Given the intensive influence of climate change and anthropogenic activities, accurate groundwater depth (GWD) prediction is essential for sustainable groundwater management. However, existing models struggle to capture spatiotemporal dependencies from complex factors. This study develops a novel Hierarchical Hydrological knowledge-guided Attention Network (HHA-Net) that processes multi-source heterogeneous data through physics-guided encoders, employs adaptive weight allocation and spatiotemporal attention to achieve fourteen-step GWD prediction, and provides insights into groundwater dynamics. Three distinct hydroclimatic and geographical regions in China (128 sites with 233,728 observations) serve as case studies, including the Yanshan-Taihang Mountain Region (YTMR), North China Plain (NCP), and North Jiangsu Plain (NJP). Results show that HHA-Net outperforms baseline models across different sites (natural, agricultural, and urban), with MAPE ranging from 1.02 % to 5.95 % and R<sup>2</sup> ranging from 0.71 to 0.98. The model demonstrates improved performance under droughts but slightly weaker predictive capability during rainfall events, particularly at natural sites in the YTMR. The geographical encoder dominates GWD in the mountainous YTMR (35.6 %), while the human activity encoder and historical encoder control it in the NCP (32.5 %) and the NJP (36.7 %), respectively. The GWD exhibits prolonged memory effects (25 days) and delayed responses to rainfall (7.5 days) in the YTMR, whereas the over-exploited NCP shows rapid decay (3 days) with negative rainfall thresholds (-0.16) and anthropogenic-dominated patterns. The humid NJP demonstrates low-positive thresholds (0.07) and balanced natural-anthropogenic effects. These findings demonstrate the broad applicability of HHA-Net for GWD prediction and response pattern interpretation across diverse regions, providing scientific support for groundwater management.