Novel variable reconstruction and friction term discretisation schemes for hydrodynamic modelling of overland flow and surface water flooding

Zhao, Jiaheng; Liang, Qiuhua

doi:10.1016/j.advwatres.2022.104187

Cited by 20 publications

(9 citation statements)

References 46 publications

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“…For this reason, the implementation of new formulations of the source terms capable of ensuring the greater accuracy and stability of the numerical resolution schemes is a constant subject of investigation, especially in the context of the development of Godunov-type finite-volume hydrodynamic models. Finally, particular attention is paid to providing new schemes with which to reconstruct the values of flow variables at the boundaries of grid cells and to improve and simplify the discretization of the stiff friction term [79,80].…”

Section: Recent Advancements In Numerical Modeling Of Dam-break Flows...mentioning

confidence: 99%

Advances in Dam-Break Modeling for Flood Hazard Mitigation: Theory, Numerical Models, and Applications in Hydraulic Engineering

Aureli,

Maranzoni,

Petaccia

2024

Water

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Section: Recent Advancements In Numerical Modeling Of Dam-break Flows...mentioning

confidence: 99%

Advances in Dam-Break Modeling for Flood Hazard Mitigation: Theory, Numerical Models, and Applications in Hydraulic Engineering

Aureli,

Maranzoni,

Petaccia

2024

Water

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“…Finite volume discretizations are widely used in overland flooding modeling since they provide a framework that is robust in the presence of drying regions, can capture discontinuities such as hydraulic bores or non-smooth topography, well-balanced with respect to nearly steady flows, and can resolve the inundating shoreline and run-up features; see for instance [3,[38][39][40][41], and [42].…”

Section: Finite Volume Discretizationmentioning

confidence: 99%

GeoFlood: Computational Model for Overland Flooding

Kyanjo,

Calhoun,

George

2024

Preprint

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This paper presents GeoFlood, a new open-source software package for solving shallow water equations (SWE) on a quadtree hierarchy of mapped, logically Cartesian grids managed by the parallel, adaptive library ForestClaw (Calhoun and Burstedde, 2017). The GeoFlood model is validated using standard benchmark tests from Neelz and Pender (2013) and against George (2011) results obtained from the GeoClaw software (Clawpack Development Team, 2020) for the historical Malpasset dam failure problem. The benchmark test results are compared against GeoClaw and software package HEC-RAS (Hydraulic Engineering Center - River Analysis System, Army Corp of Engineers) results (Brunner, 2018). This comparison demonstrates the capability of GeoFlood to accurately and efficiently predict flood wave propagation on complex terrain. The results from comparisons with the Malpasset dam break show good agreement with the GeoClaw results and are consistent with the historical records of the event.

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“…Using the less mathematically complex solver–neglecting convective acceleration terms– though a popular choice for UQ analysis (Beevers et al., 2020), had only a CPU version i.e., slower to run than GPU‐FV1 for a simulation on grids with more than half a million cells (Shaw et al., 2021). Hence, GPU‐FV1 is a mathematically complete and fast physical solver that can reproduce all types of hydrodynamic flow regimes and responses as accurately as second‐order finite volume solvers for realistic floodplain flow simulations (Ayog et al., 2021; Zhao & Liang, 2022).…”

Section: Uq Analysis Frameworkmentioning

confidence: 99%

Sampling‐Based Methods for Uncertainty Propagation in Flood Modeling Under Multiple Uncertain Inputs: Finding Out the Most Efficient Choice

Hajihassanpour

Kesserwani

Pettersson

et al. 2023

Water Resources Research

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In probabilistic flood modeling, uncertainty manifests in frequency of occurrence, or histograms, for quantities of interest, including the Flood Extent and hazard rating (HR). Such modeling at the field‐scale requires the identification of a more efficient alternative to the Standard Monte Carlo (SMC) method that can reproduce comparable output probability distributions with a relatively reduced sample size, including detailed histograms of quantities of interest. Latin hypercube sampling (LHS) is the most evaluated alternative for fluvial floods but yields no considerable sample size reduction. Potentially better alternatives include adaptive stratified sampling (ASS), Quasi Monte Carlo (QMC) and Haar‐wavelet expansion (HWE), which are yet unevaluated for probabilistic flood modeling. To fulfill this gap, LHS, ASS, QMC, and HWE are compared to quantify sample size reduction to reproduce output detailed histograms—for Flood Extent, and average and maximum HR—while keeping the difference below 10% to the reference SMC prediction. The comparison is done for two test cases with two (i.e., inflow discharge and Manning's coefficient) and three (i.e., further including the ground elevation) input random variables, and a real case with five input random variables. With two input random variables, all four alternatives yield sample size reductions, with QMC and HWE considerably outperforming the others; with three and more input random variables, HWE becomes inflexible and LHS underperforms. Still, QMC is a better choice than ASS to boost sample size reduction for the real case and shall be preferred in probabilistic flood modeling. Accompanying research codes are openly available online.

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Novel variable reconstruction and friction term discretisation schemes for hydrodynamic modelling of overland flow and surface water flooding

Cited by 20 publications

References 46 publications

Advances in Dam-Break Modeling for Flood Hazard Mitigation: Theory, Numerical Models, and Applications in Hydraulic Engineering

Advances in Dam-Break Modeling for Flood Hazard Mitigation: Theory, Numerical Models, and Applications in Hydraulic Engineering

GeoFlood: Computational Model for Overland Flooding

Sampling‐Based Methods for Uncertainty Propagation in Flood Modeling Under Multiple Uncertain Inputs: Finding Out the Most Efficient Choice

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