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Applications of Systems Dynamics in
Integrated Modeling of Humans Embedded in Ecological System
Robert CostanzaGordon and Lulie Gund Professor of Ecological Economics and Director, Gund Institute of Ecological EconomicsRubenstein School of Environment and Natural ResourcesThe University of VermontBurlington, VT 05405 www.uvm.edu/giee
• Intelligent Pluralism (Multiple Modeling Approaches), Testing, Cross-Calibration, and Integration
• Multi-scale in time, space, and complexity
• Can be used as a Consensus Building Tool in an Open, Participatory Process
• Acknowledges Uncertainty and Limited Predictability
• Acknowledges Values of Stakeholders
• Evolutionary Approach Acknowledges History, Limited Optimization, and the Co-Evolution of Human Culture and Biology with the Rest of Nature
Integrated Modeling of Humans Embedded in Ecological System
SUMBER: www.iseesystems.com/community/...2008/...Modeling/ISEE_Stella.ppt
Degree of Understanding of the System Dynamics
EXPERT MODELING
Typical result:Specialized modelwhose recommendationnever gets implementedbecause they lackstakeholder support
STATUS QUO
Typical res ult:Confrontational debateand no improvement
MEDIATED DISCUSSION
Typical result:Consensus on goals orproblems but no help onhow to achieve the goals orsolve the problems
MEDIATED MODELING
Typical result:Consensus on bothproblems/goals and process -leading to effective andimplementable policies
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+
Degree of Consensus among Stakeholders
Major opportunities exist to enhance acceptance of models for decision-making through participation in model development
From: Van den Belt, M. 2004. Mediated Modeling: A System Dynamics Approach To Environmental Consensus Building. Island Press, Washington, DC.
1. Scoping Models high generality, low resolution models produced with broad participation by all the stakeholder groups affected by the problem.
2. Research Models more detailed and realistic attempts to replicate the dynamics of the particular system of interest with the emphasis on calibration and testing.
3. Management Models medium to high resolution models based on the previous two stages with the emphasis on producing future management scenarios - can be simply exercising the scoping or research models or may require further elaboration to allow application to management questions
Three Step Modeling Process*
Increasing Complexity,
Cost, Realism,and Precision
*from: Costanza, R. and M. Ruth. 1998. Using dynamic modeling to scope environmental problems and build consensus. Environmental Management 22:183-195.SUMBER: www.iseesystems.com/community/...2008/...Modeling/ISEE_Stella.ppt
Scale
Two elements:•Resolution: grain size, time step, pixel size, etc.•Extent: size of the map, time frame, etc.
In three dimensions:•Space•Time•Complexity
SUMBER: www.iseesystems.com/community/...2008/...Modeling/ISEE_Stella.ppt
Modules
Site/PatchUnit Models
Small Watersheds
Large Watersheds
Global
Natural Capital Built Capital Human Capital Social Capital
hydrology,nutrients,plants
buildings,roads,power grid
population,education,employment,income
institutions,networks,well being
Biome BGC,UFORE
General Ecosystem Model (GEM)
Everglades Landscape Model (ELM)Patuxent Landscape Model (PLM)Gwyns Falls Landscape Model (GFLM)
General Unified Metamodel of the BiOsphere (GUMBO)
RHESSysHSPF
Spa
tial E
xten
t
Suite of interactive and intercalibrated models over a range of spatial, temporal and system scales (extents and resolutions)
SUMBER: www.iseesystems.com/community/...2008/...Modeling/ISEE_Stella.ppt
Ln of Resolution
Higher(smaller grain)
Lower(larger grain)
Ln
of
Pre
dic
tab
ilit
y
Data Predictability
Model Predictability(different models have different slopes and points of intersection)
"Optimum" resolutions for particular models
from: Costanza, R. and T. Maxwell. 1994. Resolution and predictability: an approach to the scaling problem. Landscape Ecology 9:47-57
SUMBER: www.iseesystems.com/community/...2008/...Modeling/ISEE_Stella.ppt
Three complementary and synergistic ways to include humans in integrated models:
1. As “stakeholders” and active participants in the model conceptualization, development, construction, testing, scenario development, and implementation processes.
2. As “players” of the models where the model is used as both a decision aid and as a research tool to better understand human behavior in complex valuation and decision processes.
