1. K.Fedra ‘97 Integrating GIS and environmental models integrated tools for spatial environmental analysis

2. K.Fedra ‘97 Integrating GIS... GIS are computer based tools to: capture, capture, manipulate, manipulate, process, and process, and display display spatial or geo-referenced data. GIS are computer based tools to: capture, capture, manipulate, manipulate, process, and process, and display display spatial or geo-referenced data.

3. K.Fedra ‘97 Integrating GIS... They combine geometry data (coordinates and topological information) andgeometry data (coordinates and topological information) and attribute data, describing the properties of geometrical objects (points, lines, areas)attribute data, describing the properties of geometrical objects (points, lines, areas) with tools for spatial (geometric) analysis.with tools for spatial (geometric) analysis. They combine geometry data (coordinates and topological information) andgeometry data (coordinates and topological information) and attribute data, describing the properties of geometrical objects (points, lines, areas)attribute data, describing the properties of geometrical objects (points, lines, areas) with tools for spatial (geometric) analysis.with tools for spatial (geometric) analysis.

4. K.Fedra ‘97 Integrating GIS... Environmental problems are spatial problems, environmental data can almost always be georeferenced. GIS is therefor an appropriate tool for environmental analysis. Environmental problems are spatial problems, environmental data can almost always be georeferenced. GIS is therefor an appropriate tool for environmental analysis.

5. K.Fedra ‘97 Integrating GIS... Basic concepts in GIS are: locationlocation spatial distributionspatial distribution spatial relationshipspatial relationship Basic elements: spatial objectsspatial objects Basic concepts in GIS are: locationlocation spatial distributionspatial distribution spatial relationshipspatial relationship Basic elements: spatial objectsspatial objects

6. K.Fedra ‘97 Integrating GIS... Basic concepts in environmental modeling are: systems statesystems state systems dynamicssystems dynamics interactioninteraction Basic elements: functional objects and processesfunctional objects and processes Basic concepts in environmental modeling are: systems statesystems state systems dynamicssystems dynamics interactioninteraction Basic elements: functional objects and processesfunctional objects and processes

7. K.Fedra ‘97 Merging Paradigms Overlap and relationship between GIS and environmental models is apparent, so the merging of the two fields of research, technologies, or sets of methods, their paradigms, is an obvious and promising idea.

8. K.Fedra ‘97 Merging Paradigms Datafile GIS ENV GIS+ENV SocSciSearch1819,8986 SciSearch1436,1187 Enviroline12125,31034 WaterRes.16544,69656 Computer DB50121,93381 INSPEC1,711105,781 266 Literature search from summer 1991

9. K.Fedra ‘97 Environmental Modeling: a mathematical representation of environmental processes, and relationships. Digital (numerical) Analog computers Scale models a mathematical representation of environmental processes, and relationships. Digital (numerical) Analog computers Scale models

10. K.Fedra ‘97 Environmental Modeling considerable tradition: 1856 Darcy’s Law, fundamental equation describing groundwater flow1856 Darcy’s Law, fundamental equation describing groundwater flow 1871 St.Venant equations describing unsteady open channel flow1871 St.Venant equations describing unsteady open channel flow 1924 Lotka’s Elements of Physical Biology1924 Lotka’s Elements of Physical Biology considerable tradition: 1856 Darcy’s Law, fundamental equation describing groundwater flow1856 Darcy’s Law, fundamental equation describing groundwater flow 1871 St.Venant equations describing unsteady open channel flow1871 St.Venant equations describing unsteady open channel flow 1924 Lotka’s Elements of Physical Biology1924 Lotka’s Elements of Physical Biology

11. K.Fedra ‘97 Environmental Modeling 1960-70 first computer models1960-70 first computer models 1971 B.Patten, Systems Analysis and Simulation in Ecology (linear systems)1971 B.Patten, Systems Analysis and Simulation in Ecology (linear systems) 1972 J.Forrester, Principles of Systems (Systems Dynamics)1972 J.Forrester, Principles of Systems (Systems Dynamics) 1972 H.T.Odum, Energy flow modeling1972 H.T.Odum, Energy flow modeling 1974,79 CLEANER multi-compartment lake models, Park et.al.1974,79 CLEANER multi-compartment lake models, Park et.al. 1960-70 first computer models1960-70 first computer models 1971 B.Patten, Systems Analysis and Simulation in Ecology (linear systems)1971 B.Patten, Systems Analysis and Simulation in Ecology (linear systems) 1972 J.Forrester, Principles of Systems (Systems Dynamics)1972 J.Forrester, Principles of Systems (Systems Dynamics) 1972 H.T.Odum, Energy flow modeling1972 H.T.Odum, Energy flow modeling 1974,79 CLEANER multi-compartment lake models, Park et.al.1974,79 CLEANER multi-compartment lake models, Park et.al.

