Author(s): Cheng-Wie Wu; Hao-Che Ho

Linked Author(s):

Keywords: Large-Scale Particle Image Velocimetry; Acoustic Doppler Current Profiler; Internet of things.

Abstract: Taiwan's rugged and steep terrain, coupled with distinct wet and dry seasons, results in significant variations in river flow. In addition, the diverse and complex land use planning in the region makes it difficult for traditional hydrological and hydraulic models to accurately represent the actual hydrological and hydraulic phenomena. In the past, to improve the accuracy of numerical simulations, extensive and long-term hydrological observations have been conducted in experimental catchments, and numerical models have been validated under different land use conditions within the catchment to improve the accuracy of hydrological and hydraulic model parameters. This research uses various measurement techniques to obtain flow data for comparison, while integrating Internet of Things (IoT) technology to provide a clearer observation interface for users. We used the Bagu drainage watershed in Taiwan as a case study and uses various hydrological monitoring stations to collect data, using different flow measurement techniques for research purposes. These observations will facilitate their application in the validation and verification of hydrological and hydraulic numerical models. To increase the diversity of observations, this study will simultaneously use real-time radar-based instrument, Recognition Measurement Technology and Acoustic Doppler Current Profiler (ADCP) for surface velocity and stage measurements and will compare and analyse the differences in data obtained from these three observation methods. The results indicate that both methods are effective in providing accurate and real-time flow estimates. These research results can be comprehensively preserved and digitized through the Internet of Things (IoT), facilitating their future application in the verification and validation of hydrological and hydraulic numerical models. This will also contribute to the development of real-time monitoring systems.

DOI: https://doi.org/10.3929/ethz-b-000675921

Year: 2024