A STREAMLINED METHODOLOGY FOR DEVELOPMENT AND CALIBRATION OF GROUNDWATER FLOW MODELS (Invited Presentation)

Groundwater flow modeling is one of the most useful tools available to hydrogeologists, providing quantification of the flow system and scenario testing such as varying pumping well rates or recharge associated with climate change. However, this tool is often viewed as too complicated and cumbersome for most applications, and there is an expectation that tremendous effort is needed to construct and then calibrate a flow model. Thus, the saying “The model is only completed when the budget runs out.” To address this situation, we have developed a methodology that tightly couples GIS with the modeling software to ease model construction and calibration. We applied this methodology to a flow model developed for a PFAS contaminated site in northern Wisconsin. The model was developed to determine contributing zones to nearby municipal wells.

We start with available GIS data such as well construction reports, hydrography, and geologic maps. Using GIS software such as ArcGIS Pro or QGIS, we build the model. We use hydrography and topography to identify the model domain and set boundary conditions. The model base is developed using the well construction reports and other data. The model top is taken from a digital elevation model. We can then look at available data and make decisions on layering, internal boundary conditions, and cell sizes, if using finite difference software. These decisions are made in GIS where other information such as data density, air photos, and land use helps inform the choices. Once the grid and layering have been set, we can intersect them with hydrography and geology to set elevations of boundary conditions and heterogeneity. This use of GIS also allows us to sort and weight calibration targets since we can more easily place them in context.

We found that by coupling GIS and modeling software we have a framework for the development of flow models that can incorporate all the available data and allows a better understanding of model calibration. The result was less structural model error and a model that can incorporate the additional data necessary to achieve current and future modeling objectives. In our next steps, we plan to incorporate FloPy scripting and contaminant transport into this coupled GIS/modeling methodology.