Predictive assimilation framework to support contaminated site understanding and remediation

Abstract:

Subsurface system behavior at contaminated sites is driven and controlled by the interplay of physical, chemical, and biological processes occurring at multiple temporal and spatial scales. Effective remediation and monitoring planning requires an understanding of this complexity that is current, predictive (with some level of confidence) and actionable. We present and demonstrate a predictive assimilation framework (PAF). This framework automatically ingests, quality controls and stores near real-time environmental data and processes these data using different inversion and modeling codes to provide information on the current state and evolution of the subsurface system. PAF is implemented as a cloud based software application which has five components: (1) data acquisition, (2) data management, (3) data assimilation and processing, (4) visualization and result deliver and (5) orchestration. Access to and interaction with PAF is done through a standard browser. PAF is designed to be modular so that it can ingest and process different data streams dependent on the site. We will present an implementation of PAF which uses data from a highly instrumented site (the DOE Rifle Subsurface Biogeochemistry Field Observatory in Rifle, Colorado) for which PAF automatically ingests hydrological data and forward models groundwater flow in the saturated zone.