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Collaborative Spatial Ecological-Economic Modeling for Sustainable Management of Watershed Resources. Thomas Maxwell Alexey Voinov Robert Costanza. Collaborative Modeling. Realistic models require multiple teams Modelers typically not computer scientists

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Collaborative Spatial Ecological-Economic Modeling for Sustainable Management of Watershed Resources

Thomas Maxwell

Alexey Voinov

Robert Costanza

Institute for Ecological Economics

collaborative modeling
Collaborative Modeling
  • Realistic models require multiple teams
  • Modelers typically not computer scientists
  • Stake holders must be integrated into the decision making process
  • Communication to a wide audience

Institute for Ecological Economics

three stage modeling process
Three Stage Modeling Process
  • Scoping models
    • Consensus building
  • Research models
    • Understanding dynamics
  • Management models
    • Exploring scenarios

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scoping workshops
Scoping Workshops
  • Constructivist learning.
  • Paradigm expansion.
    • (narrow,linear,static) ->
    • (broad,nonlinear,dynamic)
  • Conflict resolution.
  • Consensus building.
  • Collective decision making.
  • Develop management scenarios.

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supporting collaborative modeling
Supporting Collaborative Modeling
  • Graphical modeling tools
  • Modular model development
  • Transparent high performance computing
  • Integrated data access
  • Integrated visualization
  • Variety of formalisms and frames

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Model viewed and manipulated graphically.

  • Opens model development to non-programmers.
  • Facilitates rapid development of models.
  • Enforces modeling standards.
  • Facilitates collaboration in model development.
  • Graphical representation serves as a blackboard.
  • Graphical Modeling

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spatial modeling framework
Spatial Modeling Framework

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two types of modules
Two types of modules
  • Ecological Modules
    • No general theory.
    • Primary focus on modeling.
    • Examples:
      • Macrophytes, Epiphytes, Consumers, Phytoplankton
    • Modules developed in Stella/SME.
  • Physical Modules
    • Theory well known (e.g. Navier Stokes).
    • Primary focus on computation.
    • Examples:
      • hydrodynamics, atmospheric dynamics.
    • Modules developed externally and linked to SME.

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typical state variables
Typical State Variables
  • Examples of some typical state variables:
    • (Dissolved Inorganic) Nitrogen, Phosphorus
    • Water (Saturated, Unsaturated, Surface, Snow)
    • Detritus
    • Macrophyte (Non)Photosynthetic Biomass
    • Consumers
    • Deposited Organic Matter
    • Phytoplankton
    • Epiphytes

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Collaborative Spatial Modeling Workbench

  • Includes integrated support for:
    • Icon-based unit module development
    • Module archiving and reuse
    • Integration of multiple spatial representations
    • Distributed computing
    • Web-based modeling & simulation
      • Configuration, control, and visualization of remote simulations.
    • Data access and visualization
    • Real-time links to other apps (e.g. Swarm).
  • Spatial Modeling Environment

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spatial modeling environment
Spatial Modeling Environment







Unit model



Spatial model



Code Generator




SME Module




SMML Module Library

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sme java portal
SME Java Portal
  • Desktop access to remote supercomputing resources
  • Web-enabled ( using java servlets )
  • Grid enabled ( using globus gram utility )
  • Java applet <-> Java servlet <-> C++ apps
  • Portal interfaces include:
    • Workspace management
    • Module development
    • Model configuration
    • Simulation initialization, control, & visualization

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workspace manager
WorkSpace Manager

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configuration manager
Configuration Manager

Documentation Panel

Documentation of

selected command

Model Panel

Hierarchical View of model objects

Associated commands as boxes

Command Panel

Structure of selected command

Property Panel

Command Arguments

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parameter editor
Parameter Editor

Edit Simulation


Spreadsheet format

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simulation control
Simulation Control

Control Execution

View Model Structure

Trace Dependencies

View Model Equations

Configure Visualization

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sme python shell
SME Python Shell

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ViewServer Control Panel

  • Associates DataSets
  • with Viewers
  • Creates Viewers
  • Manages DataSets

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2D Animation Viewer

  • 2D Animation Control
  • Dynamic and manual rescaling
  • ColorMap editor
  • Data viewer (point/spreadsheet)
  • Export as GIF or JPG

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3D Animation Viewer

  • Dynamic Landscapes
  • Variable1 -> Altitude
  • Variable2 -> Color
  • Mouse controlled navigation

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image spreadsheet
Image Spreadsheet
  • Simultaneous display of variables at multiple timesteps
  • Useful for time series comparisons
  • Configure: start time, time step, magnification, scaling, etc.

