Evolving Driver Dominance: A 35‐Year Analysis of Xinjiang's Surface Water Area and Its Management Implications

Surface water bodies in arid Xinjiang are sensitive indicators of climate change and intense human activities, yet long‐term monitoring is hindered by complex terrain and data limitations. To address this, we developed a Random Forest framework on Google Earth Engine, generating a high‐precision surface water dataset for Xinjiang from 1989 to 2023. Our method achieves high accuracy (> 96%) and strong transferability, overcoming the limitations of existing global datasets. The resulting 35‐year analysis reveals that a statistically non‐significant trend in total water area masks a profound elevation‐driven dichotomy: significant expansion in high‐altitude alpine regions (> 3000 m) driven by cryospheric melt, starkly contrasting with significant contraction in lowland plains (< 1500 m). Critically, we unveil a temporal regime shift in the hydrological drivers within the Northern Slope Economic Zone of Tianshan Mountain. The dominant driver has transitioned from a climate‐centered “temperature‐precipitation” coupling in the 2000s to a human‐activity‐dominated “cropland‐urbanization” coupling in the 2010s. This study provides a transferable paradigm for water mapping and offers a critical scientific basis for formulating adaptive, demand‐focused water management strategies in a non‐stationary world.