# Dam-break flow over various obstacles configurations

Elisa Beteille, Frederique Larrarte, Sebastien Boyaval, Eric Demay, Minh-Hoang Le

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

## To cite this version:

Elisa Beteille, Frederique Larrarte, Sebastien Boyaval, Eric Demay, Minh-Hoang Le. Dam-break flow over various obstacles configurations. Journal of Ecohydraulics, 2025, 63, pp.156-170. 10.1080/00221686.2025.2460020. hal-05035422

HAL Id: hal-05035422

https://hal.science/hal-05035422v1

Submitted on 13 May 2025

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

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

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Research paper

Dam-break flow over various obstacles configurations

ELISA BETEILLE, PhD Student, LHSV, ENPC, Institut Polytechnique de Paris, EDF R&D, Chatou, France and EDF R&D, Laboratoire National d'Hydraulique et Environnement (LNHE), Chatou, France
Email: elisa.beteille@edf.fr (author for correspondence)

FREDERIQUE LARRARTE (IAHR Member), Researcher, LHSV, ENPC, Institut Polytechnique de Paris, EDF R&D, Chatou, France and SRO, Univ Gustave Eiffel, Marne-la-Vallée, France
Email: frederique.larrarte@univ-eiffel.fr

SEBASTIEN BOYAVAL, Researcher, LHSV, ENPC, Institut Polytechnique de Paris, EDF R&D, Chatou, France and Materials, Inria, Paris, France
Email: sebastien.boyaval@enpc.fr

ERIC DEMAY, Researcher, EDF R&D, Laboratoire National d'Hydraulique et Environnement (LNHE), Chatou, France
Email: e.demay@edf.fr

MINH-HOANG LE, Researcher, LHSV, ENPC, Institut Polytechnique de Paris, EDF R&D, Chatou, France
Email: minh-hoang.le@enpc.fr

Dam-break flow over various obstacles configurations
Dam-break flow over various obstacles configurations

ABSTRACT

Fast floods resulting from the failure of hydraulic structures can be characterised by 'dam-break' type waves. They pose catastrophic risks to downstream populations and result in severe structural damage, especially in urban areas. To assess and mitigate these risks, it is essential to forecast the influence of urban forms on flooding severity at a global scale. This paper provides datasets from reduced-scale physical experiments of transient flow through various obstacles configurations. The experiments are conducted in a rectangular horizontal open channel, where flow conditions are achieved by rapidly opening a gate holding a volume of water. To assess the impact of obstacle configurations on flow behaviour, two obstacle sizes are investigated, along with one idealised city layout. The experiments provide complete water hydrographs upstream and downstream of the gate. Additionally, the good performance of the code.saturne Computational Fluid Dynamics (CFD) solver and the Volume-Of-Fluid (VOF) method in numerically simulating the experiments is demonstrated.

*Keywords:* Dam-break flow, flow visualization and imaging, laboratory study, Particule Image Velocimetry (PIV), RANS model, urban flood

# 1 Introduction

Between 1995 and 2015, floods affected 2.3 billion people worldwide, accounting for more than half of the total number of people impacted by all weather-related disasters (Delforge et al., 2023). Nowadays, due to climate change and rapid urbanisation, the probability of flash floods has increased (Fluixá-Sanmartín et al., 2018; Yerramilli, 2013) and their impact can be particularly severe (Arnell and Gosling, 2016; Hettiarachchi et al., 2018; Hirabayashi et al., 2013; Jenkins et al., 2017; Lehmann et al., 2015; Zhang et al., 2021). Specifically, the increase in extreme rainfall and storm events poses new threats to urban flooding (Barbero et al., 2014; Manenti et al., 2020; Qin et al., 2022), while the rapid expansion of urban areas increases the vulnerability of communities to flooding (Chen et al., 2015; Todeschini, 2016).

Such extreme weather events or the sudden failure of hydraulic structures can lead to the rapid propagation of flood waves across large areas, resembling those following a dam break. To assess and mitigate associated risks, it is essential to forecast the severity of flood wave propagation, particularly in urban areas (Li et al., 2019). Consequently, the development of reliable and fast-running numerical models has gained significant attention in the research field over the past decade. However, the validation of numerical urban flood models often lacks real-case event data on transient urban flooding (Aureli et al., 2021). Physical modelling at reduced scale is a widely used approach to provide insights into geophysical flows and to collect valuable data for numerical model validation and calibration (Mignot et al., 2019).

This work aims to provide a novel experimental dataset of dam-break flow and its interaction with various obstacles, establishing a new benchmark for validating numerical models that deal with fast transient flow.

Dam-break flows have been extensively reproduced in laboratory because they are relatively 'easy' to perform at a reduced scale and are highly reproducible under controlled conditions (Aureli et al., 2023). Recently, several studies have incorporated idealized urban areas into the wave path to analyse their influence on flash flood propagation (Beteille et al., 2023; Soares-Frazão and Zech, 2008; Testa et al., 2007; Tomiczek et al., 2016). In these studies, idealised city layout consists of arrays of solid blocks with varying numbers of rows, spacing, and orientations. Dam break flows were generated with different setup types, and the water depth was generally the main analysed flow variable. The experiments varied dam-break wave characteristics and studied several obstacle configurations, including a single obstacle. In recent years, advancements in high-speed laboratory cameras have extended the application of the Large-Scale Particle Image Velocimetry (LSPIV) technique to unsteady flow measurements. In this paper, LSPIV is used for the first time to capture the instantaneous free surface flow velocity around obstacle configurations. In previous experimental studies, measurements of upstream water depth were generally limited, constraining
the reproducibility of the experiments with numerical models. This study provides comprehensive water depth measurements both upstream of the breach and around the obstacle configurations.

Experimental studies of dam-break flow are frequently employed for the validation of numerical models. The dam break topic have been widely investigated in the last decade and a large number of numerical models and solvers have been developed (Aureli et al., 2024; Di Cristo et al., 2018). The predominant approach is two-dimensional (2D) models, used in over 50% of cases (Aureli et al., 2023), especially for large-scale applications. 2D numerical approaches typically rely on depth-averaged Shallow Water (SW) equations and are commonly solved using the Finite Volume Method (FVM) (Chen et al., 2012; Sanders et al., 2008; Yu and Lane, 2006) or the Finite Difference Method (FDM) (Belloς, 2004; Wood and Wang, 2015). Several methods have been specifically tested and developed for urban areas to account for the impact of local phenomena that may appear near obstacles. For example, some models with coarser spatial resolution incorporate a porosity term into the balance equation, defined as the volume fraction of a mesh cell filled by the liquid phase (Cea and Vázquez-Cendón, 2010; Guinot, 2012; Kim et al., 2015; Schubert and Sanders, 2012).

