# Microclimate, Soil and Plant Dataset From a Mediterranean Urban Canyon With Irrigated Planters

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

► To cite this version:

Martina Garcia de Cezar, Bruno Cheviron, François Liron, Séverine Tomas, Laurent Aprin, et al. Microclimate, Soil and Plant Dataset From a Mediterranean Urban Canyon With Irrigated Planters. Geoscience Data Journal, 2025, 12 (4), pp.e70033. 10.1002/gdj3.70033. hal-05259159

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DATA ARTICLE

OPEN ACCESS

# Microclimate, Soil and Plant Dataset From a Mediterranean Urban Canyon With Irrigated Planters

Martina Garcia de Cezar<fnref n="1" />,² | Bruno Cheviron² | François Liron² | Séverine Tomas² | Laurent Aprin³ | Kevin Orlando² | Justine Catel² | Geoffrey Froment² | Cyril Dejean²

**Correspondence:** Martina Garcia de Cezar (martina.garcia-de-cezar@inrae.fr)

**Received:** 12 March 2025 | **Revised:** 25 August 2025 | **Accepted:** 29 August 2025

**Keywords:** humidity | radiation | temperature | thermal comfort | urban area | vegetation | water content | water management

## ABSTRACT

Dataset obtained from a fully instrumented experimental urban street canyon, located on a research site in Montpellier (France, Mediterranean climate) and monitored between 21 July 2023 and 31 July 2024. This east-west canyon consists of two parallel concrete walls (height 2.3 m, length 12 m, width 5 m). Three nearly adjacent planters (height 0.9 m, length 2.3 m, width 0.8 m) are aligned along its inner north wall and contain climbing plants of the *Lonicera japonica* species. Three other nearly adjacent planters (height 0.9 m, length 2.3 m, width 1.1 m) are aligned along its inner south wall and contain shrub plants of the *Abelia grandiflora* species. Each planter includes 11 sensors to measure soil temperature, water content and matric potential, collecting data every 10 min. Drip irrigation was used and each series of planters received identical irrigation amounts. The irrigation strategy changed several times, to monitor the impacts of irrigation on the overall water balance of the soil-plant-atmosphere system, especially on the local microclimatic variables. A dense network of 102 sensors was installed to monitor wind direction and velocity, air temperature, relative humidity, radiation, grey globe temperature and rainfall at 1.3 m above the ground, which is a standardised measurement height for determining the variables used to quantify thermal comfort indices. This dataset supports (i) understanding thermal, radiative and aerodynamic processes in urban canyons, (ii) detecting irrigation impact on microclimate, (iii) validating CFD-based microclimate models and (iv) identifying methods to manage urban heatwaves through water resource optimisation. By encompassing a full year of seasonal and climatic variability, this study is the first to evaluate the combined effects of vegetation type and irrigation on urban thermal comfort in a Mediterranean context, providing a significant contribution to urban microclimate research.

## 1 | Introduction

The Year 2024 marked a pivotal moment in climate history, as the Earth's average temperature exceeded 1.5°C above pre-industrial levels for the first time on a global scale (source C3S/ECMWF). Why is this significant, and how does it affect Earth's dynamics? According to IPCC studies, exceeding the critical threshold of 1.5°C–2°C could lead to irreversible changes in the Earth's systems (Roldán-Gómez et al. 2025).

Reaching this range underscores the urgency of mitigating, adapting to and anticipating the challenges of this climate reality. The impacts of this global climate crisis include a rise in the frequency and intensity of extreme events such as heavy rainfall, droughts, hurricanes, wildfires and heatwaves (Armstrong McKay et al. 2022). This article focusses on urban overheating, defined as the exceedance of locally established thermal thresholds that negatively impact human health, comfort, productivity and urban systems (Nazarian et al. 2022).

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2025 The Author(s). *Geoscience Data Journal* published by Royal Meteorological Society and John Wiley & Sons Ltd.

<footnote n="1">¹AgroParisTech, G-EAU, Univ Montpellier: AgroParisTech, BRGM, CIRAD, INRAE, Institut Agro, IRD, Montpellier, France | ²INRAE, G-EAU, Univ Montpellier: AgroParisTech, BRGM, CIRAD, INRAE, Institut Agro, IRD, Montpellier, France | ³Laboratory for the Science of Risks (LSR), IMT Mines Alès, Alès, France</footnote>
phenomenon is driven by two primary factors: global climate change and urban development (Nazarian et al. 2022). Urban development plays a central role in exacerbating urban overheating by disrupting the local thermal balance. Contributing factors include the Urban Heat Island (UHI) effect, heatwaves and heat advection from nearby high-temperature regions (Feng et al. 2023). Key mechanisms involve anthropogenic heat emissions, heat absorption and release by urban materials, reduced evaporative cooling due to limited vegetation, restricted turbulent heat transfer in densely built areas, low albedo from dark surfaces and the trapping of longwave radiation in street canyons (Feng et al. 2023; Nazarian et al. 2022; Roldán-Gómez et al. 2025; Ulpiani et al. 2024). These combined processes intensify urban temperatures, underscoring the urgent need for climate-adaptive urban planning strategies, such as increasing vegetation cover, optimising building materials and enhancing surface reflectivity.

This context necessitates concrete actions, as urban overheating is a multifaceted issue (Ulpiani et al. 2024). It impacts the environment, public health and social equity, disproportionately affecting vulnerable populations and intensifying social inequalities (Feng et al. 2023). Moreover, it raises questions about the resilience of cities, whose existing infrastructure is poorly adapted to future climate challenges (Egerer et al. 2021). One obstacle to effective urban climate adaptation is the lack of detailed experimental data with adequate spatial and temporal resolution (Yang et al. 2023). These data are fundamental to understanding the complexity of urban areas and validating numerical models, essential tools to aid decision-making in the quest for urban resilience (Wankhade et al. 2023; Wu et al. 2022; Zhao et al. 2023). This article aimed to address this gap by providing practical guidance from a case study for conducting experiments to quantify urban adaptation strategies and by offering a one-year dataset to advance theoretical and operational knowledge on this topic.

The experimental installation is situated at the INRAE laboratory, UMR G-EAU, Campus Lavalette (43°64' N, 3°87' E) in Montpellier, southern France. This set-up addresses a multidisciplinary challenge: developing strategies to mitigate urban overheating during heatwaves, with a particular focus on the thermal comfort indices. The study explores the cooling potential of urban vegetation, particularly when irrigated, as a means to alleviate extreme heat conditions. Although irrigation is essential for maintaining plant water balance, its implementation and potential impacts on urban microclimates remain insufficiently explored (Cheung, Nice, and Livesley 2022; Fu et al. 2022). Thermal comfort or discomfort experienced by individuals results from the interplay of various factors, often studied across different disciplines. These factors include large-scale climatic forces, urban geometry and materials, wind patterns, solar radiation and shading, and evapotranspiration fluxes from vegetation, which influence air temperature and humidity, and leaf surface temperature. Consequently, this research integrates knowledge from physics (aerodynamics, heat transfer), microclimate modelling, agronomy, urban engineering, water management and public health policy, thereby providing a multidisciplinary framework to better understand and design strategies for mitigating urban overheating and improving thermal comfort.

