1

Office of Research and DevelopmentNational Health and Environmental Effects Research Laboratory, Western Ecology Division, Corvallis, Oregon

Modeling the Effects of Land Use and Global Change on Ecosystem Services

~ Overview of EPA Western Ecology Division Models ~

Presentation for NCEA Global Change Research Program

March 18, 2008 – Corvallis, Oregon

Bob McKane, Research Ecologistmckane.bob epa.gov

541-754-4631

@

1

2

Abstract2

32

TongassNational Forest?

Arctic Alaska

Olympic National Park

Willamette Basin

Flint Hills Ecoregion

Chesapeake

Michigan

Carolina Coast

Ongoing Projects Ongoing Projects ~ Place~ Place--Based Modeling ~Based Modeling ~

3

43

USFS / EPA R10?(forest)

NSF LTER / EPA R10(arctic tundra)

USNPS, USGS / EPA R10(forest)

USDA-ARS, NSF LTER / EPA R10 (ag, forest, riparian)

NSF LTER / EPA R7 (prairie, rangeland)

Smithsonian ERC

NSF LTER (agriculture)

EPA-NERL / EPA R4

Modeling Partners / ClientsModeling Partners / Clients(land use)(land use)

(ag, riparian)

(ag, forest, riparian)

4

5

Hydrologic Processes within Landscapes5

snobear.colorado.edu/IntroHydro/hydro.gif

66

Flint Hills DemoFlint Hills DemoThe F l int Hil l sThe F l int Hil l s

FlintHills

10,000 mi2

KansasKansasCityCity

KonzaLTER

* *

Kilometers

Kansas Ecoregions

77

8

Kings Creek Watershed, Konza Prairie (11 km2)SSURGO soil map, superimposed on orthophoto

USGS Stream Gauge

USGS Stream Gauge

8

9GT Hydrologic Model Simulation,

Flint Hills Ecoregion, Kansas

100 km2

Precip recordDischarge at

Stream network (right)DEM (left)

Kings CreekWatershed,

11 km2

9

10

Dynamic simulations of stream discharge & soil moisture distribution

Driest WettestSoil Moisture

10

Soil Moisture (right)DEM (left)

11

Dynamic simulations of stream discharge & soil moisture distribution

Driest WettestSoil Moisture

11

1212

MODES: a MODular Ecosystem Services model for assessing human impacts on land, air & water resources

Bob McKane, Project Coordinator (EPA-WED)

Marc Stieglitz & Feifei Pan (Georgia Institute of Technology)

Ed Rastetter & Bonnie Kwiatkowski (Marine Biological Laboratory)

Nathan Schumaker (EPA-WED), Brad McRae (NCEA), Allen Solomon (USFS), Richard Busing (USGS)

Water Quality & Quantity

Forest Ecosystem Services

Habitat Quality

Wildlife Populations

Air Quality & Greenhouse Gases

Agricultural Ecosystem Services

1313The Problem:

Human actions affect multiple ecosystem services

No single model can capture all stressor effects & ES trade-offs

Ecosystem services are “bundled”increased use of one service involves trade-offs with others

• Water Quality & Quantity • Agricultural Products • Forest Products• Urban Demands & Outputs

• Terrestrial Habitats & Wildlife• Aquatic Habitats & Wildlife

• Air Quality & DepositionCO2, NO3, NH4, O3, CH4, N2O, Particulates…

Forest Products

Crops

Water Quality & Quantity

Wildlife

AquaticLife

Human Well-Being

Human Well-Being

The Problem:

Human actions affect multiple ecosystem services

No single model can capture all stressor effects & ES trade-offs

Forest Products

Greenhouse Gases

Ag Products

Water Quality & Quantity

Wildlife

AquaticLife

1414

EffectsEffects

Multi-Model ApproachMulti-Model Approach

MODES Models

Wildlife Populations

Plant Communities

Biogeochemistry

Hydrology

StressorsStressors

1515

EffectsEffects

Multi-Model ApproachMulti-Model Approach

MODES Models

Wildlife Populations

Plant Communities

Biogeochemistry

Hydrology

StressorsStressors

Stressors Land Use

• Urban• Agriculture• Forestry

Global Change• Climate• CO2

• N deposition Chemicals

• Fertilizers• Pesticides• Toxics

Terrestrial Services Ag products Forest products C sequestration Nutrient regulation GHG regulation

