# Integration of sentinel-2 data into the SIMETAW model for assessing irrigation water requirements and evapotranspiration

Pierre Rouault, Dominique Courault, Fabrice Flamain, Guillaume Pouget, Claude Doussan, Marcel M El Hajj, Samer K Al-Mashharawi, Matthew Mccabe, Marta Debolini

► To cite this version:

Pierre Rouault, Dominique Courault, Fabrice Flamain, Guillaume Pouget, Claude Doussan, et al. Integration of sentinel-2 data into the SIMETAW model for assessing irrigation water requirements and evapotranspiration. Agricultural Water Management, 2025, 317, pp.109637. 10.1016/j.agwat.2025.109637. hal-05266290

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# Integration of sentinel-2 data into the SIMETAW model for assessing irrigation water requirements and evapotranspiration

Pierre Rouault<sup>a</sup>, Dominique Courault<sup>a,*</sup>, Fabrice Flamain<sup>a</sup>, Guillaume Pouget<sup>a</sup>, Claude Doussan<sup>a</sup>, Marcel M. El Hajj<sup>c</sup>, Samer K. Al-Mashharawi<sup>c, ID</sup>, Matthew McCabe<sup>c</sup>, Marta Debolini<sup>b</sup>

## ARTICLE INFO

**Keywords:**

Model calibration

Orchards

Evapotranspiration

Water management

Crop coefficient

Remote sensing

## ABSTRACT

Water management for crop irrigation has become a major consideration given the global scale implications of climate change and its impacts on water scarcity and security, with an increasing frequency of water restrictions in many locations during the summer months. As a consequence, decision-support tools are needed to evaluate scenarios of various agricultural practices in order to provide improved water management strategies. In this study, water requirements of orchards were assessed at the plot scale using the Simulation of Evapo-Transpiration of Applied Water (SIMETAW) model. This model was applied across a small Mediterranean watershed to evaluate and compare various water use scenarios. The standard version of SIMETAW integrates a simplified water balance model with a single uniform soil layer to calculate the crop water needs at the plot scale (Quantity of Irrigation QI). Evapotranspiration is computed using a crop coefficient specific to each crop type (Kc) and a water stress coefficient (Ks) derived from the water available in the soil (SWC). In this standard version, plots are categorized by crop type, with fixed Kc unchanged across years and between plots of the same crop type. To better capture the spatial variability of crops at the watershed scale, SIMETAW was modified to incorporate remote sensing data. Kc for each crop in the basin was estimated using the relationship between normalized difference vegetation index (NDVI) and fractional vegetation cover (FCOVER) derived from Sentinel-2 images. The modified SIMETAW model was then compared to the standard version in estimating irrigation water requirements, from the field level to the watershed scale. Model calibration was undertaken using distributed soil moisture measurements collected from 2021 to 2024. Data on water volumes used for irrigation together with farmer surveys were used to assess model performance in simulating QI both at the plot level, for thirteen distributed farms, and at the basin scale. Comparisons between the standard model version and the modified version (i.e. using Kc from remote sensing) are examined in relation to the accuracy of obtained water volumes. The model simulated soil water content (SWC) across the monitored orchards with good accuracy, providing R² values of 0.7 and 0.9 for simulation in 2022 and 2023 respectively. The simulation of the quantity of irrigation water in farms shows a strong correlation with reported data (R= 0.81). The application of the SIMETAW model to all the studied crop types offered an improved performance by incorporating the remote sensed Kc compared against the standard version, with an 17% improvement of the water irrigation volume distributed against basin averaged irrigation volumes. Overall, the incorporation of Sentinel-2 data significantly enhanced the performance of the model by accounting for the variability in crop development across the catchment scale. By considering a typology of farms according to the irrigation practices (3 classes were defined according to observations and surveys, high, medium and low irrigation), the model was able to estimate water requirements with 10% difference to the values provided by the manager of the water resources at regional scale ("Association Syndicale Autorisée" - ASA). Such information can prove particularly useful in helping to guide water managers in making informed decisions with regards to the dynamic management of water resources.

<footnote><sup>a</sup> UMR 1114 EMMAH INRAE, Avignon University, Agroparc, Domaine St Paul, route de l'aérodrome, Avignon 84914, France</footnote>

<footnote><sup>b</sup> CMCC Foundation – Euro-Mediterranean Centre on Climate Change, IAFES Division, Sassari, Italy</footnote>

<footnote><sup>c</sup> Hydrology, Agriculture and Land Observation (HALO) Laboratory, Division of Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology (Kaust), Thuwal, Saudi Arabia</footnote>

<footnote>* Corresponding author.
E-mail address: dominique.courault@inrae.fr (D. Courault).</footnote>
# 1. Introduction

Across the Mediterranean, agriculture is placing additional pressure on already limited freshwater resources, leading to an increasing competition between the various water users and sectors. Almost 25% of the arable land surface in the Mediterranean is under irrigation (Azzopardi et al., 2020). With growing demand for water allocation, as well as an increased variability in water availability, irrigators are facing a greater frequency of imposed restrictions during the summer months (Eekhout et al., 2024), threatening the viability of many agricultural systems. Methods for providing the right amount of water at the right time, often based on the estimation of the actual water requirements of crops, are still relevant and needed. The use of in-situ sensors for monitoring the soil water status can provide alerts that aid improved water management, but their coverage remains limited even for low-cost sensors (Santhiya and Radhakrishnan, 2024; Vandôme et al., 2023). Various modeling approaches to estimate crop water consumption have also been proposed in the literature, most of which can be classified according to the processes taken into account, the required variables and the range of spatial and temporal scales covered (Aloui et al., 2023). An additional difficulty in simulating the water balance of agricultural fields – and orchards in particular – is their heterogeneous structure. In the latter case, orchards present inter-row and row structures that make modeling the system considerably more complicated. The introduction of vegetated groundcover in the inter-row can further impact the cropping systems and associated water consumption. Mechanistic models detail the main water transfer processes and often require a lot of input parameters describing the soil-plant system. Among them, the Hydrus model (Upadhyaya et al., 2024) is one of the most well-known and has been applied not only to annual-crops but also to orchards (e.g., apple trees, Nazari et al., 2021). The WaLIS (WAtEr baLance for Intercropped Systems) model has been developed to consider the water resource partitioning in cases where inter-row groundcover might be present, or to evaluate the water regime in intercropped systems (Gaudin and Gary, 2012). Semi-empirical models on the other hand, have a simplified representation of processes and generally involve fewer parameters to calibrate. Ultimately, a compromise is almost always required between simplified approaches to cover various crop systems and the most realistic representation of the physical processes, which often requires a large number of parameters to be calibrated and is therefore difficult to spatialize according to the data actually available.

Among the simplified approaches, the most commonly applied to estimate the water needs (FAO56 method) is based on the use of a crop coefficient (Kc) which is defined for each crop type and then combined with the potential evapotranspiration to estimate crop water use (Allen et al., 1998). Kc calibrations have been carried out for a large number of regions, crop types, soil and climatic conditions, with a large body of results assessed against in situ measurements using lysimeters or values of evapotranspiration estimated from the Bowen ratio or eddy correlation techniques (Allen et al., 2005; Pereira et al., 2015).

Whereas numerous papers have reported studies with Kc calibration on cereals, grasslands and vegetables, less literature exists about perennial crops. Likewise, measurements are more difficult to acquire over heterogeneous land with large row and inter-rows, and even when Kc values are proposed for these conditions, they are often very specific and do not differentiate species and/or varieties (Andreu-Coll et al., 2024; Guodong et al., 2016). While the FAO56 method is easy to apply at the regional scale, since it requires very few data inputs, it also presents some limitations, e.g. by not considering the water retention capacity of the soil. Additional coefficients have been proposed to improve this point, such as the “dual Kc approach”, which splits the Kc factor into a plant component Kcb and a soil component Ke, with the latter variable requiring information on the soil properties and the soil moisture content. Ke can then be estimated from using a range of methods (see Allen et al., 2005; Rallo et al., 2021; Ramos et al., 2024). The evapotranspiration partitioning using this dual Kc<sub>c</sub> method has been tested for olives

(Er-Raki et al., 2010; Rousseaux et al., 2009), vineyards (Ferreira et al., 2012), coffee (Flumignan et al., 2011), citrus (Er-Raki et al., 2009) and peach (Paço et al., 2012) orchards.

Other models based on the consideration of the energy budget can also be used to estimate the latent heat flux (and thus evapotranspiration). Often these approaches use remote sensing data in the optical and thermal ranges, as surface temperature and albedo or net radiations can be derived from various satellite data (Courault et al., 2005; Maes and Steppe, 2012). The exchanges between soil, plant and the surrounding atmosphere involve different resistance schemes, which are more or less complex to compute, depending on the data available (Derardja et al., 2024). A large amount of research, and related literature, has explored such approaches to map evapotranspiration (Anderson et al., 2007; McCabe et al., 2019; Olioso et al., 2023; Yang et al., 2023). However, few applications have been carried out on fruit trees (Al-Bakri et al., 2023; Ben-Gal et al., 2009; Cammalleri et al., 2010; Ferreira et al., 2025; Giuliani and Flore, 2000; Sepulcre-Cantó et al., 2007; Villalobos et al., 2013). A major constraint on the use of thermal data from operational satellites is their low spatial and temporal resolutions (daily data at 1 km or 100 m every 16 days) and limited suitability for monitoring at the field scale or helping water management at the small farm scale.

