Groundwater level dynamics under freeze-thaw conditions remain unclear. We use interpretable deep learning to simulate water table changes and identify seasonal drivers in seasonally frozen regions. During freeze-thaw, changes in soil water potential cause two-way exchange between soil water and groundwater, while rainfall, runoff, and irrigation dominate in other periods. These insights inform groundwater modeling and management in cold regions.

Water balance data are affected by various errors (bias and noise). To reduce these errors, this study presents a water balance data fusion approach that combines multi-scale data (from satellites and in-situ sensors) for each water balance variable and jointly calibrates them, resulting in consistent, bias-corrected and noise-filtered, water balance estimates, along with uncertainty bands. These estimates are useful for constraining process-based models and informing water management decisions.

Our research presents an improved method to enhance the understanding and prediction of water flows in rivers and streams, focusing on key runoff components: surface flow, baseflow, and total runoff. Using a streamlined model, the MPS model, we analyzed over 600 catchments in China and the US, demonstrating its accuracy in capturing the spatial and temporal variability of these components. This model offers a practical tool for water resource management.