impact of rapid land-use change in the northern great plains...

Impact of Rapid Land-Use Change in the Northern Great Plains: Integrated Modeling of Land-Use Patterns, Biophysical Responses, Sustainability, and Economic and Environmental Consequences

Objective:

Given two major drivers in the Northern Great Plain s, climate change and production of biomass for energy , model alternative landscape trajectories to test whether land-use patterns driven largely by economi c considerations are sustainable.

Team:USGS Earth Resources Observation and Science (EROS) CenterShuguang Liu (biogeochemical and hydrologic modeling)Terry Sohl (landscape forecasting)Alisa Gallant (landscape forecasting, risk analysis f or wildlife)

University of MinnesotaStephen Polasky (environmental economics)

USGS Northern Prairie Wildlife Research CenterChip Euliss (ecosystem services, wildlife habitat)David Mushet (wildlife habitat)

USGS Upper Midwest Environmental Science CenterWalt Sadinski (wildlife habitat)

PLUS – a large extended family

“First generation” biofuels: food-based biofuels that are currently commercially available:

– Corn-grain ethanol– Soybean biodiesel– Sugar cane ethanol– Palm oil biodiesel

“Second generation” biofuels: cellulosic biofuels– Switchgrass– Diverse prairie biomass

Alternative biofuels

Alternative biofuels

“First generation” biofuels: food-based biofuels that are currently commercially available:

– Corn-grain ethanol– Soybean biodiesel– Sugar cane ethanol– Palm oil biodiesel

“Second generation” biofuels: cellulosic biofuels– Switchgrass– Diverse prairie biomass

Demandmodule

FORE-SCE

Spatialallocationmodule

BASINS distributed hydrologic

analysis system

Annuallandscape

information

Land coverClimate

SoilsTerrain

Other factors

Economic &policy drivers

Land-usehistory

C

Crop pricesProduction costs

Ecosystemquality and

sustainability

Total C account.

Soil productivityAir quality

Water qualityHabitat

Climate-changepollutants

A

Climate change

scenarios

B E

Energy budget

Biogeochemicalassessments

Estimated erosion rates

Estimated nutrient inputto surface/groundwater

Wildlife habitat

Estimated biomass

Crop yield

GEMS

RefinedNLCD

(base landscape)

Refinedland-use

history data

Climate

SoilsTerrainOther factors

Ag. profitability

Est. greenhousegas emissions

Environmentalinformation

D

Fertilizerdatabase

Regional fluxtower data

extrapolation

F

Forecasting alternative landscapes

Inputdata/info.

Models Outputs. Env./econ.assessment

Demandmodule

FORE-SCE



analysis system

Annuallandscape

information

Land coverClimate

SoilsTerrain

Other factors


Land-usehistory

C



sustainability

Total C account.




A

Climate change

scenarios

B E

Energy budget




Wildlife habitat

Estimated biomass

Crop yield

GEMS

RefinedNLCD

(base landscape)

Refinedland-use

history data

Climate


Ag. profitability

Inputdata/info.




D

Fertilizerdatabase


extrapolation

F


A1b: “business as usual”

A2: IPCC high-change scenario

B1: IPCC low-change scenario

Demandmodule

FORE-SCE



analysis system

Annuallandscape

information

Land coverClimate

SoilsTerrain

Other factors


Land-usehistory

C



sustainability

Total C account.




A

Climate change

scenarios

B E

Energy budget




Wildlife habitat

Estimated biomass

Crop yield

GEMS

RefinedNLCD

(base landscape)

Refinedland-use

history data

Climate


Ag. profitability

Inputdata/info.




D

Fertilizerdatabase


extrapolation

F


Basis for prescription (incentive) for annual land-use change

• External demand• External prices• Estimated supply

Query landscape capability to respond to

demand

Demandmodule

FORE-SCE



analysis system

Annuallandscape

information

Land coverClimate

SoilsTerrain

Other factors


Land-usehistory

C



sustainability

Tot. C account.Soil productivity

Air qualityWater quality

Habitat


A

Climate change

scenarios

B E

Energy budget




Wildlife habitat

Estimated biomass

Crop yield

GEMS

RefinedNLCD

(base landscape)

Refinedland-use

history data

Climate


Ag. profitability

Inputdata/info.




D

Fertilizerdatabase


extrapolation

F

IPCC climate change

predictions

Policy/program requirements

and incentives

Example ingredients used by FORE-SCE

Contemporary land-conversion characteristics

FORE-SCE

Regression-based probability surfaces developed for each land-cover type

Demandmodule

FORE-SCE



analysis system

Annuallandscape

information

Land coverClimate

SoilsTerrain

Other factors


Land-usehistory

C



sustainability

Tot. C account.Soil productivity

Air qualityWater quality

Habitat


A

Climate change

scenarios

B E

Energy budget




Wildlife habitat

Estimated biomass

Crop yield

GEMS

RefinedNLCD

(base landscape)

Refinedland-use

history data

Climate


Ag. profitability

Inputdata/info.




D

Fertilizerdatabase


extrapolation

F


Services through time

Future Midwestern LandscapesU.S. EPA

• Improve society’s ability to incorporate ecosystem services into decision-making

• Address concerns about biofuels

• Facilitate development of ecosystem service markets

Randy BruinsNational Exposure Research LaboratoryU.S. Environmental Protection Agency

Cincinnati, OHEmail: [email protected]

impact of rapid land-use change in the northern great plains...

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