedo, potential single-leaf stomatal resistance, and shortwave transmittance model. Alternatively, using more sophisticated models, such as the Shuttleworth and Wallace two-source model, could also be considered. Data availability. All data mentioned in this document have been generated by the IRTA Efficient Use of Water in Agriculture Program team. Datasets produced during this study can be made available upon reasonable request to the corresponding author and/or the Efficient Use of Water in Agriculture Program. Author contributions. MQA: conceptualization, data curation, formal analysis, investigation, methodology, software, validation, visualization, and writing – original draft preparation. JB: conceptualization, funding acquisition, investigation, project administration, supervision, validation, and writing – original draft preparation. HN: software, validation, and writing – review and editing. XM: funding acquisition and writing – review and editing. AP: data curation and resources. Competing interests. The contact author has declared that none of the authors has any competing interests. Disclaimer. Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Acknowledgements. The authors would like to thank the Efficient Use of Water in Agriculture team, at the IRTA, for their technical support, as well as the European Union Horizon 2020 Research and Innovation Program (H2020) of the European Commission in the context of the Marie Sklodowska-Curie Research and Innovation Staff Exchange (RISE) action and ACCWA project: grant agreement no. 823965. To enhance the readability and language of this work, the author(s) employed ChatGPT-3.5 throughout the writing process. Following the use of this tool, the author(s) assumed full responsibility for the papers content and reviewed and edited it as required. Financial support. This research has been supported by the projects ET4DROUGHT (grant no. PID2021-127345OR-C31) and DIGISPAC (grant no. TED2021-131237B-C21), both funded by the Ministry of Science and Innovation (MICINN-AEI) of Spain. Review statement. This paper was edited by Miriam CoendersGerrits and reviewed by Gilles Boulet and one anonymous referee. 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