## 1.1 | Value of the Data

The primary value of this dataset lies in its originality and high precision. It represents the first experiment to investigate the specific role of irrigation in urban canyons, encompassing an extensive range of monitored variables, high spatial and temporal resolution, and year-long data collection that spans all four seasons under Mediterranean climatic conditions. The importance of such datasets and experimental studies is widely recognised in the literature, which underscores the relevance and value of the present work (Cheung, Nice, and Livesley 2022; Dey and Joshi 2023; Fu et al. 2022; Garcia de Cezar et al. 2024; Mballo et al. 2021). Modelling air, humidity and heat fluxes in real urban street canyons is inherently complex due to the interactions among multiple factors and the challenges in measuring key physical variables. The experimental set-up features a thoroughly instrumented urban street canyon with simplified yet realistic geometries, enabling precise monitoring of critical variables and processes. These include thermal, radiative and aerodynamic fluxes influenced by wind speed and direction, solar radiation (intensity and heterogeneity), relative humidity, air temperature, globe temperature, soil temperature, soil tension, soil water content, irrigation types and material properties. Additionally, spatial variations in thermal comfort indices are documented to provide comprehensive insights.

Existing literature (Wankhade et al. 2023; Wu et al. 2022) underscores the need for purpose-built datasets to improve the validation of models and performance indicators, such as perceived temperature and thermal comfort. This dataset addresses this gap by documenting key physical variables with fine temporal resolution, using appropriately positioned and calibrated sensors with suitable accuracy and range. Such data are essential for both understanding processes and validing models used to study urban microclimates. To date, few studies have examined related issues, including the role of well-irrigated trees in urban canyons (Mballo et al. 2021) and the effects of different irrigation regimes on backyard turfgrass during summer (Cheung, Jim, et al. 2022), and their influences on thermal comfort indices.

From a strategic and modelling perspective, this dataset supports an unprecedented analysis of irrigation strategies and their direct or indirect effects on critical physical variables in urban environments. These variables include vegetation evapotranspiration, air temperature, relative humidity and radiant temperature. Irrigation is viewed as a potential lever for thermal regulation in cities, closely linked to urban greening strategies such as species selection, landscape design and the restoration of permeable soils. Combined with other interventions, such as optimising urban design, materials and colours, the irrigation of urban vegetation can produce beneficial street-scale effects to mitigate increasingly frequent and intense heatwaves. This dataset focusses on the effects of shrubs and climbing plants in planters, under controlled irrigation, on urban microclimate and water balance, contributing to a broader understanding of urban vegetation's role in climate adaptation.

Additionally, the dataset addresses gaps in research (Fu et al. 2022) on the interplay between water management and
plant physiology, particularly the effects of different plant types and characteristics, such as shading capacity, responses to climatic forcings, local heat fluxes and water availability. By providing high-resolution and specialised data, this resource can enhance the accuracy of complex numerical models used in urban microclimate studies and facilitate model comparisons to evaluate the suitability of simplified approaches.

Ultimately, this dataset and similar ones are critical for informing decision-makers tasked with adapting cities to current and future climatic challenges. They support the development of actionable urban policies and promote the generalisation and transferability of modelling approaches, aiding the design of effective interventions to address the impacts of climate change on urban environments.

## 2 | Background

The INRAE UMR G-EAU Montpellier laboratory, specifically the OPTIMISTE team (Optimisation of the Piloting and Technologies of Irrigation, Minimisation of InputS, Transfers in the Environment) was responsible for the design, installation and data collection presented in this document. This project, conducted within the context of a PhD, marks the team's first venture into urban contexts (water and plant management, microclimate analysis). The closest context so far was that of 'dynamic agrivoltaic systems' in which the presence and operation of solar panels has local microclimatic effects and modifies crop irrigation demand. However, the team had to adapt their physical and agronomic expertise from the agricultural field to the urban environment. Given that the analysis of planters and irrigation in an urban context is relatively recent, this dataset provided a foundation for evaluating how to manage irrigation with limited information on soil and plant types, and different objectives than crop yield production, calling for ongoing adaptations of the team's irrigation model. It also addresses

challenges related to data interpretation and sensor positioning, as most often the soil sensors find themselves only partly within the 'irrigation bulb' produced by irrigation. Furthermore, this dataset enables the analysis of the impacts of irrigated urban vegetation on thermal comfort indices in a street's microclimate and serves for the initialisation and validation of computational fluid dynamics (CFD) methods the team wishes to establish and promote, as potentially generic good practices.

## 3 | Data Description

The dataset related to an experimental urban street canyon installed in Montpellier, France, is organised as shown in Figure 1. It consists of five folders.

Folder 01 (Soil) and Folder 03 (Air) contain data referring to the period from 21 July 2023 to 31 July 2024 (.csv file) and a readme.pdf file listing the monitored variables, their units and the time step for data collection, and a map showing the location of each sensor in the canyon.

The overall climatic conditions recorded during the study period are presented in Figures 2 and 3, which illustrate the seasonal variability and full range of conditions observed across the experimental year. These include monthly means, minima and maxima of air temperature and relative humidity, together with monthly precipitation and wind distribution. The site experiences a Mediterranean climate, characterised by hot and dry summers, mild winters and transitional seasons (spring and autumn) with moderate temperatures.

To emphasise the particularities of the experimental year, Figure 2 also reports the long-term annual means of the same variables, calculated from data collected at a nearby reference meteorological station located within the same experimental

<figure><img src="image_2.png" /><figcaption>**FIGURE 1** | Dataset organisation.</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>**FIGURE 2** | The upper graph (a) displays monthly values of relative humidity (%) recorded by the thermo-hygrometer at position TH1: Average (green), minimum (purple) and maximum (grey). Long-term reference values (1991–2022) from the nearby meteorological station are also shown: Average (green open), minimum (purple open) and maximum (grey open). Monthly accumulated precipitation (mm) is presented as bars, with filled bars indicating the experimental year (SMn) and open bars the long-term reference (REF). The lower graph (b) presents monthly air temperature (°C) data from the same sensor, including average (orange), minimum (yellow) and maximum (red) values, compared with long-term reference data: Average (orange open), minimum (yellow open) and maximum (red open).</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**FIGURE 3** | Graphs showing the distribution of wind speed and wind direction for the period analysed from 21 July 2023 to 31 July 2024. Wind speed (ms⁻¹) is categorised into different intervals. Wind direction is represented as percentages for the four cardinal points: west (W), south (S), east (E) and north (N).</figcaption></figure>

field (INRAE UMR G-EAU Montpellier laboratory), but in the
centre of the agricultural plots. These long-term records (1991–
2022) are available through the Climatik platform managed by
the AgroClim team at INRAE. Comparing the long-term aver-
ages with the experimental year highlights the distinctive cli-
matic conditions of the campaign. Specifically, the campaign
year exhibited more pronounced extremes of relative humid-
ity and air temperature compared with the past three decades,

alongside a drier summer and a wetter winter than the long-term
tendency. These emerging climatic realities further reinforce the
need for detailed, site-specific studies of urban microclimates, as
provided in the present data paper.

Regarding wind distribution, calm conditions prevailed, with
wind speeds between 0 and 1 ms⁻¹ occurring during 79% of the
measurement period. Wind direction was primarily aligned
with the street canyon axis, with a dominant west-to-east flow observed 56% of the time.

Folder 02 (Vegetation) includes a readme.pdf file commenting on file identification, the conditions in which measurements were conducted, and the raw data from the LAI-2200C measurements (.txt file).

Folder 04 (Photos) includes photographs of the experimental set-up during the measurement campaign, with each photograph (.jpg) labelled by date (yyyymmdd) and time (hhmmss).

Finally, Folder 05 (Data Paper) is a pdf file (.pdf), which explains the characteristics of the dataset in detail.

