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
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
Date: 9th May 2023 (Recorded – see button below) John Doherty, Jeremy White and Catherine Moore presented issues that occupy the boundaries between uncertainty analysis
Date: 28th February 2023 Webinar recording This webinar had two presenters. The first is Chris Li (from CDM Smith). Chris delivered a talk entitled ‘The “Cinderella Syndrome”
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
This tutorial explains four ways to explore the uncertainties of two predictions made by a relatively simple, fast-running model. These are: Linear analysis Sampling a
GMDSI presented three webinars delivered by John Doherty (author of PEST). Webinar 1: Wednesday 28th September – Video Recording and PowerPoint PresentationWebinar 2: Wednesday 12th October
We invite you to join us for an upcoming GMDSI Webinar to be held 11am-1pm (AEST) on Thursday August 18 on Zoom. Register for the
There has been a lot of discussion recently on groundwater modelling workflows – that is, how to build and history-match a model, and then use
intro_to_pyemu Intro to pyEMU¶ This notebook provides a quick run through some of the capabilities of pyemu for working with PEST(++). This run through is
GMDSI is managed by the National Centre for Groundwater Research and Training (NCGRT) and administered by Flinders University.