Preprints
https://doi.org/10.5194/hess-2023-7
https://doi.org/10.5194/hess-2023-7
 
24 Jan 2023
24 Jan 2023
Status: this preprint is currently under review for the journal HESS.

A Bayesian updating framework for calibrating hydrological parameters of road network using taxi GPS data

Xiangfu Kong1,2, Jiawen Yang2, Ke Xu3, Bo Dong1, and Shan Jiang4,5 Xiangfu Kong et al.
  • 1Research Center for AI Social Governance, Zhejiang Lab, Hangzhou, Zhejiang 311100, China
  • 2Shenzhen Graduate School, Peking University, Shenzhen, Guangdong 518055, China
  • 3Zhejiang Development and Planning Institute, Hangzhou, Zhejiang 310012, China
  • 4Department of Urban and Environmental Policy and Planning, Tufts University, Medford, MA 02155, USA
  • 5Department of Civil and Environmental Engineering, Tufts University, Medford, MA 02155, USA

Abstract. Hydrological parameters should pass through a careful calibration procedure before aiding decision-making. However, great difficulties are encountered when applying calibration methods to regions where runoff data are inadequate. To fill the gap of hydrological calibration for the ungaged road network, we proposed a Bayesian updating framework to calibrate hydrological parameters based on taxi GPS data. Hydrological parameters are calibrated by adjusting their values such that the runoff generated by the acceptable parameter sets could yield the road disruption period during which no taxi points are observed. The method is validated through 10 flood-prone roads in Shenzhen, and the result reveals that the trends of runoff could be correctly predicted for 8 out of 10 roads. This study shows that integration of hydrological model and taxi GPS data suggests viable alternative measures for the model calibration, and provides actionable insights for flood hazard mitigation.

Xiangfu Kong et al.

Status: open (until 21 Mar 2023)

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Xiangfu Kong et al.

Data sets

Data and code used in the article titled " A Bayesian updating framework for calibrating hydrological parameters of road network using taxi GPS data" Xiangfu Kong https://doi.org/10.5281/zenodo.7294880

Model code and software

Data and code used in the article titled " A Bayesian updating framework for calibrating hydrological parameters of road network using taxi GPS data" Xiangfu Kong https://doi.org/10.5281/zenodo.7294880

Xiangfu Kong et al.

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Short summary
To solve the issue of sparsity of field-observed runoff data, we proposed a methodology that leverages taxi GPS data to support hydrological parameter calibration for road networks. Novel to this study is that a new kind of data source, namely floating car data, is introduced to tackle the ungaged catchment problem, providing alternative flooding early warning supports for cities which are short of runoff data but rich of taxi data.