TJ14.2 INVITED Forecast-Based Reservoir Management for Turbidity Control: A Case Study using New York City's Operations Support Tool

Tuesday, 8 January 2013: 8:45 AM
Room 10A (Austin Convention Center)
Lucien Wang, NOVA Consulting & Engineering, New York, NY; and W. J. Weiss, G. Pyke, M. Zion, and J. Porter

The New York City Department of Environmental Protection (DEP) manages New York City's water supply, which is comprised of 19 reservoirs and supplies over 1 billion gallons of water per day to more than 9 million customers. Situated in the Catskill Mountains, the Catskill and Delaware sub-systems are comprised of 6 reservoirs and supply 90-100% of the overall demand. Together, these reservoirs constitute the largest unfiltered water supply in the United States. While water quality is usually pristine, high volume storm events occasionally cause the reservoirs to become highly turbid. In order to maintain EPA sanctioned filtration avoidance, DEP must meet a 5 NTU standard for customer delivery while minimizing in-reservoir application of aluminum sulfate as a coagulant. Fortunately, the NYC system has the flexibility and redundancy to temporally minimize or eliminate diversions from turbid reservoirs. This strategy is not without risk, as it may reduce the overall water supply reliability.

In response to this challenge, DEP has initiated the design of a state-of-the-art decision support system to provide computational and predictive support for water supply operations and planning – the Operations Support Tool (OST). OST integrates a reservoir operations model (OASIS), with 2D hydrodynamic water quality models (CE-QUAL-W2), and a database compiling a multitude of near-real-time data sources from DEP (SCADA, keypoint monitoring, in-reservoir robotic monitoring) and outside providers (USGS gauges, National Weather Service hydrologic forecasts). To support near-term (daily out to one year) operations decisions, OST is initialized with current system conditions (i.e. reservoir elevations and turbidity) and driven forward using ensemble hydrologic forecasts and reservoir operating rule sets. Operating rules may be iteratively modified, modeled, and quantitatively evaluated using performance metrics.

DEP has begun using interim versions of OST to support operations, including evaluation of operating strategies to reduce turbidity export from the Catskill reservoirs. This presentation describes results of a case study in which OST was used to evaluate two hypothetical operation alternatives for Ashokan Reservoir following Tropical Storms Irene and Lee. In the example, OST is initialized with reservoir elevations and turbidities immediately following Tropical Storm Lee and is driven forward 9 months using statistically generated hydrologic ensembles. The reservoir operating strategies considered include a realistic set of rules given the existing system infrastructure as well as a potential set of rules given future system infrastructure improvements. The operational strategies were evaluated based on their predicted effectiveness and water supply impacts. Key performance metrics included probability of reservoir refill and predicted length and magnitude of alum addition. The presentation will walk through the specific details of the considered operating strategies, interpret/compare the probabilistic results, and analyze performance tradeoffs critical to decision making.

OST simulations like the presented case study will provide the analytical support for DEP managers to better understand the risks associated with different courses of action. This case study demonstrates the value of a system modeling platform for near-term operations support as well as long-term planning. The probabilistic, risk-based framework allows for quantification of tradeoffs among competing objectives and ultimately a more robust and defensible basis for decision-making.

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