experimental hydraulics; model scaling; urban flooding; distortion effect; numerical modelling
Abstract :
[en] Laboratory studies of urban flooding often use geometrically distorted scale models due to the multi-scale nature of these specific flows. The possible bias induced by geometric distortion has never been thoroughly investigated with dedicated laboratory experiments. In this paper, we combine experimental and computational modelling to systematically assess the influence of the distortion ratio, i.e., the ratio of horizontal to vertical scale factors, on upscaled flow depths and discharge partition between streets. Three flow configurations were considered: a street junction, a street bifurcation and a small synthetic urban district. When the distortion ratio is varied up to a value of about 5, the upscaled flow depths at the model inlets decrease monotonously and the flow discharge in the branch that conveys the largest portion of the flow is greatly enhanced. For equal flow depths at the model outlets and depending on the configuration, the distortion effect induces a variation of the upstream flow depth approximately from ~4 % to ~17 % and a change in outlet discharge partition up to 24 percentage points. For a distortion ratio above 5, both upscaled upstream flow depths and outlet discharge partition tend to stabilize asymptotically. Our study indicates the direction and magnitude of the bias induced by geometric distortion for a broad range of flow cases, which is valuable for offsetting these effects in practical laboratory studies of urban flooding.
Research Center/Unit :
UEE - Urban and Environmental Engineering - ULiège
Disciplines :
Civil engineering
Author, co-author :
Li, Xuefang ; Université de Liège - ULiège > Département ArGEnCo > Hydraulics in Environmental and Civil Engineering
Kitsikoudis, Vasileios
Mignot, Emmanuel
Archambeau, Pierre ; Université de Liège - ULiège > Département ArGEnCo > HECE (Hydraulics in Environnemental and Civil Engineering)
Pirotton, Michel ; Université de Liège - ULiège > Département ArGEnCo > HECE (Hydraulics in Environnemental and Civil Engineering)
Dewals, Benjamin ; Université de Liège - ULiège > Département ArGEnCo > Hydraulics in Environmental and Civil Engineering
Erpicum, Sébastien ; Université de Liège - ULiège > Scientifiques attachés au Doyen (Sc.appliquées)
Language :
English
Title :
Experimental and numerical study of the effect of model geometric distortion on laboratory modelling of urban flooding
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
Bibliography
Araud, Q. (2012). Simulation des écoulements en milieu urbain lors d'un événement pluvieux extrême. Université de Strasbourg.
Arndt, R., Roberts, P., & Wahl, T. (2000). Hydraulic modeling: Concepts and practice. American Society of Civil Engineers.
Arrault, A., Finaud-Guyot, P., Archambeau, P., Bruwier, M., Erpicum, S., Pirotton, M., & Dewals, B. (2016). Hydrodynamics of long-duration urban floods: Experiments and numerical modelling. Natural Hazards and Earth System Sciences, 16, 1413–1429. https://doi.org/10.5194/nhess-16-1413-2016
Assumpcao, T. H., Popescu, I., Jonoski, A., & Solomatine, D. P. (2018). Citizen observations contributing to flood modelling: Opportunities and challenges. Hydrology and Earth System Sciences, 22, 1473–1489. https://doi.org/10.5194/hess-22-1473-2018
Brown, R., & Chanson, H. (2013). Turbulence and suspended sediment measurements in an urban environment during the Brisbane River Flood of January 2011. Journal of Hydraulic Engineering, 139, 244–253. https://doi.org/10.1061/(asce)hy.1943-7900.0000666
Bruwier, M., Archambeau, P., Erpicum, S., Pirotton, M., & Dewals, B. (2017). Shallow-water models with anisotropic porosity and merging for flood modelling on Cartesian grids. Journal of Hydrology, 554, 693–709. https://doi.org/10.1016/j.jhydrol.2017.09.051
Castex, L. (1969). Quelques nouveautés sur les déversoirs pour la mesure des débits. La Houille Blanche, 55, 541–548. https://doi.org/10.1051/lhb/1969043
Chanson, H. (1999). The hydraulics of open channel flow: An introduction (1st ed., p. 512). Edward Arnold.
