Process Interactions Can Change Process Ranking in a Coupled Complex System Under Process Model and Parametric Uncertainty.

For a complex hydrologic system with multiple processes and process interactions, global sensitivity analysis is often used to identify important or influential parameters for model development and improvement. The identification is complicated by process model uncertainty, when a system process can be represented by multiple process models. This study develops a new total‐effect process sensitivity index to identify influential processes under model uncertainty. This is done by extending Sobol's total‐effect parameter sensitivity index for one system model to total‐effect process sensitivity index for multiple system models to account for uncertainty in process models and model parameters. The total‐effect process sensitivity index includes not only the first‐order process sensitivity index for measuring the importance of individual processes but also higher‐order indices that account for process interactions. The total‐effect process sensitivity index can identify an influential process that itself and its interactions with other processes influence a model output. The total‐effect process sensitivity index is applied to two numerical examples: (a) Sobol's G*‐functions with analytical solutions of first‐order and total‐effect process sensitivity indices, and (b) groundwater flow models with interactions between recharge, geology, and snowmelt processes. The second evaluation shows that, due to second‐order and higher‐order process interactions, the first‐order and total‐effect process sensitivity indices give different process ranking. It is thus necessary to estimate both first‐order and total‐effect process sensitivity indices to appreciate the difference between the first‐order impact of a process alone and the overall total‐effect impact of the process itself and its interactions with other processes on a model output. Plain Language Summary: When studying a complex hydrologic system, it is necessary to identify non‐influential processes of the system so that limited resources are not spent on improving our understanding of these processes. On the other hand, it is important to identify influential processes of the system so that limited resources can be efficiently spent on better understanding the influential processes. Identification of the influential and non‐influential processes is difficult when a process can be represented by several plausible process models because it is not always clear which process model to choose. To resolve this issue, we develop a new total‐effect process sensitivity index that considers all the plausible process models without choosing one model and discarding other models. This is done by integrating the model averaging method with the Sobol's total‐effect parameter sensitivity index. We use two numerical examples to verify computer codes and to demonstrate how to use the index to identify influential and non‐influential processes. Applied to groundwater flow modeling, our new index demonstrates that accounting for interactions between recharge, geology, and snowmelt processes gives a ranking of process influence that is different from the ranking of process importance based on the first‐order process sensitivity index. Key Points: A new total‐effect process sensitivity index is derived to account for process interactions under process model and parameter uncertaintyThe total‐effect process sensitivity index has a first‐order term for process importance and higher‐order terms for process interactionsAccounting for process interactions allows for identifying influential system processes and/or screening non‐influential system processes [ABSTRACT FROM AUTHOR]