The probabilistic solution of dike breaching due to overtopping

Alhasan, Zakaraya; Duchan, David; Říha, Jaromír

doi:10.1201/b21902-88

Cited by 1 publication

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Results appear to be particularly different between configurations with no inflow discharge but an initial water level slightly higher than the initial breach depth (Tests 36 and 40 to 44) and tests with an inflow discharge (all other tests). In the latter case, uncertainties in input parameters affecting erosion, that is, effective shear stress and sediment concentration are almost systematically the most critical for both outputs, as already pointed in many previous works (Alhasan et al., 2016; Kalinina et al., 2020; Peter et al., 2018; Sattar, 2014; Westoby et al., 2015). Those include A n (parameter in Strickler formula), θ cr (critical Shields parameter) and λ (empirical coefficient involved in mixing length computation).…”

Section: Resultssupporting

confidence: 54%

“…Others performed global sensitivity analysis. Among them, Alhasan et al (2016) 2019), but also considering each only a single case study. In contrast, in the present work, Sobol indices of total order were computed for each input subset in twenty-seven embankment configurations both at laboratory and field scales.…”

Section: Step 3: Results Exploitationmentioning

confidence: 99%

“…Among them, Alhasan et al. (2016) and Bellos et al. (2020) used a Morris screening technique, whilst Ahmadisharaf et al.…”

Section: Uncertainty Quantification Data and Methodsmentioning

confidence: 99%

See 2 more Smart Citations

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

View full text Add to dashboard Cite

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 (

show abstract