Adding Prediction Uncertainty To An Online Groundwater Modeling Tool To Support Stakeholder Decision-Making
Presenter: Benjamin Mitchell1
Co-Author(s): -
Advisor(s): Dr. P.A. Ty Ferré
1Department of Hydrology and Atmospheric Sciences, University of Arizona
Panapto Presentation Video
Poster PDF
Poster Session 2
Uncertainty is introduced in scientific analyses through the innate structure of our analyses and limits to field data collection. When creating future predictions about the responses of natural systems to changing conditions, these uncertainties translate into potential prediction errors, which can affect the quality of stakeholders’ decisions. Groundwater modeling is no different. Scientific experts create models based on the abstraction and calibration to field data. Typically, these models are provided to stakeholders with model predictions with little ability for the stakeholders to use the models themselves to explore other possible solutions. Olsson has developed the Groundwater Evaluation Toolbox (GET) as an online platform to allow non-experts to use existing, calibrated models interactively, opening up interesting opportunities for multiple stakeholders to make more informed water resources decisions. While this tool gives unprecedented access to groundwater models, it does not include any consideration of prediction uncertainty. The aim of this study is to expand GET to include prediction uncertainty so that it is available for stakeholder consideration. Initially, this will be limited to models that have already been calibrated using the uncertainty capabilities of the Parameter ESTimation (PEST) software. Through Olsson, we will interface with stakeholders who have already used GET to assess the added value of including prediction uncertainties.