RESEARCH CENTRE

Inria Branch at the University of
Montpellier

2023
ACTIVITY REPORT

IN PARTNERSHIP WITH:

Université de Montpellier, CNRS

Project-Team

LEMON

Littoral Environment: M0dels and
Numerics

IN COLLABORATION WITH: HydroSciences Montpellier (HSM), Institut
Montpelliérain Alexander Grothendieck (IMAG)

DOMAIN

Digital Health, Biology and Earth

THEME

Earth, Environmental and Energy
Sciences

Contents

Project-Team LEMON

1 Team members, visitors, external collaborators

2 Overall objectives

3 Research program

3.1 Physics-driven models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.1 Upscaled urban flood modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.2 Large time steps methods for hydraulic processes . . . . . . . . . . . . . . . . . . . . .
3.1.3 Street-buildings interactions during flood events . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . .
3.1.4 Coupling coastal ocean and urban flood models
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.1 Data fusion and parameter estimation . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.2 Space-time variability of rainfalls
3.2.3 Multivariate dependence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . .
3.2.4 Clustering and sparsity models for rainfall
3.3 Hybrid modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3.2 Data-driven models

4 Application domains

4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Coastal Oceanography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 Urban Floods
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4 Hasard and Risk Assessment

5 Social and environmental responsibility

5.1 Footprint of research activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2 Impact of research results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6 Highlights of the year

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7 New software, platforms, open data

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7.1 New software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
7.1.1 SW2D-Lemon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.2 tsunamilab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

8 New results

11
8.1 Physics-driven models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
8.1.1 Street-building exchange . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
8.1.2 Porosity models for upscaled urban flood modeling . . . . . . . . . . . . . . . . . . . 12
8.1.3 Large scale floods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
8.1.4 Coupling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
8.1.5 Numerical methods for hyperbolic systems of equations . . . . . . . . . . . . . . . . . 13
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
8.2.1 Statistical approaches for extreme values . . . . . . . . . . . . . . . . . . . . . . . . . . 14
8.2.2 Heterogeneous data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
8.3 Hybrid models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
8.3.1 Downscaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
8.3.2 AI for flood forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

8.2 Data-driven models

9 Partnerships and cooperations

17
9.1 International initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
9.1.1 Associate Teams in the framework of an Inria International Lab or in the framework

of an Inria International Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
9.1.2 Participation in other International Programs . . . . . . . . . . . . . . . . . . . . . . . 17
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
9.2.1 Horizon Europe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
9.3 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

9.2 European initiatives

10 Dissemination

19
10.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
10.1.1 Scientific events: organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
10.1.2 Scientific events: selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
10.1.3 Journal
10.1.4 Invited talks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
10.1.5 Leadership within the scientific community . . . . . . . . . . . . . . . . . . . . . . . . 21
10.1.6 Scientific expertise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
10.1.7 Research administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
10.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
10.2.1 Academic involvement / responsibilities . . . . . . . . . . . . . . . . . . . . . . . . . . 22
10.2.2 Supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
10.2.3 Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
10.3 Popularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
10.3.1 Internal or external Inria responsibilities . . . . . . . . . . . . . . . . . . . . . . . . . . 23
10.3.2 Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
10.3.3 Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

11 Scientific production

11.1 Publications of the year
11.2 Cited publications

23
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Project LEMON

1

Project-Team LEMON

Creation of the Project-Team: 2019 January 01

Keywords

Computer sciences and digital sciences

A3.1.4. – Uncertain data

A3.1.10. – Heterogeneous data

A3.4.1. – Supervised learning

A3.4.2. – Unsupervised learning

A6.1.1. – Continuous Modeling (PDE, ODE)

A6.1.2. – Stochastic Modeling

A6.1.4. – Multiscale modeling

A6.1.5. – Multiphysics modeling

A6.2.1. – Numerical analysis of PDE and ODE

A6.2.2. – Numerical probability

A6.2.3. – Probabilistic methods

A6.2.4. – Statistical methods

A6.3.3. – Data processing

A6.3.4. – Model reduction

A6.3.5. – Uncertainty Quantification

A6.5.2. – Fluid mechanics

A6.5.3. – Transport

A6.5.4. – Waves

A9.6. – Decision support

Other research topics and application domains

B3.1. – Sustainable development

B3.2. – Climate and meteorology

B3.3.2. – Water: sea & ocean, lake & river

B3.3.3. – Nearshore

B3.4.1. – Natural risks

B3.4.3. – Pollution

B3.6. – Ecology

B3.6.1. – Biodiversity

B4.3.2. – Hydro-energy

B6.5. – Information systems

B8.3. – Urbanism and urban planning

B8.4. – Security and personal assistance

2

Inria Annual Report 2023

B8.4.1. – Crisis management

B9.11. – Risk management

B9.11.1. – Environmental risks

Project LEMON

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1 Team members, visitors, external collaborators

Research Scientists

• Antoine Rousseau [Team leader, INRIA, Researcher, until Sep 2023, HDR]

• Antoine Rousseau [Team leader, INRIA, Senior Researcher, from Oct 2023, HDR]

Faculty Members

• Carole Delenne [UNIV MONTPELLIER, Associate Professor, HDR]

• Pascal Finaud Guyot [UNIV MONTPELLIER, Associate Professor, HDR]

• Vincent Guinot [UNIV MONTPELLIER, Professor, HDR]

• Nicolas Meyer [UNIV MONTPELLIER, Associate Professor]

• Gwladys Toulemonde [UNIV MONTPELLIER, Professor, from Sep 2023, HDR]

• Gwladys Toulemonde [UNIV MONTPELLIER, Associate Professor, until Aug 2023, HDR]

Post-Doctoral Fellow

• Katia Ait Ameur [INRIA, Post-Doctoral Fellow, from Nov 2023]

PhD Students

• Fadil Boodoo [UNIV MONTPELLIER]

• Alexis Boulin [UNIV COTE D’AZUR]

• Cécile Choley [ENGEES, until Oct 2023]

• Omar Et Targuy [UNIV ARTOIS]

• Jose Daniel Galaz Mora [INRIA]

• Chloe Serre Combe [UNIV MONTPELLIER]

• Samuel Valiquette [CIRAD]

Interns and Apprentices

• Antoine Doize [INRIA, Intern, from Mar 2023 until Jun 2023]

• Marc Hetier [INRIA, Intern, from Mar 2023 until Aug 2023]

• Nadia Skifa [ENSIMAG, Intern, from Mar 2023 until Aug 2023]

Administrative Assistant

• Cathy Desseaux [INRIA]

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Inria Annual Report 2023

2 Overall objectives

Coastal areas are increasingly threatened by global warming-induced sea level rise. At the same time,
60% of the world population lives in a 100 km wide coastal strip (80% within 30 km from the shore in
French Brittany). This is why coastlines are concerned with many issues of various types: econom-
ical, ecological, social, political, etc. Coastal areas are natural interfaces between various media (e.g.
wind/sea/sand/land). The physical processes acting on these media have very different time scales,
hence the need to build complex systems coupling nonlinear partial differential equations and random
processes to describe them. To address these crucial issues, LEMON is an interdisciplinary team working
on the design, analysis and application of deterministic and stochastic models for inland and marine
littoral processes, with an emphasis on both standalone models and hybrid systems.

The spot of Montpellier offers large opportunities:

Important academic research community Additionally to IMAG1 and HSM2, we interact with several
local academic research partners. To mention but a few examples, we collaborate with UMR
MISTEA (pollution and remediation of water resources) and UMR LISAH (hydrology in agricultural
areas). Regular contacts are also maintained with UMR Geosciences (morphodynamics), UMR
G-Eau (hydraulics, data assimilation and flood economy), UMR MARBEC (lagoon environment).

NUMEV Labex and MUSE project The LEMON members are involved in projects funded by the current
NUMEV Labex and actively participate in new initiatives pertaining to sea and coast modeling,
both through the recently awarded MUSE project in Montpellier and through external (national,
European, international) calls.

Industrial and economic community From the transfer & innovation viewpoint, the team members
already interact with several local partners such as Cereg Ingénierie, IRT Saint-Exupéry, Tour du
Valat, Predict Services, Artelia, Montpellier Métropole and Berger-Levrault.

The general scope of the LEMON project-team is to develop mathematical and computational meth-
ods for the modeling of hydraulic and hydrodynamic processes. The mathematical tools used are
deterministic (PDEs, ODEs) and/or probabilistic (extreme value theory). Applications range from regional
oceanography to coastal management, including risk assessment for natural hazards on the coastline
(submersion and urban floods, tsunamis, pollution).

LEMON is a common research team between HSM (UM, CNRS, IRD), IMAG (UM, CNRS) and Inria,
whose faculty members have never been associated to Inria groups in the past. All fellows share a strong
background in mathematical modeling, together with a taste for applications to the littoral environment.
As reflected in the team contributions, the research conducted by LEMON is interdisciplinary 3, thanks to
the team members expertise (deterministic and stochastic modeling, computational and experimental
aspects) and to regular collaborations with scientists from other domains. We believe this is both an
originality and a strength for LEMON .

3 Research program

Foreword

Interdisciplinarity is a characteristic and a strength for LEMON. We want to build on this mix by developing
two main research axes - physics-driven and data-driven models - applied to free-surface hydraulic
processes and their coupling. These two axes will intersect through the hybridization of models and all
this work will serve the development of the SW2D-LEMON software so that it remains both an operational
easy to use software and a scientific reference of international standard.