In order to predict local and 3D effects, such as vertical velocities and stratification around obstacles in the flow, 3D numerical methods are particularly useful. These methods are based on the Navier-Stokes equations and are coupled with the Volume-of-Fluid (VOF) method to accurately track the free surface (Beteille et al., 2024; Kamra et al., 2018; Maghsoodi et al., 2022). Recently, mesh-free particle methods such as Smoothed-Particle Hydrodynamics (SPH) (Amicarelli et al., 2021; Chen et al., 2019; Wang et al., 2020; Wei et al., 2015; Zhuang et al., 2022) and Moving Particle Semi-implicit (MPS) (Garoosi et al., 2022) have been applied to study dam-break flows. Such techniques are valuable for analysing impact pressures on rigid structures due to their ability to accurately model air-water mixture areas (Monaco et al., 2011; Salis et al., 2022). While these methods benefit from being mesh-free, they are computationally expensive due to the need to compute interactions among neighbouring particles.

Previous numerical works dealing with dam-break flow around single or several obstacles can be mentioned, but their development is limited by the lack of extensive validation datasets within the obstacle configurations.

In this paper, a novel experimental study is presented along with an associated numerical model of dam-break flow over three obstacle configurations. A series of dam-break physical experiments were conducted using recent and adapted measurement techniques to provide a complete water hydrograph in and around the obstacle configurations. Acoustic and resistive wave gauges are combined to capture the water depth time history. The instantaneous 2D velocity field around the obstacles was measured using optical techniques, providing novel insights into the flow behaviour in the area. The experimental results are analysed and compared to the reference dam break flow case with no obstacles. Quantitative investigations of the flow area impacted by the obstacles are performed and discussed. Additionally, numerical simulations of the physical experiments were carried out using the open-source Computational Fluid Dynamics (CFD) software code `saturne` using RANS equations coupled with VOF method. Quantitative and qualitative comparisons with the experimental datasets were made to assess the ability of the numerical model to simulate our experiments.

# 2 Laboratory experiments

## 2.1 *Experimental set-up*

The experiments are carried out in a rectangular and horizontal channel at EDF Lab Chatou in France. The facility, Figure 1, is 16 m long, 2 m wide, and 0.5 m high with a uniform PVC bottom. A 7-m long reservoir is separated from the remaining of the channel by a 0.5 m wide and 0.31 m high rectangular PVC sluice gate. A pneumatic cylinder is used to ensure repeatability of gate opening.
<figure><img src="image_3.png" /><figcaption>Figure 1: Schematic views of the experimental setup, (a) horizontal and (b) longitudinal section. The probes location (x, y) [m]: R1 (4, 0), R2 (4, 0.5), R3 (6.5, 0), R4 (6.5, -0.15), A1 (8.35, 0), A2 (8.55, 0), A3 (8.65, -0.1), A4 (8.75, 0), A5 (8.85, -0.3), and A6 (9.15, 0).</figcaption></figure>

All experiments are realised in dry bed condition. Leaks at the breach are avoided by pressurising the cylinder when filling the tank, adding grease along the gate's guides, and using a water hoover between each test. The opening time of the gate, obtained from video tracking, is about 0.4 s and is above the recommended value from Lauber and Hager (1998), $t_{gate} = 1.25(h_0/g)^{1/2} = 0.2$ s, with $g = 9.81 \text{ m/s}^2$ the gravitational acceleration modulus and $h_0$ the initial reservoir water depth. Thus, the opening time of the gate needs to be taken into account in the numerical models for comparison.

## 2.2 *Performed tests and configurations*

The present study focuses on a reduced-scale model of unsteady urban flooding, designed to replicate an idealized suburban district at a 1/100 geometric scale. It is based on the principle of similarity theory, which states that the ratio of the dominant forces governing the flow should remain consistent between the model and the prototype. The Froude number, which is dominant in dam-break flows, is defined as $F = V'(gH')^{0.5}$, where V' is the characteristic flow velocity (m/s), g is the gravitational acceleration modulus (m/s²) and H' is the characteristic water depth (m). At $x = 8.35$ m, the Froude number was experimentally estimated to range between 2 and 4 during the first 25 seconds of the experiment. The flow conditions were selected to ensure submergence and emergence of the obstacles, where 3D local phenomena with vertical displacement of the flow appear in the urban area.

The test cases performed with different obstacles configurations are presented in this section. Note that the initial depth for all experiments is $h_0 = [0.250-0.251]$ m ($\pm$0.5 mm) and all obstacles used in this paper are 0.1 m high. The acquisition is stopped at $t = 25$ s.

Three different configurations of obstacles are studied in this paper and illustrated in Figure 2:

* 0-0 is a single cube of size 0.1 m placed at the center of the channel and aligned with the coordinate axes (Figure 2a).

* 2-10 corresponds to an idealised suburban district of 3 × 3 aligned obstacles (Figure 2b) already studied for steady flow (Doumic et al., 2021; Larrarte et al., 2022). In this case, cubes of size 0.1 m are all separated by 0.1 m, creating 2 streets along the x direction and 2 streets along the y direction.

* 4-0 is a single block of size 0.5 m (Figure 2c) equivalent to the area occupied by the 2-10 configuration.

The three configurations were selected to illustrate the global impact of building size and density within the area of interest on the flow behaviour. Two obstacles of varying sizes, designated as
<figure><img src="image_4.png" /><figcaption>Figure 2: Top view of the configurations studied and probes locations, (a) 0-0: one single obstacle of length 0.1 m, (b) 2-10: 3x3 aligned obstacles of individual length of 0.1 m, and (c) 4-0: one single obstacle of length 0.5m.</figcaption></figure>

0-0 and 4-0, were chosen to examine the influence of obstacle size on the flow's blockage effect, particularly on the width of the blocked area. Additionally, the building density effect in the urban area, between configurations 2-10 and 4-0, was investigated to underscore the impact of modelling streets in large-scale flood study. To assess the impact of the obstacles on the flow, all configurations are compared to a reference dam break case with no obstacles, called 0.

## 2.3 Flow measurement

In this study, resistive (wave gauge), acoustic (wave gauge), and optical (LSPIV) measurements are performed and detailed below. All sensors are connected to a measurement computing card, and all wave gauge data are recorded with the LabVIEW software. The button used to open the gate starts a synchronised acquisition for all sensors, including high speed camera. Thus, in this study, the initial time t = 0 s of the experimental data corresponds to the start of the breach opening. Water depth and horizontal velocity are expressed as dimensionless quantities (Amicarelli et al., 2021):

$$H = \frac{h}{h_0}, V = \frac{V_{xy}}{\sqrt{2gh_0}} \qquad (1)$$

where $V_{xy} = \sqrt{V_x^2 + V_y^2}$ denotes the horizontal velocity component.

### Wave gauge

The free surface position is measured with two types of wave gauges. Four resistive wave gauges TDH250, with an accuracy of 0.1 mm, are placed upstream of the gate (R1, R2, R3, and R4) and six acoustic gauges Mic+35/IU/TC, with an accuracy of 1 mm, are placed downstream of the gate (A1, A2, A3, A4, A5, and A6). This choice has been made because of the intrusiveness of the resistive gauge. As presented in Figure 1 and 2, the acoustic probes are located in areas of interest such as the obstacles' surroundings. The acquisition frequency of the wave gauges is set to 50 fps and is limited to the lower response time of the acoustic gauges.

