Confluences are key nodes of river systems that have the potential of disrupting downstream longitudinal trends in the main river through inputs of water, sediment, wood or ice delivered from tributaries. From a geomorphological perspective, confluence zones are particularly active and thus susceptible to increased flooding and bed instability, which, in turn, pose serious threats to infrastructure. However, not all confluences are active. Despite major advances in our knowledge of confluence dynamics, there has been limited progress in the development of low complexity, practical tools that use remotely sensed data to predict the spatial distribution of the main river's sensitivity to tributary inputs. We have thus developed a novel semi-automated GIS model that uses a fuzzy approach to integrate key factors (unit stream power, valley confinement and sediment connectivity potential) in order to map the distribution of the confluence morphological sensitivity (CMS) index at the watershed scale. The GIS model was tested using digital elevation models of high (LiDAR, 1 m) and coarser (10 m) resolution in the Coaticook and Gaspésie watersheds (Quebec, Canada). Results indicate that the model is useful for detecting geomorphologically active confluences and the resulting sensitivity of the main channel and thus has potential to be used in diverse river management applications (e.g., hazard reduction and freedom space mapping).