This article is about a collaborative process and tools we have built to adapt a clustering method for analysing temporal sequences of physico-chemical measurements done on river streams. These data are characterised by sampling variability and a great number of parameters, that are monitored in different ways. The dataset is thus heterogeneous and incomplete. A subset of about 300 sequences was selected and analysed with a specific clustering method for temporal data. Results are presented and commented, through adapted visualisations.