1. General Model Information

Name: General Ecosystem Model

Acronym: GEM

Main medium: terrestrial+air+aquatic
Main subject: biogeochemistry,hydrology
Organization level: ecosystem
Type of model: not specified
Main application:
Keywords: landscape, spacial modelling, ecosystem, environment,parallel processing, STELLA simulation modelling, wetlands, water management,canals, levees, Everglades, Florida


Robert Costanza,
Maryland International Institute for Ecological Economics
Carl Fitz,
Maryland International Institute for Ecological Ecomomics
Tom Maxwell,
Maryland International Institute for Ecological Ecomomics
Fred Sklar,
South Florida Water Management District
Fax :



We have developed a General Ecosystem Model (GEM) that is designed to simulate a variety of ecosystem types using a fixed model structure. Driven largely by hydrologic algorithms for upland, wetland and shallow-water habitats, the model captures the response of macrophyte and algal communities to simulated levels of nutrients, water, and environmental inputs. It explicitly incorporates ecological processes that determine water levels, plant production, nutrient cycling associated with organic matter decomposition, consumer dynamics, and fire. While the model may be used to simulate ecosystem dynamics for a single homogenous habitat, our primary objective is to replicate it as a unit model in heterogeneous, grid-based dynamic spatial models using different parameter sets for each habitat. Thus, we constrained the process (i.e., computational) complexity, yet targeted a level of disaggregation that would effectively capture the feedbacks among important ecosystem processes. A basic version was used to simulate the response of sedge and hardwood communities to varying hydrologic regimes and associated water quality. Sensitivity analyses provided examples of the model dynamics, showing the varying response of macrophyte production to different nutrient requirements, with subsequent changes in the sediment water nutrient concentrations and total water head. Changes in the macrophyte canopy structure resulted in differences in transpiration, and thus the total water levels and macrophyte production. The GEM´ s modular design facilitates understanding the model structure and objectives, inviting variants of the basic version for other research goals. Importantly, we hope that the generic nature of the model will help alleviate the "reinventing-the-wheel" syndrome of model development, and we are implementing it in a variety of systems to help understand their basic dynamics.

Fig. 1. The process- oriented feedbacks among the biotic and abiotic sectors of the GEM. Dynamics of live and standing dead macrophytes alter surface water runoff through changes in structure and thus surface roughness. Water losses via transpiration vary with changes in biomass (leaf area index) and physical canopy structure. Availability of water in surface, unsaturated and saturated storage is one control on plant growth and mortality. Hydrologic algorithms also transport dissolved nutrients and control their remineralization, while nutrient availability and uptake kinetics can control plant growth. Dead organic matter, in different forms of storage and with different C:N:P ratios, is the source for nutrient cycling. Consumers sequester plant biomass, delaying its incorporation into detrital pools. Fire may generally affect the whole system.

Model Structure

Fig. 2. The state variables and most of the linkages of material or information in the GEM, excluding hydrology (Figure 3). Hydrology drives many of the vertical fluxes shown and the unshown horizontal fluxes of materials into and out of the system (cell). State variables are enclosed within rectangles in All_Caps. Environmental forcing functions are in oval boxes; simulations of fire, hydrology, and hydrodynamics affect model dynamics. Metabolic sinks are not indicated. Dissolved Inorganic Nitrogen (DIN) and PO4 are separate state variables with slightly different dynamics. Although not shown, both nutrients are involved in uptake and mineralization processes.

Author of the abstract:

Robert Costanza

Maryland International Institute for Ecological Economics

II. Technical Information

II.1 Executables:

Operating System(s): scanner, digitizer, GIS data bases, computation system (Macintosh), STELLA,Spacial Modeling Package (SMP).

II.2 Source-code:

Programming Language(s): FORTRAN 77

II.3 Manuals:

II.4 Data:

Fig. 2. Simplified diagram of water storages and flows for the.i.Hydrology sector;. The depths associated with water in surface, unsaturated,and saturated storages all vary dynamically, and calculations determinethe variable soil moisture proportion of the unsaturated zone. Selected model output: Fig. 4. A 4 yr run of GEM in a fresh marsh habitat, with selectedhydrologic, macrophyte, and nutrient dynamics. The top graph shows thetotal water head relative to a constant (6 m) land elevation. The nextgraph contains (repeated, one-year) daily rainfall input and simulatedtranspiration.The third graph shows changes in macrophyte biomass densityand the overall production control function (0-1 multiplier). The bottompanel shows the concentration of PO4 dissolved in the sediment water andthe resulting control function that is part of the macrophyte productioncontrol.

III. Mathematical Information

III.1 Mathematics

III.2 Quantities

III.2.1 Input

III.2.2 Output

IV. References

V. Further information in the World-Wide-Web

VI. Additional remarks

SME version 1. has been used to develop a spacially explicit, dynamic simulationmodel of the ecosystem(s) within a particular landscape. Another exampleof such a model is the SME,ELM and CALM.

Last review of this document by: 24.June 1997
Status of the document:
last modified by Tobias Gabele Wed Aug 21 21:44:43 CEST 2002

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