Understanding and Predicting
The baseline information collected during the assessment phase can be used to develop conceptual and predictive models for groundwater and surface water systems and their interaction.
Conceptual Models describe the essential function and behaviour of surface water and groundwater systems in a catchment. A conceptual model summarises the current understanding of the processes, dependencies and impacts on the water resource. They can be used to guide further field investigations and data assessment.
Predictive Models are essentially mathematical models that contain equations that represent the physical processes of water movement in a catchment. These are developed using the conceptual model as a foundation. The value of constructing a predictive model is that it can be used as a tool to quantify the likely impact of different conjunctive water management options.
Some outcomes of this conceptualisation and predictive modeling process include:
- classification of stream-aquifer linkages, and indication of the potential impacts on water quantity, quality or beneficial use;
- depiction of the nature and geometry of groundwater flow systems and their interaction with surface water features. Models can test the existing hydrogeological understanding for a catchment, and are also useful as a visualisation and communications tool;
- quantification of the catchment water balance and how it changes through time. The magnitude and dynamics of seepage flux is placed in context with other water balance components (such as rainfall and evapotranspiration);
- predictions in terms of how seepage flux may change based on proposed changes to catchment condition (such as climate change, increased groundwater extraction);
- estimates of the likely impact of implementing different options of conjunctive water management. Models can be used as an optimising tool in the design of both policy and on-ground management options; and
- identification of key information gaps that need to be addressed. This includes planning of further field investigations or monitoring to quantify connectivity. Models can also be used in the analysis of data sensitivity and uncertainty to help define the priority datasets.