Preliminary estimation of the realistic optimum temperature for vegetation growth in China.

The estimation of optimum temperature of vegetation growth is very useful for a wide range of applications such as agriculture and climate change studies. Thermal conditions substantially affect vegetation growth. In this study, the normalized difference vegetation index (NDVI) and daily temperature data set from 1982 to 2006 for China were used to examine optimum temperature of vegetation growth. Based on a simple analysis of ecological amplitude and Shelford's law of tolerance, a scientific framework for calculating the optimum temperature was constructed. The optimum temperature range and referenced optimum temperature (ROT) of terrestrial vegetation were obtained and explored over different eco-geographical regions of China. The results showed that the relationship between NDVI and air temperature was significant over almost all of China, indicating that terrestrial vegetation growth was closely related to thermal conditions. ROTs were different in various regions. The lowest ROT, about 7.0 °C, occurred in the Qinghai-Tibet Plateau, while the highest ROT, more than 22.0 °C, occurred in the middle and lower reaches of the Yangtze River and the Southern China region.

Urbanization expands the fluctuating difference in gross primary productivity between urban and rural areas from 2000 to 2018 in China.

Urban and rural vegetation are affected by both climate change and human activities, but the role of urbanization in vegetation productivity is unclear given the dual impacts. Here, we delineated urban area (UA) and rural area (RA), quantified the relative impacts of climate change and human activities on gross primary production (GPP) in 34 major cities (MCs) in China from 2000 to 2018, and analyzed the intrinsic impacts of urbanization on GPP. First, we found that the total urban impervious surface coverage (ISC) of the 34 MCs increased by 13.25 % and the mean annual GPP increased by 211 gC m-2 during the study period. GPP increased significantly in urban core areas, but decreased significantly in urban expansion areas, which was mainly due to a large amount of vegetation loss due to land use conversion. Second, the variability of GPP in UA was generally lower than in RA. Both climate change and human activities had a positive impact on GPP in UA and RA in the 34 MCs, of which the contribution was 49 % and 51 % in UA, and 76 % and 24 % in RA, respectively. Third, under climate change and human activities, the increase in GPP offset 4.96 % and 12.35 % of the impact of land use conversion on GPP in 2000 and 2018, respectively, which indicated that the offset strengthened over time. These findings emphasize the role of human activities in promoting carbon sequestration in urban vegetation, which is crucial for better understanding the processes and mechanisms of urban carbon cycles. Decision-makers can manage urban vegetation based on vegetation carbon sequestration potential as regions urbanize, aiding comprehensive decision-making.

Satellite-based ground PM2.5 estimation using a gradient boosting decision tree.

Fine particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5) is one of the major air pollutants risks to human health worldwide. Satellite-based aerosol optical depth (AOD) products are an effective metric for acquiring PM2.5 information, featuring broad coverage and high resolution, which compensate for the sparse and uneven distribution of existing monitoring stations. In this study, a gradient boosting decision tree (GBDT) model for estimating ground PM2.5 concentration directly from AOD products across China in 2017, integrating human activities and various natural variables was proposed. The GBDT model performed well in estimating temporal variability and spatial contrasts in daily PM2.5 concentrations, with relatively high fitted model (10-fold cross-validation) coefficients of determination of 0.98 (0.81), low root mean square errors of 3.82 (11.57) µg/m3, and mean absolute error of 1.44 (7.45) µg/m3. Seasonal examinations revealed that summer had the cleanest air with the highest estimation accuracies, whereas winter had the most polluted air with the lowest estimation accuracies. The model successfully captured the PM2.5 distribution pattern across China in 2017, showing high levels in southwest Xinjiang, the North China Plain, and the Sichuan Basin, especially in winter. Compared with other models, the GBDT model showed the highest performance in the estimation of PM2.5 with a 3-km resolution. This algorithm can be adopted to improve the accuracy of PM2.5 estimation with higher spatial resolution, especially in summer. In general, this study provided a potential method of improving the accuracy of satellite-based ground PM2.5 estimation.

Estimating the Impact of Ecological Migrants on the South-to-North Water Diversion in China.

The South-to-North Water Diversion (SNWD) provides significant benefits in facilitating water security and improving ecology in northern China. However, few studies have estimated the water value of the SNWD and the corresponding subsequent subsidies of the ecological migrants in Xichuan County displaced by the project. Based on the Google Earth Engine (GEE), this study analyzed the water ecosystem changes in Xichuan County in 2000-2020 and valued the water transfer of the SNWD. We calculated the water cost, the water value of the trunk line project, and the four provinces (Hebei, Henan, Beijing, and Tianjin) of CNY 4.04, 39.64, and 120.93 billion, respectively, and the proportion of the three was 1:10:30 during 2014-2020. The water ecosystem area showed a rapid increase when the SNWD became operational since the end of 2014. The subsequent annual subsidy gap of ecological migrants was CNY 0.84 billion, which only accounted for 4.31% of the gross profit of SNWD. Our results imply that relevant water sectors have sufficient profits to support corresponding subsequent subsidies for ecological migrants. Ecological migrants are a major challenge for water transfer projects. Overall, this study fills a gap of interactions between subsequent policies and ecological migrants and provides a typical case for managing the migration problem caused by sustainable water management worldwide.

