Effects of Climate Change on Vegetation Growth in the Yellow River Basin from 2000 to 2019

Vegetation cover change and its response to climate extremes in the Yellow River Basin

Extreme climate events have increased in frequency and severity under the background of climate change, with vegetation growth exhibiting a sensitive response to them. By assimilating GIMMS NDVI and MODIS NDVI using the Residual Network, we obtained a long time series and high resolution NDVI dataset of the Yellow River Basin (YRB). The dataset was utilized for examining the spatiotemporal variability of NDVI and analyzing the response of vegetation cover to climate extremes with meteorological data. Our findings reveal the following: (1) A significant rise in NDVI was seen in the YRB, displaying a mean growth rate of 0.019/10a (p < 0.001). However, seasonal differences exist. The mean NDVI of multi-year declines from southeast to northwest, while the overall trend of vegetation cover improves. (2) The NDVI response to extreme temperature exhibits noticeable spatiotemporal differences. Daytime extreme high temperature in the northern YRB is negatively correlated with NDVI, while they are positively correlated in the lower YRB and the southern part of the middle YRB. Nighttime extreme high temperature exhibits a positive correlation with NDVI. Overall, NDVI displays a stronger response to extreme precipitation than to extreme temperature, with a negative correlation with CWD and a positive correlation with PRCPTOT. (3) The NDVI demonstrates a lagged response to climate extremes in the YRB, with a greater lag in response to extreme temperature than extreme precipitation. The research findings can provide scientific support for the future management and planning of vegetation in the YRB, as well as contribute to the promotion of ecological environment regulation and sustainable development.

Comparison of Typical Alpine Lake Surface Elevation Variations and Different Driving Forces by Remote Sensing Altimetry Method

Alpine lakes play a significant role in improving watershed ecology, adjusting water storage, and managing regional water resources. They are also a valuable freshwater reservoir, flood storage, and species gene pool in Central Asia. This article validated the accuracy of the CryoSat-2 footprints altimetry dataset for the Lake Bosten and Lake Issyk-Kul ranges. The time series for the surface elevations of the Central Asian alpine lakes Karakul and Chatyrkul were established, based on footprints altimetry data. The lake hydrological drivers were analyzed using remote sensing meteorological reanalysis data of the lake basins. The following main conclusions were reached. The CryoSat-2 footprints altimetry dataset has high confidence in lake surface elevation monitoring. Compared with Hydroweb monitoring results, the agreement between the monitoring results in the range between Lake Bosten and Lake Issyk-Kul are 0.96 and 0.84. The surface elevation of Lake Karakul shows an overall increasing trend with a variation rate of +7.7 cm/yr from 2010 to 2020, which has a positive correlation with the temperature in the basin. This indicates that the increased temperature, which results in the increased snow and ice meltwater in the basin, is the main driving force of the increased lake evolution. The lake surface elevation of Lake Chatyrkul shows an overall decreasing trend, with a variation rate of -9.9 cm/yr from 2010 to 2020, which has a negative correlation with the temperature in the basin. This suggests that Lake Chatyrkul is poorly recharged by snow and ice meltwater. The main driving force of its evolution is the increased evaporative output of the lake due to the increase in temperature. These conclusions prove that temperature and alpine glacial variability within the lake basin play an important role in lake surface elevation variations in alpine regions of Central Asia.

Effect of Vegetation Carryover and Climate Variability on the Seasonal Growth of Vegetation in the Upper and Middle Reaches of the Yellow River Basin

