Summary of an integrated hydrologic- agronomic-economic modeling framework for the Yaqui Valley Lee...

Summary of an integrated hydrologic-Summary of an integrated hydrologic-agronomic-economic modeling agronomic-economic modeling framework for the Yaqui Valleyframework for the Yaqui Valley

Lee AddamsLee Addams

International Research Institute for Climate Prediction (IRI)International Research Institute for Climate Prediction (IRI)Lamont-Doherty Earth ObservatoryLamont-Doherty Earth Observatory

Columbia UniversityColumbia University

The question to consider:The question to consider:

i.e What is the combined response of the linked human – water resources system??

What are the results when the rules governing water resource use are

changed?

What is the human economic response?What is the water resource system response?

OverviewOverview

• Model Development: Physical Models• Module-level Decision-making Models:

Development and Results• District Decision-making Model:

Development and Results• Conclusions

NATIONAL WATER

COMMISSION

IRRIGATION DISTRICT

IRRIGATION MODULES

ALLOCATION

Distribution

BASIN

VALLEY

FARMS

Yaqui Valley: Water Management InstitutionsYaqui Valley: Water Management Institutions

i.e.“Government”

i.e.“the District”

“Modules”

Alternatives to this?Alternatives to this?

1. More sustainable (disciplined) surface water management

2. Better management (incl. better incentives) conjunctive use of groundwater and surface water

An Integrated Framework Involves Several An Integrated Framework Involves Several Model ComponentsModel Components

Groundwater SIMULATIONSIMULATION

Model (Seasonal)

Surface WaterSIMULATIONSIMULATION

Model

Module-Level DECISIONDECISION

Models(42)

Irrigation District DECISIONDECISION Model

Groundwater SIMULATIONSIMULATION Model

(Interannual)Water Salinity COST

Development of Groundwater Flow Development of Groundwater Flow Simulation ModelSimulation Model

Module-Level DECISION

Models(42)


(Interannual)

Water Salinity COST

Groundwater SIMULATION

Model (Seasonal)

Surface WaterSIMULATION

Model

Irrigation District DECISION Model

OverviewOverview1. Introduction:

a) Study Area: Yaqui Valley, Mexico b) An Integrated Approach for Water Resource Policy

Evaluation

2. Model Development: Physical Models3. Module-level Decision-making Models: Development

and Results4. District Decision-making Model: Development and

Results5. Conclusions

Groundwater Modeling Groundwater Modeling

• What are the physics?• What is the economic relevance of

groundwater use?• What is the “sufficient” degree of

complexity of groundwater modeling for management applications?

Groundwater Model Development: Groundwater Model Development: Conceptual Model for FlowConceptual Model for Flow

Layer 1 (Surficial)(5-30m)

Layer 2 (confining/semi-confining)

(5-30m)

Layer 3 (Productive Aquifers)

(30-250m)

ConceptualModel Layers

Estimating Hydraulic ParametersEstimating Hydraulic Parameters

Lithology from well logs (55)

Layer elevations defined by lithology

transitions*

Layer-averaged lithology then used for estimating hydraulic

conductivity (except Layer 3)

High degree of heterogeneity!

Ocean

Mountains

Pumping Wells and Drain NetworksPumping Wells and Drain Networks

PUMPING WELLS(~330 irrigation

wells)

SURFACE DRAINS (~2300km)

Modeled Recharge Sources for Modeled Recharge Sources for Groundwater Flow ModelGroundwater Flow Model

#

Model Bound

N

0 10 20 Kilometers

Bacatete

Baroyeca

#

Yaqui Colonies Irrig

#

Bacatete Mtn. Front

#

Yaqui River

#

Baroyeca Mtn Front

#

GW-Irrigated

#

GW-Irrigated

#

Model Bound

N

0 10 20 Kilometers

N

0 20 40 Kilometers

Other Recharge Sources

1. Yaqui River2. Mountain-front Recharge3. “Extra-district” irrigation

Field-level Irrigation Losses

Irrigation Canal Infiltration

Calibration to “Steady-State” Head Data Calibration to “Steady-State” Head Data (1972-1974)(1972-1974)

RMS error=3.1m

Deep Aquifer (LAYER 3)Deep Aquifer (LAYER 3)

Shallow Water Table (LAYER 1)Shallow Water Table (LAYER 1)

RMS error=2.5mObserved Heads

Observed Heads

MO

DEL

ED H

eads

MO

DEL

ED H

eads

50

50

50

50

0

0

0

0

Transient Calibration HydrographsTransient Calibration Hydrographs

1972 1974 1976 1978 1980 1982 1984

-20

-10

0

10

20

30

40

50

60

70

YEAR

Hea

d (m

)

Well 201 K738

1974 1976 1978 1980 1982

-20

-10

0

10

20

30

40

50

60

70

YEAR

Hea

d (m

)

well 362 (airport)

1972 1974 1976 1978 1980 1982 1984

-20

-10

0

10

20

30

40

50

60

70

Hea

d (m

)

