# Untangling the Factors Governing the Catchment-Scale Impacts of On-Site Stormwater Infiltration in Shallow Groundwater Contexts

Jérémie Sage, William Pophillat, Fabrice Rodriguez, Isabelle Braud

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Jérémie Sage, William Pophillat, Fabrice Rodriguez, Isabelle Braud. Untangling the Factors Governing the Catchment-Scale Impacts of On-Site Stormwater Infiltration in Shallow Groundwater Contexts. Journal of the American Water Resources Association, 2026, 62 (1), 10.1111/1752-1688.70092. hal-05524676

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RESEARCH ARTICLE

OPEN ACCESS

# Untangling the Factors Governing the Catchment-Scale Impacts of On-Site Stormwater Infiltration in Shallow Groundwater Contexts

Jérémie Sage¹ | William Pophillat¹,²,³ | Fabrice Rodriguez⁴ | Isabelle Braud³

¹Cerema, Equipe TEAM, Trappes, France | ²DREAL Occitanie, Montpellier, France | ³INRAE, RiverLy, Villeurbanne, France | ⁴Université Gustave Eiffel, GERS-LEE, Bouguenais, France

**Correspondence:** Jérémie Sage (jeremie.sage@cerema.fr)

**Received:** 4 March 2025 | **Revised:** 28 January 2026 | **Accepted:** 5 February 2026

**Keywords:** cumulative effects | modeling | recharge | runoff | sustainable urban drainage systems | water-balance

## ABSTRACT

Urban stormwater management increasingly relies on decentralized infiltration systems to mitigate the adverse effects of soil sealing. At the catchment scale, the hydrological impacts of such practices however remain poorly understood, especially in shallow aquifers settings where interactions between infiltrated volumes, groundwater and underground infrastructures can occur. This study examines the hydrological response of a small shallow groundwater urban area to the implementation of stormwater infiltration, based on numerical modeling. The influence of climate, soil conditions, hydrogeology, urban context, and infiltration strategy is assessed. Results show that the concentration of runoff within infiltration systems causes a large increase of groundwater recharge, often leading to water-table rise. Where initial groundwater depth or low subsurface transmissivity is low, seepage into sewer pipes and underground drainage structures increases and potentially undermines the runoff volume reduction benefits of infiltration systems. Occasionally, an increase in transpiration from green spaces may also occur. For remaining settings, the additional recharge is mainly dissipated by downstream groundwater flow, without generating notable interactions with the surface or underground structures. Overall, this study illustrates the variety of stormwater infiltration outcomes and demonstrates the inability of this strategy, alone, to restore pre-development hydrological conditions.

## 1 | Introduction

Soil-sealing and drainage networks, along with other activities or features that affect water transfers at the soil-atmosphere interface and subsurface flows, play a prominent role on the hydrology of urban environments (Fletcher et al. 2013). The reduction of pervious and vegetated surfaces limits the possibilities for rainfall interception, infiltration, and transpiration, causing runoff to occur more frequently and with larger volumes than under natural conditions (McGrane 2016). At the same time, conveyance of surface runoff through pipes or channels is associated with a fast response and high peak flow

rates at catchment outlets (Fletcher et al. 2013). Combination of these two factors results in increased flood risks downstream or within the catchment when the capacity of the drainage system is exceeded. This increase in runoff volumes and rates also poses water quality threats for surface waters due to the presence of pollutants in stormwater, cross-contamination during transport in sewer systems, and potential overflows in the case of combined sewer systems (Bressy et al. 2012; McGrane 2016).

Threats to human safety and ecosystems impairment, in a context of continuing urbanization and aging drainage infrastructures, gradually led to a change in urban runoff management

Jérémie Sage and William Pophillat are first co-authors.

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.

© 2026 The Author(s). Journal of the American Water Resources Association published by Wiley Periodicals LLC on behalf of American Water Resources Association.
strategies. Instead of being directly collected in sewer pipes and conveyed downstream, stormwater is now increasingly handled on-site in small vegetated systems designed to provide volume reduction, flow-rate attenuation, or water quality control (Fletcher et al. 2014). These solutions may be designated by various terms (Fletcher et al. 2014), but are here referred to as Sustainable Urban Drainage Systems (SUDS). Their primary aim is to help restore (or mimic) hydrological functions altered by urbanization as a means of controlling urban runoff while simultaneously offering multiple other benefits (Jefferson et al. 2017; Zhang and Chui 2019a).

In this perspective, particular emphasis is often placed on infiltration-based systems (i-SUDS) due to their runoff volume reduction abilities, but also their potential for soil water replenishment and groundwater (GW) recharge (Zhang and Parolari 2022). i-SUDS encompass a variety of configurations, ranging from simple surface depressions where water can infiltrate into the native soil to more elaborated multiple-layer structures (Tunqui Neira et al. 2023). Yet, beyond their structural differences, most of these solutions are designed to meet similar volume reduction targets that in practice result in the infiltration of a large fraction of the runoff volume at the annual scale (Zhang and Chui 2019a).

The design and implementation of i-SUDS is generally conducted “at the site scale” as part of development or retrofit projects, with limited regard as to the fate of infiltrated volumes, and under the implicit assumption that the volume reduction achieved locally will translate directly into equivalent benefits at the outlet of sewer systems. Meanwhile, the cumulative impact of i-SUDS on the hydrology of urban catchments cannot be simply understood as the sum of expected “site-scale” benefits, but rather as the complex result of interactions between i-SUDS and their environment (Jefferson et al. 2017). This situation is exacerbated in shallow GW contexts where local interactions between i-SUDS, underground structures, and GW are likely to be reinforced by the cumulative impact of infiltration on GW levels (Pophillat et al. 2022).

Catchment scale effects of i-SUDS have been examined in several empirical or modeling studies, most of which primarily focused on stormflow rates or volumes (Jefferson et al. 2017). In most cases, i-SUDS were found to provide significant benefits for runoff control (both in terms of volume reduction and flow-rate attenuation), and especially for small rain events (Petrucci et al. 2013; Selbig and Bannerman 2008; Shuster and Rhea 2013). Comparatively, i-SUDS impacts on subsurface flows or storage as well as the other components of the water balance tend to be less documented. Studies focusing on baseflow showed contrasting results, with either negligible effects or increases of baseflow after i-SUDS implementation (e.g., Bhaskar et al. 2016; Fanelli et al. 2017; Loperfido et al. 2014). The impact of i-SUDS on GW was essentially assessed through modeling studies that point towards a potential increase of GW levels as a result of increased GW recharge (Göbel et al. 2004; Locatelli et al. 2017), and which may become a concern in relatively shallow GW contexts (Zhang and Chui 2019b).

Undesirable consequences of rising GW levels in urban environments include local resurgences, basement floodings, modifications of water pressure differential on building foundations, drainage by underground structures (e.g., sewer pipes, building foundation drains, ...) and reduction of i-SUDS efficiency (Pophillat et al. 2022). Some of them have the potential to alter

the outcome of infiltration strategies as to the total volume discharged at the outlet of sewer systems (Kidmose et al. 2015; Zhang and Parolari 2022). But water table rise may as well have more beneficial effects, with the potential increase of evaporation from green-spaces (Bonneau et al. 2018; Pophillat et al. 2022; Western et al. 2021) and enhanced downstream baseflow, due to a higher GW-to-stream hydraulic gradient (Locatelli et al. 2017; Poozan et al. 2024).

Understanding the impact of i-SUDS on GW levels, the resulting interactions within the built environment and their effect on the urban water-balance is therefore essential to ensure current stormwater management policies deliver the expected hydrologic benefits at the catchment scale. Yet, this understanding remains limited by (i) the inconsistency between the scale at which the above-mentioned processes can be observed and the scale at which their consequences are to be assessed (Golden and Hoghooghi 2018), leaving few other options than the analysis of strongly “integrative” streamflow measurements for empirical studies; consequently: (ii) the predominance of modeling studies where the assessment of i-SUDS impacts may often be biased by the adoption of excessively coarse spatial discretization or the omission of GW related processes (Jefferson et al. 2017; Locatelli et al. 2017; Pophillat et al. 2022); and more generally: (iii) the scattered nature of current research, with a singularity of study sites and a lack of a common basis for comparison, making it difficult to identify the factors controlling the impact of i-SUDS and to extrapolate the results to other contexts.

The objective of this article is to investigate the role of climate, soil conditions, hydrogeology, urban context and i-SUDS implementation strategy in the hydrological response of urban areas to stormwater infiltration in shallow GW contexts. The analysis is conducted using numerical modeling. Multi-annual simulations are performed with the URBS model (Li et al. 2022; Pophillat et al. 2021) for a hypothetical 0.75-km² catchment, and considering systematic variations in factors characterizing the local context (climate, soil conditions, hydrogeology, urban context, and infiltration strategy) and the spatial allocation of i-SUDS. The consequences of a generalized implementation of stormwater infiltration are examined by comparing simulation results obtained with and without i-SUDS. Special attention is given to (i) the ability of i-SUDS to control the water balance and GW impacts of urbanization and (ii) how the local functioning of i-SUDS translates to the catchment-scale. This comprehensive assessment allows exploring the potential variability of the hydrological response to stormwater infiltration at the catchment-scale, identifying the mechanisms involved and their relative importance depending on the local context. The study also provides insights for the adaptation of stormwater management strategies to catchment conditions or the objectives pursued.

