Coupled Landscape, Atmosphere, and Socioeconomic Systems (CLASS) in the High Plains Region Jinhua...

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)

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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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• 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

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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

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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)

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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