## 4 | Experimental Design

The experimental urban street canyon has an aspect ratio (height divided by width) of 0.48, which is a value encountered in certain urban centres although not among the most favourable values to obtain clear corridor effects. The situation handled here is that of a relatively wide street between relatively small buildings, before which the size of the vegetation elements is far from being negligible. The installation has two concrete walls, six wooden planters (three with climbing plants, three with shrubs) equipped with surface drip irrigation and a very dense array of sensors in the air (Figures 4 and 5) and also inside the planters, as detailed later.

The positioning of the pilot, the choice of its dimensions and that of plant species aimed to address several unresolved questions:

a. In the rather complex geometry of the semi-agricultural Lavalette Campus (offices, large trees, agricultural plots, temporary building serving as hydraulic laboratories, parking lots, free flat zones of limited extension, either on concrete or grass, Figure 6), the pilot was built where no significant cast shadow was expected from the surrounding buildings or equipment. In addition,

an east-west orientation was chosen for two reasons: It limits the cast shadows from the walls of the canyon on the planters, and it corresponds to the orientation of the dominant winds. Reducing the role played by direct radiation and shading, known to be otherwise dominant, allows focussing on the interaction between all other effects and variables.

b. Planters containing relatively small species were chosen due to the modulations they allow for creating and testing other arrangements, which have not been possible with trees. In coherence with the previous point, the absence of trees means the absence of significant drop shadows, hence the same focus on other variables. Moreover, trees are far more difficult to instal in ancient centres or pre-existing urban geometries than planters, which are possibly moveable. And surprisingly, despite their increasing presence in urban centres, studies on the microclimatic effects of small-sized vegetation elements are scarce, even more so to seek which irrigation strategies are most efficient for the assigned objectives.

c. Climbing and shrub plants were selected because of their adequacy with the local climatic conditions and because limited research results are available on their evapotranspiration characteristics in response to various climatic demands and irrigation strategies.

d. Different irrigation regimes were explored to remedy the lack identified in the literature. The challenge of mitigating heatwaves is often to be addressed during drought periods or in cities with limited water availability, hence with use conflicts around water. Knowing what to expect from the different possible water uses (here, cooling effects) becomes thus crucial for decision-makers.

As can be seen in Figure 7, the street pilot is east-west oriented, which causes relatively limited variations in the drop shadows due to its walls, depending on the time of day and the day of the year (Figure 7).

<figure><img src="image_5.png" /><figcaption>**FIGURE 4** | 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>
<figure><img src="image_6.png" /><figcaption>**FIGURE 5** | Photograph of the experimental device in July 2023.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**FIGURE 6** | Google Maps satellite image dated 6 June 2022, showing the location and surrounding environment of the experimental street canyon (outlined in red) on the INRAE Montpellier campus. The image includes geographic coordinates (in decimal degrees) and a scale bar for spatial reference. North is oriented towards the top of the image.</figcaption></figure>

The experiment analyses two popular ornamental species, *Lonicera japonica* (climbing plants) and *Abelia grandiflora* (shrubs plants), which are commonly found in Montpellier; hence, the choice made is pragmatic: Those species are drought-resistant and their foliage is evergreen (Figure 8). However, little information is available in the literature as regards their physiological characteristics and growth rate, and the evapotranspiration fluxes they would produce under different climatic demands and

in response to different, deliberate irrigation regimes. Table 1 summarises the Water Use Classification of Landscape Species (WUCOLS) that reported plant development under different irrigation conditions in multiple California regions, monitored by the California Department of Water Resources in partnership with the Davis Center for Urban Horticulture (Costello and Jones 2014). Information on the Kc crop coefficient of these species was not found in the literature. In this study, three planters with each type
<figure><img src="image_8.png" /><figcaption>**FIGURE 7** | Shade variation in the experiment on 21 August 2023 at 8 a.m., 12 a.m., 3 p.m. and 6 p.m. (simulations conducted with the Sketchup software).</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**FIGURE 8** | *Lonicera japonica* climbing plants (a) and *Abelia grandiflora* shrubs (b) on 27 July 2023.</figcaption></figure>

of plant were installed, using triplicates to check the representativeness of results and possible generalisations.

The six planters have the same type of light substrate, enriched with minerals and organic matter, and drainage systems were installed to prevent water stagnation (though not allowing flux quantification) in different ways for the two series of three planters. The smaller planters, for climbing plants, are equipped with two drains, centred at the extremities of their bottom face. The larger planters, which contain shrubs, have four drains near the corners of their bottom face. The procedure for preparing and filling the planters is detailed in Garcia de Cezar et al. (2024) and involves the following layers: a plastic lining to protect the wood, a geomembrane, a gravel layer for drainage and a substrate layer.

**5 | Materials and Methods**

Soil-air measurements were gathered and stored using Campbell Scientific data loggers (CR1000, CR3000). Sensors used in the

planters were Campbell Scientific CS650 capacitive sensors, Watermark soil matric potential sensors and OMEGA type T thermocouples. Sensors used in the air were Hygrovue5 and Hygrovue10 thermo-hygrometers, RAD10 and RAD5 shelters, SKS 1110 pyranometers, PT100/3 PT100 and ARG314 pluviometers from Campbell Scientific, Young 05103 wind monitors and home-made grey globe thermometers (Grosdemouge 2020) to measure the globe temperature used to estimate radiant temperature, designed by the INRAE UMR G-EAU technical team using ping-pong balls painted in RAL 7001 silver grey and RTD PT100 RS PRO sensors from RS France.

Data organisation and formatting were performed using the R 4.3.3. Software, compiling data into two separate documents for soil and air data. Overall, data were examined every 7–15 days during the whole experimental campaign, sometimes more frequently during summer, to check for sensor issues. This involved systematically plotting the latest time series of data, so as to easily identify obviously non-physical parameter variations, triggering maintenance, reinstallation or replacement of the data collection equipment.
<table><caption>TABLE 1 | Plant water needs for the species *Lonicera japonica* and *Abelia grandiflora* in the WUCOLS regions (Costello and Jones 2014).</caption><thead><tr><th>Type(s)</th><th>Botanical name</th><th>Common name</th><th>North-Central Coastal</th><th>Central Valley</th><th>South Coastal</th><th>South Inland</th><th>High and intermediate desert</th><th>Low desert</th></tr></thead><tbody><tr><td>Ground Cover, Shrub</td><td><i>Abelia grandiflora</i> and cultivars</td><td>Glossy Abelia</td><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>Not appropriate for this region</td></tr><tr><td>Ground Cover, Vine</td><td><i>Lonicera japonica</i></td><td>Japanese honeysuckle</td><td>Moderate</td><td>Moderate</td><td>Low</td><td>Low</td><td>Moderate</td><td>Moderate</td></tr></tbody></table>

Note: 'Moderate' indicates that between 40% and 60% of the reference evapotranspiration is needed for hydric comfort 'Low' indicates that only between 10% and 30% of the reference evapotranspiration is needed.

## 5.1 | Planter Soil Measurements

'Soil sensors' were chosen to monitor the water content and temperature of the substrate, thus also the conditions felt by plant roots (matric potential). To achieve this, the six planters were equipped with the same number of sensors, trying to position them close enough to the wetting bulbs formed under each dripper during each irrigation. The underlying operational objective was to select irrigation triggering criteria based on local climatic conditions and sensor values. However, as mentioned in the Background section and soon to be addressed in a dedicated paper, it is likely that soil sensors will only partly intersect the irrigation bulb in most cases, raising interpretation issues.

