Fiona Adamson

So we’ve ticked the uncertainty box. What happens next?

Leave a Comment / Blog, Webinars /

Date: 9th May 2023 (Recorded – see button below) John Doherty, Jeremy White and Catherine Moore presented issues that occupy the boundaries between uncertainty analysis and decision-making/policy-formulation. They discussed some of the problems that beset the making of decisions in an uncertain world. The talks are non-technical; the issues are important. They included: Uncertainties in […]

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Demystifying uncertainty and its policy repercussions

Leave a Comment / Webinars /

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” of groundwater modelling, and overcoming it through risk-orientated uncertainty analysis’ at the recent Australian Groundwater Conference. It was highly acclaimed. So we asked him to make his talk a little

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Data Space Inversion

Leave a Comment / Featured, Tutorials, Uncertainty Analysis /

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 fields. The model run burden is extremely low, and unrelated to the complexity of the complex model’s construction or parameterisation. There is considerable overlap between this tutorial and the “Four

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Four Ways to Explore Model Predictive Uncertainty

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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 linearised posterior covariance matrix Iterative ensemble smoother Data space inversion In doing this tutorial, you get to use the following programs: PEST PEST_HP PESTPP-IES DSI1 Other members of the PEST

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SEGLISTS: Interpolation along linear features

Leave a Comment / Featured, Parameterisation, Tutorials /

This tutorial demonstrates several options for spatial parameterization of linear and polylinear features. In a groundwater model, these may represent entities such as streams, rivers, faults, or fracture networks. The hydraulic properties of these features may vary along their lengths. If this variability is relevant for a prediction of interest, then its representation in a

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PLPROC: a simple pilot point example

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PLPROC allows a modeller to create and manipulate parameters that inform hydraulic properties that are represented in a numerical model. In doing this, PLPROC supports PEST in facilitating model-based decision-support which enshrines the principle that a model should encapsulate what we know and quantify what we do not. This tutorial offers a gentle introduction to

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GMDSI is managed by the National Centre for Groundwater Research and Training (NCGRT) and administered by Flinders University.

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