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Effects of Using Different Sources of Remote Sensing and Geographic Information System Data on Urban Stormwater 2D–1D Modeling

Abstract : Remote sensing (RS) and geographic information system (GIS) data is increasingly used in urban stormwater modeling. The undirected use of such data may waste economic and human resources. In order to provide guidance for practitioners to efficiently use different data collection resources, as well as give a reference for future works, this paper aims to assess the effects of using free access GIS data and ad hoc RS data on urban 2D–1D stormwater modeling. The 2D-surface Two-dimensional Runoff, Erosion, and Export model (TREX) model was published in Science of the Total Environment in 2008. The 1D-sewer CANOE (Logiciel intégré de conception et de diagnostic des réseaux d’assainissement) model was published in Journal of Hydrology in 2004. The two models are integrated in the TRENOE (TREX-CANOE) platform. The modeling approach is applied to a small urban catchment near Paris (Le Perreux sur Marne, 0.12 km2). Simulation results reveal that the detailed land-use information derived from multiple data sources is a crucial factor for accurate simulations. Nevertheless, using the very high resolution LiDAR (light detection and ranging) data is not equally significant for the water flow simulations at sewage outlets. Finally, we suggest that using the free access GIS data accompanying the urban sewer network design might be an acceptable low-cost solution for accurate urban 2D–1D stormwater modeling during moderate rainfall events. Further studies of urban stormwater modeling could focus on the development of “suitable” models with “enough” input data, depending on the management/research objectives.
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https://hal.archives-ouvertes.fr/hal-02154304
Contributor : Yi Hong <>
Submitted on : Wednesday, June 12, 2019 - 7:05:42 PM
Last modification on : Friday, June 5, 2020 - 5:38:02 AM

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Yi Hong, Céline Bonhomme, Bahman Soheilian, Ghassan Chebbo. Effects of Using Different Sources of Remote Sensing and Geographic Information System Data on Urban Stormwater 2D–1D Modeling. Applied Sciences, MDPI, 2017, 7 (9), pp.904. ⟨10.3390/app7090904⟩. ⟨hal-02154304⟩

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