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

Kong, Xiangfu; Yang, Jiawen; Xu, Ke; Dong, Bo; Jiang, Shan

doi:https://doi.org/10.5194/hess-27-3803-2023

Articles | Volume 27, issue 20

https://doi.org/10.5194/hess-27-3803-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-27-3803-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 27, issue 20

Research article

|

27 Oct 2023

Research article |

| 27 Oct 2023

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

Xiangfu Kong, Jiawen Yang, Ke Xu, Bo Dong, and Shan Jiang

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.7894921

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.7894921

Short summary

To solve the issue of sparsity of field-observed runoff data, we propose 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 ungauged catchment problem, providing alternative flooding early warning supports for cities that have little runoff data but rich taxi data.