Table 2 describes the characteristics of the sensors and the time step of data collection on the soil. Tables 3, 4 and Figure 9 illustrate the spatial arrangements of 'soil sensors' in the experiment, maintaining a consistent vertical distribution across all six planters. However, the horizontal coordinates vary, especially for the shrub plants triplicates where sensors are positioned very differently relative to the drippers due to the imperatives of experimentation, but which makes it possible to identify the relevance of the positioning of the sensors in relation to the water bulb.

## 5.2 | Monitoring Plant Development

During summer 2023, the LAI-2200C sensor from LI-COR was used to measure Leaf Area Index (LAI) values for the climbing plants and Leaf Area Density (LAD) values for shrubs. Measurements were made by pairs, either at sunrise then sunset the same day or at sunset then sunrise on consecutive days, and kept only when returning very close values. The measurement protocol, based on the manual and conversations with equipment support, was adapted for climbing plants, due to their specific geometry and architecture, which resulted in estimating the distribution of LAI values in each planter, instead of a single LAI value per planter.

The LAI-2200C sensor with the 90° view restriction cap, developed by LI-COR, is used to measure the LAI and LAD of the chosen urban vegetation. Those two variables reflect the overall properties of the canopy, controlling light interception thus biomass production via photosynthesis. Light attenuation is measured by comparing the amount of light passing through the canopy with the total amount of incident light; each reading captures five different zenith angles (7°, 23°, 38°, 53° and 68°) as shown in Figure 10. Advanced data processing and the creation of graphs to recalculate the LAI for canopies with complex, non-uniform shapes, such as the shrub plants in the experiment presented in this paper, can be done using the File Viewer FV2200 software, developed for this purpose.

As detailed in the LAI-2200C manual, the fact that planters with shrub plants have heterogeneous, isolated and non-overlapping canopies forces adapted, taylor-made measurements, for each plant, each time with specific issues in sensor positioning. The good practice opted for was to select one position (A) at which the sensor points to the sky with no interference, and three positions (B1, B2, B3) on the ground, around the main stems, from which
<table><caption>**TABLE 2** | List of the 'soil sensors' with their specifications for which the acquisition time step is 10 min.</caption><thead><tr><th>Variable</th><th>ID in the dataset</th><th>Units</th><th>Sensor name</th><th>Range of values</th><th>Accuracy</th><th>Price estimation (€ HT in 2021)</th></tr></thead><tbody><tr><td>Water content</td><td>CSij_VWC</td><td>m<sup>3</sup>m<sup>-3</sup></td><td>CS650</td><td>0%-100%</td><td>&gt; 0.05%</td><td>249</td></tr><tr><td>Electrical conductivity</td><td>CSij_EC</td><td>kS/m</td><td>CS650</td><td>0-3 dSm<sup>-1</sup></td><td>0.5% of BEC</td><td>249</td></tr><tr><td>Soil temperature</td><td>CSij_TS</td><td>°C</td><td>CS650</td><td>-50°C to 70°C</td><td>±0.02°C</td><td>249</td></tr><tr><td>Soil temperature</td><td>TSij_TC</td><td>°C</td><td>Thermocouples type T</td><td>0°C-350°C</td><td>±0.5°C or 4%</td><td>9</td></tr><tr><td>Soil matric potential</td><td>Wij_WP</td><td>kPa</td><td>Watermark</td><td>0-239 kPa</td><td>±10%</td><td>46</td></tr><tr><td>Electrical resistance</td><td>Wij_kOhms</td><td>kΩ</td><td>Watermark</td><td>0-0.3 kΩ</td><td>±10%</td><td>46</td></tr></tbody></table>

Note: In the identification of variables in the dataset, 'ij' refers to a number that differentiates the positioning of each sensor.

<table><caption>**TABLE 3** | Coordinates (x, y, z) of the CS650 (CS), tensiometers (W) and thermocouples (TC) in the planters with climbing plants.</caption><thead><tr><th colspan="4">Planter A</th><th colspan="4">Planter B</th><th colspan="4">Planter C</th></tr><tr><th>Sensor</th><th>x (cm)</th><th>y (cm)</th><th>z (cm)</th><th>Sensor</th><th>x (cm)</th><th>y (cm)</th><th>z (cm)</th><th>Sensor</th><th>x (cm)</th><th>y (cm)</th><th>z (cm)</th></tr></thead><tbody><tr><td>W1</td><td>196.4</td><td>18.5</td><td>35</td><td>W3</td><td>195.3</td><td>21</td><td>35</td><td>W5</td><td>34</td><td>15</td><td>35</td></tr><tr><td>W2</td><td>194.9</td><td>18.5</td><td>15</td><td>W4</td><td>196.8</td><td>19</td><td>15</td><td>W6</td><td>46</td><td>15</td><td>15</td></tr><tr><td>CS1</td><td>192.0</td><td>29</td><td>5</td><td>CS5</td><td>193.0</td><td>22</td><td>5</td><td>CS9</td><td>76</td><td>23</td><td>5</td></tr><tr><td>CS2</td><td>192.0</td><td>29</td><td>15</td><td>CS6</td><td>193.0</td><td>22</td><td>15</td><td>CS10</td><td>76</td><td>23</td><td>15</td></tr><tr><td>CS3</td><td>192.0</td><td>29</td><td>25</td><td>CS7</td><td>193.0</td><td>22</td><td>25</td><td>CS11</td><td>76</td><td>23</td><td>25</td></tr><tr><td>CS4</td><td>192.0</td><td>29</td><td>35</td><td>CS8</td><td>193.0</td><td>22</td><td>35</td><td>CS12</td><td>76</td><td>23</td><td>35</td></tr><tr><td>TC1</td><td>192.0</td><td>29</td><td>0</td><td>TC6</td><td>193.0</td><td>22</td><td>0</td><td>TC11</td><td>76</td><td>23</td><td>0</td></tr><tr><td>TC2</td><td>192.0</td><td>29</td><td>10</td><td>TC7</td><td>193.0</td><td>22</td><td>10</td><td>TC12</td><td>76</td><td>23</td><td>10</td></tr><tr><td>TC3</td><td>192.0</td><td>29</td><td>20</td><td>TC8</td><td>193.0</td><td>22</td><td>20</td><td>TC13</td><td>76</td><td>23</td><td>20</td></tr><tr><td>TC4</td><td>192.0</td><td>29</td><td>30</td><td>TC9</td><td>193.0</td><td>22</td><td>30</td><td>TC14</td><td>76</td><td>23</td><td>30</td></tr><tr><td>TC5</td><td>192.0</td><td>29</td><td>40</td><td>TC10</td><td>193.0</td><td>22</td><td>40</td><td>TC15</td><td>76</td><td>23</td><td>40</td></tr></tbody></table>

the sensor tries to see the sky across the canopy, in the vertical direction. Two problems arise, as (i) neither the canyon walls nor the wooden structure of the planters should be visible from the sensors, so as not to distort measurements, and (ii) the choice of the exact locations of the sensor is far from straightforward and vows to reflect the effective overall light interception by these very irregular canopies, taking the average value between the B1/A, B2/A and B3/A comparisons. An additional constraint and rule of thumb is that the (B1, B2, B3) locations should be “as close as possible” to the A location so as to visualise “the same part of the sky”. For accessibility reasons, it was decided not to perform measurements that would have needed positioning the sensor completely behind shrub plants, thus nearly against the wall. Three measurements were thus taken instead of the recommended four (B4 was thus missing) when circling around plants’ main stems. Figure 11 illustrates the “profiling” of a shrub in trying to measure the effective outer surface of its canopy with its horizontal structure and vertical stratification, thus its (approximately) axisymmetric nature. This helped selecting

the exact (B1, B2, B3) locations at which the sensors were positioned to estimate the ensemble LAD value.

