PEST++ V5 includes a wide range of tools for decision support modelling including global sensitivity analysis, data assimilation, uncertainty analysis, and management optimisation under uncertainty, with several new tools being actively developed to support increasingly diverse decision support needs. All of the PEST++ tools use the standard PEST interface and include a baked-in fault-tolerant parallel run manager. Additionally, the PEST++ tools all “play nice” with pyEMU, a python module for all-things PEST and PEST++. In this webinar, we will demonstrate how to programmatically construct a high-dimensional PEST interface around a MODFLOW-6 model using pyEMU. Then, using this model and PEST interface, we will show how to run the gamut of PEST++ tools without requiring changes to model or the PEST interface. In this way, we demonstrate how elements of reproducibility and improved efficiency can be brought to bear on increasingly complex environmental modelling workflows needed to support robust and risk-based decision making.
Jeremy White is co-author of PEST++ and of the pyEMU suite. Jeremy completed his B.S. in computer science at West Texas A&M, and M.S. and Ph.D. in geology at the University of South Florida. Dr. White has extensive experience with parameter estimation and uncertainty quantification in environmental and geophysical models, including those which simulate interactions of surface water with groundwater, and density-dependent flow of groundwater. Jeremy also has extensive experience using high-performance computing systems to solve massively and embarrassingly parallel problems in innovative ways in support of environmental management.
Mike Fienen is a Research Hydrologist at the USGS Wisconsin Water Science Center, an Assistant Adjunct Professor in the Department of Geoscience at the University of Wisconsin-Madison, and a member of the PhD Advisor Committee in the Civil and Environmental Engineering Department at the University of Parma, Italy (DICATeA – Dipartimento di Ingegneria Civile, dell’Ambiente e Territorio e Architettura – Università degli Studi di Parma).
Mikes research mission is to provide decision-making support for environmental managers that considers uncertainty in all aspects of decisions and strives to extract the most information from the data. This mission is expressed through the main research threads of model calibration and inference of environmental systems. Specific applications include groundwater quantity and quality; statistical inference and prediction of recreational water quality on beaches; mercury in water and fish; and the groundwater and habitat impacts of sea-level rise. In support of these threads, aspects of computational efficiency, statistical analysis, and data management also play important roles.