The PEST course Application & Theory (September 2025)

Description

This five-day course, led by PEST author John Doherty and supported in practical sessions by Francesca Lotti, explores the theory and application of modern inversion and uncertainty analysis in groundwater modeling. The course integrates conceptual understanding with hands-on experience using ModelMuse and specialized tools from the PEST/PEST++ software suites.

Participants will learn how to translate theoretical concepts into practical workflows, especially in areas not fully supported by standard modeling interfaces. The course emphasizes innovative approaches to model calibration and uncertainty analysis, using utility software that enhances modeling capabilities beyond traditional platforms.
A core focus is the assignment of site-specific parameters in models designed to inform high-stakes decisions. This component highlights the importance of selecting appropriate levels of structural and parameter complexity, which can greatly influence model reliability and outcomes.

Among the course’s most engaging topics is the use of non-stationary geostatistics. These methods allow for flexible model parameterization across both structured and unstructured grids, providing a more realistic representation of geological variability.
Another advanced technique covered is Data Space Inversion (DSI). This method facilitates data assimilation and predictive uncertainty assessment without the need to adjust model parameters directly. By doing so, it significantly speeds up the development and deployment of sophisticated numerical models, enhancing their utility for operational and regulatory decision-making.

Course Programme

Trainers

John Doherty

Francesca Lotti