1Institut Montpelliérain Alexander Grothendieck - UMR5149
2HydroSciences Montpellier - UMR 5569 - Note that HSM number changed from 5569 to 5151 in January 2021
3HSM UMR is a research unit affiliated to the National Institute for Sciences of the Universe (INSU) of CNRS, while the IMAG

UMR is affiliated to the National Institute for Mathematical Sciences and their Interactions (INSMI).

Project LEMON

5

3.1 Physics-driven models

3.1.1 Upscaled urban flood modeling

Participants: Carole Delenne, Pascal Finaud-Guyot, Vincent Guinot, Ant-

oine Rousseau.

Collaboration:

Brett Sanders (UCI, USA), Sandra Soarez Frazao (UCL, Belgium).

Research program Concerning the physics-driven modeling axis, we will continue to work with poros-
ity models, and more generally with upscaling mechanisms for free surface hydraulics. We know since
[38] that each upscaled model is biased, which also eventually distorts downscaling operations. We
wish to better identify these biases and take them into account in order to improve both the large-scale
simulations (development of new models), and the small-scale ones (downscaling using compensation
techniques between large-scale models).
The collaboration with University California Irvine (UCI) started in 2014 with research on the repres-
entation of urban anisotropic features in integral porosity models [41]. It has led to the development of
the Dual Integral Porosity model [39]. Ongoing research focuses on improved representations of urban
anisotropy in urban flood modeling.
Université Catholique de Louvain (UCL) is one of the few places with experimental facilities allowing for
the systematic, detailed validation of porosity models. The collaboration with UCL started in 2005 and is
still active.

3.1.2 Large time steps methods for hydraulic processes

Participants:

Pascal Finaud-Guyot, Vincent Guinot, Antoine Rousseau.

Collaboration:

Philippe Helluy (Univ Strasbourg & Inria TONUS).

In line with fast changes in the whole society, our scientific community is more and more sensitive to
the environmental footprint of research. We already claim that porosity models can be valued for their
sobriety, thanks to coarse space meshes and low computational cost simulations. We also wish to develop
a time discretization strategy that will continue to lighten our algorithms. A first theoretical work has
been carried out for 1D models, we wish to generalize it to 2D models and implement it into operational
models.
Discussions have started with team TONUS in Strasbourg, as "CFL-less" methods are also used by the
team for kinetic-relaxation approximation [40].

3.1.3 Street-buildings interactions during flood events

Participants:

Pascal Finaud-Guyot, Cécile Choley.

The improvement of realistic flood scenarios also requires the addition of specific processes: we will
continue the modeling of interaction with buildings (work initiated by Cécile Choley’s PhD thesis) and
develop the transport of log jams in an urban flow, using the functionalities allowed by the concept of
porosity to better take into account the feedback of log jams on the flow (crowding process).

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Inria Annual Report 2023

3.1.4 Coupling coastal ocean and urban flood models

Participants:

Antoine Rousseau, Jose Daniel Galaz Mora.

Collaboration: Maria Kazolea (Inria, team CARDAMOM).

Finally, we wish to continue to couple the numerical models developed by the team with other
processes: relying on collaborations external to LEMON (as is currently the case with the SURF project of
Inria for the Green-Naghdi / shallow water coupling) or recruiting new permanent members, we will use
the team’s strengths in free-surface hydraulics and in model coupling to explore new fields of application.

3.2 Data-driven models

Participants: Carole Delenne, Pascal Finaud-Guyot, Vincent Guinot, Nicolas Meyer,
Antoine Rousseau, Gwladys Toulemonde, Katia Ait Ameur, Fadil Boo-
doo, Alexis Boulin, Cécile Choley, Omar Et Targuy, Jose Daniel Galaz
Mora, Chloe Serre Combe, Samuel Valiquette.

One of the originality of LEMON is that we can count on a data-driven component that we wish to
develop further. Data are indeed essential throughout the whole modeling/forecast process: providing
source terms, bathymetric information, initial and boundary conditions; allowing model hybridization
(using data assimilation or artificial intelligence methods); processing model outputs for risk measure-
ments and decision making.

3.2.1 Data fusion and parameter estimation

Participants: Carole Delenne.

Macroscopic models such as those developed by the team have advantages in terms of computational
cost, but also in terms of time saved in the processing of the mesh which does not need to describe
complex geometries. However, these models require new parameters that reflect the statistical prop-
erties of the domain geometry or the topography/bathymetry of the modeled area. The directional
and connectivity properties of the environment have to be estimated from geographical data. In the
continuity of Vita Ayoub’s thesis [34], LEMON will work on the development of methods for the automatic
estimation of these parameters from cartographic or Earth observation data. The objective will be to
set up a methodology for continuous acquisition and automatic fusion of new information in order to
improve the mapping of the study area as often as the hydrodynamic models require it.

3.2.2 Space-time variability of rainfalls

Participants: Nicolas Meyer, Gwladys Toulemonde, Chloe Serre Combe.

Project LEMON

7

Collaboration:

Thomas Opitz (INRAe, Avignon), Philippe Naveau (LSCE, CNRS, Gif-
sur-Yvette).

Understanding the spatial and temporal variability of rainfalls that can generate flash floods is a
major challenge. This knowledge is essential to build stochastic methods for simulating scenarios in-
tegrating realistic spatiotemporal extreme rainfall fields. This modeling must be done keeping in mind
the importance of the physical interpretation of data simulated with such models. We aim to develop,
propose, study and implement models adapted to the presence of extreme values taking into account
the associated complex dependencies. One difficulty lies in modeling the transitions (in time and space)
between no rain, regular rainfall and extreme rainfall. Reproducing spatial or temporal non-stationarity
in the intensities as well as in the dependency structure is also a challenge we wish to address.

3.2.3 Multivariate dependence

Participants: Nicolas Meyer,
Samuel Valiquette.

Gwladys

Toulemonde,

Alexis

Boulin,

Collaboration:

Elena Di Bernardino (LJAD, UniCA), Thomas Laloé (LJAD, UniCA),
Eric Marchand (Université de Sherbrooke), Frédéric Mortier (Cirad,
Montpellier), Jean Peyhardi (IMAG, Université de Montpellier).

In the medium term, we want to develop appropriate risk measures that can then be used to assess
the potential impacts of extreme rainfall events. Multivariate risk measures should be considered, as flood
risk indicators are usually derived by combining different hydraulic variables. We would be interested
in the estimation of risk sets, the idea being in the simplest framework to identify all the combinations
of water height/velocity values which would lead to a risk higher than a fixed level. More generally, the
question of modeling dependence in statistics, and in particular when we consider extremes, is one to
which we want to contribute, as is the consideration of compound events.

3.2.4 Clustering and sparsity models for rainfall

Participants: Nicolas Meyer, Gwladys Toulemonde, Alexis Boulin.

Collaboration:

Elena Di Bernardino (LJAD, UniCA), Thomas Laloé (LJAD, UniCA).

Finally, our aim is to model forcing terms (rainfall, wind, etc.) for a large number of stations and
with a small time scale. In addition, many covariates will be included in the models to better explain
the phenomena. This means that we will deal with high dimensional data and with potentially many
parameters. This is a limitation in terms of computation time and from a statistical point of view. We will
therefore continue to propose methods to reduce the dimension: grouping stations for which the rainfall
has a similar behavior (clustering) and highlighting a few significant parameters that are sufficient to
explain the model (sparsity).

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Inria Annual Report 2023

3.3 Hybrid modeling

Participants: Carole Delenne, Pascal Finaud-Guyot, Vincent Guinot, Nicolas Meyer,
Antoine Rousseau, Gwladys Toulemonde, Katia Ait Ameur, Fadil Boo-
doo, Cécile Choley, Omar Et Targuy, Jose Daniel Galaz Mora,
Chloe Serre Combe.

At the interface between these two main axes, we would like to continue working with hybrid models,
in particular thanks to artificial intelligence techniques. Since our recent publication [36], our team is
interested in the techniques of physically informed neural networks (PINNs) in fluid mechanics and
participates in several working groups on this subject. Keeping in mind that we are not experts on this
topic and that the competition is intense, we will explore, notably in Fadil Boodoo’s PhD, the use of AI
methods for the simulation of rainfall-flood systems (together with rainfall-discharge and discharge-flood
intermediate steps). We would also like to explore the transfer of knowledge from configurations for
which the data are numerous and of high quality (digital terrain model accurately known, good quality
instrumentation) to more rudimentary computational domains.

To specify and carry out this work program, we hope that LEMON will be able to count on an Inria
recruitment in the next 2 or 3 years (several candidates have already expressed interest in the 2023 compet-
ition). We will also benefit from data from the Water in the City observatory, structured around the HSM
laboratory and led by members of LEMON. The SW2D-LEMON software will of course be at the core of
transfers operated by the team: we will continue to devote time of our permanent staff to its development,
while willing to integrate this tool into a larger Inria platform in which engineering time (possibly shared
with other teams) could be made available in order to enable us to focus on our primary research missions.

4 Application domains

4.1 Overview

The protection of coastal areas around the world has become an important issue of concern, including
within the scientific community. The coastline is defined as the physical separation between the sea or
ocean on the one hand and the inland on the other, but these two worlds are in fact intertwined, which
contributes to the difficulty of their modeling, both from a physical and statistical point of view.