Alternative methods requiring fewer calibration parameters have been proposed, amongst which the SIMETAW (Simulation of Evapotranspiration of Applied Water) model (Snyder et al., 2012) has been used to assess the future agricultural water demand at the state level. The main features of this model are the simulation of daily evapotranspiration from a dual coefficient approach, and the computation of an irrigation schedule for each field and year. A simplified water balance is computed at a daily time step with a minimal set of soil information (soil depth and maximum water holding capacity) and of irrigation parameters (start and end period, duration, and flow). The SIMETAW model was integrated into a GIS spatial platform and applied at a large scale over Europe by Masia et al., (2021) for analysing the impact of climatic change on water requirements and also on AmeriFlux stations in three states of the United States by Kibria et al. (2021). It was also used at a finer spatial scale for peach and orange trees and wheat crops in California (Mancosu et al., 2016) giving satisfactory results in comparison with ground measurements as confirmed by statistically significant analyses. The main advantage of this modeling approach is that it computes both evapotranspiration and irrigation quantities from relatively few input data. One limitation is that only a single set of parameters is provided for each crop type. While the Kc value varies with the season for each crop type, the model does not account for spatial variability within the same crop type across different fields in a watershed. Such variability may arise from various factors like crop variety (early or late), or farmers' management practices and environmental contexts.

Against this backdrop, several scientific questions arise, including: (1) is it possible to use Sentinel 2 data to assess crop coefficients of various Mediterranean orchards and include them in the SIMETAW model to better estimate the water requirements of orchards at the watershed scale?; (2) How does accounting for the spatial variability of plots improve upon the standard version?; and (3) What is the impact of various irrigation strategies on the total water quantities used at the watershed scale?

The central aim of this study was to address these questions while assessing the performance of SIMETAW across a small Mediterranean watershed, where most of the agricultural land use is represented by irrigated orchards and vineyards.

In answering these questions, the paper is structured as follows. In the first section, the main assumptions and equations of the SIMETAW model are presented and the underlying approach is described. A description of the database acquired for the specific case study is presented in Section 2, as is the approach used to introduce remote sensing data into the model. Finally, the results are outlined in Section 3 and further discussed in Section 4.
## 2. Methodology

### 2.1. SIMETAW model

The SIMETAW model is a crop-soil-water balance model that computes daily crop evapotranspiration (ETa), irrigation requirements (QI) and schedules using a dual coefficient approach (Kc for the crop coefficient and Ks a stress coefficient computed from the water balance) (see Fig. 1). The estimated volume of irrigation is the water needed to refill the water losses from the root zone due to the evapotranspiration. Several papers have described the various components of the model, particularly those of Mancosu et al., (2016), Masia et al., (2021) and Snyder et al., (2012). In the following sections, only the main equations and assumptions of the SIMETAW model are briefly presented. The SIMETAW R version (implemented in the R programming language), described by Masia et al., (2021) is employed herein, using the standard dataset of crop parameters supplied with the model.

In order to estimate irrigation requirements, the Daily Soil Water Depletion ($SWD_{day}$) is determined as:

$$SWD_{day} = FC - SWC_{day} \qquad (1)$$

$$FC = 2 \times PAW \qquad (2)$$

$$PAW = Root.depth \times \Theta \qquad (3)$$

where FC is the soil field capacity and PAW, the Plant Available Water. PAW depends on the root depth (which is fixed as a constant parameter of the model). The equation used to compute FC is a simplified approach proposed by Snyder et al. (2012). $SWC_{day}$ is the soil water content of the current day estimated from the water balance model considering local rainfall provided at a daily time step in input data (Eq. 4). The basic equation for the water budget considers Irrigation (Ir), Rainfall (R) and actual evapotranspiration (ETa), all in mm. In Eq. 3, $Theta$ is fixed at 0.175 mm per mm according to values given in Masia et al., (2021) and in Swelam, (2012). Orang et al. (2004) defined $Theta$ from the soil texture and proposed three values: 0.075 mm/mm for “light soils”, 0.125 for “medium soils” and 0.175 for “heavy soils”. According to Eq. 2,

the $Theta$ value can be estimated from FC value and the root depth. In numerous papers, FC can also be estimated from pedotransfer functions from the soil texture (see for example: Mahfouf and Noilhan 1996).

$$SWC_{day} = SWC_{day-1} - ETa_{day} + R_{day} + Ir_{day} \qquad (4)$$

Actual evapotranspiration (ETa) is the product of the potential evapotranspiration (ETO), computed from the Penman Monteith equation from standard climatic data (Allen et al., 1998) multiplied by a crop coefficient Kc and a stress coefficient Ks (detailed in Eq. 10).

$$ETc = ETO \times Kc \text{ and } ETa = ETc \times Ks \qquad (5)$$

where ETc is the daily maximum evapotranspiration for a given crop

#### 2.1.1. Crop coefficient (Kc) estimation

In the standard version proposed by Snyder et al., (2012), the determination of Kc slightly differs from the FAO method (Allen et al., 1998). In the SIMETAW model, nine parameters are required to define the Kc pattern per crop (see Table 1 and Fig. 2a). In this approach, five distinct periods of crop development (A to E) are defined (A before the planting date and E after the harvest). The different dates separating these periods from the sowing to the end of the vegetative cycle or the harvest date must be provided as input data for each crop, as well as the percentage of the length of each period of the cycle (growth maturity, senescence) with their corresponding Kc values (see Fig. 2a and Table 1). All these parameters try to follow the phenology of the plant.

Different Kc values were used during the growing season: Kc_B refers to the crop coefficient at date B, Kc_C is used to calculate Kc midseason which is applied from date C to date D and Kc_D is applied at the date E. Before the date B and after the date E, a null coefficient is applied for all the crops because the assumption is to consider that there is no cover at these periods (most of the orchards (including olive trees) have the same behavior). This point can be debatable since grass in the inter-row of the orchards can be observed and induce evapotranspiration. This point will be improved in the modified version described later. The season begins with rapid growth from Date B. During the second and the fourth period (B and D) of irrigation, a linear relationship is calculated between the Kc_B and the Kc midseason and between the Kc midseason and the Kc_D.

<figure><img src="image_3.png" /><figcaption>Fig. 1. General description of the main functional packages of the SIMETAW model (boxes in blue are related to the water balance and boxes in green are related to the crop characteristics). Dashed arrows indicate the feedback effect of a calculated value on the computation for the following day. (NAC: Net Application Content; MAD: Management of Allowable Depletion).</figcaption></figure>
<table><caption>**Table 1**

Input parameters used to compute Kc for each crop type and values for plum and peach trees given in the standard version of SIMETAW_R.</caption><thead><tr><th>Category of required parameters</th><th>Parameter name</th><th>Variability for 18 crop classes</th><th>Plum trees</th><th>Peach tree</th></tr></thead><tbody><tr><td>planting date/or sowing (date A)</td><td>A</td><td>[01/01-01/05]</td><td>March 1st</td><td>March 1st</td></tr><tr><td>Date of harvest/ or end of leaf (date E)</td><td>E</td><td>[18/08 - 30/12]</td><td>October 15th</td><td>August 15th</td></tr><tr><td>period duration in percentage of Kc (%)</td><td>Perc_B</td><td>[0-0]</td><td>0</td><td>0</td></tr><tr><td rowspan="5">Kc values for the specific period</td><td>Perc_C</td><td>[22-50]</td><td>50</td><td>50</td></tr><tr><td>Perc_D</td><td>[67-90]</td><td>90</td><td>90</td></tr><tr><td>KcB</td><td>[0.2-1.15]</td><td>0.55</td><td>0.45</td></tr><tr><td>KcC</td><td>[0.55-1.2]</td><td>1.15</td><td>0.85</td></tr><tr><td>KcD</td><td>[0.55-1.2]</td><td>1.15</td><td>0.85</td></tr><tr><td></td><td>KcE</td><td>[0.4-1.15]</td><td>0.65</td><td>0.6</td></tr></tbody></table>

The Kc midseason is calculated with a climate correction following Eq. 6:

$$Kc_{\text{midseason}} = KcC + 0.1654 \times (ETO_{\text{midseason}} - 7.3) \times (KcC - 1) \quad (6)$$

Where ETO\_midseason is the midseason average value of daily ETO during the C period and Kc\_C the fixed value of Kc for the C period which depends on the crop type.

In the standard version, parameters for 18 perennial classes were already defined: almonds, apples, kiwifruits, walnuts, peaches, plums, avocados, grapefruits, lemons, dates, evergreens cultures, olives, oranges and small oranges and vineyards (table and wine grapes), (see examples in Fig. 2b). A distinction was made between plum and peach trees, as measurements were available to calibrate the model coefficients. On contrast, no data were available for cherry or apricot trees in the provided database. The methodology proposed to define Kc of the orchards in this study area is described in Section 2.2.

The Kc value before the planting date (A) and after the harvesting date (E) is fixed as 0, which assumes that there is no evapotranspiration. It does not accurately represent reality. To correct this point, a Kc\_day coefficient has been added in the standard model to consider the evaporation of bare soil. A first step consists in using the formulation proposed by Ventura et al., (2006) described as follows in Eq. 7.

$$Kc_{\text{day}} = 2.54 \times \sum ETO_{\text{day}}^{-0.5} \quad (7)$$

where ΣET0 is the cumulative ETO from the beginning of the cultivation year up to the given day (d).

