ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Archive > Volume 47 Issue 7 > 2021,47(7):862-871. DOI:10.7519/j.issn.1000-0526.2021.07.009 Prev Next
Analysis of Meteorological Affecting Factors and Construction of Prediction Model for FirstFlowing Date of Platanus Acerifolia： A Case Study Based on the Situation in Xinghua， Jiangsu Province
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Abstract:
In April, the pollen and flying fruit hair from Platanus acerifolia endanger human health and bring pressure to city cleaning. The prediction of the firstflowering date （FFD) is conducive to the protection of allergy groups, and can provide theoretical basis for the timely spraying of plant growth inhibitor by the garden department. Taking the FFD of Platanus acerifolia in Xinghua, Jiangsu Province as the research object, this paper analyzes the annual variation trend from 1990 to 2020 and selects the leading meteorological factors which are significantly related to the FFD by using the observation data and synchronous meteorological date. Moreover, the effect of the leading meteorological factors is quantitatively specified by path analysis and the regression prediction model is constructed. In addition， the effective accumulated temperature threshold is statistically analyzed， and the prediction is made based on the SW accumulative temperature model. Finally， the prediction effects of the two models are evaluated. The results show that under the background of climate warming, the FFD shows a tendency of advancing year by year, about 2 days every 10 years. Light, wind speed and precipitation have little impact on the FFD, but in late February, plenty of rain and moist air are conducive to early flowering. Temperature is the main impact factor. The impact of daily maximum temperature on the FFD is higher than that of daily minimum temperature, and the impact of average surface temperature is higher than that of average temperature. However, the situation is opposite when temperature begins to warm up from March. The average winter surface temperature, the average winter air temperature and the number of days in midMarch with daily minimum air temperature <10℃ all have the significant impact on the FFD directly or indirectly. The fitting result of the regression prediction model which is established based on these factors is 89.3%. Therefore, this prediction model has high reliability and operational application value.
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