Technical Note: Can Visual Gauges Trained on Biased Contact-based Gauge Data Accurately Estimate River Stage?

Abstract. Water stage variations significantly influence biochemical and hydrological processes within river networks. River camera, with its ease of deployment and low cost, has emerged as a promising tool for water stage estimation, enabling efficient water stage interpretation from images via deep learning (DL). However, a critical challenge is the requirement of accurate water stage data for DL training, which often have biases caused by sedimentations, floating debris or water flow impacts associated with contact-based gauge observations. Previous studies have overlooked the influence of gauge data errors in real-world applications. This study introduces an imaging-based water stage estimation framework that addresses hidden errors in gauge station measurements for training DL models. The framework adopts a multi-task learning paradigm, using erroneous gauge stage data as labels and incorporating water pixel ratios automatically extracted from images to constrain model estimation ranking. Based on training loss, a thresholding method then filters error-free data to retrain an unbiased model. This framework is tested on images and bubble-gauge stage data from the Minturn River, Greenland, spanning 2019 to 2021. The results obtained show the framework successfully identified a gauge offset event on July 29, 2021, and mitigated an average water stage observation error of approximately 0.6 meters thereafter. Moreover, the trained DL model revealed water stage fluctuations under low-flow conditions that gauge observation could not reflect. This study implies that integrating contact and non-contact observations is a robust approach for river stage measurement.