4.2 Coastal Oceanography

Wave propagation models in the nearshore zone have evolved significantly over the last 15 years, with
contributions that increasingly take into account effects related to variations of bathymetry, hence the
non-hydrostatic nature of the flow. These models, very specific to the coastal zone, must be able to be
coupled (together and with external models) so as to allow wave propagation numerical models to be
integrated into numerical forecasting platforms, both in oceanography and in flood risk management.

4.3 Urban Floods

Due to climate change and rising sea levels, more and more cities are facing the risk of flooding. Whether
they are in coastal areas or near rivers, these cities, which are inherently highly artificial and therefore
poorly resistant to rising water levels, require different types of numerical models for flood risk: accurate
(and potentially costly) models for land use planning, but also fast models, which can be run in real time,
for crisis management.

Project LEMON

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4.4 Hasard and Risk Assessment

Modeling and risk assessment are at the heart of environmental science. Whether the events considered
are of natural or anthropogenic origin, their economic, ecological or human impacts are too important
to be neglected. By definition, the more extreme an event is, the lower its frequency of occurrence
and therefore the less data available to characterize it. Hence the importance of using statistical tools
dedicated to the modeling of extreme events, in order to provide risk management tools that are as safe
and effective as possible.

5 Social and environmental responsibility

5.1 Footprint of research activities

As for all Inria teams, the many calculations we perform (on our personal computers or on dedicated
clusters) do have an environmental cost. This cost is linked both to the resources needed to manufacture
the machines we use, and to the energy consumed to run them.

LEMON members are aware of the climate emergency and are participating in actions on this subject.
For example, Pascal Finaud-Guyot is involved in the "sustainable development and social responsibility"
working group at Polytech Montpellier and in "energy footprint reduction" working group at HSM with
Carole Delenne. Several members of the team also participate to the local group of Inria Montpellier
Antenna dedicated to sustainable development and social responsibility.

Several LEMON members are committed to limit their professional air travel to 10.000km per year.

5.2 Impact of research results

Our research activities have an indirect impact in terms of environmental responsibility:

• the research carried out by the team contributes to the seek of numerical frugality in numerical

hydraulic modeling;

• in addition, given the climate change already underway, the team’s work in environmental risk
assessment and management contributes to better anticipation of natural hasards which, unfortu-
nately, will continue to occur in the coming decades.

6 Highlights of the year

2023 was a year of evaluation for our project-team. This evaluation, coordinated by Inria’s Evaluation
Committee, is an important step in the team’s life cycle. In particular, it enabled us to elaborate collectively
on the future of all teams involved in the environmental sciences theme. We have produced a summary
of this foresight, which raises more questions than it answers, but which reflects the topic’s complexity
and the debate that has opened up within the Institute itself.

7 New software, platforms, open data

7.1 New software

7.1.1 SW2D-Lemon

Name: Shallow Water 2D - Lemon C++ software

Keywords: Numerical simulations, Shallow water equations, Upscaling, Finite volume methods

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Inria Annual Report 2023

Scientific Description: SW2D-LEMON (SW2D for Shallow Water 2D) is developed by the LEMON re-
search team in Montpellier. SW2D-LEMON is a multi-model software focusing on shallow water-
based models. It includes an unprecedented collection of upscaled (porosity) models used for
shallow water equations and transport- reaction processes. Porosity models are obtained by aver-
aging the two-dimensional shallow water equations over large areas containing both a water and
a solid phase. The size of a computational cell can be increased by a factor 10 to 50 compared to
a 2D shallow water model, with CPU times reduced by 2 to 3 orders of magnitude. Applications
include urban flood simulations as well as flows over complex topography. Besides the standard
shallow water equations (the default model), several porosity models are included in the platform:
(i) Single Porosity, (ii) Dual Integral Porosity, and (iii) Depth-dependent Porosity model. Various
flow processes (friction, head losses, wind, momentum diffusion, precipitation/infiltration) can be
included in a modular way by activating specific execution flags. Several examples are included to
illustrate the potential of SW2D.

Functional Description: Urban floods are usually simulated using two-dimensional shallow water mod-
els. A correct representation of the urban geometry and hydraulics would require that the average
computational cell size be between 0.1 m and 1 m. The meshing and computation costs make the
simulation of entire districts/conurbations impracticable in the current state of computer techno-
logy. An alternative approach consists in upscaling the shallow water equations using averaging
techniques. This leads to introducing storage and conveyance porosities, as well as additional
source terms, in the mass and momentum balance equations. Various versions of porosity-based
shallow water models have been proposed in the literature. The Shallow Water 2 Dimensions
(SW2D) computational code embeds various finite volume discretizations of these models. It uses
fully unstructured meshes with arbitrary numbers of edges. The key features of the models and
numerical techniques embedded in SW2D are : - specific momentum/energy dissipation models
that are active only under transient conditions. Such models, that are not present in classical
shallow water models, stem from the upscaling of the shallow water equations and prove essen-
tial in modeling the features of fast urban flow transients accurately - modified HLLC solvers for
an improved discretization of the momentum source terms stemming from porosity gradients -
higher-order reconstruction techniques that allow for faster and more stable calculations in the
presence of wetting/drying fronts.

Release Contributions: - binary file for educational purposes (including documentation) first release
- remove dependency with former package geo through mc.inria.fr ## version 0.8.1 - 04/02/2021
- now using dtk-forge (packages should be more homogeneous) (!220) - fixed frequency refresh
for simulation (!246) - fixed using the control bar before finish breaking the results (!249) - fixed
spurious call to close (!247) - added help menu (!238) - forbid loading settings during simulation
(!244) ## version 0.8.0a - 28/01/2021 - logging now appears in the GUI sw2dModeler (!212) - you can
extract values at chosen time using a dedicated text file (!217) - added various scripts and examples
(!230 !229) - output file name changed (!224)

URL: https://sw2d.inria.fr/

Publications: hal-01884110, hal-01878242, hal-01582224, hal-01541070, hal-01465071, hal-01118743,

hal-02269526, hal-02269564, hal-03224056, hal-03224050, hal-02903282

Contact: Antoine Rousseau

Participants: Vincent Guinot, Antoine Rousseau, Carole Delenne, Pascal Finaud Guyot, Joao Guilherme

Caldas Steinstraesser, Marc Hetier

Partners: Université de Montpellier, CNRS, IRD

7.1.2 tsunamilab

Name: TsunamiLab

Keywords: Tsunamis, GPGPU, Dissemination, Web

Project LEMON

11

Functional Description: TsunamiLab is an interactive tsunami simulation and visualization platform
that teaches and raises awareness about tsunamis through interactive experiences. It allows science
communicators, teachers, students and science enthusiasts to create virtual tsunamis or recreate
historical tsunamis, and study their features in various digital and augmented reality formats.

TsunamiLab-Pool: Using cameras and projectors, the "pool" format allows children and adults to
interact with their own hands, gathered around the circular screen. This allows the instructor to
teach and engage several children simultaneously, in a way that is entertaining for all.

Web Platform: The platform’s website allows anyone to simulate historical tsunamis, observe how
they propagated in the ocean, and test what would have happened if they had been of greater or
lesser magnitude.

Hologram: Through a prism, a holographic image makes it possible to observe the impact in
different parts of the world at the same time.

Large Touch Screen: Support for large touch screens allows teachers to observe and explain phe-
nomena in an engaging way in front of a group of students.

URL: http://www.tsunamilab.cl

Publications: hal-02112763, hal-03514473

Contact: Jose Daniel Galaz Mora

Participants: Jose Daniel Galaz Mora, Antoine Rousseau

Partners: Cigiden, Inria Chile

8 New results

8.1 Physics-driven models

8.1.1 Street-building exchange

Study and modeling of the physical processes controlling water exchanges between streets and build-
ings during an urban flood

Participants:

Pascal Finaud-Guyot, Cécile Choley.

Cécile Choley’s PhD [22] focuses on the modeling of urban floods and the consideration of buildings.
Flood management provided by public services is based on two-dimensional numerical models. These
models do not or only partially represent the exchanges between streets and buildings. However, feedback
and photos report that water enters homes, threatening people and their property. While some buildings
act as reservoirs and temporarily or even permanently store part of the volume of the flood, others are
crossed by the flows if they are connected to several streets. To characterize the effect of buildings on
flooding, a new numerical model is proposed, based on the integration of an additional source term in
the 2D shallow water equations. The concept of the street-building model is inspired by compartment
models, where street and building exchange a flow through openings, such as doors and windows. The
transverse flow is controlled by discharge laws, developed from three-dimensional simulations of real-
scale openings. The exchange laws are based on the weir and orifice laws from the literature, and the
discharge coefficient is determined by limiting the error on the flow calculated numerically. Laws with a
tolerance of 30% error on the discharge passing through an opening are established. The street-building
model is applied in a synthetic street.

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Inria Annual Report 2023

8.1.2 Porosity models for upscaled urban flood modeling

Single Porosity Model: Exploring the Spatial Resolution Limits in Complex Urban Patterns

Participants:

Pascal Finaud-Guyot, Antoine Rousseau.

Collaboration: Cristián Escauriaza (PUC Santiago, Chile), Sebastián Nash (PUC Santi-

ago, Chile).