Well 343, Alluvial Valley

1972 1974 1976 1978 1980 1982 1984

-20

-10

0

10

20

30

40

50

60

70

YEAR

Hea

d (m

)

well 222 veinte

1972

1984

1972 1984

1972

1972

1984

Hyd

raul

ic H

ead

Hyd

raul

ic H

ead

Hyd

raul

ic H

ead

Hyd

raul

ic H

ead

Calibrated Hydraulic Conductivity FieldCalibrated Hydraulic Conductivity Field

KV

KH

Layer 1 Layer 2 Layer 3

Intra-seasonal Simulation of Groundwater Intra-seasonal Simulation of Groundwater DrawdownDrawdown


(Seasonal)

Surface WaterSIMULATION Model


Models(42)


Groundwater SIMULATION Model

(Interannual)

Water Salinity COST

Components of Groundwater Pumping Lift Components of Groundwater Pumping Lift

F(pumping)TOTAL

LIFT

Active Pumping Well Drawdown

Piezometric Level

Pumping WellGround Surface

Initial, Non-pumping Depth-to-water

Components of Groundwater Pumping Lift Components of Groundwater Pumping Lift

Energy

Pumping Rate Pumping Rate Pumping Rate

Energy Energy

Aquifer Aquifer Aquifer

Groundwater Level Well

Development of Groundwater Flow Development of Groundwater Flow Simulation ModelSimulation Model


Models(42)


(Interannual)

Water Salinity COST


Model (Seasonal)


Surface WaterSIMULATIONSIMULATION Model

Canal Routing Model (Surface Water)Canal Routing Model (Surface Water)

Module AModule B

Module C

Reach 2

Rea

ch 3

Reach 1

Canal Wells

Module DiversionPoints

Module AModule B

Module C

Reach 2

Rea

ch 3

Reach 1

Canal Wells


Module AModule B

Module C

Reach 2

Rea

ch 3

Reach 1

Canal Wells


Canal Routing Model (Surface Water)Canal Routing Model (Surface Water)

• 33 reaches• Manning-based stage-

discharge relationships

• 300+ turnouts• ~60 wells

• Calibrated to monthly reservoir releases and module extraction

Canal Bajo120km

Canal ALTO100km

Canal Bajo:21 Reaches

Canal ALTO12 Reaches

Seasonal Crop Yield Simulation ModelingSeasonal Crop Yield Simulation Modeling

1. What are the physics? 2. What is the economic relevance of

irrigation and yield?3. What is the “sufficient” degree of

complexity of crop yield simulation…for management applications?

Seasonal Crop ModelingSeasonal Crop Modeling

Water-Yield Relationshipafter Letey and Dinar, 1995

Yield

SoilSoil Salinity-Yield Relationship

Applied Water

ETmaxAWt

SoilSoil Salinity

Ct Ymax

after Maas, 1999

•Decreased Yield•Increased Deep Percolation

Seasonal Crop ModelingSeasonal Crop Modeling

SALINITY IRRIGATION

REL

ATI

VE Y

IELD

Water-Yield Relationshipafter Letey and Dinar, 1995

SoilSoil Salinity-Yield Relationshipafter Maas, 1999

More Salt-Sensitive

MaizeGarbanzo

VegetablesCitrus

More Salt-Tolerant

WheatCotton

Safflower


(Interannual)

Module-Level Decision-making ModelsModule-Level Decision-making Models

AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN JUL

AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN JUL0.22 0.11 0.09 0.23 0.29 0.06

0.20 0.14 0.23 0.24 0.16 0.030.24 0.33 0.09 0.09 0.17 0.06 0.02

0.04 0.09 0.06 0.07 0.12 0.23 0.26 0.120.04 0.41 0.16 0.22 0.08 0.100.08 0.06 0.10 0.10 0.11 0.15 0.16 0.17 0.07

AUG SEP OCT NO V DEC JAN FEB MAR APR MAY JUN JUL

AUG SEP OCT NO V DEC JAN FEB MAR APR MAY JUN JUL0.2 2 0.1 1 0.0 9 0.23 0.2 9 0.0 6

0.2 0 0.14 0.2 3 0.2 4 0.1 6 0.0 30.2 4 0.3 3 0.0 9 0.09 0.1 7 0.0 6 0.0 2

0.0 4 0.0 9 0.06 0.0 7 0.1 2 0.2 3 0.2 6 0.120.0 4 0.4 1 0.1 6 0.2 2 0.08 0.1 00.0 8 0.0 6 0.1 0 0.1 0 0.11 0.1 5 0.1 6 0.1 7 0.0 7


AUG SEP OCT NO V DEC JAN FEB MAR APR MAY JUN JUL0.2 2 0.11 0.09 0.23 0.2 9 0.0 6

0.20 0.14 0.2 3 0.2 4 0.16 0.030.2 4 0.33 0.09 0.09 0.1 7 0.0 6 0.02

0.04 0.09 0.06 0.0 7 0.1 2 0.23 0.26 0.120.0 4 0.4 1 0.16 0.22 0.08 0.1 00.0 8 0.0 6 0.10 0.10 0.11 0.1 5 0.1 6 0.17 0.07



