Environmental time series observed over 100’s of monitoring locations usually possess some spatial structure in terms of common patterns throughout time, commonly described as temporal coherence. This paper will apply, develop and compare two methods for clustering time series on the basis of their patterns over time. The first approach treats the time series as functional data and applies hierarchical clustering while the second uses a state-space model based clustering approach. Both methods are developed to incorporate spatial correlation and stopping criteria are investigated to identify an appropriate number of clusters. The methods are applied to Total Organic Carbon data from river sites across Scotland
