Transcript Ecosystem Models

Applications of
Systems Dynamics in
Integrated Modeling of
Humans Embedded in
Ecological System
Robert Costanza
Gordon and Lulie Gund Professor of Ecological Economics
and Director, Gund Institute of Ecological Economics
Rubenstein School of Environment and Natural Resources
The University of Vermont
Burlington, VT 05405
www.uvm.edu/giee
Integrated Modeling of Humans
Embedded in Ecological System
• 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
Major opportunities exist to enhance acceptance of models for decision-making through
participation in model development
Degree of Understandingof the SystemDynamics
EXPERTMODELING
+
MEDIATEDMODELING
Typical result:
Consensusonboth
problems/goalsand processleadingtoeffectiveand
implementablepolicies
Typical result:
Specializedmodel
whoserecommendation
never gets implemented
becausethey lack
stakeholder support
-
+
Degree of
Consensus
among
Stakeholders
MEDIATEDDISCUSSION
STATUSQUO
ult:
Typical
res
Confrontational debate
andnoimprovement
-
Typical result:
Consensus ongoalsor
problemsbut nohelpon
howtoachievethegoalsor
solvetheproblems
From: Van den Belt, M. 2004. Mediated Modeling: A System
Dynamics Approach To Environmental Consensus Building.
Island Press, Washington, DC.
Three Step Modeling Process*
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
Increasing
Complexity,
Cost, Realism,
and Precision
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
*from: Costanza, R. and M. Ruth. 1998. Using dynamic modeling to scope environmental problems
and build consensus. Environmental Management 22:183-195.
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
Global
Large Watersheds
Natural Capital Built Capital Human Capital Social Capital
General Unified Metamodel of the BiOsphere (GUMBO)
HSPF RHESSys
Everglades Landscape Model (ELM)
Patuxent Landscape Model (PLM)
Gwyns Falls Landscape Model (GFLM)
Small Watersheds
Site/Patch
Unit Models
Modules
Biome BGC,
UFORE
hydrology,
nutrients,
plants
General Ecosystem Model (GEM)
buildings,
roads,
power grid
population,
education,
employment,
income
institutions,
networks,
well being
Suite of interactive and intercalibrated models over a range of
spatial, temporal and system scales (extents and resolutions)
Model Predictability
(different models have different slopes and points of intersection)
Data Predictability
"Optimum" resolutions for particular models
Higher
(smaller grain)
Lower
(larger grain)
Ln of Resolution
from: Costanza, R. and T. Maxwell. 1994 . Resolution and predictability: an
approach to the scaling problem. Landscape Ecology 9:47-57
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.
Modeling
Coastal
Landscape
Dynamics*
No Action Plan: MDM
2058 No Action Plan MDM
1988 USFWS Map
Jay F. Martin, G.
Paul Kemp, Hassan
Mashriqui, Enrique
Reyes, John W.
Day, Jr.
Coastal Ecology
Institute
Louisiana State
University
Habitat Coverage (km2 )
Swamp Int.
Fresh Brackish Salt Open
Marsh Marsh Marsh Marsh Water
Initial Conditions (1988)
No Action Plan
(2058)
461
460
219
298
727
1414
674
159
76
54
646
5
623
7
* Building on work originally reported in:
Costanza, R., F. H. Sklar, and M. L. White. 1990.
Modeling coastal landscape dynamics. BioScience 40:91-107.
The Everglades Landscape Model (ELM)
http://ecolandmod.ifas.ufl.edu/projects/index.html
The ELM is a regional scale ecological model designed to predict the
landscape response to different water management scenarios in
south Florida, USA. The ELM simulates changes to the hydrology,
soil & water nutrients, periphyton biomass & community type, and
vegetation biomass & community type in the Everglades region.
Current Developer s
South Florida Water Management Distric
t
H. Carl Fitz
Fred H. Sklar
Yegang Wu
Charles Cornwell
Tim Waring
Recent Collaborator s
s
Alexey A. Voinov
Robert Costanza
Tom Maxwell
Florida Atlantic Universit
Matthew Evett
y
The Patuxent and Gwynns Falls Watershed Model s
(PLM and GFLM)
http://www.uvm.edu/giee/PLM
This project is aimed at developing integrated knowledge and new
tools to enhance predictive understanding of watershed ecosystems
(including processes and mechanisms that govern the interconnect
ed dynamics of water, nutrients, toxins, and biotic components) and
their linkage to human factors affecting water and watersheds. The
goal is effective management at the watershed scale.