Wildlife

Aquatic Services Drinking water Flood mitigation Aquatic life

1616

LANDLAND

AIRAIR

WATERWATER

QslopeQmax fQ (s /sm )

CI SkCCv eIGU 10

23,min CONOwfpsdenit FFFL

RCm *DC (1 C )

NC

MODESMODES

16

1717

MODESMODES PhilosophyPhilosophy

Modular: different models for different suites of eco servicesModular: different models for different suites of eco services Process-Based: link effects to stressors (GCC, land use…)Process-Based: link effects to stressors (GCC, land use…) Simple: few parameters & drivers Simple: few parameters & drivers Broad Applicability: ag, forest, grassland, tundra…Broad Applicability: ag, forest, grassland, tundra… Flexible Scales: plots Flexible Scales: plots watersheds, days watersheds, days centuries centuries

Regulatory & Planning GoalsRegulatory & Planning Goals Best Management Practices: balancing multiple eco servicesBest Management Practices: balancing multiple eco services Water Quality: nutrients, contaminantsWater Quality: nutrients, contaminants Water Quantity: too little, too muchWater Quantity: too little, too much Greenhouse gases: CO2, N2O, NOx…Greenhouse gases: CO2, N2O, NOx… Habitat & Wildlife: effects of land use & toxics Habitat & Wildlife: effects of land use & toxics

1818

MODES MODES suite of modelssuite of models Climate:Climate:

*PRISM*PRISM – high resolution climate data – high resolution climate data *SNOPACK*SNOPACK – snow accumulation, drifting & melt – snow accumulation, drifting & melt SOILTEMPSOILTEMP – soil temperature & permafrost freeze/thaw – soil temperature & permafrost freeze/thaw

Hydrology:Hydrology: *GT *GT –– spatially distributed land surface hydrologyspatially distributed land surface hydrology Stream NetworkStream Network –– stream stream flow accumulation & nutrient attenuationflow accumulation & nutrient attenuation

Biogeochemistry: Biogeochemistry: *MEL*MEL – C, N, P, H – C, N, P, H22O cycling in plants & soilsO cycling in plants & soils *PSM*PSM – plant & soil C & N, losses of DIN, DON – plant & soil C & N, losses of DIN, DON *NESIS*NESIS – stable isotope simulator – stable isotope simulator

Wildlife Habitat & PopulationsWildlife Habitat & Populations *FORCLIM*FORCLIM – plant community / habitat dynamics – plant community / habitat dynamics *PATCH*PATCH – wildlife population dynamics – wildlife population dynamics

*Developed or modified through EPA-WED*Developed or modified through EPA-WED

1919

Climate:Climate: *PRISM *PRISM –– Daly, Smith, Smith & McKane 2007, J. Applied Meteorology & Climatology Daly, Smith, Smith & McKane 2007, J. Applied Meteorology & Climatology

*SNOPACK *SNOPACK –– Stieglitz 1994, Journal of Climate Stieglitz 1994, Journal of Climate

*SOIL-TEMP *SOIL-TEMP –– Stieglitz, Ducharne, Koster & Suarez 2001, J. Hydrometeorology Stieglitz, Ducharne, Koster & Suarez 2001, J. Hydrometeorology

Hydrology:Hydrology: *GT *GT –– Pan et al. in prep; McKane et al., in reviewPan et al. in prep; McKane et al., in review

Stream Network Stream Network –– Liu & Weller 2007, Environmental Modeling & Assessment Liu & Weller 2007, Environmental Modeling & Assessment

