## 1 Introduction  
In 1969, [PERSON] et al. [1] published their pioneering paper on hydrograph separation using stable isotope tracers. Since then, isotope hydrograph separation (IHS) has gradually become the principal method for determining the relative contributions of different sources of runoff or streamflow (i.e., event or pre-event water) [2, 3, 4, 5]. [PERSON] [6] summarized the hydrological processes, including groundwater ridging, lateral throughflow, preferential flow, saturation overland flow, kinematic waves, and output from surface storage, which significantly improved the knowledge of the mechanisms of runoff generation. In 2013, [PERSON] and [PERSON] [7] published a qualitative and comprehensive review of IHS and concluded that, despite certain limitations, water stable isotopes were still the most effective technology for understanding runoff generation processes and mechanisms. Moreover, several IHS reviews have focused on streamflow generation and associated processes and methods in special environmental settings, which has accelerated the accumulation of IHS studies [8, 9]. Since 2013, there has been a rapid increase in scientific output in the field, which provides an unprecedented opportunity to explore the dynamics of IHS research based on data from a large body of published scientific work. However, to our knowledge, there has not been a quantitative assessment of the publication data in this field, and there are limited documents on text mining and relevant data science methods applied in hydrology research. Utilizing a quantitative and systemic method, we can analyze the characteristics and performances of documents to gain both macro- and micro-insights into the history and development of the field. Similar to all scientific fields, the study of IHS has changed over time. Interests in certain themes have emerged and disappeared, and the breadth and depth of research content have expanded [10]. The most significant advances were made around the five well-known assumptions implicit in IHS models [9]. The five underlying assumptions are listed as follows: (1) significant isotopic differences occurred between the pre-event (old) and event (new) water; (2) the new water retains a constant isotopic signature in space and time, or any variations can be accounted for; (3) the isotopic signature of the old water is constant in space and time, or any variations can be accounted for; (4) contributions of water from the vadose zone must be negligible, or the isotopic content of soil water must be similar to that of groundwater; and (5) the contributions from surface storage to streamflow are negligible. Many IHS studies have concentrated on the validity and effects of these assumptions in recent decades [2; 11; 12; 13; 14; 15; 16; 17]. Tracing IHS literature by quantitative approaches is interesting and vital for enhancing the understanding of runoff generation and relevant hydrological processes. Text mining is the process of automatically extracting high-quality information from unstructured or structured texts with diverse formats and types using linguistic and statistical techniques [18; 19]. Thematic analysis is an important subject of textual data mining. Studies have found that temporal changes in themes, usually represented by keywords for a given research domain, can help to uncover the evolution of a topic and its trend [20; 21; 22]. For example, [PERSON] et al. [23] analyzed the frequency changes in author keywords related to nitrogen in eutrophic lakes or reservoirs at five-year intervals and successfully determined the current and future trends in nitrogen field research. [PERSON] et al. [24] analyzed research trends in management science and engineering in China based on co-keyword analysis and concluded that the foci were game theory, supply chain management, complex networks, data mining, optimization, risk management, and data envelopment analysis.