Temperature-driven mechanisms involving complex feedback and lag that affect the evolution of hydrological processes and ecological functions in cold- and arid-region lakes represent a core scientific issue in current hydrology and lake ecology research. In this study, based on month-scale temperature and environmental factor data from Daihai Lake in Inner Mongolia from January to December 2023, statistical methods (redundancy analysis, Tukey's test analysis, correlation analysis, structural equation modeling), time series analysis methods (dynamic time warping), and machine learning methods (random forest) were combined. A hierarchical and phased response framework was constructed that encompassed driver identification, path tracing, lag characterization, and contribution quantification. The framework was used to explore the short-term response mechanisms of environmental factors to temperature, analyze the response degrees of different environmental factors to temperature changes, and investigate the driving mechanism of temperature fluctuations on lake environmental factors. The results showed that the temperature (T), lake area (Z), wind speed (WS), and precipitation (P) explained 31.35%, 23.38%, 15.35%, and 22.09% of the variations in the water environmental factors, respectively ( p < 0.05), with temperature being the primary driver of Daihai's water environment changes.
ZHANG et al. (2026) studied this question.