基于逐步多元线性回归和随机森林模型预测黄河流域极端气温事件

Under the background of global warming, extreme weather events occur frequently, and cause serious harm to the economic development and people' s lives in the Yellow River Basin and other regions. Therefore, it is necessary to explore its response to the atmospheric circulation and predict extreme temperature events in the Yellow River basin. Based on the daily temperature data of 80 stations in the Yellow River Basin from 1961 to 2020, the six monthly extreme temperature indexes (ETI) were calculated. Multi-collinearity analysis was used to remove the dependent circulation indexes, and the Pearson correlation analysis was conducted considering the lag. The key circulation indexes of each ETI were selected, and the optimal lag time of the circulation index to each ETI was determined according to the maximum value of Pearson correlation coefficient (r). Then, stepwise multiple linear regression (SMLR) and random forest (RF) models were established based on the selected key circulation indexes with specific lag time to evaluate the accuracy and explore the variable importance of circulation indexes at a single station and the whole basin. Six ETI of 80 stations in the Yellow River Basin in November 2022 were predicted. The results showed that: the TXx, TX90p, TD30 and TNn in the ETI of the Yellow River Basin showed a fluctuating upward trend, while the FDO and TNlOp showed a downward trend. Spatial distribution characteristics of extreme temperature warm indexes and cold indexes were basically opposite. Taking TXx of Jingyuan station as an example, each key circulation index had different degrees of influence on TXx (0. 10 < r_max< 0.89), and the lag time corresponding to r_maxwas mainly concentrated in 5, 6, 11 and 12 months. Both SMLR and RF models had good predictive ability for ETI in the Yellow River Basin, with R²of 0.53 ~0.95 and 0.64~0.95 in the validation period, respectively. Except for TXx, RF model had better simulation effect on the other five ETI than SMLR model. For the Yellow River Basin, the Pacific Polar Vortex Intensity Index(PPVI) was the most important predictor of TXx, TNn, TX90p and FDO, and the North African-North Atlantic-North American Subtropical High Ridge Position Index (NANRP) had the greatest influence on TNlOp and TD30. The predicted extreme temperature indexes in November 2022 were basically similar to the multi-year average in spatial distribution. The results provide a reference for the prediction of extreme temperature events in the Yellow River Basin.