# Ad hoc irrigation piloting for the urban vegetation: How to find the contextually relevant sensors and criteria?

Martina Garcia de Cezar, Bruno Cheviron, François Liron, Geoffrey Froment, Cyril Dejean, Séverine Tomas, Laurent Aprin

► To cite this version:

Martina Garcia de Cezar, Bruno Cheviron, François Liron, Geoffrey Froment, Cyril Dejean, et al. Ad hoc irrigation piloting for the urban vegetation: How to find the contextually relevant sensors and criteria?. Journal of Environmental Management, 2024, 370, pp.122712. 10.1016/j.jenvman.2024.122712. hal-04749437

HAL Id: hal-04749437

https://imt-mines-ales.hal.science/hal-04749437v1

Submitted on 23 Oct 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

Research article

# Ad hoc irrigation piloting for the urban vegetation: How to find the contextually relevant sensors and criteria?

Martina Garcia de Cezar<sup>a,b</sup>, Bruno Cheviron<sup>c,*</sup>, François Liron<sup>c</sup>, Geoffrey Froment<sup>c</sup>, Cyril Dejean<sup>c</sup>, Séverine Tomas<sup>c</sup>, Laurent Aprin<sup>b</sup>

## ARTICLE INFO

Handling editor: Jason Michael Evans

Keywords:

Urban vegetation

Irrigation optimization

Water bulb

Climate change

Operational solutions

## ABSTRACT

The irrigation of urban vegetation in planters aims to keep the vegetation healthy and control the cooling effect from evapotranspiration during overheating days. The choice of sensors that characterise the water status of the plant, the soil and/or relevant criteria for triggering irrigation are crucial and far from obvious. This descriptive and analytical paper targets the identification of effective system properties, on which irrigation strategies should rely from a properly thought trial-and-error procedure relying on observational evidence. The study takes advantage of a real-scale pilot in Mediterranean climate (Montpellier, France), which simulates an east-west street canyon with three planters with shrubs on the north side and three planters with climbing plants on the south side, and a dense mesh of sensors in the substrate monitoring the condition of the planters. The provided guidelines explain how to comprehend and qualify any given context (plant species, planter dimensions, type and location of the sensors), before establishing ad hoc irrigation decision rules. This study demonstrates the developed methodology, by testing different irrigation doses, frequencies and criteria based on soil water content and tension information, which proves efficient for understanding and managing irrigation, even with limited information on plant stress tolerance and soil characteristics. Moreover, it allows for periodic reassessment of optimization criteria, which is crucial in addressing current challenges posed by sustainable cities without compromising water resources.

## 1. Introduction

Population growth, urbanization and the enhanced thermal effects encountered in the cities demand urgent re-thinking of their design, materials, colours and management, where and when levers of action are available. Finding proper ways to irrigate the urban green spaces is one of them, aiming at the mitigation of urban heatwaves. Since its establishment in 1972, the Intergovernmental Panel on Climate Change (IPCC) has consistently highlighted the importance of adapting our natural resources management practices. With compelling evidences of Climate Change, such as record-breaking temperatures in 2022 and 2023 (Liu et al., 2023; Szabó et al., 2024), along with extreme meteorological phenomena leading to severe droughts (Dunn et al., 2023) and extensive forest fires (Jones et al., 2022), addressing the associated risks in agricultural and urban areas is increasingly crucial.

The growing trend of expanding green areas in cities, driven by the

desire to reduce temperatures and preserve human health, leads to a corresponding increase in water consumption - a significant challenge for cities with arid, semi-arid, and Mediterranean climates (Nouri et al., 2019; Pan et al., 2023; Reyes-Paecke et al., 2019). Further exploration is thus needed to identify the potential reduction of irrigation, for these green spaces, without compromising the survival and functionality of urban vegetation. Some studies provide evidence of excessive irrigation practices, suggesting the feasibility of increasing urban vegetation cover, even without escalating water consumption if more efficient irrigation is implemented (Coutts et al., 2013; Guo et al., 2021; Reyes-Paecke et al., 2019). Moreover, in climates characterized by higher temperatures and lower precipitation, future dispendious water allocation scenarios would imply substantial cost rises, which would in turn serve as a limiting factor for the maintenance and sustainability of green spaces.

The key certainly lies in modifying current irrigation strategies (i.e.,

<footnote><sup>a</sup> AgroParisTech, G-EAU, Univ Montpellier: AgroParisTech, BRGM, CIRAD, INRAE, Institut Agro, IRD, Montpellier, France</footnote>

<footnote><sup>b</sup> Laboratory for the Science of Risks (LSR), IMT Mines Alès, 30100, Alès, France</footnote>

<footnote><sup>c</sup> G-EAU, Univ Montpellier: AgroParisTech, BRGM, CIRAD, INRAE, Institut Agro, IRD, Montpellier, France</footnote>

<footnote>* Corresponding author. UMR G-EAU, 361 rue Jean-François Breton, 34090, Montpellier, France. E-mail address: bruno.cheviron@inrae.fr (B. Cheviron).</footnote>
technologies and practices, equipment and decision rules) to improve irrigation efficiency, seeking compromise between performance and parsimony. This will imply the use of deficit irrigation (inducing tolerable water stresses at certain periods), supplemental irrigation (leveraging water collection, storage and reuse), selective strategies (to increase plant transpiration when really needed), or even, depending on climate, soil, topography, and plant type, no irrigation at all (Nouri et al., 2019). Specific alternative approaches for the urban contexts consists in more or less exploratory options such as rainwater harvesting (Jacque et al., 2023) or the REUSE of recycled water (Matos et al., 2012). Overall, urban water availability and consumption for green spaces is influenced by various factors, including temperature, precipitation, city size and design, lifestyle and income of the inhabitants, and water supply availability (Coutts et al., 2013; Li et al., 2020) in link with the pipeline network. Understanding the characteristics and driving forces behind urban water consumption is essential for the development of targeted urban irrigation strategies, aiming to alleviate the expected conflicts of use of the common water resource (Fan et al., 2017).

Irrigation to increase the cooling effect of green spaces in cities is a topic under research (Livesley et al., 2021). Cheung et al. (2022b) have detailed the physical and energetic mechanisms underlying this cooling effect and proposed a three-stage decision support framework: (i) evaluating climate suitability, with a focus on cities experiencing warm, dry seasons where urban green space irrigation is most beneficial; (ii) assessing the availability of irrigation water, where cities with abundant water supplies might adopt a soil moisture-controlled regime, while those with limited water might opt for a temperature-controlled approach; and (iii) considering management factors such as soil properties, weather conditions, plant types, irrigation timing, and the amount of water applied (Coutts et al., 2013).

Cheung et al. (2022b) also highlighted that irrigation increases soil moisture, which elevates daytime latent heat flux due to more evapotranspiration from vegetation planters, while reducing sensible heat flux, leading to lower daytime air and leaf surface temperatures. However, increased soil moisture can also result in higher daytime soil heat storage, as the thermal conductivity of water is greater than that of soil (Cosenza et al., 2003). This may lead to increased heat transfer within the soil, potentially causing a delayed release of heat at night. While irrigation can increase air humidity through evapotranspiration—sometimes associated with discomfort—it may ultimately reduce daily human thermal stress. This reduction occurs if the combination of temperature, humidity, and main radiant temperature results in more favourable thermal comfort indices, such as those provided by the Universal Thermal Climate Index (UTCI) (Bröde et al., 2012) or the Physiological Equivalent Temperature (PET) (Matzarakis et al., 1999).

