Low-temperature rain, snow, and freezing disasters pose severe threats to transportation, agriculture, public safety, and other sectors. However, there is currently a lack of localized, quantitative, and operable hazard warning criteria. Based on observational data from 34 national meteorological stations in Chongqing and historical disaster records from 1961 to 2022, this study employs correlation analysis, information entropy weighting, and the percentile method. Daily minimum temperature and precipitation are selected as the disaster-causing factors. Thresholds are established, including daily minimum temperatures of 2, 0, −3°C, and a process-accumulated precipitation of ≥0.1 mm. These thresholds, combined with the affected area (≥20% of stations) and duration (≥3 days), are used to construct the warning initiation criteria. Additionally, an intensity index for low-temperature rain, snow, and freezing processes is developed, and four warning levels (I–IV) are classified (Level I being the highest). Verification using criteria established via the percentile method, historical disaster back-calculation, and operational trial applications demonstrates that these criteria can effectively capture disaster processes, with warning processes aligning well with actual disaster situations. This study innovatively adopts graded temperature thresholds instead of a unified threshold and establishes, for the first time, a hazard warning standard for low-temperature rain, snow, and freezing disasters applicable to Chongqing’s complex terrain, providing a scientific basis and technical support for local disaster prevention and mitigation efforts.
Yang et al. (Thu,) studied this question.
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