Land surface temperature (LST) is an important parameter to evaluate environmental changes. In this paper, time series analysis was conducted to estimate the interannual variations in global LST from 2001 to 2016 based on moderate resolution imaging spectroradiometer (MODIS) LST, and normalized difference vegetation index (NDVI) products and fine particulate matter (PM_2.5) data from the Atmospheric Composition Analysis Group. The results showed that LST, seasonally integrated normalized difference vegetation index (SINDVI), and PM_2.5 increased by 0.17 K, 0.04, and 1.02 μg/m³ in the period of 2001–2016, respectively. During the past 16 years, LST showed an increasing trend in most areas, with two peaks of 1.58 K and 1.85 K at 72°N and 48°S, respectively. Marked warming also appeared in the Arctic. On the contrary, remarkable decrease in LST occurred in Antarctic. In most parts of the world, LST was affected by the variation in vegetation cover and air pollutant, which can be detected by the satellite. In the Northern Hemisphere, positive relations between SINDVI and LST were found; however, in the Southern Hemisphere, negative correlations were detected. The impact of PM_2.5 on LST was more complex. On the whole, LST increased with a small increase in PM_2.5 concentrations but decreased with a marked increase in PM_2.5. The study provides insights on the complex relationship between vegetation cover, air pollution, and land surface temperature.

Separating the impact of climate change and human activities on runoff is an important topic in hydrology, and a large number of methods and theories have been widely used. In this paper, we review the current papers on separating the impacts of climate and human activities on runoff, summarize the progress of relevant research methods and applications in recent years, and discuss future research needs and directions.

There is growing awareness of the need for regenerative practices in the fight against biodiversity loss and climate change. Yet, we lack a mechanistic understanding of how microbial community composition and functioning are likely to change alongside transition from high-density tillage to large-scale vegetation restoration. Here, we investigated the functional dynamics of microbial communities following a complete vegetation successional chronosequence in a subtropical zone, Southwestern China, using shotgun metagenomics approaches. The contents of total soil phosphorus (P), available P, litter P, and microbial biomass P decreased significantly during vegetation succession, indicating that P is the most critical limiting nutrient. The abundance of genes related to P-uptake and transport, inorganic P-solubilization, organic P-mineralization, and P-starvation response regulation significantly increased with successional time, indicating an increased microbial “mining” for P under P limitation. Multi-analysis demonstrated microbial P limitation strongly inhibits carbon (C) catabolism potential, resulting in a significant decrease in carbohydrate-active enzyme family gene abundances. Nevertheless, over successional time, microorganisms increased investment in genes involved in degradation-resistant compounds (lignin and its aromatic compounds) to acquire P resources in the litter. Our study provides functional gene-level insights into how P limitation during vegetation succession in subtropical regions inhibits soil microbial C metabolic processes, thereby advancing our understanding of belowground C cycling and microbial metabolic feedback during forest restoration.

The changing climate and intensifying human activities have made an impact on the hydrological processes in the upper Yangtze River (UYR), but quantifying their effects remains uncertain. This study used the Budyko framework to investigate the response of runoff (Q) to climate change and human activities during 1956–2017 and evaluate the impacts of human activities, including land use/cover change, water use, dam construction, and vegetation change, on watershed characteristic. Results show that climate change is the dominant driver of Q variations in the Wujiang River (WJR), Jialing River (JLR), and Jinsha River (JSR) watersheds, with contributions of 58.6%, 66.9%, and 67.6%, respectively. However, in Mingjiang River (MJR) and UYR watersheds, human activities contribute more to Q variations with 55.2% and 51.2%, respectively. Human activities play important roles in variation of watershed characteristics, and they can explain 22%, 26%, 36%, 25%, and 53% of the watershed character change in UYR, WJR, JLR, MJR, and JSR, respectively. This study conducts a comprehensive analysis of the causes of Q change in UYR, and provides a new perspective to explore the effects of specific human activities on watershed characteristics.