Mutual validation of remote hydraulic estimates and flow model simulations using UAV-borne LiDAR and deep learning-based imaging techniques

Abstract

Hydraulic modeling plays a pivotal role in various river-related applications, but acquiring precise datasets can be challenging due to complex environments and limited availability of field data. To address these challenges, remote sensing approaches have shown promise, but their widespread scientific use requires validation. This study investigates the utilization of an uncrewed aerial vehicle (UAV)-integrated topo-bathymetric green LiDAR system (GLS) combined with deep learning-based space-time image velocimetry (DL-STIV) techniques to remotely analyze hydraulic properties in the lower Asahi River, Japan. The research compares streamflow measurements with flow model simulations at four transects within a 1.2 km gravel bed channel with vegetation. The results demonstrate that the DL-STIV technique provides reliable velocity estimates, with root-mean-square errors ranging from 0.014 to 0.247 m/s. When using a typical velocity conversion factor of 0.85, the DL-STIV method aligns well with model predictions, yielding coefficient of determination values between 0.850 and 0.959. However, the choice of conversion index (ranging from 0.75 to 0.95) significantly affects accuracy. The study also reveals that remotely estimated discharges are reasonably consistent with model estimates, with errors ranging from 0.026 to 11.124%. However, accuracy is influenced by factors such as the presence of obstacles, uncertainties in remotely sensed water depths, and the choice of reference values. In conclusion, this research demonstrates the suitability and potential of GLS and DL-STIV for remotely analyzing hydraulic properties in rivers. The study also points to the importance of considering potential variations and uncertainties when adopting these techniques for flow modeling and validation purposes.