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

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.