Paper No. 4
Presentation Time: 9:45 AM


VESSELINOV, Velimir, Los Alamos National Laboratory, Computational Earth Science, MS T003, Los Alamos, NM 87545,

The uncertainties associated with subsurface flow and transport processes and their parameters are notoriously difficult to observe, measure, and characterize, causing poorly constrained and non-unique decision-analysis results. The subsurface uncertainties are heavily influenced by uncertainties related to properties of the geologic media where the groundwater flow and contaminant transport occur. Due to limited site data about the properties of the geologic media, decision analyses typically rely on model predictions, which are often poorly constrained as well. Currently, various techniques are commonly applied for data- and model-based decision analyses related groundwater contaminant sites. We will demonstrate the application of alternative techniques for evaluation of uncertainties associated with decision analyses. The techniques are applied to solve synthetic and real-world problems related to environmental management at the Los Alamos National Laboratory (LANL) site. The work utilizes the code MADS (Model Analysis & Decision Support; MADS is an open-source code designed as an integrated high-performance computational framework performing a wide range of model-based analyses for decision support. MADS includes a wide range model-analyses tools based on well established and newly developed computational techniques.