Channel‐reach morphology and landscape properties are linked across a large heterogeneous region. Issue 1 (18th October 2021) - Lane, Belize; Guillon, Herve; Byrne, Colin; Pasternack, Gregory B.; Kasprak, Alan; Sandoval‐Solis, Samuel
Abstract: Given the complex array of processes influencing river networks, conceptual frameworks of rivers are critical to our understanding of channel processes and response potential, as well as restoration efforts. Yet despite their wide usage, many classifications are based on limited observations over homogenous landscapes, raising questions about their general applicability and quantitative thresholds. Leveraging a large, transect‐based morphological field dataset across California, USA, we use data‐driven methods to evaluate multivariate patterns in channel morphology and linkages with landscape properties considering a diversity of physio‐climatic settings. Emergent patterns highlight the variability in channel form observed across an extensive dataset over heterogeneous but spatially linked watersheds. In general, identified dominant channel attributes and landscape properties align with established channel types defined through expert judgement, but key differences also emerge. Similar to past studies, bed sediment composition and sub‐reach depth variability were discriminating channel attributes. The dominance of landscape properties associated with sediment supply or transport capacity suggests that morphological diversity largely reflects these differences as posited by prior classifications. Results also show some channel forms to be largely independent of valley confinement, with several channel bedforms and dominant grain sizes occurring across valley settings. This data analysis study demonstrates the utility of considering channel reaches and landscapes as multi‐dimensional features to elucidate and test established geomorphic understanding over large field datasets. Abstract : Data‐driven methods highlight multivariate patterns in channel reach morphology and linkages with landscape properties based on a large field dataset spanning physio‐climatic settings.