Spatiotemporal distribution of grassland NPP in Gansu province, China from 1982 to 2011 and its impact factors

Time Effects of Global Change on Forest Productivity in China from 2001 to 2017

With global warming, the concentrations of fine particulate matter (PM_2.5) and greenhouse gases, such as CO₂, are increasing. However, it is still unknown whether these increases will affect vegetation productivity. Exploring the impacts of global warming on net primary productivity (NPP) will help us understand how ecosystem function responds to climate change in China. Using the Carnegie-Ames-Stanford Approach (CASA) ecosystem model based on remote sensing, we investigated the spatiotemporal changes in NPP across 1137 sites in China from 2001 to 2017. Our results revealed that: (1) Mean Annual Temperature (MAT) and Mean Annual Precipitation (MAP) were significantly positively correlated with NPP (p < 0.01), while PM_2.5 concentration and CO₂ emissions were significantly negatively correlated with NPP (p < 0.01). (2) The positive correlation between temperature, rainfall and NPP gradually weakened over time, while the negative correlation between PM_2.5 concentration, CO₂ emissions and NPP gradually strengthened over time. (3) High levels of PM_2.5 concentration and CO₂ emissions had negative effects on NPP, while high levels of MAT and MAP had positive effects on NPP.

Can human activities enhance the trade-off intensity of ecosystem services in arid inland river basins? Taking the Taolai River asin as an example

Driven by economic and social factors, more and more human beings intervene in nature to promote rapid economic and social development at the expense of ecosystem services (ES), which inevitably leads to the occurrence and even aggravation of ES trade-offs. Especially in the arid inland river basin is more serious. Therefore, this paper takes the Taolai River Basin as an example and uses the InVEST model to evaluate the spatial distribution of four typical ES, including carbon sequestration, oxygen release, windbreak and sand fixation, and water production, under the potential-actual states of the watershed. And use the Pearson correlation coefficient and the root mean square error (RMSE) to analyze the trade-off relationship between services from qualitative and quantitative aspects, respectively. Finally, the spatial matching types of trade-offs in the potential-actual states are discussed using Bivariate Local Indicators of Spatial Association, and the degree and scope of the impact of human activities on trade-offs are analyzed. The results show that the spatial distribution of the four ES has obvious heterogeneity in the potential-actual states, and the service volume of most services in the potential state is much higher than in the actual state. Secondly, there is a significant trade-offs relationship between Water production and Carbon sequestration and Oxygen release services under the potential state, while the actual state under the impact of human activities shows a significant synergistic relationship, which shows that human activities will not only increase the probability of trade-off will also increase the probability of synergy between ES. Finally, through the analysis of the meaning and causes of "high and low space dislocation" and "low and high space dislocation", it is shown that human activities will not only increase but also weaken the trade-off intensity of ES. The results of this study can provide a certain scientific basis for regional ecological environment planning and promote regional people to share ecological well-being.

Evolution and Climate Drivers of NDVI of Natural Vegetation during the Growing Season in the Arid Region of Northwest China

Vegetation plays an important role in linking water, atmosphere, and soil. The dynamic change in vegetation is an important indicator for the regulation of the terrestrial carbon balance and climate change. This study applied trend analysis, detrended correlation analysis, and the Hierarchical Partitioning Algorithm (HPA) to GIMMS NDVI3g data, meteorological data, and natural vegetation types for the period 1983 to 2015 to analyze the temporal and spatial changes in NDVI during the growing season and its driving factors in the arid region of northwestern China. The results showed that: (1) the growing season length (GSL) was delayed, with a regional trend of 8 d/33 a, due to a significant advancement in the start of the growing season (SOS, −7 d/33 a) and an insignificant delay to the end of growing season (EOS, 2 d/33 a). (2) The regional change in NDVI was mainly driven by temperature and precipitation, contributing to variations in NDVI of forest of 36% and 15%, respectively, and in the NDVI of grassland, of 35% and 21%, respectively. In particular, changes to forested land and medium-coverage grassland (Mgra) were closely related to temperature and precipitation, respectively. (3) The spatial distribution of the mean NDVI of forest was closely related with precipitation, temperature, and solar radiation, with these meteorological variables explaining 20%, 15%, and 10% of the variation in NDVI, respectively. Precipitation and solar radiation explained 29% and 17% of the variation in the NDVI of grassland, respectively. The study reveals the spatial–temporal evolution and driving mechanism of the NDVI of natural vegetation in the arid region of Northwest China, which can provide theoretical and data support for regional vegetation restoration and conservation.

Response of grassland net primary productivity to dry and wet climatic events in four grassland types in Inner Mongolia

Increasing frequency and intensity of climate extremes have profound impacts on grassland biodiversity functioning and stability. Using Moderate Resolution Imaging Spectroradiometer (MODIS) net primary productivity (NPP) data and standardized precipitation evapotranspiration index, we assessed the response of NPP to growing-season and annual climate extremes and time-lag of climatic conditions across four grassland types (meadow steppe, typical steppe, steppe desert, and desert steppe) in Inner Mongolia, China from the period 2000 to 2019. Results showed that annual NPP varied significantly across four grassland types, with the highest NPP in meadow steppe and the lowest in desert steppe. Annual NPP of all grassland types increased over the past 20 years, but NPP in meadow steppe and typical steppe decreased for the period 2012-2019. Irrespective of grassland type, the 1- and 2-month time-lag of climatic conditions showed significant effects on annual NPP. Growing-season climate was found the better predictor of annual NPP in all grassland types than the annual climate. Compared with growing-season normal climates, annual NPP was lowest in extreme dry events in all grasslands, while highest in extreme wet events in meadow steppe and typical steppe, and in moderate wet events in steppe desert and desert steppe. Typical steppe and steppe desert are highly vulnerable to the increasing intensity of climate extremes, as we found that the losses of NPP in these grasslands in extreme dry were almost double than that of moderate dry events. Surprisingly, for meadow steppe and desert steppe, the losses of NPP for both moderate and extreme dry events were almost the same, which highlights that a low-intensity drought may have profound impacts on the annual NPP of these grasslands. The study provides the key insight in scientific basis to improve our understanding of the effects of climate extremes on grassland NPP, which is critical to sustainable management of grassland and maintain ecosystem stability.