Factors affecting storm event turbidity in a New York City water supply stream

Abstract

Stream turbidity levels tend to increase during high stream discharge events, and it is important to quantify the suspended sediment flux during these events that could potentially lead to water quality problems. Here, a case study for estimating suspended sediment loads (as a product of turbidity and stream discharge) in streams that are part of the New York City (NYC) water supply in the Catskill region of New York State is presented. Over the 8 year study period 80% of the suspended sediment load was transported during less than 4% of the time, indicating the importance of estimating storm event suspended sediment loads. The objective of this study was to understand the underlying factors controlling the uncertainty in the discharge vs turbidity relationship at the outlet of the watershed draining into the NYC Ashokan Reservoir. High frequency (15-min) automated monitoring of stream turbidity was combined with stream discharge measurements of a similar frequency to provide an estimate of the true suspended sediment load that could be used for model testing and verification at two time scales; daily and events. Multivariate statistical analyses indicate that average daily stream turbidity during storm events can be influenced by the spatial variability in runoff, antecedent conditions, and season. A predictive relationship of event mean stream turbidity based on stream discharge alone led to a strong predictive relationship (r 2 = 0.81), but also a 10% underestimation of the cumulative measured event mean suspended sediment load. Inclusion of information on the time between events improved the regression equation (r 2 = 0.89), and reduced the cumulative difference between estimated and measured event mean suspended sediment loads to 7% underestimation. ► High frequency (15-min) automated monitoring of stream turbidity ► Factors causing variability in discharge–turbidity relationship ► Influence of spatial variability in runoff, antecedent conditions, and season