A second step computes a second KE2 coefficient according to Eq. 8 (Allen et al., 2005).

$$KE_2 = 1.22 - 0.04 \times ETO \quad (8)$$

The final Ke is the lowest value between KE2 and Kc\_day.

### 2.1.2. Stress coefficient (Ks) estimation

The stress coefficient (Ks) depends on the soil water depletion (SWD) computed from the water balance and two parameters defined in the input data: Plant Available Water (PAW - Eq. 3) and the allowable depletion (AD expressed in % of the water supplied). AD represents the amount of available water corresponding to the yield threshold depletion (the default value in the model is set at 50 %).

$$Ks_{\text{day}} = 1 - \frac{100 \times \left( \frac{\text{SWD}_{\text{day}}}{\text{PAW}_{\text{day}}} \right) - \text{AD}}{100 - \text{AD}} \quad (9)$$

### 2.1.3. Estimation of irrigation practices

The irrigation dose, indicated as the Management of Allowable Depletion (MAD), is calculated according to the irrigation characteristics (Table 2). Three techniques are possible: drip, sprinkler or flooding. For each irrigation type, six parameters are involved in the following equation and described in Table 2.

$$MAD = \frac{WAPF}{Pfap} \times \left(1 - \frac{Gr}{100}\right) \times Ot \times AR \times Du \quad (10)$$

where MAD is the water amount which must be apply to refill the soil to the field capacity for a day (in mm); Ot is the runtime per irrigation event and depends on the irrigation technique (for example for drip irrigation, it can be 1 h per day in summer); AR is the application rate (i. e. the flow of the used devices expressed in mm/h); Du is the distribution uniformity of irrigation (in m²/m²); WAPF is the percentage of water allocation as Percentage of Full (to describe the irrigation drift) and Pfap the percentage of full cultivated area of the field, with WAPF and Pfap are most often set at 100 %.

The model simulates an irrigation schedule in terms of Net Application Dose (NAD), i.e. the application of the MAD dose which is defined according to the SWD of the given day. If the depletion is higher than the

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** a) Schema of the Kc estimation with the different required parameters for each crop type. For this example, inflection points in the Kc curve occur at 20 %, 50 % and 80 %; b) examples of Kc graphs prescribed for different orchards in the standard version provided with the SIMETAW_R script (the input values for the inflection points and duration percentages of two classes (plum and peach orchards) are given in Table 1). Some colored lines overlapped for the first and last periods. Olive, peach, plum trees present also null values at the beginning and at the end of the year.</figcaption></figure>
<caption>**Table 2**</caption>

Standard parameters proposed in the irrigation computation by the standard SIMETAW Model and typical values given for two modes: M: micro-sprinkler and D: drip.

<table><thead><tr><th></th><th>Irrigation parameter</th><th>Order of magnitude for drip/ sprinkler</th></tr></thead><tbody><tr><td>Ot</td><td>runtime (number of hours of irrigation)</td><td>D:24 h/event; M:24 h/event</td></tr><tr><td>AR</td><td>Application rate (flow in mm/h)</td><td>D:1.3; M:0.7</td></tr><tr><td>Du</td><td>Distribution uniformity (in m²/m²)</td><td>D:0.85; M:0.8</td></tr><tr><td>Gr</td><td>Gravity runoff</td><td>0 (not used in this study case)</td></tr><tr><td>WAPF</td><td>Water Allocation as Percentage of Full water supplied</td><td>100 %</td></tr><tr><td>Pfap</td><td>Percentage of full plant area irrigated</td><td>100 %</td></tr></tbody></table>

possible MAD dose, the NAD dose is applied. The date of irrigation depends on the soil water content computed from the water balance and the threshold value fixed for AD (Mancosu et al., 2016). The annual sum of each applied MAD corresponds to the total volume of annual irrigation of the field (QI). The schedule of irrigation days was named NAC (Net Application Content). The number of irrigations (Ni) per year is also computed. Eq. 11 shows the relationship between Annual irrigation, Ni and MAD.

$$QI(Annual \ irrigation) = MAD \times Ni \qquad (11)$$

In addition to the input data described in Table 1 (for Kc computation) and Table 2 (for the estimation of irrigation volumes), the other data necessary to run the model are detailed in Table 3.

### 2.1.4. Ouvèze-Ventoux watershed and available data

SIMETAW was applied to a small Mediterranean watershed (99 km²) in the Ouvèze-Ventoux watershed in southeast France (center 44°13 N - 5°14E). The Ouvèze river flowing into the Rhône is the main water resource for irrigation in the area. The predominant irrigation techniques are either micro-sprinklers or pipes with drippers (Roux et al., 2019). Most crops are located on a sandy to loamy plateau ranging from 205 m to 639 m above sea level (a.s.l). The main crops according to LPIS<fnref n="1" /> 2020 are vineyards, representing 46 % of the area, with 323 ha wine vineyard and 65 ha of table vineyard and orchards comprising 31 % of the area. Table vineyards, dedicated to the production of table grapes, differ from wine vineyards (for only wine production) in their management practices. Table vineyards are generally always irrigated in Mediterranean regions, while wine vineyards are not irrigated (or according to exceptional authorization). Most represented orchards are cherry trees (Prunus avium), covered by a protected designation of origin (PDO) "Cerises du Mont-Ventoux" (Turpin, 2022) (Fig. 3). Since vineyards tend to be lightly irrigated (<150 mm/year) or not at all (particularly wine vines, in order to comply with regulations), orchards are the crops that require the most water in this area (300 mm in average). Other crops in the area include truffle trees (often little irrigated <20 mm/year) and annual crops with little or no irrigation (meadows, cereals, very small areas of market gardening, etc.).

The climate, as recorded by the Carpentras weather station located 15 km southeast of the area, typically experiences annual rainfall of 650 mm (falling predominantly during the autumn-winter period) and annual average temperatures of 14.5°C and characterized by a hot summer. Table S1 (in supplementary material) summarizes the meteorological characteristics of the period from 2021 to 2023

The distribution of water for irrigation is managed by the Association Syndicale Autorisée of the Ouvèze-Ventoux (ASA-OV) covering more than 1354 ha with pressurized water pipes linking a network of terminals. Each farmer has one or more terminals distributing water to fields. Water consumption is then invoiced to each farmer according his

<caption>**Table 3**</caption>

Other input data required for SIMETAW and range of values commonly used (Rg: Daily global Radiation, R: daily rainfall, RH: Relative Humidity, U: Average of wind speed; Temp: Daily mean temperature in °C).

<table><thead><tr><th>Category</th><th>Code or value</th><th>Comments</th></tr></thead><tbody><tr><td>Daily climatic data</td><td>Rg, R, RH, U, Temp</td><td>Used to compute daily ET0</td></tr><tr><td>albedo</td><td>0.23</td><td>Used to compute Rn in ET formulation</td></tr><tr><td>AD</td><td>50</td><td>Allowable depletion (%)</td></tr><tr><td>Presence/Absence of ground cover</td><td>Code</td><td>Add or not a complementary KcT to account the grass water consumption in inter-row</td></tr><tr><td>Maximal root depth (mm)</td><td>Vary from 50 to 2000 mm</td><td>Maximal depth of roots for a given crop type</td></tr><tr><td>Soil heat flux density (G)</td><td>0 Mj/m²/d</td><td>At daily scale for dense vegetation G= 0</td></tr><tr><td>Theta (mm/mm)</td><td>Vary from 0.1 to 0.25</td><td>Plant available water per unit depth</td></tr><tr><td>Irrigation practice</td><td>irrM1 irrM2</td><td>[R] rainfall (without irrigation) [M: micro-sprinkler or D: drip]</td></tr></tbody></table>

consumption measured on terminals each year by the ASA-OV. For this study, the ASA provided yearly values of water consumption for 13 farmers from 2018 to 2021 and for the whole watershed from 2018 to 2022. These data have been used to validate the outputs of SIMETAW. However, the ASA equipment is old (installed in the 90's) and not regularly monitored by farmers with high water losses in the network being quite usual. For these reasons, the accuracy of this water consumption data is estimated with a margin of error of around 30 % by the president of the ASA (personal communication).

To complete this dataset, accurate surveys were also performed in collaboration with several farmers, selected to cover the variability of the watershed in terms of crops, soils, and water management.

Overall, twenty-one farmers have been surveyed about their agricultural practices to get accurate data at field scale, including information on: start and end of irrigation, dose and technique used, crop cultivar, harvest dates and different labor or pruning/cuts done. These observations were compiled for 648 parcels, which included 359 orchards. The analysis of the variability of the agricultural practices is described in Rouault, (2024) and Rouault et al., (2024b). From these data, the yearly sum of the net application irrigation (NAD) was computed for each field as follows:

$$NAD_{drip} = \left( \frac{L_{tot}}{L_{d}} \times Q_{d} \times t \right) / Surf \qquad (12)$$

$$NAD_{microsprinkler} = (T_{d} \times N_{s} \times Surf \times Flow \times t) / Surf \qquad (13)$$

Where, Ltot: total pipe length, Ld: space between 2 drippers, t: irrigation duration per year, Surf: field surface, Qd: dripper flow, Td: tree density, Ns: number of micro-sprinklers per tree, Flow: sprinkler flow.

