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Coupled Landscape, Atmosphere, and Socioeconomic Systems (CLASS) in the High Plains Region
Jinhua ZhaoMichigan State University
NSF FEW WorkshopOctober 12-13, 2015Ames, IA
Study region:High PlainsAquifer (Ogallala)
3
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
Goals of the CLASS Project
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
Project Teams
Components of the CLASS project
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
• Simulates full water and energy balance– Integrated Surface Water & Groundwater– Interactions between soil water & vegetation– Fully distributed– Process based
• 4 main zones
Integrated Landscape Hydrology Model (ILHM)
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8
• 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
• Groundwater flow model for the saturated zone
– MODFLOW
Simulates the Landscape Water Cycle
ed
ILHM Predicts 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
Output ResultsInput Data
WeatherWeather
SoilSoil
ManagementManagement
CropCrop
CropCrop
SoilBiochemistry
SoilBiochemistry
SoilWater Balance
SoilWater Balance
Crop Growth Modules
Soil organic matter and
Nutrient Module
Soil Water Balance Module
Derived from CERES
Derived from Century Model
Derived from CERES
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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
Choice of irrigation technologies
Flood Central Pivot LEPA
Choice of crops
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
Drift-diffusion process of info about new tech
Precision ratio:
Decision rule:
Model representation
)(
1,, )())1()()(()1()(
tA
ijijijjj ttutSttutu
)(
)()( ,
, t
tt
j
ijij
uztu jj )(
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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)
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
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
SALUSFix location: bWeather, soil, water availability
(Matrix)
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