Multivariate event time series analysis using hydrological and suspended sediment data

Hydrological storm events are a primary driver for transporting water quality constituents such as suspended sediments and nutrients. Analyzing the concentration (C) of these water quality constituents in response to river discharge (Q), particularly when monitored at high temporal resolution during a hydrological event, helps to characterize the dynamics and flux of such constituents. A conventional approach to storm event analysis is to reduce C-Q time series to two-dimensional (2-D) hysteresis loops and analyze these 2-D patterns. While informative, this hysteresis loop approach has limitations because projecting the C-Q time series onto a 2-D plane obscures detail (e.g., temporal variation) associated with the C-Q relationships. In this paper, we address this limitation using a multivariate event time series (METS) clustering approach that is validated using synthetically generated event times series. The METS clustering is then applied to river discharge and suspended sediment data (acquired through turbidity-based monitoring) from six watersheds in the Lake Champlain Basin located in the northeastern United States, and results in identifying four common types of hydrological water quality events. Statistical analysis on the events partitioned by both methods (METS clustering and 2-D hysteresis classification) helped identify hydrometeorlogical features of common event types. In addition, the METS and hysteresis analysis were simultaneously applied to a regional Vermont dataset to highlight the complimentary nature of using them in tandem for hydrological event analysis.

Automated summary

This study combines a multivariate event time series clustering approach with traditional 2-D hysteresis analysis to analyze river discharge and suspended sediment data during hydrological storm events, successfully identifying four common types of hydrological water quality events.