3. As “agents” programmed into the model based on better understanding of their goals and behavior gleaned through 1 and 2.
SUMBER: www.iseesystems.com/community/...2008/...Modeling/ISEE_Stella.ppt
SUMBER: www.iseesystems.com/community/...2008/...Modeling/ISEE_Stella.ppt
No Action Plan: MDM
1988 USFWS Map 2058 No Action Plan MDM
Swamp Int. Fresh Brackish Salt OpenMarsh Marsh Marsh Marsh Water
Initial Conditions (1988) 461 219 727 674 76 6465No Action Plan
(2058)460 298 1414 159 54 623
7
Habitat Coverage (km2 )
Jay F. Martin, G.Paul Kemp, HassanMashriqui, EnriqueReyes, John W.Day, Jr.
Coastal EcologyInstitute Louisiana StateUniversity
ModelingCoastalLandscapeDynamics*
* Building on work originally reported in: Costanza, R., F. H. Sklar, and M. L. White. 1990. Modeling coastal landscape dynamics. BioScience 40:91-107.
SUMBER: www.iseesystems.com/community/...2008/...Modeling/ISEE_Stella.ppt
The Everglades Landscape Model (ELM)
http://ecolandmod.ifas.ufl.edu/projects/index.html
The ELM is a regional scale ecological model designed to predict thelandscape response to different water management scenarios insouth Florida, USA. The ELM simulates changes to the hydrology,soil & water nutrients, periphyton biomass & community type, andvegetation biomass & community type in the Everglades region.
Current DevelopersSouth Florida Water Management DistrictH. Carl FitzFred H. SklarYegang WuCharles CornwellTim Waring
Recent Collaboratorss
Alexey A. VoinovRobert CostanzaTom MaxwellFlorida Atlantic UniversityMatthew Evett
SUMBER: www.iseesystems.com/community/...2008/...Modeling/ISEE_Stella.ppt
The Patuxent and Gwynns Falls Watershed Models(PLM and GFLM)
http://www.uvm.edu/giee/PLMThis project is aimed at developing integrated knowledge and newtools to enhance predictive understanding of watershed ecosystems(including processes and mechanisms that govern the interconnect-ed dynamics of water, nutrients, toxins, and biotic components) andtheir linkage to human factors affecting water and watersheds. Thegoal is effective management at the watershed scale.
Participants Include:Robert CostanzaRoelof BoumansWalter BoyntonThomas MaxwellSteve SeagleFerdinando VillaAlexey VoinovHelena VoinovLisa Wainger
Costanza, R., A. Voinov, R. Boumans, T. Maxwell, F. Villa, L. Wainger, and H. Voinov. 2002. Integrated ecological economic modeling of the Patuxent River watershed, Maryland. Ecological Monographs 72:203-231. SUMBER: www.iseesystems.com/community/...2008/...Modeling/ISEE_Stella.ppt
Forest Resid Urban Agro Atmos Fertil Decomp Septic N aver. N max N min Wmax Wmin N gw c. NPP
Scenario number of cells kg/ha/year mg/l m/year mg/l kg/m2/y
1 1650 2386 0 0 56 3.00 0.00 162.00 0.00 3.14 11.97 0.05 101.059 34.557 0.023 2.185
2 1850 348 7 0 2087 5.00 106.00 63.00 0.00 7.17 46.61 0.22 147.979 22.227 0.25 0.333
3 1950 911 111 28 1391 96.00 110.00 99.00 7.00 11.79 42.34 0.70 128.076 18.976 0.284 1.119
4 1972 1252 223 83 884 86.00 145.00 119.00 7.00 13.68 60.63 0.76 126.974 19.947 0.281 1.72
5 1990 1315 311 92 724 86.00 101.00 113.00 13.00 10.18 40.42 1.09 138.486 18.473 0.265 1.654
