Ensemble space inversion (ENSI) enables efficient, regularisation-constrained calibration of complex, highly-parameterised models. This tutorial demonstrates how linear analysis can be undertaken in partnership with the
Ensemble space inversion (ENSI) is implemented through the PEST_HP suite (version 18). Using ENSI you can calibrate a complex model quickly. The calibration subspace is comprised
Building and history-matching a three-dimensional model is a difficult procedure. The third dimension increases parameter requirements, model run times, and model output uncertainty. Ideally, predictive
Description Where: EcoSciences Precinct, Dutton Park, Brisbane When: Monday 3rd June to Friday 7th June, 2024 Who should attend: Both new and experienced modellers will benefit from
This tutorial explores the use of “conceptual points” as a precursor to model parameterisation. Expected hydraulic properties are provided at these conceptual points. Just as
This tutorial explores construction of the interface between PEST/PEST++and a simple MODFLOW/MODPATH model, and how to then subject that model to history-matching and uncertainty analysis–including
View the webinar recording introducing the course here. This free guided self-study course and weekly Q & A sessions with the GMDSI team provided guidance and
GMDSI and the USGS have co-funded the development of a series of jupyter notebooks that use python scripting to demonstrate many aspects of applied decision-support
Decision-support groundwater modelling is as much an art as it is a science. Much of the “art” is in decomposing the management problem that modelling
Using the PLPROC parameter preprocessor supplied with the PEST suite, moveable polylinear and polygonal structural features such as faults and aquitard windows can be inserted
Construction of a decision support groundwater model requires that parameters be adjustable, stochastic and representative of geological conditions. This can be difficult to achieve in
This tutorial introduces data space inversion (DSI). DSI can be used to explore the uncertainties of predictions made by complex models with complicated hydraulic property
Predicting the future of any environmental system is fraught with uncertainty; nevertheless, decisions must be made and risks must be assessed. This is where decision
Led by the National Centre for Groundwater Research and Training (NCGRT) at Flinders University and in conjunction with the mining and groundwater industries, the Groundwater
GMDSI is managed by the National Centre for Groundwater Research and Training (NCGRT) and administered by Flinders University.