Linking Cryo–Hydrological Mass Redistribution to Regional Sea Level Change through Hybrid Physics–AI Modelling

Regional sea level change is driven by multiple physical processes, resulting in complex dynamics and pronounced spatio–temporal heterogeneity. This study proposes a hybrid framework that integrates the physical fingerprints with deep learning to achieve both sea level budget closure and temporal prediction of regional sea level variations. Total sea level changes are firstly decomposed into the steric and barystatic components. By further considering the mass redistribution of ice sheets, glaciers, and terrestrial water storage and their associated sea level fingerprints, the cryo–hydrological contribution (CHC) sea level, is introduced to replace the traditional barystatic term. This substitutes direct observations of local mass change with the sea level response to mass redistributions occurring elsewhere, thereby enhancing the physical interpretability of the decomposition. Subsequently, a convolutional neural network and bidirectional long short-term memory hybrid model is employed to jointly predict the total, steric, barystatic, and CHC sea level components.

We quantify the impacts of mass variations in cryo–hydrological domain on sea level changes across 20 oceanic regions, achieving a quantitative projection from mass redistribution to sea level response. Results demonstrate excellent budget closure within the analyzed regions, with mean correlation coefficients exceeding 0.9 and root mean square difference of approximately 15 mm. In the temporal domain, the deep learning network effectively reproduces both long-term trends and seasonal oscillations (correlation ≥ 0.8 in most prediction windows). From a physical perspective, the presented study establishes the regional sea level response to cryo–hydrological mass redistribution and demonstrates strong practical relevance.