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              <p>Within the framework of the Rhône Sediment Observatory, monthly time-integrated samples have been collected by Particle Traps (PT) in the last decade to monitor particulate contaminants in the Rhône River and its main tributaries. In this watershed with a very contrasted hydrology, we propose to use a clustering approach to classify the samples according to the main hydrological groups. This approach has been applied to riverine particulate organic radiocarbon signatures (Δ14C-POC) that are strongly affected by the origin of the material and the occurrence of nuclear plant releases. Suspended sediment samples were collected near the outlet of the Rhône River at Arles and analysed for 14C along with particulate organic carbon, chlorophyll a and tritium contents to confirm Δ14C-POC origins. Cluster Analyses (CA), coupled to Principal Component Analysis (PCA), were performed based on monthly average water discharges of the Upper Rhône River and the five main Rhône’s tributaries to define data sets according to their similarities. The classification obtained by fuzzy C-mean logic of the Rhône River hydrology into 5 clusters is similar to that already observed in the literature with Mediterranean/Cevenol floods, oceanic pluvial floods, nival floods, low-water levels and baseflows. The contribution of each cluster among the Δ14C-POC values demonstrate the complexity of hydrological classification of time-integrated sample. First, the samples with a unique and significantly dominant cluster are easily explained, with negative Δ14C-POC values observed in the flood clusters due to input from 14C-depleted material from soil or rock weathering, and positive values observed in the low-water level and baseflow clusters due to anthropogenic input by nuclear industry. Second, some samples present a homogeneous mixture between several clusters and demonstrate the occurrence of different hydrological events during the sampling periods. This tool appears as a solution to estimate the contribution of each hydrological event classification in time-integrated samples.</p>
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