The row number is computed for 1 ha from the inter-row distance, then the pipe length is derived per field according to the field size and the row number and considering 1 m of border in each size of the 1 ha plot. Figure S1a (Supplementary materials) shows the irrigation doses per irrigation event values obtained for the different farms and Figure S1b the Ot distributions, i.e. the number of hours per irrigation event, according to surveys. Generally, for drip irrigation, the common practice is to provide water every day or every two days (around 2 or 3 mm/day) between April and October. However, these surveys showed a large variability of drip irrigation practices. Some farms use very little water (minimum calculated 30 mm/year), while others irrigate heavily (maximum calculated around 750 mm/year). There is also a diversity of irrigation techniques with some farmers irrigating by drippers only 1 h each day (from one or two pipes per row) and others more than 20 h but only once or twice a month.

Table 4 shows the values defined for the irrigation parameters of

<footnote n="1">¹ https://www.data.gouv.fr/fr/datasets/rpg/ (LPIS: Land Parcel Identification System)</footnote>
<figure><img src="image_5.png" /><figcaption>Fig. 3. a) Map of landforms in the Ouvèze-Ventoux watershed with the main crops identified by local observation, LPIS database and remote-sensing (some farms include fields outside the watershed); b) sap flow meters installed on two branches on a cherry tree; c) photos of the diversity of agricultural practices (non-grassed and grassed orchards and vineyard); d) drip irrigation on a table vineyard; and e) drip irrigation from two pipes on a cherry tree orchard.</figcaption></figure>

SIMETAW derived from surveys. The most common practice (observed) is an irrigation every day during 2 h. So, the value of Ot has been fixed for this practice at 2. For micro-sprinkler irrigation, there is also a significant variability of practices with some plots irrigated up to 415 mm/ year and other receiving 550 mm/year, while the irrigation time can vary from 2 to 3 h. The Ot value has been set at 2.5 h per event for this practice. For the Du parameter, the same value proposed by Masia et al., (2021) was used, i.e 85 %, because the irrigation located on the row with drip irrigation and between the trees for micro-sprinkler finally covers a large part of the field. Other parameters, such as the Water Allocation as Percentage of Full (WAPF) and Pfap, were set at 100 % considering that these parameters refer to the root distribution in the soil and that the roots cover the whole plot.

Due to the lower dose applied to young orchards according to both surveys and the literature (Franco et al., 2000; Iglesias et al., 2024; Maes and Steppe, 2012; Pérez-López et al., 2007), the Ot value was decreased by considering 1-hour irrigation (Ot=1 h/irrigation event). The identification of young orchards at the watershed scale is possible from a threshold applied to the NDVI using Sentinel 2 images as shown in Rouault et al., (2024b). As these orchards are always drip-irrigated

according to surveys, this field characteristic leads to an irrigation dose (MAD) of 0.6 mm/event. This irrigation dose per event may seem low compared with the variability of irrigation practices observed from our surveys (FigureS1 in supplementary material, there are only 20 % of young orchards in the watershed). The model computed irrigation dose at a daily time step. One limitation of the model is the representation of irrigation practices having a higher dose with a long interval between two irrigations (> 10 days).

The 21 surveyed farms cover around 20 % of the agricultural area of the Ouvèze-Ventoux watershed. For the remaining plots, the farm classification resulting from a deeper analysis of the spatialized databases (LPIS, DEM,<fnref n="2" /> soil and landuse<fnref n="3" /> maps) developed by Rouault, (2024) was used.

Three groups of farms have been identified, including the farms which use a large amounts of irrigation water (average value above 500 mm per year corresponding to farms having mainly irrigated orchards or table vineyards, 500 mm is a threshold obtained from the survey analysis), farms which consume much less (<100 mm/year), characterized by a high proportion of non-irrigated fields, or with cereals and wine grape vineyards. This classification considers the diversity of practices according to farms and plots characteristics (method described in Rouault, 2024). The ratio between these three orchard categories is 20.8 % of heavily irrigated plots, 38.4 % of medium irrigated plots and 40.8 % of “light” irrigated plots.

<caption>**Table 4**</caption>

Irrigation parameters fixed from farm's surveys and MAD dose calculated from this value (these values correspond also to the fields where ground measurements were available).

<table><thead><tr><th></th><th>Drip</th><th>Micro-sprinkler</th></tr></thead><tbody><tr><td>Ot</td><td>2 h/event (1 h for young orchard)</td><td>2.5 h/event</td></tr><tr><td>Ar</td><td>0.7 mm/h</td><td>1.3 mm/h</td></tr><tr><td>Du</td><td>0.85 m<fnref n="2" />/m<fnref n="2" /></td><td>0.8 m<fnref n="2" />/m<fnref n="2" /></td></tr><tr><td>WAPF</td><td>100 %</td><td>100 %</td></tr><tr><td>Pfap</td><td>100 %</td><td>100 %</td></tr><tr><td>MAD</td><td>1.2 mm/event (0.6 mm/event for young orchards)</td><td>2.6 mm/event</td></tr></tbody></table>

### 2.1.5. Ground measurements

Data were also available from ground measurements for some fields which were used for validation at field scale. A micro-meteorological

<footnote n="2">² DEM digital numerical model from https://www.geoportail.gouv.fr/</footnote>

<footnote n="3">³ From (Abubakar et al., 2023)</footnote>
<caption>**Table 5**</caption>

Planting and irrigation characteristics of five orchard fields equipped with soil moisture measurements and representative of the diversity of cherry orchards (1401,3099,3347,50) and young plum orchard (1824) encountered in the watershed (R: rainfed crop (without irrigation), D: drip, M: micro-sprinkler, the irrigation parameters (Ot, Du, Ar and AD) were computed from the Surveys, QI, total quantity of water supplied by irrigation for one year).

<table><thead><tr><th>Identifier of the plot</th><th>1401</th><th>3099</th><th>3347</th><th>50</th><th>1824</th></tr></thead><tbody><tr><td>observed Irrigation Quantity (observed QI)</td><td>Not irrigated</td><td>184 mm</td><td>484 mm</td><td>391 mm</td><td>87 mm</td></tr><tr><td>Inter-row (grass/no)</td><td>Grass</td><td>Grass</td><td>Grass</td><td>Grass</td><td>Grass</td></tr><tr><td>Distance between row (m)</td><td>6</td><td>7</td><td>5</td><td>7</td><td>5</td></tr><tr><td>Plantation age</td><td>&gt; 10 y</td><td>&gt; 15 y</td><td>&gt; 10 y</td><td>&gt; 15 y</td><td>&lt; 5 y (young)</td></tr><tr><td>Field Capacity (FC)</td><td>0.257</td><td>0.230</td><td>0.242</td><td>0.255</td><td>0.268</td></tr><tr><td>Permanent Willing Point (PWP)</td><td>0.128</td><td>0.117</td><td>0.08</td><td>0.103</td><td>0.124</td></tr><tr><td>Theta</td><td>0.128</td><td>0.115</td><td>0.121</td><td>0.138</td><td>0.134</td></tr><tr><td>Irrigation Method</td><td>R</td><td>D</td><td>M</td><td>D</td><td>D</td></tr><tr><td>Operation time (Ot - hours/irrigation event)</td><td>0</td><td>2</td><td>2.5</td><td>4</td><td>1</td></tr><tr><td>Application rate (Ar - mm/h)</td><td>0</td><td>0.85</td><td>1.3</td><td>0.85</td><td>0.85</td></tr><tr><td>Distribution uniformity (Du - m²/m²)</td><td>0</td><td>0.7</td><td>0.8</td><td>0.7</td><td>0.7</td></tr><tr><td>Allowable Depletion (AD)</td><td>10 %</td><td>50 %</td><td>50 %</td><td>50 %</td><td>50 %</td></tr></tbody></table>

station was installed in May 2022 in the centre of the largest cherry orchard (4.5 ha) of the watershed to measure relative humidity, air temperature, wind speed in the two horizontal directions, net radiation, rainfall, surface temperature, soil and heat fluxes at a 15 min time step (see in supplementary material Part 1 the description of the sensors Table S4). For previous years, climatic data were taken from the weather station of Carpentras.<fnref n="4" /> Daily potential evapotranspiration was computed from the Penman Monteith equation and used directly as input data in the SIMETAW model.

Soil moisture was measured in five different orchards, with capacitive probes arranged at varying depths between 0 and 50 cm (0–15, 15–30, 30–50 cm). Continuous data were averaged every 30 min from 15/05/2021 up to 20/12/2024, on both the row and the inter-row. The five fields were selected to cover variability encountered at the watershed scale in terms of soil, irrigation practice and inter-row management (Table 5). Additionally, measurements of water retention in the soil were conducted and values for Field Capacity (FC) and Permanent Willing Point (PWP) were obtained at respectively 0.3 bar and 15 bars. These values allow to estimate the available water reserve (AWR) for 1 m soil depth. AWR varied between 129 mm to 162 mm. The AD value was modified at 10 % against 50 % generally for the non-irrigated orchard, as this value was more relevant according to the acceptable dynamic simulated of SWC. Table 5 reports the values of irrigation parameters used for the SIMETAW calibration for these 5 equipped orchards fields which are representative of the diversity of orchards of the watershed.

