optimization vs rule-based simulation in regional water management modeling

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”

Thank you!

Tingju Zhu

t.zhu@cigar.org