Optimization of series of reservoirs in the Savannah River Basin to retain more water for droughts and improve overall water quality.

1. 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. 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. 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. 4
Savannah as a
Prototypical Large
Basin

5. 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. 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. 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
ELV_N(ft)
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
100,000
QOUT-THURm(cfs)
ELV_N-HARTm
ELV_N-THURm
QOUT-THURm
2000 3 droughts
constant Q
1980

8. 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(cfs)
WLm(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. 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
SpecificConductance(S/cm)
-14
-12
-10
-8
-6
-4
-2
0
2
4
6
8
10
HarborWaterLevel(ft)
SC89784MAXm SC8840MAXFm WL8980MINFm WL8980MAXFm
Refuge
daily
max SC
I95 daily
max SC
harbor daily max & min WLWL 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

10. 10
Optimized
Resource
Management
Approach

11. 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)

12. 12
Artificial Neural Network Models (ANNs)
Q8500 WL8980
SC8840
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
SC8840(S/cm)
Q8500(cfs)
spikes occur after sustained low Q

13. 13
ANN accuracy
ANN R2=0.90
EFDC R2=0.10
SalinityatI95(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

14. 14
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)

15. 15
M2M3-DSS Screensoptions, setpoints & constraints
run & monitor
analyze results
visualize

16. 16
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)

17. 17
M2M3-DSS ANN Calibration
R2 = 0.81
Refuge
Interstate 95
R2 = 0.74
R2 = 0.70
R2 = 0.90

18. 18
3 Scenarios

19. 19
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

20. 20
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

21. 21
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

22. 22
Scenario 2 Conclusion
• Salinity reduced & water conserved
• Lakes Hartwell & Thurmond acreages = 56,000 & 71,100
Refuge
I95

23. 23
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.

24. 24
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

25. 25
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%

26. 26
Conclusions

27. 27
Approach is perfect for
“Real-Time” Application
models
database
signal
processing
optimization
M2M3-DSS
Input Data
USGS gaging
USACOE data
Weather
QOUTs
SCs
ELVs
min/max/D Q’s
SetpointsConstraints
Output Predictions
SCs & ELVs

28. 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