As previously mentioned, complete measurements were undertaken twice the same day (before dawn and after dusk) or in two consecutive days (after dusk then before dawn) and the measured values were kept only if ‘close enough’. The underlying (semi-quantitative and practical) argument is that the dynamics of canopy evolution are slow so that consecutive measurements should yield strongly correlated values. Conversely, if the values were not close enough, another measurement was made as shortly as possible.

For the climbing plants, the A position was in front of the green wall while the B1, B2 and B3 positions were behind the plants, between the grid and the wall. The first challenge is to keep exactly the same strictly vertical orientation so that the A and B readings are comparable, ensuring that the sensor ‘sees’ the same incoming light and that the only difference is due to the
<table><caption>TABLE 4 | Coordinates (x, y, z) of the CS650 (CS), tensiometers (W) and thermocouples (TC) in the planters with shrubs.</caption><thead><tr><th colspan="4">Planter D</th><th colspan="4">Planter E</th><th colspan="4">Planter F</th></tr><tr><th>Sensor</th><th>x (cm)</th><th>y (cm)</th><th>z (cm)</th><th>Sensor</th><th>x (cm)</th><th>y (cm)</th><th>z (cm)</th><th>Sensor</th><th>x (cm)</th><th>y (cm)</th><th>z (cm)</th></tr></thead><tbody><tr><td>W7</td><td>74</td><td>50</td><td>35</td><td>W9</td><td>191.8</td><td>57</td><td>35</td><td>W11</td><td>191.2</td><td>60</td><td>35</td></tr><tr><td>W8</td><td>74</td><td>18</td><td>15</td><td>W10</td><td>191.8</td><td>25</td><td>15</td><td>W12</td><td>191.2</td><td>29</td><td>15</td></tr><tr><td>CS13</td><td>43</td><td>30</td><td>5</td><td>CS17</td><td>195.9</td><td>57</td><td>5</td><td>CS21</td><td>192.2</td><td>45</td><td>5</td></tr><tr><td>CS14</td><td>43</td><td>30</td><td>15</td><td>CS18</td><td>195.9</td><td>57</td><td>15</td><td>CS22</td><td>192.2</td><td>45</td><td>15</td></tr><tr><td>CS15</td><td>43</td><td>30</td><td>25</td><td>CS19</td><td>195.9</td><td>57</td><td>25</td><td>CS23</td><td>192.2</td><td>45</td><td>25</td></tr><tr><td>CS16</td><td>43</td><td>30</td><td>35</td><td>CS20</td><td>195.9</td><td>57</td><td>35</td><td>CS24</td><td>192.2</td><td>45</td><td>35</td></tr><tr><td>TC16</td><td>43</td><td>30</td><td>0</td><td>TC21</td><td>195.9</td><td>57</td><td>0</td><td>TC26</td><td>192.2</td><td>45</td><td>0</td></tr><tr><td>TC17</td><td>43</td><td>30</td><td>10</td><td>TC22</td><td>195.9</td><td>57</td><td>10</td><td>TC27</td><td>192.2</td><td>45</td><td>10</td></tr><tr><td>TC18</td><td>43</td><td>30</td><td>20</td><td>TC23</td><td>195.9</td><td>57</td><td>20</td><td>TC28</td><td>192.2</td><td>45</td><td>20</td></tr><tr><td>TC19</td><td>43</td><td>30</td><td>30</td><td>TC24</td><td>195.9</td><td>57</td><td>30</td><td>TC29</td><td>192.2</td><td>45</td><td>30</td></tr><tr><td>TC20</td><td>43</td><td>30</td><td>40</td><td>TC25</td><td>195.9</td><td>57</td><td>40</td><td>TC30</td><td>192.2</td><td>45</td><td>40</td></tr></tbody></table>

<figure><img src="image_10.png" /><figcaption>FIGURE 9 | Distribution of the sensors within the planters. Thermocouples are installed on vertical profiles and provide point-wise measurements. Tensiometers are also installed vertically and provide point-wise measurements. Water content probes are installed horizontally, at the same depths in all planters and provide spatially averaged measurements in their domain of investigation (a limited volume that surrounds the two sensor pins). The setting is detailed for planter B (climbing plants).</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>FIGURE 10 | Optical Sensor LAI-2200C with the detail of the measured light from five different zenith angles.</figcaption></figure>
plants (assuming the fence on the wall has a negligible influ-
ence). The second challenge is to ensure that the sensor only
sees the sky and not the person taking the measurement, the
ground or other objects, which requires the use of lens caps with
the 90° view restriction. Figure 12 presents the results obtained
in a planter and outlines the measurement procedure, in which
four sectors on the wall were defined, estimating a specific LAI
for each sector. This approach aimed to obtain A and B measure-
ments as closely matched as possible, thereby reducing the like-
lihood of errors related to representativeness between the two
measurements in terms of intercepted.

5.3 | Microclimate Measurements

'Air sensors' installed within the canyon monitor the spatiotem-
poral dynamics of the variables (air temperature, relative hu-
midity, radiation, globe temperature, wind speed and direction)

<figure><img src="image_12.png" /><figcaption>FIGURE 11 | ‘Profile’ of the rightmost shrub in planter D, along the south wall, with coordinates in centimetres and the dimensions of the plant on 10 July 2023.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>**FIGURE 12** | Examples of distributed LAI values obtained for planter A on 18 July 2023.</figcaption></figure>

involved in the calculation of the thermal comfort index. The
overall aim of the experiment is to establish the connection (if
any) between the irrigation regime and the thermal comfort
index, calculated at different distances from the plants and loca-
tions in the canyon, and resulting from soil and plant processes
previously mentioned.

Those measurements of most mattering microclimatic variables in the experimental urban street canyon were thought to deliver sufficient information, on a fine-enough spatial and temporal grid, to successively understand, follow then eventually model the dynamics of thermal, radiative and aerodynamic fluxes. In the present case, it is not only quantification that is expected from the sensors and the analysis of the dataset; it is also process understanding or rather the identification of the contextually dominant processes, far from being straightforward in urban scenes. Hence, a crucial role is played by metrology and data collection in suggesting future modelling strategies.

As thermal comfort should be evaluated for pedestrians walking in urban canyons, guidelines from NF EN ISO 7726 (JANUARY 2002) were followed, which recommend installing the sensor network at a height of 1.3 m above ground—corresponding to the assumed centre of gravity of a standing person—to estimate average pedestrian thermal comfort. With this constraint, the aim was to create the densest possible sensor network (Figure 13) within the available space and of course for the available budget. The more expensive and theoretically precise sensors for air temperature were placed between the two planters, measuring air temperature and relative humidity via thermo-hygrometers. To allow the calculation of local thermal comfort indices, radiant temperature can be estimated via low-cost home-made grey globes sensors. Cheaper sensors (thermocouples) were positioned at the entrance and exit of the street, in line with the thermo-hygrometers. A weather station was also installed in the centre of the canyon to measure direct solar radiation, rainfall, air temperature and wind direction and speed. Additionally, a line of four pyranometers, measuring solar radiation, was set up to quantify radiation patterns. The characteristics of all sensors are listed in Table 5.

