This paper presents a novel method of image processing that combines Galois Field (GF) image representation and Space Time Volume Velocimetry (STVV) algorithms namely GF-STVV. STVV is an emerging method to analyze river water flows from video sequences. This study aimed to improve the performance of STVV when applied to close-range measurements (1 to 1.5 m) by reducing the variability (repeatibility and reproducibility) of computational results. Variability is related to the precision of the water flow measurement results. Small variability values indicate high measurement precision. Experiments were carried out using two main methods. The first method was by rotating videos data virtually, and the second one was by rotating the camera directly during video captures. The Experiment result shows that GF reduced the variability of computation results if applied to a particular Region Of Interest (ROI) in video frames of water flow. The particular ROI was obtained by dividing the video frame into several small regions. The GF-STVV generate 0.0 % for both repeatibility and reproducibility, while STVV generate 2.3 % for repeatibility and 3.3 % for reproducibility. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.