Articles | Volume 27, issue 20
https://doi.org/10.5194/hess-27-3803-2023
https://doi.org/10.5194/hess-27-3803-2023
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

Viewed

Total article views: 2,080 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,727 294 59 2,080 34 29
  • HTML: 1,727
  • PDF: 294
  • XML: 59
  • Total: 2,080
  • BibTeX: 34
  • EndNote: 29
Views and downloads (calculated since 24 Jan 2023)
Cumulative views and downloads (calculated since 24 Jan 2023)

Viewed (geographical distribution)

Total article views: 2,080 (including HTML, PDF, and XML) Thereof 2,023 with geography defined and 57 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 04 Nov 2024
Download
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.