A Study on the Hydrogen and Oxygen Stable Isotope Characteristics of Water in Small Watersheds on the Southern Slope of the Qilian Mountains

This study, based on stable hydrogen and oxygen isotope observations of multiple water bodies (precipitation, river water, soil water, and groundwater) in the Ami Dongsou alpine arid watershed on the southern slope of the Qilian Mountains during 2023–2024, reveals significant seasonal fluctuations in water isotope characteristics and water source renewal mechanisms. The results show that precipitation and soil water exhibit notable enrichment during the dry season, primarily due to enhanced evaporation causing light isotopes to evaporate and heavy isotopes to accumulate. River water, influenced by both precipitation recharge and evaporation, shows smaller seasonal fluctuations. Groundwater isotopes remain stable, reflecting a slower water source renewal process with minimal seasonal influence. Through quantitative comparisons of the evaporation line’s slope and intercept, this study finds that precipitation is most significantly affected by evaporation, while groundwater is least influenced, showing more stable isotope characteristics. Climate and topography in high-altitude areas significantly regulate water isotope characteristics, especially during the dry season, where evaporation plays a dominant role in the enrichment of precipitation and river water isotopes. This study innovatively establishes an evidence framework for the linkage of multiple water body isotopes, revealing the “seasonal strong fluctuations + differential water body responses + high-altitude regulation” mechanism of water isotopes in alpine arid regions. It provides new data support for water resource management, particularly in aspects such as water source allocation during the dry season, groundwater protection, and evaporation enrichment effect prediction. Future research could expand the sample size and integrate multi-source data and hydrological models to further improve the accuracy of hydrological process predictions, offering more precise support for watershed water resource management and ecological protection.