Probabilistic Estimation of Stream Turbidity and Application under Climate Change Scenarios.

Streamflow-based rating curves are widely used to estimate turbidity or suspended sediment concentrations in streams. However, such estimates are often inaccurate at the event scale due to inter- and intra-event variability in sediment-streamflow relationships. In this study, we use a quantile regression approach to derive a probabilistic distribution of turbidity predictions for Esopus Creek, a major stream in one of the watersheds that supply drinking water to New York City, using measured daily mean streamflow-turbidity data pairs for 2003 to 2016. Although a single regression curve can underpredict or overpredict the actual observation, quantile regression can estimate a range of possible turbidity values for a given value of streamflow. Regression relationships for various quantiles were applied to streamflows simulated by a watershed model to predict stream turbidity under: (i) the observed historical climate, and (ii) a future climate derived from 20 global climate model (GCM) scenarios. Future scenarios using quantile regression in combination with these GCMs and a stochastic weather generator indicated an increase in the frequency and magnitude of hydrological events that may generate high stream turbidity and cause potential water quality challenges to the water supply. The methods outlined in this study can be used for probabilistic estimation of stream turbidity for operational decisions and can be part of a vulnerability-based method to explore climate impacts on water resources.

Implications of industrial loads for ammonia pollution in an urban lake.

The recent history of loading of total ammonia (T-NH3) and organic nitrogen (N) from a pharmaceutical manufacturing facility to a municipal treatment plant (Metro) in Syracuse, New York, and the discharge of these constituents from Metro to N-polluted Onondaga Lake is documented. Further, the benefit of the implementation of pretreatment at the pharmaceutical plant, and the effect of an upset event at this treatment facility on loading to Metro and the lake and inlake concentrations are also documented. Models are used as analytical tools to couple loading and in-lake concentrations, to delineate the role that this pharmaceutical facility has played in the lake's ammonia pollution problem, and to evaluate the potential implications of future pretreatment upset events for the success of a rehabilitation program that is underway for the lake. The responsiveness of the lake to reductions in external loading is established by the lower T-NH3 concentration observed in the upper waters of the lake in the spring of 1999. Model analysis demonstrates this reduction was primarily (approximately 75%) because of the decrease in loading from the pharmaceutical facility achieved by pretreatment. An abrupt increase in loading in May 1999 associated with an upset event at the pretreatment facility caused a corresponding increase in the T-NH3 concentration of the lake of approximately 0.5 mg N/L. Model projections demonstrate that the load from the pharmaceutical plant before construction of the pretreatment facility exacerbated the lake's ammonia problems by increasing the occurrence and margin of violations of the toxicity standard. Continued upset events at the pretreatment plant could compromise the lake rehabilitation program.