Multiscale hydrogeologic-biogeochemical process monitoring and prediction framework. Final Report
In the 21st century society will need to address and resolve a large number of challenges related to the management and sustainable use of terrestrial ecosystems. These challenges exist across multiple domains including agriculture, water resource management and site remediation. These challenges are varied in nature and include for instance in situ remediation of contaminated site, optimal application of fertilizer and effective ground water management. For all of these challenges near real time actionable and affordable information on quantity, flux and behavior of nutrients, energy, contaminants and water is required This information will need to be based on a physics based understanding of the behavior of terrestrial ecosystems. Such understanding is not trivial as this behavior is driven and controlled by the interplay of physical, chemical, and biological processes (with both natural and anthropogenic origins). For example Variations in rainfall, groundwater and surface water levels coupled with subsurface heterogeneity, topography, and microclimates drive groundwater flow direction and rate, and are important for water resource management; Geochemical parameter variability in soil and sediment lead to redox and pH gradients and differential moisture, vegetation and microbial activity, which in turn impact contaminant mobility/degradation, agricultural productivity, carbon biosequestration, and other ecosystem services, and Injection of carbon rich amendments results in increased biological activity and associated breakdown or sequestration of contaminants. These processes occur at multiple temporal and spatial scales and are intimately coupled. The resulting multi-scale, multi process environmental complexity is both an essential attribute of subsurface environments and a major complication in developing an understanding of such environments. While scientific studies are more and more able to combine complex data and models to provide such an understanding after in depth data analysis, in order to be useful for operational efforts, such as contaminated site remediation, such an understanding should be both forward looking or predictive (to the extent possible given the uncertainty associated with prediction), actionable and near real time. It should be analogous to the kind of understanding that one currently has of weather and tides: a forward looking understanding - with associated well articulated uncertainties - which is derived from the assimilation of data and models and which provides sufficient information about likely future behavior that stakeholders can use for planning and decision-making. Such a forward looking understanding will require real time or near real time subsurface and environmental data, something which is increasingly common at many sites. However, data availability (while a required condition) is not sufficient: one also needs the ability to assimilate this data into models which can provide this understanding. Performing this assimilation of data and models can be achieved at highly instrumented field sites which have large research budgets and dedicated scientific staff. However, up to now no tools have been available which can perform this assimilation semi automatically in near real time. This was the opportunity that motivates the development of predictive assimilation framework (PAF) for site monitoring and understanding. This framework (which can discover, ingest and manage and process data) was developed and demonstrated for a range of sites and conditions.
- Research Organization:
- Subsurface Insights
- Sponsoring Organization:
- Office of Science (SC), Biological and Environmental Research (BER). Earth and Environmental Systems Science Division
- DOE Contract Number:
- SC0009732
- OSTI ID:
- 1501753
- Type / Phase:
- STTR (Phase IIB)
- Report Number(s):
- SSI 2019-01; 6034432202
- Country of Publication:
- United States
- Language:
- English
Similar Records
Reactive transport benchmarks for subsurface environmental simulation
Earth Sciences Division Research Summaries 2006-2007