Biogeochemistry:Biogeochemistry: *MEL *MEL –– Rastetter, Perakis, Shaver & Agren 2005, Ecological Applications Rastetter, Perakis, Shaver & Agren 2005, Ecological Applications

*PSM *PSM –– Stieglitz, McKane & Klausmeier 2006, Global Biogeochemical Cycles Stieglitz, McKane & Klausmeier 2006, Global Biogeochemical Cycles

*NESIS *NESIS –– Rastetter , Kwiatkowski & McKane 2005, Ecological Applications Rastetter , Kwiatkowski & McKane 2005, Ecological Applications

Wildlife Habitat & Population DynamicsWildlife Habitat & Population Dynamics *FORCLIM *FORCLIM –– Busing, Solomon, McKane, Burdick 2007, Ecological Applications Busing, Solomon, McKane, Burdick 2007, Ecological Applications

*PATCH *PATCH –– McRae, Schumaker, McKane, Busing, Solomon, Burdick, Ecol. Mod. in press McRae, Schumaker, McKane, Busing, Solomon, Burdick, Ecol. Mod. in press

Recent PublicationsRecent Publications

2020

ClimateClimate

LANDLAND

AIRAIR

WATERWATER

QslopeQmax fQ (s /sm )

CI SkCCv eIGU 10

23,min CONOwfpsdenit FFFL

RCm *DC (1 C )

NC

MODESMODES

2121

PRISMClimate Model

Daly, Smith, Smith & McKane 2007

South Santiam Watershed, 500 km2

Climate Stations forPRISM Calibration

High Resolution Climate Data High Resolution Climate Data

2222

PRISMClimate Model

Daly, Smith, Smith & McKane 2007 June 19, 2003

Feb. 17, 2003

Tmax(RAD adjusted) Tmax Tmin

RAD RAIN SNOW

Tmax(RAD adjusted) Tmax Tmin

RAD RAIN SNOW

PRISM Climate Data: PRISM Climate Data: • Daily time-stepDaily time-step• 1-hectare grid1-hectare grid

2323

M. Stieglitz 1994 M. Stieglitz 1994

SNOWPACKSnow Dynamics

Snowpack Accumulation & MeltSnowpack Accumulation & Melt Sleepers River Watershed, VTSleepers River Watershed, VT

Winter 1970 - 1971Winter 1970 - 1971

Sn

ow

Dep

th (

cm) Observed

Simulated

Dec-70 Jan-71 Feb-71 Mar-71 Apr-71

2424

Snow Accumulation & Drifting in Snow Accumulation & Drifting in Complex TerrainComplex Terrain

SNOPACKSnow Dynamics

M. Stielglitz

Wind direction

Wind direction

2525

Simulated Permafrost Region

Observed Permafrost Boundary

Observed Discontinuous Permafrost Boundary

SOIL TEMPERATURE MODELSOIL TEMPERATURE MODEL

Stieglitz, Ducharne, Koster & Suarez 2001

SOIL-TEMPSoil Thermodynamics

2626

LANDLAND

AIRAIR

WATERWATER

QslopeQmax fQ (s /sm )

CI SkCCv eIGU 10

23,min CONOwfpsdenit FFFL

RCm *DC (1 C )

NC

MODESMODES

HydrologyHydrology

2727

Georgia Tech (GT) Hydrology Model Spatially Distributed Hydrologic Processes

snobear.colorado.edu/IntroHydro/hydro.gif

GTHydrology

2828

GT is relatively simple3 “free” parameters vs. dozens for some hydrology models (e.g., HSPF)

434

3323

22212

s1111

QDdt

ds

QDDdt

ds

QETDDdt

ds

QQETDPdt

ds

PET1Qs

D1ET2

D2

Q1

Q2

Q3

s1

s2

s3

Bedrock

Q4

D3

s4

S = storage S = storage P = precipitation P = precipitation D = drainage (infiltration)D = drainage (infiltration)Q = runoffQ = runoffET = evapotranspiration ET = evapotranspiration