12. K.Fedra ‘97 Environmental Modeling Development through increasing complexity: number of interacting compartments, types of interactions. No explicit spatial distribution in early process models. First spatially explicit models in the physical domain (flow), linkage of transport and ecological processes by the mid 70’s and 80’s. Development through increasing complexity: number of interacting compartments, types of interactions. No explicit spatial distribution in early process models. First spatially explicit models in the physical domain (flow), linkage of transport and ecological processes by the mid 70’s and 80’s.

13. K.Fedra ‘97 Environmental Modeling Simplified block Simplified block diagram of the diagram of the aquatic ecosystem aquatic ecosystem model CLEANER model CLEANER (Park et al., 1975) (Park et al., 1975) Simplified block Simplified block diagram of the diagram of the aquatic ecosystem aquatic ecosystem model CLEANER model CLEANER (Park et al., 1975) (Park et al., 1975)

14. K.Fedra ‘97 Types of Models scale models (architecture, construction, mechanical engineering)scale models (architecture, construction, mechanical engineering) conceptual models (qualitative, block and flow diagrams, show major components and interrelationships)conceptual models (qualitative, block and flow diagrams, show major components and interrelationships) mathematical models:mathematical models: – analytical, analog, digital scale models (architecture, construction, mechanical engineering)scale models (architecture, construction, mechanical engineering) conceptual models (qualitative, block and flow diagrams, show major components and interrelationships)conceptual models (qualitative, block and flow diagrams, show major components and interrelationships) mathematical models:mathematical models: – analytical, analog, digital

15. K.Fedra ‘97 Types of Models scale models (architecture, construction, mechanical engineering)scale models (architecture, construction, mechanical engineering) conceptual models (qualitative, block and flow diagrams, show major components and interrelationships)conceptual models (qualitative, block and flow diagrams, show major components and interrelationships) mathematical modelsmathematical models scale models (architecture, construction, mechanical engineering)scale models (architecture, construction, mechanical engineering) conceptual models (qualitative, block and flow diagrams, show major components and interrelationships)conceptual models (qualitative, block and flow diagrams, show major components and interrelationships) mathematical modelsmathematical models

16. K.Fedra ‘97 Types of Models mathematical models conceptual or empiricalconceptual or empirical deterministic or stochasticdeterministic or stochastic steady-state or dynamicsteady-state or dynamic analytical or numericalanalytical or numerical spatially aggregated or distributedspatially aggregated or distributed mathematical models conceptual or empiricalconceptual or empirical deterministic or stochasticdeterministic or stochastic steady-state or dynamicsteady-state or dynamic analytical or numericalanalytical or numerical spatially aggregated or distributedspatially aggregated or distributed

17. K.Fedra ‘97 Types of Models conceptual or empiricalconceptual or empirical based on basic laws of nature or theoretical conceptsbased on basic laws of nature or theoretical concepts derived from observationsderived from observations (input-output relations), providing phenomenological descriptions (input-output relations), providing phenomenological descriptions conceptual or empiricalconceptual or empirical based on basic laws of nature or theoretical conceptsbased on basic laws of nature or theoretical concepts derived from observationsderived from observations (input-output relations), providing phenomenological descriptions (input-output relations), providing phenomenological descriptions

18. K.Fedra ‘97 Types of Models deterministic or stochasticdeterministic or stochastic all model inputs and parameters are assumed to be exactly knownall model inputs and parameters are assumed to be exactly known inputs and parameters can be represented by probability distributions, resulting in probabilistic state and outputinputs and parameters can be represented by probability distributions, resulting in probabilistic state and output deterministic or stochasticdeterministic or stochastic all model inputs and parameters are assumed to be exactly knownall model inputs and parameters are assumed to be exactly known inputs and parameters can be represented by probability distributions, resulting in probabilistic state and outputinputs and parameters can be represented by probability distributions, resulting in probabilistic state and output