Chanson, H., & Wang, H. (2013). Unsteady discharge calibration of a large V-notch weir. Flow Measurement and Instrumentation, 29, 19–24. https://doi.org/10.1016/j.flowmeasinst.2012.10.010
Chen, Y., Zhou, H., Zhang, H., Du, G., & Zhou, J. (2015). Urban flood risk warning under rapid urbanization. Environmental Research, 139, 3–10. https://doi.org/10.1016/j.envres.2015.02.028
Costabile, P., Costanzo, C., Lorenzo, G. D., & Macchione, F. (2020). Is local flood hazard assessment in urban areas significantly influenced by the physical complexity of the hydrodynamic inundation model? Journal of Hydrology, 580, 124231. https://doi.org/10.1016/j.jhydrol.2019.124231
El Kadi Abderrezzak, K., Lewicki, L., Paquier, A., Rivière, N., & Travin, G. (2011). Division of critical flow at three-branch open-channel intersection. Journal of Hydraulic Research, 49, 231–238. https://doi.org/10.1080/00221686.2011.558174
El Kadi Abderrezzak, K., & Paquier, A. (2009). Numerical and experimental study of dividing open-channel flows. Journal of Hydraulic Engineering, 135, 1111–1112. https://doi.org/10.1061/(asce)hy.1943-7900.0000009
Erpicum, S., Meile, T., Dewals, B. J., Pirotton, M., & Schleiss, A. J. (2009). 2D numerical flow modeling in a macro-rough channel. International Journal for Numerical Methods in Fluids, 61, 1227–1246. https://doi.org/10.1002/fld.2002
Güney, M. S., Tayfur, G., Bombar, G., & Elci, S. (2014). Distorted physical model to study sudden partial dam break flows in an urban area. Journal of Hydraulic Engineering, 140, 05014006. https://doi.org/10.1061/(asce)hy.1943-7900.0000926
Heller, V. (2011). Scale effects in physical hydraulic engineering models. Journal of Hydraulic Research, 49, 293–306. https://doi.org/10.1080/00221686.2011.578914
Hettiarachchi, S., Wasko, C., & 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, 2041–2056. https://doi.org/10.5194/hess-22-2041-2018
Jenkins, K., Surminski, S., Hall, J., & Crick, F. (2017). Assessing surface water flood risk and management strategies under future climate change: Insights from an Agent-Based Model. The Science of the Total Environment, 595, 159–168. https://doi.org/10.1016/j.scitotenv.2017.03.242
Jongman, B. (2018). Effective adaption to rising flood risk. Nature Communication, 9. https://doi.org/10.1038/s41467-018-04396-1
Jonkman, S. N. (2005). Global perspectives on loss of human life caused by floods. Natural Hazards, 34, 151–175. https://doi.org/10.1007/s11069-004-8891-3
Kitsikoudis, V., Becker, B. P. J., Huismans, Y., Archambeau, P., Erpicum, S., Pirotton, M., & Dewals, B. (2020). Discrepancies in flood modelling approaches in transboundary river systems: Legacy of the past or well-grounded choices? Water Resources Management, 34, 3465–3478. https://doi.org/10.1007/s11269-020-02621-5
Kobus, H. (1984). Symposium on scale effects in modelling hydraulic structures. Water Resources Publications.