### LSPIV analysis

The instantaneous free surface flow velocity around obstacles is retrieved using LSPIV digital image technique. To do so, a high-velocity laboratory camera (FASTCAM Mini UX50), used with a resolution of 1024x1024 pixels and a recording rate of 250 fps, is placed vertically above the channel.
Different types of seeding particles are added to the flow: 2 cm to 0.5 cm spherical polystyrene and 1 cm foam rectangular particles. The different shapes and sizes are used to capture all flow patterns and improve free-surface detection. Some of them are dropped inside the channel before the beginning of the experiment to capture the propagation of the wavefront. Also, in order to optimise image resolution and given the flow symmetry in the longitudinal direction, the camera is placed to capture only one side of the channel, as sketched in Figure 1. The recorded image sequences were analysed with the open-source software Fudaa-LSPIV (Jodeau et al., 2022; Le Coz et al., 2014) and then the instantaneous velocity field were post-processed with python programming language. First, a lens distortion correction and background removal routine are applied to the raw images. Then, a 3D orthorectification of the images is applied based on 8 Ground Reference Points (GRP) obtained by photogrammetry of the channel. Next, a velocity analysis of each image is performed on a uniform grid of approximately 50 pixels per cell in the x and y directions. Since a LSPIV calculation uses a flat free surface hypothesis, a constant water depth is set to 0.02 m. This value is the mean numerical water depth obtained for a case without obstacles in the measurement zone. At this point, the instantaneous velocity fields are available at each time step. Then, a filter is applied on each instantaneous velocity field, to remove erroneous vectors based on correlation threshold and vector component threshold. A correlation threshold is set between [0.4,1] and a median test (Westerweel and Scarano, 2005) as a spatial coherency filter is applied to the vector component threshold. The next step calculates the average velocity field between the three repetitions for each filtered velocity field. Finally, a variable time-averaged is computed from the previous averaged velocity fields. In this paper, the time average is set to 0.1 s, so all velocity fields are averaged over 25 instantaneous velocity fields.

## Setup repeatability

The repeatability of the experiment was investigated for each configuration. First, the repeatability of the experimental setup (i.e., the gate opening and the initial conditions) was studied. For case 0, all wave gauges were compared for different numbers of replicates between 2 and 15. Since the standard deviation of each probe was similar and smaller than 1 mm for all tests, the mean of three replicates was considered a good approximation for the other cases. Thus, for each configuration, six experiments were conducted: three for acoustic wave gauge measurements and three for LSPIV analysis. Resistive probes, upstream of the gate, ensured repeatability across all six experiments.

Then, the repeatability of the experiment was verified for all studied configurations. Figure 3a presents the water depth variation for probe R3 as the average between six experiments, verifying the test condition repeatability across the entire measurement campaign. The water height standard deviation had larger values at the beginning of the experiment, with maximum values up to 3 mm. Otherwise, the maximum value of the standard deviation was kept below 1 mm. Similarly, Figure 3b shows the water depth variation for probe A1 as the average of three replicates. Downstream of the gate, the value of the standard deviation depended on the configuration studied. For cases 0-0 and 2-10, the standard deviation was lower than 2 mm except at the wave impact, where it could reach 1 cm for a few data points. The rise in the standard deviation compared to case 0 can be explained by the presence of droplets. For case 4-0, the confidence interval was larger, with maximum values reaching 1.5 cm from 1.5 s to the end of the experiment. This can be attributed to the blockage effect of case 4-0, with the impacted area upstream of the obstacle being longer and larger than in other cases. The blocked area reaches location A1 at 1.5 seconds, resulting in a highly unstable free surface. Despite this, the primary oscillations of the free surface remained similar between the replicates, and thus the experiments are considered reproducible.
<figure><img src="image_5.png" /><figcaption>Figure 3: Water depth variation for all studied configurations, 95% confidence interval for probes (a) R3 and (b) A1.</figcaption></figure>

## 3 Numerical model

The experimental flow conditions were selected to ensure the occurrence of 3D local phenomena associated with the emergence and submergence of the obstacles. Accounting for the impact of such phenomena presents challenges for current 2D SW models. The simplifying assumptions in SW models, such as hydrostatic pressure distribution and depth-averaged velocity, do not accurately represent the local vertical flow phenomena close to the obstacles. Moreover, initialising the flow with the vertical displacement of the gate to account for the opening's impact cannot be accomplished easily with SW models.

In contrast, models based on 3D RANS equations can account for moving obstacles, such as gate openings, as well as the local 3D phenomena induced by obstacles in the path of a dam-break wave. Such models can complement experimental datasets by providing inaccessible flow variables, while maintaining reasonable computational times for industrial applications or for studying a variety of urban configurations.

To select the appropriate modelling approach for simulating the experiments, two numerical models have been set up. The first approach is based on the numerical solution of 3D RANS equations for two-phase flow, using the CFD (Computational Fluid Dynamics) solver code_saturne, and the second is based on the 2D SW equations, using the solver TELEMAC-2D. The computational domain is 14 meters long and 2 meters wide for both numerical models and 0.5 meters high for code_saturne. The 2D computational domain from TELEMAC-2D is a triangular unstructured grid with a uniform average size of 0.007 m, composed of 1.4 million tetrahedral cells. On the other hand, the 3D computational domain used for code_saturne is discretised using 6.1 millions hexahedral cells, as illustrated in Figure 5. In both models, all walls are assumed to be smooth, as the roughness coefficient of PVC is very low.

Initially, a reference dam break flow case, referred to as case 0, was performed in the experimental setup and then compared between the two solvers. The water height variations downstream of the gate for both solvers were similar and close to the experimental data. The wave's arrival time in TELEMAC-2D is faster, which can be attributed to the model not numerically considering the gate opening time. Then, case 0-0 was compared between the two modelling approaches. Figure 4 illustrates the water depth variations upstream (at position A2) and downstream (at position A4) of the obstacles. In this case, TELEMAC-2D does not adequately account for the presence of an obstacle in the flow, and the water height upstream of the obstacle, as shown in Figure 4a, is similar to the water depth in case 0. Downstream of the obstacle, as seen in Figure 4b, the SW approach underestimates strongly the water depth. On the other hand, the RANS approach tends to water depth predictions quite similar to the experimental results, though some improvements can be made in the future. Additionally, Table 1 summarises the parameters and computational time for
<figure><img src="image_6.png" /><figcaption>Figure 4: Case 0-0. Water depth comparison between experiments, *code_saturne*, and TELEMAC-2D simulations for probes: (a) A2 and (b) A4.</figcaption></figure>

the two approaches, clearly highlighting the cost difference between the 3D RANS approach and the 2D SW approach.

In summary, while shallow water models offer computational efficiency and can be highly effective for large-scale applications, their simplifications can limit their accuracy in capturing the detailed flow patterns around obstacles. Navier-Stokes models, despite their higher computational demands, are better equipped to handle the complex, three-dimensional flow phenomena associated with such scenarios. Note that, although the RANS model underestimates the water depth peak at the impact, it accurately captures the 3D local phenomena, making it a useful tool for predicting the overall impact of the urban area on the flow.