For the largest field (identifier 3099 – 4.2 ha, cherry orchard), tree water-uptake rates were monitored using sap flows (SFM1x-ICT International) located at four different locations in the field (Al-Mashharawi et al., 2024). The four locations were not collecting data over the same periods. At each location, two sensors were installed on two branches of one representative tree. It was assumed that the measured water-uptake rate of the selected trees was similar to the majority of trees within that

field. The collected water uptake data were processed for 10 min intervals using the Sap Flow Tool software (ICT International Pty. Ltd). The total flow rate of each of the four trees was determined by taking the average of the two SFMs in a tree and multiplying it by the number of branches. Finally, the average flow rate of the four trees was multiplied by the number of trees in the field obtained from ground observations (Rouault et al., 2024b) and divided by the area of the field. This measurement gives an estimation of the average transpiration of trees in this field and can be compared to the value obtained with the model.

Fig. 4 shows the different measurements of the field 3099 for the year 2022. Even if drip irrigation brings water every day during the summer (2 h/day – 1.5 mm), that amount is insufficient to maintain the water stock at a high level. The climatic demand is high (ET0 >5 mm/day in July and in August up to 7.5 mm/day) and the transpiration derived from the sap flow average is around 3.5 mm/day. The stock of water available in the soil, integrated over a soil depth of 50 cm, decreases in summer but does not reach the level of the permanent wilting point (PWP) of the plot which, for this example, is 51 mm.

### 2.1.6. Sentinel data and Kc estimation from remote sensing variables

Sentinel 2 images acquired on the Ouvèze watershed (tile 31TFJ level 2 A) were downloaded each week from the Theia platform for the period 2021–2023. These approximately 100 images per year for each field were corrected for atmospheric effects according to the methods described by Hagolle et al., (2008), reflectance are provided for each spectral band with cloud masks at each acquisition date. Finally, 94 cloudless images were used in 2021, 78 in 2022, 109 in 2023. The commonly used Normalized Difference Vegetation Index (NDVI) (Baret and Guyot, 1991; Virlet et al., 2015) was computed for each available image. Additionally, the main biophysical variables, including fractional vegetation cover (FCOVER) were computed using the BVNET model (Baret et al., 2007; Courault et al., 2022; Weiss et al., 2020; Yan et al., 2019). The shapefile containing all the field boundaries of the watershed was overlaid on all NDVI and FCOVER images and mean values computed for each field and each date (the cloudy images were not considered). As some gaps exist due to cloudy dates, the “WhittakerFilt” function in R (Vuolo et al., 2011; Whittaker, 1935) was used to temporally interpolate the data and obtain daily values for each field. The crop coefficient, Kc, was computed according to the method described in Saadi et al. (2015). Basically, the method considers a linear relation between FCOVER and NDVI and considers a Kc= 0 for a NDVI corresponding to a theoretical FCOVER= 0 and a maximum Kc for a NDVI relative to a theoretical FCOVER= 1. Kc is interpolated linearly from NDVI between these two bounds. The correlation between NDVI and FCOVER was analyzed for each field and allowed to compute Kc following Eq. 14 as a function of NDVI and FCOVER (see Fig. 5a). The maximum Kc value is selected from a table proposed by Allen and Pereira, (2009) (see supplementary material Table S2) and is dependent on the crop and its management. For example, in the case of cherry orchards, a maximum Kc value at Kcmax= 1.15 was considered for non-grassed fields with high density of trees and Kcmax = 1.2 for grassed orchards with the same density classes, which is representative of a fairly dense, non-weeded cherry orchard.

$$Kc = \frac{(NDVI_{day} - NDVI_{FCOVER0})}{NDVI_{FCOVER1} - NDVI_{FCOVER0}} \times Kc_{max} \quad (14)$$

with NDVI$_{day}$ the NDVI value for each acquisition date, NDVI$_{FCOVER0}$ the NDVI for a theoretical totally bare soil (FCOVER=0 deduced from the graph Fig. 5a), NDVI$_{FCOVER1}$ the NDVI value for a theoretical full covered soil (FCOVER=1) and Kc$_{max}$ the Kc$_{mid}$ proposed by the FAO method (Allen and Pereira, 2009), denoted by a value of 1.15 in Fig. 5b.

Fig. 6a shows the resulting Kc obtained from Eq. 14 for a) different cherry trees in 2022, and in Fig. 6b) for the same field (plot 3099) for the three studied years, compared to the original Kc values (in red) prescribed in the standard version of SIMETAW for peach trees (considered

<footnote n="4">⁴ https://donneespubliques.meteofrance.fr/metadonnees_publiques/fiches/fiche_84031001.pdf</footnote>
<figure><img src="image_6.png" /><figcaption>Fig. 4. Ground measurements on the largest cherry tree field (3099) in 2022 with a) ET0 (mm/day), b) water uptake from sap flows measurements (mm/day); c) water stock (mm/day); and d) daily rainfall (mm/day). The two vertical red lines show the irrigation period.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 5. a) Linear relationship between FCOVER and NDVI computed for a cherry orchard (plot 3099) in 2022 b) Example of Kc extrapolation from the NDVI for a NDVI value at 0.5 from the linear relation between the maximal and minimal NDVI identified into the Fig. 5a.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** a) Comparison between *Kc* calculated from remote sensing for different cherry trees in 2022 and the proposed value from SIMETAW (in red) in its standard version, for peach orchard and b) *Kc* comparisons between 3 years for the same plot (3099) with *Kc* for the standard version of SIMETAW.</figcaption></figure>

here as the closest crop to cherry trees). The *Kc* values from the standard version of SIMETAW are higher in the summer period than values derived from remote sensing data, and do not vary according to the fields and to the different climatic years. The use of Sentinel 2 data allowed to consider the spatial variability between the fields which can be due to different irrigation practices, inter-row management, pruning, soil or cultivars (early or late varieties). The differences observed between the three studied years (Fig. 6b) are mainly explained by the climatic variations. With 2021 being wetter than a particularly dry 2022, the leaf development was consequently impacted by the drought in summer and *Kc* is logically lower for this period. Due to the higher cloud cover, more important in winter associated with the observation of optical data by Sentinel 2 (Hagolle et al., 2010; Li et al., 2022), the estimated *Kc* values are fixed with a constant value before day 74 and after day 333 of the year. During this period, the value was arbitrarily set at 0.2, representing a common value for winter herbaceous cover under orchards. This value does not influence the estimate of irrigation water consumption because it is outside the irrigation period.

To define *Kcmax*, the management of the inter-row and the tree density are needed. Information at the watershed scale on whether or not plots are grassed in the inter-row and on the tree density are provided by the methods described in Rouault et al., (2024b). Classifications were made using images acquired from very high spatial resolution Pleiades and Google-satellite-hybrid data.<fnref n="5" /> The density diagram proposed in the supplementary material (Figure S2) shows the variability of the density of trees per hectare. Three density classes were defined considering 1/3 of the distribution (each class has the same number of fields).

## 2.2. Simulation strategies used with the SIMETAW model

The analysis of the key parameters impacting the most the water stock was assessed by means of a preliminary sensitivity analysis carried out without automatic method. The key parameters chosen to vary were: Theta, OT, DU, Kc and AD within a realistic range, based on the observations (Rouault, 2024). The main parameters impacting the soil moisture are Theta (see results in supplementary material figure S3), and Ot, also in agreement with Mancosu et al. observations (2016).

The SIMETAW model was evaluated at three scales: the plot, farm and watershed (see Fig. 7). At the plot scale, the performances of the simulations were assessed by comparing daily soil moisture variability to the water stock measurements for the five monitored cherry orchards, and for the field 3099 (where sap flow data were available), a comparison was added of the simulated evapotranspiration (Eta) with the values derived from the average of sap flow measurements. Classical

criteria were computed, including the correlation coefficient (R), standard error (RMSE) and bias between simulation and observations. The correlations between simulations and observations were significant if the P values were < 0.05. Additionally, for the five fields described Table 5, accurate data on the irrigation practices are known from surveys, so the net irrigation applications and the total year volumes simulated by the model were also compared to the observations and to the data provided by the ASA. At farm and watershed scale, only the ASA data were used to evaluate the simulations of irrigation volumes per year (Fig. 7).

In previous applications, SIMETAW tended to be used at larger scales, with climatic data from ERA5 climatic projections or SAFRAN data at coarse spatial resolution (>= 8 km for one grid) (Kibria et al., 2021; Masia et al., 2021). For this study, the assessment of ETO by SIMETAW was not necessary as more precise local data is available which directly provided daily potential evapotranspiration (ETO). With the size of the watershed being relatively small (< 12 km and 8 km in length and width) climatic variations are therefore considered negligible on this scale.

A constant soil depth at 1 m was considered for the simulations of the whole watershed. The 1 m maximal soil depth was also considered for each field due to the presence of a high density of rocks at depth in soil which restricts root expansions. Even if this value depends on the soil type, it is consistent with values found in the literature (Li et al., 2020: maximum effectiveness of cherry roots of 60 cm, Kadayifçi et al., (2010): around 1 m for apple trees and Doorenbos and Kassam, 1979 1.2 m for each of their studied cultivated trees). For the vineyard's simulations, since only information practices from the farmers surveys were available, the Ot parameter was adjusted according to this information (Ot was set at 2 h/event and Kc was computed for each field from remote sensing data). Table S2 in the supplementary material shows the parameters retained for the table vineyards.

