A changing climate has been posing significant impacts on vegetation growth, especially in the Yellow River Basin (YRB) where agriculture and ecosystems are extremely vulnerable. In this study, the data for normalized difference vegetation index (NDVI) obtained from moderate-resolution imaging spectroradiometer (MODIS) sensors and climate data (precipitation and temperature) derived from the national meteorological stations were employed to examine the spatiotemporal differences in vegetation growth and its reaction to climate changes in the YRB from 2000‒2019, using several sophisticated statistical methods. The results showed that both NDVI and climatic variables exhibited overall increasing trends during this period, and positive correlations at different significant levels were found between temperature/precipitation and NDVI. Furthermore, NDVI in spring had the strongest response to temperature/precipitation, and the correlation coefficient of NDVI with temperature and precipitation was 0.485 and 0.726, respectively. However, an opposite situation was detected in autumn (September to November) since NDVIs exhibited the weakest responses to temperatures/precipitation, and the NDVI’s correlation with both temperature and precipitation was 0.13. This indicated that, compared to other seasons, increasing the temperature and precipitation has the most significant effect on NDVI in spring (March to May). Except for a few places in the northern, southern, and southwestern regions of the YRB, NDVI was positively correlated with precipitation in most areas. There was an inverse relationship between NDVI and temperature in most parts of the central YRB, especially in summer (June to August) and growing season (May to September); however, there was a positive correlation in most areas of the YRB in spring. Finally, continuous attention must be given to the influence of other factors in the YRB.

Global warming and rapid socioeconomic development increased the risk of regional and global disasters. Particularly in China, annual heatwaves (HWs) caused many fatalities and substantial property damage, with an increasing trend. Therefore, it is of great scientific value and practical importance to analyze the spatiotemporal changes of HW in China for the sustainable development of regional socioeconomic and disaster risk management. In this study, based on gridded maximum temperature product and specific humidity dataset, an HW evaluation algorithm, considering the impact of humidity on the human body and the characteristics of HW in China, was employed to generate daily HW state at light, moderate, and severe levels for the period 1979–2018. Consequently, the regional differences at three HW levels were revealed, and the changing trend of HW onset, termination, and duration in each subregion was analyzed. The results show that in the three levels, the frequency and duration of HW in China had a significant increasing trend, generally characterized by the advancement of HW onset and the postponement of HW termination. The HW influence at light, moderate and severe levels decreased gradually, with the light level occurring the earliest and terminating the latest. Among the seven subregions, the largest HW frequency happened to be mainly in XJ (Xinjiang), SC (Southern China), and NC (Northern China), while the variations of HW onset and termination had noticeable regional differences at the three levels. The findings presented in this study can provide the essential scientific and technological support for national and regional disaster prevention mitigation and adaptation to extreme climate events.

The source region of the Yellow River Basin (SRYRB) is not only sensitive to climate change and the vulnerable region of the ecological environment but also the primary runoff generating region of the Yellow River Basin (YRB). Its changes of drought and wetness profoundly impact water resources security, food production and ecological environment in the middle and downward reaches of YRB. In the context of global warming, based on daily precipitation, maximum and minimum temperature of 12 national meteorological stations around and within SRYRB during 1960–2015, this study obtained standardized precipitation index (SPI) and reconnaissance drought index (RDI) on 1-, 3-, 6- and 12-month scales, and then compared the consistency of SPI and RDI in many aspects. Finally, the temporal and spatial variation characteristics of drought and wetness in the SRYRB during 1960–2015 were analyzed in this study. The results showed that SPI and RDI have high consistency on different time scales (correlation coefficient above 0.92). According to the average distribution and change trend of the RDI, SRYRB presented an overall wetness state on different time scales. We found an increasing trend in wetness since the early 1980s. In terms of wetness events of different magnitudes, the highest frequency for moderate and severe ones was in June (12.7%) and February (5.5%), respectively, and for extreme wetness events, both September and January had the highest frequency (1.8%). Among the four seasons, the change rate of RDI in spring was the largest with a value of 0.38 decade−1, followed by winter (0.36 decade−1) and autumn (0.2 decade−1) and the smallest in summer (0.1 decade−1). There was a greater consistency between RDI values of larger time scales such as annual and vegetation growing seasonal (VGS) scales in SRYRB. There was generally a growing trend in wetness in the VGS time scale. These findings presented in this study can provide data support for drought and wetness management in SRYRB.