19. K.Fedra ‘97 Types of Models steady-state or dynamicsteady-state or dynamic input and parametes are time- invariant, a solution independent of time can be derivedinput and parametes are time- invariant, a solution independent of time can be derived some model elements are described as functions of timesome model elements are described as functions of time steady-state or dynamicsteady-state or dynamic input and parametes are time- invariant, a solution independent of time can be derivedinput and parametes are time- invariant, a solution independent of time can be derived some model elements are described as functions of timesome model elements are described as functions of time

20. K.Fedra ‘97 Types of Models analytical or numericalanalytical or numerical the model equations can be solved analytically and exactlythe model equations can be solved analytically and exactly equations require a numerical approximation for solution, based on some form of discretizationequations require a numerical approximation for solution, based on some form of discretization analytical or numericalanalytical or numerical the model equations can be solved analytically and exactlythe model equations can be solved analytically and exactly equations require a numerical approximation for solution, based on some form of discretizationequations require a numerical approximation for solution, based on some form of discretization

21. K.Fedra ‘97 Types of Models spatially aggregated or distributedspatially aggregated or distributed model is assumed to be independent of spatial locationmodel is assumed to be independent of spatial location models uses average (lumped) values to describe a larger areamodels uses average (lumped) values to describe a larger area inputs, parameters or the transfer function vary with location, state is defined for more than one location, spatial elements interactinputs, parameters or the transfer function vary with location, state is defined for more than one location, spatial elements interact spatially aggregated or distributedspatially aggregated or distributed model is assumed to be independent of spatial locationmodel is assumed to be independent of spatial location models uses average (lumped) values to describe a larger areamodels uses average (lumped) values to describe a larger area inputs, parameters or the transfer function vary with location, state is defined for more than one location, spatial elements interactinputs, parameters or the transfer function vary with location, state is defined for more than one location, spatial elements interact

22. K.Fedra ‘97 Modeling Domains Atmospheric systemsAtmospheric systems Hydrologic systemsHydrologic systems Land surface and subsurfaceLand surface and subsurface Biological and ecological systemsBiological and ecological systems Risks and hazardsRisks and hazards Management and policy modelsManagement and policy models Atmospheric systemsAtmospheric systems Hydrologic systemsHydrologic systems Land surface and subsurfaceLand surface and subsurface Biological and ecological systemsBiological and ecological systems Risks and hazardsRisks and hazards Management and policy modelsManagement and policy models

23. K.Fedra ‘97 Modeling Domains Atmospheric systemsAtmospheric systems weather forecastingweather forecasting climate modelsclimate models air pollution: industry, traffic, domestic sources, accidental releases (hazardous substances)air pollution: industry, traffic, domestic sources, accidental releases (hazardous substances) Atmospheric systemsAtmospheric systems weather forecastingweather forecasting climate modelsclimate models air pollution: industry, traffic, domestic sources, accidental releases (hazardous substances)air pollution: industry, traffic, domestic sources, accidental releases (hazardous substances)

24. K.Fedra ‘97 Modeling Domains Air pollution modelingAir pollution modeling estimation of the source term:estimation of the source term: – rate and duration of release – source size, location –initial buoyancy and momentum Air pollution modelingAir pollution modeling estimation of the source term:estimation of the source term: – rate and duration of release – source size, location –initial buoyancy and momentum

25. K.Fedra ‘97 Modeling Domains Air pollution modelingAir pollution modeling pollutant transportpollutant transport – advection by wind – turbulent and molecular diffusion – buoyancy effects (gases, particles) – deposition, chemical reactions, radioactive decay radioactive decay Air pollution modelingAir pollution modeling pollutant transportpollutant transport – advection by wind – turbulent and molecular diffusion – buoyancy effects (gases, particles) – deposition, chemical reactions, radioactive decay radioactive decay

