Spatiotemporal Evolution and Attribution Analysis of Groundwater Storage in Heilongjiang Province Based on Interpretable Machine Learning

Groundwater is central to regional water security and sustainable agricultural development. As a major commodity grain base in China’s high-latitude cold region, Heilongjiang Province depends on groundwater resources closely linked to national food and ecological security. By integrating GRACE satellite data with the water balance equation, this study derived the Groundwater Storage Anomaly (GWSA) spanning 2002–2025. The Binary Segmentation (Binseg) algorithm was applied to identify structural change points and characterize the spatiotemporal evolution of GWSA. Furthermore, the XGBoost model interpreted with SHAP was then used to explore the nonlinear associations between stage-specific GWSA trends and eight hydro-climatic and anthropogenic factors. The results indicate that: (1) Binseg detected three change points (September 2014, June 2018, and November 2023), dividing GWSA evolution into four stages (P1–P4). (2) Over the full period, provincial GWSA showed a declining trend (−1.32 mm/yr) with marked spatial heterogeneity: the Songnen Plain showed a slight recovery (+1.70 mm/yr), whereas the Sanjiang Plain showed an overall declining trend (−4.61 mm/yr). (3) Among the measured factors, paddy-rice variables were the leading correlates in P1, P3 and over the full period, and more paddy rice and faster paddy expansion were consistently associated with more negative GWSA trends, most strongly in the Sanjiang Plain; temperature ranked first in the weaker P2 model, which is not interpreted as a warming effect. These findings provide uncertainty-qualified evidence for stage-sensitive groundwater monitoring and for managing irrigation-related groundwater stress in high-latitude agricultural regions.