1

Optimally Managing

Water Resources in Large

River Basins for an

Uncertain Future

Ed Roehl – Advanced Data Mining Int’l

Paul Conrads – U.S. Geological Survey

2

Large Basin Issues Savannah Basin has

Needs - wildlife habitat, water supply,

wastewater assimilative capacity, flood

control, hydroelectricity, recreation,

development

Stakeholders with Competing Interests

federal and state agencies, utilities, industrials,

communities, environmentalists

Droughts – severe, recurring, drains

resources

Uncertainty - climate change / sea-level rise

3

Project Thesis

1. Basin Management Problem = “optimizing”

water use to meet multiple objectives prioritized

by resource managers and stakeholders

2. Fact of Life = water needs and availability

change every day

3. Solution = save water FOR LATER by limiting

regulated flows to the minimums needed EACH

DAY

– NEED continuous data about changing conditions

– NEED a model that reliably predicts how to allocate the

resource for changing conditions

4

Savannah as a

Prototypical Large

Basin

5

• 02198980: water level (WL) in harbor = WL8980

• 021989784: specific conductance (SC) in

Savannah National Wildlife Refuge = SC89784

• 02198840 – SC near intakes = SC8840

• 02198500 – streamflow (Q) at Clyo = Q8500

Richard B. Russell Lake (1984; minimal storage)

Coastal Plain

Lake Hartwell (1963)

J. Strom Thurmond Lake

(1952)

USGS Gages

Study Gages

Water Intakes

USGS gages & parameters of interest

6

Recent History

• 3 major droughts since 2000

– L. Hartwell 1st, 3rd lowest ever = -22.5,-15.2 ft

– L. Thurmond – 2nd, 3rd lowest ever: -16.1,-15.1 ft

• Salinity Intrusion

– Savannah National Wildlife Refuge freshwater

marshes reduced > 50% since 1970’s

• Planned Harbor Deepening – includes

extensive salinity mitigation features

– models unable to accurately predict outcomes

7

Lakes Hartwell & Thurmond (HART, THUR)

elevations (ELV) & THUR outflow (QOUT-THUR)

• ELV_N = normalized ELV = ELV – full pond elev.

• During droughts QOUT-THUR held nearly

constant at regulatory minimum flow

-30

-25

-20

-15

-10

-5

0

5

1/63 1/66 1/69 1/72 1/75 1/78 1/81 1/84 1/87 1/90 1/93 1/96 1/99 1/02 1/05 1/08 1/11 1/14

EL

V_

N (

ft)

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

QO

UT

-TH

UR

m (

cfs

)

ELV_N-HARTm

ELV_N-THURm

QOUT-THURm

2000 3 droughts

constant Q

1980

8

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

2/07 5/07 8/07 11/07 2/08 5/08 8/08 11/08 2/09 5/09 8/09 11/09 2/10 5/10 8/10 11/10 2/11 5/11 8/11 11/11

Q (

cfs

)

-35

-30

-25

-20

-15

-10

-5

0

5

10

WL

(ft

)

Q8500m QOUT-THURm WL8980MAXFm WL8980MINFm

2007-2011 Study Period WL8980,

QOUT-THUR, Q8500

El Niño

drought more drought

m m

Qm

(c

fs)

WL

m (

ft)

WL is multiply periodic + perturbations by

offshore weather; lows/highs occur Feb/Aug

more drought

Clyo flow (Q8500) greater due to

intervening rainfall and groundwater flows

m = measured data

harbor max & min WL

QOUT-THUR

• Study period has 2 droughts separated by an

El Niño episode

9

WL’s & SC’s

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

11/2006 4/2007 10/2007 4/2008 10/2008 4/2009 10/2009 4/2010 10/2010 4/2011 10/2011

Sp

ec

ific

Co

nd

uc

tan

ce

(

S/c

m)

-14

-12

-10

-8

-6

-4

-2

0

2

4

6

8

10

Ha

rbo

r W

ate

r L

ev

el (f

t)

SC89784MAXm SC8840MAXFm WL8980MINFm WL8980MAXFm

Refuge

daily

max SC I95 daily

max SC

harbor daily max & min WL WL cycles

modulate SC

El Niño

• I95 & Refuge SC’s driven by WL at low streamflow

• SC spikes are nonlinear response to WL dynamics

• High El Niño streamflow suppresses WL influence

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Optimized

Resource

Management

Approach

11

Optimization with models

MODEL = “virtual process”

controllable inputs modulated by optimizer

x1

x2

x3

x4

x5

x6

x7

y1

y2

y3

outputs evaluated relative to setpoints

optimization routine

setpoints & constraints uncontrollable inputs

• A model is a “virtual process” that computes outputs y (e.g. SC at I95

& the Refuge) from inputs x (e.g. QOUT-THUR, WL8980)

• Inputs x are either controllable (e.g. QOUT-THUR) or uncontrollable

(e.g. WL8980)

• As uncontrollable x’s change, an “optimization routine” modulates

controllable x’s to compensate until y’s match desired target values

(setpoints)

• Limits (constraints) control the values that controllable x’s can have

(e.g. QOUT-THUR ≥ the regulatory minimum flow)

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Artificial Neural Network Models (ANNs)

Q8500 WL8980

SC

8840 measured

data ANN nonlinear curve fit

• For optimization -

model must be

reliably accurate and

execute fast for input

value search

• ANNs are “machine

learning” method for

multivariate,

nonlinear cure fitting

• ANNs execute

instantaneously

SC

884

0 (

S/c

m)

Q850

0 (

cfs

)

spikes occur after sustained low Q

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ANN accuracy

ANN R2=0.90 EFDC R2=0.10

Salin

ity a

t I9

5 (

psu)

Q8500 (

cfs

) FROM - Conrads, P.A., and Greenfield, J.M., 2010, Potential Mitigation Approach to Minimize Salinity Intrusion in the Lower Savannah River Estuary Due to Reduced Controlled Releases from Lake Thurmond, Proc. Federal Interagency Hydrologic Modeling Conference, Las Vegas, NV, June.