<table><caption>Table 1: Computation time comparison between TELEMAC-2D and code_saturne.</caption><thead><tr><th></th><th>code_saturne</th><th>TELEMAC-2D</th></tr></thead><tbody><tr><td>Equations type</td><td>RANS*</td><td>SW**</td></tr><tr><td>Dimensions</td><td>3D</td><td>2D</td></tr><tr><td>Number of cells</td><td>6 134 200</td><td>1 372 532</td></tr><tr><td>Number of processors</td><td>420</td><td>72</td></tr><tr><td>Total CPU Time for 1s of physical time</td><td>43 hours</td><td>21 seconds</td></tr></tbody></table>

*: Reynolds-Averaged Navier-Stokes.

**: Shallow Water.

Thus, the experiments have been numerically simulated using the CFD open-source solver code_saturne, as described in Archambeau et al. (2004). The model is based on the numerical solution of 3D RANS equations for two-phase flow, with the VOF technique to model the water-air mixture. The $k-\epsilon$ model with linear production is used and the free surface is compressed using the drift method (Deshpande et al., 2012).

The computational domain is 12.00 m long, 2 m wide, and 0.5 m high, and is discretised using hexahedral cells, as illustrated in Figure 5. Refinements are made along the z-axis for cells of low height and in the x and y axes in areas of interest, such as the breach surroundings or around the obstacles. For case 2-10, a mesh sensitivity analysis was conducted with three different levels of mesh refinement. The initial mesh, composed of 6 million cells (as shown in Figure 5), was modified to create two additional meshes: one finer, with cells size halved, and one coarser, with cells size doubled. The comparison of the water height profiles for the three meshes, presented in Figure 6, shows few differences between the meshes 48 million and 6 million, demonstrating that mesh convergence is achieved with 6 million cells. Therefore, the 6 million cell mesh was retained for the study. Concerning the boundary conditions, the top of the domain is defined as a symmetry and the surface $x = 12$ m is set as a free outlet. All other boundaries are set as smooth wall. The
<figure><img src="image_7.png" /><figcaption>Figure 5: Mesh representation of the numerical model along the (a) x-axis, (b) y-axis, and (c) z-axis. Each black line represents the center of an individual cell, except along the (a) x-axis, where black lines are plotted every 10 cells for enhanced clarity.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Figure 6: Case 2-10. Convergence of water height profiles at $y = 0$ m for (a) $x = [0, 12]$ m, and (b) $x = [8, 10]$ m at $t = 4$ s. Gray rectangles represent the obstacle positions. 48M: 48 million cells mesh, 6M: 6 million cells mesh, and 800K: 800 thousand cells mesh.</figcaption></figure>

effect of the gate opening is modelled as a rectangular moving wall with a velocity of 0.78 m/s, determined from video tracking.

Note that using the VOF module involves an additional post-processing step to retrieve the water height. Here, a volume fraction of water, $\alpha$ is used to calculate the position of the free surface for each probe. The following method is applied: first, a control volume, $\Omega$, of one cell wide and over the full height of the domain, $h_m$, is defined. The resulting water height, $h$, at each time step is the ratio on this column of the volume of water and its total volume, $V_t$, multiplied by its total height as described by equation 2.
$$h = \frac{\int_{\Omega} \alpha V_t}{\int_{\Omega} dV_t} \times h_m \qquad (2)$$

The above formula is typically used to calculate water depth in domains characterized by a clear separation between two phases. However, this post-processing method has limitations when applied to aerated flows, where an air-water mixture may be present with air cells located under the free-surface. Integrating these cells can lead to an underestimation of the free-surface position, which may explain the underestimation of water height during the impact (see Figure 4).

# 4 Experimental results

The flow behaviour of a dam-break wave influenced by various obstacle configurations was investigated through a series of dam-break experiments. First, a general description of the flow based on the free surface velocity is given in Section 4.1, followed by an analysis of the impacted area around the obstacle configurations in Section 4.2.

## 4.1 Free surface flow description

After the opening of the gate, the water stored in the tank begins to propagate along the channel as a 3D dam-break wave. Upon reaching the side walls of the channel, a reflecting wave is generated, forming a hydraulic jump. The two symmetric hydraulic jumps reunite downstream of the obstacle configurations and does not interfere with the measurement zone. As the water reaches the obstacles, a surge occurs, leading to water jets and droplets rising vertically along the z-axis. The surge then transforms into a splash-up of varying height and width. A thin film of water passes over the first row of obstacles, with most of it skirting around them. Additionally, downstream of the configurations, a wake zone with recirculations and lower velocity is formed.

Figure 7 describes the evolution of the free surface flow for cases 0-0, 2-10, and 4-0 at $t = 4$ s, $t = 9$ s and $t = 14$ s. The color map represents the norm of the horizontal free-surface velocity, and the vector field is normalised with respect to velocity. Given the LSPIV acquisition frequency of 250 fps and after a sensitivity analysis on the time interval, each velocity field is averaged from 25 instantaneous frames. The horizontal velocity fields highlight the existence of a splash-up zone in front of the obstacles, generating a low-velocity magnitude zone. Additionally, the vectors indicate that the flow bypasses the obstacle on both sides. Finally, a wake zone is observable with varying shapes over time downstream of the configurations. The overall velocity magnitude decreases over time as the reservoir empties and the flow rate through the gate decreases.

Specifically for case 0-0, the splash-up zone remains 0.1 m long and evolves from 0.6 m wide at $t = 4$ s to 0.4 m at $t = 14$ s. Downstream of the obstacles, a wake zone can be observed with a near-zero flow velocity. The width of the wake zone is similar to the obstacle's width at all time steps, and its length decreases with time.

For case 2-10, the splash-up area is 0.15 m long and 0.8 m wide (almost constant for all time intervals) and merges with the reflection zone at $x = 8.7$ and $y = 0.6$ m. A significant flow velocity larger, than in the vicinity, is observed along the street in the main direction of the flow. At $t = 4$ s, the flow passing through the configuration continues downstream and divert the wake zone from the side row of obstacles. The deflected wake zone merge the reflection zone at $x = 9.4$ m and $y = 0.4$ m. The intensity of the wake zone becomes stronger with time and go up the flow. It creates a blockage effect downstream of the configuration, resulting in a decrease in velocity magnitude over time along the street in the x direction (Figure 7d to f). Furthermore, recirculations are noticeable
$t_{avg} = [4, 4.1] s$

$t_{avg} = [9, 9.1] s$

$t_{avg} = [14, 14.1] s$

<figure><img src="image_9.png" /><figcaption>Figure 7: Free surface velocity variations for cases 0–0, 2–10 and 4–0 at 3 different time step. Black surfaces illustrate the obstacles position. (a) Case 0–0; (b) Case 0–0; (c) Case 0–0; (d) Case 2–10; (e) Case 2–10; (f) Case 2–10; (g) Case 4–0; (h) Case 4–0; and (i) Case 4–0.</figcaption></figure>

downstream of each obstacle. In the central row, the recirculations appear symmetrical, whereas in the side rows, water is ejected from the inside of the configuration to the outside. At t = 14s, an additional hydraulic jump is created at the meeting point between the splash-up zone and the reflection zone with a low velocity magnitude.

Finally, for case 4-0 at t = 4 s, the splash-up induces a deviation in velocity direction within the reflection zone, creating an additional hydraulic jump with lower velocity magnitude. This effect intensifies over time until it completely diverts the reflection zone at t = 14 s. A wake zone with distinct recirculations is visible downstream of the obstacle. Similar to configuration 0-0, the wake zone size is approximately the width of the obstacle.

