关键词: 物候期, 物候期预报, 集合模型, 预报时效, 权重确定, 气候变化

Abstract: The phenological forecast is mostly based on the single model which is with poor accuracy and stability. It is difficult to realize the business application. Taking the mature stage of peach as an example, the feasibility of the collection model in the refined prediction of phenological period was explored. By using the phenological data and meteorological data collected from major peach producing areas in Ningbo from 2005 to 2017, the maturity prediction models of peach with different time scale (hours, days, 5-days, 10-days and month) and multiple starting point (phenological period, fixed date) were built. The weighted sum method was employed to construct the collection models with different forecast aging for maturity forecasting. The weights of ensemble forecasting members were determined by using arithmetic mean method, regression coefficient method, correlation coefficient method and absolute error method based on the accuracy and stability of the model prediction results. The results showed that: the collection models, constructed with four kinds of weight coefficient determination methods, were with high accuracy and stability. The AE (absolute error) of the collection models’ regression test was only 0.69 (0.56-0.87) days, RMSE (root mean square error) was 0.90 (0.69-1.14) days, R (correlation coefficient) was 0.95 (0.92-0.98). Compared with the single model, the AE and RMSE of the collection model were 0.5 days and 0.6 days lower, and R was 0.12 higher. The collection model based on the absolute error method was the best, the average value of AE and RMSE by back-generation test were 0.66 and 0.88 days, respectively. The AE of maturity forecasting for main peach producing area in Ningbo was less than 2 days. The prediction error could be reduced by 0.2-0.3 days by the fusion of stone hardening observation in the collection model. We suggest that collection model provide a good proxy for fine phenological prediction and can meet the needs of business applications.

Key words: Phenological, Phenoloyical forecast, Collection model, Period of validity, Weight determination, Climate change