When modeling large-scale urban floods, the use of porosity non-linear shallow water equations
emerges as an interesting sub-grid approach for reducing computation time while preserving the struc-
ture of the solution. In such models, fine-scale topographic information is represented at a coarser scale
through porosity parameters, enabling a speed-up in computations at the expense of losing accuracy
while computing hydrodynamic variables. In [28], we use the Single Porosity model (SP) in Cartesian
coordinates to simulate flows in both an idealized and a real-world urban area, while gradually increasing
the spatial resolution. During such partial coarsening, in which we move from fine-scale to macro-scale,
the porosity distribution changes within the urban zone from a highly heterogeneous field to a more
uniform one. At an intermediate meso-scale, where the cell size is of the order of the street width and the
reduction in computation time is still significant, the main preferential flow paths within the urban area
can be captured by means of the porosity gradient. At such a scale, good agreement with refined classical
model solutions is found for flow depth, flood extension, and hazard index, both in magnitude and spatial
distribution. Numerical results highlight the importance of porosity models for quickly assessing flow
properties during an event and improving real-txme decision-making through reliable information.

8.1.3 Large scale floods

Assimilation of flood maps into large scale hydraulic models to retrieve bathymetry information

Participants: Carole Delenne, Vita Ayoub.

Collaboration:

Renaud Hostache (UMR Espace-Dev, Montpellier).

Following the work of [35] in Vita Ayoub’s PhD thesis, we are currently working on data assimilation
using a tempered particle filter to retrieve an estimation of the river bathymetry. Indeed, a major challenge
tied to the hydrodynamic modeling is the lack of hydraulic parameter data that are needed as inputs, such
as the riverbed shape and elevation. While the knowledge of such information is critical for flood models,
it is rarely available from remote sensing observations, digital elevation models (DEMs), or ground data
measurements. Most studies have estimated river discharges and depths assuming the bathymetry and
bed roughness to be known a priori. We propose to make use of Synthetic Aperture Radar (SAR) imagery
due to its ability to provide frequent updates of flooded areas at a large scale, regardless of atmospheric
conditions. The porosity functions of the SW2D-DDP hydraulic model enable a straightforward repres-
entation of the riverbed geometry using porosity parameters. Probabilistic flood maps derived from SAR
data are thus assimilated using a Tempered Particle Filter into the SW2D-DDP model, in order to retrieve
a simplified spatially distributed riverbed geometry (i.e., riverbed depth assuming trapezoidal shape).
The first results presented in [5] are very encouraging as the model predictions reach water level errors
below 0.5 m as a result of the assimilation although the retrieved river bottom elevation is not matching
the real one.

Project LEMON

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Numerical modeling of large debris transport during floods

Participants:

Pascal Finaud Guyot, Marc Hetier, Antoine Rousseau.

Collaboration:

Vincent Acary (Inria TRIPOP).

Flood risk is the natural phenomenon that affects the most people in the world, and climate change
is likely to increase this trend. Floods, whether in rivers or in urban areas, carry debris that can have a
significant impact on hydrodynamics and therefore on risk. Although numerical models are frequently
used to anticipate the impacts of floods in order to improve land-use planning and facilitate crisis
management, there are few models capable of representing accurately both the flood hydrodynamics
and the associated debris transport process. Numerical models, when they exist, are generally based on
simplifying assumptions. In [26], we propose a new operational model that is validated and compared
with analytical results, other models and experimental data. The proposed model, implemented in the
SW2D software, paves the way for a better representation of debris clogging on hydrodynamics, even
with a large number of transported objects.

8.1.4 Coupling

Coupling dispersive and non-dispersive shallow water models

Participants:

Antoine Rousseau, José Galaz.

Collaboration: Maria Kazolea (Inria CARDAMOM).

The calculation of wave shoaling and breaking is essential in coastal applications like risk analysis.
Due to the high cost of 3D models, 2D vertically averaged models are normally used. For the propagation
and shoaling, higher order models are needed to describe the dispersion of waves with enough accuracy.
For breaking some dissipation mechanism is normally introduced, of which the simple switch from
the dispersive high order models to the first order Saint-Venant equations has been very popular for
two reasons: the non parametric dissipation of energy of shock waves and its simple implementation.
However, it has been shown that this model becomes unstable as the resolution is increased. In [32] we
show how domain decomposition methods can propose a different way to "switch off" the dispersion by
exchanging the information through boundary conditions and overlapping zones, obtaining different
performances.

8.1.5 Numerical methods for hyperbolic systems of equations

Large CFL explicit scheme for hyperbolic systems

Participants:

Vincent Guinot, Antoine Rousseau.

In [27], a large CFL algorithm is presented for the explicit, finite volume solution of hyperbolic
systems of conservation laws. The Riemann problems used in the flux computation are determined
using averaging kernels that extend over several computational cells. The usual Courant-Friedrichs-
Lewy stability constraint is replaced with a constraint involving the kernel support size. This makes

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Inria Annual Report 2023

the method unconditionally stable with respect to the size of the computational cells, allowing the
computational mesh to be refined locally to an arbitrary degree without altering solution stability. The
practical implementation of the method is detailed for the shallow water equations with topographical
source term. Computational examples report applications of the method to the linear advection, Burgers
and shallow water equations. In the case of sharp bottom discontinuities, the need for improved, well-
balanced discretizations of the geometric source term is acknowledged.

8.2 Data-driven models

8.2.1 Statistical approaches for extreme values

High-dimensional variable clustering based on sub-asymptotic maxima of a weakly dependent ran-
dom process.

Participants: Gwladys Toulemonde, Alexis Boulin.

Collaboration:

Elena Di Bernardino (Université de Côte d’Azur), Thomas Laloé (Uni-
versité de Côte d’Azur).

In the recent preprint [24], we adress both novel modeling techniques and practical algorithmic
applications. In the first aspect of our contribution, we introduce a new class of models called Asymptotic
Independent Block (AI-block) models. These models are based on a model-based approach to variable
clustering, where clusters are delineated at the population level based on the independence of the
extremes between these clusters. The second aspect of our contribution revolves around the development
and rigorous evaluation of an algorithm specifically tailored for AI block models.

In addition, we situate our work in the context of multivariate stationary mixing random processes.
To demonstrate the practical utility of our proposed AI block models and the associated algorithm, we
present two compelling data analyses. These analyses are performed in neuroscience for the first one
and in environmental sciences for the second one. This work has also been presented by Alexis Boulin
three times in 2023 [21, 31, 6].

High-dimensional clustering of compound precipitation and wind extremes over Europe

Participants: Gwladys Toulemonde, Alexis Boulin.

Collaboration:

Elena Di Bernardino (Université de Côte d’Azur), Thomas Laloé (Uni-
versité de Côte d’Azur).

Catastrophic climate events such as floods, forest fires and heat waves are often caused by the
simultaneous extreme behavior of several interacting processes. Since in these compound events several
spatio-temporal factors are jointly extreme and are inherently high dimensional, a proper understanding
of them requires the development of dependence summary measures that are appropriate for extreme-
value random vectors. The latter is a key component to propose spatial clustering of these temporal
processes. Based on the recent development of an algorithm specifically tailored to asymptotic block
models (see also [24]), we propose in this preprint [25] a clustering method adapted to compound extreme
events. We illustrate this method by proposing a regionalization task. Specifically, we identify regions
based on gridded data from observations and climate model ensembles over Europe. This approach uses
daily precipitation sums and daily maximum wind speed data from from the ERA5 reanalysis dataset
from 1979 to 2022. This work was also presented at ICSDS [4], at the JDS[12] and at a workshop on data
science for coastal risk in Roscoff in November.

Project LEMON

15

Multivariate sparse clustering for extremes

Participants: Nicolas Meyer.

Collaboration: Olivier Wintenberger (Sorbonne University).

The concept of sparse regular variation introduced in [42] allows to infer the tail dependence of a
random vector X . This approach relies on the Euclidean projection onto the simplex which, in comparison
with standard methods, better exhibits the sparsity structure of the tail of X . We develop a procedure
which enables to capture the clusters of extremal coordinates of this vector. This approach also includes
the identification of the threshold above, where the values taken by X are considered as extreme. We
provide an efficient and scalable algorithm called MUSCLE which is applied to numerical examples and
real-world data, such as wind speed measurements.

Asymptotic tail properties of Poisson mixture distributions

Participants:

Samuel Valiquette, Gwladys Toulemonde.

Collaboration:

Jean Peyhardi (Univ Montpellier), Eric Marchand (Univ Sherbrooke,
CA), Fredéric Mortier (CIRAD).

Count data are omnipresent in many applied fields, often with overdispersion due to an excess of
zeroes or extreme values. With mixtures of Poisson distributions representing an elegant and appealing
modeling strategy, we focus in the published paper [2] on the study of how extreme value theory can be
used in such a context. This work has also been presented in an international conference by the PhD
student Samuel Valiquette, see [20].

Multivariate peaks-over-threshold with latent variable representations of generalized Pareto vectors

Participants: Gwladys Toulemonde.

Collaboration: Carlo Gaetan (Venice), Thomas Opitz (Inrae), Jean-Noël Bacro (Univ

Montpellier).

