Using explainable artificial intelligence as a diagnostic tool

Recent applications have demonstrated the strength of deep learning (DL) in information extraction and prediction. However, its limitations in interpretability have delayed its popularity for use in facilitating advancement of hydrologic understanding. Here we present a framework using explainable artificial intelligence (XAI) as a diagnostic tool to investigate distributed soil moisture dynamics within a watershed. Soil moisture and its movement generated by physically based hydrologic model were used to train a long short-term memory (LSTM) network, whose feature attribution was then evaluated by XAI methods. The aggregated feature importance presents abrupt rise in the model’s nodes located in riparian area, indicating threshold behavior in runoff generation and development of hydrologic connectivity at the watershed scale, which helps explain the rapid increase in streamflow. This work represents a demonstration of the potential of XAI to uncover underlying physical mechanisms and to help develop new theories from observed data.