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Title: Validating city-scale surface water flood modelling using crowd-sourced data
Authors: Yu, Dapeng
Yin, Jie
Liu, Min
Keywords: Surface water flooding
Storm sewer modelling
Urban flooding
FloodMap
Shanghai
Issue Date: 2016
Publisher: © IOP Publishing
Citation: YU, D., YIN, J. and LIU, M., 2016. Validating city-scale surface water flood modelling using crowd-sourced data. Environmental Research Letters, 11 (12), pp. 124011-124011.
Abstract: Surface water and surface water related flood modelling at the city-scale is challenging due to a range of factors including the availability of subsurface data and difficulty in deriving runoff inputs and surcharge for individual storm sewer inlets. Most of the research undertaken so far has been focusing on local-scale predictions of sewer surcharge induced surface flooding, using a 1D/1D or 1D/2D coupled storm sewer and surface flow model. In this study, we describe the application of an urban hydro-inundation model (FloodMap-HydroInundation2D) to simulate surface water related flooding arising from extreme precipitation at the city-scale. This approach was applied to model an extreme storm event that occurred on 12 August 2011 in the city of Shanghai, China, and the model predictions were compared with a ‘crowd-sourced’ dataset of flood incidents. The results suggest that the model is able to capture the broad patterns of inundated areas at the city-scale. Temporal evaluation also demonstrates a good level of agreement between the reported and predicted flood timing. Due to the mild terrain of the city, the worst-hit areas are predicted to be topographic lows. The spatio-temporal accuracy of the precipitation and micro-topography are the two critical factors that affect the prediction accuracies. Future studies could be directed towards making more accurate and robust predictions of water depth and velocity using higher quality topographic, precipitation and drainage capacity information.
Description: This is an Open Access Article. It is published by IOP under the Creative Commons Attribution 3.0 Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/.
Sponsor: National Natural Science Foundation of China (Grant No: 41201550, 41371493) and the Project of Joint Center for Shanghai Meteorological Science and Technology (Grant No:2015-03).
Version: Published
DOI: 10.1088/1748-9326/11/12/124011
URI: https://dspace.lboro.ac.uk/2134/23380
Publisher Link: http://dx.doi.org/10.1088/1748-9326/11/12/124011
Appears in Collections:Published Articles (Geography)

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