0.20 0.14 0.2 3 0.2 4 0.16 0.030.2 4 0.33 0.09 0.09 0.1 7 0.0 6 0.02

0.04 0.09 0.06 0.0 7 0.1 2 0.23 0.26 0.120.0 4 0.4 1 0.16 0.22 0.08 0.1 00.0 8 0.0 6 0.10 0.10 0.11 0.1 5 0.1 6 0.17 0.07



0.20 0.14 0.23 0.24 0.16 0.030.24 0.33 0.09 0.09 0.17 0.06 0.02

0.04 0.09 0.06 0.07 0.12 0.23 0.26 0.120.04 0.41 0.16 0.22 0.08 0.100.08 0.06 0.10 0.10 0.11 0.15 0.16 0.17 0.07



0.2 0 0.14 0.2 3 0.2 4 0.1 6 0.0 30.2 4 0.3 3 0.0 9 0.09 0.1 7 0.0 6 0.0 2

0.0 4 0.0 9 0.06 0.0 7 0.1 2 0.2 3 0.2 6 0.120.0 4 0.4 1 0.1 6 0.2 2 0.08 0.1 00.0 8 0.0 6 0.1 0 0.1 0 0.11 0.1 5 0.1 6 0.1 7 0.0 7



0.2 0 0.14 0.2 3 0.2 4 0.1 6 0.0 30.2 4 0.3 3 0.0 9 0.09 0.1 7 0.0 6 0.0 2

0.0 4 0.0 9 0.06 0.0 7 0.1 2 0.2 3 0.2 6 0.120.0 4 0.4 1 0.1 6 0.2 2 0.08 0.1 00.0 8 0.0 6 0.1 0 0.1 0 0.11 0.1 5 0.1 6 0.1 7 0.0 7



0.20 0.14 0.2 3 0.2 4 0.16 0.030.2 4 0.33 0.09 0.09 0.1 7 0.0 6 0.02

0.04 0.09 0.06 0.0 7 0.1 2 0.23 0.26 0.120.0 4 0.4 1 0.16 0.22 0.08 0.1 00.0 8 0.0 6 0.10 0.10 0.11 0.1 5 0.1 6 0.17 0.07



0.20 0.14 0.23 0.24 0.16 0.030.24 0.33 0.09 0.09 0.17 0.06 0.02

0.04 0.09 0.06 0.07 0.12 0.23 0.26 0.120.04 0.41 0.16 0.22 0.08 0.100.08 0.06 0.10 0.10 0.11 0.15 0.16 0.17 0.07



0.20 0.14 0.23 0.24 0.16 0.030.24 0.33 0.09 0.09 0.17 0.06 0.02

0.04 0.09 0.06 0.07 0.12 0.23 0.26 0.120.04 0.41 0.16 0.22 0.08 0.100.08 0.06 0.10 0.10 0.11 0.15 0.16 0.17 0.07



0.2 0 0.14 0.2 3 0.2 4 0.1 6 0.0 30.2 4 0.3 3 0.0 9 0.09 0.1 7 0.0 6 0.0 2

0.0 4 0.0 9 0.06 0.0 7 0.1 2 0.2 3 0.2 6 0.120.0 4 0.4 1 0.1 6 0.2 2 0.08 0.1 00.0 8 0.0 6 0.1 0 0.1 0 0.11 0.1 5 0.1 6 0.1 7 0.0 7



0.20 0.14 0.2 3 0.2 4 0.16 0.030.2 4 0.33 0.09 0.09 0.1 7 0.0 6 0.02

0.04 0.09 0.06 0.0 7 0.1 2 0.23 0.26 0.120.0 4 0.4 1 0.16 0.22 0.08 0.1 00.0 8 0.0 6 0.10 0.10 0.11 0.1 5 0.1 6 0.17 0.07


Models(42)

Water Salinity COST


Model (Seasonal)


Model




Evaluation2. Model Development: Physical Models

3. Module-level Decision-making Models: Development and Results

4. District Decision-making Model: Development and Results

5. Conclusions

Module-level Decision ModelModule-level Decision Model(42 in Valley)(42 in Valley)


0.20 0.14 0.23 0.24 0.16 0.030.24 0.33 0.09 0.09 0.17 0.06 0.02

0.04 0.09 0.06 0.07 0.12 0.23 0.26 0.120.04 0.41 0.16 0.22 0.08 0.100.08 0.06 0.10 0.10 0.11 0.15 0.16 0.17 0.07


Crop Decisions

Irrigation Decisions

GW Pumping Infiltration to GW

Crop / Irrigation SchedulingCrop Yield

Essentially, a single-farmer optimization framework, conditioned to act like a “MODULE” group of farmers

Module-level Decision Model FormulationModule-level Decision Model Formulation

OBJECTIVE: Maximize Total Module Profits

CONSTRAINTS: (1) Water Allocation, (2) Crop Scheduling, (3) Available Area, (4) Private Well Pumping Capacity, (5) Soil Drainage Constraints


0.20 0.14 0.23 0.24 0.16 0.030.24 0.33 0.09 0.09 0.17 0.06 0.02

0.04 0.09 0.06 0.07 0.12 0.23 0.26 0.120.04 0.41 0.16 0.22 0.08 0.100.08 0.06 0.10 0.10 0.11 0.15 0.16 0.17 0.07