Numerous uncertainties indeed persist, within the ongoing attempt of reducing human thermal stress through green space irrigation in dry, warm cities. The diverse utilization of urban vegetation, including various plant types (trees, shrubs, grasses, climbers) and settings (roofs, walls, gardens, planters, in isolated patches or corridors of biodiversity) complicates the quantification of irrigation effects on human thermal comfort. Serious challenges arise from (i) the heterogeneous and artificial nature of urban soils, whose hydrodynamic properties may not be evaluated from classical approaches, (ii) the multiplicity of geometrical effects governing water fluxes (irrigation bulbs) and constraining sensor installation, and (iii) the poorly-known "crop coefficient" of ornamental plants, a coefficient that describes crop transpiration and soil evaporation (Allan et al., 1998) in response to the climatic demand, thus eventually controls the cooling effects. Efficient irrigation schemes therefore requires relevant irrigation technologies (to avoid excessive drainage or evaporation) and appropriate trigger criteria, relying on expertise (shared experiences and knowledge), metrology (sensors of soil water status) and modelling (decision support tools for the assigned objectives).

To establish urban irrigation, two aspects must be determined: the criteria for triggering irrigation and the irrigation technique to be used.

Irrigation criteria can be based either on the experience of the operator or, more effectively, on sensor data reflecting the water status of the plant and soil. Commonly used sensors for irrigation control are tensiometers, which measure how strongly water molecules are held by soil particles, very near the sensor itself, and soil water content sensors, which assess the volumetric soil water content, in a quite wide domain of investigation around the sensor probes. These sensors can be used individually or together, and in some cases, integrated into closed-loop systems to control irrigation valves (Al-Hamed et al., 2017). A challenge with using such sensors is that they often only partially overlap with the irrigation bulb, or in some cases, may fall completely outside it. This creates interpretation issues, particularly when water is unevenly distributed within planters. Surprisingly, this issue has not been fully addressed in the literature, despite its relevance to the choice of irrigation criteria. Drip irrigation is the most popular technique in urban settings, very efficient to deliver water near plant stems thus straight to the root zone (Ferguson, 1987; Narayanamoorthy, 1997) while creating irrigation bulbs. Micro-sprinklers are also used, for a more even but less efficient water distribution, especially in hot and dry conditions.

This study focuses on the surface drip irrigation of planters containing shrubs and climbing plants, in an experimental installation that mimics one of the popular settings of urban vegetation. The primary objective of this article is to provide guidelines for irrigation decision rules, in typical urban contexts where vegetation has grown up in planters, when seeking a balance between preserving water resources and adapting to climate change and urban overheating events. This implies controlling the response of vegetation evapotranspiration to specific irrigation strategies during heatwaves or when a heatwave is approaching. The proposed generic methodology allows thus setting up irrigation piloting criteria from assumed vegetation characteristics, known location of the sensors and inferred soil properties.

## 2. Material and methodology

### 2.1. Site description

The experimental installation is run by France's National Research Institute for Agriculture, Food and Environment (INRAE) for the Joint Research Unit "Water Management, Actors, Territories" (UMR G-EAU) in Montpellier (France, 43°64'N, 3°87'E), under a Mediterranean climate, with mild Springs then hot and dry Summers (see Fig. 1 for 2023). The pilot used to constitute an urban canyon has dimensions of a narrow pedestrian street, fully equipped with multiple sensors for the sake of several experimental and modelling studies (Fig. 2). The east-west orientation of the canyon favours the occurrence of drop shadows: the planters along the north wall are more exposed to the sun. Planters with climbing plants lie along the north wall, planters with shrubs along the south wall. Multiple sensors are installed at 1.3 m above the ground to monitor micro-climatic variables and calculate thermal comfort indices in link with irrigation. The planters themselves are fully instrumented to follow the direct effects of irrigation on the dynamics of soil water content and evapotranspiration, which initiates and controls all other observable effects and therefore is the focus of the present study.

As shown in Fig. 2, the planters with climbing plants (0.8 m) have a smaller width because they support plants with smaller volumes compared to the planters with shrubs (1.1 m). However, both types of planters were designed with the same depth (2.3 m) and height (0.9 m) to ensure a substantial substrate volume ($1.66 m^3$ climbing plants and $2.28 m^3$ shrubs), which is important for effective irrigation management and monitoring of substrate-plant-environment interactions. The procedure for preparing and filling the planters is detailed in Fig. 3 (see Fig. 4).

### 2.2. Urban vegetation

The planters are representative of the limited soil water reserve
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Weather data collected at the centre of the pilot, showing daily rainfall, daily maximum and daily minimum of air relative humidity (a), then daily-averaged wind speed together with the daily maximum, minimum and average of air temperature (b).</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Representation of the pilot urban canyon, in a west-east side view (a) and a top-down view (b). The drop shadows of the walls have been drawn for August 21st at 2:00 p.m.</figcaption></figure>

conditions experienced by the small urban vegetation in real settings. They contain two popular ornamental species, commonly found in the centre of Montpellier, *Lonicera japonica* (climbing plants) and *Abelia grandiflora* (shrubs). Those species have been chosen for their different geometries and their resilient behaviour under various deficit irrigation conditions (Costello and Jones, 2014; Reid and Oki, 2016), assuming irrigation strategies covering 10%-60% of the climatic demand were acceptable *Lonicera japonica*, against 20-80% for *Abelia grandiflora*. However, the cited ranges certainly depend on the local climatic conditions as well as on the objectives assigned to irrigation. Here, keep plants in good condition during standard periods to later maximize evapotranspiration during heatwaves. Three planters were devoted to each specie (each planter containing several individuals) to ensure robustness of the approach and allow comparisons.

Unfortunately, very few information is available from literature as regards the classical crop coefficient (Kc) of the two species, likely as they do not offer any agricultural interest. Hence, the potential soil-plant response, i.e. actual evapotranspiration (AET), to climatic demands is a *priori* largely unknown. More precisely, the only reported orders of magnitude of Kc values or daily AET rates were obtained from different experimental conditions, namely from much smaller containers (pots instead of planters). Smaller reservoirs expectedly yield smaller Kc or AET values under identical climatic forcings, unless irrigation is such that water stress conditions are totally avoided. For *Lonicera japonica*,

Picchioni et al. (2007) investigated comfort irrigation conditions, during 22 days in the south-western United States. The "experimental maximal Kc" obtained was 1.65 with 3.8-L pots, and 2.15 with 18-L pots, corresponding to AET rates of 0.5 L d⁻¹ and 2.2 L d⁻¹, respectively per plant. For *Abelia grandiflora*, Niu et al. (2006) conducted a four-month experiment in Texas, using 2.6-L pots in the same comfort irrigation conditions, and obtained Kc values ranging from 0.84 to 0.93, with water use per plant ranging from 0.23 to 0.31 L d⁻¹.

