Preprints
https://doi.org/10.5194/hess-2020-59
https://doi.org/10.5194/hess-2020-59
04 Mar 2020
 | 04 Mar 2020
Status: this preprint has been withdrawn by the authors.

Using unmanned aerial vehicle and volunteered geographic information to sophisticate urban flood modelling

Yuan-Fong Su, Yan-Ting Lin, Jiun-Huei Jang, and Jen-Yu Han

Abstract. Sophisticated flood simulation in urban areas is a challenging task due to the difficulties in data acquisition and model verification. This study incorporates three rapid-growing technologies, i.e. volunteered geographic information (VGI), unmanned aerial vehicle (UAV), and computational flood simulation (CFS) to reconstruct the flash flood event occurred in 14 June 2015, GongGuan, Taipei. The high-resolution digital elevation model (DEM) generated by a UAV and the real-time VGI photos acquired from social network are served to establish and validate the CFS model, respectively. The DEM data are resampled based on two grid sizes to evaluate the influence of terrain resolution on flood simulations. The results show that flood scenario can be more accurately modelled as DEM resolution increases with better agreement between simulation and observation in terms of flood occurrence time and water depth. The incorporation of UAV and VGI lower the barrier of sophisticated CFS and shows great potential in flood impact and loss assessment in urban areas.

This preprint has been withdrawn.

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Yuan-Fong Su, Yan-Ting Lin, Jiun-Huei Jang, and Jen-Yu Han

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Yuan-Fong Su, Yan-Ting Lin, Jiun-Huei Jang, and Jen-Yu Han
Yuan-Fong Su, Yan-Ting Lin, Jiun-Huei Jang, and Jen-Yu Han

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Short summary
This study incorporates volunteered geographic information (VGI), unmanned aerial vehicle (UAV), and computational flood simulation (CFS) for flood analysis in urban areas. The digital elevation model (DEM) generated by a UAV and the VGI photos acquired from social network are served to establish and validate the CFS model, respectively. The results show that the flood scenarios can be more accurately modeled and the cost of sophisticated CFS can be greatly reduced with the help of UAV and VGI.