6 1997 1195 460 115 672 91.00 94.00 105.00 18.00 11.09 55.73 0.34 147.909 18.312 0.289 1.569
7 BuildOut 312 729 216 1185 96.00 155.00 61.00 21.00 12.89 83.03 2.42 174.890 11.066 0.447 0.558
8 BMP 1195 460 115 672 80.00 41.00 103.00 18.00 5.68 16.41 0.06 148.154 16.736 0.23 1.523
9 LUB1 1129 575 134 604 86.00 73.00 98.00 8.00 8.05 39.71 0.11 150.524 17.623 0.266 1.494
10 LUB2 1147 538 134 623 86.00 76.00 100.00 11.00 7.89 29.95 0.07 148.353 16.575 0.269 1.512
11 LUB3 1129 577 134 602 86.00 73.00 99.00 24.00 7.89 29.73 0.10 148.479 16.750 0.289 1.5
12 LUB4 1133 564 135 610 86.00 74.00 100.00 12.00 8.05 29.83 0.07 148.444 16.633 0.271 1.501
13 agro2res 1195 1132 115 0 86.00 0.00 96.00 39.00 5.62 15.13 0.11 169.960 17.586 0.292 1.702
14 agro2frst 1867 460 115 0 86.00 0.00 134.00 18.00 4.89 12.32 0.06 138.622 21.590 0.142 2.258
15 res2frst 1655 0 115 672 86.00 82.00 130.00 7.00 7.58 23.50 0.10 120.771 20.276 0.18 1.95
16 frst2res 0 1655 115 672 86.00 82.00 36.00 54.00 9.27 39.40 1.89 183.565 9.586 0.497 0.437
17 cluster 1528 0 276 638 86.00 78.00 121.00 17.00 7.64 25.32 0.09 166.724 17.484 0.216 1.792
18 sprawl 1127 652 0 663 86.00 78.00 83.00 27.00 8.48 25.43 0.11 140.467 17.506 0.349 1.222
Patuxent Watershed Scenarios*
* From: Costanza, R., A. Voinov, R. Boumans, T. Maxwell, F. Villa, L. Wainger, and H. Voinov. 2002. Integrated ecological economic modeling of the Patuxent River watershed, Maryland. Ecological Monographs 72:203-231.
Land Use Nitrogen Loading Nitrogen to Estuary Hydrology N in GW NPP
SUMBER: www.iseesystems.com/community/...2008/...Modeling/ISEE_Stella.ppt
GUMBO (Global Unified Model of the BiOsphere)
From: Boumans, R., R. Costanza, J. Farley, M. A. Wilson, R. Portela, J. Rotmans, F. Villa, and M. Grasso. 2002. Modeling the Dynamics of the Integrated Earth System and the Value of Global Ecosystem Services Using the GUMBO Model. Ecological Economics 41: 529-560
Atmosphere
Anthropo-sphere
EcosystemServices
HumanImpacts
Natural Capital Human-madeCapital(includes Built CapitalHuman Capital,and Social Capital
SolarEnergy
Hydrosphere
Lithosphere
Biosphere
11 Biomes
SUMBER: www.iseesystems.com/community/...2008/...Modeling/ISEE_Stella.ppt
Global Unified Metamodel of the BiOsphere (GUMBO)• was developed to simulate the integrated earth system and assess the dynamics and
values of ecosystem services. • is a “metamodel” in that it represents a synthesis and a simplification of several
existing dynamic global models in both the natural and social sciences at an intermediate level of complexity.
• the current version of the model contains 234 state variables, 930 variables total, and 1715 parameters.
• is the first global model to include the dynamic feedbacks among human technology, economic production and welfare, and ecosystem goods and services within the dynamic earth system.
• includes modules to simulate carbon, water, and nutrient fluxes through the Atmosphere, Lithosphere, Hydrosphere, and Biosphere of the global system. Social and economic dynamics are simulated within the Anthroposphere.
• links these five spheres across eleven biomes, which together encompass the entire surface of the planet.