Pan, Stieglitz & McKane in prep

GTHydrology

2929

Climate Station

A Forest Application: HJ AndrewsA Forest Application: HJ AndrewsWestern Oregon Cascades

Photo: Al Levno

GTHydrology

3030

Climate Station

A Forest Application: HJ AndrewsA Forest Application: HJ AndrewsWestern Oregon Cascades

Photo: Al Levno

Effects of harvest, fire and climate change on:• stream water quality and quantity• forest productivity• carbon sequestration

GTHydrology

3131 31

3232Daily Stream Hydrograph, 1996 - 2001

HJ Andrews Watershed 10S

trea

m D

isch

arg

e (m

m/d

ay)

140

120

100

80

60

40

20

01996 1997 1998 1999 2000 2001

Simulated Annual Surface Runoff & Baseflow

1998

1996 1997 1998 1999 2000 2001

S

trea

m D

isch

arg

e (m

m/d

)

Str

eam

Dis

char

ge

(mm

/d)

GTHydrology

32

333333

3434

But, we need to move nutrients with waterBut, we need to move nutrients with water

StreamStream

NHNH44 , NO, NO

33 , PO, PO

44

DON, DOC

DON, DOC

Topographic control of Topographic control of HH22O, C, N, P cyclingO, C, N, P cycling

Plants

Soils

HH22 OO

NHNH44 , NO, NO

33 , PO, PO44

DON, DOC,

DON, DOC,

HH22 OO

Plants

Soils

3535

LANDLAND

AIRAIR

WATERWATER

QslopeQmax fQ (s /sm )

CI SkCCv eIGU 10

23,min CONOwfpsdenit FFFL

RCm *DC (1 C )

NC

MODESMODES

BiogeochemistryBiogeochemistry

36

Simulates acclimation of plants & microbes to changing resources Resources: H2O, PO4, NH4, NO3, DON, N fixation, CO2, light Effects of climate, land use, & chemicals Daily to century-scale responses Simulates grasslands, forests, tundra, agricultural systems, wetlands...

N Leaching

Denitrification

SoilVegetation

MEL: Multiple Element Limitation MEL: Multiple Element Limitation ModelModel

Rastetter et al., 2005, Ecological Applications 15(1)

3737

http://ecosystems.mbl.edu/Research/Models/mel/welcome.html

3838

Export of Dissolved Organic Nitrogen (DON)HJ Andrews Watershed 10

DO

N D

isch

arg

e (k

g N

/ha)

GT-MELEco-Hydrology

39

Accumulation of C, N & P during forest succession

HJ Andrews WS-10

Ed Rastetter

39

0

25

50

75

100

125

0 100 200 300 400 500g

N m

-2

900

925

950

975

1000

g N

m-2

(P

has

e II

SO

M)

Nit

roge

n g/

m2

Ph

ase

II N

g/m

2

0

2

4

6

8

10

12

14

0 100 200 300 400 500Years

g P

m-2

90

92

94

96

98

100

g P

m-2

(Pha

se II

SO

M)

Ph

osph

orus

g/m

2

Ph

ase

II P

g/m

2

YEARSClearcut, Burn

GT-MELEco-Hydrology

0

10000

20000

30000

40000

50000

0 100 200 300 400 500

g C

m-2

13500

14000

14500

15000

15500

g C

m-2

(P

has

e II

SO

M)

