Image velocimetry techniques are emerging as a powerful tool for river monitoring, offering the ability to quantify surface flow dynamics while overcoming the structural limitations of traditional monitoring systems. However, their performances remain highly sensitive to the environmental conditions – such as lighting, shadows, seeding density and distribution. One of the main challenges in real-world applications is the need for detectable surface tracers or a homogeneous distribution of materials across the flow section. In their absence, velocity fields—particularly near riverbanks—can exhibit high variance and systematic underestimation.In this study, the SSIMS-Flow software was tested to estimate surface flow velocities under different seeding conditions. The application was carried out on the Arrow River (UK) along two meandering river reaches under low-flow conditions. Seven experiments ranging from low (natural) to medium-high (artificial) seeding density were performed, varying the upstream tracer quantities. Conventional velocity measurements across various transects were used as benchmarks.Performances of SSIMS-Flow under different settings were evaluated and compared with those obtained with PIVlab software. The analysis demonstrates that the MultiTMP pooling technique within SSIMS-Flow achieved high predictive accuracy with Nash-Sutcliffe Efficiency (NSE) values often exceeding 0.8 and Root Mean Square Error (RMSE) as low as 0.02 m/s under low to high seeding densities and provided comparable results to PIVlab’s Ensemble correlation method even in unseeded conditions. The results underscore the importance of selecting appropriate parameters based on field conditions and provide specific recommendations for optimizing parameters like spatial pooling block size and framerate for image velocimetry applications.
Keywords: Flow velocity, Image velocimetry, SSIMS-Flow, LSPIV, Optical flow, Seeding density
How to cite: Dal Sasso, Silvano Fortunato and Ljubicic, Robert and Zindovic, Budo and Pizarro, Alonso and Pearce, Sophie and Maddock, Ian and Manfreda, Salvatore, Evaluating Ssims-Flow Velocimetry Performances Under Varying Seeding Densities: A Proof-of-Concept Field Study. Available at SSRN: http://dx.doi.org/10.2139/ssrn.5293007