Research on vegetation variation is an important aspect of global warming studies. The quantification of the relationship between vegetation change and climate change has become a central topic and challenge in current global change studies. The source region of the Yellow River (SRYR) is an appropriate area to study global change because of its unique natural conditions and vulnerable terrestrial ecosystem. Therefore, we chose the SRYR for a case study to determine the driving forces behind vegetation variation under global warming. Using the Normalized Difference Vegetation Index (NDVI) and climate data, we investigated the NDVI variation in the growing season in the region from 1998 to 2016 and its response to climate change based on trend analysis, the Mann–Kendall trend test and partial correlation analysis. Finally, an NDVI–climate mathematical model was built to predict the NDVI trends from 2020 to 2038. The results indicated the following: (1) over the past 19 years, the NDVI showed an increasing trend, with a growth rate of 0.00204/a. There was an upward trend in NDVI over 71.40% of the region. (2) Both the precipitation and temperature in the growing season showed upward trends over the last 19 years. NDVI was positively correlated with precipitation and temperature. The areas with significant relationships with precipitation covered 31.01% of the region, while those with significant relationships with temperature covered 56.40%. The sensitivity of the NDVI to temperature was higher than that to precipitation. Over half (56.58%) of the areas were found to exhibit negative impacts of human activities on the NDVI. (3) According to the simulation, the NDVI will increase slightly over the next 19 years, with a linear tendency of 0.00096/a. From the perspective of spatiotemporal changes, we combined the past and future variations in vegetation, which could adequately reflect the long-term vegetation trends. The results provide a theoretical basis and reference for the sustainable development of the natural environment and a response to vegetation change under the background of climate change in the study area.

Forest ecosystems play an important role in the global carbon cycle. Clarifying the large-scale dynamics of net primary productivity (NPP) and its correlation with climatic factors is essential for national forest ecology and management. Hence, this study aimed to explore the effects of major climatic factors on the Carnegie–Ames–Stanford Approach (CASA) model-estimated NPP of the entire forest and all its corresponding vegetation types in China from 1982 to 2015. The spatiotemporal patterns of interannual variability of forest NPP were illustrated using linear regression and geographic information system (GIS) spatial analysis. The correlations between forest NPP and climatic factors were evaluated using partial correlation analysis and sliding correlation analysis. We found that over thirty years, the average annual NPP of the forests was 887 × 10¹² g C/a, and the average annual NPP per unit area was 650.73 g C/m²/a. The interannual NPP of the entire forest and all its corresponding vegetation types significantly increased (p < 0.01). The increase in the NPP of evergreen broad-leaved forests was markedly substantial among forest types. From the spatial perspective, the NPP of the entire forest vegetation gradually increased from northwest to southeast. Over the years, the proportions of the entire forest and all its corresponding vegetation types with a considerable increase in NPP were higher than those with a significant decrease, indicating, generally, improvements in forest NPP. We also found climatic factors variably affected the NPP of forests over time considering that the rise in temperature and solar radiation improved the interannual forest NPP, and the decline in precipitation diminished the forest NPP. Such varying strength of the relationship between the interannual forest NPP and climatic factors also varied across many forest types. Understanding the spatiotemporal pattern of forest NPP and its varying responses to climatic change will improve our knowledge to manage forest ecosystems and maintain their sustainability under a changing environment.