## 2 | Methodology

### 2.1 | General Approach

The analysis is based on the modeling of a 1500×500-m domain representing a gently sloping urbanized hillside. The underground compartment is described as a two-layer system, delimited by an impervious bedrock, and where a GW extends from
<figure><img src="image_4.png" /><figcaption>**FIGURE 1** | Construction of individual modeling scenarios from the factors investigated.</figcaption></figure>

a no flux boundary condition upstream ($x=0$ m) to a fixed head boundary condition downstream ($x=1500$ m). The surface layer is 1-m thick and potentially has different hydrodynamic properties than those of the underlying layer. The domain is divided into three $500 \times 500 \text{ m}^2$ areas, with the same land-cover and underground infrastructure configuration. Stormwater infiltration can be implemented in one of these three areas.

This setup serves as a starting point for the elaboration of multiple scenarios, differing by their climate, soil and hydrogeology conditions, urban contexts, and stormwater infiltration strategies. Three broad types of scenarios are introduced: (i) those with i-SUDS, referred to as “i-SUDS scenarios”; (ii) those without i-SUDS, used as a reference for assessing the impact of stormwater infiltration, and referred to as “urban-reference scenarios”; and (iii) additional simulations without urbanization (i.e., 100% green-spaces cover), interpreted in terms of pre-development conditions, and referred to as “natural-reference scenarios”. For each of them, individual modeling scenarios are constructed by considering systematic variations of “factors” characterizing climate, soil conditions, hydrogeology, urban context and i-SUDS implementation strategy (if applicable). The procedure is illustrated in Figure 1. The combination of the 10 factors with 2 to 3 “modalities” here results in $1.7 \times 10^4$ i-SUDS scenarios, $2.9 \times 10^3$ urban-reference scenarios and $2.9 \times 10^3$ natural-reference scenarios. A detailed description of these factors and their modalities is given in Section 2.3.

Simulations are carried out with URBS, a distributed hydrological model dedicated to urban environments (Rodriguez et al. 2008) that incorporates a relatively detailed description of the underground compartment and its interactions with surface

hydrology (Pophillat et al. 2021), making it suitable for a comprehensive examination of the consequences of stormwater infiltration. For each scenario, a set of variables is generated to describe the hydrological functioning of the studied domain. The results associated with the multiple simulations hence make up a large database composed of (i) the modalities associated with the various factors (explanatory variables) and (ii) a set of hydrological functioning indicators (response variables). The relationship between explanatory variables and response variables is then analyzed based on different statistical tools and data mining techniques. These methods and the indicators to which they apply are detailed in Section 2.4.

<figure><img src="image_5.png" /></figure>

## 2.2 | Model Setup

The URBS model involves different levels of discretization. The simulation domain is first divided into 75 to 214 surface units (depending on urban pattern and i-SUDS scenarios) described as a combination of 4 possible land-use profiles: green spaces, streets (or other impervious areas), buildings, and i-SUDS. Each of these profiles is represented as a succession of the following reservoirs: tree interception (optional), surface storage, unsaturated zone (UZ), and saturated zone (SZ). Processes accounted for at this scale include rainfall, interception by vegetation, surface evaporation, infiltration, runoff, root water extraction (for vegetation transpiration), leakage from water supply networks, exchanges between UZ and SZ including capillary upwelling. Runoff is either directed to i-SUDS or to the drainage network (from which water directly leaves the modeling domain). The UZ is vertically divided into 8 reservoirs, with the top 7 reservoirs being 50-cm thick and the last one extending from -3.5 m
to the bedrock. A refinement of surface units in triangular cells
is finally introduced for the modeling of SZ, resulting in 495
to 704 underground features. Processes described at this level
of discretization include GW flow (lateral exchange between
units), seepage into drainage pipes, or intentional drainage by
underground constructions.

Simulations are performed for a 4-year period at a 5-min time-
step using rainfall and hourly potential evapotranspiration
(PET) records from 3 different meteorological stations, each
of them associated with a "climate" modality (the rainfall and
evapotranspiration characteristics of these 3 "climates" are sum-
marized in Section 2.3). Initialization of subsurface conditions
is achieved by repeating the first year of meteorological records
until a 1 cm or less difference in average and maximum GW lev-
els and a 1 mm or less difference in GW recharge is obtained
between two repetitions. Once this condition is met, the simula-
tion continues for the three remaining years (and the analysis is
conducted for the last 4 years of the simulation).

The following parameterization is adopted for the different
land-use profiles: tree coverage is respectively set to 10% and
30% for streets and green spaces profiles; vegetation interception
capacity is set at 1 mm; root depth is set at 50 cm; surface storage
capacity is respectively set to 0.2, 0.5 and 4 mm for buildings,
paved areas and green spaces; permeability of paved areas is set
at 10<sup>-9</sup> m/s (references for these different values can be found as
*Supporting Information*—cf. Data S1). The storage capacity of i-
SUDS is calculated as the volume associated with a 10-mm rain-
fall depth over their drainage area, in accordance with French
stormwater management guidelines that require frequent rain
events to be infiltrated on-site (Sage et al. 2024).

## 2.3 | Factors Investigated

The analysis primarily focuses on 8 factors pertaining to cli-
mate, urban context, soil and hydrogeology conditions, and i-
SUDS spatial allocation strategy. Two additional factors related
to boundary conditions settings are introduced for the construc-
tion of the modeling scenarios, allowing different configurations
to be considered as part of the results database (Although these
two factors are set aside during the analysis due to their less di-
rect interpretability in terms of hydrogeological conditions, their
impact is nevertheless discussed at the end of the article).

2.3.1 | Climate

The influence of climate is examined from a single factor (de-
noted as *Clim*) with 3 modalities corresponding to French (main-
land) locations with contrasting rainfall and PET conditions:
Nantes (47.15° N, 1.61° W) (NTS); Paris (48.82° N, 2.34° E) (PRS);
and Montpellier (43.57° N, 3.96° E) (MTP). For each of them, cli-
mate records cover the period from March 2015 to March 2019
and are provided by the French National Meteorological Service.

During this period, NTS is characterized by a balance between
annual precipitation (765 mm/year) and PET (740 mm/year).
PRS exhibits a slight imbalance, with lower annual precipitation
(642 mm/year) than NTS but similar PET (742 mm/year). MTP,

with similar annual rainfall to PRS (634 mm/year) but much
higher PET (1131 mm/year), shows the lowest rainfall to PET
ratio. Rainfall distribution differs between the three locations,
with: noticeably less frequent and more intense precipitations
for MTP than NTS or PRS; slightly more frequent and intense
precipitations for NTS than PRS; a marked seasonality for NTS
and MTP, with approximately 58% of the rainfall volume be-
tween October and February instead of 49% in the case of PRS.
In all cases, PET exhibits a strong seasonality with more than
75% of the volume from March to September. Further details
regarding climate data are provided as Supporting Information
(cf. Data S2).

2.3.2 | Urban Context

The impact of the urban context is examined from a single fac-
tor (denoted as *Urba*) with 3 modalities associated with contrast-
ing hypothetical urban patterns: discontinuous residential area
(RES); commercial units (COM); and dense urban fabric (DEN).

RES is characterized by a limited proportion of sealed surfaces (30%), moderate leakage from the water supply network (50 mm/year) and the presence of stormwater pipes along the streets at a 2 m depth. COM exhibits similar characteristics but has a higher proportion of sealed surfaces (70%). DEN is similar to COM, but features higher water leakage (200 mm/year), reflecting the fact that dense areas often consist of city centers with the oldest water pipes. DEN additionally includes impervious and draining building foundations at a 2.5-m depth. The different patterns are shown in Figure 1 (Justifications for the water leakage values can be found as Supporting Information—cf. Data S2).

2.3.3 | Soil Conditions and Hydrogeology

Soil conditions and hydrogeology are described through 4 dif-
ferent factors: surface soil (S<sub>s</sub>), underlying soil (S<sub>U</sub>), initial UZ
extension before i-SUDS implementation (eUZ<sub>ini</sub>), and bedrock
slope (Sl).

Three soil types are considered for S<sub>s</sub> and S<sub>U</sub>: a fine texture,
low permeability material (LP) (saturated hydraulic conductiv-
ity K<sub>s</sub> = 5 × 10<sup>-6</sup> m.s<sup>-1</sup>); a moderate permeability material (MP)
(K<sub>s</sub> = 5 × 10<sup>-5</sup> m.s<sup>-1</sup>); and a coarse texture, high permeability
material (HP) (K<sub>s</sub> = 5 × 10<sup>-4</sup> m.s<sup>-1</sup>) (Other hydrodynamic prop-
erties can be found in Supporting Information—cf. Data S2).

The initial UZ extension before stormwater infiltration eUZ<sub>ini</sub> represents a spatially uniform average GW depth during the first year of “reference urban” scenarios. Three values are considered for eUZ<sub>ini</sub>: 3, 5 and 7 m. In practice, the equilibrium GW elevation depends on GW recharge, hydrogeology as well as the distance to boundary conditions, and thus varies throughout the simulation domain. Setting uniform initial GW depth without introducing artificial movements of the water table hence required adapting the longitudinal profile of the soil surface to ensure a spatially uniform UZ extension (and follow the natural equilibrium level of the water table in the various contexts). The details of the procedure are available as Supporting Information (cf. Data S2).
Finally, 3 values are considered for the longitudinal bedrock slope $S_l$: 0, 1 or 2% (constant slope over the flow domain).

### 2.3.4 | Spatial Allocation of i-SUDS

The impact of stormwater infiltration strategy is examined from 2 factors: the spatial distribution of i-SUDS (Dis) and the ratio between infiltration area and drainage area ($S_l/S_d$).

