# Monitoring and modelling of stormwater trees to study their water balance under present and future climate

Rémi Combeaux, Frederic Cherqui, Katharina Tondera, Laurent Lassabatere, Jeremie Bonneau

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Rémi Combeaux, Frederic Cherqui, Katharina Tondera, Laurent Lassabatere, Jeremie Bonneau. Monitoring and modelling of stormwater trees to study their water balance under present and future climate. Urban Drainage Modelling 2025, Sep 2025, Innsbruck, Austria. hal-05288109

HAL Id: hal-05288109

https://hal.science/hal-05288109v1

Submitted on 29 Sep 2025

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# Monitoring and modelling of stormwater trees to study their water balance under present and future climate

Auteurs : **Rémi COMBEAUX**¹ : remi.combeaux@insa-lyon.fr ; Frédéric CHERQUI¹ ; Katharina TONDERA² ; Laurent LASSABATERE² ; Jérémie BONNEAU¹
¹ INSA Lyon, DEEP, UR 7429, 69621, Villeurbanne, France. ² Univ Lyon, Université Claude Bernard Lyon 1, CNRS, ENTPE, UMR 5023 LEHNA, Vaulx-en-Velin, 69518, France.

## Context

Stormwater trees are nature-based stormwater control measures (SCM). These are often retrofitted roadside trees with an improved infiltration capacity, to increase collection of runoff from neighbouring streets and sidewalks. Stormwater trees are considered as pertinent in highly urbanized areas, but the contribution of the tree (evapotranspiration) to the water balance is not well known, since it is difficult to measure.

## Research objectives

* Continuous monitoring to study the water balance of:
  * five stormwater trees and two control trees
  * soil moisture in the tree pit and sap flow

* Mechanistic modelling of the tree pit to close water balance

* Performance assessment under present and future climate

## Stormwater trees

The infiltration capacity of roadside trees is increased by enlarging the pit (≥ 10 m²) and adding an infiltration trench down the slope.

<figure><img src="image_2.png" /><figcaption>Stormwater tree retrofitting in 2022</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>Figure 1: a. before and b. after stormwater tree retrofitting.</figcaption></figure>

### Water balance

* $S_W$ estimation

* $h$ from close rain gauge

* ET cumulated sap flow up to 24h after the rain event

* $\Delta S$ sum of water increase at each depth

* $Inf$ the remainder

<table><thead><tr><th>Period</th><th>h (mm)</th><th>Inflow (L) = h * S<sub>W</sub></th><th>ΔS (L)</th><th>ET (L)</th><th>Inf (L)</th></tr></thead><tbody><tr><td>19 - 22 July</td><td>26,3</td><td>4002</td><td>0</td><td>1464 (37%)</td><td>2538 (63%)</td></tr><tr><td>19 - 21 August</td><td>18,8</td><td>2853</td><td>0</td><td>712 (25%)</td><td>2141 (75%)</td></tr></tbody></table>

* Preliminary data; requires further calibration.

<figure><img src="image_4.png" /><figcaption>Figure 4: a. water balance summary; and b. preliminary measurements.</figcaption></figure>

## Study site

<figure><img src="image_5.png" /><figcaption>Figure 3: Experimental setup of a stormwater tree pit to measure its water balance. Adapted from: Métropole de Lyon².</figcaption></figure>

<figure><img src="image_6.png" /></figure>

### Soil characterization

* Beerkan estimation of soil retention curve¹

* Comparison with experimental data

* Inverse modelling estimation

<figure><img src="image_7.png" /><figcaption>Figure 5: experimental soil water retention curve. VWC = volumetric water content.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Figure 6: Modelling results on a single rainfall event.</figcaption></figure>

* Large uncertainty on inverse modelling estimation of parameters

* Need more events

## Further research

* New experiments to estimate water balance fluxes (artificial events, leaf porometer, inflow characterization)

* Modelling on characteristic rainfall events

* Scenario assessment of water balance under future climate, with downscaled climatic projections³

## References

1. Lassabatère, L. et al. (2006) 'Beerkan Estimation of Soil Transfer Parameters through Infiltration Experiments—BEST', *Soil Science Society of America Journal*, 70(2), pp. 521–532.

2. Métropole de Lyon (2022) *Livret technique : les arbres de pluie*.

3. Pons, V. et al. (2022) 'Forecasting green roof detention performance by temporal downscaling of precipitation time-series projections', *Hydrology and Earth System Sciences*, 26(11), pp. 2855–2874.

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anr

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H₂O' Lyon
Université de Lyon

SOLUBIOD

Living Lab Antharès

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OTHU

MÉTROPOLE
GRAND LYON

INSA
deep

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