A flexible multivariate threshold exceedances modeling is defined based on component-wise ratios
between any two independent random vectors with exponential and Gamma marginal distributions.
This construction allows flexibility in terms of extremal bivariate dependence. More precisely, asymptotic
dependence and independence are possible, as well as hybrid situations. Two useful parametric model
classes will be presented. One of the two, based on Gamma convolution models, will be illustrated
through a simulation study. Good performance is shown for likelihood-based estimation of summaries of
bivariate extremal dependence for several scenarii. This work has been presented in an invited seminar
in UCL and is submitted in [23].

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Inria Annual Report 2023

8.2.2 Heterogeneous data

Data collection and representation for urban water networks

Participants: Carole Delenne, Omar Et-Targuy, Yassine Belghaddar.

Collaboration: Nanée Chahinian (IRD), Salem Benferhat (CRIL Lens), Ahlame Beg-

douri (Université Sidi Mohamed Ben Abdellah).

A lot of work is currently being done in the framework of ANR CROQUIS4 and European project STAR-
WARS5 to collect data of different nature/format on wastewater and stormwater networks. Problematic
around these kind of heterogeneous, unprecise, uncertain geographical data have been presented in [3]
and an automn school (IA2).

Concerning water network representation, even if geographic information systems (GIS) are powerful
tools for geographical data, each type of elements: i.e. points, lines and polygons, are defined in separate
files, so that pipes and manhole covers or devices are disconnected and linked through attributes in the
associated database. One approach explored in Omar Et-Targuy’s PhD is thus a graph-based approach
that ensures connectivity and consistency [7].

Dealing with uncertain data Omar Et-Targuy’s PhD concerns the practical fusion and heterogeneous
conditioning of uncertain data, with an application to urban water data. Conditioning is an important
task for updating and revising uncertain information when new information, often considered reliable, is
added. This paper deals with the so-called Fagin and Halpern (FH-)conditioning within the framework of
possibility theory [37]. In two conferences, Omar Et-Targuy presented his first work on the computation
of FH-conditioning when it is applied to weighted knowledge bases. He also compared FH-conditioning
with the two forms of standard possibilistic conditioning (min-based conditioning and product-based
conditioning) [19] [7].

8.3 Hybrid models

8.3.1 Downscaling

Neural networks and boosted trees for multiscale shallow water equations

Participants: Kilian Bakong, Vincent Guinot, Antoine Rousseau, Gwladys Toulem-

onde.

During K. Bakong’s internship, supported by IRT Saint-Exupery, we considered downscaling al-
gorithms for shallow water flows thanks to artificial intelligence techniques. Neural networks and boosted
trees are used for the simulation of high resolution flow variables computed from low resolution inputs.
Various numerical configurations are addressed, with or without using principle component analysis to
reduce the computational coast of training and forecasting steps.
This work has been published in [36].

8.3.2 AI for flood forecasting

4Collecting, Representing, cOmpleting, merging and Querying heterogeneous and UncertaIn waStewater and stormwater

network data, see Section 9.3

5STormwAteR and WastewAteR networkS heterogeneous data AI-driven management, see Section 9.2

Project LEMON

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Rainfall-runoff modeling with AI

Participants: Carole Delenne, Fadil Boodoo, Nadia Skifa.

The aim of Fadil Boodoo’s PhD is to take advantage of new AI approaches to rapidly generate flood
extent maps in view of flood forecasting. In the first part of the PhD, we focused on rainfall-runoff models,
via a comparison of classical hydrological and Long Short Term Memory (LSTM) models [18], [29], [30].
The next step will be to generate flood extent maps with another kind of neural network (such as
graph neural networks), that will be trained thanks to physical simulation of the flood with SW2D-DDP
model.

9 Partnerships and cooperations

9.1 International initiatives

9.1.1 Associate Teams in the framework of an Inria International Lab or in the framework of an Inria

International Program

Participants:

Pascal Finaud-Guyot, Antoine Rousseau, Katia Ait Ameur, Marc Hetier.

FLOTTE

Title: FLOod and TransporT Equations

Duration: 2023 ->

Coordinator: Cristián Escauriaza (cescauri@ing.puc.cl)

Partners:

• Pontificia Universidad Católica de Chile Santiago (Chili)

Inria contact: Antoine Rousseau

Summary: The overall objective of the research program is to develop a numerical tool able to represent,
in urban area, flood and transport (sediment, debris and vehicle) propagation as the potential
feedback from transport to the flow. Several directions are identified:

• Shallow water and transport models coupling

• Upscaling of transport model

• Sensitivity analysis

Papers [28] and [26] are related to this project.

9.1.2 Participation in other International Programs

Participants:

Pascal Finaud-Guyot, Vincent Guinot, Carole Delenne.

• Pascal Finaud-Guyot and Vincent Guinot are members of the French-Tunisian International Labor-
atory Naïla, 2016-2023. The laboratory research focuses on the management of the water resources
in agricultural Tunisian catchement. In this context, LEMON members develop DDP model for fine
scale runoff modeling to better understand the agricultural practices-soil erosion interactions.

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Inria Annual Report 2023

• Carole Delenne is a member of the project "Abidjan: eaux et ville en mutation"6 funded by the
UNESCO Center on water ICIREWARD, lead by Jean-Louis Perrin (HSM) in collaboration with
Université Nangui Abrogoua. Abidjan, the economic capital of Côte d’Ivoire, is set to see its
population double by 2050. Faced with the far-reaching changes observed over the last 10 years,
this project aims to gain a better understanding of their impact on the urban water cycle, with
major issues relating to flooding, domestic effluent management, resource pollution and the city’s
drinking water supply.

9.2 European initiatives

9.2.1 Horizon Europe

Participants: Carole Delenne, Omar Et-Targuy.

Carole Delenne is a member of the STARWARS European project steering committee: STormwAteR
and WastewAteR networkS heterogeneous data AI-driven management (MSCA Staff Exchange program,
Grant Number 101086252).

Public and private stakeholders of the wastewater and stormwater sectors are increasingly faced with
large quantities and multiple sources of information/data of different nature: databases of factual data,
geographical data, various types of images, digital and analogue maps, intervention reports, incomplete
and imprecise data (on locations and the geometric features of networks), evolving and conflicting data
(from different eras and sources), etc. The main objective of this multidisciplinary project is to provide
novel proposals for the management of heterogeneous data with an application to stormwater and
wastewater networks. The STARWARS project aims to bring together researchers from the AI and Water
Sciences communities in order to enhance the emergence of new practical solutions for representing,
managing, modeling, merging, completing, reasoning, explaining and query answering over data of
different forms pertaining to stormwater and wastewater networks. The project is implemented through
five work packages (WP):

• Data Collection and Data Completion

• Unreliable and Heterogeneous Data/Information Modeling

• Practical Merging, Inconsistency and Clustering

• Tractable Query-answering, Explainability, Algorithms

• Validations Project management, Communication, Dissemination and Training

The scheduled secondment plan is designed with the aim of maximizing knowledge transfer and training
between the two fields of Water Sciences and AI and thus facilitating the achievement of the project
objectives.

9.3 National initiatives

Participants: Carole Delenne, Pascal Finaud Guyot, Nicolas Meyer, Ant-

oine Rousseau, Gwladys Toulemonde.

• Pascal Finaud-Guyot, Antoine Rousseau and Gwladys Toulemonde are members of ANR MUFFINS
- MUltiscale Flood Forecasting with INnovating Solutions - led by Pierre-André Garambois (INRAE)
including the following partners: IMT, Univ Eiffel, Cerema IMFT, CCR, Météo/SPCME, SCHAPI. The
objective of the MUFFINS project is to develop new accurate and computationally efficient flood

6Abidjan: water and the changing city

Project LEMON

19

forecasting approaches, enabling the transfer of information between models (meteo-hydrology-
hydraulic-damage) and scales (from local runoff generation over areas lesser than 1km2 to flood
propagation on catchments of thousands km2), and taking advantage of innovative data (in situ,
remote observation, opportunistic) to reduce forecasts uncertainties.

• Carole Delenne is a member (+co-leader of several tasks and a WP) of ANR CROQUIS - Collecting,
Representing, cOmpleting, merging and Querying heterogeneous and UncertaIn waStewater and
stormwater network data - led by Salem Benferhat (CRIL) and funded in 2022. In this project,
we refer to data of different nature such as geographical databases, various types of images, di-
gital/analogue maps, intervention reports, etc. Heterogeneity also refers to the imperfection of
the available information where data may be unreliable, imprecise, incomplete, dynamic and con-
flicting. One of the objectives of CROQUIS is to answer the need for establishing a methodological
framework to collect, complete, centralize, update and archive data. Approaches based on Machine
Learning (ML) techniques enhanced with basic additional knowledge will be developed along
with knowledge-driven query answering and reasoning mechanisms to infer new data needed for
hydraulic modeling. In particular, we aim to develop an enhanced query answering tool that should
be easily integrated into existing information systems in order to fully exploit available resources
and to better exploit meta information such as uncertainty.

• Gwladys Toulemonde is member of the ANR-18-CE02-0025 project GAMBAS - Generating Ad-
vances in Modeling Biodiversity And ecosystem Services - lead by Frédéric Mortier (CIRAD) and
involving 6 partners (CIRAD, INRAE, LECA at Grenoble, IMAG at Montpellier, the Museum d’histoire
naturelle and the LMO at Paris-Saclay) for a total of 569k€. GAMBAS gathers a collective comprised
of quantitative ecologists and mathematicians with aspirations in ecology in order to expand Joint
Species Distribution Models (JSDMs). The PhD grant of Samuel Valiquette is fully funded by this
project.