Crop Decisions




Yield Yield PotentialPotential

PumpingPumpingCapacityCapacity

Land Land ResourcesResources

Distribution Distribution EfficiencyEfficiency

EnergyEnergyCostsCosts

ProductionProductionCostsCosts CropCrop

PricesPrices

DrainageDrainageConstraintsConstraints

DISTRICTALLOCATION


0.20 0.14 0.23 0.24 0.16 0.030.24 0.33 0.09 0.09 0.17 0.06 0.02

0.04 0.09 0.06 0.07 0.12 0.23 0.26 0.120.04 0.41 0.16 0.22 0.08 0.100.08 0.06 0.10 0.10 0.11 0.15 0.16 0.17 0.07


Crop Decisions





0.20 0.14 0.23 0.24 0.16 0.030.24 0.33 0.09 0.09 0.17 0.06 0.02

0.04 0.09 0.06 0.07 0.12 0.23 0.26 0.120.04 0.41 0.16 0.22 0.08 0.100.08 0.06 0.10 0.10 0.11 0.15 0.16 0.17 0.07


Crop Decisions





PumpingPumpingCapacityCapacity

Land Land ResourcesResources

Distribution Distribution EfficiencyEfficiency

EnergyEnergyCostsCosts

ProductionProductionCostsCosts CropCrop

PricesPrices

DrainageDrainageConstraintsConstraints

DISTRICTALLOCATION

Module-Specific ResourcesModule-Specific ResourcesValley-Wide ParametersValley-Wide Parameters

Lobell et al, 2002

4 4.5 5 5.5 6 6.5 70

2

4

6

8

10

12

14

Wheat Yield (tons/ha)

Cou

nt

Wheat Yields (t/ha)4.5 - 5.55.5 - 5.95.9 - 6.16.1 - 6.36.3 - 6.56.5 - 6.9

N

0 10 20 Kilometers

4 4.5 5 5.5 6 6.5 70

2

4

6

8

10

12

14

Wheat Yield (tons/ha)

Cou

nt

Wheat Yields (t/ha)4.5 - 5.55.5 - 5.95.9 - 6.16.1 - 6.36.3 - 6.56.5 - 6.9

N

0 10 20 Kilometers

Spatial Heterogeneity:Spatial Heterogeneity: Irrigation Efficiency & Yield Potential Irrigation Efficiency & Yield Potential

0.4 0.5 0.6 0.7 0.8 0.9 10

2

4

6

8

10

12

Field Irrigation Efficiency

Cou

nt

Wheat Irrig. Efficiency0.57 - 0.620.62 - 0.670.67 - 0.710.71 - 0.750.75 - 0.790.79 - 0.84

N

0 10 20 Kilometers

0.4 0.5 0.6 0.7 0.8 0.9 10

2

4

6

8

10

12

Field Irrigation Efficiency

Cou

nt

Wheat Irrig. Efficiency0.57 - 0.620.62 - 0.670.67 - 0.710.71 - 0.750.75 - 0.790.79 - 0.84

N

0 10 20 KilometersIrrigation Irrigation EfficiencyEfficiency




Evaluation2. Model Development: Physical Models3. Module-level Decision-making Models: Development


Results5. Conclusions

Module-Level Crop and Irrigation Modeling Module-Level Crop and Irrigation Modeling Using Historical Water Allocations (1996-2002)Using Historical Water Allocations (1996-2002)



0.20 0.14 0.23 0.24 0.16 0.030.24 0.33 0.09 0.09 0.17 0.06 0.02

0.04 0.09 0.06 0.07 0.12 0.23 0.26 0.120.04 0.41 0.16 0.22 0.08 0.100.08 0.06 0.10 0.10 0.11 0.15 0.16 0.17 0.07



0.2 0 0.14 0.2 3 0.2 4 0.1 6 0.0 30.2 4 0.3 3 0.0 9 0.09 0.1 7 0.0 6 0.0 2

0.0 4 0.0 9 0.06 0.0 7 0.1 2 0.2 3 0.2 6 0.120.0 4 0.4 1 0.1 6 0.2 2 0.08 0.1 00.0 8 0.0 6 0.1 0 0.1 0 0.11 0.1 5 0.1 6 0.1 7 0.0 7



0.20 0.14 0.2 3 0.2 4 0.16 0.030.2 4 0.33 0.09 0.09 0.1 7 0.0 6 0.02

0.04 0.09 0.06 0.0 7 0.1 2 0.23 0.26 0.120.0 4 0.4 1 0.16 0.22 0.08 0.1 00.0 8 0.0 6 0.10 0.10 0.11 0.1 5 0.1 6 0.17 0.07



0.20 0.14 0.2 3 0.2 4 0.16 0.030.2 4 0.33 0.09 0.09 0.1 7 0.0 6 0.02

0.04 0.09 0.06 0.0 7 0.1 2 0.23 0.26 0.120.0 4 0.4 1 0.16 0.22 0.08 0.1 00.0 8 0.0 6 0.10 0.10 0.11 0.1 5 0.1 6 0.17 0.07



0.20 0.14 0.23 0.24 0.16 0.030.24 0.33 0.09 0.09 0.17 0.06 0.02

0.04 0.09 0.06 0.07 0.12 0.23 0.26 0.120.04 0.41 0.16 0.22 0.08 0.100.08 0.06 0.10 0.10 0.11 0.15 0.16 0.17 0.07



