A rainfall-tracking travel time distribution model to quantify mixing and storage release preference in a large shallow lake by two-year stable isotopic data

Abstract. Lake Taihu is the largest eutrophic lake in China that is shallow and connected to a dense river network. Severe eutrophication is frequently observed in Lake Taihu due to excess pollutant loadings. Understanding water cycle dynamics is essential for investigating this problem. The travel time distribution (TTD), residence time distribution (RTD) and storage selection (SAS) function describing how water is stored, mixed and released in the lake provide fundamental information on water cycle dynamics. In this study, a rainfall mixing model is established and coupled with the age master equation model to estimate the time-variant TTDs and RTDs of rainwater, river water and all water in Lake Taihu based on the two-year high-resolution isotopic data. In the rainfall mixing model, a novel rainfall mixing factor is introduced to quantify the mixing of rainwater with older lake water. The estimated range of travel time varies between 2–4 months and 7–9 months, depending on lake volume. Lake Taihu shows an inverse storage effect, i.e., the release preference shifts toward younger water when lake volume is large. The results of rainfall mixing model reveal distinct patterns and control factors between the TTDs of rainwater and river water, with rainwater contributing to 15 %–25 % of outflow. Then, the SAS functions of rainwater and river water are analyzed, which are controlled by different source zones and flow patterns. Temporal variation of spatial distribution of deuterium isotope composition illustrates storage release preference is controlled by the variation of horizontal flow paths and velocities in the lake.