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Suprapermafrost groundwater (SPG) plays a critical role in hydrological and ecological functioning of permafrost regions, yet its spatiotemporal dynamics and controlling mechanisms remain poorly understood on the Qinghai-Tibet Plateau (QTP). Here, we integrated in situ observations, geophysical surveys, and machine learning (ML) models (including XGBoost, LightGBM, and RandomForest) to investigate the seasonal variation, drivers, and projections of SPG dynamics in alpine meadow (AM) and alpine wet meadow (AWM) ecosystems. Results showed that SPG tables ranged from -1.1 to -0.1 m in AM and from -1.3 to -0.2 m in AWM during the warm season. SPG fluctuations were primarily driven by thaw depth (TD) and rainfall infiltration and exhibited similar seasonal patterns across both ecosystems. A greater TD was associated with a deeper SPG table, as deeper thawing expanded the unsaturated zone and enhanced vertical drainage, indicating an exponential relationship between TD and SPG table position, and a linear relationship with aquifer thickness. In contrast, rainfall infiltration increased shallow soil moisture and elevated SPG tables, with responses influenced by rainfall intensity, duration, and infiltration pathways. Spatial heterogeneity in SPG distribution was further shaped by vegetation structure and microtopographic variation. Furthermore, ML models projected that mean summer SPG table depths in the 2090s would increase by 0.06 m under SSP126 and 0.64 m under SSP585 in AWM ecosystems, and by 0.37 m under SSP126 and 0.87 m under SSP585 in AM ecosystems. These findings provide new insights into how climate warming affects hydrological processes in permafrost regions of the QTP.

来源平台：WATER RESOURCES RESEARCH