A Powerful New Tool in the Suite of Visual MODFLOW Software Facilitates a Conceptual Approach to Ground Water Modeling

For a ground water model to be accurate, reliable, and robust, it requires a tremendous amount of information and understanding of the aquifer. The first step in developing a ground water model, and perhaps the most important, involves the design of a conceptual model. Developing a good conceptual model requires compiling detailed information on the geological formations, ground water flow directions, recharge, rivers, hydraulic parameters, extraction or injection from wells, and the ground water quality.

Today’s ground water modeler has at his/her disposal, a variety of tools and data sources for designing a conceptual model. The challenge is bringing together this data, into one common application. A revolutionary new tool encourages a conceptual approach to ground water modeling.  The modeler loads the raw conceptual data (wells, surfaces, cross-sections, lines, polygons, XYZ points, maps, etc.), and conceptualizes the geological structure, properties, and boundary conditions, independent of any particular numerical simulator. Once complete, the modeler selects the “right simulator for the job”, then generates the input for the appropriate numerical model, whether it be finite difference, finite element model, or even analytical models, etc.  Since the conceptual data remains in one location, it is a simple task to generate or update multiple numerical models, of different types. Using a conceptual model also allows for generating a variety of numerical discretizations from the same source, such as a variety of finite difference grids (deformed, uniform, or a combination), or finite element mesh.

A case study using a conceptual model was built for a landfill site in Ontario, Canada. By following this approach, it was possible to quickly and easily generate several numerical models from the same conceptual model. The most suitable numerical model was selected, then simulated using the USGS MODFLOW code. The result was improved quality, credibility, and efficiency of the modeling.