Time‐Varying Hydroclimatic and Oceanic Controls on Arctic Submarine Groundwater Discharge Inferred Using Explainable AI

Abstract Submarine groundwater discharge (SGD), an important component of coastal water and nutrient budgets, is challenging to monitor and predict in the Arctic given the remoteness and harsh conditions. Here, we used explainable artificial intelligence to quantify the time‐varying importance of hydroclimatic and oceanic drivers of SGD at an Arctic beach from the early thaw period to late summer. Deep learning models were trained on in situ observations and reanalysis data, and feature contributions to model prediction were quantified using SHAP (Shapley Additive Explanations). We learned the following: potential evaporation is the dominant control on groundwater storage and SGD over seasonal scales; winds regulate short‐term groundwater levels and intermediate‐term discharge; soil temperature modulates groundwater storage via thaw‐driven processes; and precipitation is important for short‐term groundwater flushing and seasonal‐scale storage. These insights on the scale‐dependent SGD controls establish a framework for hindcasting and forecasting groundwater dynamics in remote Arctic environments.