Over the past 50 years, temperatures on the Qinghai-Tibet Plateau (QTP) have risen roughly twice as fast as the global average, making it the most unpredictable region of environmental change due to global warming. In this paper, an Environmental Area Index model was developed using data from the Coupled Model Intercomparison Project to assess the ecological risk faced by QTP ecosystems under the influence of climate factors. The results show that ecological risk gradually decreases from northwest to the southeast, and there are different trends in ecological risk for each class in areas with different elevation gradients. As elevation increased, the proportion of potential risk areas gradually decreased, and the proportion of high- and higher-risk areas gradually increased. We predict that in the period 2021–2100, the overall ecological risk change trend on the QTP will not be obvious, but there will be a more obvious change on the vertical gradient. In general, under the existing global climate change scenario, the ecological risk faced by the QTP show a decreasing trend under the influence of climate factors, and the decrease in ecological risks is much higher at higher elevations than at lower elevations.

The land use and land cover pattern of the Qinghai-Tibet Plateau (QTP) is an important basis for the structure and function of the QTP ecological barrier. It is of great significance to simulate the land use pattern and landscape ecological risk of the QTP under future scenarios for the construction of the QTP barrier area and to promote the sustainable use of land resources. The QTP was selected as the study area. Based on the spatial pattern of land use in 2010 and 2020, the PLUS model was used to predict the land use patterns of the QTP in 2030 under the two scenarios of natural development and ecological conservation. The landscape ecological risk index was constructed to evaluate the past, present, and future landscape ecological risk of the QTP. The natural break point method was used to divide the landscape ecological risk index into five levels: lower ecological risk, low ecological risk, medium ecological risk, high ecological risk, and higher ecological risk. The results showed that: (1) Under the natural development scenario, the area of cropland, forestland, grassland, and unused land decreased continuously, while the areas of water and built-up land increased gradually. Under the ecological conservation scenario, the areas of forestland and grassland increased by 130 km² and 2293 km², respectively, compared with the natural development scenario. (2) Under the natural development scenario, the overall ecological risk of the QTP increased from 2010 to 2030, which showed that the proportions of lower ecological risk area decreased, while the proportion of medium and high ecological risk area increased. Under the ecological conservation scenario, compared with the natural development scenario, the area of lower, low, and high ecological risk increased by 4044 km², 2484 km², and 6401 km², respectively, while the areas of medium and higher ecological risk decreased by 6333 km² and 6597 km², respectively.

Drought has great negative impacts on crop growth and production. In order to select appropriate drought indices to quantify drought influences on crops to minimize the risk of drought-related crops as much as possible, climate and spring wheat yield-related data from eight sites in the Qinghai Province of China were collected for selecting better drought index between standardized precipitation evapotranspiration index (SPEI, denoting meteorological drought) and soil moisture deficit index (SMDI, denoting agricultural drought) as well as the key parameters (timescale and month) in denoting drought impacts on spring wheat yields. The spring wheat yields during 1961–2018 were simulated by the DSSAT–CERES–Wheat model. Pearson correlations were used to investigate the relationship between SPEI and SMDI and between spring wheat yields and drought indices at different timescales. The results showed that: (1) SMDI reflected more consistent dry/wet conditions than SPEI when the timescales changed and (2) There were one- and two-month lags in SMDI compared to SPEI (with the higher correlation coefficients values of 0.35–0.68) during May to August and (3) May (the jointing period of spring wheat) and the two-month timescale of SMDI_0–10 (with the higher correlation coefficients values of 0.21–0.37) were key parameters denoting drought influences on spring wheat yield and (4) The correlations between the linear slopes of spring wheat yield reduction rate and linear slopes of SMDI_0–10 in May at the studied eight sites were considerable between 1961–2018 (r = 0.85). This study provides helpful references for mitigating the drought risk of spring wheat.