Ecosystems Climate Energy and Minerals Natural Hazards ...€¦ · climate change Barney and...

Ecosystems Climate Energy and Minerals Natural Hazards Environment and Human Health Water

U.S. Department of the InteriorU.S. Geological Survey

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A spatially explicit land-use model for the assessment of biofuelsTerry SohlU. S. Geological SurveyEarth Resources Observation and Science (EROS) CenterSioux Falls, [email protected]

Ryan RekerARTS, Contractor to:USGS Earth Resources Observation and Science (EROS) CenterSioux Falls, [email protected]

December 5 2010

mailto:[email protected]�

mailto:[email protected]�


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Land Use Modeling, Scenarios, and Issues of Scale


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“Demand” vs. “Spatial Allocation” modules Originally based on CLUE model approachModular approach allows easier handling of scale issues Demand – Non-spatial, provides overall proportions of change Spatial Allocation – Uses land-use “prescription” from DEMAND, allocates change across the landscape

Basic FORE-SCE structure: Handling issues of scale


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“Demand” provides non-spatial prescription for annual LULC change

Simple table of LULC proportions for each year

Alternatively, table of LULC transitions (change matrix)

Source of “Demand” can vary widely:

Simple extrapolations of recent LULC trends

USGS Land Cover Trends

Economic Models FASOM, Lubowski, etc.

Integrated Models

IMAGE, POLYSYS, etc.

Scenario Construction

FORE-SCE – “Demand”


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“Seed” pixel is placed on probability surface, patch size is assigned

Historical patch size distribution (from USGS Trends data) modeled

Patch size selected within historical range (mimicking modeled distribution)

Once patch sized assigned to seed, a “patch library” accessed

Patch of assigned size selected from patch library, placed on probability surface

Seed Pixel

Patch Library

Patch Size Distribution

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FORE-SCE: Spatial Allocation – Patch Approach


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1992 to 2050 Projected Change:

Southeast U.S.


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1992 to 2050 ProjectedLULC Change:

Montgomery, AL area


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2007 Energy Independence and Security Act (EISA) – Section 712Mandates Department of Interior to conduct a resource assessment for biological carbon sequestration and reduction of GHG (CO2, N2O and CH4) emissions in the United States

1. Resource assessment of the nation’s ecosystems: both terrestrial (uplands, wetlands) and aquatic systems (freshwater, coastal water)

2. Assessment of both current and future potential carbon storage and GHG fluxes (2001-2050)

3. Relate to policy applications (potential mitigation strategies and impact on other ecosystem services)

4. Address effects of climate change and other controlling processes such as climate change, LULC change and ecosystem disturbances

Energy Independence and Security Act (EISA)


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Validation, uncertainty, risks, and probabilities

GIS maps, tables, and charts of carbon

sequestration and GHG flux (by ecosystems, watersheds, other

reporting units)

GIS data informing mitigation actions or adaptation strategies

Monitoring protocols

Collateral/ancillaryeffects on ecosystem

services

Collaboration/coordination with

other agencies

Carbon and GHG flux modeling

Land use change

modeling

Ecosystem disturbance

modeling

Policy and land management

analysisTerrestrial and

aquatic BGC

Policy and land

management scenarios

Future Climate

Scenarios

Regional workshops for model

parameters

Biophysical, inventory, flux tower data inputs

LandCarbon – Major project components


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Assessing Climate Change and Mitigation Scenarios

Scenario based approach, using IPCC SRES storylines

Land-use projections provided for 2010 to 2050

2001 to 2010 model model validation/calibration period

For each IPCC SRES storyline, a “reference” and “alternative” scenario

Alternative scenarios focus on land use and land management options for sequestering carbon, mitigating GHGs


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Landcarbon Scenarios – Role of Biofuels Scenario based approach, using IPCC SRES storylines

Storylines primarily provide global socioeconomic assumptions

How to “downscale” storylines to U.S., including biofuels assumptions?

Need “demand” consistent with IPCC SRES storylines


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Landcarbon Scenarios – Role of Biofuels

“Reference” scenarios must include assumptions consistent with current policy and land-use/land-cover conditions

EISA – Renewable Fuels Standards


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Socioeconomic activities and drivers of change elaborated for 24 regions Climate, LULC change processes represented at 0.5 x 0.5 degree grid Biofuels explicitly represented Corn, woody, and non-woody biomass

Graphics from: MNP (2006) (Edited by A.F. Bouwman, T. Kram, and K. Klen Goldewijk). Integrated modelling of global environmental change. An overview of IMAGE 2.4. Netherlands Environmental Asessment Agency (MNP), Bilthoven, The Netherlands

Other information sources – IMAGE Model


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U.S. - Non-woody Biomass (km2)

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,00020

00

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

2055

2060

2065

2070

2075

2080

2085

2090

2095

2100

B1A1BA1FA1TA2B2

Other information sources – IMAGE Model


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“Baseline” case: Business-as-usual Use of “demand” from variety of studies Some dependence on historic trendsAssumes no GHG policies are in place

“Alternative scenarios”: Primarily based on modification of economic variables Example on left –Scenarios based on varying GHG prices Less emphasis on global scenarios, global demographics, demand, etc.