• simulates the dynamics of eleven major ecosystem goods and services for each of the biomes
SUMBER: www.iseesystems.com/community/...2008/...Modeling/ISEE_Stella.ppt
1000
800
600
400
200
0
Wetland 3000
2500
2000
1500
1000
500
0
Ice and Rock
2000
1500
1000
500
0
Tundra6000
5500
5000
4500
4000
3500
3000
Grasslands
6000
5500
5000
4500
4000
3500
3000
Forests1000
800
600
400
200
0
Urban
4000
3000
2000
1000
0
21002050200019501900
Croplands2000
1500
1000
500
0
21002050200019501900
Desert
Years
Landuse Changes
23
22
21
20
°C
Global Temp
1300
1200
1100
1000
900
800
700
Gig
a T
on
C
Atmospheric Carbon
0.4
0.3
0.2
0.1
0.0
mete
rs
Sealevel 2000
1500
1000
500
0
Wast
e e
qu
ivale
nts
(n
orm
aliz
ed
for
19
00
)
Waste
4.0
3.5
3.0
Gig
a T
on
C e
qu
ivale
nts Alternative
Energy
12
10
8
6
4
2
0
Gig
a T
on
C
Fossil Fuel extraction
1.0
0.8
0.6
0.4
0.2
0.0
Foss
il_Fu
el_
Mark
et_
Sh
are
eq
uiv
ale
nts
(n
orm
aliz
ed
for
19
00
)
21002050200019501900
Year
Fossil FuelMarket share
16
14
12
10
8
6
4
Gig
a T
on
C e
qu
ivale
nts
2050200019501900
Year
Total Energy
Startrek Big Goverment Ecoptopia Mad Max
Basecase Observations
Physics
20
15
10
5bill
ion
s of
ind
ivid
uals Human Population
4.0
3.5
3.0
2.5
2.0
1.5
SO
CIA
L_N
ETW
OR
K e
qu
ivale
nts
(n
orm
aliz
ed
for
19
00
)21002050200019501900
Year
The Social Network
2000
1500
1000
500
0
Pro
du
cti
vit
y I
nvest
ed
Knowledge
8000
6000
4000
2000
0
Pro
du
cti
vit
y I
nvest
ed
Built Capital
800
600
400
200
0
Pro
du
cti
vit
y I
nvest
ed Built capital
per capita
300
250
200
150
100
50
0
Pro
du
cti
vit
y I
nvest
ed
Knowledgeper capita
1.2
1.0
0.8
0.6
0.4
0.2
SO
CIA
L_N
ETW
OR
K_P
erC
ap
eq
uiv
ale
nts
(n
orm
aliz
ed
for
19
00
)
21002050200019501900
Year
Social network per capita
Ecotopia Startrek Mad Max Big Goverment Basecase Observations
0.030
0.025
0.020
0.015
0.010
0.005
0.000
Price on waste treatment
30
25
20
15
10
5
0
Price on soil formation
3.0
2.5
2.0
1.5
1.0
0.5
0.0
Price on Cultural and recreational service
20
15
10
5
0
Price on Nutrient cycling
10
8
6
4
2
0
Price on gas regulation
30
25
20
15
10
5
0
Price on Disturbance regulatiuon
10
8
6
4
2
0
2050200019501900
Year
Climate price100
80
60
40
20
0
21002050200019501900
Year
Energy price
7000
6000
5000
4000
3000
Waste_Treatment7.2
6.8
6.4
6.0
Soil Formation
24
20
16
12
Recreation and_Culture 0.9
0.8
0.7
0.6
Nutrient_Cycling
12
10
8
6
4
2
Gas_regulation
2.76
2.72
2.68
2.64
Disturbance Regulation
10.90
10.85
10.80
10.75
10.70
10.65
21002050200019501900
Year
Climate Regulation500
400
300
200
100
21002050200019501900
Year
Ecosystem services value
Ecotopia StartrekMad Max Big Goverment Basecase
1.0
0.8
0.6
0.4
0.2
Global_Welfare
80
60
40
20
GWP_per_Capita120
100
80
60
40
20
19
89
dolla
rs
GWP
0.16
0.12
0.08
0.04
Welfare_per_capita
0.20
0.16
0.12
0.08
21002050200019501900
Year
food_per_capita2.0
1.5
1.0
0.5
21002050200019501900
Year
Energy_per_Capita
10-4
10-3
welfare
per
cap
ita e
qu
ivale
nts
(n
orm
aliz
ed
for
19
00
)
Welfare_GNP_Index
Ecotopia Startrek MadMax Big GovermentBasecase Observations
SUMBER: www.iseesystems.com/community/...2008/...Modeling/ISEE_Stella.ppt
1000
800
600
400
200
0
Wetland 3000
2500
2000