## 2.3. *Site instrumentation*

The experimental street canyon has a dense network of sensors in the planters and in the ambient environment, to fully monitor the various direct and indirect effects of irrigation. This paper focuses on the difficult choice of relevant soil sensors, seeking those able to provide representative measures of the soil water status, on which irrigation criteria and decisions could be based. Fig. 3 illustrates the spatial arrangements of all sensors disposed in each planter. This arrangement is specific to each planter, which in particular results in specific distances to the drippers for the installed water content probes, tensiometers and thermocouples (Table 1). This deliberate variability in plausible sensor positioning creates a collection of different cases, making the acquired data more or less easily exploitable, hence the necessity for *ad hoc* data analysis and proceeding procedures.
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Successive steps in preparing the planters with illustrations of a planter with climbing plants.</figcaption></figure>

## 2.4. Soil hydrodynamic properties

The six planters have the same type of substrate, enriched with minerals and organic matter, and drainage systems were installed to prevent water stagnation (though not allowing flux quantification). However, soil hydrodynamic properties, as the Kc values, should also be considered a priori unknown, first as they really are in most cases, and second because the purpose here is to work with very limited prior knowledge on the plant and soil properties. Generic approaches are available for the context-specific estimation of the key soil hydrodynamic parameters (saturated water content θsat, field capacity θfc) (Brooks and Corey, 1964; Mualem, 1976; Van Genuchten, 1980). In this study, these values were estimated through the dynamics of the measured volumetric soil water content, after an episode of significant rain or irrigation, strong enough to generate drainage in the system (Bean et al., 2018; Zotarelli et al., 2010). As the saturation water content represents the maximum amount of water the soil can hold, all soil pores being filled with water (Bean et al., 2018; Sumon et al., 2017), it is the highest point on the curve, thus allegedly identifiable as such. Field capacity being the amount of water retained in the soil after excess water has quickly drained down due to gravity (Bean et al., 2018; Hammami and Maalej, 2005), it is usually observable as the point where the soil

water curve levels off after drainage, changing from “rapid” to “slower” decrease in soil water content (Zotarelli et al., 2010).

The initial (or "pre-event") soil moisture content (θi) is also known to significantly influence soil water retention capacity and water distribution during rainfall or irrigation, and after it, hence also governing the elongation of the irrigation bulb and its ability to quickly develop (Kozak et al., 2005; Philip, 1989; Raats, 2007; Rothfuss et al., 2010). Medium and homogeneous θi values have been reported to enhance soil capacity to retain additional water, being associated with less preferential flow paths, at least in soils prone to changes in their structure in link with drying (Merdun et al., 2008). Consequently, the increase in "final" soil water content is typically greater when irrigating initially moist soil compared to dry soil, especially in well-draining soils. The adopted procedure, therefore, involves: (i) avoiding excessive drying of the soil before the next irrigation to maximize water use efficiency, (ii) implementing repeated irrigation cycles, and (iii) establishing irrigation criteria based on specific soil water content thresholds.

## 2.5. Irrigation piloting

One dripper was installed per plant, near plant stems, resulting in four drippers for the planters (A, B, C) with climbing plants and three
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Arrangement of the sensors within planters A, B and C containing climbing plants of the species *Lonicera japonica* and planters D, E and F containing shrubs of the species *Abelia grandiflora*. Focus on planter B with an example of the vertical distribution of the sensors CS650, thermocouples and tensiometers, indicated with CS, TS and W, respectively.</figcaption></figure>

<table><caption>**Table 1**
Type and characteristics of the sensors installed in the planters.</caption><thead><tr><th>Variables</th><th>Unit</th><th>Sensor</th><th>Range of values</th><th>Accuracy</th><th>Time step</th></tr></thead><tbody><tr><td>Water volume</td><td>m<sup>3</sup> m<sup>-3</sup></td><td>CS650 - Soil Water Content Reflectometers</td><td>0 to 1</td><td>&lt;0.05%</td><td>10 min</td></tr><tr><td>Temperature</td><td>°C</td><td>CS650 - Soil Water Content Reflectometers</td><td>-50 to 70 °C</td><td>ca. 0.02 °C</td><td>10 min</td></tr><tr><td>Temperature</td><td>°C</td><td>Thermocouples type T Copper/constantan</td><td>0 to 350</td><td>ca. 0.5 °C or 4%</td><td>10 min</td></tr><tr><td>Water tension</td><td>kPa</td><td>Watermark - Tensiometer</td><td>0 to 239</td><td>ca. 0.5 °C or 4%</td><td>10 min</td></tr></tbody></table>

drippers (D, E, F) for the planters with shrubs. The irrigation flow rate was 1.9 Lh<sup>-1</sup> for each dripper, ensuring controlled and consistent water delivery to the plants, throughout the entire test period. Different irrigation strategies and criteria could thus be followed for so-called "standard", or current or announced "overheat" days. In the standard case, the purpose attributed to irrigation was to prevent water stress while minimizing water consumption. In the overheat case, irrigation was decided from both the current soil water status and weather forecasts, for anticipation, to allow optimal evapotranspiration rates.

Early attempts at irrigation control were done in May (Spring), then re-thought later on, after the identification of soil hydrodynamic parameters and also assuming the existence of soil water stress thresholds, for irrigation piloting under the climatic conditions of interest (Summer). The study in May focused on evaluating the effects of distinct irrigation doses, delivered during 20, 30, 40 and 50 min, on the dynamics of soil water content and soil tension, at different locations in the soil. To trace and quantify those effects, a few descriptors were used. The response time ($t_0$) to the "irrigation pulse", is the time needed for a signal change in the monitored soil water content or tension values. The duration of the irrigation effect was estimated from three different perspectives, first by recording the time of occurrence ($t_m$) of the maximum water content value, the magnitude of this short-term effect being $\Delta\theta_m$, second by seeking the persistence of a mid-term effect $\Delta\theta_{mt}$, a few hours after irrigation ($t_{mt}$), and third by recording the time needed for a stabilisation ($t_s$) of water content values, likely on a longer term and

more influenced by the vanishing of evaporation and transpiration fluxes after dusk. A strong point of this approach based on data collection and analysis, is to allow progressive refinements or updated information on the descriptors ($t_0$, $t_m$, $\Delta\theta_m$, $t_{mt}$, $\Delta\theta_{mt}$, $t_s$, $\theta_{sat}$, $\theta_{fc}$, $\theta_i$) as more and more data become available.

From a different angle, indications on the relevance of irrigation strategies may be found from the observation of indirect and delayed effects of the irrigation on air temperature in urban street canyons – but those finding are still fragile. For example, Cheung et al. (2022b), argued that a single irrigation a day should better take place in the morning or late in the afternoon then around noon, to induce a daily cooling effect. However, two of three irrigations seem more appropriate to maintain soil water content closer to field capacity for a longer time, thus favour evapotranspiration and the cooling effect. In accordance, irrigation was scheduled at 8 a.m. or 6 p.m. at first and by default, before re-examining the situation from the descriptors first, then for the non-standard conditions of heatwaves.

Finally, poorly-known cultural coefficients prompt us to strategies that are cautious for the plant but dispitious in terms of irrigation, to avoid any underestimation of plant water needs and favour its maximal functioning. With such intentions, any underestimation of the current water content values in the irrigation bulb will inevitably trigger excessive irrigation, and "misplaced" sensors will only lead to underestimations, never to overestimations (as stated in the Introduction). Without prejudging the identification of contextually-relevant sensors, i. e. these able to deliver representative values, the previously mentioned estimates of $\theta_{sat}$ and $\theta_{fc}$ can be used to define the setpoints for repeated irrigations. On a standard day, the initial strategy will be to seek compromise between staying away from significant water stresses and reducing water consumption. In such conditions, a reasonable operational target is water contents lower than field capacity though not thought to induce any stress. By contrast, during heatwaves, irrigation should allow maximal plant transpiration, at the risk of flirting with drainage when favouring root water uptake. This implies to keep soil water content as close as possible to field capacity, if not slightly above, where root activity and density is expected to be maximal.
# 3. Results and discussions

## 3.1. Descriptors of the irrigation effects

Seeking to understand and describe the process based on the values of the descriptors mentioned ($t_0$, $t_m$, $\Delta\theta_m$, $t_{mt}$, $\Delta\theta_{mt}$, $t_s$, $\theta_{sat}$, $\theta_{fc}$, $\theta_i$), the dynamics of soil water contents were analysed, for all planters and soil depths, the days irrigation took place (May 2^nd^ to 22^nd^). To make it specific, the analysis excluded rainy days. Unfortunately, to a few exceptions, soil tension results were mostly non-exploitable, either due to a bad position of the tensiometers outside the irrigation bulb or to saturated conditions near the bottom of the planters, owing to insufficient drainage. Conversely, a quasi-systematic and very coherent response in the dynamics of soil water content was observed after each irrigation for the triplicates of planters A, B, and C (climbing plants), and D, E, and F (shrubs). However, some specificities also arise. For example, planter A showed a consistently higher soil water content at 15 cm than at the other depths in the same planter and at the same depth in the other planters. This could be attributed to various individual or combined factors such as sensor defects, poor local root density or activity, or existence of a preferential flow path towards the sensor. Consequently, the atypical values provided by this sensor were excluded from the overall analysis, relying on the above descriptors, summarized in a supplementary material.