Among the various thermal comfort indices found in the litera-
ture, the Universal Thermal Climate Index (UTCI) is the most
widely used for evaluating outdoor urban thermal comfort (Li
and Liu 2020). UTCI is based on a human energy balance model
and incorporates several microclimatic parameters, including
wind speed, air temperature, relative humidity and mean radi-
ant temperature (Bröde et al. 2012). In terms of measurement
accuracy, the sensor network includes devices with varying lev-
els of precision. While the grey globe sensors used to estimate
mean radiant temperature are less accurate than commercial-
grade instruments, as discussed in the Limitations section,
they still offer valuable insights, although further validation
is needed under a broader range of environmental conditions.
Nonetheless, studies such as Weihs et al. (2012) suggest that
even in worst-case uncertainties, where uncertainties in mean
radiant temperature reach ±6°C for example, the resulting vari-
ation in UTCI is limited to approximately ±2°C. This margin is
considered acceptable, as UTCI is derived from four interrelated
microclimatic inputs, and its calculation framework inherently
dampens the influence of any single variable affected by moder-
ate uncertainty.
<figure><img src="image_14.png" /><figcaption>FIGURE 13 | Mapping of the ‘air sensors’ used in the experiment, detailing the spatial scale of the distribution of sensors.</figcaption></figure>

## 5.4 | Irrigation Technique and Rates Adopted

Drip irrigation was chosen for its efficiency and precision in water management, as it delivers water directly to the root zone, reducing losses from evaporation, transport and distribution (Narayanamoorthy 1997). In this experiment, one dripper was installed per plant, near plant stems, resulting in four drippers for the planters with climbing plants and three drippers for the planters with shrubs. The irrigation flow rate was 1.9 L h⁻¹ for each dripper, ensuring controlled and consistent water delivery to the plants.

Different irrigation strategies and criteria were tested during the campaign for the so-called standard and current or announced overheat days. In the former, the purpose attributed to irrigation was to prevent water stress while minimising water consumption. In the latter, irrigation was decided based on both the current soil water status and weather forecasts, for anticipation, to allow optimal evapotranspiration rates in the vicinity of the plants, when really needed, at the cost of increased water consumption. These decision rules aim to regulate the energy balance within the plant-substrate-environment system, taking into account the locally available resources within the city while coping with the imperative to adapt to extreme events.

However, soil properties in terms of maximum soil water content and plant properties in terms of crop coefficient, resistance

to water stress or evapotranspiration fluxes under water abundance were a priori not known. The trial-and-error process described in Garcia de Cezar et al. (2024) produced knowledge on some of these factors and resulted in the formulation of several irrigation strategies. Different irrigation criteria and controls were explored during the experiment, relying on data collected in planter C, representing climbing plants, and planter E, representing shrubs (Table 6). The depth of 25 cm was chosen as the depth at which water content criteria could be used to trigger irrigation.

The irrigation performed on each day of the campaign should be verified through data analysis. Similar irrigation patterns observed in the triplicates for each plant (such as an increase in soil moisture content) in the absence of rainfall suggest either programmed and automated irrigation (then Stations 2 and/or 4 return NAN data for some of the variables, which is a weak point of the measurement architecture) or manual irrigation (when complete data is available during the irrigation period).

## 6 | Limitations

The dataset presents several limitations that should be considered in the analysis and interpretation of the results. One of the main issues arises from the Campbell Scientific CR3000 data
<table><caption>**TABLE 5** | List of the 'air sensors' with their specifications for which the acquisition time step is 30s.</caption><thead><tr><th>Variable</th><th>ID in the dataset</th><th>Units</th><th>Sensor name</th><th>Range of values</th><th>Accuracy</th><th>Price estimation (€ HT)</th></tr></thead><tbody><tr><td>Air temperature</td><td>TCij_TA</td><td>°C</td><td>Thermocouples type T</td><td>0°C-350°C</td><td>±0.5°C or 4%</td><td>9 (2021)</td></tr><tr><td>Air temperature</td><td>THij_TA</td><td>°C</td><td>Hygrovue5</td><td>+20°C to +60°C</td><td>±0.3°C</td><td>283 (2021)</td></tr><tr><td>Air temperature</td><td>THij_TA</td><td>°C</td><td>Hygrovue10</td><td>+20°C to +60°C</td><td>±0.1°C</td><td>383 (2023)</td></tr><tr><td>Air temperature</td><td>SMn_TARef</td><td>°C</td><td>PT100<br>PT100/3</td><td>-100°C to +200°C</td><td>&lt;±0.15°C<br>at -100°C<br>&lt;±0.1°C at 0°C<br>&lt;±0.19°C<br>at 100°C<br>&lt;±0.31°C<br>at 200°C</td><td>342 (2021)</td></tr><tr><td>Air relative humidity</td><td>THij_HR</td><td>%</td><td>Hygrovue5</td><td>25°C and range of 0%-80% HR<br>25°C and range of 80%-100% HR</td><td>±1.8%/±3%</td><td>283 (2021)</td></tr><tr><td>Air relative humidity</td><td>THij_HR</td><td>%</td><td>Hygrovue10</td><td>25°C and range of 0%-80% HR<br>25°C and range of 80%-100% HR</td><td>±1.5%/±2%</td><td>383 (2023)</td></tr><tr><td>Solar radiation</td><td>SlrW_RN SMn_ij</td><td>W/m<sup>2</sup></td><td>Pyranometer SKS 1110</td><td>0-5000 W m<sup>-2</sup><br>350-1100 nm</td><td>typ. &lt;3%,<br>5% max</td><td>300 (2023)</td></tr><tr><td>Wind speed</td><td>SMn_Wspd</td><td>m/s</td><td>Wind monitor 05103 young</td><td>0-100 m s<sup>-1</sup></td><td>±0.3 m s<sup>-1</sup> or<br>1% reading</td><td>1000 (2023)</td></tr><tr><td>Wind direction</td><td>SMn_Wdir</td><td>°</td><td>Wind monitor 05103 young</td><td>0°-360° mecanical,<br>355° eletrical</td><td>±3°</td><td>1000 (2023)</td></tr><tr><td>Globe temperature</td><td>Gij_TA</td><td>°C</td><td>Grey Globe</td><td>-50 to +250<br>and range of<br>Wind speed<br>0.1-4.0 m s<sup>-1</sup><br>Solar radiation<br>100-850 W m<sup>-2</sup></td><td>±3.5°C<sup>a</sup></td><td>45 (2023)</td></tr><tr><td>Pluviometry</td><td>SMn_P</td><td>mm</td><td>EML ARG314</td><td>0-1000 mm h<sup>-1</sup></td><td>99% up to<br>120 mm h<sup>-1</sup></td><td>500 (2023)</td></tr></tbody></table>

Note: In the identification of variables in the dataset, 'ij' refers to a number, letter or nothing that differentiates the positioning of each sensor.

loggers used for the experiment. When the irrigation system is triggered, the data loggers controlling the irrigation (Loggers 2 and 4) stop collecting data until the irrigation cycle ends, leading to gaps in the dataset during those periods. This loss of data occurs specifically during irrigation events, affecting the continuity of the collected information.

Another limitation concerns the grey globe sensor, which was custom-built in the laboratory following the guidelines in Grosdemouge (2020). While it has been used in the study, further validation is necessary to confirm its accuracy across a wider range of measurement conditions. In addition, the thermocouples used in the study were calibrated in the laboratory by submerging them in water at various temperatures to ensure

consistency. While the calibration provides a precision of ±0.5°C or 4% relative to the measured value, it is important to acknowledge the potential for small errors that may arise in real-world conditions.