In recent years, extreme droughts have been frequent and widespread, and understanding the spatiotemporal variations in drought events in China plays an important role in drought risk management. In this study, using monthly meteorological data at 618 stations, we computed the Standardized Precipitation Evapotranspiration Index with potential evapotranspiration based on the Penman–Monteith equation (SPEI_PM) for drought assessment. Furthermore, we analysed the trend, affected area, frequency and duration of drought in China over the period of 1960–2014 by the piecewise linear regression method (LRM) and Mann–Kendall (MK) method. The results showed that (a) the slight wetting trend reflected during 1960–2014 by the SPEI_PM (0.03/10a (p > .05). This trend was allocated to most parts of northern and southeastern China and was prone to arrive in winter. In contrast, the most significant drying trend was in the Loess Plateau and Yunnan–Kweichow Plateau. (b) The turning point (TP) year of drought trend was 1993. The SPEI_PM was increased in most parts of northern China before 1993 according to the LRM and MK method. However, it decreased after 1993, especially in the south and northwest of China; (c) Over the past 55 years, the areas affected by drought decreased at a rate of −1.23%/10a (p < .05). In detail, these areas decreased at a rate of −3.41%/10a before 1993 (p < .05) and increased at a rate of 2.71%/10a after 1993 (p > .05). (d) The drought frequency in most parts of China was between 15 and 20 during 1960–2014. Furthermore, compared with that in 1960–1977, the drought frequency was decreased in 1978–1995 and then increased in 1996–2014. These findings suggested that the long‐term trend in drought events during 1960–2014 was not significant in China. However, the severity, affected area and frequency of droughts increase after 1993. These results allowed us to understand the changes in the drought across China over the last 55 years, which is important for guiding relevant agricultural activities.

The variation of river runoff (Q) and suspended sediment load (S), in addition to their influential factors, is an important area of focus in hydrological sciences. Here, Mann–Kendall tests and double mass curve analyses were used with hydrometric data from four hydrological stations in the upper Fenhe River Basin in China to evaluate temporal trends in annual Q (Q_a) and S (S_a) values between 1955 and 2015. Based on the observed inflection points, three distinct periods were identified, namely, 1955–early 1980s (period I), the early 1980s–1996 (period II), and 1996–2015 (period III). The Q_a and S_a values for the four stations, except for Q_a values for the Jingle station, significantly decreased over the study period, with average reduction rates of 0.68–1.07 mm∙km⁻²∙a⁻¹ and 9.24–54.39 t∙km⁻²∙a⁻¹, respectively. Decreased rainfall, implementation of soil and water conservation program, and reservoir construction were primarily responsible for decreased Q_a and S_a values for the three stations during period II, while the first two factors led to variation in Q_a and S_a for the Jingle station during the same period. During period III, the Q_a, S_a, and Q_a–S_a relationships for the four stations were intensively affected by increased anthropogenic activities, including water diversion, cross-basin water transfers, soil and water conservation measures, revegetation efforts, and sand excavation. Further, the Q_a, S_a, and Q_a–S_a relationships at the Lancun station were affected by the construction of the No. 2 Fenhe Reservoir. Effective water use and supply strategies should be implemented in the future for the upper Fenhe River Basin.

The distribution of global warming has been varying both diurnally and seasonally. Little is known about the spatiotemporal variations in the relationships between vegetation greenness and day- and night-time warming during the last decades. We investigated the global inter- and intra-annual responses of vegetation greenness to the diurnal asymmetric warming during the period of 1982–2015, using the normalized different vegetation index (NDVI, a robust proxy for vegetation greenness) obtained from the NOAA/AVHRR NDVI GIMMS3g dataset and the monthly average daily maximum (T_max) and minimum temperature (T_min) obtained from the gridded Climate Research Unit, University of East Anglia. Several findings were obtained: (1) The strength of the relationship between vegetation greenness and the diurnal temperature varied on inter-annual and seasonal timescales, indicating generally weakening warming effects on the vegetation activity across the global. (2) The decline in vegetation response to T_max occurred mainly in the mid-latitudes of the world and in the high latitudes of the northern hemisphere, whereas the decline in the vegetation response to T_min primarily concentrated in low latitudes. The percentage of areas with a significantly negative trend in the partial correlation coefficient between vegetation greenness and diurnal temperature was greater than that of the areas showing the significant positive trend. (3) The trends in the correlation between vegetation greenness and diurnal warming showed a complex spatial pattern: the majority of the study areas had undergone a significant declining strength in the vegetation greenness response to T_max in all seasons and to T_min in seasons except autumn. These findings are expected to have important implications for studying the diurnal asymmetry warming and its effect on the terrestrial ecosystem.