Using Bayesian Networks for Sensitivity Analysis of Complex Biogeochemical Models
[1];
[2];
[3];
[2];
[4];
[5];
[2]
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Jinan Univ., Guangzhou (China). Inst. of Groundwater and Earth Sciences
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Florida State Univ., Tallahassee, FL (United States)
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Jinan Univ., Guangzhou (China)
Sensitivity analysis is a vital tool in numerical modeling to identify important parameters and processes that contribute to the overall uncertainty in model outputs. We developed here a new sensitivity analysis method to quantify the relative importance of uncertain model processes that contain multiple uncertain parameters. The method is based on the concepts of Bayesian networks (BNs) to account for complex hierarchical uncertainty structure of a model system. We derived a new set of sensitivity indices using the methodology of variance-based global sensitivity analysis with the Bayesian inference. The framework is capable of representing the detailed uncertainty information of a complex model system using BNs and affords flexible grouping of different uncertain inputs given their characteristics and dependency structures. We have implemented the method on a real-world biogeochemical model at the groundwater-surface water interface within the Hanford Site's 300 Area. The uncertainty sources of the model were first grouped into forcing scenario and three different processes based on our understanding of the complex system. The sensitivity analysis results indicate that both the reactive transport and groundwater flow processes are important sources of uncertainty for carbon-consumption predictions. Within the groundwater flow process, the structure of geological formations is more important than the permeability heterogeneity within a given geological formation. Our new sensitivity analysis framework based on BNs offers substantial flexibility for investigating the importance of combinations of interacting uncertainty sources in a hierarchical order, and it is expected to be applicable to a wide range of multi-physics models for complex systems.
- Research Organization:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Florida State Univ., Tallahassee, FL (United States); Jinan Univ., Guangzhou (China)
- Sponsoring Organization:
- USDOE Office of Science (SC), Biological and Environmental Research (BER); PNNL Laboratory Directed Research and Development (LDRD) Program; National Science Foundation (NSF); National Natural Science Foundation of China (NSFC)
- Grant/Contract Number:
- SC0008272; SC0019438; AC05‐76RL01830; AC02‐05CH11231; NA0003525; AC05-76RL01830; AC02-05CH11231; 1552329; 41807182; SBR PNNL SFA
- OSTI ID:
- 1542940
- Alternate ID(s):
- OSTI ID: 1502972; OSTI ID: 1506160
- Report Number(s):
- SAND-2019-2924J
- Journal Information:
- Water Resources Research, Vol. 55, Issue 4; ISSN 0043-1397
- Publisher:
- American Geophysical Union (AGU)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
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