0.2 0 0.14 0.2 3 0.2 4 0.1 6 0.0 30.2 4 0.3 3 0.0 9 0.09 0.1 7 0.0 6 0.0 2

0.0 4 0.0 9 0.06 0.0 7 0.1 2 0.2 3 0.2 6 0.120.0 4 0.4 1 0.1 6 0.2 2 0.08 0.1 00.0 8 0.0 6 0.1 0 0.1 0 0.11 0.1 5 0.1 6 0.1 7 0.0 7



0.2 0 0.14 0.2 3 0.2 4 0.1 6 0.0 30.2 4 0.3 3 0.0 9 0.09 0.1 7 0.0 6 0.0 2

0.0 4 0.0 9 0.06 0.0 7 0.1 2 0.2 3 0.2 6 0.120.0 4 0.4 1 0.1 6 0.2 2 0.08 0.1 00.0 8 0.0 6 0.1 0 0.1 0 0.11 0.1 5 0.1 6 0.1 7 0.0 7



0.20 0.14 0.2 3 0.2 4 0.16 0.030.2 4 0.33 0.09 0.09 0.1 7 0.0 6 0.02

0.04 0.09 0.06 0.0 7 0.1 2 0.23 0.26 0.120.0 4 0.4 1 0.16 0.22 0.08 0.1 00.0 8 0.0 6 0.10 0.10 0.11 0.1 5 0.1 6 0.17 0.07



0.20 0.14 0.23 0.24 0.16 0.030.24 0.33 0.09 0.09 0.17 0.06 0.02

0.04 0.09 0.06 0.07 0.12 0.23 0.26 0.120.04 0.41 0.16 0.22 0.08 0.100.08 0.06 0.10 0.10 0.11 0.15 0.16 0.17 0.07



0.20 0.14 0.23 0.24 0.16 0.030.24 0.33 0.09 0.09 0.17 0.06 0.02

0.04 0.09 0.06 0.07 0.12 0.23 0.26 0.120.04 0.41 0.16 0.22 0.08 0.100.08 0.06 0.10 0.10 0.11 0.15 0.16 0.17 0.07



0.2 0 0.14 0.2 3 0.2 4 0.1 6 0.0 30.2 4 0.3 3 0.0 9 0.09 0.1 7 0.0 6 0.0 2

0.0 4 0.0 9 0.06 0.0 7 0.1 2 0.2 3 0.2 6 0.120.0 4 0.4 1 0.1 6 0.2 2 0.08 0.1 00.0 8 0.0 6 0.1 0 0.1 0 0.11 0.1 5 0.1 6 0.1 7 0.0 7



0.20 0.14 0.2 3 0.2 4 0.16 0.030.2 4 0.33 0.09 0.09 0.1 7 0.0 6 0.02

0.04 0.09 0.06 0.0 7 0.1 2 0.23 0.26 0.120.0 4 0.4 1 0.16 0.22 0.08 0.1 00.0 8 0.0 6 0.10 0.10 0.11 0.1 5 0.1 6 0.17 0.07


Models(42)


(Interannual)

Water Salinity COST


Model (Seasonal)


Model


HISTORICAL DATA

Crop Prices

WHEAT

0

20

40

60

80

100

120

140

160

180

1996 1997 1998 1999 2000 2001 2002

CR

OP

AR

EA (x

100

0 ha

)

DATA

MODEL

MAIZE

0

10

20

30

40

50

60

70

80

90

1996 1997 1998 1999 2000 2001 2002

CR

OP

AR

EA (x

100

0 ha

)

DATA

MODEL

SAFF

0

10

20

30

40

50

60

70

80

90

1996 1997 1998 1999 2000 2001 2002

CR

OP

AR

EA (x

100

0 ha

)

DATA

MODEL

Cotton

0

10

20

30

40

50

60

70

80

90

1996 1997 1998 1999 2000 2001 2002

CR

OP

AR

EA (x

100

0 ha

)

DATA

MODEL

Cro

p A

rea

Cro

p A

rea

Cro

p A

rea

Cro

p A

rea

Aggregate Crop Area Comparisons Aggregate Crop Area Comparisons for Historical Period—for Historical Period—End-of-Season PricesEnd-of-Season Prices

WHEAT

0

20

40

60

80

100

120

140

160

180

1996 1997 1998 1999 2000 2001 2002

CR

OP

AR

EA (x

100

0 ha

)

DATA

MODEL

MAIZE

0

10

20

30

40

50

60

70

80

90

1996 1997 1998 1999 2000 2001 2002

CR

OP

AR

EA (x

100

0 ha

)

DATA

MODEL

SAFF

0

10

20

30

40

50

60

70

80

90

1996 1997 1998 1999 2000 2001 2002

CR

OP

AR

EA (x

100

0 ha

)

DATA

MODEL

Cotton

0

10

20

30

40

50

60

70

80

90

1996 1997 1998 1999 2000 2001 2002

CR

OP

AR

EA (x

100

0 ha

)