Note that some erroneous velocity gradients on the lower-left side of the velocity fields are due to highly transparent water and a lack of seeding particles passing through the area during acquisition. These effects are erased in the surroundings of the obstacles where the flow is agitated and foam, hollows, and humps appears on the free surface that allow the LSPIV analysis to accurately track the free surface.
## 4.2 Investigation of the flow area impacted by the obstacles

To access quantitatively the impact of the obstacles on the flow, each velocity map presented in Figure 7 is compared to the velocity map of the reference dam break, case 0, at the same time interval. Figure 8 presents the velocity magnitude variation computed as $|V_{X-X} - V_0|/|V_0|$ with $V_0$ the velocity of case 0. Similar to the previous subsection, erroneous velocity gradients on the lower-left side of the zone of interest are observed and have been filtered out. Table 2 quantifies the flow area impacted by the configurations.

Figure 8 demonstrates the hydrodynamic neutrality of configuration 0-0 throughout the entire experiment, in fact no differences are visible near the wall. For case 2-10, some interaction between the impacted flow and the channel side wall are visible at the last time interval, and for case 4-0, this interaction is observed throughout the entire experiment. The area affected by the 4-0 configuration is as large as the experimental set-up, highlighting the limitations of studying larger obstacle configurations in the current experimental set-up. For case 0-0, the flow in the reflection zone remains largely unaffected by the obstacles, with values below 10%. The intensity decreases gradually with distance downstream from the obstacle. For case 2-10, the flow is mostly affected along each longitudinal row of obstacles. The higher flow velocity magnitude in the streets aligned with the main direction of the flow, and its interaction with the wake zone are also visible. Towards the last time interval, the flow inside the reflection zone is impacted as well. For the 4-0 case, almost the entire measurement zone is affected by the obstacle. The flow is severely slowed down, with important changes in the splash-up zone, wake zone and meeting zone between the two.

Table 2 summarises the interval height of the relative absolute free surface velocity for three different cases (0-0, 2-10, and 4-0) at three different time intervals. The values are presented as percentages and indicate the proportion of points belonging to each interval weight. Each field consists of approximately 250,000 point coordinates. Points belonging to the interval [100, 200] are considered erroneous since they are all situated in the low-left side of the velocity fields, as shown in Figure 8. These results quantify the influence of the obstruction effect on flow dynamics.

<table><caption>Table 2: Interval height of the velocity variation (%) at 3 different time steps.</caption><thead><tr><th rowspan="2">Case</th><th rowspan="2">Time interval (s)</th><th colspan="6">Interval weight [%]</th></tr><tr><th>[0, 20]</th><th>[20, 40]</th><th>[40, 60]</th><th>[60, 80]</th><th>[80, 100]</th><th>[100, 200]</th></tr></thead><tbody><tr><td rowspan="3">0-0</td><td>[4, 4.1]</td><td>78.48</td><td>10.52</td><td>4.38</td><td>3.76</td><td>2.57</td><td>0.30</td></tr><tr><td>[9, 9.1]</td><td>78.23</td><td>12.39</td><td>2.89</td><td>2.30</td><td>3.07</td><td>1.12</td></tr><tr><td>[14, 14.1]</td><td>80.64</td><td>12.20</td><td>2.46</td><td>2.02</td><td>2.12</td><td>0.57</td></tr><tr><td rowspan="3">2-10</td><td>[4, 4.1]</td><td>48.45</td><td>15.67</td><td>13.17</td><td>10.92</td><td>11.43</td><td>0.34</td></tr><tr><td>[9, 9.1]</td><td>45.42</td><td>13.76</td><td>10.48</td><td>11.14</td><td>18.33</td><td>0.85</td></tr><tr><td>[14, 14.1]</td><td>33.11</td><td>13.89</td><td>13.65</td><td>18.44</td><td>20.49</td><td>0.43</td></tr><tr><td rowspan="3">4-0</td><td>[4, 4.1]</td><td>24.67</td><td>26.59</td><td>17.5</td><td>11.83</td><td>19.21</td><td>0.20</td></tr><tr><td>[9, 9.1]</td><td>24.48</td><td>16.96</td><td>16.81</td><td>14.69</td><td>26.04</td><td>1.01</td></tr><tr><td>[14, 14.1]</td><td>8.00</td><td>26.69</td><td>21.72</td><td>18.15</td><td>25.20</td><td>0.24</td></tr></tbody></table>

Comparing the free surface velocity between cases 2-10 and 4-0 highlights the importance of modelling the streets in urbanised areas. The size of the surge zone, the direction of the flow in the vicinity, and the recirculation processes are clearly impacted by the presence or absence of streets. Additionally, the impact of obstacle size, as seen between cases 0-0 and 4-0, seems to be directly linked to the width of the obstacle. However, the analysis is limited by the width of the experimental setup.

# 5 Assessment of the numerical model

The numerical model is assessed by comparing water depths with experimental results across the three studied configurations. The evolution of water height over time is examined at three key
$t_{avg} = [4, 4.1] \text{ s}$

$t_{avg} = [9, 9.1] \text{ s}$

$t_{avg} = [14, 14.1] \text{ s}$

<figure><img src="image_10.png" /><figcaption>Figure 8: Free surface velocity comparison between case 0 and cases 0–0, 2–10 and 4–0 computed as $\frac{|V_{x-x}-V_0|}{|V_0|}$ [%] at 3 different time step. Black surfaces illustrate the obstacles position. (a) Case 0–0; (b) Case 0–0; (c) Case 0–0; (d) Case 2–10; (e) Case 2–10; (f) Case 2–10; (g) Case 4–0; (h) Case 4–0; and (i) Case 4–0.</figcaption></figure>

locations: R3, situated upstream of the gate; A1, located upstream of the first row of obstacles; and A6, positioned downstream of the configurations. These probe locations aim to validate the numerical model’s capability in accurately replicating the influence of the 3D local phenomena at a global scale. The water height’s temporal evolution in the numerical model is derived using the post-processing method outlined in Section 3.

For probes located upstream of the gate, one can see the presence of oscillations of the water height (Figure 9). As presented in Figure 3, these variations are similar for all configurations; hence, only 0-0 results are presented. Figure 9 illustrates a good similarity between experimental and numerical curves showing the ability of the numerical model to reproduce the opening of the gate and the resulting oscillations inside the reservoir.

