Predicting field calibration factors for suspended sediment monitoring based on particle size and shape

Reliable real-time measurement of suspended sediment mass concentration (SSC) is essential for effective environmental monitoring and management. It is also important for the operation and maintenance of hydropower schemes, particularly in managing reservoir sedimentation and mitigating turbine abrasion. However, sensor readings are strongly influenced by variable sediment properties, particularly size and shape, hindering reliable monitoring. This study systematically investigates the effects of particle size (median particle diameter d₅₀ and Sauter Mean Diameter SMD) and shape (sphericity Ψ) on the responses of several turbidimeters and acoustic sensors (single- and multi-frequency), and develops methods for practical application. A customized recirculating cylindrical tank with a volume of 246 L and a maximum upward flow velocity of 0.2 ms⁻¹ enabled testing various natural and artificial particles (up to 2 mm) across SSCs from 0.5 to 25 gl⁻¹. We analyzed the specific outputs of the instruments, defined as the outputs divided by SSC, representing the calibration factors for each particle type. We found that for turbidimeters, the specific output scaled with inverse power-law relations of d₅₀ as well as SMD, and decreased nearly linearly with Ψ. SMD and Ψ proved effective for combining size/shape effects and representing shape-related output, offering a basis for generalized field calibration. We developed three generic models to predict sensor output conversion factors for improved real-time SSC monitoring and calibration. The best-performing data-driven model, applied to a natural sediment sample, showed good agreement for turbidimeters but overestimated acoustic sensor response, highlighting refinement needs. The findings advance the understanding of sensor responses and support the feasibility of generic prediction models across diverse sediment types and sensor technologies. This study contributes to better informed sensor selection and calibration, directly enabling more effective and sustainable monitoring and management of water and sediment resources.