For the climbing plants, the 20-min irrigation, resulted in variations in soil water content of magnitude $\Delta\theta_m = 0.035$, 0.064, 0.045 and 0.000 $m^3 m^{-3}$ at depths of 5, 15, 25 and 35 cm, respectively. The 30-min irrigation yielded values of $\Delta\theta_m = 0.056$, 0.079, 0.071 and 0.038 $m^3 m^{-3}$ at the same depths, against $\Delta\theta_m = 0.047$, 0.060, 0.066 and 0.088 $m^3 m^{-3}$ for the 40-min irrigation. The 50-min irrigation finally gave $\Delta\theta_m = 0.050$, 0.085, 0.092 and 0.117 $m^3 m^{-3}$. For the same planters, the estimation of $\theta_{sat}$ and $\theta_{fc}$ values at different depths was made following the irrigation of May 21^st^: This irrigation took place after a rainfall event the day before, ensuring high initial $\theta_i$ values. It was the occasion to create drainage on purpose, for the sake of this analysis. For planter C, the estimation gave $\theta_{sat} = 0.351$, 0.337, 0.354 and 0.329 $m^3 m^{-3}$ at 5, 15, 25, and 35 cm, respectively, then $\theta_{fc} = 0.300 m^3 m^{-3}$ at 5, 15 and 25 cm but only 0.262 $m^3 m^{-3}$ at 35 cm.

For shrub plants, the sensors in planters D and E were not positioned properly with respect to the irrigation water bulb, as attested by the lack of significant variations in readings after each irrigation event, which in turn provided information as regards the limited spatial extension of that bulb. In consequence, the analysis focused on planter F. As expected, the overall impact of irrigation on the $\Delta\theta$ increase was less pronounced than for planters A, B and C. The latter have smaller volumes and more drippers which also increases the probability of larger overlaps between the irrigation bulbs and the domain of investigation of the sensors, thus creating better conditions for the representativeness of measurements made. Additionally, planter F is also less exposed to solar radiation, which also likely reduces the AET term. Finally, there are altogether less effects of irrigation and less effects of the climatic demand in planter F, hence less pronounced variations in water content values. Their measured magnitude was only $\Delta\theta_m = 0.020 m^3 m^{-3}$ for all irrigation durations, at 5, 15, and 35 cm depths, of the order of the resolution of the sensor. Surprisingly, larger magnitudes were obtained at 25 cm. These were all between $\Delta\theta_m = 0.032$ and 0.056 $m^3 m^{-3}$ for the various irrigation durations. For planter F, the inferred soil hydrodynamic parameters were $\theta_{sat} = 0.258$, 0.265, 0.452 and 0.469 $m^3 m^{-3}$ at 5, 15, 25 and 35 cm, respectively, then $\theta_{fc} = 0.252$, 0.236, 0.412 and 0.464 $m^3 m^{-3}$ at the same depths.

Water content probes located at 25 cm were found the most suitable for irrigation piloting: their dynamic variations are clues of large-enough intersections with the irrigation bulb, thus of representative measures of the actual water content values in the irrigation bulb. To fulfil the criteria discussed in Section 2.5, a 30-min irrigation duration was adopted. Planters C and F were selected as references, for their

large-enough solar exposure. The following rules were adopted as the outcome of this preliminary analysis conducted in May. For overheat conditions, that is if the air temperature is above 24 °C at 8am or 33 °C at 6 p.m., irrigation takes place only if $\theta_i < 0.30 m^3 m^{-3}$ for the climbing plants, or $\theta_i < 0.45 m^3 m^{-3}$ for the shrubs. For standard conditions, irrigation occurs at 8 a.m. and 6 p.m. if $\theta_i < 0.20 m^3 m^{-3}$ for climbing plants, $\theta_i < 0.35 m^3 m^{-3}$ for the shrubs.

## 3.2. Re-evaluation of irrigation criteria for summer conditions

### 3.2.1. Planters with climbing plants

To cope with the much higher temperatures and climatic demands of the Summer period, even in the so-called standard conditions, the new $\theta_i < 0.25 m^3 m^{-3}$ irrigation triggering criterion was set closer to field capacity, and examined now three times a day instead of twice, at 8 a.m., 12 p.m. and 6 p.m. As an example, this allowed maintaining an average water content of $0.197 m^3 m^{-3}$ between 15 and 25 cm depth, between 1pm and 6pm, for June 5^th^-7^th^ (Fig. 5). By contrast, with the irrigation criterion initially scheduled after the analysis in May, for the same depths and hours, the average water content value was only $0.150 m^3 m^{-3}$ for June 1^st^-3^rd^, which is way below the target.

As the result of the modified irrigation piloting criteria, allowing three irrigations per day, the averaged water content value was 0.269 $m^3 m^{-3}$ between 15 and 25 cm, and between 1 p.m. and 6 p.m. for the three consecutive days of extreme heat that occurred for July 19^th^-21^st^ (Fig. 6). In comparison, for the standard days preceding this episode (July 14^th^-16^th^) this average water content value was only 0.209 $m^3 m^{-3}$. This was the desired result, as water abundance in the root zone region is needed to maximize root water uptake and plant transpiration during heatwaves.

### 3.2.2. Planters with shrubs

As for the climbing plants, two irrigations a day were not enough for Summer conditions and the planters with shrubs were drying out. So three irrigations were allowed at 8 a.m., 12 p.m. and 6 p.m., provided the triggering criteria were met. This change took place on July 5^th^ and yielded good, observable results in terms of soil water content chronicles (Fig. 7). By contrast, it was clear that soil tension probes were not exploitable: the sensor at 15 cm was obviously always outside the irrigation bulb, indicating too high pressure values, while the sensor at 35 cm consistently reported saturated conditions, which is highly questionable – unless the drainage device was clogged. With those changes in the irrigation schedule, a noticeable shift in soil water content is visible in the reference planter F. Under the previously suggested irrigation regime, the average water content was 0.184 and 0.328 $m^3 m^{-3}$, between 1 p.m. and 6 p.m., at 15 and 25 cm, for June 1^st^-3^rd^. With the new irrigation regime, this average water content increased to 0.190 and 0.363 $m^3 m^{-3}$ at the same depths, for the same hours, for June 5^th^-7^th^: This adjustment keeps the soil water content slightly higher, approaching the reference field capacity for each level, ensuring that the plant remains in the water comfort zone.

As a consequence of the irrigation criteria for heatwave days (Fig. 8), the average soil water content values, between 1 p.m. and 6 p.m., over three consecutive days for July 19^th^-21^st^, were 0.197 and 0.366 $m^3 m^{-3}$ at 15 and 25 cm, respectively. For the standard conditions preceding this episode those values were 0.189 and 0.345 $m^3 m^{-3}$, for July 14^th^-16^th^: It proved thus possible to reach and maintain the target soil moisture levels, even under extreme meteorological conditions.