26. K.Fedra ‘97 Modeling Domains Air pollution modelingAir pollution modeling impacts and hazardsimpacts and hazards – human end environmental exposure – damage through explosion and fire – damage through chemical reactions (corrosion) (corrosion) Air pollution modelingAir pollution modeling impacts and hazardsimpacts and hazards – human end environmental exposure – damage through explosion and fire – damage through chemical reactions (corrosion) (corrosion)

27. K.Fedra ‘97 Modeling Domains Hydrologic systemsHydrologic systems hydrological cycle, rainfall-runoffhydrological cycle, rainfall-runoff river flow and floodingriver flow and flooding water distribution and allocationwater distribution and allocation reservoir operationsreservoir operations water quality, eutrophication,water quality, eutrophication, waste allocation waste allocation groundwater systemsgroundwater systems Hydrologic systemsHydrologic systems hydrological cycle, rainfall-runoffhydrological cycle, rainfall-runoff river flow and floodingriver flow and flooding water distribution and allocationwater distribution and allocation reservoir operationsreservoir operations water quality, eutrophication,water quality, eutrophication, waste allocation waste allocation groundwater systemsgroundwater systems

28. K.Fedra ‘97 Modeling Domains Coastal waters and oceansCoastal waters and oceans currents and energy balance (climate modeling)currents and energy balance (climate modeling) coastal water qualitycoastal water quality nutrient cycles, eutrophicationnutrient cycles, eutrophication fisheries (sustainable yield)fisheries (sustainable yield) Coastal waters and oceansCoastal waters and oceans currents and energy balance (climate modeling)currents and energy balance (climate modeling) coastal water qualitycoastal water quality nutrient cycles, eutrophicationnutrient cycles, eutrophication fisheries (sustainable yield)fisheries (sustainable yield)

29. K.Fedra ‘97 Modeling Domains Land surface and subsurfaceLand surface and subsurface erosion, soil processeserosion, soil processes vegetation, land covervegetation, land cover groundwater (unsaturated and saturated zones, links to the hydrological domain)groundwater (unsaturated and saturated zones, links to the hydrological domain) Land surface and subsurfaceLand surface and subsurface erosion, soil processeserosion, soil processes vegetation, land covervegetation, land cover groundwater (unsaturated and saturated zones, links to the hydrological domain)groundwater (unsaturated and saturated zones, links to the hydrological domain)

30. K.Fedra ‘97 Modeling Domains Biological and ecological systemsBiological and ecological systems population models, predator-prey systems, food chainspopulation models, predator-prey systems, food chains ecosystem models (multi- compartment combining physical and biological elements)ecosystem models (multi- compartment combining physical and biological elements) Biological and ecological systemsBiological and ecological systems population models, predator-prey systems, food chainspopulation models, predator-prey systems, food chains ecosystem models (multi- compartment combining physical and biological elements)ecosystem models (multi- compartment combining physical and biological elements)

31. K.Fedra ‘97 Modeling Domains Agriculture and ForestryAgriculture and Forestry agricultural productionagricultural production livestock and grazing modelslivestock and grazing models forest models (stands, growth, yield, deforestation and reforestation)forest models (stands, growth, yield, deforestation and reforestation) Agriculture and ForestryAgriculture and Forestry agricultural productionagricultural production livestock and grazing modelslivestock and grazing models forest models (stands, growth, yield, deforestation and reforestation)forest models (stands, growth, yield, deforestation and reforestation)

32. K.Fedra ‘97 Modeling Domains Risks and hazardsRisks and hazards floods and droughtsfloods and droughts erosion, desertificationerosion, desertification spills and accidental releasesspills and accidental releases epidemiological models (pests, infectuous diseases)epidemiological models (pests, infectuous diseases) Risks and hazardsRisks and hazards floods and droughtsfloods and droughts erosion, desertificationerosion, desertification spills and accidental releasesspills and accidental releases epidemiological models (pests, infectuous diseases)epidemiological models (pests, infectuous diseases)

33. K.Fedra ‘97 Modeling Domains Management and policy modelsManagement and policy models all of the above, but containing explicit representation of control and decision variablesall of the above, but containing explicit representation of control and decision variables economic evaluation economic evaluation Management and policy modelsManagement and policy models all of the above, but containing explicit representation of control and decision variablesall of the above, but containing explicit representation of control and decision variables economic evaluation economic evaluation

