Yao Li

Understanding potential drought characteristics under climate change is essential for reducing vulnerability and establishing adaptation strategies, especially in North China, a major grain production area. In this study, the key growth period of winter wheat was taken as the entry point. We comprehensively utilized data from meteorological stations and circulation factors and employed methods such as the modified Mann–Kendall test, run theory, wavelet analysis, and cluster analysis. We quantitatively assessed the drought conditions during the winter wheat growth stages using the Standardized Precipitation Evapotranspiration Index at a 1-month timescale (SPEI-1). We analyzed the spatiotemporal evolution characteristics of drought and explored the inherent correlation between drought and atmospheric circulation factors. Results indicate that the SPEI-1 index increased significantly during the entire growth period, the overwintering-jointing stage, and the heading-maturity stage at rates of 0.0058, 0.0044, and 0.0140 per year, respectively, showing a wetting trend. Higher drought frequency values were observed in northern Shanxi, northern Hebei, western Henan, and southern Shandong during the entire growth period, with the drought frequency of the overwintering-jointing stage approximately twice that of the emergence-tillering and heading-maturity stages. Furthermore, drought frequency values in southern Hebei and southern Henan decreased from high to low frequency during the heading-maturity stage compared to the overwintering-jointing period. The SPEI-1 is predominantly influenced by the Arctic Oscillation (AO), Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO), varying by growth stages.

Understanding variations in drought characteristics is of great importance for water resource planning and agriculture risk management. Despite increasing interest in exploring spatiotemporal drought patterns, long-term drought event characteristics and their future changes are unclear in major grain-producing areas in China. In this study, we applied Run theory, Sen’s slope, the modified Mann–Kendall method, wavelet analysis, and three machine learning models to systematically examine drought variation patterns, their future trends, and agricultural exposure in Henan Province, China, from 1961 to 2019. The results indicated that the SPEI-12 showed a significant increase at a rate of 0.0017/month during 1961–1999, but this has gradually changed to a drying trend since the 21st century. Drought event characteristics shifted markedly during these two periods, with drought duration and severity gradually shifting from east to west. The BO-LSTM model performed better than the LSTM and BP models, indicating that the drought frequency, higher drought duration, and drought peak would greatly increase 1.28–3.40-fold and cropland exposure is predicted to increase 1.61-fold in the near future compared to the first two decades of the 21st century. This finding not only helps developing meteorological drought predicting models, but also provides the scientific groundwork for drought disaster prevention and mitigation in Henan Province.