Thomas Maxwell Alexey Voinov Robert Costanza

Institute for Ecological Economics

Collaborative Spatial Ecological-Economic

Modeling for Sustainable Management of Watershed

ResourcesThomas MaxwellAlexey Voinov

Robert Costanza


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


Three Stage Modeling Process

• Scoping models– Consensus building

• Research models– Understanding dynamics

• Management models– Exploring scenarios


Scoping Workshops

• Constructivist learning.• Paradigm expansion.

– (narrow,linear,static) ->– (broad,nonlinear,dynamic)

• Conflict resolution.• Consensus building.• Collective decision making.• Develop management scenarios.


Supporting Collaborative Modeling

• Graphical modeling tools• Modular model development• Transparent high performance

computing• Integrated data access• Integrated visualization• Variety of formalisms and frames


Graphical Modeling

•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.


STELLA Model


Spatial Modeling

Framework


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.


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


Spatial Modeling Environment

• 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).



STELLA

PowerSim

SME ModuleEditor

ModuleConstructor

SMML Module Library

ModuleRepository

ModuleBuilder

SimulationDriver

Code Generator

HPC

JavaPortal

Unit model Spatial modelGraphical modeling


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


WorkSpace Manager


Documentation PanelDocumentation of selected command

Model PanelHierarchical View of model objects

Associated commands as boxes

Command PanelStructure of selected command

Property Panel Command Arguments

Configuration Manager


Parameter Editor

Edit Simulation Parameters

Spreadsheet format


Simulation Control

Control Execution

View Model Structure

Trace Dependencies

View Model Equations

Configure Visualization


SME Python Shell


Associates DataSets with Viewers

Creates Viewers

Manages DataSets

ViewServer Control Panel


2D Animation Viewer

2D Animation Control

Dynamic and manual rescaling

ColorMap editor

Data viewer (point/spreadsheet)

Export as GIF or JPG


3D Animation Viewer

Dynamic Landscapes

Variable1 -> Altitude

Variable2 -> Color Mouse controlled

navigation


Image Spreadsheet

Simultaneous display of variables at multiple timesteps Useful for time series comparisons

Configure: start time, time step, magnification, scaling, etc.


View spatial data Attach to vis panels

Follows animation Export to Stat

packages.

Numerical Spreadsheet


Agent Based Modeling in SME

• Swarm agents can populate SME landscapes.• SME-Swarm integration:

– http://iee.umces.edu/~villa/swarmsme

• 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:

– http://iee.umces.edu/~villa/sni

http://iee.umces.edu/~villa/swarmsme


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.


Spatial grid partitioned over processors

Highly parallel application

Recursive N-section: excellent load balancing

Fully transparent to user

Distributed Processing


Model Calibration toolkit

• Built on MPE toolkit:– http://iee.umces.edu/~villa/svp/

• 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.


Example Applications

• Everglades Landscape Model– http://www.sfwmd.gov/org/erd/esr/elm/intro/welcome.htm

• Patuxent Landscape Model– http://iee.umces.edu/PLM

• Baltimore Ecosystem Study– http://baltimore.umbc.edu/lter

• Great Bay Estuarine Model– http://iee.umces.edu/GrBay

• Illinois TES Models– http://blizzard.gis.uiuc.edu/

• IGERT & CoreModels programs

http://baltimore.umbc.edu/lter

http://baltimore.umbc.edu/lter

http://blizzard.gis.uiuc.edu/




CavernSoft

Collaborative

Environment

Environmental Hydrology Applications Team

Inputs to multiple models

Environmental Modeling Workbench

Integrated wirelessSensor web

CoupledBio-HydroSimulation



• Links components: – Circulation (OM3) – Ecology (SME)– Atmospheric coupling

Environmental Hydrology Applications Team

Chesapeake Bay Model

Institute for Ecological EconomicsEnvironmental Hydrology Applications Team

Collaborative Virtual Environment

Chesapeake Bay data in CVE with Cave5D/Virtual Director


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MM

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MMMMMMMMMMM MMMMMMMMMMMMMMM

MMMMMMMMMMMMMMMMM

MMMMMM

STELLAª Model

Population

M

EX LANDUSE ALT LANDUSE

CHANGE

LU MAP

EXISTING

DEV PROBABILITY

O CHANGE

OPEN SPACE

OPEN SPACE SWITCH

ECONOMICS

SOCIAL MODEL

UTILITIES

SPONTANEOUS

NEIGHBORS

DEV PROBABILITY

~

ECON TRENDS

DEM

GROWTH TRENDSPLANNING MAP

TRANSPORTATION MODEL

OPEN SPACE SWITCH

Landuse Evolution and Impact Assessment ModelLanduse Evolution and Impact Assessment ModelLEAM, University of Illinois at Urbana-ChampaignLEAM, University of Illinois at Urbana-Champaign


LEAM Framewor

kL E A ML E A ML E A ML E A M

planning groupplanning group planning groupplanning groupsimulationsimulation

model driversmodel drivers

random geography transport open space neighbor-

hood economic population social

landuse changelanduse change

water air habitat tes fiscal energy waste environ

sustainable indicessustainable indices

impact assessment


LEAM Portal


• Resolution - 1 km (200m for subwatersheds)

• 2562 grid cells• A model in each cell:

hydrologynutrients (N, P)vegetation

• Forcing functions:climatic conditionsland use mapnutrient loadings from

-atmosphere-fertilizers-septics-point sources

Patuxent Landscape ModelPatuxent Landscape Model(PLM)(PLM)


PhotosyntheticBiomass

Surface Water

Unsaturated Water

Saturated Water

PrecipitationEvaporation

Overland flow

Surface -Saturatedexchanges

Percolation& upflow

Groundwaterflow

Transpiration

Infiltration

SnowIce

Non-PhotoBiomass

Runoff

N insediment

Photosynthesis

N onsurface

Detritus

DOM

Translocation

Mortality

Decomposition

Uptake

State variables and main processes in the State variables and main processes in the landscape modellandscape model


Library of Hydro-Ecological Modules

SurfaceHydrology& NutrientTransport

SimulationModule

Markup LanguageSpatial

ModelingEnvironemnt

- SME. . .

. . .

Hydrology

Nutr ientCycling

PlantGrowth

Dead OrganicDecomposition

PhysicalConditions

Local Dynamics

GroundwaterHydrology& NutrientTransport

LanduseChange

CropRotation

Spatial Dynamics


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


Results

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


SME Distribution

The SME home page:

http://www.uvm.edu/giee/SME3/

Includes:– Overview.– Technical documentation.– Publications.– Source code (C++ and java).– Links

Thomas Maxwell Alexey Voinov Robert Costanza

Documents

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