1500
1000
500
0
Ice and Rock
2000
1500
1000
500
0
Tundra6000
5500
5000
4500
4000
3500
3000
Grasslands
6000
5500
5000
4500
4000
3500
3000
Forests1000
800
600
400
200
0
Urban
4000
3000
2000
1000
0
21002050200019501900
Croplands2000
1500
1000
500
0
21002050200019501900
Desert
Years
Landuse Changes
SUMBER: www.iseesystems.com/community/...2008/...Modeling/ISEE_Stella.ppt
23
22
21
20
°C
Global Temp
1300
1200
1100
1000
900
800
700
Gig
a T
on
C
Atmospheric Carbon
0.4
0.3
0.2
0.1
0.0
mete
rs
Sealevel 2000
1500
1000
500
0
Waste
eq
uiv
ale
nts
(n
orm
alized
for
19
00
)
Waste
4.0
3.5
3.0
Gig
a T
on
C e
qu
ivale
nts
Alternative Energy
12
10
8
6
4
2
0
Gig
a T
on
C
Fossil Fuel extraction
1.0
0.8
0.6
0.4
0.2
0.0
Fossil_Fu
el_
Mark
et_
Sh
are
eq
uiv
ale
nts
(n
orm
alized
for
19
00
)
21002050200019501900
Year
Fossil FuelMarket share
16
14
12
10
8
6
4
Gig
a T
on
C e
qu
ivale
nts
2050200019501900
Year
Total Energy
Startrek Big Goverment Ecoptopia Mad Max
Basecase Observations
Physics
SUMBER: www.iseesystems.com/community/...2008/...Modeling/ISEE_Stella.ppt
20
15
10
5billion
s o
f in
div
idu
als
Human Population
4.0
3.5
3.0
2.5
2.0
1.5
SO
CIA
L_N
ETW
OR
K e
qu
ivale
nts
(n
orm
alized
for
19
00
)
21002050200019501900
Year
The Social Network
2000
1500
1000
500
0
Pro
du
cti
vit
y I
nveste
d
Knowledge
8000
6000
4000
2000
0
Pro
du
cti
vit
y I
nveste
d
Built Capital
800
600
400
200
0Pro
du
cti
vit
y I
nveste
d
Built capital per capita
300
250
200
150
100
50
0
Pro
du
cti
vit
y I
nveste
d
Knowledgeper capita
1.2
1.0
0.8
0.6
0.4
0.2
SO
CIA
L_N
ETW
OR
K_PerC
ap
eq
uiv
ale
nts
(n
orm
alized
for
19
00
)
21002050200019501900
Year
Social network per capita
Ecotopia Startrek Mad Max Big Goverment Basecase Observations
SUMBER: www.iseesystems.com/community/...2008/...Modeling/ISEE_Stella.ppt
7000
6000
5000
4000
3000
Waste_Treatment7.2
6.8
6.4
6.0
Soil Formation
24
20
16
12
Recreation and_Culture 0.9
0.8
0.7
0.6
Nutrient_Cycling
12
10
8
6
4
2
Gas_regulation
2.76
2.72
2.68
2.64
Disturbance Regulation
10.90
10.85
10.80
10.75
10.70
10.65
21002050200019501900
Year
Climate Regulation500
400
300
200
100
21002050200019501900
Year
Ecosystem services value
Ecotopia StartrekMad Max Big Goverment Basecase
SUMBER: www.iseesystems.com/community/...2008/...Modeling/ISEE_Stella.ppt
0.030
0.025
0.020
0.015
0.010
0.005
0.000
Price on waste treatment
30
25
20
15
10
5
0
Price on soil formation
3.0
2.5
2.0
1.5
1.0
0.5
0.0
Price on Cultural and recreational service
20
15
10
5
0
Price on Nutrient cycling
10
8
6
4
2
0
Price on gas regulation
30
25
20
15
10
5
0
Price on Disturbance regulatiuon
10
8
6
4
2
0
2050200019501900
Year
Climate price100
80
60
40
20
0
21002050200019501900
Year
Energy price
SUMBER: www.iseesystems.com/community/...2008/...Modeling/ISEE_Stella.ppt
1.0
0.8
0.6
0.4
0.2
Global_Welfare
80
60
40
20
GWP_per_Capita120
100
80
60
40
20
19
89
d
ollars
GWP
0.16
0.12
0.08
0.04
Welfare_per_capita
0.20
0.16
0.12
0.08
21002050200019501900
Year
food_per_capita2.0
1.5
1.0
0.5
21002050200019501900
Year
Energy_per_Capita
10-4
10-3
welfare p
er cap
ita eq
uiv
ale
nts (n
orm
alized
for 1
90
0)
Welfare_GNP_Index
Ecotopia Startrek MadMax Big GovermentBasecase Observations