Vegetation dynamics are often affected by climate variability, but the past state of vegetation has a non-negligible impact on current vegetation growth. However, seasonal differences in the effects of these drivers on vegetation growth remain unclear, particularly in ecologically fragile areas. We used the normalized difference vegetation index (NDVI), gross primary productivity (GPP), and leaf area index (LAI) to describe the vegetation dynamic in the upper and middle reaches of the Yellow River basin (YRB). Three active vegetation growing seasons (early, peak, and late) were defined based on phenological metrics. In light of three vegetation indicators and the climatic data, we identified the correlation between the inter-annual variation of vegetation growth in the three sub-seasons. Then, we quantified the contributions of climate variability and the vegetation growth carryover (VGC) effect on seasonal vegetation greening between 2000–2019. Results showed that both the vegetation coverage and productivity in the study area increased over a 20-year period. The VGC effect dominated vegetation growth during the three active growing seasons, and the effect increased from early to late growing season. Vegetation in drought regions was found to generally have a stronger vegetation carryover ability, implying that negative disturbances might have severer effects on vegetation in these areas. The concurrent seasonal precipitation was another positive driving factor of vegetation greening. However, sunshine duration, including its immediate and lagged impacts, had a negative effect on vegetation growth. In addition, the VGC effect can sustain into the second year. The VGC effect showed that initial ecological restoration and sustainable conservation would promote vegetation growth and increase vegetation productivity. This study provides a comprehensive perspective on understanding the climate–vegetation interactions on a seasonal scale, which helps to accurately predict future vegetation dynamics over time in ecologically fragile areas.

Vegetation Dynamics and Their Influencing Factors in China from 1998 to 2019

Vegetation is a critical component of ecosystems that is influenced by climate change and human activities. It is therefore of great importance to investigate trends in vegetation dynamics and explore how these are influenced by climate and human activities. This will help formulate effective ecological restoration policies and ensure sustainable development. As the Normalized Difference Vegetation Index (NDVI) is strongly correlated with vegetation dynamics and may be used as a proxy measure for vegetation condition, the spatiotemporal characteristics of NDVI derived from SPOT/VEGETATION NDVI data in China over the 1998–2019 period were assessed using the Mann–Kendall test and the Hurst exponent. The Pearson correlation analysis and residual analysis methods were employed to analyze the influencing factors of NDVI dynamics. Integrating the results of the Hurst exponent and the NDVI trend analysis, it was found that the majority area of China is presenting an increasing NDVI trend at present but is likely to reverse in the future. A significant positive correlation between the NDVI and temperature was observed on the southeast coast of China and the north Qinghai–Tibet Plateau. Precipitation was the dominant factor affecting vegetation dynamics as indicated by a positive correlation with the NDVI for most parts of China except for the inland area in the Northwest and the Hengduan Mountains in Southwest China. Extreme temperature and extreme precipitation have also shown varying degrees of influence on vegetation dynamics at various locations. In addition, this study revealed trends of increasing NDVI, suggesting improved vegetation condition attributable to the implementation of ecological engineering projects. This study is helpful for studying the interaction mechanisms between terrestrial ecosystems and climate and for sustaining the ecological environment.

Changes in Vegetation Greenness and Their Influencing Factors in Southern China

Since the 21st century, China has experienced rapid development, and the spatial and temporal changes in vegetation cover have become increasingly significant. Southern China is a representative region for human activities, climate change, and vegetation change, but the current human understanding of the interactions between vegetation and its influencing factors is still very limited. In our study, we use NDVI as the vegetation greenness data, land cover data, temperature, precipitation, downgradient shortwave radiation, and CO2 data to investigate the interrelationship among vegetation, climate change, and human activities in southern China. The changes and their consistency were studied by trend analysis and Hurst exponent analysis. Then, the contribution of each influencing factor from 2001 to 2020 was quantified by random forest. The results showed that the vegetation in southern China showed an overall rising trend, and areas with a continuous changing trend were concentrated in the Pearl River Delta, western Guangdong, and eastern Guangdong, with a growth rate of 0.02∼0.04%. The vegetation in northern Guangdong did not change significantly. The main factor of NDVI spatial variation in southern China is the land-use factor, accounting for 79.4% of the variation, while climate factors produce further differences. The contributions and lagged effects of NDVI factors on different land-use types and the lagged effects of different climate factors are different and are related to the climate and vegetation background in Sourthern China. Our study is useful in estimating the contribution of NDVI change by each considered factor and formulating environmentally friendly regional development strategies and promoting human–land harmony.