Inter-model Comparison of Turbidity-Discharge Rating Curves and the Implications for Reservoir Operations Management

This study compares several statistical rating curve techniques to estimate turbidity, a proxy for suspended sediment concentration, in fluvial systems based on available discharge data. Seven models were tested, including variants of quadratic rating curves, quantile regression, local regression, dynamic linear models (DLMs), and Box-Jenkins models. Two comparisons were conducted in a case study of the Esopus Creek watershed in New York, a major water source for the New York City Water Supply System (NYCWSS). First, the models were tested in their ability to forecast turbidity at 1-7 day lead times assuming perfect forecasts of discharge using two daily datasets of varying record lengths and resolution. Second, the models were used to gap-fill turbidity data based on available discharge data, and the resulting continuous turbidity time series were used to assess optimal reservoir operations in the NYCWSS to manage water quality. Results suggest that DLMs coupled with additional time series modeling on the residuals produce the most robust forecasts across lead times for both high and low turbidity values. During average conditions, differences between rating curves have little impact on inferred reservoir operations due to the buffering effect of storage. But during extreme events, rating curve differences lead to large differences in inferred operations, suggesting that rating curve choice can play an important role in assessing the risk of reservoir-based water quality management.

Automated summary

This study compared various statistical rating curve techniques to estimate turbidity in fluvial systems, with results showing that dynamic linear models coupled with additional time series modeling on the residuals provide the most robust forecasts.