New approach to quantify uncertainty in large scale models helps researchers predict fluid flow through porous subsurface media at more detailed scale.
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.
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)
Subsurface Biogeochemical Research
Subsurface Biogeochemical Research
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]
SC-23.1 Climate and Environmental Sciences Division, BER
BER supports basic research and scientific user facilities to advance DOE missions in energy and environment. More about BER
May 10, 2019
Quantifying Decision Uncertainty in Water Management via a Coupled Agent-Based Model
Considering risk perception can improve the representation of human decision-making processes in age [more...]
May 09, 2019
Projecting Global Urban Area Growth Through 2100 Based on Historical Time Series Data and Future Scenarios
Study provides country-specific urban area growth models and the first dataset on country-level urba [more...]
May 05, 2019
Calibrating Building Energy Demand Models to Refine Long-Term Energy Planning
A new, flexible calibration approach improved model accuracy in capturing year-to-year changes in bu [more...]
May 03, 2019
Calibration and Uncertainty Analysis of Demeter for Better Downscaling of Global Land Use and Land Cover Projections
Researchers improved the Demeter model’s performance by calibrating key parameters and establi [more...]
Apr 22, 2019
Representation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles
Representation of warm temperature events varies considerably among global climate models, which has [more...]
List all highlights (possible long download time)