Climate and landscape controls on spatio-temporal patterns of stream water stable isotopes in a large glacierized mountain basin on the Tibetan Plateau.

The spatio-temporal variations of stream water stable isotopes are often assumed to follow atmospheric moisture transport over the Tibetan Plateau (TP). However, the isotopic composition of streamflow can be modified by the extensive variation in landscape properties in large glacierized mountain basins. In this study, the isotopic composition of stream water and its dominant controls in terms of spatial variation and potential water sources of rainfall, snow and glacier melt, and groundwater are analyzed based on synoptic water sampling from September 2018 to August 2019 over the Lhasa River basin (LRB) in the Southern TP. Results showed that: (1) δO variation in stream water is linearly proportional to longitude and latitude in the north. This spatial pattern is primarily controlled by cold mountainous environments, where stream water δO is more depleted and d-excess is higher towards the northwest and higher elevation in glacier-fed streams. Glacial melt could contribute considerably to streamflow generation, especially in the late monsoon season. (2) In the south, stream water δO does not simply follow depleted δO in precipitation along the strengthened Indian monsoon moisture gradient, but is enriched by strengthened local moisture recycling and increased groundwater contributions. The rainfall recharge is highly regulated and mixes with storage before it reaches the mainstem of the river. (3) The seasonal variations of stream water δO and d-excess are distinct, resulting from different contribution sources and catchment controls. In the pre-monsoon season, the strongest local moisture recycling obscures any simple stream water isotope lapse with elevation. These identified source areas and seasonal variations in the isotopic composition in stream water of LRB help us understand diverse water sources and flow paths to streams in this complex environment, which is a prerequisite for projecting potential future change.