Different simulation cases were considered as irrigation scenarios according to assumptions made at the watershed scale. These situations were compared and described in Table 6. Three irrigation strategies were simulated considering: a) "heavy" irrigation (S1): farmers providing significant amounts of water to orchards (parametrisation corresponding to the field 3347 with micro-sprinkler, called "heavy" in Table 6), b) famers providing medium amount of water irrigation less than 300 mm, parametrisation corresponding to the field 3099 called "medium irrigation" in Table 6 and c) "light" irrigation, farmers providing limited water by irrigation, (parametrisation corresponding to the field 1824, young plum trees, see Table 5). For each irrigation strategy, the assumption is to consider the same irrigation practice for all the fields of the watershed. For those three strategies, comparisons were performed between simulations using the standard version of SIMETAW (simulations 1 without using remote sensing information), and simulations using *Kc* computed from remote sensing (S2RS and S3RS). The differences between S2 and S3 are as follows: S3 considered all the plots with *Kc* computed for each field from Sentinel 2 data, and S2 considered only the representative plot for the irrigation class studied. The integration at the watershed scale is then achieved by taking into account the surface covered by the orchards. The *QI* simulated for the plot was multiplied by the total surface area corresponding to this crop type in the watershed, i.e. respectively 338 ha for orchards and 40 ha for table vineyards. The performances are evaluated by comparison to the ASA data provided at the watershed for different years.

A final case was added more related to the ground observations, mixing these three irrigation strategies with the percentages presented previously for the areas with light, medium or heavily irrigation. The outputs of irrigation quantity (QI) simulated for the 6 years were compared to the ASA data to assess the global accuracy of the model.

<footnote n="5">⁵ https://qms.nextgis.com/geoservices/1135/</footnote>
<figure><img src="image_9.png" /><figcaption>Fig. 7. Main steps to summarize the use of the SIMETAW model combined with remote sensing data.</figcaption></figure>

Table 6

Different irrigation strategies simulated with SIMETAW (S1 without remote sensing data), RS means Kc computed from remote sensing, the parameters for each case correspond to the field noted between parenthesis).

<table><thead><tr><th>Simulation</th><th>Parametrisation and option for the simulation</th></tr></thead><tbody><tr><td>S1light</td><td>Standard Kc values, 1 orchard (param field 1824), 1 table vineyard (53) * watershed surface</td></tr><tr><td>S1medium</td><td>Standard Kc values, 1 orchard (param field 3099), 1 table vineyard (53) * watershed surface</td></tr><tr><td>S1heavy</td><td>Standard Kc values, 1 orchard (field 3347), 1 table vineyard (53) * watershed surface</td></tr><tr><td>S2RSlight</td><td>Rs Kc values, 1 orchard (field 1824), 1 table vineyard (53) * watershed surface</td></tr><tr><td>S2RSmedium</td><td>Rs Kc values, 1 orchard (field 3099), 1 table vineyard (53) * watershed surface</td></tr><tr><td>S2RSheavy</td><td>Rs Kc values, 1 orchard (field 3347), 1 table vineyard (53) * watershed surface</td></tr><tr><td>S3RSlight</td><td>Rs Kc values for all the fields, param orchard (field 1824), 1 table vineyard () * plot surface</td></tr><tr><td>S3RSmedium</td><td>Rs Kc values for all the fields, param orchard (field 3099 and 1824 for young), table vineyard (53) * plot surface</td></tr><tr><td>S3RSheavy</td><td>Rs Kc values for all the fields, param orchard (field 3347 and 1824 for young), 1 table vineyard (53) * plot surface</td></tr><tr><td>S4RSmultimodal</td><td>Application of the ratios of 20.8 % of the S3RShigh value, 38 % of the S3RSmedium and 40.8 % of the S3RSlow.</td></tr></tbody></table>

3. Results

3.1. Simulations obtained on the monitored plots

The simulation obtained for the largest cherry tree field (3099) is presented for 2022 in Fig. 8. As accurate information on the soil was available (see Tables 5 - 6), a good agreement between soil moisture measurements and simulations is observed with an R= 0.86. Some peaks due to heavy rainfall in the observations are slightly lower with the model compared to measurements. Such appear smoothed in the model simulations. *Et*<sub>a</sub> simulated by SIMETAW is slightly greater than *Et*<sub>a</sub> derived from the average of sap flow values, particularly during two periods between April and May and around mid-August.

The same parametrisation was applied in 2021 and 2023 for the plot 3099. The results remain satisfactory with a high correlation between

soil moisture measurements and simulations (R=0.9 for 2021, R=0.86 for 2022, R= 0.71 for 2023 with respective RMSE of 12, 13 and 17 mm). Small differences are also observed for the irrigation variables (for 2022, simulated number: 181 applications and 123 observed, for a total amount of 208 mm simulated against 184 mm observed). The performances for the SWC simulations for 2023 were not as strong as in previous years, but remain globally satisfactory with significant correlation coefficients. 2023 was classified as dry and hot climatic year and follows 2022, a year of low rainfall. The reduced 2022 winter rain was not sufficient to refill the water stocks and very few rainfall events were observed in the spring of 2023. The model was calibrated for a normal climatic year with more water in the spring and in winter and less in the summer. This calibration considered all the available data provided from the surveys on the irrigation practices (Table 2) and the ground measurements. The comparison between the soil water stock observed in these 5 different orchards and the simulations allowed to evaluate if the model was well calibrated. There were no assimilation methods or inverse procedures applied to retrieve some key input parameters of the model.

Another field (cherry tree with drip irrigation, number 50, belonging to the same farmer) was evaluated with SIMETAW from the soil measurements (there is no sap flow data in this case). The irrigation parametrisation was different than the previous plot (3099), Ot= 4 h/event, since the irrigation device comprises more drippers on the pipe, Theta was set at 0.127 according to FC values in Table 6. The results are presented in Fig. 9 for 2021 (n.b the land use was modified by the farmer in 2022 around the day of year 280 by uprooting the trees). The results are satisfactory for the soil moisture estimations compared to measurements with a R coefficient at 0.9. A difference of 10 mm for QI is observed between simulations and surveys for the year 2021 (see Fig. 9b).

For the youngest monitored orchard (field 1814), *Theta* was set at 0.134 and *Ot* was set at 1 h/event, since less water is required due to the lower canopy growth rate. Fig. 10 shows the results obtained for 2022. There are more variations between row and inter-row for the soil moisture measurements, which is normal since trees are not developed and a lot of water is provided on the row by the farmer (beyond their needs). The simulations remain within the range of variation of the ground measurements, but a small overestimation at the end of the
<figure><img src="image_10.png" /><figcaption>**Fig. 8.** Comparisons between simulations (in red) and observations for the field 3099, cherry tree with drip irrigation in 2022; a) soil water stock; b) Eta; and c) irrigation quantity per months.</figcaption></figure>

season is observed, maybe due to too much irrigation supplied by the model.

For micro-sprinkler irrigation (case of the field 3347, Fig. 11), the calibration was carried out on both *Theta* (from FC measurement *Theta*=0.124 see Eq. 1) and the irrigation parameters in order to get as close as possible the total quantity of water supplied each year. As SIMETAW does not allow to define accurately both a low frequency and high duration, an irrigation set of parameters was fixed as follows: *Ot*= 2.5 (2 h and 30 min), *Du*= 0.8 and *Ar*= 1.3 that lead to an irrigation dose (MAD) of 2.6 mm, which appears below the observed dose of 7.2 mm. The total cumulated irrigation quantity was well modeled, with 443 mm simulated against 484 mm observed in 2022. The soil moisture simulated by the model are quite satisfactory globally compared to the measurements (Fig. 11a). The peak observed near day 205 corresponds to a high irrigation quantity supplied by the farmer (due to the dry spell during the 15 days preceding this date). However, this does not appear in the model since the irrigation dose defined per event in SIMETAW is smaller and the event number is more often.

Table 7 shows the results obtained for *QI* for the different monitored fields and years and the comparisons between simulations performed with SIMETAW using *Kc* from remote sensing and *Kc* from the standard version (peach tree parametrisations for *Kc*).

The use of a *Kc* derived from remote sensing provides an inter-annual and inter-crop variability. Globally, the use of *Kc* computed from remote sensing data improves the accuracy of results obtained for *QI*, reducing the amount of water required by the plant, except for the field 3347, corresponding to micro-sprinkler irrigation for which the irrigation quantities (*QI*) was already underestimated by the model (or over-estimated by the farmer). For this last case, it was found that the determination of the irrigation parameter was difficult to reproduce via

a low frequency of irrigation (every 15 days with a high water quantity up to 20 mm). In this case, the priority has been given to more accurately reproducing the soil moisture variations than the irrigation dose. *Kc* from remote sensing (RS case) are also lower than *Kc* values from the standard version, resulting in lower *ETA* (RS) compared to values simulated with the *Kc* given with standard version. Consequently, soil moisture (RS) remains higher, reducing the irrigation requirements (Table 7).

## 3.2. Results obtained at farm scale (comparison with the ASA data)

Fig. 12 presents the irrigation quantities (*QI*) estimated by SIMETAW using *Kc* computed with or without remote sensing data from 2016 to 2022, aggregated at farm scale and compared with the ASA values (gray bars) given with 30 % of uncertainties (for the last year 2022, the ASA references were not provided). The selected farm, named A2 in this case (38.33 ha with 40 fields) has mostly cherry trees moderately irrigated with drippers (parametrisation of the field 3099 shown in Fig. 9 and Table 5), few plum and apricot trees and table vineyards. It represents a typical case encountered in the watershed. The computation of *Kc* from Sentinel-2 is lower than *Kc* prescribed from the table provided with the standard version of the model, this leads to lower *ETA* values and consequently less need for irrigation. Estimations of *QI* using remote sensing data are closer to the ASA values with a Mean Absolute Percentage Error (MAPE) of 8 % for the studied years (overestimated) against 37 % overestimation with the standard version of SIMETAW compared to ASA data.