Figures 10 and 11 illustrate a comparison of water heights between wave gauge data and the numerical model at positions A1 (upstream of the first row of obstacles) and A6 (downstream of the configurations) for the three studied cases. For case 0-0 at position A1 (Figure 10), the water depth
<figure><img src="image_11.png" /><figcaption>Figure 9: Case 0-0. Water depth variations comparison between experimental and numerical data at position R3.</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>Figure 10: Water height comparison at position A1 between wave gauge measurements (black dashed line), and numerical model (blue line) for cases (a) 0-0, (b) 2-10, and (c) 4-0.</figcaption></figure>

is not impacted by the presence of the obstacle, and there is a good agreement between experimental and numerical results. A slight decrease in water depth observed at $t = 10$ s, induced by the variation of the flow rate through the gate (Figure 9), is accurately captured by the numerical model. For case 2-10, the numerical model demonstrates a good ability to replicate the water depth evolution, except for numerical oscillations, particularly during the first 15 seconds of the experiment, and then progressively reduces its error. More significant difference between experimental and numerical results can be observed for case 4-0. Here, the splash-up zone is located in A1, causing an 80% increase in water level at $t = 20$ s compared to case 0. While the numerical model correctly identifies the presence of the splash-up zone, with a higher water depth than other cases, general variations in flow depth show differences. The splash-up zone, characterised by air-water mixture cells, presents challenges for the numerical post-processing method. Further work on the numerical post-processing water height calculation might be of interest.

For probes located downstream of the obstacles, larger differences are observable (Figure 11). This can be explained by the presence of hollows and humps due to the obstacle, leading to very-small scale free surface variations. Initially, for case 0-0, the numerical simulation underestimates water height for a brief period after the passage of the dam-break wave. Subsequently, the numerical model shows a similar shape compared to experimental data. Note that the sudden rise in water level at the end of the acquisition for case 0-0 (Figure 11) is the result of wave reflecting from downstream caused by a too-slow emptying of the channel. The wave arrives faster in the measurement zone for case 0-0 compared to other study cases due to lower water depth in this area. In the cases 2-10 and 4-0, where position A6 is closer to the obstacles and within the wake zone, a similar tendency is observed, but the numerical model generally underestimates the water depth. Particularly for 4-0, the model fails to accurately predict the arrival time of the dam-break wave, with too much water passing over the obstacle. This difference can be explained by the use of several 0.1 m cubes
<figure><img src="image_13.png" /><figcaption>Figure 11: Water height comparison at position A6 between wave gauge measurements (black dashed line), and numerical model (blue line) for cases (a) 0-0, (b) 2-10, and (c) 4-0.</figcaption></figure>

to build the geometry of the 4-0, which may not be completely watertight.

The comparison of water height variations at various key locations, both upstream and downstream of the gate and obstacles, reveals a generally good agreement between the numerical predictions and experimental data. Notably, the model accurately captures the presence of splash-up zones, despite the challenges posed by the air-water mixture environment within these areas. While some deviations are observed during the early stages of certain experiments, the model progressively refines its predictions over time. Accurately reproducing the hydrograph at the gate is fundamental for high-quality simulations downstream of the gate. These effects justify the approach of conducting numerical simulations over the entire tank domain, with mesh refinement particularly around the gate.

Additionally, the surface velocity obtained with the LSPIV analysis is compared to the depth-averaged velocity field from the numerical simulation. The validity of such comparisons for unsteady flow has been discussed in Soares-Frazão and Zech (2007) and Soares-Frazão and Zech (2008). Indeed, velocity profiles under transient flow conditions are complex, as the flow is not yet fully developed and may present complex shapes. However, qualitative comparisons can be made to obtain information about the velocity direction, magnitude and shape around an obstacle (caution is still required for quantitative comparisons).

The experimental velocity field, displayed in the left column of Figure 12, is averaged over 25 frames ($t_{avg} = 0.1$ s). The numerical post-processing method to compute the velocity is as follows: the velocities of cells having a value of $\alpha \le 0.5$ (i.e. water cells) are averaged over each column following the z-axis to obtain 2D depth-averaged fields. The numerical velocity fields are displayed in the centre column of each figure, for different time steps. The colour map represents the norm of the horizontal velocity $V$ (-). Finally, the absolute difference between the experimental and numerical velocity magnitudes is displayed in the right column of each figure.

For all cases, the specific flow behaviour, such as the splash-up and wake zones or the reflection between the flow and the channel walls is predicted by code_saturne enhanced with the VOF module. Given the experimental and numerical post processing methods, which come with uncertainties, some differences are present, that will be discussed in the next paragraphs.

Specifically, for case 2–10 (Figure 12), the lengths of the splash-up zone (along x-axis) over time are smaller for the numerical simulation; as the time increases, the differences decrease. However, higher velocities are observed numerically within the streets aligned with the main direction of the flow. Additionally, the flow ejected from the inside to the outside of the configuration was predicted numerically. Downstream of the configurations, the wake zone is better represented; at $t = 4$ s, the shape and intensity of the velocity field closely match between the two fields. At $t = 9$ s, a recirculation on the low left side of the images appears in both fields. Finally, the interaction between the splash-up area and the reflection zone at $t = 14$ s is slightly underestimated, even if a low velocity area is reproduced along the line $x = 9$ m. This can be explained by the numerical
<figure><img src="image_14.png" /><figcaption>Figure 12: Case 2 – 10. Surface velocity field comparison at three different time steps. $V_{\text{EXP}}$ represents the horizontal time-averaged velocity obtained from the LSPIV analysis and $V_{\text{NUM}}$ refers to the vertically averaged horizontal velocity for $\alpha \le 0.5$. Black surfaces illustrate the obstacles’ positions. (a) $V_{\text{EXP}}(t_{\text{avg}} = [4, 4.1]$ s); (b) $V_{\text{NUM}}(t = 4]$ s); (c) $V_{\text{EXP}} - V_{\text{NUM}}(t = 4]$ s); (d) $V_{\text{EXP}}(t_{\text{avg}} = [9, 9.1]$ s); (e) $V_{\text{NUM}}(t = 9]$ s); (f) $V_{\text{EXP}} - V_{\text{NUM}}(t = 9]$ s); (g) $V_{\text{EXP}}(t_{\text{avg}} = [14, 14.1]$ s); (h) $V_{\text{NUM}}(t = 14]$ s); and (i) $V_{\text{EXP}} - V_{\text{NUM}}(t = 14]$ s).</figcaption></figure>

underestimation of the splash-up width.

Quantitative (water height) and qualitative (velocity field) comparisons emphasize that CFD can predict the overall behaviour of dam-break flow in the presence of idealized urban areas. Indeed, the different zones identified previously in the analysis of the experimental results were accurately predicted using code_saturne. On the other hand, differences have been observed, mostly related with unsteady phenomena, such as an underestimation of the wave impact and water height downstream of the obstacles. Despite the general good agreement, this comparison underscores the importance of detailed validation datasets for numerical models and reveals some difficulties in replicating the observed flow with current 'established' codes. Further work on RANS-based VOF models might be of interest to complement the experimental findings on the processes involved in dam-break flows in the presence of obstacles.
# 6 Conclusion

This paper presents a novel experimental study on the propagation of fast transient dam-break waves over two single obstacles of different sizes and one configuration representative of an idealised city layout. A comparison with a VOF-based RANS model is also conducted to assess the ability of this method to forecast and complement experimental dataset for additional urban forms. Essential physical parameters, such as water height and the horizontal velocity field of the free surface, are investigated within specific regions of interest. Flow tracking employs recent measurement techniques adapted to the fast physical phenomenon measured. The examined obstacle configurations highlighted interesting flow features, such as the formation of splash-ups and wake zones around the obstacles. The surroundings of these obstacles are compared to a reference dam-break case and analysed. The experimental dataset is then compared to numerical results from the open-source CFD software, code_saturne, focusing on water depth and horizontal velocity. Overall, the results of the laboratory experiments provide a valuable new dataset for validating both 2D and 3D models used to simulate fast transient flows around obstacle configurations.