Participants Include:
Robert Costanza
Roelof Boumans
Walter Boynton
Thomas Maxwell
Steve Seagle
Ferdinando Villa
Alexey Voinov
Helena Voinov
Lisa 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.
Patuxent Watershed Scenarios*
Land Use
Forest
Resid
Scenario
Nitrogen Loading
Urban Agro
Atmos
number of cells
Fertil
Nitrogen to Estuary
Decomp
Septic
N aver.
kg/ha/year
N max
Hydrology
N min
mg/l
Wmax
N in GW
Wmin
m/year
N gw c.
mg/l
NPP
NPP
kg/m 2/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
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
7 BuildOut
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
* 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.
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
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
Landus e Changes
1000
Wetland
20
3000
800
billions of individuals
600
2000
1500
400
1000
200
Human Population
Ice and Rock
2500
7000
Ec otopia
St artrek
Mad Max
Big Gov erment
Bas ec ase
Obs erv at ions
15
10
5
6.8
5000
6.4
4000
6.0
0
3000
6000
Tundr a
Grasslands
2000
Productivity Invested
5500
1500
5000
1000
4500
4000
500
3500
0
1500
1000
500
Urban
400
200
4000
2000
0
0
4000
2000
Deser t
Cropl ands
3000
1500
2000
1000
1000
500
0
0
1900
1950
2000
2050
2100
1900
1950
2000
2050
Y ears
Physics
Global Temp
1300
1200
Giga Ton C
23
°C
22
21
20
0.9
20
0.8
16
0.7
12
0.6
Nutrient_Cycling
50
12
600
400
Gas_r eg ulation
Distur bance Reg ul ati on
10
2.76
8
2.72
6
200
2.68
4
0
2100
4.0
The Social Network
3.5
3.0
2.5
2.0
1.5
1900
1950
2000
2050
Year
0.030
Atmospher ic
Car bon
1100
0.025
Price on waste tr eatment
2100
Social network
per capi ta
1.2
2.64
10.90
Ecosystem ser vi ces val ue
500
Cli mate Reg ulation
10.85
400
1.0
10.80
0.8
300
10.75
0.6
200
10.70
0.4
100
10.65
0.2
1900
1950
2000
2050
2100
1900
1950
Year
2000
2050
2100
1900
0.020
20
0.015
15
800
0.010
10
0.005
5
0.000
0
2000
1.0
2050
2100
Year
Global_Welfare
Wel fare_per_capita
0.16
Price on soil formation
25
900
1950
Year
30
1000
St artrek
Big Gov erment
Ec opt opia
Mad Max
Recreation and_Culture
2
SOCIAL_NETW ORK equivalents (normalized for 1900)
3000
6000
SOCIAL_NETW ORK_PerCap equivalents (normalized for 1900)
3500
100
Buil t capital
per capi ta
Productivity Invested
Productivity Invested
600
4500
4000
150
Buil t Capital
8000
800
5000
24
200
800
1000
For ests
5500
Knowl edge
per capi ta
250
0
0
3000
6000
300
Knowl edge
Productivity Invested
2000
Ec otopia
St artrek
Mad Max
Big Gov erment
Bas ec ase
Soil For mati on
7.2
500
0
Waste_Treatment
6000
0.8
0.12
0.6
700
0.08
0.4
0.04
0.2
meter s
0.2
0.1
3.0
Waste
2.5
1000
1.5
5
0
0.0
30
Price on g as reg ulati on
8
10
20
8
6
15
6
4
10
2
Giga Ton C equival ents
0.6
0.4
0.2
2
5
0
0
10
16
Fossi l Fuel
Mar ket share
0.8
Pri ce on
Disturbance
r eg ul atiuon
25
4
1.0
100
Total Ener gy
8
2000
Year
2050
2100
20
Wel fare_GNP_Index
10
10
Ec otopia
St artrek
MadMax
Big Gov erment
Bas ecas e
Obs erv at ions
-3
-4
Energ y_per_Capita
food_per _capita
0.20
1.5
10
8
6
60
4
40
2
20
0
0
0.16
1.0
0.12
6
0.5
4
1950
40
40
80
12
0.0
1900
60
2.0
Energ y pri ce
Climate pr ice
14
GWP_per _Capita
60
0
10
Fossi l Fuel
extr action
12
3.0
80
80
20
0.5
0
uel_Mar ket_Shar e equival ents (nor mal ized for 1900)
10
1.0
500
Alternative
Energ y
GWP
100
15Price on Nutrient cycl ing
2.0
4.0
3.5
120
20
Price on Cul tur al
