PEST: Model-Independent Parameter Estimation and Uncertainty Analysis
PEST, the software package, automates highly-parameterized calibration, and calibration-constrained uncertainty analysis of any numerical model. It interacts with a model through the model’s own input and output files. While estimating or adjusting its parameters, it runs a model many times. These model runs can be conducted either in serial or in parallel. Advanced dimensional-reduction methods can dramatically reduce the number of model runs required for attainment of a high level of model-to-measurement fit.
PEST-support software suites perform a plethora of tasks that assist and complement model parameter estimation and uncertainty analysis. These include:
- setup facilitation;
- flexible spatial parameterization, including pilot points and structural overlay parameters;
- stochastic parameter field generation using stationary and non-stationary geostatistics;
- multi-component objective function definition;
- linear prior and posterior uncertainty analysis;
- nonlinear prior and posterior uncertainty analysis;
- data space inversion.
The principal tasks of decision-support environmental modeling are to harvest information from site data, and to illuminate the repurcussions of information insufficiency. This is not necessarily a straightforward matter where environmental systems are complex. A GMDSI monograph discusses the many pitfalls and how to avoid them.
Many thanks to ESI and SSPA for funding these pages.
Latest change: April 23, 2025 - updates of PEST_HP and Groundwater Utilities.