Uncertainty Assessment of Flood Hazard Due to Levee Breaching

Goeury, Cédric; Bacchi, Vito; Zaoui, Fabrice; Bacchi, Sophie; Pavan, Sara; Abderrezzak, Kamal El Kadi

doi:10.3390/w14233815

Cited by 7 publications

(8 citation statements)

References 57 publications

(73 reference statements)

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“…Goeury et al. (2022) also used the moment‐independent delta method because of the presence of multi‐modal output distributions. Variance‐based methods, such as Sobol indices, were used by Kalinina et al.…”

Section: Uncertainty Quantification Data and Methodsmentioning

confidence: 99%

Global Sensitivity Analysis of a Dam Breaching Model: To Which Extent Is Parameter Sensitivity Case‐Dependent?

Schmitz

Arnst

Abderrezzak

et al. 2023

Water Resources Research

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With the surge of extreme meteorological events and intensification of urbanization downstream of hydraulic structures, the need for predicting failure of dams and dikes has become of paramount importance for establishing emergency response procedures (Zhong et al., 2021). To this end, numerical models are instrumental tools for simulating the embankment breaching process. Existing models can be classified into three categories. First, statistical (or parametric) models are based solely on regression analysis of data from past events or laboratory campaigns. They describe some breaching parameters (e.g., final breach width, failure duration or maximum breach discharge) as a function of dam or reservoir properties. These simple, computational-efficient models may lack generality because they entirely rely on data from specific cases without considering underlying physics (Chen et al., 2019;De Lorenzo & Macchione, 2014;Lee, 2019). Conversely, distributed physically based models can describe the phenomenon in greater detail, as they solve the flow and sediment governing equations using a computational mesh of the domain. Their results may be accurate but only if reliable data is available and if physical processes are numerically well represented, for example, erosion of non-homogeneous dam material, slope failure or 3D-flow patterns (Cantero-Chinchilla et al., 2019;Onda et al., 2019;Pheulpin et al., 2020;Shimizu et al., 2020). Additionally, the time required to run distributed physically based models can be substantial (ASCE, 2011). Simplified physics-based models offer a good trade-off (Wu, 2013). Without spatially distributing the flow description nor the embankment morphology, they enable simulating hydraulic and dam breach variables (e.g., time-evolution of breach discharge and dimensions) by describing selected physical processes (

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Section: Uncertainty Quantification Data and Methodsmentioning

confidence: 99%

Global Sensitivity Analysis of a Dam Breaching Model: To Which Extent Is Parameter Sensitivity Case‐Dependent?

Schmitz

Arnst

Abderrezzak

et al. 2023

Water Resources Research

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“…Depending on the uncertain variable, a uniform distribution (e.g., Goeury et al. (2022)), or a beta distribution was used. Beta distributions involve two parameters ( α and β ), as detailed in Section D in Supporting Information and described in McDonald and Xu (1995).…”

Section: Methodsmentioning

confidence: 99%

“…The considered distributions were either directly derived from observations, or theoretical distributions were adjusted to match observations. Depending on the uncertain variable, a uniform distribution (e.g., Goeury et al (2022)), or a beta distribution was used. Beta distributions involve two parameters (α and β), as detailed in Section D in Supporting Information S1 and described in McDonald and Xu (1995).…”

Section: Stochastic Frameworkmentioning

confidence: 99%

Drift of a Drowning Victim in Rivers: Conceptualization and Global Sensitivity Analysis Under Idealized Flow Conditions

Delhez,

Rivière,

Erpicum

et al. 2023

Water Resources Research

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We present a conceptual model of the drift of a drowning victim in a river, accounting for the mechanical effects of body decomposition. The model was tested under idealized flow conditions (uniform flow) and a global sensitivity analysis was performed. Uncertainties in body morphology, flow‐body interaction parameters (e.g., drag coefficient) and drowning characteristics were considered. The results emphasize the importance of predicting the body vertical position since it considerably influences the streamwise body drift velocity, due to the significant difference between near‐surface and near‐bed velocity. The results also highlight the value of testimonies providing information on body mass and height, as decreasing uncertainty on these input data strongly reduces uncertainties on the predicted body streamwise position. Next step of the research will consist in coupling the model with an Eulerian multi‐dimensional model of river hydrodynamics.

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“…While Mengwa diverted the riverine floods in 2003 and 2007, the water stages at Caoji in inner Mengwa were measured synchronously with the discharge of the flood diversion sluice. Bed resistance in the 2D model was related to land use and land cover [41]. From the land use and land cover map with a resolution of 30 m, there were six types of land use and land cover in the Mengwa flood storage area: village, shrub, dryland, paddy, roads, and waterbody.…”

Section: Scenario Settingsmentioning

confidence: 99%

Flood Modeling in a Composite System Consisting of River Channels, Flood Storage Areas, Floodplain Areas, Polder Areas, and Flood-Control-Protected Areas

Hu,

Qin,

Dong

et al. 2024

Water

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The Linhuaigang flood control project (LFCP), situated on the Huaihe River, China, uses the river channels upstream of the LFCP, together with the hinterland areas outside the channels, to retain and store fluvial floodwaters that exceed the downstream channel’s discharge capacity. The hinterland areas are split into seven flood storage areas, three floodplain areas, eight polder areas, and three flood-control-protected areas, and they are connected to the river in various ways. A coupled hydrodynamic model was established to simulate the hydrodynamic and water volume exchange between the river channels and the hinterland areas. The flood storage area, under the control of a flood diversion sluice, was simulated with a 2D hydrodynamic model, and the inflow process initiated by the flood diversion sluice was simulated as a control structure. The polder area was generalized as a reservoir that would be filled in several hours once put into use because of its small size. The uncontrolled inflow process between the flood-control-protected areas and the channel was simulated by means of a dam break model, which could simulate levee breaching. The flooding within the flood-control-protected area, which represents a vast space, was simulated with a 2D hydrodynamic model. The floodplain area was laterally connected to the river channel along the river levee. The difference between the simulated and the measured flood peak water stage did not exceed 0.2 m in 2003 and 2007, indicating that the accuracy of the model was relatively high. In the scenario of a design flood with a return period of 100 years, the flood storage areas and the LFCP were used in the following order: Mengwa, Qiujiahu, Nanrunduan, Shouxihu, Jiangtanghu, Chengxihu, Chengdonghu, and the LFCP. When the Huaihe River encounters a flood with a return period of 1000 years that exceeds the design standard, the highest water stage upstream of the LFCP and Zhengyangguan shall not exceed 29.30 m and 27.96 m after the use of all the flood storage areas, floodplain areas, and flood-control-protected areas. The results of this research can provide technical support for the flood risk management of the LFCP.

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Uncertainty Assessment of Flood Hazard Due to Levee Breaching

Cited by 7 publications

References 57 publications

Global Sensitivity Analysis of a Dam Breaching Model: To Which Extent Is Parameter Sensitivity Case‐Dependent?

Global Sensitivity Analysis of a Dam Breaching Model: To Which Extent Is Parameter Sensitivity Case‐Dependent?

Drift of a Drowning Victim in Rivers: Conceptualization and Global Sensitivity Analysis Under Idealized Flow Conditions

Flood Modeling in a Composite System Consisting of River Channels, Flood Storage Areas, Floodplain Areas, Polder Areas, and Flood-Control-Protected Areas

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