applications of systems dynamics in integrated modeling of humans embedded in ecological system...

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

- +

-

+

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


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)


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


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.


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.


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


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


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


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


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2.72

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10.65

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Ecotopia StartrekMad Max Big Goverment Basecase

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GUMBO

0

1

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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


Energy

Agriculture

Freshwater

Biogeochemistry

Natural Systems


DICE

0

1

2

3

4Atmosphere

Water Cycle

Land - Soil

Demographic

Political

Development

Cultural-Values

Economics

Landuse change


Energy

Agriculture

Freshwater

Biogeochemistry

Natural Systems


IFs

0

1

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3

4Atmosphere

Water Cycle

Land - Soil

Demographic

Political

Development

Cultural-Values

Economics

Landuse change


Energy

Agriculture

Freshwater

Biogeochemistry

Natural Systems


IMAGE-2

0

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4Atmosphere

Water Cycle

Land - Soil

Demographic

Political

Development

Cultural-Values

Economics

Landuse change


Energy

Agriculture

Freshwater

Biogeochemistry

Natural Systems


IMAGE

0

1

2

3

4Atmosphere

Water Cycle

Land - Soil

Demographic

Political

Development

Cultural-Values

Economics

Landuse change


Energy

Agriculture

Freshwater

Biogeochemistry

Natural Systems


WORLD3

0

1

2

3

4Atmosphere

Water Cycle

Land - Soil

Demographic

Political

Development

Cultural-Values

Economics

Landuse change


Energy

Agriculture

Freshwater

Biogeochemistry

Natural Systems


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.


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


Ecosystem Services

Climate Regulation

Biological Regulation

Natural Hazard Mitigation

Cultural Heritage

Genetic Information

Inorganic Resources


applications of systems dynamics in integrated modeling of humans embedded in ecological system...

Documents

model development

model conceptualization

integrated models

ecological system sumber

particular models sumber

intercalibrated models

acceptance of models

resolutions sumber