Dis reflects the proportion of the catchment area managed by a single facility and thus controls the number of i-SUDS. Its impact is assessed from 3 modalities: centralized (CEN), median (MED), or distributed (DIS) stormwater infiltration. Corresponding configurations are shown in Figure 1. $S_l/S_d$ reflects the hydraulic loading associated with i-SUDS, independently from their distribution over the study area and may be set to 2% or 10%.

### 2.3.5 | Boundary Conditions Setting

Model setup regarding boundary conditions is controlled by two factors: the extension of the SZ at the downstream limit of the GW domain ($eSZ_{BC}$) and the position of the 500×500-m² area on which infiltration is implemented (Pos). $eSZ_{BC}$ directly affects GW transmissivity at the downstream boundary condition. Pos controls the distance to upstream and downstream boundary conditions, and thus their influence on model outputs.

Two modalities are adopted for these two factors: 1 or 5 m for $eSZ_{BC}$ and upstream or middle (i.e., $x=0$ to 500 m and $x=500$ to 1000 m respectively) for Pos.

## 2.4 | Analysis of Simulation Results

The hydrological functioning of the study area and the impact of stormwater infiltration is evaluated from the variables listed in Table 1. The analysis primarily focuses on i-SUDS scenarios and on the 500×500-m² area over which stormwater infiltrations are implemented. Results associated with urban and natural reference scenarios are used for the calculation of variables characterizing the changes caused by stormwater infiltration and as complementary interpretation elements. The impact of climate, urban context, soil and hydrogeology, and i-SUDS spatial allocation on the distribution of the different variables is assessed from Kruskal-Wallis (KW) and Fligner-Killeen tests (FK), that respectively allow quantifying stochastic dominance and variance heterogeneity among modalities.

The analysis is structured in four stages, with consistent use of FK and KW tests along with distribution-based data visualization. Two of them additionally involve more specific exploratory data analysis techniques, as explained below.

An overview of simulation results is first provided (Section 3.1), with an emphasis on the ability of stormwater infiltration to mitigate the water-balance impacts of urbanization. It relies

<table><caption>TABLE 1 | Variables considered for the analysis.</caption><thead><tr><th>Hydrologic variables</th><th>Description</th></tr></thead><tbody><tr><td>IF (mm/year)</td><td>Mean annual infiltration</td></tr><tr><td>EV (mm/year)</td><td>Mean annual surface evaporation</td></tr><tr><td>RE (mm/year)</td><td>Mean annual root-extraction</td></tr><tr><td>GR (mm/year)</td><td>Mean annual groundwater recharge</td></tr><tr><td>DR (mm/year)</td><td>Mean annual drainage by underground infrastructures</td></tr><tr><td>BC (mm/year)</td><td>Mean annual flow at downstream limit of GW domain<sup>a</sup></td></tr><tr><td>SZS (m)</td><td>Mean annual water storage in the saturated zone</td></tr><tr><td>E<sub>i-SUDS</sub> (%)</td><td>I-SUDS volume reduction efficiency over the simulation period<sup>b</sup></td></tr><tr><td>IF<sub>i-SUDS</sub> (mm/year)</td><td>Mean annual infiltration within i-SUDS<sup>a</sup></td></tr><tr><td>RE<sub>i-SUDS</sub> (mm/year)</td><td>Mean annual root-extraction within i-SUDS<sup>a</sup></td></tr><tr><td>GR<sub>i-SUDS</sub> (mm/year)</td><td>Mean annual groundwater recharge within i-SUDS<sup>a</sup></td></tr><tr><td>Z<sub>S</sub></td><td>Space and time average GW elevation</td></tr><tr><td>dZ<sub>S,i-SUDS</sub></td><td>Difference between average GW level below i-SUDS and overall GW level</td></tr><tr><td>RE<sub>other</sub> (mm/year)</td><td>Mean annual root-extraction outside i-SUDS<sup>a</sup></td></tr><tr><td>Δ(X)</td><td>Difference from urban reference condition for variable X</td></tr><tr><td>SD<sub>y</sub>(X)</td><td>Standard deviation for the variable X along the y-axis (perpendicular to bedrock slope)</td></tr><tr><td>SD<sub>l</sub>(X)</td><td>Mean annual value of the standard deviation for the variable X over time</td></tr></tbody></table>

on a principal component analysis (PCA) performed on water-balance terms to illustrate how i-SUDS scenarios compares to urban and natural reference scenarios. The variables considered for the PCA are mean annual infiltration (IF), surface evaporation (EV), transpiration (i.e., root water extraction, RE), groundwater recharge (GR), drainage by underground structures (DR), GW flow at downstream boundary condition (BC) and saturated-zone storage (SZS) (all the variables are expressed over the 500×500 m² area on which i-SUDS are implemented). The impact of i-SUDS is additionally assessed from the differences

<footnote><sup>a</sup>Expressed over the 500×500 m² area on which i-SUDS are implemented, unless otherwise specified.</footnote>

<footnote><sup>b</sup>Proportion of the incoming volume evacuated through infiltration or evaporation.</footnote>
<figure><img src="image_6.png" /><figcaption>**FIGURE 2** | Distribution of simulation results—a and c: representation of the simulations in the first two plans of the PCA and corresponding correlation circle; b and d: corresponding correlation circles; e to k: distribution for the different terms of the water balance across simulation types as violin plots (with median and quartiles).</figcaption></figure>

in the above-terms between i-SUDS and urban reference scenarios, denoted by the prefix Δ.

The controls over the increase of GW recharge are then investigated (Section 3.2), with a focus on the partition of infiltration between recharge and transpiration within i-SUDS. The variables considered are: (Δ)IF<sub>i-SUDS</sub>, (Δ)RE<sub>i-SUDS</sub> and (Δ)GR<sub>i-SUDS</sub> (where the subscript “i-SUDS” refers to the scale at which these variables are assessed), as well as i-SUDS volume reduction efficiency (E<sub>i-SUDS</sub>).

An examination of the GW level impacts of stormwater infiltration is later conducted (Section 3.3). The variables considered are: average GW depth (Z<sub>S</sub>), standard deviation of time-averaged GW depths along y (σ<sub>y</sub>Z<sub>S</sub>), the mean annual value of the standard deviation of daily space-averaged GW depths (σ<sub>t</sub>Z<sub>S</sub>), corresponding differences between i-SUDS and urban reference scenarios (Δ) (Further details about the calculation of these variables are provided as Data S3).

The water-balance contribution of the additional GW recharge within i-SUDS (GR<sub>i-SUDS</sub>) is finally analyzed (Section 3.4), considering the increase of RE (outside i-SUDS, later denoted as RE<sub>other</sub>), DR or BC as the three possible mechanisms for balancing the increase in the volume directed to the SZ. Modeling scenarios are first categorized depending on the relative importance of these three components, using hierarchical-clustering from a PCA on ΔRE<sub>other</sub>/GR<sub>i-SUDS</sub>, ΔDR/GR<sub>i-SUDS</sub> and ΔBC/GR<sub>i-SUDS</sub>. The distribution of the factors among the different families of behavior is examined to highlight the role of local context and i-SUDS allocation strategy. A more specific analysis of the mechanisms controlling the increase of RE, DR, and BC is then conducted, considering the following variables: (Δ)DR, (Δ)BC, and root-extraction variation outside i-SUDS (ΔRE<sub>other</sub>).

(For all investigated variables, violin-plots showing the influence are available as Supporting Information—cf. Data S4).

## 3 | Results

### 3.1 | Overview of Simulation Results

The results of the PCA, along with the distribution of the water-balance terms across i-SUDS, urban-reference, and natural-reference scenarios, are shown in Figure 2. The results of KW/FK tests for the corresponding variables are presented in Table 2.

As shown with the two first plans of the PCA (~80% of output variance) (Figure 2), i-SUDS scenarios clearly stand apart from urban and natural reference simulations, evidencing both the strong impact of stormwater infiltration and inability to recover “pre-development” water-balance in most configurations. i-SUDS scenarios are additionally characterized by a higher dispersion than reference ones, suggesting an increased sensitivity to the different factors considered for the analysis and highlighting the potential complexity in the catchment-scale response to stormwater infiltration.