Leitao, J. P., Pena-Haro, S., Lüthi, B., Scheidegger, A., & Vitry, M. (2018). Urban overland runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry. Journal of Hydrology, 565, 791–804. https://doi.org/10.1016/j.jhydrol.2018.09.001
Li, X., Erpicum, S., Bruwier, M., Mignot, E., Finaud-Guyot, P., Archambeau, P., et al. (2019). Technical note: Laboratory modelling of urban flooding: Strengths and challenges of distorted scale models. Hydrology and Earth System Sciences, 23, 1567–1580. https://doi.org/10.5194/hess-23-1567-2019
Li, X., Erpicum, S., Mignot, E., Archambeau, P., Rivière, N., Pirotton, M., & Dewals, B. (2020). Numerical insights into the effects of model geometric distortion in laboratory experiments of urban flooding. Water Resources Research, 56, e2019WR026774. https://doi.org/10.1029/2019wr026774
Liu, H., Wang, Y., Zhang, C., Chen, A. S., & Fu, G. (2018). Assessing real options in urban surface water flood risk management under climate change. Natural Hazards, 94, 1–18. https://doi.org/10.1007/s11069-018-3349-1
Macchione, F., Costabile, P., Costanzo, C., & Lorenzo, G. D. (2019). Extracting quantitative data from non-conventional information for the hydraulic reconstruction of past urban flood events. A case study. Journal of Hydrology, 576, 443–465. https://doi.org/10.1016/j.jhydrol.2019.06.031
McCabe, M. F., Rodell, M., Alsdorf, D. E., Miralles, D. G., Uijlenhoet, R., Wagner, W., et al. (2017). The future of Earth observation in hydrology. Hydrology and Earth System Sciences, 21, 3879–3914. https://doi.org/10.5194/hess-21-3879-2017
Mignot, E., Li, X., & Dewals, B. (2019). Experimental modelling of urban flooding: A review. Journal of Hydrology, 568, 334–342. https://doi.org/10.1016/j.jhydrol.2018.11.001
Mignot, E., Paquier, A., & Haider, S. (2006). Modeling floods in a dense urban area using 2D shallow water equations. Journal of Hydrology, 327, 186–199. https://doi.org/10.1016/j.jhydrol.2005.11.026
Momplot, A., Kouyi, G. L., Mignot, E., Rivière, N., & Bertrand-Krajewski, J.-L. (2017). Typology of the flow structures in dividing open channel flows. Journal of Hydraulic Research, 55, 63–71. https://doi.org/10.1080/00221686.2016.1212409
Moy de Vitry, M., Kramer, S., Wegner, J. D., & Leitao, J. P. (2019). Scalable flood level trend monitoring with surveillance cameras using a deep convolutional neural network. Hydrology and Earth System Sciences, 23, 4621–4634. https://doi.org/10.5194/hess-23-4621-2019
Musolino, G., Ahmadian, R., Xia, J., & Falconer, R. A. (2020). Mapping the danger to life in flash flood events adopting a mechanics based methodology and planning evacuation routes. Journal of Flood Risk Management, 23(4), e12627. https://doi.org/10.1111/jfr3.12627
Neal, J. C., Bates, P. D., Fewtrell, T. J., Hunter, N. M., Wilson, M. D., & Horritt, M. S. (2009). Distributed whole city water level measurements from the Carlisle 2005 urban flood event and comparison with hydraulic model simulations. Journal of Hydrology, 368, 42–55. https://doi.org/10.1016/j.jhydrol.2009.01.026
Novak, P., Guinot, V., Jeffrey, A., & Reeve, D. E. (2018). Hydraulic modelling—An introduction. CRC Press.
Paquier, A., Bazin, P.-H., & El Kadi Abderrezzak, K. (2020). Sensitivity of 2D hydrodynamic modelling of urban floods to the forcing inputs: Lessons from two field cases. Urban Water Journal, 17, 457–466. https://doi.org/10.1080/1573062x.2019.1669200
Perks, M. T., Russell, A. J., & Large, A. R. G. (2016). Technical note: Advances in flash flood monitoring using unmanned aerial vehicles (UAVs). Hydrology and Earth System Sciences, 20, 4005–4015. https://doi.org/10.5194/hess-20-4005-2016
Proudovsky, A. M. (1984). General principles of approximate hydraulic modelling. In H. Kobus (Ed.), Symposium scale effects modelling hydraulic structures (pp. 0.2-1–0.2-14). International Association for Hydraulic Reasearch.
