Identification of Key Factors Driving Dissolved Oxygen in Riparian Aquifers Through Deep Learning‐Assisted Global Sensitivity Analysis

Abstract We rely on a global sensitivity analysis (GSA) approach to identify the dominant physical and biogeochemical controls on dissolved oxygen (DO) dynamics in riparian aquifers. The study is motivated by the observation that availability of DO is key to regulating redox conditions and associated processes in the subsurface. Yet, the complexity of coupled flow and transport models, combined with model input uncertainty challenges our ability to fully characterize system behavior. To address this issue, we integrate Bayesian network‐based and variance‐based methods into a comprehensive GSA framework, enabling a robust evaluation of parameter and process sensitivities. To overcome the high computational…

• NN
  National Natural Science Foundation of China

  Awards: 42025703, 42422208