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SUSTAIN:Urban Modeling Systems Integrating
Optimization and Economics
September 25, 2012
Joong Gwang Lee, Ph.D.
U.S. Environmental Protection Agency
John Riverson, P.E.
Tetra Tech
5/9/2014 1U.S. Environmental Protection Agency
What is SUSTAIN?
System for Urban Stormwater Treatment and Analysis INtegration
� A decision support system (DSS) to select/locate/size stormwater BMP/LID/GIs in urban watersheds
� Extends the capabilities and functionality of traditionally available models by providing integrated analysis of water quantity, quality, and cost factors.
5/9/2014U.S. Environmental Protection Agency
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SWMMSWMM
HSPFHSPF
OthersOthers
GISGIS
CostsCosts
OptimizationOptimization
Cost-benefit analysis for decision supportCost-benefit analysis for decision support
Modeling Components of SUSTAIN
U.S. Environmental Protection Agency35/9/2014
8. Case Study
Presentation
Outline:
1. Framework Manager The ArcGIS platform provides:
� Data management and
spatial visualization
� Placement of BMPs
� Drainage areas
delineation and flow
network connectivity
� Land use summaries
within catchment
drainage areas
�SUSTAIN is a plug-in extension to ArcGIS5/9/2014
U.S. Environmental Protection Agency4
2. BMP Siting Tool
Identifies potential suitable locations/areas for implementing different types of BMPs or LID techniques
� Results derived from spatial analysis using relevant criteria
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3. Watershed Module
• Consists of a land simulation module and a conveyance module
– Land simulation module:• Internally simulates land runoff/pollutant loads OR
• Imports externally derived runoff/pollutant time series
– Conveyance module performs routing of flow and water quality through a conduit
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Conveyance Module
• Simulates one-dimensional open channel flow with pollutant and sediment routing
– Flow and pollutant routing are simulated using algorithms from the EPA SWMM5 transport compartment
– Sediment routing is simulated using reach sediment transport algorithms from HSPF
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4. BMP Simulation ModuleProvides a process-based simulation of flow and pollutant transport routing for structural BMPs
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Why Continuous Simulation?
5/9/2014U.S. Environmental Protection Agency
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Comprehensive Analysis in Stormwater BMP/LID/GI Dynamics
5. Cost Module
• Estimates the overall costs of implementing BMPs based on fundamental construction components:
where a, b, c, d, e, f, and g are cost parameters based on initiation, length,
area, or volume of a BMP component
• Supports a built-in cost database organized by general BMP construction components
5/9/2014U.S. Environmental Protection Agency
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6. Optimization Module
• BMP Configuration
– Map all potential locations
– Typical routing configuration
– Unit cost (scalable)
• Decision Variables
– BMP Size (0 to maximum)
– BMP Location (on or off)
• Objectives
– Minimize Cost
– Maximize Volume Reduction
BMP Types &
Locations
BMP Sizes
The Watershed
∆, $
∆, $
∆, $
Reduction
Opportunity
Matrix
%
$
NSGAII (Non-dominated Sorting Genetic Algorithm II)
Scatter Search
%
$
Genera
tions
Iterations
Evolutionary Optimization Techniques
% ∆
$
% ∆∆∆∆
% ∆∆∆∆
% ∆∆∆∆
Storm Size
A point along this curve represents an aggregated
response of a BMP-scenario over time and space
CE-Curve vs. Time Series
7. Post Processor
• Analyzes and interprets simulation outputs
• Compares simulation scenarios:– Pre-development condition
– Existing condition (no BMPs)
