1. 1 A Turbidity Model For Ashokan Reservoir Rakesh K. Gelda, Steven W. Effler Feng Peng, Emmet M. Owens Upstate Freshwater Institute, Syracuse, NY Donald C. Pierson New York City Department of Environmental Protection 2009 Watershed Science & Technical Conference September 14 th -15 th, Thayer Hotel, West Point, New York

2. 2 network of 19 reservoirs network of 19 reservoirs three controlled lakes three controlled lakes Croton, Catskill, Delaware systems Croton, Catskill, Delaware systems watershed: 1930 mi 2 watershed: 1930 mi 2 storage: 550 BG storage: 550 BG unfiltered supply unfiltered supply 1.2 BG/day 1.2 BG/day Ashokan Reservoir Ashokan Reservoir watershed: 257 mi 2 watershed: 257 mi 2 storage: 130 BG storage: 130 BG Catskill Aq.: 600 MGD Catskill Aq.: 600 MGD Turbidity < 8 NTU (90 th percentile; 1987-2008) Turbidity < 8 NTU (90 th percentile; 1987-2008) *

3. 3 Ashokan Reservoir West Basin East Basin

4. 4 West Basin East Basin Upper Gate Chamber Bridge and Dividing Weir

5. 5 Gates (4)

6. 6 East Basin Diversion Wall

7. 7 Upper Gate Chamber

8. 8 Intake Structure

9. 9 Turbidity Problem  stream channel and banks erosion – glacial and fluvial sediment; Esopus Creek 85% of the inflow  turbidity in waters leaving Ashokan Reservoir can be high following major runoff events  alum treatment before it enters Kensico – Nine alum events, 524 days during 1987-2007  turbidity model to evaluate management alternatives

10. 10 Features of Turbidity Model  Two-dimensional (longitudinal-vertical), laterally averaged transport framework (CE-QUAL-W2)  State variables: Temperature (T) and turbidity (Tn)  Three size classes of Tn  Source of Tn: external loading  Sinks: settling, export (via withdrawal, spill, waste channel diversion)  Two basins simulated separately

11. 11 Model Grid – West Basin 27 segments (~330 m avg) 47 layers (1 m) 1 branch Esopus Creek dividing weir

12. 12 Model Grid – East Basin 37 segments (~ 300 m avg) 26 layers (1 m) 1 branch spill dividing weir

13. 13 Model Grid – Vertical Layers dividing weir west basineast basin

14. 14 Turbidity (Tn)  primary metric of quality for water supplies  measure of light scattering by particles at 90° collection angle, units of NTU  Tn α b; supported in peer-reviewed literature  b, Tn = f (particle concentration, size distribution, composition, shape) 0° 90° incident beam scattered light 1 Tn α light scattering coefficient (b, m -1 ) 1

15. 15 Scattering (b) and Turbidity (Tn): Behaves Like Intensive Properties  mass balance calculations can be done well-established in optical literature ( Davies-Colley et al. 1993 ) well-established in optical literature ( Davies-Colley et al. 1993 ) Q 1, b 1, T n 1 Q 2, b 2, T n 2 Q, b, T n example Q = Q 1 + Q 2

16. 16 Turbidity: As the Model State Variable  Tn is the regulated parameter  disadvantages of TSS (a gravimetric measurement) as an alternative (would have to rely on Tn = k · TSS) differences in particle size and composition dependencies of Tn and TSS differences in particle size and composition dependencies of Tn and TSS Tn, b (scattering) and c (beam attenuation) measurements more precise Tn, b (scattering) and c (beam attenuation) measurements more precise limitations in temporal and spatial resolution; e.g., robotic and rapid profiling capabilities for Tn and c limitations in temporal and spatial resolution; e.g., robotic and rapid profiling capabilities for Tn and c pore size for TSS measurements too large (1.7 µm) pore size for TSS measurements too large (1.7 µm) variation in relationship between Tn and TSS in time and space (i.e., k is not really a constant) variation in relationship between Tn and TSS in time and space (i.e., k is not really a constant)  Tn, [and c] supported in peer-reviewed literature, without published critical comments

17. 17 Model Inputs  Model testing period: 2003-2007 supported by UFI’s intensive (Robohut on Esopus Creek, in- reservoir robots) and DEP’s routine monitoring data supported by UFI’s intensive (Robohut on Esopus Creek, in- reservoir robots) and DEP’s routine monitoring data constrained by the availability of operations data constrained by the availability of operations data  Additional (secondary) validation period: 1995-2002  Operations data  Hydrologic inputs/outputs  Loading of turbidity  Creek temperature  Meteorological data

18. 18 In-Reservoir Robots: Example, 2007 April – November (June in 2007) depth-profiles every 6 hours depth interval 1 m

19. 19 In-Reservoir Rapid Profiling Example, 11/30/2006 after major runoff events depth interval 0.25 m

20. 20 Example of Driving Conditions and Reservoir Response: June 2006

21. 21 Turbidity-Causing Particles Four Features: 1.number concentration 2.size distribution 3.composition 4.shape Individual Particle Analysis (IPA) Technology 75-80% clay75-80% clay Tn associated with 1-10 µTn associated with 1-10 µ sub-µ particles unimportantsub-µ particles unimportant TSS filter pore size 1.7 µm; misses some turbidity causing particlesTSS filter pore size 1.7 µm; misses some turbidity causing particles April 2005 b m (660) – minerogenic particle scattering coefficient, m -1

22. 22 “Turbidity” Size-Classes for Model Class size (µm) Size range vel(m/d)11 < 1.75 0.075 23.14 1.75- 5.75 0.75 38.11 > 5.75 5.0 Class Q ≤ 40 m 3 /s Q > 40 m 3 /s 110%10% 265%45% 325%45% Fractions in Esopus Creek Stokes Law: coefficient specification constrained by reality of particle characteristics as obtained from IPA Esopus Creek

23. 23 Hydrothermal Model Performance * withdrawal temperature (T w ) * importance of withdrawal depth information 2003-20071995-2002

24. 24 Turbidity Model Performance * withdrawal turbidity (T n,w ) * importance of detailed monitoring of forcing conditions

25. 25 Turbidity Model Performance

26. 26 Turbidity Model Performance East Basin 6/30/2006

27. 27 Alum treatment events * * Normalized RMSE (Gelda and Effler, 2007) performance for well monitored years consistent with that reported for Schoharie Reservoir (Gelda and Effler, 2007) Performance Summary

28. 28Summary  2-D model CE-QUAL-W2 as transport framework  Turbidity as a state variable  Characterization of turbidity-causing particles  Three size classes  Model performed well in simulating in-reservoir and withdrawal temperature and turbidity  Model is suitable for evaluating management alternatives  Future research: resuspension, particle-based modeling including aggregation Gelda, R. K., S. W. Effler, F. Peng, E. M. Owens and D. C. Pierson, 2009. Turbidity model for Ashokan Reservoir, New York: Case Study. J. Environ. Eng. 135: 885-895. e-mail: RKGelda@UpstateFreshwater.Org

29. 29 Ashokan Reservoir East Basin Spillway 4/2005