Ecological engineering induced carbon sinks shifting from decreasing to increasing during 1981-2019 in China.

Substantial evidence shows that most of China's terrestrial ecosystems are important carbon sinks. However, the nonlinear trend of the carbon sinks and their nonlinear response to driving factors are unclear. Taking the net ecosystem productivity (NEP) as a proxy for the ecosystem carbon sink, the nonlinear relationships between the monotonically increasing trends and decreasing to increasing shifts in the carbon sink to climate change and ecological engineering were investigated based on ensemble empirical mode decomposition (EEMD) and machine learning algorithm (boosted regression tree model, BRT). The results suggest that 16.75 % of the carbon sinks in China experienced a monotonic increase. Additionally, 20.55 % of the carbon sinks shifted from decreasing to increasing trends, primarily after 1995, and these carbon sinks were located in the key ecological engineering areas, such as the middle reaches of the Yellow River shelterbelt program area, the Liaohe shelterbelt program area, the Grain to Green program area, and the Three-North Forest shelterbelt program area. Moreover, carbon sinks exhibited strong spatial autocorrelation with low-low clustering in the north and high-high clustering in the south. The increase in CO2 (slope of CO2 < 1.8 g/m2/s/y) and solar radiation (slope of radiation >1 w/m2/y) promoted the monotonic increase in the carbon sinks in the center of China. The increase in the areas of forest and grassland shifted the carbon sink trend from decreasing to increasing in the key ecological engineering program areas, and economic development reversed the carbon sink reduction in the Pearl River shelterbelt program area. These findings highlight the positive effect of ecological engineering on carbon sinks and provide adaptation strategies and guidance for China to achieve the "carbon neutrality" target.

Spatial variations and mechanisms for the stability of water use efficiency in China.

A clearer understanding of the stability of water use efficiency (WUE) and its driving factors contributes to improving water use efficiency and strengthening water resource management. However, the stability of WUE is unclear. Based on the EEMD method, this study analyses the spatial variations and mechanisms for the stability of WUE in China, especially in the National Forest Protection Project (NFPP) areas. It is found that the stable WUE was dominated by non-significant trends and increasing trends in China, accounting for 33.59% and 34.19%, respectively. The non-significant trend of stable WUE was mainly located in the Three-North shelterbelt program area, and the increasing trend of stable WUE was in Huaihe and Taihu, Taihang Mountains, and Pearl River shelterbelt program areas. Precipitation and soil moisture promoted the stable WUE in these project areas. The unstable WUE was dominated by positive reversals or negative reversals of WUE trends. The positive reversals of unstable WUE were mainly located in the Yellow River shelterbelt program areas, which was promoted by temperature and radiation, while the negative reversals of unstable WUE were mainly distributed in the Yangtze River and Liaohe shelterbelt program areas, which were mainly induced by saturation water vapor pressure difference (VPD). Our results highlight that some ecological restoration programs need to be improved to cope with the negative climate impact on the stability of WUE.

Persistence of increasing vegetation gross primary production under the interactions of climate change and land use changes in Northwest China.

Substantial evidence suggests a widespread increase in global vegetation gross primary production (GPP) since the 1980s. If the increasing trend of GPP remains unchanged in the future, it is considered to be the persistence of increasing GPP. However, it is still unknown whether the vegetation increasing GPP is persistent under the interactive effects of climate change and land use changes in Northwest China. Using the Mann-Kendall and boosted regression tree models, we constructed the relationship between the increasing GPP and environmental variables, and further explored its persistence under the interactions between climate change and land use changes under SSP245 and SSP585 scenarios. The results indicated that: (1) Land use change (8.01%) was the most important variable for the increasing GPP. The surface net solar radiation (6.79%), and maximum temperature of the warmest month (6.78%) were also very important. Moreover, mean temperature of the warmest quarter had strong interactions with mean precipitation of the warmest quarter (9.82%) and land use change (8.24%). (2) Under the SSP245 scenario, the persistence of increasing GPP accounted for 65.06% of the area in 2100, mainly located in Qinghai, Ningxia, and Shaanxi, while it only accounted for 19.50% under the SSP585 scenario. (3) The SSP245 scenario moderate warming leads to a slight ecosystem benefit, with more areas developing an increase in GPP due to climate and land use change factors. On the other hand, under SSP585 scenario, there are widespread losses of increasing GPP, driven largely by climate change, while ecological engineering is conducive to the persistence of increasing GPP in southern Qinghai. The results highlight the importance of the interactive effects of climate change and land use changes for predicting the persistence of vegetation change.