DATA

MODEL

Cro

p A

rea

Cro

p A

rea

Cro

p A

rea

Cro

p A

rea

Aggregate Crop Area Comparisons Aggregate Crop Area Comparisons for Historical Period—for Historical Period—Adjusted PricesAdjusted Prices

RESULT: Crop Decision-making modeling on a “decentralized” basis reasonably estimated aggregate Valley-wide crop patterns




and Results

4. District Decision-making Model: Development and Results

5. Conclusions

Putting it all together: Multiple Decision Putting it all together: Multiple Decision Model and Physical Process ModelsModel and Physical Process Models



0.20 0.14 0.23 0.24 0.16 0.030.24 0.33 0.09 0.09 0.17 0.06 0.02

0.04 0.09 0.06 0.07 0.12 0.23 0.26 0.120.04 0.41 0.16 0.22 0.08 0.100.08 0.06 0.10 0.10 0.11 0.15 0.16 0.17 0.07



0.2 0 0.14 0.2 3 0.2 4 0.1 6 0.0 30.2 4 0.3 3 0.0 9 0.09 0.1 7 0.0 6 0.0 2

0.0 4 0.0 9 0.06 0.0 7 0.1 2 0.2 3 0.2 6 0.120.0 4 0.4 1 0.1 6 0.2 2 0.08 0.1 00.0 8 0.0 6 0.1 0 0.1 0 0.11 0.1 5 0.1 6 0.1 7 0.0 7



0.20 0.14 0.2 3 0.2 4 0.16 0.030.2 4 0.33 0.09 0.09 0.1 7 0.0 6 0.02

0.04 0.09 0.06 0.0 7 0.1 2 0.23 0.26 0.120.0 4 0.4 1 0.16 0.22 0.08 0.1 00.0 8 0.0 6 0.10 0.10 0.11 0.1 5 0.1 6 0.17 0.07



0.20 0.14 0.2 3 0.2 4 0.16 0.030.2 4 0.33 0.09 0.09 0.1 7 0.0 6 0.02

0.04 0.09 0.06 0.0 7 0.1 2 0.23 0.26 0.120.0 4 0.4 1 0.16 0.22 0.08 0.1 00.0 8 0.0 6 0.10 0.10 0.11 0.1 5 0.1 6 0.17 0.07



0.20 0.14 0.23 0.24 0.16 0.030.24 0.33 0.09 0.09 0.17 0.06 0.02

0.04 0.09 0.06 0.07 0.12 0.23 0.26 0.120.04 0.41 0.16 0.22 0.08 0.100.08 0.06 0.10 0.10 0.11 0.15 0.16 0.17 0.07



0.2 0 0.14 0.2 3 0.2 4 0.1 6 0.0 30.2 4 0.3 3 0.0 9 0.09 0.1 7 0.0 6 0.0 2

0.0 4 0.0 9 0.06 0.0 7 0.1 2 0.2 3 0.2 6 0.120.0 4 0.4 1 0.1 6 0.2 2 0.08 0.1 00.0 8 0.0 6 0.1 0 0.1 0 0.11 0.1 5 0.1 6 0.1 7 0.0 7



0.2 0 0.14 0.2 3 0.2 4 0.1 6 0.0 30.2 4 0.3 3 0.0 9 0.09 0.1 7 0.0 6 0.0 2

0.0 4 0.0 9 0.06 0.0 7 0.1 2 0.2 3 0.2 6 0.120.0 4 0.4 1 0.1 6 0.2 2 0.08 0.1 00.0 8 0.0 6 0.1 0 0.1 0 0.11 0.1 5 0.1 6 0.1 7 0.0 7



0.20 0.14 0.2 3 0.2 4 0.16 0.030.2 4 0.33 0.09 0.09 0.1 7 0.0 6 0.02

0.04 0.09 0.06 0.0 7 0.1 2 0.23 0.26 0.120.0 4 0.4 1 0.16 0.22 0.08 0.1 00.0 8 0.0 6 0.10 0.10 0.11 0.1 5 0.1 6 0.17 0.07



0.20 0.14 0.23 0.24 0.16 0.030.24 0.33 0.09 0.09 0.17 0.06 0.02

0.04 0.09 0.06 0.07 0.12 0.23 0.26 0.120.04 0.41 0.16 0.22 0.08 0.100.08 0.06 0.10 0.10 0.11 0.15 0.16 0.17 0.07



0.20 0.14 0.23 0.24 0.16 0.030.24 0.33 0.09 0.09 0.17 0.06 0.02

0.04 0.09 0.06 0.07 0.12 0.23 0.26 0.120.04 0.41 0.16 0.22 0.08 0.100.08 0.06 0.10 0.10 0.11 0.15 0.16 0.17 0.07



0.2 0 0.14 0.2 3 0.2 4 0.1 6 0.0 30.2 4 0.3 3 0.0 9 0.09 0.1 7 0.0 6 0.0 2

0.0 4 0.0 9 0.06 0.0 7 0.1 2 0.2 3 0.2 6 0.120.0 4 0.4 1 0.1 6 0.2 2 0.08 0.1 00.0 8 0.0 6 0.1 0 0.1 0 0.11 0.1 5 0.1 6 0.1 7 0.0 7



