During the planning step we will work together to set expectations and agree on various aspects of the model and the developmental process. A key component of the planning process is to identify the decisions that the modelling needs to support and the most important predictions that need to be made. Agreed modelling objectives and the intended use of the model will be documented and guide further stages of the modelling project.
At this point our team will present you with a pathway to the best possible solution for your needs.
In this step we will undertake a review of all available site-specific data and information to identify and describe the processes that control or influence the movement and storage of groundwater and solutes in the hydrogeological system. The physical processes, hydrologic and geologic features that are deemed to be most important to the predictions of interest will considered in the development of a conceptual model of groundwater flow. The dominant sources of uncertainty in model predictions will be qualitatively identified and project planning revised to pursue to goal of reducing uncertainty in important predictions and decisions. The conceptual model(s) will form the basis for further numerical modelling.
Construction is the step when selection of the numerical method, modelling software, and appropriate model dimension occurs. The model domain and the spatial and temporal discretisation to be used in the model is defined. This process is based on the conceptual model and simplifications are guided by the predictions necessary for decision support and available groundwater observations that can reduce uncertainty in predictions through calibration.
Uncertainty analysis is a critical step in the modelling process because all model predictions are uncertain as they are simplifications of reality. Predictive uncertainty is considered throughout all stages of modelling with the goal of reducing the uncertainty in model-based decisions. Model predictive uncertainty is considered through predictive simulations based on multiple alternative model parameter sets and potentially, alternative conceptual models.
Modelling and uncertainty analysis prior to data collection can be used to optimize field programs to collect the best dataset for reducing the uncertainty in key predictions.
Calibration involves an iterative process to estimate parameters describing hydrogeological properties and boundary conditions so that the models results closely match historical observations.
For risk-based decision-making model calibration involves estimating a suite of alternative model parameter sets that all cause the models to adequately reproduce observations and allow for calibration constrained prediction uncertainty analysis. Through this process collection of observation data reduces uncertainty in key model predictions and improves confidence in model-based decisions.
Model reporting includes documentation and communication at different stages of the model through a written technical document. The report will describe the model, all data collected and information created through the modelling process and will be accompanied by an archive of all the model files and all supporting data so the results presented in the report can, if necessary, be reproduced and the model used in future studies.