HydrocamCollect: A robust data acquisition and cloud data transfer workflow for camera-based hydrological monitoring

Reliable, high-resolution streamflow data are essential for hydrologic research, flood forecasting, and water management. Camera-based monitoring provides a promising non-contact alternative to traditional sensors. However, deployment challenges exist in remote environments, including intermittent connectivity, limited storage, and the need for manual oversight. This paper presents a robust, cloud-integrated data acquisition workflow enabling autonomous operation of camera-based hydrologic stations. Built with low-cost Raspberry Pi computers and IP cameras, the system automates image capture, verifies file integrity during cloud upload, stores metadata locally, and deletes confirmed files to conserve storage. A serverless cloud monitoring component checks for expected uploads and triggers alerts on failure. The system includes failsafe mechanisms like scheduled reattempts, data integrity safeguards, and environment-variable-based configuration for flexibility. Deployed at two sites in Utah, it operated continuously for more than four months. This work offers a fault-tolerant, scalable solution for reliable imagery collection and transfer in large-scale environmental sensing networks. • A robust workflow was developed for remote camera-based streamflow monitoring. • The system automates image capture, cloud upload, and file integrity verification. • Local storage is efficiently managed using metadata tracking and retention policies. • Alerts are triggered via cloud services when data is missing, or errors occur. • The workflow supports unattended operation using Raspberry Pi and IP cameras.