Contribution of anthropogenic CO2 in China to global radiative forcing and its offset by the ecosystem during 2000-2015.

As the world's largest developing country, quantifying China's CO2 contribution to global warming is important for assessing the climate effects of anthropogenic and natural factors. We used global CO2 assimilation data from 2000 to 2015 and a carbon-climate parameterized scheme to analyze anthropogenic carbon emissions and their climatic effects while considering the climate effects of the terrestrial ecosystem carbon sink. Three results are notable: (1) From 2000 to 2015, global anthropogenic emissions increased from 2.48 to 3.45 mol m-2 , and net emission (sum of anthropogenic and natural emissions) rose from 1.24 to 2.51 mol m-2 ; China's contribution of anthropogenic emissions to global anthropogenic emission was 34.78% and to net emission 39.65%. (2) By 2015, radiative forcing (RF) caused by CO2 absorption in the global terrestrial ecosystem was -0.18 Wm-2 , and this offset accounts for 30.96% of the warming effect of global anthropogenic carbon emissions; in China, RF caused by the terrestrial ecosystem was -0.04 Wm-2 , and this offset accounts for 20.27% of the warming effect of China's anthropogenic carbon emissions. (3) Using CO2 assimilation data and sectoral inventory data, China's contribution of carbon emissions to global RF was 10.02% and 9.73%, respectively, and China's contribution of net RF to global RF was 7.93%. Our findings highlight the importance of ecosystems on mitigating climate warming.

Spatial Association of Urbanization in the Yangtze River Delta, China.

China is urbanizing rapidly, but current research into the spatiotemporal characteristics of urbanization often ignores the spatial and evolutionary associations of cities. Using the theory of spatial polarization and diffusion, together with a systematic analysis method, this study examined the spatial development process of urbanization in the Yangtze River Delta (YRD) region of China during 1995-2015. Results showed clear patterns in the scale and hierarchy of regional urbanization. Shanghai ranked first as the regional growth pole, while Nanjing, Hangzhou, and Suzhou ranked second. The spatial linkage index of urbanization showed that 10 cities (including Shanghai, Suzhou, and Hangzhou) constituted the densest spatial linkage network. The diffused area often became spatially polarized before the polarization then weakened as a new diffusion stage developed. The study also revealed that the spatial correlation urbanization differences in the YRD generally decreased. The polarization index revealed increasing spatial integration and correlation of urbanization in the YRD. This study proved that each city had a different spatial role in relation to other cities during different stages of development. Investigation of the driving mechanism of regional urbanization indicated that industrial modernization and relocation within the region provided the main endogenous driving force for the formation of spatial polarization or diffusion. Our research provides important scientific support for regional development planning. Furthermore, our analysis of the impact of spatial correlation within cities or a region could provide an important reference in relation to the regional environment and public health.

Land Cover Change Intensifies Actual and Potential Radiative Forcing through CO2 in South and Southeast Asia from 1992 to 2015.

Land cover change (LCC) and its impact on CO2 sequestration and radiative forcing (RF) could dramatically affect climate change, but there has been little effort to address this issue in South and Southeast Asia over a long period of time using actual land cover information. In this study, annual land cover data from 1992 to 2015 were used to assess the CO2 flux and corresponding RF due to LCC in South and Southeast Asia. The results showed that 553.2 × 103 km2 of the region experienced LCC during this period, mostly due to land reclamation, urban expansion, and deforestation. These LCC caused a marked net decrease in net ecosystem productivity (NEP) as a composite of the various land cover categories during the whole study period, especially since 2001. The CO2 sequestration was 2160 TgCO2 during the early 1990s however cumulative sequestration decreased by 414.95 TgCO2 by 2015. Correspondingly, the cooling effect of NEP, i.e. the total actual RF, was -0.366 W m-2 in South and Southeast Asia between 1992 and 2015. However, the potential RF of the cumulatively reduced NEP due to LCC relative to the 1990s resulted in a warming effect of 2.33 × 10-3 W m-2 in 2015. Our study provides an applicable framework to accurately assess the potential effect of large-scale LCC on climate.

Temporal consistency between gross primary production and solar-induced chlorophyll fluorescence in the ten most populous megacity areas over years.

The gross primary production (GPP) of vegetation in urban areas plays an important role in the study of urban ecology. It is difficult however, to accurately estimate GPP in urban areas, mostly due to the complexity of impervious land surfaces, buildings, vegetation, and management. Recently, we used the Vegetation Photosynthesis Model (VPM), climate data, and satellite images to estimate the GPP of terrestrial ecosystems including urban areas. Here, we report VPM-based GPP (GPPvpm) estimates for the world's ten most populous megacities during 2000-2014. The seasonal dynamics of GPPvpm during 2007-2014 in the ten megacities track well that of the solar-induced chlorophyll fluorescence (SIF) data from GOME-2 at 0.5° × 0.5° resolution. Annual GPPvpm during 2000-2014 also shows substantial variation among the ten megacities, and year-to-year trends show increases, no change, and decreases. Urban expansion and vegetation collectively impact GPP variations in these megacities. The results of this study demonstrate the potential of a satellite-based vegetation photosynthesis model for diagnostic studies of GPP and the terrestrial carbon cycle in urban areas.