and recr eational servi ce
1500
Giga Ton C
Giga Ton C equival ents
0.0
2000
1989 dollars
Sealevel
0.3
welfar e per capita equivalents (normalized for 1900)
Waste equivalents (normalized for 1900)
Bas ec ase
Obs erv at ions
0.4
1900
1950
2000
Year
2050
1900
1950
2000
Year
2050
0.08
1900
1950
2000
Year
2050
2100
1900
1950
2000
Year
2050
2100
1900
1950
2000
Year
2050
2100
Landus e Changes
1000
Wetland
3000
800
2500
600
2000
Ice and Rock
1500
400
1000
200
500
0
0
2000
6000
Tundr a
Grasslands
5500
1500
5000
1000
4500
4000
500
3500
0
3000
6000
1000
For ests
Urban
5500
800
5000
600
4500
400
4000
200
3500
3000
0
4000
2000
Cropl ands
3000
1500
2000
1000
1000
500
0
Deser t
0
1900
1950
2000
2050
2100
1900
Y ears
1950
2000
2050
2100
Ph ysics
Global Temp
1200
Giga Ton C
23
1300
°C
22
21
1100
1000
900
800
St artrek
Big Gov erment
Ec opt opia
Mad Max
20
Atmospher ic
Car bon
700
0.4
Waste equivalents (normalized for 1900)
Bas ec ase
Obs erv at ions
Sealevel
meters
0.3
0.2
0.1
Waste
1500
1000
500
0
Alternative
Energ y
Fossi l Fuel
extr action
12
4.0
10
Giga Ton C
Giga Ton C equivalents
0.0
2000
3.5
8
6
4
3.0
2
1.0
16
Fossi l Fuel
Mar ket share
0.8
Giga Ton C equivalents
uel_Market_Share equivalents (normalized for 1900)
0
0.6
0.4
0.2
14
Total Ener gy
12
10
8
6
4
0.0
1900
1950
2000
Year
2050
2100
1900
1950
2000
Year
2050
billions of individuals
20
Human Population
Ec otopi a
St artrek
Mad Max
Big Gov erment
Bas ec ase
Obs erv at ions
15
10
5
300
Knowl edge
Productivity Invested
Productivity Invested
2000
1500
1000
500
Knowl edge
per capi ta
250
200
150
100
50
0
0
800
Productivity Invested
Productivity Invested
Buil t capital
per capi ta
Buil t Capital
8000
6000
4000
2000
4.0
400
200
0
The Social Network
3.5
3.0
2.5
2.0
1.5
1900
1950
2000
Year
2050
2100
NETW ORK_PerCap equivalents (normalized for 1900)
IAL_NETW ORK equivalents (normalized for 1900)
0
600
Social network
per capi ta
1.2
1.0
0.8
0.6
0.4
0.2
1900
1950
2000
Year
2050
2100
7000
Waste_Treatment
Soil For mati on
7.2
6000
6.8
Ec otopia
St artrek
Mad Max
Big Gov erment
Bas ec ase
5000
6.4
4000
6.0
3000
24
Recreation and_Culture
0.9
20
0.8
16
0.7
12
0.6
12
Nutrient_Cycling
Gas_r eg ulation
Distur bance Reg ul ati on
10
2.76
8
2.72
6
2.68
4
2.64
2
10.90
500
Cli mate Reg ulation
10.85
Ecosystem ser vi ces val ue
400
10.80
300
10.75
200
10.70
100
10.65
1900
1950
2000
Year
2050
2100
1900
1950
2000
Year
2050
2100
0.030
0.025
30
Price on soil formation
Price on waste tr eatment
25
0.020
20
0.015
15
0.010
10
0.005
5
0.000
0
3.0
2.5
20
Price on Cul tur al
and recr eational servi ce
15Price on Nutrient cycl ing
2.0
1.5
10
1.0
5
0.5
0.0
0
10
30
Price on g as reg ulati on
8
Pri ce on
Disturbance
r eg ul atiuon
25
20
6
15
4
10
2
5
0
0
10
100
Energ y pri ce
Climate pr ice
8
80
6
60
4
40
2
20
0
0
1900
1950
2000
Year
2050
1900
1950
2000
Year
2050
2100
1.0
Global_Welfare
Wel fare_per_capita
0.16
0.8
0.12
0.6
0.08
0.4
0.04
0.2
120
GWP
80
1989 dollars
100
80
60
60
40
40
20
20
welfare per capita equivalents (normalized for 1900)
GWP_per _Capita
Wel fare_GNP_Index
10
10
Ec otopia
St artrek
MadMax
Big Gov erment
Bas ecas e
Obs erv at ions
-3
-4
2.0
Energ y_per_Capita
food_per _capita
0.20
1.5
0.16
1.0
0.12
0.5
0.08
1900
1950
2000
Year
2050
2100
1900
1950
2000
Year
2050
2100
W ORLD3
MODEL COMPLEXITY
0 = Not addressed in model.