Expectedly, infiltration (IF) is much higher for i-SUDS scenarios than for urban-reference scenarios (and approaches the values obtained for natural-reference ones). As shown by KW/FK tests (Table 2), the variability of IF in urban-reference scenarios is primarily governed by climate (Clim) and urban pattern (Urba) that respectively control rainfall volume and impervious cover. With stormwater infiltration, additional controls related to soil conditions or i-SUDS spatial allocation are introduced. Their effect is examined in Section 3.2.1.
<table><caption>**TABLE 2** | Summary of KW/FK tests on water-balance variables for i-SUDS and urban reference scenarios (results shown as *p*-values classes considering the minimum *p*-value [$p_{min}$] between FK and KW tests).</caption><thead><tr><th></th><th>Clim</th><th>Urba</th><th>S<sub>s</sub></th><th>S<sub>U</sub></th><th>eUZ<sub>ini</sub></th><th>Sl</th><th>Dis</th><th>S<sub>i</sub>/S<sub>d</sub></th></tr></thead><tbody><tr><td colspan="9">i-SUDS scenarios</td></tr><tr><td>IF</td><td>****</td><td>****</td><td>****</td><td>****</td><td></td><td></td><td>**</td><td>****</td></tr><tr><td>EV</td><td>****</td><td>****</td><td>**</td><td></td><td></td><td></td><td>*</td><td>***</td></tr><tr><td>RE</td><td>**</td><td>****</td><td>****</td><td>**</td><td>*</td><td></td><td></td><td>**</td></tr><tr><td>GR</td><td>****</td><td>****</td><td>****</td><td>**</td><td>*</td><td></td><td>*</td><td>**</td></tr><tr><td>DR</td><td>****</td><td>****</td><td>**</td><td>****</td><td>****</td><td>**</td><td>**</td><td>**</td></tr><tr><td>BC</td><td>****</td><td>****</td><td>****</td><td>****</td><td>***</td><td>*</td><td></td><td></td></tr><tr><td>SZS</td><td>***</td><td>****</td><td>**</td><td>****</td><td>**</td><td>****</td><td></td><td></td></tr><tr><td colspan="9">Urban reference scenarios</td></tr><tr><td>IF</td><td>****</td><td>****</td><td>**</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>EV</td><td>****</td><td>****</td><td>**</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>RE</td><td>*</td><td>****</td><td>****</td><td>**</td><td></td><td></td><td></td><td></td></tr><tr><td>GR</td><td>***</td><td>****</td><td>****</td><td>*</td><td></td><td></td><td></td><td></td></tr><tr><td>DR</td><td>***</td><td>**</td><td>*</td><td>**</td><td>***</td><td></td><td></td><td></td></tr><tr><td>BC</td><td>***</td><td>****</td><td>****</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SZS</td><td>**</td><td>**</td><td>**</td><td>****</td><td></td><td>***</td><td></td><td></td></tr></tbody></table>

The volumes returned to the atmosphere are essentially associated with transpiration (RE). Surface evaporation (EV) always represents a very limited fraction of the rainfall volume (Figure 2) and is thus neglected for the rest of the analysis. RE shows slightly higher values for i-SUDS than for urban reference scenarios but remains far lower than in natural reference settings. Without i-SUDS, the main controls over RE are *Urba*, through its impact on the proportion of green areas, and surface (and to a lesser extent underlying) soil characteristics ($S_s$ and $S_U$) that determine the water holding capacity in the unsaturated zone. *Clim*, which relates to rainfall and PET, also affects the distribution of RE but with a more limited impact than previous factors. The situation remains similar for i-SUDS scenarios, with a slightly higher effect of *Clim* and an additional role of the ratio between infiltration area and drainage area ($S_i/S_d$).

Logically, high IF values along a relatively limited difference in RE between i-SUDS and urban-reference scenarios result in noticeably higher GW recharge (GR) values for i-SUDS scenarios. These values are also much higher than for the natural-reference scenario. The controls over GR (Table 2) are consistent with those on IF and RE. Finally, GR exhibits a much higher variability in i-SUDS than in other scenarios, which again points towards a broad range of possible catchment responses to stormwater infiltration.

The high GR values of i-SUDS scenarios translate into a slight increase of saturated zone storage (SZS)—that is, GW levels—as

compared to reference scenarios. The controls over the variability of SZS are quite similar to those affecting GR, with an additional or reinforced influence of factors governing GW transmissivity (i.e., underlying soil permeability $S_U$ and initial unsaturated zone extent $eUZ_{ini}$). The correlation between GR and SZS however remains very weak (spearman's rho $\rho$ < 0.1 for i-SUDS scenarios), which indicates that the variability of SZS is not only controlled by GR but by other factors or interactions. Drainage by underground structures (DR), which was mostly inexistent for urban reference scenarios (because of relatively low GW levels), potentially represents a large fraction of the annual rainfall volume in i-SUDS scenarios. For these scenarios, DR is strongly related to SZS ($\rho$ = 0.63) as a result of the dependence of this term on GW levels. The variability of DR is thus affected by the same factors as SZS, with an additional impact of i-SUDS spatial allocation (*Dis* and $S_i/S_d$). Finally, the distribution of downstream GW flow (BC) remains relatively similar across the different families of scenarios, with only slightly higher values with stormwater infiltration. Controls over BC are similar to those over GR or SZS, but BC only correlates to GR ($\rho$ = 0.75 vs. $\rho$ < 0.05 for SZS).

## 3.2 | Understanding i-SUDS Controls on GW Recharge

The large variability of the GW recharge impacts of stormwater infiltration (cf. Section 3.1) reflects the complex influence

<footnote>***$p_{min} \le 10^{-100}$.</footnote>

<footnote>**$10^{-100} < p_{min} \le 10^{-50}$.</footnote>

<footnote>$10^{-50} < p_{min} \le 10^{-10}$.</footnote>

<footnote>$10^{-10} < p_{min} \le 10^{-2}$.</footnote>
<table><caption>**TABLE 3** | Summary of KW/FK tests for the variables related to the water balance of i-SUDS (results shown as *p*-values classes considering the minimum *p*-value [$p_{min}$] between FK and KW tests).</caption><thead><tr><th></th><th>Clim</th><th>Urba</th><th>S<sub>S</sub></th><th>S<sub>U</sub></th><th>eUZ<sub>ini</sub></th><th>Sl</th><th>Dis</th><th>S<sub>i</sub>/S<sub>d</sub></th></tr></thead><tbody><tr><td>ΔIF<sub>i-SUDS</sub></td><td>****</td><td>****</td><td>****</td><td>***</td><td></td><td></td><td>**</td><td>***</td></tr><tr><td>E<sub>i-SUDS</sub></td><td>****</td><td>**</td><td>****</td><td>****</td><td>*</td><td>*</td><td>****</td><td>****</td></tr><tr><td>RE<sub>i-SUD</sub></td><td>***</td><td>****</td><td>****</td><td>**</td><td></td><td></td><td>*</td><td>****</td></tr><tr><td>ΔRE<sub>i-SUD</sub></td><td>****</td><td>****</td><td>***</td><td>*</td><td></td><td></td><td>*</td><td>****</td></tr><tr><td>GR<sub>i-SUD</sub></td><td>****</td><td>****</td><td>****</td><td>***</td><td></td><td></td><td>**</td><td>**</td></tr><tr><td>ΔGR<sub>i-SUD</sub></td><td>****</td><td>****</td><td>****</td><td>***</td><td></td><td></td><td>**</td><td>**</td></tr></tbody></table>

***$p_{min} \le 10^{-100}$.

**$10^{-100} < p_{min} \le 10^{-50}$.

**$10^{-50} < p_{min} \le 10^{-10}$.

*$10^{-10} < p_{min} \le 10^{-2}$.

<figure><img src="image_7.png" /><figcaption>**FIGURE 3** | Impact of climate, urban context, soil conditions, and i-SUDS spatial allocation on the increase of infiltration volumes.</figcaption></figure>

of some of the factors considered in the analysis on infiltrated volumes and on their partition between transpiration and deep infiltration. These controls are described below, based on the results of the KW/FK tests presented in Table 3.

### 3.2.1 | Infiltration Within i-SUDS

The increase of infiltration within i-SUDS ($\Delta IF_{i-SUDS}$) depends on both the amount of water to be infiltrated and the ability of i-SUDS to infiltrate the volumes they receive, here characterized by the volume reduction efficiency $E_{i-SUDS}$ (on which surface evaporation only plays a very limited role).

As shown in Table 3, the variability of $\Delta IF_{i-SUDS}$ and $E_{i-SUDS}$ is mainly controlled by 6 factors, among which can be distinguished those with a direct impact on runoff (Clim and Urba—i.e., climate and urban pattern), those determining surface infiltration potential ($S_S$ and $S_i/S_d$—i.e., surface soil permeability and ratio between infiltration area and drainage area) and those that do not belong to previous categories ($S_U$ and Dis—i.e., underlying soil permeability and i-SUDS distribution level).

Regarding Clim and Urba, larger $\Delta IF_{i-SUDS}$ values are obtained for contexts associated with high impervious cover and annual rainfall volumes (Figure 3). While the greater the runoff volume, the greater the increase in $IF_{i-SUDS}$ ($\rho=0.81$), the same does not

apply to $E_{i-SUDS}$ that slightly decreases as runoff volumes increases ($\rho=-0.11$) and seems to be predominantly affected by Clim, suggesting an impact of rainfall distribution rather than runoff volume. In the case of $S_S$ and $S_i/S_d$, the effect on $\Delta IF_{i-SUDS}$ obviously results from the control over $E_{i-SUDS}$ (i.e., larger infiltration area and soil permeability resulting in higher efficiencies). Finally, the noticeable impact of $S_U$ and Dis on $\Delta IF_{i-SUDS}$ can essentially be interpreted as the result of an indirect control on the ability of i-SUDS to infiltrate (i.e., $E_{i-SUDS}$) through GW interactions. As shown later on (cf. Section 3.3), low-permeability and centralized infiltration settings are associated with significant GW mounding below i-SUDS. Consistently, $\Delta IF_{i-SUDS}$ decreases as i-SUDS allocation strategy becomes more centralized and underlying soil permeability decreases (due to the reduction of $E_{i-SUDS}$).

