Natural rainfall drives dramatic changes in the hydrology and ecosystems of lakes in cold-arid regions through complex cascading and lagged pathways. Our understanding of these processes remain a key scientific challenge in current hydrology and lake ecology research. This study is based on monthly-scale rainfall and environmental factor data from Chagannaoer Lake, Inner Mongolia, for the period from January to December 2024. We used a redundancy analysis, correlation analysis, structural equation modeling, dynamic time warping, and Random Forest algorithms to construct a systematic analytical framework that encompassed "driver identificationpathway tracing-contribution quantification." The results of the study showed that changes in the water environment of Chagannaoer Lake were primarily influenced by rainfall. The study revealed the "four-level cascading response system" and the "three-stage lagged response mechanism" triggered by rainfall, and we quantified the rainfall contribution rate to water environmental factors. The tiered and staged theoretical framework proposed in this study overcomes the limitations of traditional single linear response models, with the aim to provide scientific evidence for algal bloom risk warnings and ecologically precise management in cold-arid region lakes.