1 = Exogenous input to model.
2 = Endogenous w/o feedback in model
3 = Endogenous w/ feedback (mid-complexity)
4 = Endogenous w/ feedback (very complex)
Natural Systems
Atmosphere
Biogeochemistry 4
Water Cycle
3
Freshwater
Land - Soil
2
1
Agriculture
Demographic
0
Energy
DEGREE OF HISTORIC CALIBRATION
Low
High
Political
Industry - Pollution
Development
Landuse change
Cultural-Values
Economics
Human - Environment
Feedback
IMAGE
Natural Systems
Atmosphere
Biogeochemistry 4
Water Cycle
3
Freshwater
Land - Soil
IMAGE-2
Natural Systems
Atmosphere
Biogeochemistry 4
Water Cycle
3
Freshwater
1
Demographic
1
Agriculture
Energy
Political
Industry - Pollution
Development
Landuse change
Cultural-Values
Energy
Human - Environment
Feedback
Social Systems
Atmosphere
4
Biogeochemistry
Water Cycle
Land - Soil
2
1
Agriculture
Development
Landuse change
Cultural-Values
Economics
Natural Systems
3
Political
Industry - Pollution
Economics
Freshwater
Demographic
Human - Environment
Feedback
DICE
Natural Systems
3
Freshwater
Energy
Political
Development
Landuse change
Cultural-Values
Economics
Human - Environment
Feedback
1
Demographic
Social Systems
Natural Systems
3
Land - Soil
2
1
Energy
Human - Environment
Feedback
GUMBO
Cultural-Values
Economics
Human - Environment
Feedback
Social Systems
Natural Systems
Atmosphere
Biogeochemistry 4
Water Cycle
Freshwater
3
Agriculture
1
Land - Soil
Demographic
0
Development
Landuse change
Cultural-Values
2
Demographic
Political
Industry - Pollution
Development
Landuse change
0
Energy
Political
Industry - Pollution
Economics
Atmosphere
Biogeochemistry 4
Water Cycle
Agriculture
Land - Soil
2
Agriculture
0
Industry - Pollution
Freshwater
Social Systems
Atmosphere
Biogeochemistry 4
Water Cycle
0
TARGETS
Demographic
0
0
IFs
Land - Soil
2
2
Agriculture
Social Systems
Social Systems
Energy
Political
Industry - Pollution
Development
Landuse change
Cultural-Values
Economics
Human - Environment
Feedback
Social Systems
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.
MIMES
Multi-scale Integrated Models of Ecosystem Services
Biosphere
Location
Anthroposphere
Cultures
Earth Surfaces
Nutrient
Cycling
Ecosystem
Services
Biodiversity
Social Capital
Human Capital
Built Capital
Hydrosphere
Water
by
Reservoir
Lithosphere
Atmosphere
Geological
Carbon
Earth Energy
Ores
Gasses
Exchanges
Between
Locations
Ability to select specific areas to model at variable spatial and
temporal resolution, in their global and regional context
A range of
calibration
sites used by
project partners
to test model
applicability and
performance.
These include in
the first phase:
Amazon, Pacific
northwest,
Winoski
watershed,
Vermont, and
Global
Land Use
Land
use
Soil Drainage type
Soil
drainage
type
Water Regulation
Water
regulation
Ecosystems (% Area)
Croplands
Tundra
Forests
Wetlands
Urban
Oceans
43
1990 economic production in $ PPP by country
Research
Transportation
Tourism
Households
Agriculture
Fisheries
44
Ecosystem Services
Climate Regulation
Cultural Heritage
Biological Regulation
Genetic Information
Natural Hazard Mitigation
Inorganic Resources
45
Atmosphere
Thank You
Papers mentioned in this talk available at:
www.uvm.edu/giee/publications: