A new multi-model absolute difference-based sensitivity (MMADS) analysis method to screen non-influential processes under process model and parametric uncertainty

Journal Article · 18 February 2022 · Journal of Hydrology

DOI: https://doi.org/10.1016/j.jhydrol.2022.127609 · OSTI ID:1977304

Yang, Jing ^[1]; Ye, Ming ^[2]

---

1. Northwest A&F University, Shaanxi (China); Florida State Univ., Tallahassee, FL (United States); OSTI
2. Florida State Univ., Tallahassee, FL (United States)

Process-based models have been widely used for hydrologic modeling, and it is a common practice to use sensitivity analysis methods for excluding non-influential hydrologic processes from further investigation and/or model improvement. This study develops a new method called multi-model absolute difference-based sensitivity (MMADS) analysis method to screen non-influential system processes and parameters. MMADS is conceptually similar to the Morris method for addressing parametric uncertainty, but has a unique feature to address both process model uncertainty (i.e., a process may be represented by multiple process models) and process model parameter uncertainty (i.e., parameters associated with a process model are random). MMADS first evaluates absolute differences of a quantity of interest (i.e., a system model output) by varying process models and/or process model parameter values, and then calculates the mean and variance of the differences for investigating process influence. The mean measures overall influence of the process on the quantity of interest, and the variance estimates influence of nonlinear effects of the process and/or its interactions with other processes. MMADS is an extension of the Morris method from a parameter space to a joint parameter-model space for explicitly addressing both process model uncertainty and model parameter uncertainty. The performance of MMADS is evaluated by using two numerical experiments. One experiment is based on Sobol’s G*-function with ten product elements, and has analytical solutions of the MMADS mean and variance of absolute differences. The other experiment is for groundwater flow modeling which considers three processes (i.e., recharge, geology, and snowmelt) that interact with each other. Finally, results indicate that MMADS is computationally efficient and can identify non-influential processes of complex hydrological systems.

• Research Organization: Florida State Univ., Tallahassee, FL (United States)
• Sponsoring Organization: National Science Foundation (NSF); USDOE; USDOE Office of Science (SC)
• Grant/Contract Number: SC0019438
• OSTI ID: 1977304
• Journal Information: Journal of Hydrology, Journal Name: Journal of Hydrology Journal Issue: C Vol. 608; ISSN 0022-1694
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English