ANN in M2M-DSS

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Decision Support Systems (DSS)

• Delivers best science to all types of users – Integrates databases, predictive models, optimization

routines, GUI, graphics

– Excel front-end - familiar & easy to use

• DSS’s for South Carolina & Georgia – M2M-DSS (2006) – Savannah Harbor deepening impacts

– M2M2-DSS (2012) – climate change impacts near intakes in Savannah estuary

– M2M3-DSS (now) – Savannah Basin management optimization

– Savannah River Chlorides (2011) – harbor deepening impact at Savannah City intake

– Pee Dee Basin (2007) – FERC hydropower licensing

– Beaufort River DO TMDL (2004)

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M2M3-DSS Screens options, setpoints & constraints

run & monitor

analyze results

visualize

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About M2M3-DSS

• Predicts – SCs at USGS gages in estuary

– Lake ELVs

• Optimizes QOUT-THUR each day – Setpoints for avg & max SCs in Refuge & I95, ELVs

of Hartwell, Russell, Thurmond

– Constraints for min & max QOUT-THUR, ELVs

– Setpoint Priorities • SC > ELV

• Russell ELV > Hartwell & Thurmond ELVs

• Hartwell & Thurmond QOUTs are balanced to be equidistant to their ELV setpoints (mimics USACE historical practice)

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M2M3-DSS ANN Calibration

R2 = 0.81

Refuge

Interstate 95

R2 = 0.74

R2 = 0.70

R2 = 0.90

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3 Scenarios

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Scenario 1

• Objective – “clip” SC spikes in the Refuge by

pulsing flow from THUR (QOUT-THURu)

– SC89784AVG max “setpoint” = 1,000 S/cm ~ upper

drinking water limit

setpoint

QOUT-THUR increases

per optimization

Measured

Predicted

Refuge

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Scenario 1 - Conclusion

• Clipping spikes reduces lake elevations

more severely during droughts than the

current management practice

full pond

full pond -4.4 ft

-5.7 ft

-3.3 ft

Measured

Predicted

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Scenario 2

• Objective – reduce salinity in the Refuge & at I95, AND conserve lake water during droughts – Setpoints: SC89784 avg/max = 650/2,000, SC8840 max

=1,000 (S/cm)

setpoint

Measured

Predicted flow modulated daily

to meet need

Refuge

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Scenario 2 Conclusion

• Salinity reduced & water conserved

• Lakes Hartwell & Thurmond acreages = 56,000 & 71,100

Refuge

I95

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Scenario 3 Rationale

• Objective – demonstrate how to quickly detect differences between pre and post -deepening salinity behaviors for change management – Given inherently high inter-seasonal and inter-annual

salinity variability, it would take a long time to collect enough post data for statistical comparison to pre data.

• Being developed with pre data, the M2M3-DSS’s ANN models represent the estuary’s pre physical processes.

• Running the M2M3-DSS with post input data and comparing its predictions to post measured SC’s will quickly determine if the post measured SC’s are higher or lower, and if the mitigation features have worked.

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Scenario 3 Steps

• Step 1 – the M2M3-DSS calibration runs use historical input values represents pre conditions. – Let predicted daily-average SC89784 = SCpre

– 95% of the measured data fell within ±348 S/cm of the calibration predictions

• Step 2 – since we don’t have a post dataset, we created one for this demonstration by raising WL8980 1.5 ft and running the M2M3-DSS with all other inputs set to historical conditions. – The average predicted SC89784 increased by 61%

– The demo post dataset = SCpost

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Scenario 3 Steps – cont.

• Step 3 – add ±348 S/cm to account for model inaccuracy and compare SCpost to SCpre±348 – Running% = % of days from start when SC-post exceeded

SCpre+348

• Running% = 30% after 3 months

• Smaller deepening impact would increase detection time

• Better model accuracy would decrease detection time

• post SC reductions would be apparent relative to SCpre-348

SCpost

SCpre+348

Running%

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Conclusions

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Approach is perfect for

“Real-Time” Application

mo

de

ls

da

tab

ase

sig

na

l p

roce

ssin

g

op

tim

iza

tio

n

M2M3-DSS

Input Data USGS gaging

USACOE data

Weather

QOUTs

SCs

ELVs

min/max/D Q’s

Setpoints Constraints

Output Predictions

SCs & ELVs

28

Conclusions

• Optimized flow regulation – Could decrease salinity intrusions

– Could conserve water

– Could meet power generation requirements - via minimum flow constraints

– Can add other objectives – e.g. dissolved oxygen (DO) • Beaufort River DO TMDL DSS similar to M2M3-DSS

– Inherently Adaptive – e.g. to post-deepened conditions with new data

• DSS uses – Change Management - detect / correct problems early

– Operational Tool – optimally meet needs as conditions change

– Set Management Policy - evaluate policy alternatives