### 3.2.2 | Transpiration Increase Within i-SUDS

As discussed in Section 3.1, the distribution of transpiration (RE) across the different scenarios is only marginally affected by stormwater infiltration. Consistently, transpiration increase within i-SUDS ($\Delta RE_{i-SUDS}$) only represents a very limited volume at the scale of the 500×500-m² domain (at best 38 mm/year, and 8 mm/year on average). This situation essentially originates from the relatively low $S_i/S_d$ values considered in the analysis, resulting in a limited proportion of i-SUDS area at the catchment scale.
<figure><img src="image_8.png" /><figcaption>**FIGURE 4** | Transpiration at i-SUDS location (expressed as mm over i-SUDS area) as function of climate and surface soil permeability for i-SUDS and urban-reference scenarios. Blue violins: i-SUDS scenarios; black violins: reference scenarios (simple green-spaces); red-dashed line: potential evapotranspiration (theoretical maximum value for the simulations).</figcaption></figure>

Examination of the factors affecting ΔREᵢ-SUDS nevertheless provides relevant information on i-SUDS functioning and the limitation of transpiration (Table 3). The impact of Sᵢ/Sₐ and Urba primarily reflects the role of i-SUDS area (the latter being controlled by Urba via impervious area). These factors left aside, ΔREᵢ-SUDS is mostly controlled by *Clim* and Sₛ (cf. Table 3) that dictate atmospheric demand and soil water availability. Their effect is further illustrated in Figure 4. Comparison of transpiration within i-SUDS (REᵢ-SUDS) with the one simulated in urban-reference scenarios at i-SUDS location (i.e., simple green space) primarily indicates that, locally, the redirection of runoff within i-SUDS does result in a significant increase of transpiration in most configurations (although still very low compared to the volumes infiltrated). This is particularly true for MTP climate, due to a high atmospheric demand and water deficit in the reference scenario. Expectedly, lower permeability soils (e.g., Sₛ=LP or MP), which more efficiently retain water within the root zone, are associated with higher REᵢ-SUDS values. For PRS and NTS, the moderate differences between PET and REᵢ-SUDS across soil types however suggests that the potential for increasing REᵢ-SUDS through improved soil water retention remains limited. This potential tends to be higher for MTP, but, in any case, cannot be expected to compensate for the low proportion of catchment area covered by i-SUDS.

(It may finally be noted from Figure 4 that a drop of REᵢ-SUDS below reference values is obtained for a few configurations. This result originates from the description of transpiration in URBS, which assumes that root extraction falls to zero under saturated conditions. Further analysis of the impact of the various factors on ΔREᵢ-SUDS (as provided in S4) indeed indicates that the phenomenon occurs for centralized infiltration and low-permeability soil settings, for which highest GW elevations below i-SUDS are obtained).

### 3.2.3 | Additional Recharge Within i-SUDS

The concentration of runoff volumes within relatively small infiltration systems, where transpiration remains limited by

the area over which it applies, results in GW recharge within i-SUDS (GRᵢ-SUDS) being primarily determined by cumulative infiltration volume (ρ > 0.99). Factors controlling the variability of ΔGRᵢ-SUDS are thus the same as those reported for ΔIFᵢ-SUDS (cf. Table 3), and so is their impact (as shown in Data S4).

While factors controlling transpiration do not dominate the absolute increase in GW recharge, they nevertheless affect the proportion of the infiltrated volume contributing to GW recharge at the i-SUDS scale. As shown in Figure 5, this proportion is logically lower for contexts associated with the highest atmospheric demand (Clim=MTP) and soil water availability (Sₛ=LP). But beyond these two factors, the partition between transpiration and recharge here essentially depends on Sᵢ/S₉, which controls both the potential for transpiration and how fast infiltrated volumes can reach deeper soil horizons. Moving from Sᵢ/S₉=2 to Sᵢ/S₉=10% hence results in a strong increase in the contribution of transpiration, although the corresponding water balance remains far from the one simulated for simple urban green spaces (cf. Figure 5).

## 3.3 | Impacts on GW Levels

The additional GW recharge associated with stormwater infiltration affects overall GW levels as well as their spatial and temporal variability. The impact of the different factors on the nature and magnitude of these GW level modifications is analyzed below, based on the results of the KW/FK tests (Table 4).

### 3.3.1 | Overall Impact

Average GW levels modifications (ΔZₛ) are mainly controlled by the initial extent of the unsaturated zone (eUZᵢₘᵢ), underlying soil permeability (Sₛ), urban pattern (Urba), and to a lesser extent Climate (*Clim*), bedrock slope (Sᵢ), and surface soil permeability (Sₛ) (Table 4). Because the impact of eUZᵢₘᵢ essentially reflects the limitation of ΔZₛ by the initial distance between the water
<figure><img src="image_9.png" /><figcaption>**FIGURE 5** | GW recharge within i-SUDS as a fraction of infiltrated volume as a function of climate, soil type, and $S_i/S_a$. Dark blue violins: $S_i/S_a = 2\%$; light blue violins: $S_i/S_a = 10\%$; black violins: urban reference (simple green spaces).</figcaption></figure>

<table><caption>**TABLE 4** | Summary of KW/FK tests on GW level related variables (results shown as *p*-values classes considering the minimum *p*-value [$p_{min}$] between FK and KW tests).</caption><thead><tr><th></th><th>Clim</th><th>Urba</th><th>S<sub>S</sub></th><th>S<sub>U</sub></th><th>eUZ<sub>ini</sub></th><th>Sl</th><th>Dis</th><th>S<sub>i</sub>/S<sub>d</sub></th></tr></thead><tbody><tr><td>ΔZ<sub>S</sub></td><td>**</td><td>****</td><td>**</td><td>****</td><td>****</td><td>**</td><td></td><td></td></tr><tr><td>ΔSD<sub>y</sub>(Z<sub>S</sub>)</td><td>***</td><td>****</td><td>**</td><td>****</td><td>****</td><td>****</td><td>****</td><td></td></tr><tr><td>dZ<sub>S,i-SUDS</sub></td><td>**</td><td>****</td><td>***</td><td>****</td><td>**</td><td>****</td><td>****</td><td>*</td></tr><tr><td>ΔSD<sub>i</sub>(Z<sub>S</sub>)</td><td>***</td><td>****</td><td></td><td>****</td><td>***</td><td>**</td><td>**</td><td>***</td></tr></tbody></table>

***$p_{min} \le 10^{-100}$.

**$10^{-100} < p_{min} \le 10^{-50}$.

$10^{-50} < p_{min} \le 10^{-10}$.

$10^{-10} < p_{min} \le 10^{-2}$.

table and the soil surface, the effect of the other factors' levels is subsequently examined from the distribution of $Z_S$ across the three $eUZ_{ini}$ classes (Figure 6).

Aside $eUZ_{ini}$ the most influential factor is by far $S_U$ that controls (via GW transmissivity) the ability to evacuate the additional recharge upstream and downstream the sector over which i-SUDS are implemented. Low permeability settings ($S_U$=LP) are thus associated with the most pronounced rise in GW levels, resulting in very shallow GW depths in i-SUDS scenarios (less than 2.2 m for 90% of the simulations). Conversely, highest permeability settings ($S_U$=HP) only show a moderate water-table rise for most simulations (0.8 m on average) and GW levels remain predominantly determined by $eUZ_{ini}$. Although less important than $S_U$, the impact of Urba is also noticeable. Interestingly, higher GW elevations are achieved for commercial (COM) than dense (DEN) settings despite similar $\Delta GR_{i-SUDS}$ values, whereas relatively comparable elevations are obtained for DEN and residential (RES) settings despite lower $\Delta GR_{i-SUDS}$ values for RES. GW level changes across the 3 modalities hence do not directly follow the increase of GW recharge described in Section 3.2. This situation, along with the sharp limitation of maximum GW levels around -2 m for DEN, indicates that GW levels impacts are

not only determined by the additional recharge within i-SUDS but also drainage by underground structures (here, more significant for DEN as detailed in Section 3.4.2).

The influence of $S_S$ and Clim is less marked. It primarily results from their impact on GW recharge within i-SUDS as well as their control over transpiration. This last point, suggested in particular by the limitation of $Z_S$ for Clim = MTP characterized by a higher atmospheric demand, reflects the limitation of GW rise imposed by root water extraction (for transpiration), with a limiting effect similar to that observed for underground structures. Finally, the effect of $S_i$ reflects its influence on downstream GW flows. Because this influence decreases as the extent of the SZ increases (which may simply be shown from Darcy's law), this effect is mainly visible at lower $Z_S$ values (i.e., for high permeability soil settings, characterized by a limited extent of the SZ in reference urban scenarios).

### 3.3.2 | Spatial and Temporal Variability

As shown in Figure 7, stormwater infiltration overall leads to a marked increase in both the spatial and temporal variability
of GW levels, as captured by the standard deviations SD<sub>y</sub>(Z<sub>S</sub>) and SD<sub>t</sub>(Z<sub>S</sub>). The impact of the various factors on this increase is summarized in Table 4.

Regarding spatial variability (SD<sub>y</sub>[Z<sub>S</sub>]), although most factors come into play, the impact is predominantly controlled by *Urba*, *S<sub>U</sub>*, *Sl*, and i-SUDS distribution level (*Clim*) (Table 4). This impact primarily originates from GW mounding below i-SUDS and interactions with underground structures, as suggested by the correlation of ΔSD<sub>y</sub>(Z<sub>S</sub>) with (i) the additional GW elevation below i-SUDS relative to Z<sub>S</sub> (ρ=0.76) and (ii) the increase of drainage volumes (ΔDR, ρ=0.53). The influence of *S<sub>U</sub>* and *Dis* reflects their control on GW transmissivity and the possibilities for lateral dissipation of infiltrated volumes (see D'Aniello et al. 2019). Low-permeability (*S<sub>U</sub>*=LP) or centralized infiltration (*Dis*=CEN) settings that favor GW mounding are hence associated with the largest ΔSD<sub>y</sub>(Z<sub>S</sub>) values (on average, 2.5× higher than other *S<sub>U</sub>* settings for LP, and 2.4× higher than other DIS settings for CEN). The effect of *Urba* stems from its influence on drainage by underground infrastructures and on the amount of water to be infiltrated through impervious cover (that

may in turn affects GW mounding). DEN settings, characterized by a high imperviousness and the presence of draining building foundations, hence results in largest ΔSD<sub>y</sub>(Z<sub>S</sub>) values (1.7 and 2.6× higher than COM and RES resp.) (The impact of *Sl* reflects its influence on GW transmissivity in urban reference scenarios, that in turn controls the magnitude of mounding—cf. Data S4).

