Probabilistic Estimation of Stream Turbidity and Application under Climate Change Scenarios

Area of Science:

• Environmental Science
• Hydrology
• Water Quality

Background:

• Streamflow-based rating curves are common for estimating stream turbidity and sediment but lack accuracy at event scales.
• Variability in sediment-streamflow relationships causes inaccuracies in traditional methods.

Purpose of the Study:

• To apply a quantile regression approach for probabilistic turbidity predictions in Esopus Creek.
• To assess potential climate change impacts on stream turbidity and water quality.

Main Methods:

• Utilized daily mean streamflow-turbidity data from 2003-2016 for Esopus Creek.
• Employed quantile regression to model the distribution of turbidity predictions.
• Integrated a watershed model, 20 global climate models (GCMs), and a stochastic weather generator for future climate scenarios.

Main Results:

• Quantile regression provides a range of turbidity values, improving upon single regression curves.
• Future climate scenarios predict an increase in the frequency and magnitude of high stream turbidity events.
• These events may present challenges to drinking water quality.

Conclusions:

• The developed methods enable probabilistic turbidity estimation for operational decisions.
• The approach can be used in vulnerability assessments for climate impacts on water resources.
• Findings highlight potential water quality challenges for New York City's water supply under future climate conditions.