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