Modular ecosystem modeling
Section snippets
Nomenclature
- Bt
biological time counter (°C)
- BMa
above ground biomass (kg/m2)
- br
biological time threshold for reproductive organs to develop (°C)
- Ch
horizontal conductivity (m/day)
- CS
infiltration rate for a given type of soil (m/day)
- CHab
habitat type modifier for infiltration (0<CHab<1)
- CSl
slope modifier for infiltration (°)
- Cij
cell size weighted horizontal conductivity in cell (i,j) (m/day)
- Cn
half-saturation coefficient (n=N for nitrogen, or P for phosphorus) (g/m2)
- Ctr
habitat dependent transpiration rate (1/day)
- Cvc
General conventions
There is a good variety of software currently available that can help build and run models. Between the qualitative conceptual model and the computer code, we may place a number of software tools that can assist us in converting conceptual ideas into a running model. Usually there is a trade off between universality and user-friendliness. On the one extreme, we see computer languages that can be used to translate any concepts and any knowledge into working computer code. On the other extreme,
Variables and major assumptions
There are no state variables in this module. The variables defined here are the forcing functions and parameters that describe the physical environment and include:
- •
Climatic factors—precipitation, temperature, humidity, wind speed, solar radiation;
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Surface geomorphology—such as elevation, bathymetry, soils;
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Auxiliary variables shared by other modules—such as day length, Julian day, and habitat type.
The module is designed mostly to simplify data preprocessing. It takes care of various conversions
Variables and major assumptions
The traditional scheme of vertical water movement (Novotny and Olem, 1994), also implemented in GEM (Fitz et al., 1996), assumes that water is fluxed along the following pathway: rainfall→surface water→water in the unsaturated layer→water in the saturated zone. Snow is yet another storage that is important to mimic the delayed response caused by certain climatic conditions. In each of the stages, some portions of water are diverted due to physical (evaporation, runoff) and biological
State variables
As in GEM, the nutrients considered in the LHEM are nitrogen and phosphorus. Various nitrogen forms, NO2−, NO3− and NH4+ are aggregated into one variable representing all forms of nitrogen that are directly available for plant uptake. Available inorganic phosphorus is simulated as orthophosphate. There are two nutrient modules currently available. The distinction appears in the conceptualization of nutrients in the vertical dimension. In terrestrial ecosystems, nutrients on the surface are no
State variables
In the plant module, we simulate the growth of higher vegetation. It will be the macrophytes in an aquatic environment, trees in forests, crops in agricultural habitats, grasses and shrubs in grasslands. The plant biomass (kg/m2) is assumed to consist of the photosynthetic (PH) and the non-photosynthetic (NPH) components. In addition to that we distinguish between the above ground and the below ground biomasses (Fig. 3).
Another state variable (Bt) is employed to track the so-called biological
State variables
At present, this module serves predominantly to close the nutrient and material cycles in the system, it does not go into all the details of the multi-scale and complex processes of leaching and bacterial decomposition. As biomass dies off, part of it turns into stable detritus, DS, whereas the rest becomes labile detritus, DL. The proportions between the two are driven by the lignin content, which is relatively low for the PH biomass and is quite high for NPH biomass. Labile detritus is
Calibration and test runs
We have been mostly using the LHEM for modeling of the Patuxent watershed as well as several of its subwatersheds. Another watershed that was modeled is the Gwynns Falls, a highly urbanized watershed in Baltimore. The details of the PLM and its results have been reported elsewhere (Voinov et al., 1999a, Costanza et al., 2002), and may also be found at http://giee.uvm.edu/PLM. This brief description of the application of the LHEM in a particular project is primarily to illustrate how the modules
Conclusions
Somewhat in contrast to GEM, in the modular approach, we do not intend to design a unique general model. In this case, our goal is to offer a framework that can be easily extended and is flexible to be modified. A module that performs best in one case may not be sufficient in another. The goals and scale of a particular study may require a completely different set of modules that will be invoked and further translated by the SME into a working model. Though STELLA may not be perfect for all
Acknowledgements
Our thanks go to Thomas Maxwell for his responsiveness to our needs in the development of SME, and to Helena Voinov for much needed help with data sets for calibrating the Patuxent model. The EPA STAR (Science to Achieve Results) program, Office of Research and Development, National Center for Environmental Research and Quality Assurance (R82716901), has provided funding for this research. We are also grateful to an anonymous reviewer for careful editing of the text and helpful suggestions.
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