Regarding temporal variability (SD<sub>t</sub>[Z<sub>S</sub>]), the impact of stormwater infiltration mainly originates from the overall rise of GW levels that leads to a more important and faster response to climate forcings. Such interpretation is supported by the strong correlation between SD<sub>t</sub>(Z<sub>S</sub>) and Z<sub>S</sub> (ρ=0.69), as well as the correlation between these terms and the temporal variability of GW recharge SD<sub>t</sub>(GR) (ρ=0.79 and 0.59). The influence of GW levels is also reflected in the identification of *S<sub>U</sub>*, *Urba*, and *eUZ<sub>ini</sub>* as key controls over ΔSD<sub>t</sub>(Z<sub>S</sub>), just as for ΔZ<sub>S</sub>. Expectedly, the role of *Clim* is here exacerbated as the latter directly governs the temporal variability of GW recharge that in turn affects SD<sub>t</sub>(Z<sub>S</sub>). Finally, the impact of *S<sub>t</sub>*/*S<sub>d</sub>* results from its control over the temporal variability of GR within i-SUDS (30% higher for *S<sub>t</sub>*/*S<sub>d</sub>*=10% than *S<sub>t</sub>*/*S<sub>d</sub>*=2%), which subsequently influences SD<sub>t</sub>(Z<sub>S</sub>).

<figure><img src="image_10.png" /><figcaption>**FIGURE 6** | Impact of the most influential factors on GW level increase, shown from the distribution of average GW level Z<sub>S</sub> after i-SUDS implementation for the three initial GW level (*eUZ<sub>ini</sub>*) classes.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>**FIGURE 7** | Impact of stormwater infiltration on the spatial (SD<sub>y</sub>[Z<sub>S</sub>]) and temporal variability (ΔSD<sub>t</sub>[Z<sub>S</sub>]) of GW levels.</figcaption></figure>
## 3.4 | Water Balance Contribution of the Additional GW Recharge

The results of the hierarchical clustering, associated with the PCA on the relative contribution of the additional GW recharge to downstream GW flow (ΔBC/GRᵢ-SUDS), drainage by underground structures (ΔDR/GRᵢ-SUDS), and transpiration outside i-SUDS (ΔREᵢ-other/GRᵢ-SUDS), are presented in Figure 8.

Based on this classification, three types of behaviors are identified: for cluster 1 (45% of the simulations), the additional recharge associated with i-SUDS is essentially balanced by the increase of downstream GW flow (BC); (ii) for cluster 2 (48% of the simulations), the equilibration is mainly achieved via drainage by underground structures (DR), with a potential contribution of BC; (iii) for cluster 3 (7% of the simulations), the dominant process is transpiration (RE), even if DR and BC remain significant. The mechanisms underlying these behaviors are discussed below, focusing on the distribution of the different factors across clusters (Figure 9). The results of KW/FK over the different variables are presented in Table 5.

<figure><img src="image_12.png" /></figure>

**FIGURE 8** | Results of the hierarchical clustering from PCA on the possible water-balance contribution of GRᵢ-SUDS. a: Representation of the simulations associated with the 3 clusters (using the first 2 directions of the PCA); b: correlation circle for two first directions of the PCA; c to e: distribution of PCA variables across clusters; f: distribution of investigated factors across clusters.

<figure><img src="image_13.png" /></figure>

**FIGURE 9** | Relationship between the increase of transpiration outside i-SUDS (ΔREᵢ-other) and average GW level (Zₛ) as a function of climate, urban context, surface soil characteristics and underlying soil characteristics.

### 3.4.1 | Increase of Transpiration Outside i-SUDS

As shown in Figure 8, the increase of transpiration outside i-SUDS (ΔREᵢ-other) is only significant for a very limited number of simulations consisting of low surface permeability settings (Sₛ = LP or MP), predominantly associated with high evapotranspiration clients (Clim = MTP), residential (i.e., low imperviousness) contexts (Urba = RES) and low underlying soil permeability (Sₛ = LP). In line with the frequent occurrence of Sₛ = LP, these simulations are characterized by limited GW depths (Zₛ), with 5th to 95th centiles values ranging from -2.8 to -1.7m.

The magnitude of ΔREᵢ-other primarily depends on three factors: the evapotranspiration demand (PET), the availability of water in the root zone and the proportion of vegetated surfaces available for transpiration. Because of the influence of GW levels on soil water availability (through capillary rise), the factors identified as potentially affecting ΔREᵢ-other are similar to those controlling the variability of ΔZₛ (Table 5). Accordingly, ΔREᵢ-other/GRᵢ-SUDS and ΔREᵢ-other also show a positive correlation with ΔZₛ (ρ = 0.53 and 0.54). The direct
<table><caption>**TABLE 5** | Summary of KW/FK for variables associated with the increase of transpiration, drainage by underground structures or downstream GW flow (results shown as *p*-values classes considering the minimum *p*-value [$p_{min}$] between FK and KW tests).</caption><thead><tr><th></th><th>Clim</th><th>Urba</th><th>S<sub>S</sub></th><th>S<sub>U</sub></th><th>eUZ<sub>ini</sub></th><th>Sl</th><th>Dis</th><th>S<sub>i</sub>/S<sub>d</sub></th></tr></thead><tbody><tr><td>ΔRE<sub>other</sub></td><td>**</td><td>****</td><td>****</td><td>****</td><td>****</td><td>*</td><td>*</td><td>**</td></tr><tr><td>ΔRE<sub>other</sub>/GR<sub>i-SUDS</sub></td><td>***</td><td>****</td><td>****</td><td>****</td><td>****</td><td>**</td><td>*</td><td>**</td></tr><tr><td>ΔDR</td><td>****</td><td>****</td><td>***</td><td>****</td><td>****</td><td>**</td><td>**</td><td>**</td></tr><tr><td>ΔDR/GR<sub>i-SUDS</sub></td><td>****</td><td>****</td><td>***</td><td>****</td><td>****</td><td>**</td><td>**</td><td>*</td></tr><tr><td>ΔBC</td><td>****</td><td>****</td><td>**</td><td>****</td><td>****</td><td>**</td><td></td><td></td></tr><tr><td>ΔBC/GR<sub>i-SUDS</sub></td><td>****</td><td>****</td><td>**</td><td>****</td><td>****</td><td>**</td><td>*</td><td>*</td></tr></tbody></table>

***$p_{min} \le 10^{-100}$.

**$10^{-100} < p_{min} \le 10^{-50}$.

$10^{-50} < p_{min} \le 10^{-10}$.

$10^{-10} < p_{min} \le 10^{-2}$.

contribution of GW levels to ΔRE<sub>other</sub> may however be separated from the other mechanisms involved by examining the variability caused by the different factors on the relation between ΔRE<sub>other</sub> and Z<sub>S</sub> (Figure 9). The role of PET is clearly evidenced by the higher values of ΔRE<sub>other</sub> for Clim = MTP as compared to the two other settings, despite lower GW levels. The results associated with S<sub>S</sub> (and to a lesser extent S<sub>U</sub>), with the occurrence of highest transpiration increase for low-permeability settings, reflect the impact of soil texture on water holding capacity and capillary rise. Finally, the impact of green-spaces is illustrated in the higher range of ΔRE<sub>other</sub>, where the similar values between Urba = RES (30% sealed) and COM (70% sealed) suggest that a high availability of vegetated areas for transpiration can sometimes compensate for lower GW levels. The moderate effect of S<sub>i</sub>/S<sub>d</sub> reported in Table 5 reflects the influence of this parameter on (i) infiltrated volumes and (ii) the proportion of green-spaces other than i-SUDS (cf. Data S3).

Ultimately, it should be noted that the water-balance contribution of ΔRE<sub>other</sub> in any case remains limited, with highest values ranging between only 55 to 120 mm/year (9th decile) and minimal significance in the majority of simulations. In other words, the relatively high ΔRE<sub>other</sub>/GR<sub>i-SUDS</sub> values within cluster 3 (Figure 8) here dissimulate low GR<sub>i-SUDS</sub> values due to lower runoff volumes and i-SUDS efficiency, consistent with the distribution of Urba, S<sub>S</sub>, S<sub>U</sub>, and Clim shown Figure 8.

### 3.4.2 | Increase of Drainage by Underground Structures

Overall, the increase of drainage (ΔDR) is significant for a large proportion of simulations associated with cluster 2 and 3 (between 69 and 424 mm/year for the highest 90% values). This increase directly results from localized infiltrations through sewer pipes and drainage from building foundations following the rise of GW levels. As for ΔRE<sub>other</sub>, the latter is thus mainly observed for contexts associated with shallow GW levels (between -2.3 and -1.0 m for the 90% highest ΔDR values within cluster 2 and 3), among which underlying soil permeability (S<sub>U</sub>) and low initial GW depths (eUZ<sub>ini</sub>) configurations are over-represented (cf. Figure 8). More specifically, cluster 2, characterized by higher drainage contribution and limited ΔRE<sub>other</sub> values, also

comprises a large proportion of dense urban settings, where the increase of DR is facilitated by the presence of additional drainage structures.