Sensor interactions within the network also pose a challenge, potentially affecting the quality of the data. For example, some sensors may experience shadowing from other equipment, such as the anemometer, which could alter their readings. Furthermore, the sensor network itself might influence the flow and propagation of thermal fluxes, leading to slight distortions in the measurements. A specific issue also arose with one of the pyranometers. On 6 December 2023, the pyranometer located furthest north (SlrMJ_1) was moved from its original support

<footnote>ªAccuracy for estimating mean radiant temperature from the grey globe (Thorsson et al. 2007).</footnote>
<table><caption>TABLE 6 | Irrigation implemented from 21 July 2023 to 31 July 2024, detailing periods when irrigation is conducted based on soil characteristics (e.g., moisture content) and air temperature.</caption><thead><tr><th rowspan="3">Period</th><th rowspan="3">To</th><th colspan="4">Criteria for triggering irrigation—During 30 min</th></tr><tr><th colspan="2">Standard day</th><th colspan="2">Overheated day</th></tr><tr><th>8 a.m. &lt;24°C</th><th>12 a.m. 6 p.m.<br/>&lt;33°C &lt;33°C</th><th>8 a.m. &gt;24°C</th><th>12 a.m. &gt;33°C &gt;33°C</th></tr></thead><tbody><tr><td>21/07/23</td><td>30/07/23</td><td>Soil moisture content below 0.25 m<sup>3</sup> m<sup>-3</sup> for climbing plants and below 0.35 m<sup>3</sup> m<sup>-3</sup> for shrubs</td><td></td><td>Soil moisture content below 0.30 m<sup>3</sup> m<sup>-3</sup> for climbing plants, and below 0.45 m<sup>3</sup> m<sup>-3</sup> for shrubs</td><td></td></tr><tr><td>31/07/23</td><td></td><td>Specific situation: during a day of overheating, we tested a day with 4 irrigations (8 a.m., 12 a.m., 2 p.m. and 6 p.m.), and drainage was identified in device A</td><td></td><td></td><td></td></tr><tr><td>01/08/23</td><td>10/08/23</td><td>Soil moisture content below 0.25 m<sup>3</sup> m<sup>-3</sup> for climbing plants and below 0.35 m<sup>3</sup> m<sup>-3</sup> for shrubs</td><td></td><td>Soil moisture content below 0.30 m<sup>3</sup> m<sup>-3</sup> for climbing plants, and below 0.45 m<sup>3</sup> m<sup>-3</sup> for shrubs</td><td></td></tr><tr><td>11/08/23</td><td>20/08/23</td><td>Specific situation: 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. Then, irrigation take place at 8 a.m. and 6 p.m. for August 19th and August 20th</td><td></td><td></td><td></td></tr><tr><td>21/08/23</td><td>14/09/23</td><td>Specific situation: Irrigation is activated at 8 a.m., 12 a.m. and 6 p.m. when the conditions meet the following criteria: 8 a.m. and 6 p.m. if soil moisture content below 0.30 m<sup>3</sup> m<sup>-3</sup> for climbing plants, and below 0.45 m<sup>3</sup> m<sup>-3</sup> for shrubs. The irrigation duration remains at 30 min.</td><td></td><td></td><td></td></tr><tr><td>15/09/23</td><td>08/12/23</td><td>Specific situation: Irrigation was activated at 8 a.m. and 6 p.m. under the following conditions: 8 a.m. if soil moisture content below 0.25 m<sup>3</sup> m<sup>-3</sup> for climbing plants and below 0.35 m<sup>3</sup> m<sup>-3</sup> for shrubs. The irrigation duration is set to 30 min</td><td></td><td></td><td></td></tr><tr><td>09/12/23</td><td></td><td>Specific situation: A leak in the irrigation system identified</td><td></td><td></td><td></td></tr><tr><td>10/12/23</td><td>26/05/24</td><td>Specific situation: No irrigation</td><td></td><td></td><td></td></tr><tr><td>27/05/24</td><td>30/05/24</td><td>Specific situation: manual irrigation</td><td></td><td></td><td></td></tr><tr><td>03/06/24</td><td>18/07/24</td><td>Soil moisture content below 0.20 m<sup>3</sup> m<sup>-3</sup> or tension below 50 kPa at 15 cm depth for climbing plants and below 0.30 m<sup>3</sup> m<sup>-3</sup> or tension below 50 kPa at 15 cm depth for shrubs</td><td></td><td>Soil moisture content below 0.30 m<sup>3</sup> m<sup>-3</sup> for climbing plants, and below 0.45 m<sup>3</sup> m<sup>-3</sup> for shrubs</td><td></td></tr><tr><td>19/07/24</td><td>31/07/24</td><td>Soil moisture content below 0.20 m<sup>3</sup> m<sup>-3</sup> or tension below 50 kPa at 15 cm depth for climbing plants and below 0.30 m<sup>3</sup> m<sup>-3</sup> for shrubs</td><td></td><td>Soil moisture content below 0.30 m<sup>3</sup> m<sup>-3</sup> for climbing plants, and below 0.45 m<sup>3</sup> m<sup>-3</sup> for shrubs</td><td></td></tr></tbody></table>

Note: Irrigation is triggered for 30 min or adjusted for specific situations when the set criteria are met.
and repositioned at a height of 0.9 m, unlike the other three pyranometers which are all placed at 1.3 m. This change in positioning could result in discrepancies in radiation measurements across the different sensors.

It is also important to note that external interventions took place on 10 to 11 June and 4 July 2024, which may have temporarily affected the microclimate within the canyon, particularly around Planter D. These events should be considered when interpreting data from those specific dates.

Finally, a broader limitation common to experimental studies in urban microclimate research lies in the inherent site specificity of such investigations. The characteristics of the present dataset are closely linked to the unique geometry of the experimental canyon, particularly its low height-to-width (H/W) ratio, as well as the Mediterranean climatic context. Additional specificities include the vegetation type selected, the developmental stage of each plant and their spatial arrangement within the study area. These factors may constrain the direct applicability of the results to other urban settings with different climatic, morphological or vegetative contexts. Nonetheless, the dataset provides valuable insights that can inform both local analyses and comparative studies, while also contributing to methodological improvements for future research.

# 7 | Conclusion

This dataset provides a comprehensive and unique resource for advancing the understanding of urban overheating and its mitigation through irrigation strategies and urban vegetation. By capturing a wide range of thermal, radiative, and aerodynamic variables across a fully instrumented experimental urban canyon, it fills a critical gap in experimental data necessary for studying and modelling urban microclimates. Its high spatial and temporal resolution, combined with year-long monitoring, offers valuable insights into the interplay of factors influencing thermal comfort and microclimatic conditions in Mediterranean urban environments. The dataset's originality lies in its unprecedented focus on the role of irrigation and plant types in regulating urban temperatures, particularly during heatwaves. It supports the validation of numerical models, enhances our understanding of the impacts of shrubs and climbing plants under controlled irrigation, and contributes to the development of climate-adaptive urban planning strategies.

