Modular ecosystem modeling

doi:10.1016/S1364-8152(03)00154-3

Environmental Modelling & Software

Volume 19, Issue 3, March 2004, Pages 285-304

https://doi.org/10.1016/S1364-8152(03)00154-3 Get rights and content

Abstract

The Library of Hydro-Ecological Modules (LHEM, http://giee.uvm.edu/LHEM) was designed to create flexible landscape model structures that can be easily modified and extended to suit the requirements of a variety of goals and case studies. The LHEM includes modules that simulate hydrologic processes, nutrient cycling, vegetation growth, decomposition, and other processes, both locally and spatially. Where possible the modules are formulated as STELLA® models, which adds to transparency and helps reuse. Spatial transport processes are presented as C++ code. The modular approach takes advantage of the spatial modeling environment (http://giee.uvm.edu/SME3) that allows integration of various STELLA models and C++ user code, and embeds local simulation models into a spatial context. Using the LHEM/SME the Patuxent landscape model (PLM) was built to simulate fundamental ecological processes in the watershed scale driven by temporal (nutrient loadings, climatic conditions) and spatial (land use patterns) forcings. Local ecosystem dynamics were replicated across a grid of cells that compose the rasterized landscape. Different habitats and land use types translate into different modules and parameter sets. Spatial hydrologic modules link the cells together. These are also part of the LHEM and define horizontal fluxes of material and information. This approach provides additional flexibility in scaling up and down over a range of spatial resolutions. Model results show good agreement with data for several components of the model at several scales. Other applications include several subwatersheds of the Patuxent, the Gwynns Falls watershed in Baltimore, and others.

Section snippets

Nomenclature

B_t

biological time counter (°C)

BM_a

above ground biomass (kg/m²)

b_r

biological time threshold for reproductive organs to develop (°C)

C_h

horizontal conductivity (m/day)

C_S

infiltration rate for a given type of soil (m/day)

C_Hab

habitat type modifier for infiltration (0<C_Hab<1)

C_Sl

slope modifier for infiltration (°)

C_ij

cell size weighted horizontal conductivity in cell (i,j) (m/day)

C_n

half-saturation coefficient (n=N for nitrogen, or P for phosphorus) (g/m²)

C_tr

habitat dependent transpiration rate (1/day)

C_vc

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;
•
Surface geomorphology—such as elevation, bathymetry, soils;
•
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 (B_t) 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, D_S, whereas the rest becomes labile detritus, D_L. 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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View full text

Modular ecosystem modeling

Abstract

Section snippets

Nomenclature

General conventions

Variables and major assumptions

Variables and major assumptions

State variables

State variables

State variables

Calibration and test runs

Conclusions

Acknowledgements

Ecological Modelling

Ecological Modelling

Environmental Modelling and Software

Ecological Modelling

Ecological Modelling

Ecological Modelling

Ecological Modelling

Ecological Modelling

Ecological Modeling

Ecological Modelling

Environmental Modelling and Software

Ecological Modelling

MASCAP—MD’s Agronomic Soil Capability Assessment Program

Integrated ecological economic modeling of the Patuxent river watershed, Maryland

Ecological Monographs