• Gwladys Toulemonde is involved in the ANR project McLaren - Machine Learning and Risk Evalu-
ation - under the grant ANR-20-CE23-0011 (289k€). The University of Côte d’Azur, the University
of Montpellier, INRAE, Inria and the CNRS are involved in this project. The overall objective of
the project is to bring significant innovations in these two areas of statistical learning and risk
assessment. The PhD grant of Alexis Boulin is fully funded by this project.

• Nicolas Meyer is member of the ANR project EXSTA - EXtremes, STatistical learning and Applica-
tions -, PI Anne Sabourin, Université de Paris, 2024-2028. This project aims at developing machine
learning techniques to study extreme values.

• Nicolas Meyer obtained a PEPS grant on Graphical models for extremes. This project aims at using

statistical tools developed for graphs to study the dependence structure of extreme events.

10 Dissemination

10.1 Promoting scientific activities

Participants: Carole Delenne, Pascal Finaud Guyot, Jose Daniel Galaz Mora, Vin-
cent Guinot, Nicolas Meyer, Antoine Rousseau, Gwladys Toulemonde.

10.1.1 Scientific events: organisation

General chair, scientific chair

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Inria Annual Report 2023

• Gwladys Toulemonde co-organized a BIRS workshop "Modern Statistical and Machine Learning
Approaches for High-Dimensional Compound Spatial Extremes" at the University of Granada in
Spain, from May 7 - 12, 2023.

Member of the organizing committees

• Nicolas Meyer, member of the organizing comittee of the workshop Statlearn2023

• Carole Delenne was member of the organizing comitee of the IA2 school in Sete. The Autumn
School in Artificial Intelligence (IA2) aims to provide high-level courses in the various techniques
used in artificial intelligence. This 2023 autumn school edition concerned the management of
heterogeneous and imperfect information and data. The program of this school, spread over 5 days
(from Monday, October 30 to Friday, November 3), was mainly composed of fundamental courses
(mornings) supplemented by advanced presentations (afternoons) on specific research topics.

10.1.2 Scientific events: selection

Reviewer Carole Delenne was reviewer for ENIGMA workshop organized next to KR conference in
Rhodes. AI-driven heterogeneous data management: Completing, merging, handling inconsistencies
and query-answering 2023. Rhodes, Greece, September 3-4, 2023.

10.1.3 Journal

Member of the editorial boards

• Antoine Rousseau is associate editor of Discrete and Continuous Dynamical Systems - Series S.

Reviewer - reviewing activities

• Carole Delenne is a reviewer for several Journals such as Journal of Hydraulic Research, Water,

Computers Environment and Urban Systems (1 to 3 manuscripts/year).

• Vincent Guinot is a reviewer for Journal of Hydrology, Advances in Water Resources, Mathematical

Problems in Engineering (3 manuscripts/year).

• Nicolas Meyer is a reviewer for several journals, such as Extremes, Annals of Statistics, Bernoulli (1

to 3 manuscripts per year).

• Antoine Rousseau is a reviewer for Journal of Hydrology and Environmental Modelling and As-
sessment (2 manuscripts/year), DCDS-S (1 manuscript/year) and Computer Methods in Applied
Mechanics and Engineering (1 manuscript/year).

• Pascal Finaud-Guyot is a reviewer for Journal of Hydroinformatics, Advances in Water Resources,

Environmental Modelling and Software, Journal of Hydrology (2 manuscripts/year).

• Gwladys Toulemonde is a reviewer for statistical journals (like Annals of applied statistics, Compu-
tational statistics and data analysis, Dependence modelling, Extremes, Journal of applied Statistics,
Journal of Statistical Theory and Practice, Statistics and Computing) and also Esaim or Water
Ressources research (1 to 3 manuscripts/year).

10.1.4 Invited talks

• Nicolas Meyer gave a talk at the one-day workshop Événements extrêmes et risquesentitled "Tail

inference for high-dimensional data"

• Antoine Rousseau gave a keynote [16] at the Rencontre annuelle 2023, GDR "Défis théoriques pour

les sciences du climat", June 2023, Paris, France

• Antoine Rousseau gave a lecture [15] at the conference Numhyp23 - Numerical Methods for Hyper-

bolic Problems, June 2023, Bordeaux, France

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• Gwladys Toulemonde gave a virtual lecture at UCL seminar in statistics, June 2023, London, UK.

• Carole Delenne gave a lecture at the Autumn Institute in Artificial Intelligence (IA2), co-organized

in November 2023, Sete, France

• Gwladys Toulemonde gave an invited talk at the conference Data Science pour les risques côtiers,

November 2023, Roscoff, France

• Antoine Rousseau gave a keynote [14] at the Journées Scientifiques Inria Chile, December 2023,

Santiago, Chile

• Antoine Rousseau gave a lecture [13] at the hydraulic department of Pontificia Universidad Católica,

December 2023, Santiago, Chile

• Gwladys Toulemonde gave an invited talk [4] at the IMS conference ICSDS 2023, December 2023,

Lisbon, Portugal.

10.1.5 Leadership within the scientific community

• Gwladys Toulemonde is vice-president of the French Statistical Society (Société Française de

Statistique, SFdS).

10.1.6 Scientific expertise

• Antoine Rousseau is member of the scientific board of Fondation Blaise Pascal.

10.1.7 Research administration

• Carole Delenne is elected member of the Ecole Doctorale GAIA board.

• Vincent Guinot is head of the "Eau dans la Ville" cross-disciplinary research group at HSM (20 staff

members) and of the Urban Observatory of HSM.

• Vincent Guinot is a member of the board for scientific strategy at HSM.

• Antoine Rousseau is head of the LEMON team at Inria CRI-SAM (6 staff members).

• Antoine Rousseau is a member of the Inria CRI-SAM steering board (Comité des Projets).

• Antoine Rousseau is a member of the Inria CRI-SAM scientific board (Bureau du Comité des

Projets).

• Gwladys Toulemonde is elected member of a local committee (commission de section) relative to

the CNU section 26 until september 2023.

• Gwladys Toulemonde is elected member of the French Statistical Society board (Société Française

de Statistique, SFdS).

• Gwladys Toulemonde is elected member of Environment group of the French Statistical Society

board (Société Française de Statistique, SFdS).

• Gwladys Toulemonde is elected member of the liaison commitee of the MAS Group (Modélisation

Aléatoire et Statistique), SMAI (Société de Mathématiques Appliquées et Industrielles).

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Inria Annual Report 2023

10.2 Teaching - Supervision - Juries

10.2.1 Academic involvement / responsibilities

5 UM-affiliated members of LEMON are Academics, for a total teaching load of approximately 1000
hrs/year. Moreover, these members undertook significant administrative duties (approx. 1000 hrs) in
2023:

• Carole Delenne is Program coordinator of the last year of "Eau et Génie Civil - EGC" (Water and
Civil Engineering) and the penultimate year of "Sciences et technologies de l’eau" (water science
and technology) of the engineering program at Polytech Montpellier.

• Pascal Finaud-Guyot is Program coordinator (Year 2) and Sustainable Development coordinator

for the EGC engineering program at Polytech Montpellier.

• Nicolas Meyer is Head of Master 1 Statistics and Data Science

• Vincent Guinot is Head of the EGC Academic department at Polytech Montpellier.

• Gwladys Toulemonde is Admissions office coordinator at Polytech Montpellier (500+ students/

year).

10.2.2 Supervision

PhD defended this year:

• Cécile Choley, "Numerical study of the street-building exchange during urban floods", Nov, Pascal

Finaud-Guyot and R. Mosé (Université de Strabsourg), [22]

PhD in progress

• Fadil Boodoo, "Hydrodynamic model and artificial intelligence for a flood forecasting system",
January 2021, supervised by Carole Delenne and Renaud Hostache (Université de Montpellier).

• Chloé Serre-Combe, Stochastic generators of extreme precipitation and risk assessment of urban
flooding at high spatiotemporal resolution, October 2022, supervised by Gwladys Toulemonde,
Nicolas Meyer, and T. Opitz (Inrae Avignon)

• Omar Et-Targuy, practical fusion and heterogeneous conditioning of uncertain data, October 2022,

supervised by Salem Benferhat, Ahlame Begdouri, Carole Delenne.

• Alexis Boulin, Université de Côte d’Azur (UniCA) and Université de Montpellier, October 2021,

supervised by Elena Di Bernadino, Thomas Laloé and Gwladys Toulemonde.

• Samuel Valiquette, PhD with Sherbrooke University, CIRAD and Université de Montpellier, October

2021(co-directed by Eric Marchand, Frédéric Mortier and Gwladys Toulemonde),

10.2.3 Juries

• Antoine Rousseau was reviewer for Rishabh Bbhatt’s PhD (Univ. Grenoble Alpes, 2023)

• Antoine Rousseau was member of the Inria Junior Researcher (CRCN/ISFP) comittee in Grenoble

• Gwladys Toulemonde has participated in the PhD jury of Hela Hammami from University of

Montpellier, defended in December, 2023.

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10.3 Popularization

10.3.1 Internal or external Inria responsibilities

• Antoine Rousseau is co-editor of the national blog binaire, published by Le Monde.