– Existing condition with BMPs
• Specific graphical and tabular reports:– Storm event classification
– Storm event viewer
– Storm performance summary
– Cost-effectiveness curve
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Cost Effectiveness Curve
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0%
5%
10%
15%
20%
25%
30%
35%
$0.0 $0.5 $1.0 $1.5 $2.0 $2.5 $3.0 $3.5 $4.0 $4.5
Cost ($ Million)
Eff
ective
ne
ss (
% R
ed
uctio
n)
All Solutions
Cost-Effectiveness Curve
Selected Simulation
BMP-Cost Distribution for CE Curve
5/9/2014U.S. Environmental Protection Agency
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$0.0
$0.5
$1.0
$1.5
$2.0
$2.5
$3.0
$3.5
$4.0
$4.5
9.9
%
14
.1%
16
.6%
18
.4%
20
.3%
22
.1%
23
.4%
24
.2%
24
.8%
25
.7%
26
.6%
27
.3%
27
.9%
28
.5%
29
.6%
30
.2%
30
.6%
30
.8%
31
.1%
31
.6%
31
.8%
32
.1%
32
.4%
32
.9%
Effectiveness (% Reduction)
Co
st
Dis
trib
utio
n (
$ M
illio
n)
DRYPOND BIORETENTION
RAINBARREL POROUSPAVEMENT
Selected Simulation
55%24%
0%
21%
55%24%
0%
21%
SUSTAIN Case Studies: Lessons Learned
Three case study applications:
• GI Applications for CSO Mitigation
– Kansas City, MO
– Louisville, KY
• Stormwater Management for TMDLs in an Arid Climate (poster presentation)
– Albuquerque, NM
5/9/2014 U.S. Environmental Protection Agency 19
Study Area:Kansas City, MO
�480 acres
�Residential with some Com/Ind
�35% Impervious
�XP-SWMM Model
�100-acre GI Pilot
�5 past or present parallel studies
Gray
+
Gray
+
Green
Infrastructure
Green
Infrastructure
Existing or
Planned Mgmt
Existing or
Planned Mgmt
Gray
Infrastructure
Only
Gray
Infrastructure
Only
Existing or
Planned Mgmt
Existing or
Planned Mgmt
Optimization
Scenario 1
Optimization
Scenario N
Optimization
Baseline
Existing or
Planned Mgmt
Existing or
Planned Mgmt
CSO
Calibration
Baseline
CSO
Nu
mb
er
of
CS
O E
ven
ts
Problem Formulation…
Exp
lora
tory
Allowable Exceedances Max ∆ / Min $
Co
mm
itte
d
c1
Articulated BMP NetworkN
Conduit
Stormwater inlet
Flow monitoring station
Assessment point
Junction
Basin routing
BMPs
Subwatershed
Aggregate BMP Network
Shallow Bioretention
BioretentionRain
GardenPorous
Sidewalk
Porous Pavement on Storage Cube
outletoutlet
Cascade
Bioswale
N
Drainage Area Land Distribution(Proportional to articulated network contributing areas)
Storage
Untreated Land
EL 418.83’
EL 438.66’
38th Street
Pump HouseManhole
Access
2’-9”
Weir
Pump
11’-6”
CSO 019 Regulator and Pump Station
(160’ Long)
EL 418.83’
EL 438.66’
38th Street
Pump HouseManhole
Access
2’-9”
Weir
Pump
11’-6”
CSO 019 Regulator and Pump Station
(160’ Long)
Supplemental Gray Storage
Overflow
To WWTPSupplemental
Gray
Infrastructure
Assessment
Point
CSO Regulator and Pump Station
Pump House
c2
Green vs. Gray
�Not all storage is created equal (Gray ≠ Green)
�Dewatering » Infiltration/ET, but it’s not free
GI
Gray
Infiltration/ET
Dewatering
Overflow Target
Overflow Target
Overflow Target
Overflow Target
GI
Compare
Data Collection
and Analysis
Data Collection
and Analysis
Modeling Objectives &
Problem Formulation
Modeling Objectives &
Problem Formulation
Results Interpretation
Results Interpretation
Management Objectives
Numeric Control Targets
Management Objectives
Numeric Control Targets
Project Setup
Project Setup
Applying SUSTAINApplying SUSTAIN
OptimizationOptimization
Baseline Calibration
Exploratory
Insufficient Data
Where SUSTAIN Applies?
• Evaluate and select BMP/LID/GI options to achieve loading targets set by a TMDL
• Identify protective management practices and evaluate pollutant loadings for Source Water Protection
• Develop cost-effective management options for a municipal MS4 program
• Determine a cost-effective mix of control measures to help achieve management objectives for CSO control
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