Nonlinear relationship of greening and shifts from greening to browning in vegetation with nature and human factors along the Silk Road Economic Belt.

Understanding the trends in vegetation evolution and their driving factors is critical to revealing changes in ecosystem's structure and function. However, less is known about the nonlinear relationship between greening trends and greening to browning shifts in vegetation and its driving factors. Based on ensemble empirical mode decomposition (EEMD) and Boosted Regression Tree (BRT) methods, we investigated the nonlinear relationships of the greening trends and greening to browning shifts in vegetation to climate change, elevation, and human activities along the Silk Road Economic Belt (SREB). Results indicated that: (1) For vegetation with greening trends, although the total importance of climate-related variables was greater than human activities (59.57% and 35.33%, respectively), Land use changes (14.91%) and CO2 (9.41%) were more important than the other nature-related variables. In addition, volumetric soil moisture (8.72%), precipitation of warmest quarter (6.50%), and daily snow water equivalent (5.75%) were also very important. For vegetation with greening to browning shifts, the total contributions of climate-related variables were also larger than human activities (69.10% and 19.36%, respectively). Moreover, elevation (11.54%), vapor pressure deficit (9.85%), annual mean temperature (6.84%), min temperature of the coldest month (5.98%), soil moisture (5.16%), and annual precipitation (5.11%) might be the main reasons. (2) Increased cropland and grassland, along with the land conversions from shrub land/sparse vegetated to forest could promote vegetation greening. Increased rates of CO2, volumetric soil moisture, warmest quarter precipitation, and daily snow water equivalent could also promote it. For vegetation with greening to browning shifts, low-altitude areas, increased vapor pressure deficit, decreased cold temperature, and drought could promote vegetation shift greening to browning. The nonlinear analysis can correctly reveal the actual trends in vegetation and its responses to driving factors, which will further provide adaptation strategies to protect ecology.

Pure, shared, and coupling effects of climate change and sea level rise on the future distribution of Spartina alterniflora along the Chinese coast.

AIM: Global change seriously threatens the salt marsh ecosystem, while it remains unclear how S. will respond to climate change and sea level rise. Here, we investigated interactions among variables and identified the impacts of climate change, sea level rise, and their interactions on the distribution of Spartina alterniflora. LOCATION: Northern Chinese coast and Southern Chinese coast. TAXON: Spartina alterniflora Loisel. METHODS: With global sensitivity analysis, we determined interactions among variables and their relative importance to the distribution of S. alterniflora. Integrating the Venn's four-set diagram, we built ecological niche models under current and three future scenarios to identify pure, shared, and coupling effects of climate change and sea level rise on the distribution of S. alterniflora. RESULTS: Mean diurnal range (Bio02) and Elevation were the two most critical variables controlling the distribution of S. alterniflora on the Chinese coast, and interactions among variables of the northern coast were much greater than that of the southern coast. Habitats change was mainly caused by pure effects of climate change, except habitats reduction on the southern coast. Pure effects of sea level rise were low, but it can influence habitats change through shared and coupling effects from complex interactions with climate change. Interactions of climate change and sea level rise can drive habitats change, and the changed habitats caused by shared and coupling effects were mainly distributed the areas near the landward side. MAIN CONCLUSIONS: Our research suggests paying attention to interactions among variables when calculating the relative importance of explanatory variables. Identifying pure, shared, and coupling effects of climate change and sea level rise for the distribution of S. alterniflora will provide scientific references for assessing the risk of similar coastal species.