Post-processing NWM output with spatially distributed turbidity sensing to forecast turbidity loading and source for reservoir operation management

Reservoir operations and management increasingly extend beyond considerations of water volume and water level to include water quality, especially in situations where reservoir water is unfiltered or treatment of a particular contaminant is onerous. A primary concern regarding reservoir water quality is often turbidity – a measure of water clarity that is chiefly impacted by how much sediment and other material is suspended in the water column – which can impede reservoir operations when certain levels are exceeded. Because the National Water Model (NWM) has been shown to have substantial utility for reservoir operations by providing flow forecasts that inform anticipation of future water volumes, it is sensible to leverage this forecasting capability to provide insight into future turbidity levels. Additionally, many prior studies have suggested that turbidity is primarily influenced by water discharge and other hydrologic parameters forecasted by the NWM (such as rainfall) as well as easily obtainable watershed characteristics (such as geology), indicating turbidity prediction may be readily achievable by coupling the hydrologic forecasting capability of the NWM to empirical models of turbidity production. In this project, we employ such an approach in the Esopus Creek catchment in the Catskills Mountains of New York State, which drains to the Ashokan Reservoir of New York City water supply system and has been extensively monitored for the past decade. We build off prior research to construct a machine learning-based model of turbidity as a function of antecedent conditions, storm hydrology, and watershed characteristics. In initial testing, this model, which leverages the distributed, high-resolution sensor network present in the Esopus watershed, outperforms the current model used by the New York City Department of Environmental Protection (NYC DEP) in reservoir operations. These preliminary results support the utility of our proposed approach and suggest that machine-learning models built on understanding of watershed processes can be fed NWM forecast products to successfully anticipate future turbidity loading.  Work in the second and third year of the project will continue to fine-tune turbidity models with additional site-specific data from the Esopus sensor network to further improve forecasting capabilities and enhance forward-thinking reservoir operations. In particular, the next steps will aim to forecast not only turbidity levels, but also anticipate where in the watershed turbidity will be produced. This type of source-specific forecasting has substantial importance for both operational planning and management efforts (e.g., erosion mitigation) to suppress sediment loading to Esopus streams. Overall, results of this project will emphasize the capabilities of the NWM to extend beyond hydrologic forecasts and provide a blueprint for others interested in leveraging such abilities to produce water quality predictions.