• Gwladys Toulemonde is involved in the board of Animath since october, 2019, representing the

SFdS.

• Gwladys Toulemonde is a member of the organizing committee of the Salon de la culture et des

jeux mathématiques since 2020.

• In Montpellier, Gwladys Toulemonde and a humanities colleague set up the first local branch of the
Collège des sociétés savantes. The creation was announced in June 2023 and the first action was
a round table on "Science and Society" in 3 parts: Science and the media, Science and the socio-
economic world, and Science and the general public, held during the Printemps des comédiens
festival.

10.3.2 Education

• Carole Delenne participated several times in the reception of secondary school students in the
framework of Digi’Filles (ISDM, Univ Montpellier): children’s version of the “fresque du climat”,
water cycle.

• Gwladys Toulemonde co-created the national programme Math C pour L, a mathematics research
programme for female students in their first three years of university. The first edition was in CIRM,
February 2023.

• Gwladys Toulemonde is involved in the board of the CFEM (commission française pour l’enseignement

des mathématiques) since October, 2019, representing the SFdS.

• Nicolas Meyer and Gwladys Toulemonde organized sessions of the “Fresque du climat” for the

Master 1 students in Statistics and Data Science and for Polytech students, respectively.

• Carole Delenne is part of OpenING and WidenING projects of the "Fondation Polytech", which
aims to provide a series of educational ’seeds’: these are a mixture of lectures and exercises on a
particular concept, which students can complete independently (with guidance if required) in one
or two hours. They are created using the H5P and Genially tools, e.g.

• Carole Delenne and Gwladys Toulemonde are part of Math’Start Project, that aims to provide a
Moodle-based library of mathematical exercises to remedy, restore and consolidate numeracy skills
at L1 level in mathematics. These deliverables will complement the resources already available in
the form of educational ’seeds’ of the OpenING project.

10.3.3 Interventions

• José Galaz gave a lecture "Tsunamilab, une plateforme numérique interactive pour créer des

tsunamis" at the "Feria de la Ciencia" (Chile, 2023).

11 Scientific production

11.1 Publications of the year

International journals

[1]

S. Majdalani and V. Guinot. ‘Solute transport in heterogeneous model porous media under different
flow rates: Experimental and modelling study’. In: Journal of Hydrology 616 (Jan. 2023), p. 128790.
DOI: 10.1016/j.jhydrol.2022.128790. URL: https://hal.science/hal-03881729.

24

[2]

Inria Annual Report 2023

S. Valiquette, G. Toulemonde, J. Peyhardi, É. Marchand and F. Mortier. ‘Asymptotic tail properties
of Poisson mixture distributions’. In: Stat 12.1 (2023), e622. DOI: 10.1002/sta4.622. URL: https:
//hal.science/hal-04107633.

Invited conferences

[3] N. Chahinian, B. Haydar, S. Conrad, S. Debard and C. Delenne. ‘Everything You Always Wanted to
Know About Urban Water Networks But Were Afraid to Ask -Extended Abstract’. In: CEUR Work-
shop proceedings. ENIGMA 2023 - 1st Workshop on AI-driven heterogeneous data management:
Completing, merging, handling inconsistencies and query-answering, co-located with 20th Interna-
tional Conference on Principles of Knowledge Representation and Reasoning (KR 2023). Vol. 3495.
Rhodes, Greece, 7th Oct. 2023, pp. 1–2. URL: https://hal.science/hal-04231966.

[4] G. Toulemonde, A. Boulin, E. Di Bernadino and T. Laloë. ‘High dimensional clustering of coupound
precipitation and wind extremes over Europe’. In: Programm book. ICSDS 2023 - IMS International
Conference on Statistics and Data Science. 2023 IMS International Conference on Statistics and
Data Science (ICSDS). Lisbon, Portugal, Dec. 2023. URL: https://inria.hal.science/hal-043
73309.

International peer-reviewed conferences

[5] V. Ayoub, R. Hostache, M. Chini, R. Pelich, C. Delenne and P. Matgen. ‘Assimilation of probabilistic
flood maps into large scale hydraulic models to retrieve missing river geometry data using a
tempered particle filter’. In: IEEE Xplore. IGARSS 2023 - 2023 IEEE International Geoscience and
Remote Sensing Symposium. Pasadena, United States, 16th July 2023. URL: https://hal.scienc
e/hal-04378319.

[6] A. Boulin, E. D. Bernardino, T. Laloë and G. Toulemonde. ‘High-dimensional clustering of sub-
asymptotic maxima of a weakly dependent process’. In: Proceedings of ICSDS 2023. 2023 IMS
International Conference on Statistics and Data Science (ICSDS). Lisbon, Portugal, 21st Dec. 2023.
URL: https://hal.science/hal-04397145.

[7] O. Ettarguy, A. Begdouri, S. Benferhat and C. Delenne. ‘Syntactic computation of Fagin-Halpern
conditioning in possibility theory’. In: EPiC Series in Computing. LPAR 2023 - 24th International
Conference on Logic for Programming, Artificial Intelligence and Reasoning. Vol. 94. Proceedings
of 24th International Conference on Logic for Programming, Artificial Intelligence and Reasoning.
Manizales, Colombia, 3rd June 2023, pp. 164–146. DOI: 10.29007/9pjn. URL: https://hal.scie
nce/hal-04377873.

[8] C. Peugeot, E. Le Roux, V. Wendling, G. Panthou, P.-A. Raynal, A. Ba, I. Bouzou Moussa, J.-M. Cohard,
J. Demarty, F. Gangneron, M. Grippa, B. Hector, T. Lebel, P. Hiernaux, L. Kergoat, E. Lawin, J. D.
Galaz Mora, É. Mougin, C. Pierre, J.-L. Rajot and T. Project. ‘A drought-triggered hydrological
tipping point in the central Sahel: an attribution study using system dynamics modelling’. In: 2nd
conference internationale TERENO-OZCAR. Bonn, Germany, 2023. URL: https://imt-mines-al
es.hal.science/hal-04286359.

[9] C. Serre-Combe, N. Meyer, T. Opitz and G. Toulemonde. ‘Modélisation statistique de précipitations
urbaines à fine échelle spatio-temporelle’. In: JDS 2023 - 54e Journées de Statistique de la SFds.
Bruxelles, Belgium, 3rd July 2023. URL: https://hal.science/hal-04374113.

[10] C. Serre-Combe, N. Meyer, T. Opitz and G. Toulemonde. ‘Towards statistical modelling of urban
rainfall on a fine spatio-temporal scale’. In: RESSTE 2023 - Journée "Événements extrèmes et
risques". Marseille, France, 1st June 2023. URL: https://hal.science/hal-04374066.

[11] O. Et-Targuy, A. Begdouri, S. Benferhat, C. Delenne, T.-N. Do and T.-T. Ma. ‘A Graph-Based Approach
for Representing Wastewater Networks from GIS Data: Ensuring Connectivity and Consistency’. In:
Communications in Computer and Information Science (to appear). ISDS 2023 - 1rst International
Conference on Intelligent Systems and Data Science. Vol. 1949. Can Tho (Vietnam), Vietnam:
Springer Nature Singapore, 31st Oct. 2023, pp. 243–257. DOI: 10.1007/978-981-99-7649-2_19.
URL: https://hal.science/hal-04377851.

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Conferences without proceedings

[12] A. Boulin, E. D. Bernardino, T. Laloë and G. Toulemonde. ‘Identifying regions of concomitant
compound precipitation and wind speed extremes over Europe’. In: 54es Journées de Statistique
de la SFdS. Bruxelles, Belgium, 4th July 2023. URL: https://hal.science/hal-04397129.

[13] A. Rousseau. ‘Frugal multi-scale flood modeling: new models and numerical tools’. In: Seminario
del Departamento Ingeniería Hidráulica y Ambiental. Santiago, Chile, Dec. 2023. URL: https://in
ria.hal.science/hal-04396123.

[14] A. Rousseau. ‘Multi-scale flood modeling with shallow water models’. In: Journée Scientifiques Inria
Chile 2023. Santiago, Chile, 4th Dec. 2023. URL: https://inria.hal.science/hal-04372149.

[15] A. Rousseau and V. Guinot. ‘Large CFL explicit scheme for nonlinear shallow water equations’. In:
numhyp23 - Numerical Methods for Hyperbolic Problems. Bordeaux, France, 26th June 2023. URL:
https://inria.hal.science/hal-04372201.

[16] A. Rousseau and V. Guinot. ‘Upscaled models and large CFL time schemes for hydraulic modeling’.
In: Rencontre annuelle 2023 GDR "Défis théoriques pour les sciences du climat". Paris, France,
5th June 2023. URL: https://inria.hal.science/hal-04117279.

[17] C. Serre-Combe, N. Meyer, T. Opitz and G. Toulemonde. ‘Modélisation statistique de précipitations
urbaines à fine échelle spatio-temporelle’. In: Workshop ANR MUFFINS. Aix-en -Provence, France,
28th Sept. 2023. URL: https://hal.science/hal-04374131.

[18] N. Skifa, F. Boodoo, C. Delenne, R. Hostache and M. Abily. ‘Impact of training dataset size and its
hydrometeorological typology on LSTM performance for rainfall-runoff modeling: a case study of
the Severn river’. In: SimHydro conferences. Chatou, France, 8th Nov. 2023. URL: https://hal.sc
ience/hal-04375806.