Biomass Woody debris Phase I SOMPhase II SOM

Ph

ase

II C

g/m

2

Car

bon

g/m

2

40

Climate Change Effects on C SequestrationOld-Growth Forest, HJ Andrews WS-10

GT-MELEco-Hydrology

38

Climate Change Effects on C SequestrationOld-Growth Forest, HJ Andrews WS-10

37

-1000

0

1000

2000

3000

4000

2 X CO2 +4 oC 80% Ppt 125% VPD CombinedC

um

ulative C

C

han

ge (g

C

m

-2 ) Vegetation Coarse Woody Debris

Phase I SOM Phase II SOMTotal

Cu

mu

lati

ve

Ch

ang

e A

fter

10

Yea

rs

-1000

0

1000

2000

3000

4000

2 X CO2 +4 oC 80% Ppt 125% VPD Combined

Cu

mu

lati

ve C

Ch

an

ge (

g C

m-2)

g C

m-2

Carbon

Initial total 73,600 g C m-2

g N

m-2

Nitrogen

Initial total 1,130 g N m-2

2 X CO2 +4 oC 80% 125% Combined

Ppt VPD

Ed Rastetter

GT-MELEco-Hydrology

38

Climate Change Effects on C SequestrationOld-Growth Forest, HJ Andrews WS-10

37

-1000

0

1000

2000

3000

4000

2 X CO2 +4 oC 80% Ppt 125% VPD Combined

Cu

mu

lati

ve

Ch

ang

e A

fter

10

Yea

rs

-1000

0

1000

2000

3000

4000

2 X CO2 +4 oC 80% Ppt 125% VPD Combined

g C

m-2

Carbon

Initial total 73,600 g C m-2

g N

m-2

Nitrogen

Initial total 1,130 g N m-2

2 X CO2 +4 oC 80% 125% Combined

Ppt VPD

Ed Rastetter

GT-MELEco-Hydrology

2x CO2 +4oC 80% ppt Combined

Woody Debris

Net

Seq

ues

trat

ion

aft

er 1

0 yr

(g C

/ m

2)

Ed Rastetter

40

4141

GT-MELEco-Hydrology An Agricultural ApplicationAn Agricultural Application

Crop Production & Water Quality Trade-offsCrop Production & Water Quality Trade-offs

H 2O

NO 3, N

H 4, D

ON

Nassauer

4242

0

5

10

15

20

25

0 100 200 300 400

Day

Nitr

ate

(mg

N L

-1)

Simulation 7

Simulation 12

Simulation 15

100 kg N/ha/yrNo forest buffer

100 kg N/ha/yr100-m forest buffer

50 kg N/ha/yr100-m forest buffer

Drinking Water Std

Effect of Fertilization on Water Quality

Fertilization Rate

Tradeoff: Corn Yield vs. Water Quality Simulations 12, 15 & 18 (100-m mature forest buffer)

Co

rn Y

ield

(t

DM

ha

-1 y-1

)

DIN

Ex

po

rt t

o S

tre

am

(kg

N h

a-1 y-1

)

NH4 Fertilizer (kg N ha-1 y-1)

0 50 100 150 200

BMP

Corn yield

DIN export

Trade-off: Corn Yield vs. Water Quality

GT-MELEco-Hydrology

McKane, Kwiatkowski, Stieglitz, Pan, Rastetter in review

Ammonium fertilizer added to corn field on day 150

Day

42

4343

Tradeoff: Corn Yield vs. Water Quality Simulations 12, 15 & 18 (100-m mature forest buffer)

Co

rn Y

ield

(t

DM

ha

-1 y-1

)

DIN

Ex

po

rt t

o S

tre

am

(kg

N h

a-1 y-1

)

NH4 Fertilizer (kg N ha-1 y-1)

0 50 100 150 200

BMP

Corn yield

DIN export

Trade-off: Corn Yield vs. Water Quality

McKane, Kwiatkowski, Stieglitz, Pan, Rastetter in review

Trade-off: Corn Yield vs. Water Quality

GT-MELEco-Hydrology

43

4444

• Where did all the fertilizer N go?Where did all the fertilizer N go?

• What processes were most important What processes were most important for protecting water quality?for protecting water quality?