Fig. 13 shows the results obtained for different monitored farms in 2021 for the two following strategies: a) light and b) medium irrigation. The same parametrisation is kept for the different crops of all the

<figure><img src="image_11.png" /><figcaption>**Fig. 9.** Comparisons between simulations (red line) and observations for the field 50 in 2021 a) soil water stock in mm, b) irrigation quantity.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>**Fig. 10.** Comparisons between simulations (red line) and observations for the young orchard (plot 1824) in 2022 simulation a) soil water stock, b) actual Evapotranspiration, c) irrigation quantity.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>**Fig. 11.** Comparisons between simulations and observations for Field 3347 with micro-sprinkler irrigation in 2022 for a) soil water stock (in red simulation); b) ET0 and ETa simulated; and c) irrigation quantity.</figcaption></figure>

<caption>**Table 7**</caption>

Comparison of QI (mm/year) between simulations and observations from surveys for the four irrigated monitored plots for 2021, 2022 and 2023.

<table><thead><tr><th>Plots</th><th>Year</th><th>Irrigation method</th><th>SIMETAW without remote sensing (mm/year)</th><th>SIMETAW with Remote sensing (mm/year)</th><th>Estimation from surveys (mm/year)</th></tr></thead><tbody><tr><td rowspan="2">50</td><td>2021</td><td rowspan="2">Drip</td><td>433</td><td>381</td><td rowspan="2">391</td></tr><tr><td>2022</td><td>426</td><td>405</td></tr><tr><td rowspan="3">1824</td><td>2021</td><td rowspan="3">Drip</td><td>108</td><td>104</td><td rowspan="3">87</td></tr><tr><td>2022</td><td>108</td><td>107</td></tr><tr><td>2023</td><td>108</td><td>104</td></tr><tr><td rowspan="3">3099</td><td>2021</td><td rowspan="3">Drip</td><td>217</td><td>208</td><td rowspan="3">184</td></tr><tr><td>2022</td><td>217</td><td>215</td></tr><tr><td>2023</td><td>217</td><td>207</td></tr><tr><td rowspan="3">3347</td><td>2021</td><td rowspan="3">Micro-Sprinkler</td><td>465</td><td>413</td><td rowspan="3">484</td></tr><tr><td>2022</td><td>463</td><td>442</td></tr><tr><td>2023</td><td>452</td><td>421</td></tr></tbody></table>

surveyed farms and the results are shown with a differentiation of each crop for the different monitored farms. The results showed that three farm classes were clearly identified, using high water quantities for irrigation and particularly due to the high density of cherry orchards in these farms. The QI simulations are close to the ASA data except for the first farm A1. This farm has more vineyards and plum trees and this result can be explained by these two crop classes less well calibrated since no ground measurements were made on these crops. A second farm's class (from C1 to E farms and T2) showed QI simulations higher

than the ASA data, and the third class (from I to R1 farms) presented very low values of QI more and less in agreement with the observations. The overestimation obtained for the second class can be due to the fact that the same Ot and Theta parameters were considered for all the plots, although there are significant differences in the soil types and irrigation practices between these farms. As shown in Fig. 13a, the simulations of those farms are much closer to the ASA value with the low irrigation strategy. Globally, Fig. 13b with the medium irrigation strategy showed a high correlation R coefficient between the simulations and the ASA data for the different farms at 0.81.

### 3.3. Comparison of different scenarios at the watershed scale

Table 8 shows the estimated quantity of water supplied (QI) of all 779 plots of orchards and table vines within the Ouvèze-Ventoux ASA watershed for the different irrigation strategies presented in Table 6 for 2021. The ASA comparative value for this year 2021 referred to a total water consumption of 466 899 m³. This table shows that the results are better when the model used remote sensing information to compute Kc, (simulation S3RS medium irrigation), considering that all orchards are irrigated like the plot 3099 and have a similar soil (drip medium irrigation with Ot= 2 h/event and Theta value = 0.115). 17 % difference is observed between QI simulations and the ASA data for S3RS simulations against 66 % when remote sensing is not used (S1medium). The "multimodal" simulation (S4RS multimodal) mixing low, medium and high irrigation according to a farm typology performed at the watershed scale (Rouault et al., 2024a) presents the highest performance compared to the ASA data 10 % difference. This last simulation takes into account
<figure><img src="image_14.png" /><figcaption>**Fig. 12.** Comparison of irrigation quantities (QI) obtained for a typical fruit production farm between ASA data (in gray with 30 % of uncertainty represented by black vertical bars) and SIMETAW simulations with the two-options for Kc computed with (Kc.RS) and without remote sensing data (Kc) model.</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>**Fig. 13.** Irrigation quantities (QI) simulated for 13 farms in 2021 and comparisons with the ASA values (in gray - the error bars refer to the 30 % uncertainty in ASA measurements.) for two irrigation strategies a) the S3RS low irrigation strategy and b) the S3RS medium irrigation strategy.</figcaption></figure>

better the irrigation variability encountered at the watershed level which can be due to the diversity of soils and agricultural practices.

The simulation S3RSheavy, which considers all the orchards of the watershed as highly irrigated, gives interesting results to estimate the

total water consumption in the watershed if all the farmers decide to irrigate much more for the whole watershed, if micro-sprinklers are preferred to drip irrigation for example. In contrast, the simulation S3RSlight can also provide useful information for water managers if drip
<caption>**Table 8**</caption>

Results of annual water quantities brought by irrigation (QI) in 2021 for the different irrigation strategies simulated. S1 simulations without remote sensing data, S2 and S3 simulating using remote sensing to compute Kc. The first two columns give QI simulated for a vineyard and an orchard in 2021 at plot scale, and the last column difference in percent between QI averaged at the watershed scale and ASA data).

<table><thead><tr><th>Simulation</th><th>Orchard plot simulated QI (m³/y)</th><th>Vineyard plot simulated QI (m³/y)</th><th>Watershed (Sum of QI (m³/y))</th><th>Difference from ASA value (m²)</th><th>Difference from ASA value (%)</th></tr></thead><tbody><tr><td>S1light</td><td>108</td><td>103</td><td>406, 240</td><td>-60, 659</td><td>-13 %</td></tr><tr><td>S1medium</td><td>217</td><td>103</td><td>774, 660</td><td>307,761</td><td>+ 66 %</td></tr><tr><td>S1heavy</td><td>465</td><td>103</td><td>1,612, 900</td><td>1,146,001</td><td>+ 245 %</td></tr><tr><td>S2RSlight</td><td>104</td><td>103</td><td>392,720</td><td>-74,179</td><td>-16 %</td></tr><tr><td>S2RSmedium</td><td>208</td><td>103</td><td>744,240</td><td>277, 341</td><td>+ 59 %</td></tr><tr><td>S2RSheavy</td><td>413</td><td>103</td><td>1,437,140</td><td>970,241</td><td>+ 208 %</td></tr><tr><td>S3RSlight</td><td>-</td><td>-</td><td>345,604</td><td>-121,295</td><td>-26 %</td></tr><tr><td><strong>S3RSmedium</strong></td><td>-</td><td>-</td><td>546, 602</td><td>79,703</td><td>+ 17 %</td></tr><tr><td>S3RSheavy</td><td>-</td><td>-</td><td>772,344</td><td>305, 455</td><td>+ 65 %</td></tr><tr><td><strong>S4RSmultimodal</strong></td><td>-</td><td>-</td><td>511,549</td><td>44,650</td><td>+ 10 %</td></tr></tbody></table>

irrigation is used everywhere with low annual QI used. In these two opposite cases, a change in the total consumption of the basin would modify the territory's water requirements by + 65 % and - 26 % respectively.

## 4. Discussion

This study has evaluated the SIMETAW model to estimate the water requirements on a typical small Mediterranean watershed mainly covered by irrigated cherry trees and vineyards. The main advantage of this model is that it requires very few input parameters and couples a water balance to a simplified estimation of evapotranspiration modulated by two coefficients: a crop coefficient defined for each crop type and a stress coefficient depending on the soil properties. It simulates irrigation dose for each event and estimates the total water quantity supplied for the year from mainly three parameters. In this study the key parameters have been identified and used for the calibration of the parametrisation according to the observation of irrigation water used in five representative orchards.

The results obtained at plot scale, for the field 3099 for example (the most common drip irrigation practice), have shown that the differences of SWC and ETa dynamics are close but present a difference, particularly in April and after mid-August, which can be explained by several factors. First, the ground measurements used presented a large spatial variability. The sap flows are quite different between the four measurement locations (as shown in Fig. 4b). Data were available only from the end of February (the 21st), meaning that the average is impacted by these differences. A better agreement was observed when there is more than one measurement point. This result is also not surprising as the sap flow considers only the tree canopy and is more related to transpiration, while the model uses an integrated value of Kc to compute ETa. This Kc includes the tree and the inter-row with grass which also consumes water, so ETa of SIMETAW is greater than the observations in summer. This observation correlated with the yellowing of the inter-rows and therefore the lower proportion of grass cover observed in the Sentinel 2 signal (Rouault et al., 2024c). The differences observed between ETO and ETa are in the same order as observations collected from various orchards by other authors (Cao et al., 2021; Paço et al., 2006; Zanotelli et al., 2019).