[19] O. Et-Targuy, A. Begdouri, S. Benferhat and C. Delenne. ‘Revising Weighted Knowledge Bases Using
FH-Conditioning’. In: ENIGMA 2023 - 1st Workshop on AI-driven heterogeneous data management:
Completing, merging, handling inconsistencies and query-answeringis. Rhodes, Greece, 3rd Sept.
2023. URL: https://hal.science/hal-04209092.

[20]

S. Valiquette, G. Toulemonde, J. Peyhardi, É. Marchand and F. Mortier. ‘Asymptotic tail properties of
Poisson mixture distributions’. In: 2023 Joint Statistical Meetings. Toronto ( CA ), Canada, 5th Aug.
2023. URL: https://hal.science/hal-04375734.

Edition (books, proceedings, special issue of a journal)

[21] A. Boulin, E. D. Bernardino, T. Laloë and G. Toulemonde, eds. High-dimensional clustering of
sub-asymptotic maxima of a weakly dependent process. 54es Journées de Statistique de la SFdS.
4th July 2023. URL: https://hal.science/hal-04397100.

Doctoral dissertations and habilitation theses

[22] C. Choley. ‘Study and modeling of the physical processes controlling water exchanges between
streets and buildings during an urban flood’. Université de Strasbourg, 14th Dec. 2023. URL: https:
//inria.hal.science/tel-04391846.

Reports & preprints

[23]

J.-N. Bacro, C. Gaetan, T. Opitz and G. Toulemonde. Multivariate peaks-over-threshold with latent
variable representations of generalized Pareto vectors. 26th Jan. 2023. URL: https://inria.hal.s
cience/hal-03965533.

[24] A. Boulin, E. D. Bernardino, T. Laloë and G. Toulemonde. High-dimensional variable clustering
based on sub-asymptotic maxima of a weakly dependent random process. 2023. URL: https://hal
.science/hal-03969058.

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Inria Annual Report 2023

[25] A. Boulin, E. D. Bernardino, T. Laloë and G. Toulemonde. Identifying regions of concomitant com-
pound precipitation and wind speed extremes over Europe. 19th Nov. 2023. URL: https://hal.sci
ence/hal-04293894.

[26] P. Finaud-Guyot, M. Hétier and A. Rousseau. Numerical modeling of large debris transport during

floods. Mar. 2023. URL: https://inria.hal.science/hal-04009769.

[27] V. Guinot and A. Rousseau. Large CFL explicit scheme for one-dimensional shallow water equations.

2023. URL: https://inria.hal.science/hal-03882644.

[28]

S. Nash, C. Escauriaza, P. Finaud-Guyot, W. Jahn and A. Rousseau. Single Porosity Model: Exploring
the Spatial Resolution Limits in Complex Urban Patterns. 2nd Oct. 2023. URL: https://inria.ha
l.science/hal-04224771.

Other scientific publications

[29] F. Boodoo, C. Delenne, R. Hostache and J. Freychet. ‘Comparison of a conceptual rainfall-runoff
model with an artificial neural network model for streamflow prediction’. In: 2023 EGU General
Assembly - European Geosciences Union General Assembly. Vienna, Austria, 23rd May 2023. URL:
https://hal.science/hal-04093049.

[30] F. Boodoo, C. Delenne, R. Hostache and N. Skifa. ‘Comparison of an artificial neural network
with a conceptual rainfall-runoff model for streamflow prediction’. In: Prévision des crues et
des inondations – Avancées, valorisation et perspectives. Toulouse, France, 28th Nov. 2023. URL:
https://hal.science/hal-04375766.

[31] A. Boulin, E. D. Bernardino, T. Laloë and G. Toulemonde. ‘High-dimensional clustering of sub-
asymptotic maxima of a weakly dependent process’. In: 13th International Conference on Extreme
Value Analysis. Milan (Italie), Italy, 27th June 2023. URL: https://hal.science/hal-04397078.

[32]

J. Galaz, M. Kazolea and A. Rousseau. ‘Modeling wave breaking by coupling dispersive and hyper-
bolic water wave models’. In: MOMI 2023 - Le Monde des Mathématiques Industrielles 2023: Smart
Environment. Sophia Antipolis, France, 2023. URL: https://inria.hal.science/hal-0418945
8.

[33] C. Serre-Combe, N. Meyer, T. Opitz and G. Toulemonde. ‘Modeling moderate and extreme urban
rainfall at high spatio-temporal resolution’. In: EVA 2023 - 13th International Conference on
Extreme Value Analysis. Milano, Italy, 26th June 2023. URL: https://hal.science/hal-043740
90.

11.2 Cited publications

[34] V. Ayoub. ‘Assimilation of satellite earth observation-derived flood maps for a better parameteriza-
tion of large scale hydraulic models.’ Theses. Université de Montpellier, Dec. 2022. URL: https:
//theses.hal.science/tel-04131237.

[35] V. Ayoub, C. Delenne, M. Chini, P. Finaud-Guyot, D. Mason, P. Matgen, R.-M. Pelich and R. Hostache.
‘A porosity-based flood inundation modelling approach for enabling faster large scale simulations’.
In: Advances in Water Resources 162 (Apr. 2022), p. 104141. DOI: 10.1016/j.advwatres.2022.10
4141. URL: https://hal.umontpellier.fr/hal-03558847.

[36] K. Bakong, V. Guinot, A. Rousseau and G. Toulemonde. ‘Downscaling shallow water simulations
using artificial neural networks and boosted trees’. In: Discrete and Continuous Dynamical Systems
- Series S (2022). DOI: 10.3934/dcdss.2022198. URL: https://inria.hal.science/hal-0381
8928.

[37] D. Dubois and H. Prade. ‘Possibility Theory and Its Applications: Where Do We Stand?’ In: Springer
Handbook of Computational Intelligence. Ed. by J. Kacprzyk and W. Pedrycz. Berlin, Heidelberg:
Springer Berlin Heidelberg, 2015, pp. 31–60.

[38] V. Guinot, B. F. Sanders and J. E. Schubert. ‘A critical assessment of flux and source term closures in
shallow water models with porosity for urban flood simulations’. In: Advances in Water Resources
109 (2017), pp. 133–157.

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[39] V. Guinot, B. F. Sanders and J. E. Schubert. ‘Dual integral porosity shallow water model for urban

flood modelling’. In: Advances in Water Resources 103 (2017), pp. 16–31.

[40] P. Helluy, P. Gerhard, V. Michel-Dansac and B. Weber. Quasi-explicit, unconditionally stable, discon-
tinuous galerkin solvers for conservation laws. Research Report. IRMA (UMR 7501), May 2022. URL:
https://hal.science/hal-03665248.

[41] B. Kim, B. F. Sanders, J. S. Famiglietti and V. Guinot. ‘Urban flood modeling with porous shallow-
water equations: A case study of model errors in the presence of anisotropic porosity’. In: J. Hydrol.
523 (2015), pp. 680–692. URL: http://dx.doi.org/10.1016/j.jhydrol.2015.01.059.

[42] N. Meyer and O. Wintenberger. ‘Multivariate sparse clustering for extremes’. working paper or

preprint. Dec. 2022. URL: https://hal.science/hal-02904347.
<scratchpad>
- Main topic: Research Centre LEMON (Littoral Environment: Models and Numerics)
- Key points: 
  - LEMON is an interdisciplinary team working on deterministic and stochastic models for inland and marine littoral processes.
  - The team focuses on physics-driven and data-driven models, with an emphasis on hybrid systems.
  - Research areas include urban flood modeling, coastal oceanography, and environmental risk assessment.
  - The team collaborates with various institutions, including universities, research centers, and industrial partners.
- Structure: The document is organized into sections, including activity report, project-team, research program, application domains, and dissemination.
- Potential artifacts to ignore: 
  - Web-related elements, such as links, HTML tags, and URLs
  - Unnecessary metadata, such as author information and publication dates
</scratchpad>

After analyzing the document, I have identified the following key points:

* LEMON is an interdisciplinary team working on models for inland and marine littoral processes.
* The team focuses on physics-driven and data-driven models, with an emphasis on hybrid systems.
* Research areas include urban flood modeling, coastal oceanography, and environmental risk assessment.
* The team collaborates with various institutions, including universities, research centers, and industrial partners.
* The team has made significant contributions to the development of new models and numerical tools, including the SW2D-LEMON software.

<final_summary>
The LEMON research centre is an interdisciplinary team focused on developing models for inland and marine littoral processes. The team combines physics-driven and data-driven approaches to tackle complex challenges in urban flood modeling, coastal oceanography, and environmental risk assessment. LEMON collaborates with various institutions and has made significant contributions to the development of new models and numerical tools, including the SW2D-LEMON software. The team's research has the potential to improve our understanding of complex systems and inform decision-making in various fields. </final_summary>
The LEMON research centre is an interdisciplinary team focused on developing models for inland and marine littoral processes. The team combines physics-driven and data-driven approaches to tackle complex challenges in urban flood modeling, coastal oceanography, and environmental risk assessment. LEMON collaborates with various institutions and has made significant contributions to the development of new models and numerical tools, including the SW2D-LEMON software. The team's research has the potential to improve our understanding of complex systems and inform decision-making in various fields.