GT-MELEco-Hydrology

45

Corn Field, Segment 98,000

6,000

4,000

2,000

0

10,000

0 5 10 15 20

Years

Total N Input

N Leaching

Denitrification

N Storage

Corn Field, Segment 9Corn Field, Segment 98,000

6,000

4,000

2,000

0

10,000

0 5 10 15 20

Years10,000

8,000

6,000

4,000

2,000

00 5 10 15 20

Years

Total N Input

N Leaching

Denitrification

N Storage

Corn Field, Segment 9

Total N input

N Leaching

Denitrification

N Storage

Mature Forest Buffer

kg N

/ h

akg

N /

ha

(35% less N leaching)

20-yr Cumulative N Inputs & LossesGT-MELEco-Hydrology

McKane, Kwiatkowski, Stieglitz, Pan, Rastetter in review

4646

NO3- N2O N2

Denitrification Requires:

To atmosphere

• Nitrate• Low soil O2 • Labile carbon

GT-MELEco-Hydrology

47

Chesapeake WS109

Peterjohn & Correll 1984

Willamette Valley

Agricultural Validation Sites for GT-MEL47

4848

Stable Isotope SimulatorStable Isotope SimulatorTracing HTracing H22O & Nutrients within O & Nutrients within

Organisms, Communities & LandscapesOrganisms, Communities & Landscapes

LANDLAND

AIRAIR

WATERWATER

QslopeQmax fQ (s /sm )

CI SkCCv eIGU 10

23,min CONOwfpsdenit FFFL

RCm *DC (1 C )

NC

MODESMODES

Rastetter, Kwiatkowski & McKane 2005

4949

LANDLAND

AIRAIR

WATERWATER

QslopeQmax fQ (s /sm )

CI SkCCv eIGU 10

23,min CONOwfpsdenit FFFL

RCm *DC (1 C )

NC

MODESMODES

Stream Network ModelStream Network ModelDownstream Flow Accumulation & Nutrient AttenuationDownstream Flow Accumulation & Nutrient Attenuation

Liu & Weller 2007

5050

Liu & Weller 2007

Stream NetworkModel

Stream Network ModelStream Network ModelStreamflow Accumulation & Nutrient AttenuationStreamflow Accumulation & Nutrient Attenuation

5151Stream Network

ModelStream Network ModelStream Network Model

Streamflow Accumulation & Nutrient AttenuationStreamflow Accumulation & Nutrient Attenuation

Liu & Weller 2007

Str

eam

flo

w (

1,00

0 m

3/d

ay)

Str

eam

flo

w (

1,00

0 m

3/d

ay)

Gage 251 ObservedSimulated

Gage 277 ObservedSimulated

1600

1200

800

400

0

10000

Aug-97 Aug-98 Aug-99

Gage 277

Gage 251

PatuxentPatuxent River Watershed, MDRiver Watershed, MD

Aug-97 Aug-98 Aug-99

0

6000

4000

2000

8000

5252

Habitat & WildlifeHabitat & Wildlife

LANDLAND

AIRAIR

WATERWATER

QslopeQmax fQ (s /sm )

CI SkCCv eIGU 10

23,min CONOwfpsdenit FFFL

RCm *DC (1 C )

NC

MODESMODES

5353

Present DayPresent DayDouglas Fir

Western Hemlock

Pacific Silver Fir

Basal Area (m2/ha)

Basal Area (m2/ha)

Basal Area (m2/ha)

FORCLIMForest Habitat

20502050Douglas Fir

Western Hemlock

Pacific Silver Fir

Basal Area (m2/ha)

Basal Area (m2/ha)

Basal Area (m2/ha)

ProjectedProjectedClimateClimate

ProjectedProjectedLand UseLand Use

(Mote et al. 2003)(Mote et al. 2003)

(Hulse et al. 2004)(Hulse et al. 2004)

FORCLIM

Busing, Solomon, McKane & Burdick 2007

FORCLIM Plant Community ModelFORCLIM Plant Community ModelClimate & Fire Effects on Forest HabitatClimate & Fire Effects on Forest Habitat

500 km500 km22 South Santiam Watershed, Oregon South Santiam Watershed, Oregon

5454

Wildlife Population ModelWildlife Population Model

PATCH predicts PATCH predicts population changespopulation changes

based on:based on:

