Towards district scale flood simulations using conventional and anisotropic porosity shallow water models with high-resolution topographic information
Abstract
Current topographic survey technology provides high-resolution (HR) datasets for urban environments. Incorporating this HR information in models aiming to provide flood risk assessment is desirable because the flood wave propagation is depending on the urban topographic features, i.e. buildings, bridges and street networks. Conceptual, numerical and practical challenges arise from the application of shallow water models to HR urban flood modeling. For example, numerical challenges are occurrence of wet-dry fronts, geometric discontinuities in the urban environment and discontinuous solutions, i.e. shock waves. These challenges can be overcome by using a Godunov-type scheme. However, the computational cost of this type of schemes is high, such that HR two-dimensional shallow water simulations with practical relevance have to be run on supercomputers. The porous shallow water model is an alternative approach that aims to reduce computational cost by using a coarse resolution and accounting for unresolved processes by means of the porosity terms. Usually, a speedup between two and three orders of magnitude in comparison to HR simulations can be obtained. This study presents preliminary results of a practical test case concerning pluvial flooding in a district of the city of Nice, France, caused by the intense rainfall event on October 3rd, 2015.more »
- Authors:
-
- Technische Univ. Berlin, Berlin (Germany)
- Univ. of Nice Sophia Antipolis (UNS), Nice (France)
- Univ. of Cambridge, Cambridge (United Kingdom)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1506301
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- La Houille Blanche
- Additional Journal Information:
- Journal Volume: 2018; Journal Issue: 2; Journal ID: ISSN 0018-6368
- Publisher:
- Societe Hydrotechnique de France
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; 42 ENGINEERING; anisotropic porosity; shallow water model; urban area; FullSWOF_2D; hms; Nice
Citation Formats
Özgen, Ilhan, Abily, Morgan, Zhao, Jiaheng, Liang, Dongfang, Gourbesville, Philippe, and Hinkelmann, Reinhard. Towards district scale flood simulations using conventional and anisotropic porosity shallow water models with high-resolution topographic information. United States: N. p., 2018.
Web. doi:10.1051/lhb/2018023.
Özgen, Ilhan, Abily, Morgan, Zhao, Jiaheng, Liang, Dongfang, Gourbesville, Philippe, & Hinkelmann, Reinhard. Towards district scale flood simulations using conventional and anisotropic porosity shallow water models with high-resolution topographic information. United States. https://doi.org/10.1051/lhb/2018023
Özgen, Ilhan, Abily, Morgan, Zhao, Jiaheng, Liang, Dongfang, Gourbesville, Philippe, and Hinkelmann, Reinhard. Fri .
"Towards district scale flood simulations using conventional and anisotropic porosity shallow water models with high-resolution topographic information". United States. https://doi.org/10.1051/lhb/2018023. https://www.osti.gov/servlets/purl/1506301.
@article{osti_1506301,
title = {Towards district scale flood simulations using conventional and anisotropic porosity shallow water models with high-resolution topographic information},
author = {Özgen, Ilhan and Abily, Morgan and Zhao, Jiaheng and Liang, Dongfang and Gourbesville, Philippe and Hinkelmann, Reinhard},
abstractNote = {Current topographic survey technology provides high-resolution (HR) datasets for urban environments. Incorporating this HR information in models aiming to provide flood risk assessment is desirable because the flood wave propagation is depending on the urban topographic features, i.e. buildings, bridges and street networks. Conceptual, numerical and practical challenges arise from the application of shallow water models to HR urban flood modeling. For example, numerical challenges are occurrence of wet-dry fronts, geometric discontinuities in the urban environment and discontinuous solutions, i.e. shock waves. These challenges can be overcome by using a Godunov-type scheme. However, the computational cost of this type of schemes is high, such that HR two-dimensional shallow water simulations with practical relevance have to be run on supercomputers. The porous shallow water model is an alternative approach that aims to reduce computational cost by using a coarse resolution and accounting for unresolved processes by means of the porosity terms. Usually, a speedup between two and three orders of magnitude in comparison to HR simulations can be obtained. This study presents preliminary results of a practical test case concerning pluvial flooding in a district of the city of Nice, France, caused by the intense rainfall event on October 3rd, 2015. HR topography data set on a 1 m resolution is available for the district, whereby street features of infra-metric dimensions have been included. A reference solution is calculated by a HR shallow water model on a 1 m by 1 m structured computational grid. The porous shallow water model is run on a 10 m by 10 m grid and the influence of the drag source term is studied. The model calculations show a large deviation, which is caused by the poor meshing strategy of the porous shallow water (AP) model. The study also summarizes practical challenges that arise during the application of the AP and HR models to a large urban catchment. The main difficulty is to obtain a good mesh. In smaller scale investigations, the mesh is currently constructed by hand such that the cell edges align with buildings. This approach is not feasible for large scale urban catchments with a large number of buildings. Future steps that have to be taken, such as a strategy for automatic mesh generation, are reported on.},
doi = {10.1051/lhb/2018023},
journal = {La Houille Blanche},
number = 2,
volume = 2018,
place = {United States},
year = {2018},
month = {6}
}
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