Influences of atmospheric circulation factors on agricultural drought prediction with long lead time in the Yellow River basin

Frequent droughts have posed a serious threat to food security in the agricultural production bases of the Ningmeng Hetao Plain, FenWei Basin, and Yellow River irrigation area within the Yellow River basin. Accurately predicting drought is crucial for ensuring regional food production and water allocation. Among them, atmospheric circulation factors can significantly influence the occurrence, development, and transmission of agricultural drought via remote correlation. It is unclear whether introducing atmospheric circulation factors to the prediction model will improve the prediction performance of agricultural drought. Furthermore, the Meta-Gaussian (MG) model has been applied to drought prediction and achieved great prediction results. This study aims to determine the predictors of agricultural drought using correlation analysis, which includes agricultural drought, high temperature, and atmospheric circulation factors. Three groups of predictors were set: 1) agricultural drought (MG2 model), 2) agricultural drought and atmospheric circulation factors (MG3 model), and 3) agricultural drought, high temperature, and atmospheric circulation factors (MG4 model). The MG2, MG3 and MG4 models were used to predict agricultural drought in the Yellow River basin in typical years under the different forecast periods (1, 12, 24, 36, and 48 months). Then, a systematic investigation was made to explore the influence of atmospheric circulation factors on the prediction performance of agricultural drought in the MG model using the Nash-Sutcliffe efficiency coefficient (NSE) and root mean square error (RMSE). The results showed that the agricultural drought and high temperature were suitable for the predictors to predict the later agricultural drought. The standardized western Pacific subtropical high intensity index (SWPSHI) on 12-month time scale shared the most significant correlation with agricultural drought compared to other standardized atmospheric circulation indexes. Furthermore, 2014 was selected as a typical year because of the severe drought event in the Yellow River basin of 2014. Predictions of typical agricultural drought events in 2014 showed that the MG models with the different predictors in the long forecast period were different from the actual observed drought in the spatial scope and severity of the drought. The difference of NSE and RMSE indexes between different MG models were compared. It was found that the atmospheric circulation factor spatially varied in the prediction performance of the MG model. Compared to the one-factor prediction, the MG3 model with atmospheric circulation factor could improve the prediction performance of agricultural drought in the Yellow River basin by up to 46%, and the MG4 model with atmospheric circulation factor and high temperature had the largest improvement grid by 50%. Moreover, the prediction performance of the MG3 model with atmospheric circulation factor was improved in the Inner Mongolia Autonomous Region, Ningxia, Gansu, and Shaanxi provinces in the forecast period of more than one year. However, the prediction performance declined slightly in the rest region. Compared with the MG3 model, the MG4 model further improved the overall prediction accuracy of agricultural drought in the larger spatial range. Thus, it is recommended to take the atmospheric circulation factor into account when predicting droughts. The findings can provide guidance to predict the agricultural drought in the Yellow River basin.