Coupled Landscape, Atmosphere,
and Socioeconomic Systems
(CLASS) in the High Plains Region
Jinhua Zhao
Michigan State University
NSF FEW Workshop
October 12-13, 2015
Ames, IA
Study region:
High Plains
Aquifer
(Ogallala)
Goals of the CLASS Project
1. Synthesize existing efforts
 Build from existing efforts including a major USGS project
2. Link climate, hydrology, crop, and economics models
 Explore historical changes to understand feedbacks
3. Predict impacts of changing climate, technologies, policies,
and management on:
 Water levels and streamflows
 Yields and economic output
4. Offer results to stakeholders to
 help improve water and economic sustainability
3
Project Teams
 Hydrology and Plant Biophysics Team
 D. Hyndman, A. Kendall, W. Wood, B. Basso & E. King - MSU
• Hydrology and Crop modeling
 M. Sophocleous, J. Butler, D. Whittemore, & D. Fross- KGS
• Hydrology, data acquisition, and outreach
 Socioeconomics Team
 S. Gasteyer & M. Rabb - MSU
• Social aspects of water management
 J. Zhao - MSU
• Agricultural Economics
 Climate Team
 N. Moore, S. Zhong & L. Pei - MSU
• Regional climate modeling
Components of the CLASS project
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Climate downscaling
Hydro model
Agronomy model
Social/econ decision model
Coupling. Not only econ  land use  biophysical
model, but also biophysical model  econ.
Model linkages
Integrated Landscape Hydrology Model
(ILHM)
• Simulates full water and energy balance
–
–
–
–
Integrated Surface Water & Groundwater
Interactions between soil water & vegetation
Fully distributed
Process based
• 4 main zones
7
Simulates the Landscape Water Cycle
• Canopy & Litter intercept P
• Snow pack stores water
• Root Zone
– Variable root mass with depth
– Dynamic moisture zone
• Water percolates through rest
of unsaturated zone
ed
• Groundwater flow model for
the saturated zone
– MODFLOW
8
ILHM Predicts
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Streamflows
Groundwater levels
Soil moisture
Snowpack
Water Temperature in Lakes
 Time Scale: Hourly water cycle for ~160 years
 1930’s Current
 Scenarios: Current  2100
 Spatial Scale: ~1 km2 cells across the aquifer
 ~450,000 cells per layer
 3 domains: South, Central, and North
9
SALUS model
Derived from
CERES
Input
Data
Output Resul
Weather
Crop
Soil
Soil
Biochemistry
Soil
Management
Water Balance
Crop
Derived from CERES
10
Derived from Century
Model
Key components of econ model
 Adoption and diffusion of irrigation technologies.
 Micro level data (well level): need to be careful about
decision framework.
 Sunk costs, uncertainty, learning,
 Bounded rational adoption behavior.
 Crop choices and management practices
Corn/soybean
Sorghum
winter wheat
Alfalfa
Choice of farming practices
Tillage practice
Other conservation practices
Input use: fertilizer, pesticides…
Key factor of econ model: for policy
 Institutions on water use: water rights
 Use-it-or-lost-it: three year window
 Not sure how limiting the factor is – how farmers consider
water rights in water use decisions
 Data: use small amounts of water at some wells
 Econometric approach to estimate impacts of water rights
 inform structural model.
• Mostly not limiting, esp with newer irrigation technologies
• But incentive to preserve water rights.
 Econometric model also shows rebound effect, mainly
through extensive margin (irrigated acreage, crop choice) –
not modeled yet
Irrigation technology model: structural
 Drift-diffusion model of technology adoption
 “incentive to adopt” follows a diffusion process, driven by
expected profits, informed by signals/shocks. Learning can
be non-Bayesian
 “threshold” of adoption, determined by adoption costs,
learning potential (future adopters), irreversibility
 Adopt when incentive crosses threshold
 Captures a range of behaviors, from fully rational (game
theoretic) to heuristics
Model representation
 Drift-diffusion process of info about new tech
A( t )
u j (t )  u j (t  1)   j ,i (t )( S j ,i (t )  u j (t  1))   (t )
i 1
 j ,i (t )
 Precision ratio:  j ,i (t ) 
 j (t )
 Decision rule:
u j (t )  z j  u
u  c(1  1 /  j (t ))
Data (for Kansas)
 Observed data (WIMAS): location specific irrigation
technologies, 1991-2010: diffusion process.
 “Calibrates” model parameters to match observed
data: behavioral parameters (errors in Bayesian
updating, adoption barrier parameter, responsiveness
to new info)
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Flood
Center Pivot
LEPA
Data
 Input data
 Location specific: weather (rainfall, temperature), depth to
water, soil characteristics, water rights, remote sensed crop
cover
 Prior: expected profits and variances for three irrigation
technologies – from SALUS, and climate/hydro models
 Costs: equipment, operating (energy)
 Basically obtain “production functions” from
SALUS, and future uncertainties from
climate/hydro/SALUS models
 Location specific matrices of “input – output” for historical
weather patterns
Econ max search: x
Draw x
Get f(x,b)
Finney county, simple calibration (Cheng, 2014)
Spatial adoption pattern
Challenges in econ modeling
 Reduced form vs structural models
 Reduced form works the best to fit historical data:
behavioral distortions implicitly included in econometric
model
 Structural model might be needed for out of sample
predictions
 Structural model can also be much easier to be linked with
crop, hydro and climate models
 But structural models with too many parameters can
become black boxes
 Our solution: incorporate behavioral distortions in a
parsimonious structural model. Semi-structural?
Challenges in model linkages
 Temporal scale of models: input use
 Econ model: annual
 SALUS: intraseasonal
 ILHM: hourly
 “Simplified” expectations of other models
 Econ’s expectation from SALUS: y=f(x). But, SALUS
doesn’t generate any production function
 Econ’s expectation from climate models: distribution of
weather variables. But, they produce assembles of models
and scenarios
 Others’ expectation from Econ: tell me how land will be
used in 2050.
Challenges in modeling FEW systems
 Influence policy? Influence farmer behavior?
 Communication: not only model results and not
sufficient
 Stakeholder involvement: participatory modeling
 Powerful tool for local decisions, e.g., adaptation