Reproducing surface water isoscapes of δ18O and δ2H across China: A machine learning approach

The availability of comprehensive stable isotope data in China is limited, which hinders a thorough understanding of interpreting runoff sources and land–atmosphere water fluxes on a national scale. In this study, we have undertaken the task of establishing a dataset of surface water isotopes (δ¹⁸O and δ²H) to create surface water isoscapes, and identify its controlling factors. Our analysis, utilizing a random forest model (RF), indicates that the isotopic patterns observed in precipitation are well-reflected in river water across China. Specifically, the δ¹⁸O isoscape in river demonstrates enrichment in the Western arid zone and depletion in the Tibetan Plateau. These patterns are strongly influenced by hydro-climatic factors such as relative humidity, precipitation, and catchment properties, such as elevation. Notably, elevation is a significant variable in the RF model, governing the isotopic composition (δ¹⁸O and d-excess) of rivers throughout China, primarily due to the rainout effect resulting in isotopically-depleted precipitation from lowlands to elevated mountain regions. In contrast, surface water d-excess isoscape reveals a more complex spatial variability in China, mainly associated with contrasting moisture sources including maritime vapor from tropical oceans and inland recycling vapor. In addition, secondary evaporation processes resulted from cumulative dams and developed irrigation systems also contribute to this variability. Hence, catchment-scale evapotranspiration and instream evaporative processes contribute to the enrichment of downstream river water isotopes. The predictive surface water isoscapes will help understand the impact of changes in the hydrological cycle on a larger scale and provide practical guidance for future monitoring efforts and isotopic simulations.