Because seepage into sewer pipes or dewatering systems directly depends on the hydraulic head above draining structures, ΔDR/GR<sub>i-SUDS</sub> and ΔDR exhibit a strong correlation with Z<sub>S</sub> ($\rho$=0.78 and 0.75). Once again, the factors controlling the increase of DR are thus largely consistent with those identified for ΔZ<sub>S</sub> (cf. Table 5). Beyond the direct impact of GW levels, ΔDR also depends on the nature and density of draining structures. This influence is highlighted in Figure 10, where the dense urban setting (Urba = DEN) leads to similar ΔDR values at lower GW levels compared to the two other settings due to the presence of deeper drainage systems. In such configurations, ΔDR is additionally controlled by S<sub>U</sub> that limits drainage from building foundations, as shown by the slight shift of higher permeability soils towards lower Z<sub>S</sub> values for Urba = DEN (and not observed for RES and COM, where DR is limited to seepage into sewer pipes and governed by a sewer conductance parameter—cf. Data S1). Finally, the influence of Clim (and to a lesser extent S<sub>S</sub>), as evidenced in Figure 10 with lower ΔDR values for high evapotranspiration (and, to a lesser extent, low surface permeability) settings at similar Z<sub>S</sub> values, mirrors their impact on the concurrent transpiration process (cf. Section 3.4.1). The slight effect of i-SUDS spatial allocation parameters reported in Table 5 results from (i) their control over infiltrated volumes and (ii) the localized nature of interactions between draining structures and GW (cf. Data S4).

### 3.4.3 | Increase of Downstream GW Flow

The dominance of BC, as observed for simulations belonging to cluster 1, reflects the absence (or very limited contribution) of near surface interactions between GW and underground structures or vegetation. In such configurations, most of the additional recharge volume within i-SUDS is thus evacuated downstream, yielding to significant ΔBC values (between 129 and 471 mm/year for the 90% highest values). Consistently, cluster 1 essentially consists of configurations resulting in relatively low GW levels (below -2 m for 90% of the simulations), among which high permeability underlying soils (S<sub>U</sub>) and low initial
GW (*eUZ*<sub>in</sub>) are over-represented (cf. Figure 8). Accordingly, both ΔBC/GR<sub>i-SUDS</sub> and ΔBC exhibit a negative correlation with Z<sub>S</sub> (ρ = -0.75 and -0.63) and the factors controlling their variability are again similar to those identified for ΔZ<sub>S</sub> (cf. Tables 4 and 5).

At low Z<sub>S</sub> values (where the contribution of ΔRE and ΔDR be-
comes negligible), the impact of *Clim*, *Urba*, and S<sub>S</sub> additionally
encompasses their control over GR<sub>i-SUDS</sub>, through their influ-
ence on runoff, IF<sub>i-SUDS</sub> and its partition between transpiration
and recharge (discussed in Section 3.2). For this reason, configu-
rations characterized by low imperviousness (Urba = RES), low
surface soil permeability (S<sub>S</sub> = LP), and high precipitation deficit
(Clim = MTP) generally show lower ΔBC values (cf. Figure 11).

4 | Discussion

4.1 | Hydrological Impact of i-SUDS and Management Implications

Simulation results demonstrate the potential variability in
the hydrological response of urban catchment to stormwa-
ter infiltration depending on local context, and suggest that
in most cases, i-SUDS alone do not allow returning towards
"pre-development" water-balance conditions, due to the large
increase of GW recharge, which eventually often exceeds "pre-
development" levels.

This increase of GW recharge primarily originates from the
concentration of runoff volumes within i-SUDS that limit the

potential contribution of transpiration. For the Sᵢ/S₉ settings considered in this study, the latter remains relatively low, with less than 13% of the infiltrated volume for 90% of the simulations—a value consistent with previous findings (De-Ville et al. 2024; Thom et al. 2020). Despite locally high transpiration rates, systematically exceeding those of non-irrigated green-spaces and approaching PET provided soil water retention capacity is sufficient, transpiration increase within i-SUDS thus cannot compensate for the low evapotranspiration from the impervious drainage area, leading to excessive GW recharge.

At the catchment scale, i-SUDS impacts on GW levels then re-
sults from the complex balance between the increase of GW
recharge and underground or near surface processes, that both
depend on and affect GW levels (Pophillat et al. 2022). In most
cases, the rise of GW level associated with i-SUDS implementa-
tion is significant, with more than 1-m difference from urban-
reference scenarios for 66% of the simulations. The latter is also
highly variable, with values that may reach several meters for
low to moderate transmissivity aquifers, as found in previous
research (Göbel et al. 2004; Maimone et al. 2011). In such cases,
or where initial GW depth is already limited, stormwater infil-
tration is then likely to result in greater interactions between
GW and draining structures. The subsequent increase in the
volumes flowing through sewer network (significant in about
half of the scenarios) may require special attention, as it poten-
tially undermines i-SUDS runoff reduction objectives (Zhang
and Parolari 2022), despite the attenuated dynamics of drain-
age induced sewer flows as compared to the ones associated
with surface-runoff. These interactions can also be expected
to restrict the rise of GW level, especially in the case of deep,

<figure><img src="image_14.png" /><figcaption>**FIGURE 10** | Relationship between the increase of drainage by underground structures (ΔDR) and average GW level (Z<sub>S</sub>) as a function of climate, urban context, surface soil characteristics, and underlying soil characteristics.</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>**FIGURE 11** | Relationship between the increase of downstream GW flow (ΔBC) and average GW level (Z<sub>S</sub>) as a function of climate, urban context, surface soil characteristics, and underlying soil characteristics.</figcaption></figure>
dense or conductive underground draining structures. In contexts where drainage possibilities are more limited, the rise of GW level may result in an increase of transpiration but also unwanted interactions with i-SUDS or localized GW resurgences. Conversely, for high transmissivity aquifers or low initial GW levels, the rise is generally not sufficient to produce interactions with draining structure, vegetation or soil surface. In such cases, i-SUDS mainly contribute to the increase of downstream GW flow (and thus baseflow) with values that potentially exceed “pre-development conditions,” a behavior previously evidenced by Bhaskar et al. (2016).

These contrasts in the potential response to i-SUDS implementation highlight the importance of adapting stormwater management strategies to the local context and environmental objectives, as already stated in previous research (Göbel et al. 2004; Poozan et al. 2024). For situations where infiltration essentially translates into an increase of downstream GW flow, the concerns are probably minimal, even if possibilities of over-compensating for the impact of urbanization exist. Inversely, where GW level rise is likely to result in higher interactions with the surface or underground structures, limiting or at least controlling recharge (or its consequences) may be needed.

A notable result from this study is the limited influence of i-SUDS spatial allocation strategy on the catchment scale consequences of stormwater infiltration, as compared to other factors investigated. As opposed to the findings of Poozan et al. (2024), the current analysis, for instance, does not show a clear influence of i-SUDS distribution level (Dis) on transpiration or downstream GW flow. Such a difference may however stem from (i) the less extreme nature of the scenarios considered here, where “centralized” infiltration still involves some degree of spatial distribution over the catchment, and (ii) the impact of Dis on i-SUDS efficiency and thus GW recharge, that dominates over other mechanisms. Similarly, drainage by underground structures is only marginally affected by i-SUDS allocation strategy. Current results however confirm the primary importance of the levels of distribution (Dis) and concentration ($S_i/S_d$) of infiltration on the local scale functioning of i-SUDS. The potential reduction in i-SUDS volume control efficiency for centralized infiltration settings, due to GW interactions, is consistent with previous findings (Locatelli et al. 2015; Sage et al. 2024) and highlights the benefits of adapting size of system to maximize lateral dissipation of GW mounds for contexts where the GW rise is likely to be significant. The decision between distributed and more centralized infiltration systems may however not always be straightforward, as centralizing infiltration may in some cases allow controlling the spatial extent of GW interactions with drainage infrastructures, surface or vegetation. The results finally demonstrate the key role of the level of concentration ($S_i/S_d$) on the partition between transpiration and recharge at the i-SUDS scale.

At the catchment scale, limiting the undesirable consequences of stormwater infiltration or moving towards a more “natural” hydrological functioning, presumably requires controlling the increase of GW recharge through adapted stormwater management strategies. The adoption of a larger $S_i/S_d$ ratio, combined with the selection of high water-holding capacity substrate and

adapted plant species, could certainly provide opportunities for enhancing transpiration within i-SUDS. However, unless considering a $S_i/S_d$ ratio approaching 1, impractical in most contexts and for which the effective infiltration area could in any case be smaller than expected (as suggested by Tedoldi et al. 2017), transpiration will presumably remain limited by atmospheric demand and it is hence unlikely that i-SUDS alone can help restore pre-development water balance. The increase of transpiration beyond i-SUDS, previously evidenced (Poozan et al. 2024; Western et al. 2021), may help compensate for the increase of GW recharge. Current results, however, suggest that this phenomenon is only significant for a limited number of contexts, characterized by shallow GW levels and low permeability settings where drainage by underground structures might as well be substantial. Hence, maintaining the balance between evapotranspiration and recharge at the catchment scale presumably requires combining stormwater infiltration with evapo(transpi)ration-based runoff reduction practices such as green roofs. Obviously, limiting soil sealing should also be considered as the magnitude of i-SUDS impacts primarily depends on the amount of water to be infiltrated. Finally, facilitating the “reuse” of water from the underground compartment, for instance by adequately combining i-SUDS and green spaces (as suggested in Thom et al. 2020), presumably remains a promising approach to ensure water availability for the vegetation while locally promoting evapotranspiration.