Riviere, N., Travin, G., & Perkins, R. J. (2011). Subcritical open channel flows in four branch intersections. Water Resources Research, 47, W10517. https://doi.org/10.1029/2011wr010504
Rivière, N., Travin, G., & Perkins, R. J. (2014). Transcritical flows in three and four branch open-channel intersections. Journal of Hydraulic Engineering, 140(4), 04014003. https://doi.org/10.1061/(ASCE)HY.1943-7900.0000835
Rubinato, M., Lashford, C., & Goerke, M. (2020). Advances in experimental modelling of urban flooding (pp. 235–257). International Water Association.
Rubinato, M., Martins, R., Kesserwani, G., Leandro, J., Djordjevic, S., & Shucksmith, J. (2017). Experimental calibration and validation of sewer/surface flow exchange equations in steady and unsteady flow conditions. Journal of Hydrology, 552, 421–432. https://doi.org/10.1016/j.jhydrol.2017.06.024
Schindfessel, L., Creëlle, S., & Mulder, T. D. (2015). Flow patterns in an open channel confluence with increasingly dominant tributary inflow. Water, 7, 4724–4751. https://doi.org/10.3390/w7094724
Sharp, J. J., & Khader, M. H. A. (1984). Scale effects in harbour models involving permeable rubble mound structures. In H. Kobus (Ed.), Symposium scale effects modelling hydraulic structures (pp. 7.12-1–7.12-5). International Association for Hydraulic Reasearch.
Smith, G. P., Rahman, P. F., & Wasko, C. (2016). A comprehensive urban floodplain dataset for model benchmarking. International Journal of River Basin Management, 14, 345–356. https://doi.org/10.1080/15715124.2016.1193510
Teng, J., Jakeman, A. J., Vaze, J., Croke, B. F. W., Dutta, D., & Kim, S. (2017). Flood inundation modelling: A review of methods, recent advances and uncertainty analysis. Environmental Modelling & Software, 90, 201–216. https://doi.org/10.1016/j.envsoft.2017.01.006
Wakhlu, O. N. (1984). Scale effects in hydraulic model studies. In H. Kobus (Ed.), Symposium scale effects modelling hydraulic structures (pp. 2.13-1–2.13-6). Technische Akademie Esslingen
Wang, R.-Q., Mao, H., Wang, Y., Rae, C., & Shaw, W. (2018). Hyper-resolution monitoring of urban flooding with social media and crowdsourcing data. Computers & Geosciences, 111, 139–147. https://doi.org/10.1016/j.cageo.2017.11.008
Xia, J., Falconer, R. A., Wang, Y., & Xiao, X. (2014). New criterion for the stability of a human body in floodwaters. Journal of Hydraulic Research, 52, 93–104. https://doi.org/10.1080/00221686.2013.875073
Yu, D., & Lane, S. N. (2006). Urban fluvial flood modelling using a two-dimensional diffusion-wave treatment, Part 1: Mesh resolution effects. Hydrological Processes, 20, 1541–1565. https://doi.org/10.1002/hyp.5935
Zhou, Q., Leng, G., & Huang, M. (2018). Impacts of future climate change on urban flood volumes in Hohhot in northern China: Benefits of climate change mitigation and adaptations. Hydrology and Earth System Sciences, 22, 305–316. https://doi.org/10.5194/hess-22-305-2018
This website uses cookies to improve user experience. Read more
Save & Close
Accept all
Decline all
Show detailsHide details
Cookie declaration
About cookies
Strictly necessary
Performance
Strictly necessary cookies allow core website functionality such as user login and account management. The website cannot be used properly without strictly necessary cookies.
This cookie is used by Cookie-Script.com service to remember visitor cookie consent preferences. It is necessary for Cookie-Script.com cookie banner to work properly.
Performance cookies are used to see how visitors use the website, eg. analytics cookies. Those cookies cannot be used to directly identify a certain visitor.
Used to store the attribution information, the referrer initially used to visit the website
Cookies are small text files that are placed on your computer by websites that you visit. Websites use cookies to help users navigate efficiently and perform certain functions. Cookies that are required for the website to operate properly are allowed to be set without your permission. All other cookies need to be approved before they can be set in the browser.
You can change your consent to cookie usage at any time on our Privacy Policy page.
