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Evaluating Loose & Tight Integration of a Water Quality Model With GIS

Author describes a comparative study of the integration of GIS with a water quality model & the model as a stand-alone application. Paper by Chitra Gowda & Rajesh Seth

The use of geographical information systems (GIS) in enhancing the predictive capabilities of environmental software models as well as being a decision support tool is well known and documented. Spatially distributed parameter models may be coupled with GIS to enable visual assessments and such integration benefits decision makers. GIS allows the user to analyse and display spatially referenced data, and provides an interactive environment to construct models and to make models easier to use (Goodchild et al., 1996) and aid in modeling complex resource and environmental systems (Rolando et al., 1995). The strategy for integrating environmental models with GIS can range from loose coupling through the exchange of data files to tight coupling where model algorithms are embedded within the GIS using programming languages (Maidment, 1993; Tim, 1996) or GIS functionality is built within models (Karimi, 1995). Literature reports that a tight coupling between the model engine and GIS, depicted in Figure 1, results in a powerful up-to-date tool that facilitates better data storage, manipulation and analysis than conventional methods (Yoon, 1998).

However, some researchers prefer a loose coupling in which GIS would act as a data analyzer, pre and post model run, as shown in Figure 2, as it allows for flexibility in modelling and increased options in the use of GIS. In a loose coupling, data generated from the environmental model is analyzed and reformatted for display by the GIS (Crawford et al., 1998). In order to efficiently couple models with GIS, the desired aims of the using the model must be known prior to the integration, such as required prediction scenarios. Understanding how GIS can enhance modeling is a necessary step towards a meaningful integration.

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