34. K.Fedra ‘97 Modeling Domains All environmental model domains have an obvious spatial dimension. Most recent environmental models are spatially explicit (inputs and state are functions of space) X (x,y,z,t) X (x,y,z,t) All environmental model domains have an obvious spatial dimension. Most recent environmental models are spatially explicit (inputs and state are functions of space) X (x,y,z,t) X (x,y,z,t)

35. K.Fedra ‘97 Distributed Models are based on partial differential equations; dependent variables are functions of two or more other variables: dQ dQ dx dy (continuity equation for 2D groundwater flow) are based on partial differential equations; dependent variables are functions of two or more other variables: dQ dQ dx dy (continuity equation for 2D groundwater flow) + = 0

36. K.Fedra ‘97 Distributed Models and the partial differentials dQ/dx and dQ/dy dQ/dx and dQ/dy describe the gradient of discharge Q in the horizontal x and y directions. and the partial differentials dQ/dx and dQ/dy dQ/dx and dQ/dy describe the gradient of discharge Q in the horizontal x and y directions.

37. K.Fedra ‘97 Distributed Models The partial differential equations are solved with a numerical scheme like finite elements or finite differences. or finite differences. This requires the This requires the solution domain to solution domain to be discretized. be discretized. The partial differential equations are solved with a numerical scheme like finite elements or finite differences. or finite differences. This requires the This requires the solution domain to solution domain to be discretized. be discretized.

38. K.Fedra ‘97 Distributed Models Process equations are solved for each of the discrete units in the model domain. Process equations are solved for each of the discrete units in the model domain.

39. K.Fedra ‘97 Distributed Models coupling of cells is achievedthroughtransferprocesses such as advectionanddiffusion.coupling of cells is achievedthroughtransferprocesses such as advectionanddiffusion.

40. K.Fedra ‘97 Merging Paradigms Use GIS functionality for data capture, processing and display; Use GIS functionality for static, geometric analysis; Use model functionality for dynamic processes and complex analysis. Use GIS functionality for data capture, processing and display; Use GIS functionality for static, geometric analysis; Use model functionality for dynamic processes and complex analysis.

41. K.Fedra ‘97 GIS-Model coupling data exchange between two separate systemsdata exchange between two separate systems common interface, shared datacommon interface, shared data common interface, fully integrated functionalitycommon interface, fully integrated functionality data exchange between two separate systemsdata exchange between two separate systems common interface, shared datacommon interface, shared data common interface, fully integrated functionalitycommon interface, fully integrated functionality

42. K.Fedra ‘97 GIS-Model coupling data exchange between two separate systems: GIS acts as a pre- and post- processor for a dynamic environmental model. data exchange between two separate systems: GIS acts as a pre- and post- processor for a dynamic environmental model.

43. K.Fedra ‘97 GIS-Model coupling separate user interfaces, shared files separate user interfaces, shared files user interface GIS MODEL shared files

44. K.Fedra ‘97 GIS-Model coupling common user interface, shared files and memory common user interface, shared files and memory common user interface GIS MODEL shared files and memory

45. K.Fedra ‘97 GIS-Model coupling full integration of GIS and models together with a DSS component representing a problem-oriented user interface. full integration of GIS and models together with a DSS component representing a problem-oriented user interface. GIS DSS MODELS

46. K.Fedra ‘97 GIS-Model coupling GIS MODELS KBDBMS data filesrule base pre- processor post- processor interactive user interface help/explainvisualizationscenario manager

47. K.Fedra ‘97 Example: groundwater modeling Spatially distributed aquifer characteristics (conductivity, porosity) and inputs (recharge) are derived from appropriate maps; Model output is displayed as (animated) map overlays. Spatially distributed aquifer characteristics (conductivity, porosity) and inputs (recharge) are derived from appropriate maps; Model output is displayed as (animated) map overlays.

48. K.Fedra ‘97 from a digitized geological map …... from a digitized geological map …...

49. K.Fedra ‘97 a rasterized data set of aquifer properties is derived...

50. K.Fedra ‘97 The map is background and input to the model...

51. K.Fedra ‘97 The model output is yet another map layer.

52. K.Fedra ‘97 Different display styles are supported...

53. K.Fedra ‘97 … including pseudo 3D display of functional values.