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

Abstract. Hydrological parameters should pass through a careful calibration procedure before being used in a hydrological model that aids decision making. However, significant difficulty is encountered when applying existing calibration methods to regions in which runoff data are inadequate. To achieve accurate hydrological calibration for ungauged road networks, we propose a Bayesian updating framework that calibrates hydrological parameters based on taxi GPS data. Hydrological parameters were calibrated by adjusting their values such that the runoff generated by acceptable parameter sets corresponded to the road disruption periods during which no taxi points are observed. The proposed method was validated on 10 flood-prone roads in Shenzhen and the results revealed that the trends of runoff could be correctly predicted for 8 of 10 roads. This study demonstrates that the integration of hydrological models and taxi GPS data can provide viable alternative measures for model calibration to derive actionable insights for flood hazard mitigation.