The Tibet Autonomous Region (TAR) is located in the mid-latitude and high-cold regions, and the ecological environment in most areas is fragile. Studying its surface vegetation coverage can identify the ecosystem’s development trends and provide a specific contribution to global environmental change. The normalized difference vegetation index (NDVI) can better reflect the coverage of surface vegetation. Therefore, based on remote sensing data with a resolution of 1 km², air temperature, precipitation, and other data in the same period in the study area from 1998 to 2019, this paper uses trend analysis, F-significance tests, the Hurst index, and the Geodetector model to obtain the spatial distribution, change characteristics, and evolution trends of the NDVI in the TAR in the past 22 years. At the same time, the quantitative relationship between natural and human factors and NDVI changes is also obtained. The study results show that the NDVI in the southern and southeastern parts of the TAR is higher, with mean values greater than 0.5 showing that vegetation cover is better. The NDVI in the western and northwestern parts of the TAR is lower, with mean values less than 0.3, indicating vegetation cover is worse. NDVI in the TAR showed an overall increasing trend from 1998 to 2019 but a decreasing trend in ridgelines, snow cover, and glacier-covered areas. The areas where NDVI values show a trend of increasing and then decreasing in the future account for 53.69% of the total area of the TAR. The most crucial factor affecting NDVI changes in the TAR is soil type, followed by influencing factors such as vegetation cover type, average annual air temperature, and average annual precipitation. The influence of natural elements is generally more significant than anthropogenic factors. The influencing factors have synergistic effects, and combining anthropogenic factors and other factors will show mutual enhancement and non-linear enhancement relationships. This study provides a theoretical basis for natural resource conservation, ecosystem restoration, and sustainable human development strategies in the TAR.

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.

The Qinghai–Tibet Plateau (QTP) is among one of the most sensitive regions to global environmental change worldwide. Although climate change and engineering construction on the QTP have jointly modified the regional vegetation activity, little is known about how this affects the vegetation variation. Using Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) data from 2000–2021, this study investigated the spatiotemporal variation of vegetation activity and the compound effects of climate change and reconstruction along the Tibetan section of the G318 national highway (TG318) through a novel contribution quantification model and partial correlation analysis, as well as through a structural equation model (SEM). The results showed that the mean growing-season EVI increased significantly at a rate of about 0.0020/year in the western side of the TG318 after reconstruction but fluctuated in the east. Reconstruction generally had a significant effect on the mean growing-season EVI, with contributions of 7.67%, 19.12%, 18.24%, and −4.15% in different sections of the TG318, whereas climate change contributed from −10.14% to 8.84% of the total variation. The mean growing-season EVI negatively correlated with snow cover and minimum temperature in humid and sub-humid regions, whereas it was positively related with vapor pressure in semi-arid regions. Moreover, there existed an obvious lag effect of climate change on the mean growing-season EVI, with lag time generally decreasing from west to east and apparent heterogeneity among different months and regions. These findings will help better understand the environmental impacts along the engineering corridors and provide a scientific basis for ecological conservation in the QTP regions.

North China is a major production base for winter wheat and one of the regions most severely affected by meteorological droughts. However, the stage-specific characteristics of drought variability and their response to large-scale atmospheric teleconnections remain insufficiently understood, limiting sustainable winter wheat production and food security. Based on monthly observational records from 1961 to 2021 and large-scale atmospheric circulation data, this study investigated the spatiotemporal evolution of long-term meteorological drought across the entire winter wheat growing season, as well as its linkages with atmospheric circulation through the integrated application of Sen’s slope estimation, Modified Mann-Kendall (MMK) test, run theory, and wavelet analysis. The results showed that the 12-month Standardized Precipitation Evapotranspiration Index (SPEI-12) for each winter wheat growth stage exhibited significant upward trends over the study period, especially during the overwintering–jointing stage (0.0092 yr⁻¹). The trends in SPEI-12 varied considerably across different growth stages, with pronounced drying detected in Shanxi Province, northern Hebei Province, and eastern Shandong Province. Drought frequency in Shanxi, northern Hebei, and southern Henan during the whole growth period was relatively high, yet drought duration and severity were generally low. SPEI-12 responded differently to circulation factors across the growth stages. The Pacific Decadal Oscillation (PDO) showed the strongest coherence with SPEI-12 during the emergence–tillering stage (Percentage of Significant Coherence Area, PASC = 12.63%), whereas the Niño 3.4 Sea Surface Temperature Index (Niño 3.4) was most strongly associated with SPEI-12 during the overwintering–jointing (PASC = 12.27%) and booting–maturity (PASC = 10.00%) stages. These findings advance the understanding of winter wheat drought risk and teleconnection-driven drought patterns, with implications for sustainable agricultural strategies.