In some cases, as for the plot 3099, the irrigation doses simulated and observed were very close for this type of orchard (dose = 1.2 mm for simulation and 1.5 mm for observations from surveys), the number of irrigations 181 events simulated against 122 observed implies a difference on the total amount of water (QI) of 31 mm (less than 17 % of the total amount which can be considered a more than satisfactory result).

In other cases, irrigation doses differ from the actual applied dose (observed in surveys, 6–8 h, one every or two weeks with 10 irrigation events per year for the plot 50 as illustrated in Fig. 9). This discrepancy arises because the model estimates daily irrigation needs based on the soil water deficit. The difference is computed each day between the SWC

and the FC values and an irrigation dose (MAD) is applied to refill the soil reserve (see Eq. 1). This parametrisation of the model allows to derive the real dose by considering a daily dose applied. A lowest dose (fixed by the parametrisation) applied more frequently is the best compromise to simulate the soil water content dynamic in the summer period and the total amount of water for the year. Generally, the simulation produced a good dynamic response of soil water content with relatively few differences between the amount of irrigation (QI) simulated and that provided from the surveys (difference of 20 mm).

At farm level, results can be validated only from surveys on irrigation practices and ASA data. The irrigation parameters have not changed for the same crop type. An automatic optimization procedure could be considered to improve these results if more observations were available on the irrigation variability. Therefore, additional data on soil moisture variability at the farm scale would be required. Only few spatially punctual (5 plots) continuous measurements were available for this study. Additional information can be provided from remote sensing using the complementarity of radar and optical images to obtain soil moisture maps as proposed by (Doussan et al., 2024; El Hajj et al., 2019; Elfarkh et al., 2023). Such soil moisture maps are not yet available at fine scale on this studied site to improve this point.

The model simulation estimates the optimum irrigation with a potential daily dose applicable. This optimal irrigation can be very different than the one applied at plot scale by some farmers, especially for those small farms with few orchards where the fruit production is not the main economical factor (Rouault, 2024). For these farms, the choice can be followed by farmers to underirrigate and accept more hydric stress for plants, leading to a lower ASA value than the possible optimum simulated with SIMETAW. This explains some of the overestimates visible between simulations and observations.

Most of the heavily irrigated crops identified are located in the municipality of Beaumont-du-Ventoux in the Est of the watershed. This information is very important for the watershed manager to better distribute the available water in restriction periods.

The utilization of the model has demonstrated that one of the limitations is the assumption of providing water for irrigation to fill the soil to field capacity, along with the related frequency calculated on a daily time step. This approach makes it challenging to accurately reproduce certain irrigation practices that involve low frequency and high doses per event. In this case, it appears difficult to simulate correctly both the irrigation dose and the soil moisture variability. It appears necessary to modify the parametrisation of irrigation to better reproduce this practice.

Another limitation of this model in its standard version was that it did not allow to consider the spatial variability of the crop development and the difference between years for a same crop type. This work has contributed to improve this point by proposing an approach that is easy to implement from plot to watershed scale. The use of NDVI and FCOVER variables to compute Kc for each field from Sentinel2 allowed to reproduce the spatial variability due to the various cultivars, soils and
irrigation management. The results have shown that the model performances are better correlated to the observations of soil water content, *ETa* and irrigation volume estimation with the use of *Kc* computed from remote sensing than using fixed *Kc* values defined in the standard version. This approach is very similar to that used in the SAMIR model (Elfarkh et al., 2023; Saadi et al., 2015; Simonneaux et al., 2009), and successfully applied on citrus orchard with only 2.3 % differences between simulated irrigation volume and observed (Tazekrit et al., 2018). The main difference relies in the computation of the water stress coefficient which required more accurate input data to describe the soil properties in SAMIR. Other models based on Dual coefficient approaches to simulated *ET* such as the AqYield model (Tribouillois et al., 2018) or SIMDualKc (Almeida et al., 2024; Cao et al., 2021; Paço et al., 2014; Ramos et al., 2023) have also shown good performances for applications on various crops but most of the time the highest scores are linked with accurate ground data to calibrate the models, either from the knowledge of the soil properties (texture or hydrodynamic parameters) or from a fine description of the crop development. The access to this information is sometimes particularly difficult to obtain at the watershed scale because the available soil maps are often not enough accurate to provide such data. Let us mention that numerical soil maps approaches are developing based on the use of various remote sensing data to get better resolution of soil properties (Bousbih et al., 2019; Gomez et al., 2019). Such approaches will be very useful in the future to improve the soil parametrisations in water balance models.

This study is focused on few parameters of SIMETAW which are relevant based on a sensitivity analysis to calibrate the model from ground measurements and according to the data obtained from surveys. Different optimization tools are now available to better fit the simulations to the observations (Buis et al., 2023, 2011; Wallach et al., 2024). Surveys are sometimes not accurate enough and often farmers describe their irrigation practices for one year and have not quantified their time according to the different years or to the different plots of a same crop at farm scale, and ASA equipment includes losses on the network and sometimes wrong declarations concerning the irrigated surfaces. It appears therefore difficult to estimate the uncertainty related to these ground data. To check if a field is irrigated or not, different papers have proposed operational methods based on the use of Sentinel 1 and 2 which gave satisfactory results (Abubakar et al., 2022, 2023; Bazzi et al., 2021; Pageot et al., 2020). Such methods could be applied on the Ouvèze-Ventoux watershed to monitor the real irrigated surfaces. To improve the observations, soil moisture maps obtained from remote sensing can also be used for optimizing input parameters (Doussan et al., 2024). Another useful data source in the future will be the use of the thermal data from the TRISHNA mission which will be launched in 2026. This new satellite should provide high spatial and temporal data in the thermal range (every 3 days at 60 m) with evapotranspiration maps computed from the EVASPA operational chain (Gallego-Elvira et al., 2013; Roujean et al., 2021). Such data should really improve the decision support models for water management, especially during restriction periods.

This modeling is also limited by a lack of information of the irrigation technique used for each plot of the watershed, which cannot yet be identified using current remote sensing data. The determination of irrigation techniques (micro-sprinkler or drip) still remains difficult and can be based only on ground surveys and statistical assumptions for the whole area. If some studies have already shown a slight variation in biophysical variables in the context of micro-sprinkling (Rouault et al., 2024c), to our knowledge no studies using optical remote sensing have identified this type of irrigation.

## 5. Conclusion and perspectives

The SIMETAW model gave good performances when calibrated from ground measurements acquired on various irrigated orchards. This study shows the first application of the SIMETAW model to cherry

orchards using measured data, irrigation schedules and ASA data. The simulations are significantly improved when *Kc* is derived from remote sensing to consider the spatial variability of crop development. The results at watershed scale for the water consumption for irrigation were quite satisfactory with only 17 % deviation from the actual quantity of water measured by the water managers in 2021 (against 66 % without remote sensing). This simulation is even more precise considering farmers irrigation practices for orchards with a ratio of low, medium and high irrigated parcels identified by a statistical typology. This multi-modal modeling makes it possible to estimate the quantity of irrigation water supplied to the watershed within 10 % of the value measured by the ASA.

Future improvements should aim to improve the identification of micro-sprinkler irrigation plots. These improvements could increase the accuracy and reliability of the model, leading to the development of better water management strategies for cherry orchards and to further spatialize the simulations, particularly by considering the variability of practices in the field.

The use of the SIMETAW model with a high contribution of remote sensing can be a useful tool for watershed management. The model is able to estimate the irrigation water consumption of a large territory with a dominance of orchards and vineyards for various scenarios. Considering the challenges that await arborist farmers in the Mediterranean region, and in particular droughts and increasingly frequent restrictions on water use, this tool is an interesting step forward for estimating consumption but also for providing the consumption in the event of climatic changes but also of behavioral changes or available equipment.

## CRediT authorship contribution statement

**Claude Doussan:** Writing – review & editing, Methodology, Investigation, Conceptualization. **Marcel M. El Hajj:** Writing – review & editing, Methodology, Data curation. **Fabrice Flamain:** Methodology, Data curation. **Guillaume Pouget:** Software, Methodology, Data curation. **Marta Debolini:** Supervision, Software, Formal analysis. **Samer K Al-Mashharawi:** Writing – review & editing, Validation, Formal analysis, Data curation. **Matthew McCabe:** Writing – review & editing, Methodology, Funding acquisition, Formal analysis. **Pierre Rouault:** Writing – original draft, Validation, Software, Methodology, Investigation, Formal analysis, Data curation. **Dominique Courault:** Writing – review & editing, Supervision, Project administration, Methodology, Funding acquisition, Conceptualization.

## Declaration of Competing Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: COURAULT reports financial support was provided by French National Institute for Agricultural Research INRAE. COURAULT reports financial support was provided by King Abdullah University of Science and Technology. COURAULT reports financial support was provided by Regional Council Provence-Alpes-Cote d'Azur.

## Acknowledgments

This study was funded by several research projects, including the PACA region, a collaborative project with King Abdullah University of Science and Technology in Saudi Arabia and funds from INRAE. The authors thank the ASA president who has provided useful data for the validation of this work and the surveyed farmers, who kindly facilitated field access and measurements. The authors would also like to thank the colleagues who contributed their expertise to the processing and acquisition of the data, in particular Mr. Borgo Andrea (CMCC) for the presentation of the model.
# Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.agwat.2025.109637.

## Data availability

Data will be made available on request.

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