Abstract:

The price of jasmine flowers in Hengzhou, Guangxi has been significantly affected by heavy rains. It is of great importance to develop a model for assessing the risk of heavy rain disasters for jasmine, in order to reduce the economic losses caused by heavy rain disasters. Based on the meteorological observation and jasmine flower prices during 2022−2024, this study constructed a risk assessment model for heavy rain disasters. By combining GIS and remote sensing techniques, the model was built to predict daily heavy rain risk for jasmine flower using the information entropy weight method. The results showed that: (1) the key factor influencing the disaster risk of heavy rain on jasmine was the duration of the heavy rain, which accounted for 40.6% of impact. These were followed by total precipitation over 24 hours and the accumulated precipitation during heavy rain process. Typically, heavy rain events lead to a decrease in the daily price of jasmine flowers by 15.9% to 25.5%. (2) The risk of heavy rain disasters for jasmine in Hengzhou was classified into low (26.4%), medium (44.6%), and high (29.0%) levels. The high risk areas were mainly located in the southern and eastern parts of Hengzhou. (3) Based on the heavy rain disaster risk assessment model, a heavy rain disaster risk forecasting system for jasmine was constructed. The prediction accuracy reached 63.1% for the 38 heavy rain events in 2024. It precisely verified the heavy rain process from June 3 to 5 in 2024. The simulation slightly underestimated the risk on June 3 and 4, while its results on 5 were relatively consistent with the actual decline in flower prices. In conclusion, based on the refined prediction and warning information, the heavy rain disaster risk prediction system for jasmine can help flower farmers formulate response strategies, reduce production losses, and improve production efficiency. As a result, the meteorological disaster resistance of the jasmine industry in Hengzhou can be enhanced. 

Key words: Hengzhou, Heavy rain disaster, Jasmine price, Meteorological disaster risk forecasting