A Probabilistic Model for Turbidity and Temperature in the

Schoharie Reservoir Withdrawal

Steven W. Effler and Rakesh K. GeldaUpstate Freshwater Institute, Syracuse, NY

Donald C. PiersonNew York City Department of Environmental Protection

2009 Watershed Science & Technical ConferenceSeptember 14th-15th,Thayer Hotel, West Point, New York

2

site 3

site 1.5

Schoharie Creek

dam

1 km

x

Schoharie ReservoirWater Supply Withdrawal and Esopus Creek

• water quality issues for withdrawal– temperature (T)– turbidity (Tn)

Ashokan ReservoirEsopus Creek

ShandakenTunnel ~ 29 km

withdrawal depthwhen full = 18 m

3

Variations in Water Quality of Withdrawal and Thresholds of Concern

1987-2004

J F M A M J J A S O N D

Tw

(°C

)

0

10

20

30

21.1 °C 1987-2004

J F M A M J J A S O N D

Tn

,w (

NT

U)

1

10

100

1000

15 NTU

threshold: 21.1 °C

drivers of variability:• meteorology• reservoir drawdown

threshold: ~ 15 NTU

drivers of variability:• runoff• reservoir drawdown• meteorology

related management modeling goal for Schoharie Reservoir:develop and implement a modeling strategy to represent this variability in model applications

4

Development of Modeling Strategy

• a “probabilistic” framework is desired to represent variability

• long-term records of environmental and operational drivers (model inputs), together with tested water quality models, offer opportunity to represent variability

• these historic conditions are inherently representative of the system

Withdrawal Temperature

% O

ccur

renc

e

21.1 °C

existing

Withdrawal Temperature

% O

ccur

renc

emanagementalternative

existing

21.1 °C

5

Design of Probabilistic Model Framework for Schoharie Reservoir

Water Quality Model• transport/hydrothermal sub model

• turbidity submodel with resuspension

Stream Temperature

(empirical model)

Stream Turbidity Loading

(empirical model)

Stream Flow(USGS)

Reservoir Operations(NYC DEP)

Met. Data (NOAA)

Withdrawal Temperature and

Turbidity

Wave Model

MLI Optimization Algorithm

long-term records independent emp. models multi-level opt. algo.

6

Water Quality Model (W2Tn)

• transport/hydrothermal submodel (W2/T)– mechanistic, dynamic, two-dimensional from CE-

QUAL-W2 (USACE)see Gelda and Effler 2007. J. Environ. Eng. Sci. 6:73-84

• turbidity submodel– three particle sizes of turbidity– sources – external loads (primarily Scoharie Creek),

resuspension (circulation and wave-driven)

– sinks – export and settlingsee Gelda and Effler 2007. J. Environ. Eng. Div. ASCE133:139-148

Water Quality Model• transport/hydrothermal sub model

• turbidity submodel with resuspension

Stream Temperature

(empirical model)

Stream Turbidity Loading

(empirical model)

Stream Flow(USGS)

Reservoir Operations(NYC DEP)

Met. Data (NOAA)

Withdrawal Temperature and

Turbidity

Wave Model

MLI Optimization

Algorithm

Predicted Tw (°C)

0 5 10 15 20 25

Obs

erve

d T

w (

°C)

0

5

10

15

20

25Tw,obs = 0.8676 Tw,prd + 1.2861

(r2 = 0.95; n = 1380)RMSE = 1.89 °C

2003

M J J A S Oc

660

(m

-1)

0

5

10

15

20(c) 10 m - bottom

7

Water Quality Model (W2Tn) Segmentation and a Simulation

Distance from dam (m)

0 2000 4000 6000 8000

Ele

vatio

n (m

)

300

310

320

330

340

350

site 3

site 1.5

Schoharie Creek

water supplyintake

dam

1 km

x

Bear Kill

Manor Kill

intake

8

Long-Term Records to SpecifyInputs for Probabilistic Model

Water Quality Model• transport/hydrothermal sub model

• turbidity submodel with resuspension

Stream Temperature

(empirical model)

Stream Turbidity Loading

(empirical model)

Stream Flow(USGS)

Reservoir Operations(NYC DEP)

Met. Data (NOAA)

