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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

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Page 1: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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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

Page 2: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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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

Page 3: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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Kensico Reservoir

New York City’s 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 km2

-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)

Page 4: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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Kensico Reservoir Watershed

N5-1

E10

BG9

MB-1

N-12

WHIP

E11

E9

")

")

")

")

")

")

")

")

CATLEFF

N1 ")

DEL18

")

")

CATALUM

DEL17

")

")

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%

Page 5: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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Kensico Reservoir Watershed

N5-1

E10

BG9

MB-1

N-12

WHIP

E11

E9

")

")

")

")

")

")

")

")

CATLEFF

N1 ")

DEL18

")

")

CATALUM

DEL17

")

")

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%

Page 6: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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Base Flow Storm Flow

Page 7: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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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”

Page 8: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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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

Page 9: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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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

Page 10: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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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

ExamplesNon-detect =

Detection Limit

Non-detect =

½ Detection Limit

Non-detect =

0

<1 1 0.5 0

<100 100 50 0

Page 11: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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MB-1 (All) Detection Limit ½ Detection Limit Zero

Mean 1617 1611 1605

Median 250 238 220

Handling of FC Non-Detects

11

DL ½ Zero % ND

BG9 47 42 42 9.8

E10 100 100 100 1.3E11 100 100 100 13.7E9 100 100 100 3.5MB-1 250 238 220 6.0N12 50 50 46 5.2

N5-1 3500 3500 3500 2.9

WHIP 52 50 50 5.8

Medians

Page 12: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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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 hours 0.20”+ 0.50”+ 2.00”+

24 – 48 hours 1.00”+ 1.50”+ 3.00”+

48 – 72 hours 2.00”+ 2.50”+ 4.00”+

Page 13: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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Separation of Storm Influence

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Sample N using historical data

BG9 E10 E11 E9 MB-1 N5-1 N12 WHIP

Low Flow

101 274 72 313 110 98 341 356

Bin #1 Light

18 57 7 59 23 23 64 90

Bin #2 Mod

12 42 39 47 64 136 52 90

Bin #3 Heavy

1 9 20 8 4 52 8 13

Between 132 and 549 samples from each site

Page 14: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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Separation of Storm Influence

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MB-1 FC ConcentrationsSignificant difference between low flow and storm flow

Mean and median increase with rainfall bin

Page 15: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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Separation of Storm Influence

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N5-1 FC ConcentrationsSignificant difference between low & storm flow,

but doesn’t increase for every rainfall bin

Page 16: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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Separation of Storm Influence

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Mean FC concentration using historical data

Page 17: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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Separation of Storm Influence

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Median FC concentration using historical data

Page 18: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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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

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Page 19: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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Concentrations Used to Create Load Estimates

WHIP Flow (10-min)

Median Concentration

Sampled on site

Page 20: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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Concentrations Used to Create Load Estimates

WHIP Flow (10-min)

Median Concentration

Sampled on site

Mean Concentration

Page 21: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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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

Page 22: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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Kensico Perennial Stream Loading Estimate

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* 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

Page 23: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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Kensico Perennial Stream Loading Estimate

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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

E9WHIP

E11MB-1

E10N12

(~46% by area)

Page 24: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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Kensico Input Loading Estimates

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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

WHIPCATALUM

E9BG9

DEL17

Page 25: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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Mean Loading Estimate

Total load = 249.0 trillion FC

Breakdown of Loading Estimates

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Median Loading Estimate Total load = 162.0 trillion FC

Estimated Watershed Load - 85.4% Estimated Watershed Load - 77.5%

Page 26: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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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

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Alderisio, Kerri
i still think you should do yours first and then say this and then compare
Page 27: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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Tropical Storms Irene and Lee

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Alderisio, Kerri
i would move this later with previous slide
Page 28: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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Tropical Storms Irene and Lee

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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

Con

cent

ratio

ns (

FC

/ 10

0mL)

Page 29: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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Kensico Input Loading Estimates

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Arithmetic Mean Estimate 249.0 trillion FC

Median Estimate 162.0 trillion FC

JV Median Estimate61 trillion FC

JV Interpolated Estimate170 trillion FC

Page 30: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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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 siteJV 58 - 184 samples from each site

DEP estimates:High estimates are almost 1.5X JV highLow estimate is more than 2.6X JV low

Page 31: 1 Using Historical Data to Assess Potential Fecal Coliform Contribution During Storms at Kensico Reservoir: A Case Study Christian Pace, NYCDEP and Kerri

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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.

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THANK YOU!

Questions?