## 3.3. Soil saturation, de-saturation and stress tests

### 3.3.1. Planters with climbing plants

As previously stated, the retained irrigation strategy should be able to reach near-saturation water contents for overheat conditions. This section compares two different approaches for this purpose: using a single "maximum irrigation event" (Imax in Figs. 5, 7 and 9) or repeated
<figure><img src="image_7.png" /><figcaption>Fig. 5. Chronicle of soil water content values, between June 28th and July 13th, 2023, for Planter C (climbing plants, *Lonicera japonica*). Orange shades are darker with larger depths (5, 15, 25 and 35 cm). Light blue bars indicate irrigations, dark blue bars rain events. Parallel black dotted lines indicate hours between 1pm and 6pm day used in the analysis of averaged water contents during hours of strong climatic demands. The "Imax" notation positions the maximum irrigation event that lasted 4h30 (Section 3.1.2). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Fig. 6. Chronicle of soil water content values, between June 14th and July 23rd, 2023, for Planter C (climbing plants, *Lonicera japonica*). Orange shades are darker with larger depths (5, 15, 25 and 35 cm). Light blue bars indicate irrigations. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 7. Chronicles of soil water content values, between June 28th and July 13th, 2023, for Planter F (shrubs, *Abelia grandiflora*). Orange shades are darker with larger depths (5, 15, 25 and 35 cm for soil water content, 15 and 25 cm for soil tension). Light blue bars indicate irrigations, dark blue bars rain events. The black dotted lines indicate the hours between 1 p.m. and 6 p.m. Also indicated is the maximum irrigation event (Imax). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

irrigations separated by short time intervals. It also describes the succession of saturation and depletion (stress) conditions.

On July 10th, soil saturation was reached via the maximum irrigation

event taking place between noon and 4.30 p.m., causing drainage from all planters (A, B and C). For planter C, the maximum soil water contents reached were $\theta_{sat} = 0.333, 0.327, 0.370$ and $0.364 \text{ m}^3 \text{ m}^{-3}$ at 5, 15, 25
<figure><img src="image_10.png" /><figcaption>**Fig. 8.** Chronicles of soil water content values, between June 28th and July 13th, 2023, for Planter F (shrubs, *Abelia grandiflora*). Orange shades are darker with larger depths (5, 15, 25 and 35 cm). Light blue bars indicate irrigations. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>**Fig. 9.** Chronicles of soil water content (a) and tension (b) values, between June 27th and September 23rd, 2023, for Planter C (climbing plants, Lonicera Japonica). Orange shades are darker with larger depths (5, 15, 25 and 35 cm for soil water content, 15 and 25 cm for soil tension). Light blue bars indicate irrigations, dark blue bars rain events. Also indicated are the maximum irrigation event (Imax) and the stress test of the soil-plant system (Stress) before the August heatwave. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

and 35 cm, slightly different from the values obtained in May ($\theta_{sat} = 0.351, 0.337, 0.354$ and $0.329$). The planter was then left without irrigation until July 13th and the water content decreased to $0.094, 0.115, 0.151$ and $0.150 \text{ m}^3 \text{ m}^{-3}$ for the 5, 15, 25 and 35 cm depths. For comparison, soil saturation was sought on July 31st, during an overheated day, from four irrigations of identical 30-min durations: an extra irrigation took place at 2 p.m., in addition to those at 8 a.m., 12 p.m. and 6 p.m. Under this irrigation regime, drainage was observed in planter A, which is less exposed to the sun and seemingly has preferential flow paths in the soil, as indicated by higher soil moisture content values

(Section 3.1). Thus, four irrigations of 30 min on the same day may also be too much, although the diagnostic is less obvious as for the Imax experiment. The water contents achieved for planter C after the 2 p.m. irrigation were $\theta_{sat} = 0.382, 0.383, 0.407$ and $0.392 \text{ m}^3 \text{ m}^{-3}$ for the depths of 5, 15, 25 and 35 respectively.

The soil-plant system was then stressed before the heatwave of August 20th-25th (until the vertical dotted line indicated as "Stress" in Fig. 8) prior to the application of the irrigation strategy dedicated to heatwave periods. The purpose was to check how long (or how many repeated irrigation cycles) it would take for the soil to respond and reach
the desired water content (and tension) levels, starting from relatively low water content values. From August 11th to 16th, irrigation only took place at 8 a.m., then both at 8 a.m. and 12 p.m. for August 17th and August 18th, as the plants looked a little too dry and the idea was to stay within a tolerable water stress range. Under this deficit irrigation regime, minimum soil water contents were reached on August 20th (8 a. m.), as 0.095, 0.123, 0.160 and 0.162 m³ m⁻³ for the depths of 5, 15, 25 and 35 cm, corresponding to tensions of -71.78 kPa and -163.50 kPa for the depths of 15 and 35 cm, in one of the few cases in which tension values were exploitable. Then three irrigations per day took place during each of the following days (August 21st-25th) and the water content rose rapidly and progressively, in successive steps followed by a small decrease, likely water consumption by evapotranspiration after each irrigation.

For soil tension, there was a nearly immediate response at the 15 cm depth, while it took two days for the 35 cm depth, suggesting two ex- planations, not excluding one another. Either water movement kept on very slowly so as to finally reach the larger depths, or non-negligible hysteresis exists in the soil water retention curve, so that it takes high- enough water contents to trigger significant variations in soil tension, along the wetting curve, as the result of a threshold effect (Haines, 1930; Mualem, 1974; Nuth and Laloui, 2008). Overall, the very partial and uncertain correlation between soil water content and tension values has multiple possible causes in the geometrical setting and the processes themselves, which rather complicates the analysis in the present case.

### 3.3.2. Planters with shrubs

As for the climbing plants, three different tests took place: (a) the Imax irrigation to create drainage thus favourable conditions to identify θsat values, (b) four irrigations of 30 min in a single day, as a possible alternative to the selected regime for overheat days, allowing three ir- rigations, and (c) a reduction in irrigation followed by the irrigation regime decided for overheat days, to identify the number of irrigation cycles required to reach the target soil water content values. See Fig. 10 for all associated observations.

For point (a), the Imax irrigation resulted in drainage out of the planters and yielded estimates of θsat = 0.107, 0.255, 0.438, and 0.489 m³ m⁻³ at depths of 5, 15, 25, and 35 cm, respectively. As for point (b), four irrigations of 30 min each took place at 8 a.m., 12 p.m., 2 p.m., and 6 p.m., on July 31st. No drainage was observed in planter F the day those irrigations occurred, but some drainage was observed the day after (August 1st) just after the 8 a.m. irrigation. Soil water contents after the 2 p.m. irrigation on July 31st were 0.100, 0.248, 0.415 and 0.490 m³ m⁻³ at 5, 15, 25, and 35 cm, respectively. On August 1st, the water contents causing drainage were nearly the same, approximately θsat = 0.094,

0.233, 0.411 and 0.491 m³ m⁻³. For point (c), as previously, moderate water stress conditions were created before the heatwave of August 20th to 25th, to observe the progressive rise of soil water content and tension when transitioning from low-water conditions to the maximum irrigation regime. The minimum water content values recorded were 0.061, 0.142, 0.322 and 0.465 m³ m⁻³ at 5, 15, 25 and 35 cm, respectively, at 8 a.m., on August 20th. With the start of the heatwave irrigation regime, a quick response of soil water content was observed. At the depths of 5, 25 and 35 cm, it only took two days to reach and maintain the target water content values, while the probe at 15 cm showed a slower, gradual increase, with a daily pattern.

