| Abstract： |
The rampant COVID-19 pandemic swept the globe rapidly in 2021, causing a tremendous impact on human health and the global economy. This pandemic has stimulated an explosive increase of related studies in various disciplines, including geography, which has observed sub-pixel river widths to achieve an effective HD model calibration. The unknown channel Manning coefficient, river bed elevation and cross-sectional shape in the HD model are calibrated simultaneously through a Bayesian optimization algorithm. The complete workflow is implemented in a flood-prone reach in Yiluo River basin. Results show that the WSE retrieved by the new approach has a higher spatiotemporal resolution in narrower rivers than that of Sentinel-3 altimetry derived WSE, and the root-mean-square error (RMSE) varies from 0.25 to 0.59 m against in-situ WSE. The HD model calibrated by WSE retrieved by the new approach simulates the WSE with RMSE of 0.36 m and the flood extent with critical success index (CSI) of 0.60 in validation, outperforming the results of the HD model calibrated by Sentinel-3 altimetry derived WSE, with corresponding results of 0.49 m and 0.58, respectively. Additional case studies in the Maqu and Lingkou reaches further demonstrate the applicability of the WSE retrieval approach proposed in this study for HD model calibration in different types of rivers. This study highlights the potential of the proposed WSE retrieval approach in HD model calibration to improve flood forecasting in poorly gauged regions.lse 㸋ᬠ㛐လ. Alࠀ㻶⮸᪔谸脨erved.꣸ |
