Assessing Sources of Uncertainty in Predictions from a Reactive Transport Model
Researchers used Bayesian networks to develop a new method for measuring and ranking which components contribute the most uncertainty to outputs from a reactive transport model.
A multi-institutional team of scientists developed a new sensitivity analysis framework using Bayesian networks to quantify which parameters and processes in complex multiphysics models are least understood. The method can guide continued development and refinement of predictive models of environmental systems by highlighting which components of complex systems require enhanced characterization data to reduce uncertainty.
Sensitivity analysis is a numerical tool used to identify important parameters and processes that contribute to the overall uncertainty in model outputs. This new research applies a Bayesian network approach to sensitivity analysis frameworks. This approach increases the flexibility and power of the sensitivity analysis by quantifying the uncertainty contribution from a variety of controlling factors and ranking them; the results can better inform decisions on where to focus resources to improve the predictive capability of various multiphysics models.
Numerical modeling is an important tool for predicting the future behavior of complex systems that impact the environment and for managing natural resources. For example, Pacific Northwest National Laboratory researchers are developing numerical models to study the factors that control the exchange of river and groundwater in the Hanford Reach, the last free-flowing stretch of the Columbia River that defines the north and east boundaries of the Department of Energy’s Hanford Site.
Predictive uncertainty is inevitable in numerical models of systems such as the Hanford Reach because of the complex hydrological and biogeochemical properties of the natural system and limited site characterization data. To effectively and efficiently reduce predictive uncertainty with limited resources, researchers perform sensitivity analysis to rank the importance of different uncertainty sources that contribute to overall uncertainty in model predictions.
Current state-of-the-art sensitivity analysis frameworks are unable to describe the entire range of uncertainty sources involved in predictive models of complex systems. The integration of Bayesian network-based methods into these frameworks allows the full representation of uncertainty sources and the relationships between them, opening the door to performing sensitivity analysis on complex systems. For example, the networks allow researchers to computationally and graphically understand how uncertainty in one node of the network, or group of nodes, propagates through a network and impacts a model’s overall predictive uncertainty.
The authors implemented their Bayesian network–based method on a real-world biogeochemical model of the groundwater–surface water interface within the Hanford Site’s 300 Area. They used the framework to run model simulations to predict how factors such as variation in river stage under future climate scenarios and the release or damming of water in upstream hydroelectric dams would contribute to variations in groundwater–surface water exchange and impact biogeochemical processes that affect the rate of organic carbon consumption.
The team found that groundwater flow and reactive transport processes contribute most significantly to the predictive uncertainty in carbon consumption rate, and that future states of the climate, which defines the system’s driving forces, were less significant. Further analysis of the uncertainty contributed by groundwater flow processes revealed that the geological structural information, such as the thickness of the confining layer between the river and groundwater, was more important than the within-formation permeability field in controlling the flow processes.
The Bayesian network–based methodology in this research was implemented on a complex biogeochemical model of the Hanford Site 300 area, but it is mathematically rigorous and generally applicable for reducing uncertainty in a wide range of Earth system models.
Pacific Northwest National Laboratory
Funding for this research came from the Office of Biological and Environmental Research (BER), within the U.S. Department of Energy (DOE) Office of Science, Subsurface Biogeochemical Research SFA at Pacific Northwest National Laboratory.
Dai, H., Chen, X., Ye, M., Song, X., Hammond, G., Hu, B., & Zachara, J.M. “Using Bayesian networks for sensitivity analysis of complex biogeochemical models.” Water Resources Research 55(4), 3541–55 (2019). DOI:10.1029/2018WR023589