Again, different soil responses were observed for different irrigation characteristics (repetition, duration). Significant drainage was observed during and after the Imax irrigation, with higher water contents at depths of 5, 15, and 25 cm than at 35 cm, whereas limited drainage took place for in the four-irrigation trial. Overall, for the planters with shrubs, the response of soil water content sensors was relatively rapid (quicker than in the planters with climbing plants) when transitioning from a rather dry soil state to an intense irrigation regime. This is likely attributable to their reduced soil exposure to solar radiation: the dense plant cover and the shading provided by the south wall on the planters probably allowed for greater water retention.

### 3.4. Evaluation of the campaign and future studies

Given the trial-and-error nature of the methodology opted for, in a system with no prior information about the soil-plant characteristics, what could be expected at most was (i) the identification of the key system properties, i.e. descriptors to bear in mind for irrigation piloting, and (ii) associated attempts at relevant irrigation piloting strategies, producing particular cases to gain knowledge on good practices and feed more formal interpretation and piloting "models", as a predecessor study.

As for the first expectation (i), this approach succeeds in "unveiling" the apparent θfc and θsat values, as well as the characteristic times (t₀, tₘ, tmt, t₅) and magnitudes (Δθm, Δθtm) of the observed effects of irrigation, also hypothesizing their dependence on the initial θt values. It also establishes that system geometry plays a significant role in the measured soil water content or soil tension values, as the monitored values change dramatically (and become non-interpretable) when the probes obviously find themselves outside the irrigation bulb. Of course, the expectedly frequent case of water content probes at least partly within the irrigation bulb is more difficult to handle with this descriptive approach but the next section will show it can still be addressed in indirect ways. On the one hand, it seems a priori impossible to identify those partial overlaps

<figure><img src="image_12.png" /><figcaption>**Fig. 10.** Chronicles of soil water content values, between June 27th and September 23rd, 2023, for Planter C (climbing plants, Lonicera Japonica). Orange shades are darker with larger depths (5, 15, 25 and 35 cm for soil water content). Light blue bars indicate irrigations, dark blue bars rain events. Also indicated are the maximum irrigation event (Imax) and the stress test of the soil-plant system (Stress) before the August heatwave. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>
through their effects on the measured water content values. This would
definitely need a numerical model linking the measured water content
value (say, θm) to the overlap (α), the actual water content value in the
bulb (θb) and the water content exterior to the bulb (θe) but within the
domain of investigation of the probe. But on the other hand, interest-
ingly, the network of sensors in the planters is dense enough to guess the
contours of the irrigation bulbs from a 3D analysis of the sensors that are
responding, or not, or with significant delay, to the irrigation pulses.
This could, in turn, inform interpretation models with approximations as
regards the development (or persistence) and the shape of the bulbs (e.g.
depth Zb, radius Rb) which in reality control the overlap mentioned
above. All previously mentioned quantities (θfc, θsat, t0, tm, tmt, ts, Δθm,
Δθtm, α, θm, θb, θe, Zb, Rb) can be organized in such a model, so as to fulfil
the second expectation (ii) and pave the way for future studies.

A thorough evaluation of the approach would thus require simula-
tions and numerical methods to test various scenarios and to project
them for future conditions, or to model the effects of different irrigation
regimes in planters, under similar sun exposure, plant development and
sensor arrangements. However, based on visual analysis (observable
evidence), on the team's expertise and on rough estimates of the soil-
plant water balance, it is possible to evaluate irrigation piloting. First,
apart from dedicated tests, it did not cause any drainage. Second, the
plants remained consistently in bloom and exhibited a healthy appear-
ance. Third, soil water content stayed within the desired ranges for both
the standard and overheat days. Fourth, evapotranspiration rates, and
especially transpiration, were likely higher in overheated conditions.

The latter argument arises from the water balance method, restricted
to days without rainfall or drainage in the planters, thus relying on the
variations of soil water content in the profile, as the result of irrigation
and AET only. It should be noted that this approach rather aims to
compare the magnitude of the AET term across different irrigation re-
gimes, for different periods, than to provide exact calculations. The main
reason to be that cautious is that the actual dimensions of the irrigation
bulb (shape, elongation, width) remain unknown while this bulb is ex-
pected to significantly feed the evapotranspiration processes. For the
indicated purposes, two simplifying hypotheses were made: (i) a con-
stant bulb radius of 15 cm was assumed, corresponding to a cylindrical
shape of the bulb, and (ii) the variations of soil water content monitored
by the sensor at 25 cm were extrapolated to the whole profile. This
seems a strong assumption but the bulb water content is likely similar
over the entire profile and the sensor at 25 cm is the one thought to
provide the most reliable measures. Finally, estimating overall water
budgets from soil depths a priori not concerned with evaporation means
insisting on the role played by transpiration, which may be perceived as
a weak point of the approach but also is a specific point of interest in
evaluating irrigation strategies. During the standard period from July
23rd to 24th, the AET per plant was estimated as 2.5 L day⁻¹ per climbing
plant and 0.6 L day⁻¹ per shrub. In contrast, during the heatwave from
July 19th-20th, the AET were 5.3 and 1.7 L day⁻¹ plant⁻¹. On August
20th, under water stress conditions, the AET dropped to 2.7 and 0.2 L
day⁻¹ plant⁻¹. Then the average AET for the subsequent four days under
the re-established full irrigation regime rose up to 5.4 and 1.2 L day⁻¹
plant⁻¹ (similar orders of magnitude as in Section 2.1).

In an overview, the followed procedure gave insights into the soil's
behaviour in response to irrigation. More specifically, soil water reten-
tion capacity seemed to progressively increase with the adopted irriiga-
tion regime (Fig. 10, after the "Stress" bar) as also reported by Cheung
et al. (2022a, 2022b). This suggested to adopt relatively limited irriiga-
tion durations (30 min) used more frequently throughout the day, which
is a fifth outcome of the study. Aiming to further optimise irrigation, a
more restricted irrigation regime could likely be adopted on standard
days, since the plants proved to be robust and tolerant, and also because
the measured water contents can only underestimate the bulb water
content, potentially leading to excessive irrigation. In this logic, accu-
rately positioning the sensors in relation to the (vertical of the) drippers
becomes even more important, to ensure the appropriate monitoring of

soil water dynamics.

3.5. Semi-quantitative information on the water bulb

Using the variation in soil water content recorded by the sensors, and
knowing the position of those sensors with respect to the drippers, it is
possible to guess the approximate geometric characteristics (shape,
elongation, depth, radius) of the water bulbs, as sketched out in Figs. 11
and 12. The next step would then be to re-position the sensors. From
literature, a reasonable hypothesis is a relatively homogeneous water
content within the irrigation bulb, slightly above θfc, apart from the
immediate vicinity of the dripper where θsat is expected. The magnitude
of the Δθ effect is thus closely related to the overlap between the bulb
and the sensor, hence to the radius and depth of the bulb. Additionally,
regularizing hypotheses as regards the expected shape of the bulb
(resembling an ellipsoid) to complete the drawings. In the chosen ex-
amples, the sensors positioned at 25 cm depth exhibited the largest
overlaps with the irrigation bulbs, followed by these at 15, 5 and 35 cm,
hence the slight distortions of the represented ellipsoids.

