A Multi-Scale Method for Objective River Segmentation Using Geomorphological Indicators: Application to Sicilian Rivers

European countries have transposed the Water Framework Directive (2000/60/EC), adapting it to their ecosystems and river morphologies. Although the Directive allows methodological flexibility, it requires watercourses to be subdivided into homogeneous reaches for proper characterization and management. However, river segmentation is strongly influenced by the spatial scale of analysis, often introducing subjectivity into the process. This study presents a semi-automated approach for river segmentation based on the combined use of the Sinuosity Index (SI) and the Confinement Index (CI), aiming to improve objectivity and consistency in morphological classification. The proposed framework is designed as a rapid and transferable tool that can support river analyses across different European contexts, complementing existing national methodologies. Sinuosity is calculated every 50 m along the channel using multiple moving windows ranging from 100 to 2000 m. Each SI value represents the sinuosity computed within a moving window, covering a distance equal to its size. This multi-scale analysis helps understanding sinuosity variations across different spatial scales and identifying the most representative scale by comparing and analysing SI distributions along the river profile. Graphical analysis of SI variability highlights morphological transitions and meander spacing along the analysed reaches. After selecting the most representative SI scale, sinuosity is combined with the Confinement Index, also computed every 50 m, based on the ratio between channel width and floodplain width. A Python-based workflow integrates both indices to automatically delineate morphologically homogeneous reaches. The resulting segments are classified into Rectilinear, Sinuous or Meandering categories (SI-based) and Confined, Semi-confined or Unconfined settings (CI-based). The method was tested on several Sicilian rivers with contrasting geomorphological characteristics, demonstrating its adaptability and interpretative value. Results confirm that segmentation outcomes are highly scale-dependent, emphasizing the need for careful selection of analysis parameters. By integrating only parameters related to floodplain extent and fluvial morphology, the approach provides an efficient and reproducible tool for fluvial geomorphology and river management, reducing subjectivity in river classification.