Dynamics and Drivers of Suprapermafrost Groundwater on the Qinghai-Tibet Plateau Under Climate Change

Suprapermafrost groundwater (SPG) plays a critical role in the hydrological and ecological functioning of permafrost regions, yet its spatiotemporal dynamics and controlling mechanisms remain poorly understood on the Qinghai–Tibet Plateau (QTP). In this study, we integrated in-situ observations, geophysical surveys, and machine learning models to investigate the seasonal variation, key drivers, and future projections of SPG dynamics in alpine meadow (AM) and alpine wet meadow (AWM) ecosystems. Results showed that SPG tables ranged from –1.1 m to –0.1 m in AM and from –1.3 m to –0.2 m in AWM during the warm season. SPG fluctuations were primarily driven by thaw depth and rainfall infiltration and exhibited similar seasonal patterns across both ecosystems. Specifically, increasing thaw depth tended to lower the SPG table by expanding the unsaturated zone and enhancing vertical drainage, showing an exponential relationship with SPG table depth 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, machine learning models projected that mean summer SPG tables in the 2090s would rise 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. This dataset includes the observed and ML-simulated SPG tables, and the corresponding ML model codes (example).