In contrast to linear uncertainty analysis, non-linear methods do not suffer from the limitation of assuming a linear relationship between model predictions and model parameters. Given that such relationships are rarely linear, this is an important consideration. Nevertheless, non-linear methods are not free of assumptions, can sometimes incur a higher implementation cost than linear methods, […]
Non-Linear Uncertainty Analysis Read More »
A variance-covariance matrix, often referred to as a covariance matrix, is a square matrix that provides covariances between pairs of elements of a random vector. A covariance matrix of model parameters describes the variance of each parameter and the covariance of that parameter with that of every other parameter. For cases in which parameters represent points
Covariance Matrices: The PPCOV Suite Read More »
The present tutorial addresses the ability (or otherwise) of yet-ungathered data to reduce the uncertainties of decision-critical predictions using linear analysis utilities from the PEST suite. Data worth analysis is applied to a model which was calibrated in another GMDSI tutorial; this model was subjected to a suite of common linear analysis tasks in yet
Data Worth Analysis Read More »
Linear uncertainty analysis is also known as “first order second moment” (or “FOSM”) analysis. It provides approximate mathematical characterisation of prior predictive probability distributions, and of posterior parameter and predictive probability distributions. It has other uses as well. It can be used to demonstrate how the history-matching process bestows worth on data. It can also
Linear Uncertainty Analysis Read More »
This is the first in a series of tutorials which demonstrate workflows for parameter estimation and uncertainty analysis with the PEST/PEST++ suites. These are not the only (or necessarily the best) workflows; their purpose is to take the reader through the fundamentals of how to accomplish common tasks whilst also providing insights in how to apply the
Calibration – A Simple Model Read More »
https://vimeo.com/566929903 by Catherine More and John Doherty Because environmental systems are complex, models must be complex too. Otherwise, how can they simulate the impact of human management on natural systems? This applies especially to groundwater systems. After all, water flows through heterogeneous media and interacts with surface water bodies in complex ways. This line of
How Complex Should a Groundwater Model Be? Read More »
https://vimeo.com/548316380 by Catherine Moore and John Doherty We have talked about uncertainty before – in last year’s webinar series. We have been asked to talk about it some more. Our target audience for this webinar are those who are not necessarily modellers but who must work with modellers, and who must assess the credibility of
Uncertainty in Groundwater Modelling 101 Read More »
OLPROC is a model dancing partner. Its role is to postprocess model outputs in order to match them with field measurements, as well as to assist in automatic creation of PEST input datasets involving complex, multi-component objective functions. Starting from a set of field measurement data and model output files, this tutorial demonstrates how OLPROC
OLPROC: Processing Observations Made Easy Read More »
Simultaneous fitting of drawdowns induced by pumping at multiple sites in an area of complex geology revealed important patterns of connected permeability Over a six month period in July 2014, BHP conducted six pumping tests of between 5 and 11 days duration near the site of its OB31 pit in the Pilbara region of Western
Worked Example: Simultaneous Interpretation of Six Pumping Tests Read More »
GMDSI is managed by the National Centre for Groundwater Research and Training (NCGRT) and administered by Flinders University.
