1. 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri Alderisio, NYCDEP NYC Watershed/Tifft Science and Technical Symposium Thayer Hotel West Point, NY September 19, 2013

2. 2 Presentation Outline Kensico Reservoir Effect of storms on concentration Case Study: TS Irene / Lee Historical FC data Loading estimate from flow and concentration Compare low flow + storm loads to other estimates Summary

3. 3 Kensico Reservoir New York Citys terminal source water reservoir - 30.6 BG storage Aqueduct Monitoring Sites Influents – CATALUM – DEL17 Effluent – CATLEFF – DEL18 Kensico also has its own small watershed = 34.3 km 2 -Mixed land use -approx. <1% of annual flow During storms… direct runoff pushes stream flow contribution from 0.1% at base flow up to 4% (or more in extreme cases)

4. 4 Kensico Reservoir Watershed Aqueducts provide most of the inflow >1 BGD Eight perennial streams -Monitoring began: MB-1 1987 Others 1991 -WQ samples taken monthly -Each equipped for flow monitoring Ungauged area = approx. 54%

5. 5 Kensico Reservoir Watershed Aqueducts provide most of the inflow >1 BGD Eight perennial streams -Monitoring began: MB-1 1987 Others 1991 -WQ samples taken monthly -Each equipped for flow monitoring Ungauged area = approx. 54%

6. 6 Base Flow Storm Flow

7. 7 Small stream flow increases 10X + during moderate events and 100X + during extreme events Pathogens more concentrated in storm runoff - Generally consistent for bacteria and protozoans Storm Event Effects 7 Flow and G/C composite sample results for stream N5-1MAIN Storm on 10/17/2006, Rainfall total = 0.82

8. 8 Tropical Storm Irene Aug 27–28, 2011 Preceded by wet August (>7 rain) - 2.85 rain 3 weeks before - 3.14 rain 2 weeks before - almost 1 in 7 days prior Intense rain (up to 0.92 in 10min) Rainfall total = 6.60 – 7.06 Sharp rise in fecal coliform at effluents (less than 24 hours) Extreme Events : Case Study TS Irene/Lee 8 TS Lee (+ Katia) Sept 6–8, 2011 8 days after TS Irene Less intense (<1 per hour) Rainfall total = 6.27 – 6.80 Sharp rise in fecal coliform at effluents Elevated FC counts at Kensico into October

9. 9 Methods created for Kensico protozoan budget (2009) Vital to separate storm and base flow for loading estimates Importance to factor in whole watershed (gauged and ungauged sub-basins) Apply method to estimate fecal coliform loading Prior DEP Work with Loading Estimates 9

10. 10 Fecal Coliform Data Data Handling Utilize only data for existing stream conditions (ex. post-BMP) (BMP data from ~2000 and unmodified stream data from 1991) Coliform data issues Confluent growth – samples removed Too Numerous To Count (TNTC) – samples removed Greater than or equal, estimated – Used value given Non-detects or Less thans (ex. <1, <10, etc.) - ??? < 14% of samples for any stream were non-detects Examples Non-detect = Detection Limit Non-detect = ½ Detection Limit Non-detect = 0 <1 10.50 <100 100500

11. 11 MB-1 (All)Detection Limit½ Detection LimitZero Mean 161716111605 Median 250238220 Handling of FC Non-Detects 11 DL ½ Zero% ND BG9 4742 9.8 E10 100 1.3 E11 100 13.7 E9 100 3.5 MB-1 2502382206.0 N12 50 465.2 N5-1 3500 2.9 WHIP 5250 5.8 Medians

12. 12 Separation of Storm Influence Rainfall Bin Classification System Divide long-term routine and storm event dataset into bins according to: - Amount of precipitation - Time interval since precipitation Allows for use of data prior to flow measurement Westchester County Airport and DEL18 met station data used #1 (Light Influence) #2 (Moderate Influence) #3 (Heavy Influence) < 24 hours0.20+0.50+2.00+ 24 – 48 hours1.00+1.50+3.00+ 48 – 72 hours2.00+2.50+4.00+

13. 13 Separation of Storm Influence 13 Sample N using historical data BG9E10E11E9MB-1N5-1N12WHIP Low Flow 1012747231311098341356 Bin #1 Light 185775923 6490 Bin #2 Mod 12423947641365290 Bin #3 Heavy 19208452813 Between 132 and 549 samples from each site

