1. 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

2. Study region: High Plains Aquifer (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 3 Goals of the CLASS Project

4.  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

5. 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.

6. Model linkages

7. 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) 7

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 8 Simulates the Landscape Water Cycle ed

9. 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 km 2 cells across the aquifer  ~450,000 cells per layer  3 domains: South, Central, and North 9

10. SALUS model Output Results Input Data Weather Soil Management Crop Soil Biochemistry Soil Biochemistry Soil Water Balance Soil Water Balance Derived from CERES Derived from Century Model Derived from CERES 10

11. 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

12. Corn/soybeanSorghumwinter wheat Alfalfa Choice of farming practices Tillage practiceOther conservation practices Input use: fertilizer, pesticides…

13. 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

14. 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

15.  Drift-diffusion process of info about new tech  Precision ratio:  Decision rule: Model representation

16. 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)

18. 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

19. Econ max search: x Draw x Get f(x,b)

20. Finney county, simple calibration (Cheng, 2014)

21. Spatial adoption pattern

22. 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?

23. 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.

24. 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