0.20 0.14 0.2 3 0.2 4 0.16 0.030.2 4 0.33 0.09 0.09 0.1 7 0.0 6 0.02

0.04 0.09 0.06 0.0 7 0.1 2 0.23 0.26 0.120.0 4 0.4 1 0.16 0.22 0.08 0.1 00.0 8 0.0 6 0.10 0.10 0.11 0.1 5 0.1 6 0.17 0.07


Models(42)


(Interannual)


(Seasonal)

Surface WaterSIMULATIONSIMULATION Model

Irrigation District DECISIONDECISION Model

Water Salinity COST

District Decision-Making District Decision-Making Optimization FrameworkOptimization Framework

1. Historical Policy2. Historical-Redistribution Policy3. Historical-Improved Policy4. “Salinity Limit” Custodial Policy5. “Equivalent Yield” Custodial Policy

Various “policies” of the Irrigation District, formulated as

optimization problems

Performance of Historical vs. “Historical-Performance of Historical vs. “Historical-Improved” Policy (1)Improved” Policy (1)

0

500

1000

1500

2000

2500

3000

1995 1996 1997 1998 1999 2000 2001 2002 2003

Tota

l Mod

ule

Allo

catio

n (M

CM)

"Historical Policy"

"Historical-Improved Policy"

0

200

400

600

800

1000

1200

1400

1600

1800

1995 1996 1997 1998 1999 2000 2001 2002 2003

Avg

Mod

ule

Prof

it (M

illio

n Pe

sos)

"Historical Policy"

"Historical-Improved Policy"

Total Water Allocation

Total Profit

Mod

ule

Allo

catio

nTo

tal M

odul

e Pr

ofit

Historical Policy “rule-based” objective“Historical-Improved” Policy operational efficiency

Performance of Historical “Redistribution” Performance of Historical “Redistribution” ProcedureProcedure

0

200

400

600

800

1000

1200

1400

1600

1800

1995 1996 1997 1998 1999 2000 2001 2002

Year

Tota

l Mod

ule

Prof

it (m

illio

n pe

sos)

Historical Policy

Historical-RedistributionPolicy

Modules with unused allocation

Tabulate unusedallocation at current salinity and prices

Subtract unused water from current

allocation

Use difference as “new” allocation

(fixed)

Modules with positive marginal

price for water

Distribute 80% of total “unused” allocation to

modules(weight by shadow price and module

area)

Use new quantity as for new allocation

(lower bound)

Re-Run District Allocation Model

Modules with unused allocation

Tabulate unusedallocation at current salinity and prices

Subtract unused water from current

allocation

Use difference as “new” allocation

(fixed)

Modules with positive marginal

price for water

Distribute 80% of total “unused” allocation to

modules(weight by shadow price and module

area)

Use new quantity as for new allocation

(lower bound)

Re-Run District Allocation Model

0%

5%

10%

15%

20%

25%

1995 1996 1997 1998 1999 2000 2001 2002Season

Allo

catio

n ex

chan

ges

betw

een

mod

ules

(% o

f tot

al M

odul

e A

lloca

tions

) Data

Historical-RedistributionPolicy

no d

ata

no d

ata

no d

ata

no d

ata

0 0.1 0.2 0.3 0.40

5

10

15

20

25Before "Exchanges"

Cou

nt o

f Mod

ules

0 0.1 0.2 0.3 0.40

5

10

15

20

25After "Exchanges"

Shadow Price for Water (pesos/m3)

Cou

nt o

f Mod

ules

Num

ber o

f Mod

ules

Num

ber o

f Mod

ules

Shadow Price for Water

Before Transfer After TransferW

ater

Tra

nsfe

rs B

etw

een

Mod

ules

(% o

f tot

al)

Tota

l Mod

ule

Prof

its

““Salinity Limits” Custodial Policy—More Salinity Limits” Custodial Policy—More water, but higher water priceswater, but higher water prices

0

200

400

600

800

1000

1200

1995 1996 1997 1998 1999 2000 2001 2002 2003

Tota

l GW

Pum

ping

(MCM

)

"Salinity Limits" Policy"Historical-Improved" Policy

0

500

1000

1500

2000

2500

3000

3500

1995 1996 1997 1998 1999 2000 2001 2002 2003

Tota

l Mod

ule

Allo

catio

n (M

CM)


(A)

(B)

Mod

ule

Allo

catio

nD

istr

ict G

roun

dwat

er P

umpi

ng

““Salinity Limits” Custodial Policy—More Salinity Limits” Custodial Policy—More water, but higher water priceswater, but higher water prices

0

200

400

600

800

1000

1200

1995 1996 1997 1998 1999 2000 2001 2002 2003

Tota

l GW

Pum

ping

(MCM

)


0

500

1000

1500

2000

2500

3000

3500

1995 1996 1997 1998 1999 2000 2001 2002 2003

Tota

l Mod

ule

Allo

catio

n (M

CM)


(A)

(B)

Mod

ule

Allo

catio

nD

istr

ict G

roun

dwat

er P

umpi

ng

0

200

400

600

800

1000

1200

1400

1600

1800

1995 1996 1997 1998 1999 2000 2001 2002 2003

Avg

Mod

ule

Prof

it (M

illio

n Pe

sos)

"Salinity Limits" Policy

"Historical-Improved" Policy

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

0.400

0.450

1995 1996 1997 1998 1999 2000 2001 2002 2003

Dist

rict W

ater

Pric

e

"Salinity Limits" Policy"Historical-Improved" PolicyACTUAL

(A)

(B)

Mod

ule

PRO

FITS

Wat

er P

RIC

E to

mod

ules

2X Water Price with more GW pumping!