14. 14 Separation of Storm Influence 14 MB-1 FC Concentrations Significant difference between low flow and storm flow Mean and median increase with rainfall bin

15. 15 Separation of Storm Influence 15 N5-1 FC Concentrations Significant difference between low & storm flow, but doesnt increase for every rainfall bin

16. 16 Separation of Storm Influence 16 Mean FC concentration using historical data

17. 17 Separation of Storm Influence 17 Median FC concentration using historical data

18. 18 Following the same rainfall criteria used to create means/medians … Apply the appropriate concentration to flow measurements (10-min) Must consider cumulative amount of rainfall and time interval since accumulation Historical mean represents high estimate (worst-case scenario) Historical median represents lower estimate Utilize samples taken on site during the time period DEP assigned these measured values to a 6-hr timespan Concentrations Used to Create Load Estimates 18

19. 19 Concentrations Used to Create Load Estimates WHIP Flow (10-min) Median Concentration Sampled on site

20. 20 Concentrations Used to Create Load Estimates WHIP Flow (10-min) Median Concentration Sampled on site Mean Concentration

21. 21 FC Loading Estimate – Whippoorwill Brook Flow Loading estimate Closely follows flow because mean is applied consistently (except when samples were collected) Historical means when missing values

22. 22 Kensico Perennial Stream Loading Estimate 22 * Estimated flow – above rating curves Aug 28 8 Streams = 10.6% Kensico Input Volume* Volume (L / 10 min) and fecal coliform load (accumulating) for 8 streams Aug 23 – Sept 12, 2011

23. 23 Kensico Perennial Stream Loading Estimate 23 Median Estimate 57.7 trillion Arithmetic Mean Estimate 97.7 trillion Cumulative loading estimate for 8 streams (Aug 23 – Sep 29, 2011) N5-1 BG9 E9 WHIP E11 MB-1 E10 N12 (~46% by area)

24. 24 Kensico Input Loading Estimates 24 Arithmetic Mean Estimate 249.0 trillion FC Median Estimate 162.0 trillion FC Ungauged Watershed (54% by area) N5-1 N12 E10 MB-1 E11 WHIP CATALUM E9 BG9 DEL17

25. 25 Mean Loading Estimate Total load = 249.0 trillion FC Breakdown of Loading Estimates 25 Median Loading Estimate Total load = 162.0 trillion FC Estimated Watershed Load - 85.4% Estimated Watershed Load - 77.5%

26. 26 HDR/Gannett Fleming (JV) contracted to : - Review events and DEP operational response - Create fecal coliform loading estimate for these storms - Assess function of BMPs during the storm - Make recommendations on future response measures and program enhancements to protect WQ Final Summary Report – May 2012 Tropical Storms Irene and Lee 26

27. 27 Tropical Storms Irene and Lee 27

28. 28 Tropical Storms Irene and Lee 28 80,000 60,000 40,000 20,000 JV used 2 approaches to fill in daily concentration data for FC load estimates: 1.Interpolated concentrations between samples & geometric means for ungauged areas 2.Missing values set to the median concentration from historical data (Jul 99 – Nov 11) MB-1 Hydrograph from Aug 26 – Sept 13, 2011 FC Concentrations (FC / 100mL)

29. 29 Kensico Input Loading Estimates 29 Arithmetic Mean Estimate 249.0 trillion FC Median Estimate 162.0 trillion FC JV Median Estimate 61 trillion FC JV Interpolated Estimate 170 trillion FC

30. 30 Summary Many ways to do loading calculations for a complex system such as Kensico Goal to estimate worst case scenario during timeframe Separating historical samples by storm size allowed us to differentiate loading calculations by storm size Use of 0 for non-detect samples did not significantly affect mean or median concentrations Worst case load estimate = 249.0 trillion FC Sample sizes: DEP 132 - 549 samples from each site JV 58 - 184 samples from each site DEP estimates: High estimates are almost 1.5X JV high Low estimate is more than 2.6X JV low

31. 31 Acknowledgements WWQO East of Hudson Field Staff Kensico Laboratory Staff Kurt Gabel and James Alair Kelly Seelbach Glenn Horton and Jim Machung *2012. HDR Gannett Fleming. Kensico Reservoir Watershed Assessment, Fecal Coliform Occurrence, and Operation Response During and After Tropical Storms Irene and Lee – Final Summary Report. May 2012. 31

32. 32 THANK YOU! Questions?