Moreover, the open availability of this dataset enhances its value by fostering collaboration and knowledge exchange within the scientific community. Open data initiatives empower researchers to test hypotheses, compare models and replicate findings, thereby accelerating progress in urban climate adaptation studies. It ensures transparency and inclusivity, allowing diverse stakeholders to benefit from the insights generated and promoting the development of universally applicable solutions. Also, this dataset not only aids researchers and modellers in refining tools for urban microclimate studies but also equips policymakers with evidence-based strategies to address the pressing challenges posed by climate change. By advocating open data principles, it serves as a critical foundation for fostering resilient, sustainable and inclusive urban environments.

# Acknowledgements

We acknowledge the support 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.

# Disclosure

Dataset Details: The dataset is available on the Recherche Data Gouv platform (Recherche Data Gouv>Data INRAE>UMR G-EAU>PRESTI).

Repository name: Soil, plant and microclimate data set for an experimental urban street canyon with planters and different irrigation patterns in a Mediterranean climate.

Data identification number: https://doi.org/10.57745/0MYJU4.

Direct URL to data: https://entrepot.recherche.data.gouv.fr/privateurl.xhtml?token=f3a9cfa3-fe13-4784-8acd-bf8c5986ae58.

# Conflicts of Interest

The authors declare no conflicts of interest.

# Data Availability Statement

The data that support the findings of this study are openly available in Soil, plant and microclimate dataset for an urban canyon at https://entrepot.recherche.data.gouv.fr/privateurl.xhtml?token=f3a9cfa3-fe13-4784-8acd-bf8c5986ae58, reference number https://doi.org/10.57745/0MYJU4.

# References

Armstrong McKay, D. I., A. Staal, J. F. Abrams, et al. 2022. "Exceeding 1.5°C Global Warming Could Trigger Multiple Climate Tipping Points." *Science* 377: eabn7950. https://doi.org/10.1126/science.abn7950.

Bröde, P., D. Fiala, K. Błażejczyk, et al. 2012. "Deriving the Operational Procedure for the Universal Thermal Climate Index (UTCI)." *International Journal of Biometeorology* 56: 481–494. https://doi.org/10.1007/s00484-011-0454-1.

Cheung, P. K., C. Y. Jim, N. Tapper, K. A. Nice, and S. J. Livesley. 2022. "Daytime Irrigation Leads to Significantly Cooler Private Backyards in Summer." *Urban Climate* 46: 101310. https://doi.org/10.1016/j.uclim.2022.101310.

Cheung, P. K., K. A. Nice, and S. J. Livesley. 2022. "Irrigating Urban Green Space for Cooling Benefits: The Mechanisms and Management Considerations." *Environmental Research: Climate* 1: 015001. https://doi.org/10.1088/2752-5295/ac6e7c.

Costello, L. R., and K. S. Jones. 2014. "WUCOLS IV: Water Use Classification of Landscape Species."

Dey, S., and Y. Joshi. 2023. "Recent Progress and Challenges in Microscale Urban Heat Modeling and Measurement for Urban Engineering Applications." *Journal of Thermal Science and Engineering Applications* 15: 010801. https://doi.org/10.1115/1.4056054.

Egerer, M., D. Haase, T. McPhearson, et al. 2021. "Urban Change as an Untapped Opportunity for Climate Adaptation." *Npj Urban Sustainability* 1: 22. https://doi.org/10.1038/s42949-021-00024-y.

Feng, J., K. Gao, H. Khan, et al. 2023. "Overheating of Cities: Magnitude, Characteristics, Impact, Mitigation and Adaptation, and Future Challenges." *Annual Review of Environment and Resources* 48: 651–679. https://doi.org/10.1146/annurev-environ-112,321-093021.

Fu, J., K. Dupre, S. Tavares, D. King, and Z. Banhalmi-Zakar. 2022. "Optimized Greenery Configuration to Mitigate Urban Heat: A Decade
Systematic Review.” *Frontiers of Architectural Research* 11: 466–491.
https://doi.org/10.1016/j.foar.2021.12.005.

García de Cezar, M., B. Cheviron, F. Liron, et al. 2024. "Ad Hoc Irrigation Piloting for the Urban Vegetation: How to Find the Contextually Relevant Sensors and Criteria?" *Journal of Environmental Management* 370: 122712.

Grosdemouge, V. 2020. "Proposition D'indicateurs de Confort Thermique et Estimation de la Température Radiante Moyenne en Milieu Urbain Tropical." *Contribution à la méthode nationale d'évaluation des ÉcoQuartiers. Thermique* [physics.class-ph].

Li, J., and N. Liu. 2020. "The Perception, Optimization Strategies and Prospects of Outdoor Thermal Comfort in China: A Review." *Building and Environment* 170: 106614. https://doi.org/10.1016/j.buildenv.2019.106614.

Mballo, S., S. Herpin, M. Manteau, S. Demotes-Mainard, and P. E. Bournet. 2021. "Impact of Well-Watered Trees on the Microclimate Inside a Canyon Street Scale Model in Outdoor Environment." *Urban Climate* 37: 100844. https://doi.org/10.1016/j.uclim.2021.100844.

Narayanamoorthy, A. 1997. "Economic Viability of Drip Irrigation: An Empirical Analysis From Maharashtra."

Nazarian, N., E. S. Krayenhoff, B. Bechtel, et al. 2022. "Integrated Assessment of Urban Overheating Impacts on Human Life." *Earth's Future* 10: e2022EF002682. https://doi.org/10.1029/2022EF002682.

Roldán-Gómez, P. J., P. De Luca, R. Bernardello, and M. G. Donat. 2025. "Regional Irreversibility of Mean and Extreme Surface Air Temperature and Precipitation in CMIP6 Overshoot Scenarios Associated With Interhemispheric Temperature Asymmetries." *Earth System Dynamics* 16: 1–27. https://doi.org/10.5194/esd-16-1-2025.

Thorsson, S., F. Lindberg, I. Eliasson, and B. Holmer. 2007. "Different Methods for Estimating the Mean Radiant Temperature in an Outdoor Urban Setting." *International Journal of Climatology* 27: 1983–1993. https://doi.org/10.1002/joc.1537.

Ulpiani, G., A. Treville, P. Bertoldi, et al. 2024. "Are Cities Taking Action Against Urban Overheating? Insights From Over 7500 Local Climate Actions." *One Earth* 7: 848–866. https://doi.org/10.1016/j.oneear.2024.04.010.

Wankhade, R., G. Pernigotto, and M. Larcher. 2023. "A Literature Review on Methods and Metrics for the Analysis of Outdoor Air Displacement Conditions in the Urban Environment." *Energies* 16: 2577. https://doi.org/10.3390/en16062577.

Weihs, P., H. Staiger, B. Tinz, et al. 2012. "The Uncertainty of UTCI due to Uncertainties in the Determination of Radiation Fluxes Derived From Measured and Observed Meteorological Data." *International Journal of Biometeorology* 56: 537–555. https://doi.org/10.1007/s00484-011-0416-7.

Wu, H.-W., P. Kumar, and S.-J. Cao. 2022. "Implementation of Green Infrastructure for Improving the Building Environment of Elderly Care Centres." *Journal of Building Engineering* 54, no. 104: 682. https://doi.org/10.1016/j.jobe.2022.104682.

Yang, S., L. Wang, T. Stathopoulos, and A. M. Marey. 2023. "Urban Microclimate and Its Impact on Built Environment—A Review." *Building and Environment* 238: 110334. https://doi.org/10.1016/j.buildenv.2023.110334.

Zhao, Y., L. W. Chew, Y. Fan, et al. 2023. "Fluid Tunnel Research for Challenges of Urban Climate." *Urban Climate* 51, no. 101: 659. https://doi.org/10.1016/j.uclim.2023.101659.