““Salinity Limits” Custodial Policy…ok when Salinity Limits” Custodial Policy…ok when crop prices are high!crop prices are high!

0

200

400

600

800

1000

1200

1400

1600

1800

1995 1996 1997 1998 1999 2000 2001 2002 2003

Avg

Mod

ule

Prof

it (M

illio

n Pe

sos)

S.L. = 0.6S.L. = 0.8S.L. = 1.0S.L. = 1.2

““Equivalent Yields” Custodial Policy Equivalent Yields” Custodial Policy

0.5

0.6

0.7

0.8

0.9

1

1.1

0 0.5 1 1.5 2

Dep

th o

f Allo

catio

n to

Mod

ule

(m)

0.5

0.6

0.7

0.8

0.9

1

1.1

0 0.5 1 1.5 2

Effective Salinity of Allocation (dS/m)

Dep

th o

f Allo

catio

n to

Mod

ule

(m)

““Equivalent Yields” Custodial Policy (2) Equivalent Yields” Custodial Policy (2)

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

mod

01 k63

K66

K70

K79

Mod

06

K91

Sur

San

t1

K10

5

Mod

10

nain

ari

dos

seis

diez

doce

diec

isei

s

diec

ioch

o

vein

te

K88

5

4p6

4p10

Varia

tion

in A

lloca

tion

Dep

th (m

)

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

mod

01 k63

K66

K70

K79

Mod

06

K91

Sur

San

t1

K10

5

Mod

10

nain

ari

dos

seis

diez

doce

diec

isei

s

diec

ioch

o

vein

te

K88

5

4p6

4p10

MODULE

Varia

tion

in A

lloca

tion

Dep

th (m

)

StdDev=0.05 m

StdDev=0.01 m

Change in allocation depthwith consideration for equity

in water quality





Results

5. Conclusions

Summary/Conclusions (methodology)Summary/Conclusions (methodology)

Developed a Developed a integrated hydrologic-integrated hydrologic-agronomic-economic frameworkagronomic-economic framework consisting of:consisting of:

A. A three-layer, distributed-parameter groundwater flow model a first for the Yaqui Valley

B. A physically-based canal routing model for flow and salinity

C. Seasonal water-salinity-yield models for relevant crops

SALINITY IRRIGATIONR

ELA

TIVE

YIE

LD

Summary/Conclusions (methodology)Summary/Conclusions (methodology)

Developed a integrated hydrologic-integrated hydrologic-agronomic-economic agronomic-economic frameworkframework consisting of :

D. A set of 42 Module-Level Decision Models for de-centralized crop and irrigation decisions

E. An Irrigation District Decision Model to determine optimal water distribution, groundwater pumping, and canal operations

Summary/Conclusions (insights)Summary/Conclusions (insights)

With the integrated set of decision and physical models:

A. Matched historical aggregate crop production (human behavior)

B. Showed that the “Historical Policy” for District distribution of surface and groundwater could be made more efficient

C. Demonstrated a method (“Historical-Redistribution Policy”) to represent transfers of (unused) water between modules

Summary/Conclusions (insights)Summary/Conclusions (insights)

With the integrated set of decision and physical models, also:

D. Explored a potential “Custodial Policy” for District allocations that increased allocations to Valley farmers, but decreased overall profits

E. Explored a second “Custodial Policy” that allocated water more equitably based on crop-growing potential.

Planned future work @ IRIPlanned future work @ IRI• Valuation:

– What is the “value” of groundwater under various levels of pumping capacity and climate scenario?

• Multi-level Decision Making: – What are appropriate groundwater management goals for a

“sustainably minded” Irrigation District– What incentives to groundwater users help achieve those

results?• Crop Decision-Making

– Use of climate forecasts and groundwater to increase value to farmers

• General Model “Upgrades”– Couple with reservoir model of JLM

Acknowledgements Acknowledgements

Funding:• UPS Foundation, Packard

Foundation• GES Department

Ph.D. Committee: Steve Gorelick (advisor), Pamela Matson, Walter Falcon, Paul Switzer

Research Assistants: Tod Johnston and Cynthia Chen (Stanford), Andrea Harrison (SJSU), Francisco Flores (ITSON)

ThanksThanks to the Stanford Yaqui Group to the Stanford Yaqui Group

David LobellEllen McCulloughJosh GoldsteinMike BemanToby AhrensPeter JewettJose Luis Minjares

Stanford-Yaqui Research GROUP (past and present)

Amy LuersLindsey ChristiansenJohn HarrisonIvan Ortiz-MonasterioJose Luis MinjaresRoz Naylor+ many others!!

Yaqui support group:

Mary Smith, Lori McVay, Ashley Dean

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