Oscillation of the water height are observed upstream of the breach for all configurations. This phenomenon leads to variations in the flow rate through the gate, subsequently impacting the flow behaviour downstream of it. This is an important condition to take into account for future experimental and numerical studies in order to accurately reproduce the observed dam break wave. Two key features of the flow are significantly influenced by the presence of obstacle configuration: the splash-up upstream of it and the position and temporal variation of the wake zone downstream. Results obtained with the VOF module of code_saturne correctly replicate the formation of the splash-up and wake zone around the obstacle configurations. Overall, the numerical model reproduces the wake zone water depth and velocity well, yielding that one could use such numerical strategy to study the state of the flow downstream of the urban areas. Still, a numerical underestimation of the water height closer to the obstacles was observed for some cases, partially due to the applied post-processing method and to limitations of the VOF method in accurately capturing two-phase dynamics. Specific diphasic models may be more suitable for detailed analysis in such areas. Thus, further analysis is suggested to refine the post-processing methods for VOF numerical models. Finally, future work aims to extend this experimental protocol to new obstacle configurations representative of suburban areas and to study their impact on the flow on a larger scale combining both experimental and numerical results.

# Acknowledgments

The authors thank the POMPHY team (EDF Lab Chatou, France) for support during the manufacturing of the experimental device and the code_saturne development team for the valuable discussions on parameterising the numerical model. All computational facilities were provided by EDF R&D and the LHSV laboratory.

# Funding

Elisa Beteille acknowledges the financial support of her PhD research program provided by the French ANRT (Association Nationale de la Recherche et de la Technologie) with CIFRE Grant 2021/0912.
Notation

F = Froude number (-)

g = gravity acceleration (m s⁻²)

h = water depth (m)

h₀ = initial reservoir water depth (m)

h_m = mesh height (m)

H = dimensionless water depth (-)

H' = characteristic water depth (m)

V = dimensionless horizontal velocity (-)

V' = characteristic flow velocity (m s⁻¹)

V_t = total volume (m³)

V_x = longitudinal velocity component (m s⁻¹)

V_{X-X} = horizontal velocity of case X-X (m s⁻¹)

V_y = lateral velocity component (m s⁻¹)

V_{xy} = horizontal velocity component (m s⁻¹)

V₀ = horizontal velocity of case 0 (m s⁻¹)

t = time (s)

t_{avg} = time interval (s)

t_{gate} = opening time of the gate (s)

α = void fraction (-)

Ω = volume (m³)

ρ = density (kg m⁻³)

References

Amicarelli, A., Manenti, S., and Paggi, M. (2021). Sph modelling of dam-break floods, with damage assessment to electrical substations. *International Journal of Computational Fluid Dynamics*, 35(1-2):3-21.

Archambeau, F., Méchitoua, N., and Sakiz, M. (2004). Code Saturne: A Finite Volume Code for the computation of turbulent incompressible flows - Industrial Applications. *International Journal on Finite Volumes*, 1(1):http://www.latp.univ-mrs.fr/IJFV/spip.php?article3. Publisher: Institut de Mathématiques de Marseille, AMU.

Arnell, N. W. and Gosling, S. N. (2016). The impacts of climate change on river flood risk at the global scale. *Climatic Change*, 134:387-401.

Aureli, F., Maranzoni, A., and Petaccia, G. (2021). Review of Historical Dam-Break Events and Laboratory Tests on Real Topography for the Validation of Numerical Models. *Water*, 13(14):1968.

Aureli, F., Maranzoni, A., and Petaccia, G. (2024). Advances in dam-break modeling for flood hazard mitigation: Theory, numerical models, and applications in hydraulic engineering. *Water*, 16(8).

Aureli, F., Maranzoni, A., Petaccia, G., and Soares-Frazão, S. (2023). Review of Experimental Investigations of Dam-Break Flows over Fixed Bottom. *Water*, 15(6):1229.

Barbero, G., Moisello, U., and Todeschini, S. (2014). Evaluation of the areal reduction factor in an urban area through rainfall records of limited length: A case study. *Journal of Hydrologic Engineering*, 19(11):05014016.

Bellos, C. (2004). Experimental measurements of flood wave created by a dam break. *Eur. Water*, 7(8).

Beteille, E., Boyaval, S., Larrarte, F., and Demay, E. (2024). Experimental and numerical investigation of dam break flow propagation through various obstacle configurations. In Gourbesville,
P. and Caignaert, G., editors, *Advances in Hydroinformatics—SimHydro 2023 Volume 1*, pages 269–287, Singapore. Springer Nature Singapore.

Beteille, E., Larrarte, F., Boyaval, S., and Demay, E. (2023). Providing Validation Data for Numerical Codes Dealing with Unsteady Urban Flooding. In *Proceedings of the 40th IAHR World Congress*, pages 1456–1464, Vienna, Austria.

Cea, L. and Vázquez-Cendón, M. E. (2010). Unstructured finite volume discretization of two-dimensional depth-averaged shallow water equations with porosity. *International Journal for Numerical Methods in Fluids*, 63(8):903–930.

Chen, A. S., Evans, B., Djordjević, S., and Savić, D. A. (2012). A coarse-grid approach to representing building blockage effects in 2d urban flood modelling. *Journal of Hydrology*, 426-427:1–16.

Chen, H.-X., Li, J., Feng, S.-J., Gao, H.-Y., and Zhang, D.-M. (2019). Simulation of interactions between debris flow and check dams on three-dimensional terrain. *Engineering Geology*, 251:48–62.

Chen, Y., Zhou, H., Zhang, H., Du, G., and Zhou, J. (2015). Urban flood risk warning under rapid urbanization. *Environmental Research*, 139:3–10. Environmental Research on Hydrology and Water Resources.

Delforge, D., Wathelet, V., Below, R., Sofia, C., Margo, T., van Loenhout, J., and Speybroeck, N. (2023). Em Dat: the emergency events database.

Deshpande, S. S., Anumolu, L., and Trujillo, M. F. (2012). Evaluating the performance of the two-phase flow solver interFoam. *Computational Science & Discovery*, 5(1):014016.

Di Cristo, C., Evangelista, S., Greco, M., Iervolino, M., Leopardi, A., and Vacca, A. (2018). Dam-break waves over an erodible embankment: experiments and simulations. *Journal of Hydraulic Research*, 56(2):196–210.

Doumic, A., Larrarte, F., Rtimi, R., and Goutal, N. (2021). Investigation of the hydraulics in flooded housing estate. In *SimHydro*, page 9.

Fluixá-Sanmartín, J., Altarejos-García, L., Morales-Torres, A., and Escuder-Bueno, I. (2018). Climate change impacts on dam safety. *Natural Hazards and Earth System Sciences*, 18(9):2471–2488.

Garoosi, F., Nicole Mellado-Cusicahua, A., Shademani, M., and Shakibaeinia, A. (2022). Experimental and numerical investigations of dam break flow over dry and wet beds. *International Journal of Mechanical Sciences*, 215:106946.