## 4.2 | Impact of Catchment Conceptualization

Despite the consideration of a variety of factors regarding catchment characteristics, the analysis involves several assumptions that potentially affect simulation results, while at the same time making them easier to interpret.

In particular, the hypothetical “sloping hillside” configuration, used as a basis for constructing the different scenarios, inevitably influences the ability to evacuate the additional GW recharge associated with i-SUDS outside the modeling domain. A discussion about the implications of the limited catchment area and the no-flux lateral boundary condition hypothesis is provided in Pophillat et al. (2022), where a similar setting was adopted. Compared to this previous application, the adoption of different settings regarding (i) the position of the 500×500-m² area on which infiltration is implemented (Pos) and (ii) the extension of the SZ at the downstream limit of the GW domain (eSZ<sub>BC</sub>) here allows introducing some variability as to the influence of boundary conditions. The examination of their impact, as provided in *Supporting Information* (cf. Data S4), indicates that the variations considered do not significantly alter the identified trends in the effect of the infiltration or the role of other factors. Expectedly, *Pos* is nevertheless found to influence the rise of GW level and related processes, with a more significant impact of i-SUDS when the latter are implemented close to upstream no-flow boundary conditions (a result consistent with the findings of Zhang and Chui 2020). Conversely, the impact of eSZ<sub>BC</sub> remains limited, with only a slight effect on SZ processes. Regarding this factor, it may be noted that the use of fixed eSZ<sub>BC</sub> values for different underlying soil permeability settings leads to significant transmissivity contrasts between each soil type that in turn largely affect downstream GW flow and thus control the
GW level differences across soil categories. In other words, the variability of downstream GW flow is rather controlled by underlying soil permeability than $eSZ_{BC}$ setting. In addition to the elements discussed above, the limited extent of the simulation domain, combined with extensive i-SUDS implementation over a large portion of it, is likely to amplify the water table's response to infiltration compared to real-world situations.

Other notable conceptualization assumptions include the homogeneity of soil characteristics, sewer conductance or vegetation characteristics, as well as the choices regarding urban context or stormwater management strategies. Obviously, simulated scenarios thus cannot be expected to be representative of all contexts and fully capture the complexity of urban catchments. They nevertheless remain consistent with the objective of the study to generically assess the potential impacts of stormwater infiltration on the hydrological functioning of small urban catchments and to explore the factors governing these impacts.

## 4.3 | Suggestions for Future Research

While covering a diversity of contexts and providing a comprehensive assessment of the potential catchment-scale impacts of stormwater infiltration in shallow GW contexts, this study leaves several aspects open for future research.

Here, the focus on “generalized” infiltration scenarios, where runoff is entirely and exclusively managed in i-SUDS, facilitates the analysis of the factors controlling the impact of stormwater infiltration. However, the relatively extreme nature of this hypothesis, along with the expected non-linearity in the catchment response to the proportion of impervious areas “treated” by i-SUDS (Golden and Hoghooghi 2018; Jefferson et al. 2017), could motivate the exploration of intermediary implementation scenarios. The analysis could also be extended to a broader range of stormwater management practices, to examine how to effectively allocate and combine them in shallow GW contexts (as discussed by Zhang and Chui 2019b). Besides, another notable methodological choice of this study is the consideration of “stabilized” hydrological conditions, after an initialization period whose duration significantly varied from a context to another, and often exceeded several years in moderate to low permeability settings. In other words, the manifestation of i-SUDS impact might often be gradual, and occurs concurrently with land use or stormwater management changes. Hence, the temporality in SUDS deployment and corresponding catchment response should probably be given further attention in scenario modeling contexts or for observational studies.

The study illustrates the relevance of hydrological modeling, as complementary approach to observation, for improving our understanding of the potential impacts of stormwater management practices. It more specifically demonstrates the relevance of the URBS model for describing the various interactions between surface hydrology, GW and underground structures in a multiple scenario analysis context, involving continuous and long period simulations. Yet, the approach does not address the impact of soil heterogeneities (e.g., backfills) or preferential flows (e.g., along man-made trenches) that may partly control the fate of infiltrated volumes (Bonneau et al. 2017), and thus

presumably deserves further attention. The highly uncertain and heterogeneous nature of the urban underground compartment, along with the potentially significant influence of very localized processes such as preferential flows, also raises question of the optimal representation scale for distributed urban hydrological models (Golden and Hoghooghi 2018). In any case, maintaining the focus on the verification of models against observation will be essential, as monitoring capabilities develop, to make sure that the latter are plausible representations of urban environment and able to depict the impact of stormwater management strategies.

Ultimately, the practical relevance of modeling results databases that depict the potential impact of i-SUDS across a broad range of scenarios, such as the one generated in this study, could be examined. In the perspective of supporting practitioners in adapting stormwater strategies to the local context, a straightforward approach would be to build on such simulation results to derive trends regarding the potential impact of stormwater infiltration for a limited number of representative contexts. Such elements, which could be incorporated into guidance documents, would offer insights into the key factors to be considered when implementing infiltration at the neighborhood or catchment scale and help identify areas where infiltration may have undesirable consequences. Looking ahead, further developments could entail integrating simulation results into a decision-support system that would allow practitioners to anticipate the potential for GW elevation and water-balance modifications based on local context characteristics and assumptions about i-SUDS implementation.

## 5 | Conclusion

A numerical modeling analysis was conducted to examine the hydrological consequences of a generalized implementation of stormwater infiltration at the scale of a small urban catchment. Continuous and multi-annual simulations were performed with the URBS model over a 0.75-km² domain, considering a diversity of contexts regarding climate, urbanization, and hydrogeology along with different spatial allocation strategies for infiltration systems (i-SUDS). The impact of i-SUDS was then assessed by comparing corresponding results regarding water balance and GW levels to those obtained for reference simulations without i-SUDS and for theoretical “pre-development” conditions. Overall, about 1.7×10⁴-stormwater infiltration scenarios were considered, allowing a systematic investigation of the role of local context and i-SUDS implementation strategy on the hydrological response to stormwater infiltration. The findings may be summarized as follows:

* Stormwater infiltration showed highly variable water balance impacts depending on the local context, but systematically resulted in a significant increase of GW recharge at the catchment scale as compared to the reference situation without i-SUDS, with values often exceeding those estimated for “pre-development” conditions. The increase of GW recharge was essentially driven by the concentration of runoff within i-SUDS, which strongly limited the potential contribution of transpiration in the evacuation of infiltrated volumes (for the scenarios considered). This contribution was found to be primarily controlled by the ratio between
infiltration and drainage areas, along with climate and surface i-SUDS soil characteristics.

* This increase of GW recharge resulted in an overall rise in GW levels, often exceeding several meters for low to moderate underlying soil permeability settings. Beyond aquifer transmissivity, an influence of climate and urban context—here controlling both the volume to be managed and GW interactions with vegetation or man-made structures—was also highlighted. The rise in GW level was also found to be highly variable in time and space. The increase of the temporal variability was mainly driven by the overall elevation of GW levels, resulting in faster response to climate forcings, and the specific dynamic of GW recharge within i-SUDS. For the spatial variability, the main driver was the formation of GW mounds below i-SUDS, exacerbated for low-permeability and centralized infiltration settings.

* For low initial GW depths and low-permeability settings, the rise of GW level led to a notable increase of drainage by underground structures (sewer pipes or dewatering systems), with volumes comparable to the additional recharge within i-SUDS. In a more limited number of configurations, additionally characterized by low surface soil permeability, high evapotranspiration demand and relatively low imperious cover, a significant increase of transpiration outside i-SUDS was also observed. For remaining settings, most of the additional recharge was dissipated by downstream GW flow, without generating significant interactions with the surface or underground structures.

This study demonstrates the inability of stormwater infiltration strategies alone to restore “pre-development” hydrological conditions, due to the overcompensation of the impact of soil-sealing on GW recharge. It further highlights the critical role of interactions between GW, surface and underground structures—induced by the increase in GW recharge—on the hydrological outcome of stormwater infiltration strategies. This work therefore underscores the potential importance of managing these interactions by adapting infiltration practices to local contexts and environmental objectives. It more generally suggests that, in contexts where a natural hydrological functioning is desired or where interactions might lead to undesirable effects, i-SUDS should be implemented in combination with other stormwater management practices such as green-roofs.

## Author Contributions

**William Pophillat:** conceptualization, methodology, investigation, formal analysis, writing – review and editing; **Jérémie Sage:** conceptualization, methodology, supervision, writing – original draft; **Fabrice Rodriguez:** conceptualization, methodology, supervision, writing – review and editing; **Isabelle Braud:** conceptualization, methodology, supervision, writing – review and editing.

## Acknowledgments

This study was carried out under the OPUR program and supported by the French Ministry for the Ecological Transition and the French National Institute for Agriculture, Food, and Environment. The study also contributes to the research conducted within the OTHU (Field Observatory in Urban Hydrology) and the ONEVU (Nantes Urban Environment Observatory).

## Conflicts of Interest

The authors declare no conflicts of interest.

## Data Availability Statement

The URBS model is available at: https://zenodo.org/records/5140710. The synthetic database used for the analysis is available as Data S5.

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## Supporting Information

Additional supporting information can be found online in the Supporting Information section. **Data S1:** Further information on model parameterization. **Data S2:** Additional details on the modalities associated with the factors investigated. **Data S3:** Procedure for the calculation of spatial and temporal variability indicators. **Data S4:** Violin plots for all factors and variables. **Data S5:** Synthetic database used for the analysis.