U.S. Department of Energy Office of Biological and Environmental Research

BER Research Highlights

Improving Accuracy of Subsurface Flow and Transport Models
Published: October 16, 2017
Posted: April 18, 2018

New approach to quantify uncertainty in large scale models helps researchers predict fluid flow through porous subsurface media at more detailed scale.

The Science
Researchers improved the predictive capabilities of subsurface flow models by developing new, more efficient equations that account for length scales at which predictions are made and the hydrological measurements that are made in the field.

The Impact
To protect humans and the environment, there is a need to accurately predict the fate and transport of contaminants in the groundwater and subsurface sediments. Armed with more accurate predictive models, land stewards and managers can take appropriate actions to isolate and remove contaminants.

What scientists know about complex natural systems is inherently uncertain mainly because of very incomplete knowledge of the structure and function of the subsurface environment. Depending on the amount and type of available data, uncertainty in predictions can be so large that it makes them useless. For this reason uncertainty quantification is now an essential part of predictive modeling. A group of scientists from the Pacific Northwest National Laboratory has now proposed a new computational method that allows a researcher to identify the scale at which predictions can be made with an acceptable level of uncertainty, as defined by the researcher. At a given scale, this method can provide guidance regarding where and how many additional measurements are required to make predictions with that desired level of uncertainty.

Contacts (BER PM)
David Lesmes
Subsurface Biogeochemical Research
David.Lesmes@science.doe.gov, 301-903-2977

Paul Bayer
Subsurface Biogeochemical Research
Paul.Bayer@science.doe.gov, 301-903-5324

(PI Contact)
Alex Tartakovsky

This research was supported by the U.S. Department of Energy (DOE) Office of Advanced Scientific Computing Research (ASCR) as part of the Early Career Award ‘‘New Dimension Reduction Methods and Scalable Algorithms for Multiscale Nonlinear Phenomena' and by DOE's Office of Biological and Environmental Research (BER) through the PNNL Subsurface Biogeochemical Research Scientific Focus Area project. Funding also came from the Italian Ministry of Education, Universities and Research (MUIR); from the Water Joint Programming Initiative's WaterWorks 2014 project WE-NEED:WatEr NEEDs availability, quality and sustainability; and from the European Union's Horizon 2020 Research and Innovation programme.‘‘Furthering the Knowledge Base for Reducing the Environmental Footprint of Shale Gas Development' (FRACRISK).

Tartakovsky, A. M., M. Panzeri, G.D.Tartakovsky and A. Guadagnini. "Uncertainty Quantification in Scale-Dependent Models of Flow in Porous Media." Water Resources Research, 53, 9392-9401 (2017). [DOI:10.1002/2017WR020905]

Topic Areas:

  • Cross-Cutting: Early Career

Division: SC-23.1 Climate and Environmental Sciences Division, BER


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