The dimensions of the hydric bulb after each irrigation are obviously
not self-replicating, as the results show they vary with the irrigation
amount and the initial water content (which meets literature elements)
but also with weather conditions and plant development, as seen from
the different dynamics between Spring and Summer. Given the naturally
heterogeneous soil properties and the occurrence of preferential path-
ways, which are more common in well-drained soils like the one used in
the experiment, the water bulb tends to have a likely modified and
uneven ellipsoidal shape, retaining less water in the upper layers of the
soil (Hao et al., 2007). The experimental results also showed a trend for
the water bulb to increase in all dimensions with the frequency of irri-
gation. This visual representation vows to help understand the tested
cases by organizing all available information in a very incarnate
manner, focusing on designing the quantity object of interest: the irri-
gation bulb. Another aim is to emphasize the importance of accurately
positioning the sensors to efficiently assess the impact of each irrigation
 expecting less difficulties from the 1D nature of rain events). Ulti-
mately, this is the location and shape of the irrigation bulb that dictates
the amount of water available for the plant, thus, in turn, the evapo-
transpiration of the soil-plant system.

3.6. *Irrigation piloting guide*

There is a clear stake of converting the results of this study in
operational guidelines for good practices in irrigation piloting, for
limited prior knowledge on the soil-plant properties and imperfectly-
gauged contexts. To summarize the leads followed here, (a) the objec-
tives assigned to irrigation should be defined first, as they will require
different strategies if one wishes to maximize biomass production (e.g.
vegetables grown on rooftops) or to produce cooling effects from
maximal evapotranspiration. The control options should be listed then
(b), some of them active (irrigation amounts, duration, frequency and
triggers) for system operation, some others passive (sensor type and
positioning, depending on the irrigation type and strategy, plant species)
but prone to deliver useful feedbacks. Among the various sensors,
identify those effectively relevant (c) to qualify the key soil hydrody-
namic properties, and possibly plant tolerance or aversion for water
stress, from a basic signal analysis. Choose performance indicators as
descriptors of the irrigation effects (d) then assess their ranges of vari-
ation to check how sensitive and informative they are, for a series of
contrasted irrigation piloting experiments. Jointly select the indicators
and sensors that provide the most accurate and timely information on
the irrigation bulb (e) which is the key quantity of interest. Define, re-
adjust and update the optimal control criteria (f) as more information
becomes available from data acquisition.
<figure><img src="image_13.png" /><figcaption>**Fig. 11.** Estimation of the water bulb considering the summer months and a 30-min (1.9 L/h) irrigation, in the climbing planter C, different perspectives of the same device.</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>**Fig. 12.** Estimation of the water bulb considering the summer months and a 30-min (1.9 L h⁻¹) irrigation, in the shrubs planter F, different perspectives of the same device.</figcaption></figure>

## 4. Conclusion

The primary goal of this study was to develop a general approach to enhance evapotranspiration through irrigation during heatwaves for cooling effects, while maintaining plant health during standard periods with minimal water use, maximising efficiency. The findings underscore the challenges of conventional assumptions about soil-plant dynamics, especially in non-standard contexts such as small planters and varied climatic conditions. Due to the complexity of these interactions, the study recommends identifying key factors—such as initial soil moisture, irrigation duration, and sensor placement—as critical controls for devising effective irrigation strategies.

This study has shed light on key aspects of the functioning and resilience of native Mediterranean plant species when placed in planters and irrigated under different conditions, depending on the local climate and irrigation goals. Starting with minimal data, as would be typical for new urban vegetation installations, the study demonstrates how to design context-specific irrigation strategies through a trial-and-error process, informed by observational evidence. The selected irrigation control criteria, applied to appropriate soil moisture sensors, resulted in significantly different evapotranspiration rates between standard and heatwave conditions. Centrally positioned sensors relative to the irrigation bulb provided more accurate monitoring of soil conditions, while horizontally placed capacitive sensors covered a wider area, improving positioning accuracy compared to tensiometers. For the system studied, smaller, more frequent irrigation doses resulted in better soil water retention compared to larger, less frequent applications.

Building on these findings, the study advocates for the creation of comprehensive guides to help select plant species suitable for various cultivation methods, along with the optimal choice of planter size, soil types, and irrigation system design. These efforts will support informed decision-making, promoting the sustainable development of urban environments while conserving water resources.

## Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

### CRediT authorship contribution statement

**Martina Garcia de Cezar:** Writing – original draft, Visualization, Methodology, Formal analysis, Data curation, Conceptualization. **Bruno Cheviron:** Writing – review & editing, Supervision, Formal analysis, Conceptualization. **François Liron:** Validation, Investigation, Data curation. **Geoffrey Froment:** Validation, Investigation, Data curation. **Cyril Dejean:** Validation, Investigation, Data curation. **Séverine Tomas:** Supervision, Project administration, Conceptualization. **Laurient Aprin:** Supervision, Project administration, Conceptualization.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgements

We acknowledge the supporting of the INRAE UMR G-Eau technical team for their support in building and maintaining the experiment and the team of developers for their support in assembling the experiment database.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jenvman.2024.122712.
# Data availability

Data will be made available on request.

## References

Al-Hamed, S.A., Wahby, M.F., Aboukarima, A.M., Marazky, M.S.E., 2017. Effect of soil and water characteristics on yield and properties of "Spunta" potatoes. Chil. J. Agric. Res. 77, 250-256. https://doi.org/10.4067/S0718-58392017000300250.

Allan, R., Pereira, L., Smith, M., 1998. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56.

Bean, E.Z., Huffaker, R.G., Migliaccio, K.W., 2018. Estimating field capacity from volumetric soil water content time series using automated processing algorithms. Vadose Zone J. 17, 1-12. https://doi.org/10.2136/vzj2018.04.0073.

Bröde, P., Fiala, D., Blažejczyk, K., Holmér, I., Jendritzky, G., Kampmann, B., Tinz, B., Havenith, G., 2012. Deriving the operational procedure for the universal thermal climate index (UTCI). Int. J. Biometeorol. 56, 481-494. https://doi.org/10.1007/s00484-011-0454-1.

Brooks, R.H., Corey, A.T., 1964. Hydraulic properties of porous media. Hydrology Papers. Colorado State University.

Cheung, P.K., Jim, C.Y., Tapper, N., Nice, K.A., Livesley, S.J., 2022a. Daytime irrigation leads to significantly cooler private backyards in summer. Urban Clim. 46, 101310. https://doi.org/10.1016/j.uclim.2022.101310.

Cheung, P.K., Nice, K.A., Livesley, S.J., 2022b. Irrigating urban green space for cooling benefits: the mechanisms and management considerations. Environ. Res.: Climate 1, 015001. https://doi.org/10.1088/2752-5295/ac6e7c.

Cosenza, P., Guérin, R., Tabbagh, A., 2003. Relationship between thermal conductivity and water content of soils using numerical modelling. Eur. J. Soil Sci. 54, 581-588. https://doi.org/10.1046/j.1365-2389.2003.00539.x.

Costello, L.R., Jones, K.S., 2014. WUCOLS IV: water use classification of landscape species. http://ucanr.edu/sites/WUCOLS/.

Coutts, A.M., Tapper, N.J., Beringer, J., Loughnan, M., Demuzere, M., 2013. Watering our cities: the capacity for Water Sensitive Urban Design to support urban cooling and improve human thermal comfort in the Australian context. Prog. Phys. Geogr. Earth Environ. 37, 2-28. https://doi.org/10.1177/0309133312461032.

Dunn, R.J.H., Sandford, C., Kendon, M., Morice, C.P., Kennedy, J.J., Rayner, N.A., 2023. Global and regional climate in 2022. Weather 78, 328-336. https://doi.org/10.1002/wea.4500.

Fan, L., Gai, L., Tong, Y., Li, R., 2017. Urban water consumption and its influencing factors in China: evidence from 286 cities. J. Clean. Prod. 166, 124-133. https://doi.org/10.1016/j.jclepro.2017.08.044.

Ferguson, B.K., 1987. Water conservation methods in urban landscape irrigation: an exploratory overview. J American Water Resour Assoc 23, 147-152. https://doi.org/10.1111/j.1752-1688.1987.tb00794.x.