Other information sources – FASOM-GHG Model


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Other information sources – POLYSYS Model

Policy Analysis System (POLYSYS) System of modules simulating: Crop supply and demand Crop prices Livestock supply and demand Agricultural Income Crop rotation and management Production of energy crops

Graphic from “The POLYSYS Modeling Framework: An Overview”. http://www.agpolicy.org/polysys.html


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IPCC SRES Modeling

A1B LULC A2 LULC

B1 LULC

B2 LULCA1T LULC

A1F1 LULC

SRES Storylines

A1Family

A2Family

B1Family

B2Family

Averaging of all modeling results to derive OECD LULC

For each storyline

Model means applied to NLCDand compared with LCT

for verification

National LULC Demandfor each Storyline

(NET Change)

Agriculture

Grasslands

Forest

National LULC Demandfor each Storyline

(NET Change)

Calculation of national net change for each LULC class

for each scenario

Regional downscalingusing historic LULCchange information(TRENDS) and a“tier rank method”

Regional workshopRefinement of LULC

change scenarios

Produces regional netchange for each LULC class

under each storyline.

Calculation of regionalGross Change based

on historical data(ratio of gross to net

from Trends)

Gains Losses

Calculation of specificland cover conversions

(from Trends)

Ag. to forest, developed, etc

Forest to Ag, GS, Dev, etc

…to Developed

Incorporation ofICLUS Demographic

Projections(SRES based)

National workshopRefinement of LULC

change scenarios

Step 1: National Demand Step 2: Regional Demand Step 3: Deliverables

Final package of LULCChange products

Six SRES Storylines

FORE-SCESpatial Allocation

Final package of LULCChange products

LULC DatabaseL2/L3 Ecoregions

From Demand ModuleNet National LULC Demand

Net Demand Level II EcoregionsNet Demand Level III EcoregionsGains and Losses by LULC type

Land Cover Conversions

Landscape PatternFrom (Trends)

Patch Size, Clumpiness,Dispersal, Elasticity

SRES Derived AssumptionsDevelopment densities

Biofuels and cropsOther implicit LULC parameters

Input from Exogenous Modelse.g. RPA forest dynamics,

forest management practices

NarrativesDescriptive storylines at national

and regional scales nestedwithin SRES

LandCarbon – Biofuels handled through “top-down”

scenario development


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LandCarbon: Prototype land-cover data


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Integrated modeling assessment focused on impact of expanded biofuels industry in Northern Great Plains

Linked models: FORE-SCE (land cover) GEMS (biogeochemical) Economic Model

Integrated Landscape Assessment:Northern Great Plains

Demand for land area change fed by

annual rates of land-cover change associated with the

scenarios (and informed by LCTrends

contemporary data)

Land cover data updated by FORE-

SCE (at annual time step)

GEMS builds new JFDs using the new

land-cover layer

Economic model provides

probabilities for crop types. These

are statistically applied by GEMS.

Gems provides yield estimates

based on new JFDs

GEMS uses the probabilities to

statistically distribute the crop types within the cropland JFDs


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What landscape patterns are likely to result from an expanded biomass-for-biofuel economy and what are our estimates of the uncertainties?

What are the environmental consequences? What are the full economic costs and benefit of biomass production for

energy, including agricultural sector profitability? How will projected climate change impact agricultural production and

profitability? How will projected climate change impact the provision of ecosystem

services? What are the feedbacks among land use change, economic and policy

drivers, climate, biophysical processes, and a variety of ecosystem services?

Key Research Questions


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Integrated Landscape Assessment:Northern Great Plains


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Economic Module Overview – Bottom-up Approach

Economic model – Haochi Zheng and Stephen Polasky (U. of Minnesota) Utilizes input from GEMS on crop yields, along with information on crop

prices and production costs, to assess agricultural profitability.

Where:

USDA crop prices used, as well as projections of crop prices for various biofuels production scenarios from the commodities database of the Food and Agricultural Policy Research Institute (www.fapri.org).

Data on production costs from U. of Minnesota extension sources Will provide probabilities of crop choice for each JFD, modeled by

FORE-SCE (along with other land-use/land-cover types)

http://www.fapri.org/�


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Integrated Modeling and Deliverables FORE-SCE and land-use/land-cover modeling component is only one

part of integrated modeling used for these projects. Other components include modeling of disturbance (fire),

biogeochemistry, hydrology, and economics Integrated modeling framework provides: LULC change Fire occurrence Carbon storage and flux CH4 and N2O flux Biomass Crop yield Sediment transport Nutrient input to surface

or groundwater More


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Sidney, NE and Julesburg, CO Area – 1949 to 2008


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FORE-SCE: Spatial Allocation Module:Determining areas suitable for cellulosic feedstock Difficulty determining

areas of suitability for biofuels crops not under widespread current use

Utilization of existing research on habitat suitability, and suitability under future potential climate change Barney and

DiTomaso 2010 paper on switchgrass

A2 scenario at right

From: Barney, J.N. and DiTomaso, J.M., 2010. Bioclimatic predictions of habitat suitability for the biofuel switchgrass in North America under current and future climate scenarios. Biomass and Bioenergy 34, pp. 124-133.


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Oak Ridge National Lab and U. of Tennessee


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Areas with high site potential that underperformed

FORE-SCE: Spatial Allocation Module:Determining areas suitable for cellulosic feedstock

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