• Habitat qualityHabitat quality• ContaminantsContaminants• PesticidesPesticides• Other human Other human

activitiesactivities

PATCHWildlife Populations

N. Schumaker

5555South Santiam Watershed, Oregon

FORCLIM Demographic Data Habitat Suitability Maps

Area ~ 200 mi2

Winter Wren FORCLIM–PATCH

Habitat & Wildlife

55

PATCH

Conservation Trend

Plan Trend

Development Trend

Conservation

BAU

Development

Winter Wren Response to 3 Land Use Plans3000

2500

2000

1500

1000

Pop

ula

tion

Siz

e

2000 2020 2040 2060 2080 2100 YEAR

Population Trends• 3 land use scenarios• 3 climate scenarios

McRae, Schumaker, McKane, Busing, Solomon & Burdick, in press

5656

Potential Human Health ApplicationsPotential Human Health Applications

Simulated Soil MoistureSimulated Soil Moisture15 km15 km22 area area

Driest Wettest

5757

LANDLAND

AIRAIR

WATERWATER

QslopeQmax fQ (s /sm )

CI SkCCv eIGU 10

23,min CONOwfpsdenit FFFL

RCm *DC (1 C )

NC

MODESMODES

Incorporation of Incorporation of Human DecisionsHuman Decisions

5858MODES

+ENVISION

Incorporating Eco-hydrologyIncorporating Eco-hydrology (MODES) (MODES) in in a Decision-Making Frameworka Decision-Making Framework (ENVISION) (ENVISION)

John Bolte, Oregon State University

Landscape Evaluators:

Generate landscape metrics reflecting scarcity

Landscape:Spatial Domain in which land use changes are depicted

Autonomous Change Processes:

Models of nonhuman change

Actions

Policies:Constraints and actions

defining land use management

decisionmaking

PolicySelection

Actors:Decisionmakers making landscape change by selecting

policies responsive to their objectives

Landscape Feedback

Evoland – General Structure

(MODES)(ES Maps)

Update

Input

Landscape GIS:Maps of current

land use, vegetation, soils,

climateetc.

Human Actions

Policy Selection

Landscape Feedback

Modified from John Bolte, Oregon State University

Changes in Ecosystem Processes

MODES

5959

Chan et al.

Client-oriented goals: Client-oriented goals: Mapping Ecosystem Services in Response to Human DecisionsMapping Ecosystem Services in Response to Human Decisions

6060

Are MODES GCC modeling goals Are MODES GCC modeling goals achievable within next 5 years?achievable within next 5 years?

Technical feasibilityTechnical feasibility

? Programmatic feasibilityProgrammatic feasibility• Funding?Funding?• Technical support? Technical support? • Programmatic aids & obstacles?Programmatic aids & obstacles?

6161

GHGGHGCO2, N2O, CO2, N2O,

NOx, …NOx, …

Water Water QuantityQuantityDrinking water, Drinking water, flood mitigationflood mitigation

Water Water Quality Quality

N, P, C, N, P, C,

sedimentsediment

HabitatHabitat&&

WildlifeWildlife

MODESMODES

RHEESysRHEESys

SWATSWAT

AGWAAGWA

BASINSBASINS

SPARROWSPARROW

Comparison of Some Models for Comparison of Some Models for GCC AssessmentsGCC Assessments

EcosystemsEcosystems

Ag

Ag

Fo

res

tF

ore

st

Arc

tic

Arc

tic

6262

PlotPlot HillslopeHillslope Water-Water-shedshed RegionRegion

MODESMODES

RHEESysRHEESys

SWATSWAT

AGWAAGWA

BASINSBASINS

SPARROWSPARROW

Ease of Application

Explanatory Power

Scale of ProcessesScale of Processes

63

Questions?

Arctic LTER greenhouse experiments near Toolik Lake, AlaskaMcKane et al. 1997a, 1997b, Ecology 78(4)

Photo courtesy of Jim Laundre

63