Withdrawal Temperature and

Turbidity

Wave Model

MLI Optimization

Algorithm

Model Driver/Input Specifications

meteorology1948-2004 (57 years); off-site Albany (NOAA) since 1948, on-site since 1997

inflows (gaged)Schoharie Creek since 1948; others more recent (USGS)

outflows (operations)1948-1996, NYCDEP; 1997-2004, USGS

9

Independent Empirical Models to Specify Inputs for Probabilistic Model

Water Quality Model• transport/hydrothermal sub model

• turbidity submodel with resuspension

Stream Temperature

(empirical model)

Stream Turbidity Loading

(empirical model)

Stream Flow(USGS)

Reservoir Operations(NYC DEP)

Met. Data (NOAA)

Withdrawal Temperature and

Turbidity

Wave Model

MLI Optimization

Algorithm

Stream temperature (plunging)Ts,i = a0 + a1 Tair,i-3 + a2 log (Qi)

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31

Ts

(°C

)

16

20

24

28

observedpredicted - long-term stream T predicted

from Tair and Q records

Turbidity-Flow Relationship (external loads)Tn = 2.5 C660

- long-term stream Tn loads predicted from Q records

9/18/2004

Distance from Creek Mouth (km)

3 4 5 6 7 8

De

pth

(m

)

10

20

30

0

20

40

60

80

100

120

Flow (m3·s-1)

0.1 1 10 100 1000

c 660

(m

-1)

0.1

1

10

100

10001996-2001best fit (r2=0.70)

10

Performance of Probabilistic Model in Representing Variability of Withdrawal T

• observations: 1987-2004• prediction bounds: for driving conditions of 1987-2004

J F M A M J J A S O N D

Tw (

°C)

0

5

10

15

20

25

30

• probabilistic model succeeds in representing range of observations

11

Performance of Probabilistic Model in Representing Variability of Withdrawal Turbidity

• observations: 1987-2004• prediction bounds: for driving conditions of 1987-2004

• probabilistic model succeeds in representing range of observations

J F M A M J J A S O N D

Tn

,w (

NT

U)

1

10

100

1000

12

Performance of Probabilistic Model in Simulating Water Quality in the Withdrawal

Withdrawal Temperature (°C)0 5 10 15 20 25 30

Cu

mu

lativ

e O

ccu

rre

nce

(%

)

0

20

40

60

80

100

predictedobserved

21

.1 °

C (

70

°F

)

Withdrawal Turbidity (NTU)

1 10 100

Cu

mu

lativ

e O

ccu

rre

nce

(%

)

0

20

40

60

80

100

predictedobserved

15 NTU

(a)

(b)

generally good performance

13

Example Application of the Probabilistic Model: Scenario Description

• potential benefits of multi-level intakes (MLI) and location in the reservoir

• is there a benefit to “spatial avoidance” of turbid plumes?E

leva

tion

(m)

300

310

320

330

340

350

Col 17 vs Col 18 Col 20 vs Col 19 Plot 2 Mean

site 3

site 1.5

spillway elevation

multi-levelintakes

14

Projections for MLI Scenario with Probabilistic Model: Site 3 versus Site 1.5

• for 57 years of historic conditions• summary statistic of number of days withdrawal Tn > 15

NTU, for individual years of record

- no noteworthy benefit for MLI at site 1.5 versus site 3E

leva

tion

(m)

300

310

320

330

340

350

Col 17 vs Col 18 Col 20 vs Col 19 Plot 2 Mean

site 3

site 1.5

spillway elevation

multi-levelintakes

Schoharie Cr.

Exceedences of Tn,w for site 1.5 MLI

0 50 100 150 200

Exc

eed

en

ces

of

Tn

,w

for

site

3 M

LI

0

50

100

150

200hypotheticaloutcome:site 1.5 betterthan site 3

equivalence; i.e.,no benefit from location change

Exceedences of Tn,w for site 1.5 MLI

0 50 100 150 200

Exc

eed

ence

s o

f T

n,w

fo

r si

te 3

MLI

0

50

100

150

200

hypotheticaloutcome:site 1.5 betterthan site 3

equivalence; i.e.,no benefit from location change

15

Projections for MLI Scenarios with Probabilistic Model,

Comparisons to existing Withdrawal Case

• for 57 years of historic conditions• cumulative distribution format for presentation of results

Withdrawal Turbidity (NTU)

