Analysis of Hydrological and Suspended Sediment Events from Mad River Watershed using Multivariate Time Series Clustering

TL;DR

This study employs multivariate time series clustering to analyze hydrological storm events and suspended sediment transport, revealing detailed relationships beyond traditional 2-D hysteresis loop methods in watersheds.

Contribution

It introduces a multivariate time series clustering method for storm event analysis, capturing complex dynamics overlooked by conventional hysteresis loop approaches.

Findings

Clusters correlate with hysteresis classifications and watershed locations.

Clustering reveals additional storm event characteristics.

Meteorological features influence clustering results.

Abstract

Hydrological storm events are a primary driver for transporting water quality constituents such as turbidity, suspended sediments and nutrients. Analyzing the concentration (C) of these water quality constituents in response to increased streamflow 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 the C-Q time series to two-dimensional (2-D) hysteresis loops and analyze these 2-D patterns. While effective and informative to some extent, 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 issue using a multivariate time series clustering approach. Clustering is applied to sequences of river discharge and suspended sediment data (acquired through turbidity-based monitoring) from six watersheds located in the Lake Champlain Basin in the northeastern United States. While clusters of the hydrological storm events using the multivariate time series approach were found to be correlated to 2-D hysteresis loop classifications and watershed locations, the clusters differed from the 2-D hysteresis classifications. Additionally, using available meteorological data associated with storm events, we examine the characteristics of computational clusters of storm events in the study watersheds and identify the features driving the clustering approach.