Guinot, V. (2012). Multiple porosity shallow water models for macroscopic modelling of urban floods. *Advances in Water Resources*, 37:40–72.

Hettiarachchi, S., Wasko, C., and Sharma, A. (2018). Increase in flood risk resulting from climate change in a developed urban watershed - the role of storm temporal patterns. *Hydrology and Earth System Sciences*, 22(3):2041–2056.

Hirabayashi, Y., Mahendran, R., Koirala, S., Konoshima, L., Yamazaki, D., Watanabe, S., Kim, H., and Kanae, S. (2013). Global flood risk under climate change. *Nature climate change*, 3(9):816–821.

Jenkins, K., Surminski, S., Hall, J., and Crick, F. (2017). Assessing surface water flood risk and management strategies under future climate change: Insights from an agent-based model. *Science of The Total Environment*, 595:159–168.

Jodeau, M., Hauet, A., Le Coz, J., Faure, J.-B., and Bodart, G. (2022). Fudaa-LSPIV User Manual (v 1.9.2). Technical report.

Kamra, M. M., Mohd, N., Liu, C., Sueyoshi, M., and Hu, C. (2018). Numerical and experimental investigation of three-dimensionality in the dam-break flow against a vertical wall. *Journal of Hydrodynamics*, 30(4):682–693.

Kim, B., Sanders, B. F., Famiglietti, J. S., and Guinot, V. (2015). Urban flood modeling with porous shallow-water equations: A case study of model errors in the presence of anisotropic porosity. *Journal of Hydrology*, 523:680–692.

Larrarte, F., Goutal, N., and Doumic, A. (2022). Floods in housing estate: some experimental data.
In *Proceedings of the 39th IAHR World Congress*, pages 6981–6986. International Association for Hydro-Environment Engineering and Research (IAHR).

Lauber, G. and Hager, W. H. (1998). Experiments to dambreak wave: Horizontal channel. *Journal of Hydraulic Research*, 36(3):291–307.

Le Coz, J., Jodeau, M., Hauet, A., Marchand, B., and Le Boursicaud, R. (2014). Image-based velocity and discharge measurements in field and laboratory river engineering studies using the free Fudaa-LSPIV software. In Schleiss, A., de Cesare, G., Franca, M., and Pfister, M., editors, *River Flow 2014*, pages 1961–1967. CRC Press.

Lehmann, J., Coumou, D., and Frieler, K. (2015). Increased record-breaking precipitation events under global warming. *Climatic Change*, 132.

Li, W., Li, Z., Ge, W., and Wu, S. (2019). Risk Evaluation Model of Life Loss Caused by Dam-break Flood and Its Application. *Water*, 11(7):1359.

Maghsoodi, R., Khademalrasoul, A., and Sarkardeh, H. (2022). 3D numerical simulation of dam-break flow over different obstacles in a dry bed. *Water Supply*, 22(4):4015–4029.

Manenti, S., Amicarelli, A., Palazzolo, N., Bordoni, M., Creaco, E., and Meisina, C. (2020). Post-failure dynamics of rainfall-induced landslide in oltrepò pavese. *Water*, 12(9).

Mignot, E., Li, X., and Dewals, B. (2019). Experimental modelling of urban flooding: A review. *Journal of Hydrology*, 568:334–342.

Monaco, A. D., Manenti, S., Gallati, M., Sibilla, S., Agate, G., and Guandalini, R. (2011). Sph modeling of solid boundaries through a semi-analytic approach. *Engineering Applications of Computational Fluid Mechanics*, 5(1):1–15.

Qin, W., Xu, H.-m., Liu, M., Liu, L., Xiao, C., Mallakpour, I., Rahnamay Naeini, M., Hsu, K., and Sorooshian, S. (2022). Projected impacts of climate change on major dams in the upper yangtze river basin. *Climatic Change*, 170.

Salis, N., Luo, M., Reali, A., and Manenti, S. (2022). Wave generation and wave-structure impact modelling with wsph. *Ocean Engineering*, 266:113228.

Sanders, B. F., Schubert, J. E., and Gallegos, H. A. (2008). Integral formulation of shallow-water equations with anisotropic porosity for urban flood modeling. *Journal of Hydrology*, 362(1):19–38.

Schubert, J. E. and Sanders, B. F. (2012). Building treatments for urban flood inundation models and implications for predictive skill and modeling efficiency. *Advances in Water Resources*, 41:49–64.

Soares-Frazão, S. and Zech, Y. (2007). Experimental study of dam-break flow against an isolated obstacle. *Journal of Hydraulic Research*, 45(sup1):27–36.

Soares-Frazão, S. and Zech, Y. (2008). Dam-break flow through an idealised city. *Journal of Hydraulic Research*, 46(5):648–658.

Testa, G., Zuccalà, D., Alcrudo, F., Mulet, J., and Soares-Frazão, S. (2007). Flash flood flow experiment in a simplified urban district. *Journal of Hydraulic Research*, 45(sup1):37–44.

Todeschini, S. (2016). Hydrologic and environmental impacts of imperviousness in an industrial catchment of northern italy. *Journal of Hydrologic Engineering*, 21(7):05016013.

Tomiczek, T., Prasetyo, A., Mori, N., Yasuda, T., and Kennedy, A. (2016). Physical modelling of tsunami onshore propagation, peak pressures, and shielding effects in an urban building array. *Coastal Engineering*, 117:97–112.

Wang, K., Yang, P., Yu, G., Yang, C., and Zhu, L. (2020). 3d numerical modelling of tailings dam breach run out flow over complex terrain: A multidisciplinary procedure. *Water*, 12(9).

Wei, Z., Dalrymple, R. A., Hérault, A., Bilotta, G., Rustico, E., and Yeh, H. (2015). SPH modeling of dynamic impact of tsunami bore on bridge piers. *Coastal Engineering*, 104:26–42.

Westerweel, J. and Scarano, F. (2005). Universal outlier detection for PIV data. *Experiments in Fluids*, 39(6):1096–1100.

Wood, A. and Wang, K.-H. (2015). Modeling dam-break flows in channels with 90 degree bend using an alternating-direction implicit based curvilinear hydrodynamic solver. *Computers &
Fluids, 114:254–264.

Yerramilli, S. (2013). Potential impact of climate changes on the inundation risk levels in a dam break scenario. *ISPRS international journal of geo-information*, 2(1):110–134.

Yu, D. and Lane, S. N. (2006). Urban fluvial flood modelling using a two-dimensional diffusion-wave treatment, part 2: development of a sub-grid-scale treatment. *Hydrological Processes*, 20(7):1567–1583.

Zhang, Y., Wang, Y., Chen, Y., Xu, Y., Zhang, G., Lin, Q., and Luo, R. (2021). Projection of changes in flash flood occurrence under climate change at tourist attractions. *Journal of Hydrology*, 595:126039.

Zhuang, Y., Jin, K., Cheng, Q.-g., Xing, A., and Luo, H. (2022). Experimental and numerical investigations of a catastrophic tailings dam break in daye, hubei, china. *Bulletin of Engineering Geology and the Environment*, 81.