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Optimization vs Rule-based Simulation in Regional
Water Management Modeling
Tingju Zhu
International Food Policy Research Institute
Washington, DC
Systematic Analysis of Climate Resilient Development Workshop,
IFPRI and UNU-WIDER, Washington, DC, October 7-8, 2010
Regional Water Management Issues
River Basins are
Coupled Natural-
Human Systems!
Engineering-Economic IssuesWater Management Issues
Water Management Issues
Variants of Optimization and Simulation Models
for Regional Water Management
Optimization models
Rule-based simulation models
Optimization-driven simulation models (e.g.
priority driven models – WEAP, OASIS,
DWRSIM, CALSIM)
‘Optimized rules’-based models (common in
reservoir operation)
An Optimization Model
Example:
Climate Change & California
Water Resources
California Value Integrated Network
Statewide integrated
engineering-optimization
model (CALVIN)
Integrates hydrology,
infrastructure, operations,
economics, and environmental
flows
Models adaptations to changed
conditions
Highlights importance of
North-South flows
(Courtesy of Lund and Howitt)
Optimization Components
Objective: Maximize net economic benefits
Decisions: Reservoir releases, storage
allocations
Constraints: Mass balance, physical
capacities, environmental flows, policies
Optimized Rules vs Dynamic
Optimization for Flood Protection under
Climate Change
Sacramento Valley, California
Yolo Bypass Downtown
Sacramento
Levee and Dam
Safety
0
10
20
30
40
50
60
70
0 200 400 600 800 1000 1200 1400 1600
Existing Levee Setback (ft)
Ex
isti
ng
Le
ve
e H
eig
ht
(ft)
Do nothing
Raise to optimal height at
current setbackRebuild - inward
Rebuild
- outward
Optimal
setback
First
Critical
Setback
Second
Critical
Setback
X*h0
Xch0
Alw
ays r
ebuild
- inw
ard
Xch0
Levee Re-design Rules based on Cost
Minimization
(Details: Zhu & Lund, 2009)
95 9099 75 50 25 10 1 0.15 0.5210
100
1,000
10,000
100,000
Percent Chance Exceedence
Th
ree-d
ay
Flo
w (
m3/s
)
HCM2000
HCM2025
HCM2065
HCM2090
Sacramento,
California
Flood control under
Urbanization & a Changing
Climate …
Stochastic Dynamic
Programming Model
(Details: Zhu et al., 2007)
Move Backward?
Levees height increases over time,
and setback expansion seems
desirable in distant future …
Setback expansion
for increased
channel capacity
Continuously increasing
flood protection
standard driven by
economic growth
Expected annual
flood damage
Land value
loss
Levee
construction cost
Optimization vs rule-based simulation
Optimization can explore many options ‘quickly’ and
identify promising solutions for detailed study by simulation
models
More simplifications are usually needed in optimization
models; simulation models can consider more details
Optimization models can provide useful economic
information (e.g. scarcity value); simulation models usually
cannot
For distant future: rule-based simulation models face the
difficulty of specifying operating rules; similar challenge
exists for optimization models, but seems “more doable”
