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10 3. The development of modelling frameworks 3.1 Modelling environments and tools For something like 3 decades, the need for some type of software support for the modelling process has been recognised, and various solutions have been proposed and prototypes developed (Rizzoli and Young, 1997). In the 1970's, various large-scale models were developed for different biomes as part of the IBP - the International Biological Programme. See Patten (1975) for a summary paper on each biome. Several groups recognised major problems with the development of these models as large Fortran programs. Innis (1975) and Overton (1975) reported on the development of Fortran pre-processors (SIMCOMP and FLEX respectively) to aid in the development of models in their respective studies in the IBP. Initial efforts in this area tended to fall into one of two main categories. First, several groups adopted a simulation language, most commonly one for continuous systems simulation. De Wit and his crop modelling group in Wageningen were the main proponents of this approach, using CSMP (the Continuous Systems Modeling Program) (de Wit and Goudriaan, 1974). CSMP subsequently died, though more recently ACSL (the Advanced Continuous Systems Modeling Language) [see URLs] has been used, notably by Thornley (1998) in developing the Hurley Pasture Model [see URLs]. The other main approach was to develop Fortran-based frameworks specifically tailored for modelling. Typically, this involved separating out the subroutines that held the model itself from those involved with support tasks (controlling the simulation, input of data and output of results). FSE, the Fortran Simulation Environment, (van Kraalingen, 1995) is a current example of this approach, which has been adopted by ICASA (the International Consortium for Agricultural Systems Applications) [see URLs] for in an initiative involving the re-implementation in a modular fashion of the DSSAT series of crop models. Over the last decade or so, there has been a significant shift in the nature of the modelling frameworks proposed or actually used. Two main types can be recognised. First, there has been widespread use of visual modelling environments, usually based on the System Dynamics paradigm (Forrester, 1971). System Dynamics is based on conceptualising a system in terms of compartments (stocks) and flows, and provides an intuitive way of modelling with differential or difference equations. Stella [see URLs]is the best known and probably most widely used visual modelling environment for System Dynamics: 3 special issues of the journal Ecological Modelling have been devoted to its use in ecology (Costanza et al, 1998; Costanza and Gottlieb, 1998; Costanza and Voinov, 2001). It is mainly used in an educational context, or for making relatively simple models, but van Noordwijk (1999) for example has used it for making a complex model (WANULCAS [see URLs]) of tree-crop-soil interactions involving several hundred equations. Although Stella has no explicit spatial-modelling capability, Maxwell and Costanza (1997) have linked Stella modelling to Geographic Information Systems (GIS) by using Stella to model a single patch, then using the exported equations to produce a program that is then coupled to the GIS. Other similar packages include Modelmaker, Powersim, Simile and Vensim [see URLs]. Simile was developed under the author's guidance, specifically as a proof-of-concept demonstrator for declarative modelling. For this reason, extensive reference will be made to it in this paper, particularly in Sections 7 and 8. The main characteristics of the use of visual modelling tools is that they tend to be used for wholesystems modelling: the model is essentially a flat structure, consisting of System Dynamics elements. Such modularity as there is typically for logically dividing a complex model up, rather than for building up a model from submodels.
11 3.2 Modular modelling, component-based approaches, and integrated modelling frameworks In contrast, the second major current type of modelling framework is based on some notion of modularity. For a number of commentators, the need for modularity in model structure is the defining requirement for a modelling framework (Reynolds and Acock 1997: special issue of Ecological Modelling). It paves the way for the re-use of model components, enables a model to be constructed by a simple process of linking modules together, and allows for 'plug-and-play' use of alternative modules for a particular component of the system. (From the early days of modelling, the program implementing a model has often been split into subroutines/functions/procedures, corresponding to separate submodels. However, this approach typically provided little support for submodel re-use, or plug-and-play modularity.) One way of handling modularity has been through the development of a modular modelling framework, in which modellers can select and link modules with no or little programming. A well-known example in the area of crop modelling is APSIM [see URLs] (McCown et al, 1996), which enables a cropping systems model to be constructed by selecting submodels for (e.g.) crop growth, soil nutrient dynamics and management to be plugged into a central simulation 'engine'. Another approach for achieving modularity has been through the implementation of models in an objectoriented programming language, such as C++. Indeed, all the papers in the above-cited special issue of Ecological Modelling describe systems of this type. The approach has other claimed benefits for ecological modelling, including the analogy between composition and inheritance hierarchies in nature (a sheep has legs; a sheep is a type of mammal), and analogous constructs in object-oriented software design. However, by far the most significant development in modular-based modelling in the last few years has been the adoption of a component-based approach as the way to organise the modelling effort within an organisation or in a large-scale project. A component is an independent software object that can be linked with other components. Its internal structure is hidden. It communicates with other components through the exchange of information across one or more interfaces. Table 3.1 lists a number of such activities; there are doubtless numerous others. In addition, a more detailed description of two component-based frameworks - MODCOM and IMA - is given in Appendix 1. A component-based approach can be applied at two levels: • It can be applied at the level of decision-support systems (DSS), with individual parts of the system (the model, the user interface, the data management, the GIS, the results analysis) being separate components. Potter et al (2000) consider the design issues and choice of component-based technology for such systems in the context of forest DSS. In this view, the model is itself one component. This will not be considered further in this document: there is little to argue about, since a DSS is a large software product, and it is clear that component-based approaches offer a sound engineering approach to the development of such products. • It can be applied at the level of the parts of a model, each part (submodel) being conceptualised as a component. For example, a cropping systems model might include 3 components: a crop growth model, a soil water model, and an insect pest model. It is this use of the term 'component-based approach' that interests us here. A key characteristic of many such initiatives is the adoption of Microsoft's COM technology. COM (the Component Object Model) enables interoperability between modules developed in a wide variety of languages. Some use DCOM, which is a distributed version of COM (for operating over the internet). The principal alternative to COM is CORBA (the Common Object Request Broker Architecture), which works under both MS Windows and Unix operating systems, but is much less widely adopted. Raj (1998) and Chung et al (1997) provide an excellent technical comparison of the two technologies. There are a couple of issues to consider about component-based approaches:
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