GUMBO
0
1
2
3
4Atmosphere
Water Cycle
Land - Soil
Demographic
Political
Development
Cultural-Values
Economics
Landuse change
Industry - Pollution
Energy
Agriculture
Freshwater
Biogeochemistry
Natural Systems
Social SystemsHuman - Environment Feedback
TARGETS
0
1
2
3
4Atmosphere
Water Cycle
Land - Soil
Demographic
Political
Development
Cultural-Values
Economics
Landuse change
Industry - Pollution
Energy
Agriculture
Freshwater
Biogeochemistry
Natural Systems
Social SystemsHuman - Environment Feedback
DICE
0
1
2
3
4Atmosphere
Water Cycle
Land - Soil
Demographic
Political
Development
Cultural-Values
Economics
Landuse change
Industry - Pollution
Energy
Agriculture
Freshwater
Biogeochemistry
Natural Systems
Social SystemsHuman - Environment Feedback
IFs
0
1
2
3
4Atmosphere
Water Cycle
Land - Soil
Demographic
Political
Development
Cultural-Values
Economics
Landuse change
Industry - Pollution
Energy
Agriculture
Freshwater
Biogeochemistry
Natural Systems
Social SystemsHuman - Environment Feedback
IMAGE-2
0
1
2
3
4Atmosphere
Water Cycle
Land - Soil
Demographic
Political
Development
Cultural-Values
Economics
Landuse change
Industry - Pollution
Energy
Agriculture
Freshwater
Biogeochemistry
Natural Systems
Social SystemsHuman - Environment Feedback
IMAGE
0
1
2
3
4Atmosphere
Water Cycle
Land - Soil
Demographic
Political
Development
Cultural-Values
Economics
Landuse change
Industry - Pollution
Energy
Agriculture
Freshwater
Biogeochemistry
Natural Systems
Social SystemsHuman - Environment Feedback
WORLD3
0
1
2
3
4Atmosphere
Water Cycle
Land - Soil
Demographic
Political
Development
Cultural-Values
Economics
Landuse change
Industry - Pollution
Energy
Agriculture
Freshwater
Biogeochemistry
Natural Systems
Social SystemsHuman - Environment Feedback
MODEL COMPLEXITY0 = Not addressed in model.1 = Exogenous input to model.2 = Endogenous w/o feedback in model3 = Endogenous w/ feedback (mid-complexity)4 = Endogenous w/ feedback (very complex)
DEGREE OF HISTORIC CALIBRATIONLow High Amoeba diagram of
complexity with which Integrated Global Models (IGMs) capture socioeconomic systems, natural systems, and feedbacks (from Costanza, R., R. Leemans, R. Boumans, and E. Gaddis. 2006. Integrated global models. Pp 417-446 in: Costanza, R., L. J. Graumlich, and W. Steffen (eds.). Sustainability or Collapse?: An Integrated History and future Of People on Earth. Dahlem Workshop Report 96. MIT Press. Cambridge, MA.
SUMBER: www.iseesystems.com/community/...2008/...Modeling/ISEE_Stella.ppt
LocationBiosphere
Earth Surfaces
NutrientCycling
Hydrosphere Lithosphere Atmosphere
Anthroposphere
Cultures
Biodiversity
EcosystemServices
Water by
Reservoir
Geological Carbon
Ores
Earth Energy
Gasses
ExchangesBetweenLocations
Social Capital
Human Capital
Built Capital
MIMESMulti-scale Integrated Models of Ecosystem Services
SUMBER: www.iseesystems.com/community/...2008/...Modeling/ISEE_Stella.ppt
Ecosystem Services
Climate Regulation
Biological Regulation
Natural Hazard Mitigation
Cultural Heritage
Genetic Information
Inorganic Resources
SUMBER: www.iseesystems.com/community/...2008/...Modeling/ISEE_Stella.ppt