summary of an integrated hydrologic- agronomic-economic modeling framework for the yaqui valley lee...
DESCRIPTION
Overview Model Development: Physical Models Module-level Decision-making Models: Development and Results District Decision-making Model: Development and Results ConclusionsTRANSCRIPT
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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
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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?
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OverviewOverview
• Model Development: Physical Models• Module-level Decision-making Models:
Development and Results• District Decision-making Model:
Development and Results• Conclusions
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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”
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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
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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
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Development of Groundwater Flow Development of Groundwater Flow Simulation ModelSimulation Model
Module-Level DECISION
Models(42)
Groundwater SIMULATIONSIMULATION Model
(Interannual)
Water Salinity COST
Groundwater SIMULATION
Model (Seasonal)
Surface WaterSIMULATION
Model
Irrigation District DECISION Model
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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
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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?
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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
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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
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Pumping Wells and Drain NetworksPumping Wells and Drain Networks
PUMPING WELLS(~330 irrigation
wells)
SURFACE DRAINS (~2300km)
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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
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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
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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
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Calibrated Hydraulic Conductivity FieldCalibrated Hydraulic Conductivity Field
KV
KH
Layer 1 Layer 2 Layer 3
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Intra-seasonal Simulation of Groundwater Intra-seasonal Simulation of Groundwater DrawdownDrawdown
Groundwater SIMULATIONSIMULATION Model
(Seasonal)
Surface WaterSIMULATION Model
Module-Level DECISION
Models(42)
Irrigation District DECISION Model
Groundwater SIMULATION Model
(Interannual)
Water Salinity COST
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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
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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
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Development of Groundwater Flow Development of Groundwater Flow Simulation ModelSimulation Model
Module-Level DECISION
Models(42)
Groundwater SIMULATION Model
(Interannual)
Water Salinity COST
Groundwater SIMULATION
Model (Seasonal)
Irrigation District DECISION Model
Surface WaterSIMULATIONSIMULATION Model
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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 DiversionPoints
Module AModule B
Module C
Reach 2
Rea
ch 3
Reach 1
Canal Wells
Module DiversionPoints
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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
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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?
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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
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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
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Groundwater SIMULATION Model
(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 NOV DEC JAN FEB MAR APR MAY JUN JUL
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
AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN JUL
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
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 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 NOV DEC JAN FEB MAR APR MAY JUN JUL
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
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 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 NOV DEC JAN FEB MAR APR MAY JUN JUL
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
Module-Level DECISIONDECISION
Models(42)
Water Salinity COST
Groundwater SIMULATION
Model (Seasonal)
Surface WaterSIMULATION
Model
Irrigation District DECISION Model
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OverviewOverview1. Introduction:
a) Study Area: Yaqui Valley, Mexico b) An Integrated Approach for Water Resource Policy
Evaluation2. Model Development: Physical Models
3. Module-level Decision-making Models: Development and Results
4. District Decision-making Model: Development and Results
5. Conclusions
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Module-level Decision ModelModule-level Decision Model(42 in Valley)(42 in Valley)
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 NOV DEC JAN FEB MAR APR MAY JUN JUL
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
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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
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 NOV DEC JAN FEB MAR APR MAY JUN JUL
Crop Decisions
Irrigation Decisions
GW Pumping Infiltration to GW
Crop / Irrigation SchedulingCrop Yield
Yield Yield PotentialPotential
PumpingPumpingCapacityCapacity
Land Land ResourcesResources
Distribution Distribution EfficiencyEfficiency
EnergyEnergyCostsCosts
ProductionProductionCostsCosts CropCrop
PricesPrices
DrainageDrainageConstraintsConstraints
DISTRICTALLOCATION
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 NOV DEC JAN FEB MAR APR MAY JUN JUL
Crop Decisions
Irrigation Decisions
GW Pumping Infiltration to GW
Crop / Irrigation SchedulingCrop Yield
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 NOV DEC JAN FEB MAR APR MAY JUN JUL
Crop Decisions
Irrigation Decisions
GW Pumping Infiltration to GW
Crop / Irrigation SchedulingCrop Yield
Yield Yield PotentialPotential
PumpingPumpingCapacityCapacity
Land Land ResourcesResources
Distribution Distribution EfficiencyEfficiency
EnergyEnergyCostsCosts
ProductionProductionCostsCosts CropCrop
PricesPrices
DrainageDrainageConstraintsConstraints
DISTRICTALLOCATION
Module-Specific ResourcesModule-Specific ResourcesValley-Wide ParametersValley-Wide Parameters
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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
Yield Yield PotentialPotential
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OverviewOverview1. Introduction:
a) Study Area: Yaqui Valley, Mexico b) An Integrated Approach for Water Resource Policy
Evaluation2. Model Development: Physical Models3. Module-level Decision-making Models: Development
and Results4. District Decision-making Model: Development and
Results5. Conclusions
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Module-Level Crop and Irrigation Modeling Module-Level Crop and Irrigation Modeling Using Historical Water Allocations (1996-2002)Using Historical Water Allocations (1996-2002)
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 NOV DEC JAN FEB MAR APR MAY JUN JUL
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
AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN JUL
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
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 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 NOV DEC JAN FEB MAR APR MAY JUN JUL
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
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 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 NOV DEC JAN FEB MAR APR MAY JUN JUL
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
Module-Level DECISIONDECISION
Models(42)
Groundwater SIMULATIONSIMULATION Model
(Interannual)
Water Salinity COST
Groundwater SIMULATION
Model (Seasonal)
Surface WaterSIMULATION
Model
Irrigation District DECISION Model
HISTORICAL DATA
Crop Prices
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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
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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
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OverviewOverview1. Introduction:
a) Study Area: Yaqui Valley, Mexico b) An Integrated Approach for Water Resource Policy
Evaluation2. Model Development: Physical Models3. Module-level Decision-making Models: Development
and Results
4. District Decision-making Model: Development and Results
5. Conclusions
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Putting it all together: Multiple Decision Putting it all together: Multiple Decision Model and Physical Process ModelsModel and Physical Process 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 NOV DEC JAN FEB MAR APR MAY JUN JUL
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
AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN JUL
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
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 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 NOV DEC JAN FEB MAR APR MAY JUN JUL
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
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 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 NOV DEC JAN FEB MAR APR MAY JUN JUL
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
Module-Level DECISIONDECISION
Models(42)
Groundwater SIMULATIONSIMULATION Model
(Interannual)
Groundwater SIMULATIONSIMULATION Model
(Seasonal)
Surface WaterSIMULATIONSIMULATION Model
Irrigation District DECISIONDECISION Model
Water Salinity COST
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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
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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
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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
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““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)
"Salinity Limits" Policy"Historical-Improved" Policy
(A)
(B)
Mod
ule
Allo
catio
nD
istr
ict G
roun
dwat
er P
umpi
ng
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““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)
"Salinity Limits" Policy"Historical-Improved" Policy
(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!
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““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
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““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)
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““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
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OverviewOverview1. Introduction:
a) Study Area: Yaqui Valley, Mexico b) An Integrated Approach for Water Resource Policy
Evaluation2. Model Development: Physical Models3. Module-level Decision-making Models: Development
and Results4. District Decision-making Model: Development and
Results
5. Conclusions
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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
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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
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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
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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.
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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
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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)
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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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Questions?Questions?