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Numerical Spreadsheet

  • View spatial data
  • Attach to vis panels
  • Follows animation
  • Export to Stat packages.

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agent based modeling in sme
Agent Based Modeling in SME
  • Swarm agents can populate SME landscapes.
  • SME-Swarm integration:
  • Swarm classes serve as wrappers for:
    • SME model.
    • SME grid layers.
    • SME spatial variables.
  • Two-way remote data transfer.
  • Built on SNI simulation server architecture:

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multi grid library
Multi-Grid Library
  • Integrates multiple spatial representations
  • Implements space in SME
  • Major Components include:
    • Cell: Spatially referenced area (or volume) element.
    • Grid: Distributed set of Cells + links.
    • Frame: Hierarchy of distributed Grids.
    • Link: Connection between Cells.
      • Intra-Grid: spatial contiguity.
      • Inter-grid: scaling relations or mappings.
    • Activation Layer: Subset of Cells in a Frame.
    • Coverage: Mapping:: Activation Layer -> floats.

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distributed processing
Distributed Processing
  • Spatial grid
  • partitioned over processors
  • Highly parallel application
  • Recursive N-section:
    • excellent load balancing
  • Fully transparent to user

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model calibration toolkit
Model Calibration toolkit
  • Built on MPE toolkit:
  • Calculate performance measure (MPE)
    • Estimate of match between model & system.
    • Weighted sum of tests (Bounds, Theil, Freq, etc).
  • Search parameter space to maximize MPE.
    • Evolutionary and gradient searches.
  • Params, tests, & searches configured in SME.

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example applications
Example Applications
  • Everglades Landscape Model
  • Patuxent Landscape Model
  • Baltimore Ecosystem Study
  • Great Bay Estuarine Model
  • Illinois TES Models
  • IGERT & CoreModels programs

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environmental modeling workbench
Environmental Modeling Workbench

Spatial Modeling Environment

Inputs to multiple models




Integrated wireless

Sensor web




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Environmental Hydrology Applications Team

chesapeake bay model
Links components:

Circulation (OM3)

Ecology (SME)

Atmospheric coupling

Chesapeake Bay Model

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Environmental Hydrology Applications Team

collaborative virtual environment
Collaborative Virtual Environment

Chesapeake Bay data in CVE with Cave5D/Virtual Director

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Environmental Hydrology Applications Team



Landuse Evolution and Impact Assessment Model

LEAM, University of Illinois at Urbana-Champaign

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leam framework






open space

neighbor- hood










LEAM Framework


model drivers


planning group

planning group

landuse change

impact assessment

sustainable indices

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leam portal
LEAM Portal

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patuxent landscape model plm
Patuxent Landscape Model(PLM)
  • Resolution - 1 km (200m for subwatersheds)
  • 2562 grid cells
  • A model in each cell: hydrology nutrients (N, P) vegetation
  • Forcing functions: climatic conditions land use map nutrient loadings from
  • -atmosphere -fertilizers -septics -point sources

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library of hydro ecological modules
Library of Hydro-Ecological Modules

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main drivers
Main Drivers
  • Landuse change - number if cells in different habitat categories and their patterns
  • The total amount of nutrients that is contributed annually from various sources to the watershed. At this time atmospheric deposition is the main source of non-point pollution

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  • historical land use in 1650, 1850, 1950, 1972, 1990 and 1997;
  • a “buildout” scenario based on fully developing all the land currently zoned for development;
  • four future development patterns based on an empirical economic land use conversion model;
  • agricultural “best management practices” which lower fertilizer application;
  • four “replacement” scenarios of land use change to analyze the relative contributions of agriculture and urban land uses; and
  • two “clustering” scenarios with significantly more and less clustered residential development than the current pattern.

We analyzed 18 scenarios including

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sme distribution
SME Distribution

The SME home page:


  • Overview.
  • Technical documentation.
  • Publications.
  • Source code (C++ and java).
  • Links

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