1 10 100

Cum

ulat

ive

Occ

urre

nce

(%)

0

20

40

60

80

100

site 3, 3-level

site 1.5, 3-level

15 NTU

existing

MLI scenarios

- modest benefit of MLI; exceedences decrease from 27 to 16% of days

16

Summary

• probabilistic modeling framework for temperature and turbidity for Schoharie Reservoir developed, tested and preliminarily applied– key components: tested mechanistic water quality models, long-

term records for drivers, and empirical models

• insights from preliminary applications concerning multi-level intake alternatives

• broad utility of approach– other issues and systems (Ashokan, Kensico)– flexibility to accept upgrades/updates– coupling with hydrologic model (OASIS)

• to integrate water quantity needs of overall system

17

Related Professional Journal Citation

• a more complete treatment of material addressed in this presentation can be found in the following peer-reviewed journal paper

Gelda, R. K. and S. W. Effler, 2008. Probabilistic model for temperature and turbidity in a reservoir withdrawal. Lake and Reserv. Manage. 24: 219-230.

18

Investigation of Model and Input Updates/Upgrades (2009)

• turbidity submodel and stream turbidity loading model

Water Quality Model• transport/hydrothermal sub model

• turbidity submodel with resuspension

Stream Temperature

(empirical model)

Stream Turbidity Loading

(empirical model)

Stream Flow(USGS)

Reservoir Operations(NYC DEP)

Met. Data (NOAA)

Withdrawal Temperature and

Turbidity

Wave Model

MLI Optimization

Algorithm

19

Investigation of Model and Input Updates/Upgrades (2009)

• turbidity submodel and stream turbidity loading model

Updates based on1. new particle characterizations

(Peng et al. 2009)2. resuspension studies (Cornell)

and modeling (Owens et al. 2009)3. expansion of model testing for

additional years of detailed monitoring (Owens et al. 2009)

4. correction of coding error for resuspension

Water Quality Model• transport/hydrothermal sub model

• turbidity submodel with resuspension

Stream Temperature

(empirical model)

Stream Turbidity Loading

(empirical model)

Stream Flow(USGS)

Reservoir Operations(NYC DEP)

Met. Data (NOAA)

Withdrawal Temperature and

Turbidity

Wave Model

MLI Optimization

Algorithm

based on additional stream monitoring data

Schoharie Creek Flow (m3·s-1)

1 10 100 1000

Tu

rbid

ity (

NT

U)

1

10

100

1000

10000

Phase II

Schoharie Creek Flow (m3·s-1)

1 10 100 1000

Tu

rbid

ity (

NT

U)

1

10

100

1000

10000Upgraded - multipleUpgraded - singlePhase II

20

Effects of Updates/Upgrades on Probabilistic Model Projections

• an example

Exceedences of Tn,w for site 1.5 MLI

0 50 100 150 200

Exc

ee

de

nce

s o

f T

n,w

fo

r si

te 3

ML

I

0

50

100

150

200Phase II

equivalence

Exceedences of Tn,w for site 1.5 MLI

0 50 100 150 200

Exc

ee

de

nce

s o

f T

n,w

fo

r si

te 3

ML

I

0

50

100

150

200

equivalence

Updated/Upgraded with errorPhase II

Exceedences of Tn,w for site 1.5 MLI

0 50 100 150 200

Exc

ee

de

nce

s o

f T

n,w

fo

r si

te 3

ML

I

0

50

100

150

200

equivalenceUpdated/Upgraded with error corrected

Phase IIUpdated/Upgraded with error

Ele

vatio

n (m

)

300

310

320

330

340

350

Col 17 vs Col 18 Col 20 vs Col 19 Plot 2 Mean

site 3

site 1.5

spillway elevation

multi-levelintakes

Schoharie Cr.

• management perspectives on MLI/location alternatives remain unchanged

21

Summary

• probabilistic modeling framework for temperature and turbidity for Schoharie Reservoir developed, tested and preliminarily applied– key components: tested mechanistic water quality models, long-

term records for drivers, and empirical models

• insights from preliminary applications concerning multi-level intake alternatives

• broad utility of approach– other issues and systems (Ashokan, Kensico)– flexibility to accept upgrades/updates– coupling with hydrologic model (OASIS)

• to integrate water quantity needs of overall system