Quantitative analysis of channel characteristics of distributive fluvial systems

ABSTRACT Distributive fluvial systems (DFSs) are characterised by a radial distributive channel pattern in planform and dominate modern‐day sedimentary basins. Where aggradation occurs, such as in sedimentary basins, there is increased preservation potential, and therefore, DFSs are hypothesised to constitute a significant portion of the continental fluvial rock record. However, despite their prevalence, channel characteristic variations longitudinally and between different climates are yet to be fully investigated. This study quantifies and analyses five representative DFSs across different climate zones: Río Fragua Chorroso (Colombia, tropical), Canning (Alaska, polar), Nabesna (Alaska, continental), Brahmaputra (India, subtropical) and an Unnamed Iranian DFS (Iran, dryland). The analysis quantifies downstream changes in channel belt and active channel widths (wetted channel/dry riverbed), and the proportion of bars present at the system, domain (proximal, medial and distal) and reach scales. For each DFS, 10 m Sentinel‐2 imagery was digitised to map channel belt, active channels and barforms (vegetated and non‐vegetated). Images were acquired between 2019 and 2021 across different seasons, which may influence the temporal variability of bar and channel configurations observed in each DFS. Analysis of channel belt width shows that three of the five DFSs (Río Fragua Chorroso, Nabesna and Unnamed Iranian) exhibit downstream decreases, whilst two DFSs (Brahmaputra and Canning) show contrasting downstream increases. Bars occupy more area than active channels in the proximal domain of all five DFSs and decrease in prevalence downstream, whilst vegetated and unvegetated bar complexes vary across all five DFSs. This analysis provides a comprehensive dataset to refine the DFS conceptual model and aid in the identification and characterisation of fluvial deposits across different climates. The findings also explain why some DFS do not conform to current models.