New York City’s Operations Support Tool: Utilizing Hydrologic Forecasts for Water Supply Management

  • James PorterEmail author
  • Gerald Day
  • John C. Schaake
  • Lucien Wang
Reference work entry


The New York City Department of Environmental Protection (DEP) supplies over one billion gallons per day (BGD) of water to more than nine million people in the New York City metropolitan area, making it one of the largest suppliers of surface water in the United States. DEP’s water supply system is as complex as it is large; it draws water from three distinct watersheds and features a number of interconnections and redundancies allowing for a large number of potential operating conditions. The system has a wide range of objectives – from supplying clean, reliable water for municipal demand to meeting environmental flow requirements for downstream stakeholders. Combined with the existing system complexity, these disparate objectives can make operational decision making a challenge.

In 2013, DEP launched the Operations Support Tool (OST) – a state-of-the-art model built to assist the utility in water supply operation decisions. OST consists of a system model (OASIS) to simulate water supply operation decisions and a linked hydrodynamic two-dimensional water quality model (CE-QUAL-W2). The model is initialized using current system conditions (e.g., reservoir elevations, water quality conditions) and is driven forward in time using ensemble hydrologic forecasts. This setup gives DEP the ability to simulate a wide variety of operational strategies in near real-time, allowing for objective alternative analysis prior to making operational decisions. Ensemble hydrologic forecasts are a critical part of the success of this approach, as they enable DEP to evaluate decisions probabilistically by explicitly considering hydrologic uncertainty.

This chapter provides an overview of the New York City water supply system, details the hydrologic forecasts used in OST, and reviews a handful of real operational applications of OST and the hydrologic forecast system.


Water resource management Applications of hydrologic ensemble forecasts Reservoir operations Decision support Water quality management Turbidity Probabilistic risk System modeling Supply reliability Conservation releases Real-time modeling Data visualization New York City water supply 


  1. E.A. Anderson, N.H. Crawford, The Synthesis of Continuous Snowmelt Runoff Hydrographs on a Digital Computer (Department of Civil Engineering, Stanford University, Stanford, 1964)Google Scholar
  2. R. Burnash, R. Ferral, R. McGuire, A Generalized Streamflow Simulation System, Conceptual Modeling for Digital Computers (California-Nevada River Forecast Center, Sacramento, 1973)Google Scholar
  3. T. Cole, S. Wells, CE-QUAL-W2: 2: A Two-Dimensional, Laterally Averaged, Hydrodynamic and Water Quality Model (U.S. Army Engineering and Research Development, Vicksburg, 2006)Google Scholar
  4. G. Day, Extended streamflow prediction using NWSRFS. J. Water Resour. Plan. Manag. 111, 157–170 (1985)CrossRefGoogle Scholar
  5. Decree Parties to the 1954 U.S. Supreme Court, Flexible Flow Management Program (Decree Parties to the 1954 U.S. Supreme Court Decree, Trenton, 2016)Google Scholar
  6. Delaware River Basin Commission, Water Quality Programs of the Delaware River Basin Commission (Delaware River Basin Commission, West Trenton, 2015)Google Scholar
  7. J. Demargne, L. Wu, S. Regonda, J. Brown, H. Lee, M. He, … Y. Zhu, The science of NOAA’s operational hydrological ensemble forecast service. Bull. Am. Meteorol. Soc. 79–98 (2014)CrossRefGoogle Scholar
  8. R. Gelda, S. Effler, Modeling turbidity in a water supply reservoir: advancements and issues. J. Environ. Eng. Div. 133, 139–148 (2007a)CrossRefGoogle Scholar
  9. R. Gelda, S. Effler, Simulation of operations and water quality performance of reservoir multilevel intake configurations. J. Water Resour. Plan. Manag. 133, 78–86 (2007b)CrossRefGoogle Scholar
  10. R. Gelda, S. Effler, Testing and application of a two-dimensional hydrothermal model for a water supply reservoir: implications of sedimentation. J. Environ. Eng. Sci. 6, 73–84 (2007c)CrossRefGoogle Scholar
  11. G. Gong, L. Wang, L. Condon, A. Shearman, U. Lall, A simple framework for incorporating seasonal streamflow forecasts into existing water resource management practices. J. Am. Water Resour. Assoc. 46(3), 574–585 (2010)CrossRefGoogle Scholar
  12. R. Hirsch, Stochastic hydrology model for drought management. J. Water Resour. Plan. Manage. 107, 303–313 (1981)Google Scholar
  13. New York City Department of Environmental Protection, History of New York City's Water Supply System (2016), Retrieved from New York City Department of Environmental Protection:
  14. New York Codes, Rules, and Regulations, 6 NYCRR Part 701. Classifications – Surface Waters and Groundwaters (New York State Department of Environmental Conservation, Albany, 1991)Google Scholar
  15. New York State Department of Environmental Conservation/New York City Department of Environmental Protection (DEC/DEP), New York State Department of Environmental Conservation/New York City Department of Environmental Protection (DEC/DEP) Interim Ashokan Release Protocol. DEC/DEP (2011)Google Scholar
  16. NYCDEP, Refinement of New York City Water Demand Projections. Prepared by Hazen and Sawyer (2014)Google Scholar
  17. NYCDEP, History of Drought and Water Consumption (2016), Retrieved from New York City Department of Environmental Protection:
  18. NYSDEC, The Lower Hudson River Basin Waterbody Inventory and Priority Waterbodies List (Division of Water, Bureau of Watershed Assessment and Management, Albany, 2008)Google Scholar
  19. Office of the Delaware River Master, Historical Background (2015), Retrieved from Office of the Delaware River Master:
  20. R. Seager, N. Pederson, Y. Kushnir, J. Nakamura, The 1960s drought and the subsequent shift to a wetter climate in the Catskill Mountains region of the New York City watershed. J. Clim. 25, 6721–6742 (2012)CrossRefGoogle Scholar
  21. W. Sittner, C. Schauss, J. Monro, Continuous hydrograph synthesis with an API-type hydrologic model. Water Resour. Res. 5, 1007–1022 (1969)CrossRefGoogle Scholar
  22. W.J. Weiss, G. Pyke, W. Becker, D. Sheer, R. Gelda, P. Rush, T. Johnstone, Integrated water quality modeling to support long-term planning. J. Am. Water Works Assoc. 105, 217–228 (2013)Google Scholar
  23. L. Zhao, Q. Duan, J. Schaake, A. Ye, J. Xia, A hydrologic post-processor for ensemble streamflow predictions. Adv. Geosci. 29, 51–59 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • James Porter
    • 1
    Email author
  • Gerald Day
    • 2
  • John C. Schaake
    • 4
  • Lucien Wang
    • 3
  1. 1.New York City Department of Environmental Protection, Bureau of Water SupplyNew YorkUSA
  2. 2.RTI InternationalFt. CollinsUSA
  3. 3.Hazen and SawyerSan FranciscoUSA
  4. 4.U.S. National Weather Service (retired)AnnapolisUSA

Section editors and affiliations

  • Hannah Cloke
    • 1
  • Massimiliano Zappa
    • 2
  • Schalk Jan van Andel
    • 3
  1. 1.University of ReadingReadingUK
  2. 2.Swiss Federal Research Institute WSLBirmensdorfSwitzerland
  3. 3.IHE Delft Institute for Water EducationDelftNetherlands

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