1. 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

2. Regional Water Management Issues

3. River Basins are
Coupled Natural-
Human Systems!

4. Engineering-Economic IssuesWater Management Issues

5. Water Management Issues

6. 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)

7. An Optimization Model
Example:
Climate Change & California
Water Resources

8. 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)

9. Optimization Components
Objective: Maximize net economic benefits
Decisions: Reservoir releases, storage
allocations
Constraints: Mass balance, physical
capacities, environmental flows, policies

10. Optimized Rules vs Dynamic
Optimization for Flood Protection under
Climate Change

11. Sacramento Valley, California
Yolo Bypass Downtown
Sacramento
Levee and Dam
Safety

12. 0
10
20
30
40
50
60
70
0 200 400 600 800 1000 1200 1400 1600
Existing Levee Setback (ft)
ExistingLeveeHeight(ft)
Do nothing
Raise to optimal height at
current setback
Rebuild - inward
Rebuild
- outward
Optimal
setback
First
Critical
Setback
Second
Critical
Setback
X*h0
Xc
h0
Alwaysrebuild-inward
Xc
h0
Levee Re-design Rules based on Cost
Minimization
(Details: Zhu & Lund, 2009)

13. 95 9099 75 50 25 10 1 0.15 0.52
10
100
1,000
10,000
100,000
Percent Chance Exceedence
Three-dayFlow(m3
/s)
HCM2000
HCM2025
HCM2065
HCM2090
Sacramento,
California
Flood control under
Urbanization & a Changing
Climate …
Stochastic Dynamic
Programming Model
(Details: Zhu et al., 2007)

14. Move Backward?
Levees height increases over time,
and setback expansion seems
desirable in distant future …

15. Setback expansion
for increased
channel capacity
Continuously increasing
flood protection
standard driven by
economic growth

16. Expected annual
flood damage
Land value
loss
Levee
construction cost

17. 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”

18. Thank you!
Tingju Zhu
t.zhu@cigar.org