Guo, J., Niu, H., Xiao, D., Sun, X., Fan, L., 2021. Urban Green-space Water-consumption characteristics and its driving factors in China. Ecol. Indicat. 130, 108076. https://doi.org/10.1016/j.ecolind.2021.108076.

Haines, W.B., 1930. Studies in the physical properties of soil. V. The hysteresis effect in capillary properties, and the modes of moisture distribution associated therewith. J. Agric. Sci. 20, 97-116. https://doi.org/10.1017/S002185960008864X.

Hammami, M., Maalej, M., 2005. Prédiction de la profondeur de sol humidifié sous goutteur. rseau 12, 273-284. https://doi.org/10.7202/705352ar.

Hao, A., Marui, A., Haraguchi, T., Nakano, Y., 2007. Estimation of wet bulb formation in various soil during drip irrigation. J. Fac. Agric. Kyushu Univ. 52, 187-193. https://doi.org/10.5109/9304.

Jacque, H., Knox, J.W., Gush, M., Holman, I.P., 2023. Modelling the potential of rainwater harvesting to improve the sustainability of landscape and public garden irrigation. J. Environ. Manag. 348, 119167. https://doi.org/10.1016/j.jenvman.2023.119167.

Jones, M.W., Abatzoglou, J.T., Veraverbeke, S., Andela, N., Lasslop, G., Forkel, M., Smith, A.J.P., Burton, C., Betts, R.A., Van Der Werf, G.R., Sitch, S., Canadell, J.G., Santín, C., Kolden, C., Doerr, S.H., Le Quéré, C., 2022. Global and regional trends and drivers of fire under climate change. Rev. Geophys. 60, e2020RG000726. https://doi.org/10.1029/2020RG000726.

Kozak, J.A., Ahuja, L.R., Ma, L., Green, T.R., 2005. Scaling and estimation of evaporation and transpiration of water across soil textures. Vadose Zone J. 4, 418-427. https://doi.org/10.2136/vzj2004.0119.

Li, W., Hai, X., Han, L., Mao, J., Tian, M., 2020. Does urbanization intensify regional water scarcity? Evidence and implications from a megaregion of China. J. Clean. Prod. 244, 118592. https://doi.org/10.1016/j.jclepro.2019.118592.

Liu, B., Zhu, C., Xu, K., Ma, S., Lu, M., Han, X., Hua, L., 2023. Record-breaking pre-flood rainfall over south China in 2022: role of historic warming over the northeast pacific and maritime continent. Clim. Dynam. 61, 3147-3163. https://doi.org/10.1007/s00382-023-06734-6.

Livesley, S.J., Marchionni, V., Cheung, P.K., Daly, E., Pataki, D.E., 2021. Water smart cities increase irrigation to provide cool refuge in a climate crisis. Earth's Future 9, e2020EF001806. https://doi.org/10.1029/2020EF001806.

Matos, C., Sampaio, A., Bentes, I., 2012. Greywater use in irrigation: characteristics, advantages and concerns. In: Garcia-Garizabal, I. (Ed.), Irrigation - Water Management, Pollution and Alternative Strategies. InTech. https://doi.org/10.5772/30026.

Matzarakis, A., Mayer, H., Iziomon, M.G., 1999. Applications of a universal thermal index: physiological equivalent temperature. Int. J. Biometeorol. 43, 76-84. https://doi.org/10.1007/s004840050119.

Merdun, H., Meral, R., Riza Demirkiran, A., 2008. Effect of the initial soil moisture content on the spatial distribution of the water retention. Eurasian Soil Sci. 41, 1098-1106. https://doi.org/10.1134/S1064229308100128.

Mualem, Y., 1976. A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resour. Res. 12, 513-522. https://doi.org/10.1029/WR012i003p00513.

Mualem, Y., 1974. A conceptual model of hysteresis. Water Resour. Res. 10, 514-520. https://doi.org/10.1029/WR010i003p00514.

Narayanamoorthy, A., 1997. Economic viability of drip irrigation: an empirical analysis from Maharashtra. Indian J. Agric. Econ. 52.

Niu, G., Rodriguez, D.S., Cabrera, R., McKenney, C., Mackay, W., 2006. Determining water use and crop coefficients of five woody landscape plants. J. Environ. Hortic. 24, 160-165. https://doi.org/10.24266/0738-2898.24.3.160.

Nouri, H., Chavoshi Borujeni, S., Hoekstra, A.Y., 2019. The blue water footprint of urban green spaces: an example for Adelaide, Australia. Landsc. Urban Plann. 190, 103613. https://doi.org/10.1016/j.landurbplan.2019.103613.

Nuth, M., Laloui, L., 2008. Advances in modelling hysteretic water retention curve in deformable soils. Computers and Geotechnics, Special Issue on Unsaturated Soils: Models, Algorithms and Applications 35, 835-844. https://doi.org/10.1016/j.compgeo.2008.08.001.

Pan, L., Zhao, Y., Zhu, T., 2023. Estimating urban green space irrigation for 286 cities in China: implications for urban land use and water management. Sustainability 15, 8379. https://doi.org/10.3390/su15108379.

Philip, J.R., 1989. The scattering analog for infiltration in porous media. Rev. Geophys. 27, 431-448. https://doi.org/10.1029/RG027i004p00431.

Picchioni, G.A., Singh, J., Mexal, J.G., Goss, R.M., 2007. Merging the roles of learning, research, and outreach in a nursery irrigation exercise. hortte 17, 379-385. https://doi.org/10.21273/HORTTECH.17.3.379.

Raats, P.A.C., 2007. Uptake of water from soils by plant roots. Transport Porous Media 68, 5-28. https://doi.org/10.1007/s11242-006-9055-6.

Reid, K., Oki, L.R., 2016. Evaluation of ornamental plant performance on four deficit irrigation levels: working with industry to promote sustainable plant choices for summer-dry regions. Acta Hortic. 155-162. https://doi.org/10.17660/ActaHortic.2016.111222.

Reyes-Paecke, S., Gironás, J., Melo, O., Vicuña, S., Herrera, J., 2019. Irrigation of green spaces and residential gardens in a Mediterranean metropolis: gaps and opportunities for climate change adaptation. Landsc. Urban Plann. 182, 34-43. https://doi.org/10.1016/j.landurbplan.2018.10.006.

Rothfuss, Y., Biron, P., Braud, I., Canale, L., Durand, J., Gaudet, J., Richard, P., Vauclin, M., Bariac, T., 2010. Partitioning evapotranspiration fluxes into soil evaporation and plant transpiration using water stable isotopes under controlled conditions. Hydrol. Process. 24, 3177-3194. https://doi.org/10.1002/hyp.7743.

Sumon, Datta, Taghvaean, S., Stivers, J.W., 2017. Understanding soil water content and thresholds for irrigation management. https://doi.org/10.13140/RG.2.2.35535.89765.

Szabó, P., Bartholy, J., Pongrácz, R., 2024. Seasonal temperature and precipitation record breakings in Hungary in a warming world. Int. J. Geom. 15, 2. https://doi.org/10.1007/s13137-023-00241-w.

Van Genuchten, M.Th, 1980. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. SSBA (Soil Sci. Soc. Am.) J. 44, 892-898. https://doi.org/10.2136/ssaj1980.03615995004400050002x.

Zotarelli, L., Dukes, M., Morgan, K., 2010. Interpretation of soil moisture content to determine soil field capacity and avoid over-irrigating sandy soils using soil moisture sensors: ae460/ae460, 2/2010. Environ. Data Inf. Serv. 2010. https://doi.org/10.3247/edis-ae460-2010.