Carbon dioxide (CO2) is a major climate forcing factor, closely related to human activities. Quantifying the contribution of CO2 emissions to the global radiative forcing (RF) is therefore important to evaluate climate effects caused by anthropogenic and natural factors. China, the United States (USA), Russia and Canada are the largest countries by land area, at different levels of socio-economic development. In this study, we used data from the CarbonTracker CO2 assimilation model (CT2017 data set) to analyze anthropogenic CO2 emissions and terrestrial ecosystem carbon sinks from 2000 to 2016. We derived net RF contributions and showed that anthropogenic CO2 emissions had increased significantly from 2000 to 2016, at a rate of 0.125 PgC yr-1. Over the same period, carbon uptake by terrestrial ecosystems increased at a rate of 0.003 PgC yr-1. Anthropogenic CO2 emissions in China and USA accounted for 87.19% of the total, while Russian terrestrial ecosystems were the largest carbon sink and absorbed 14.69 PgC. The resulting cooling effect was -0.013 W m-2 in 2016, representing an offset of -45.06% on climate warming induced by anthropogenic CO2. This indicates that net climate warming would be significantly overestimated if terrestrial ecosystems were not included in RF budget analyses. In terms of cumulative effects, we analyzed RFs using reference atmospheres of 1750, at the start of the Industrial Revolution, and 2000, the initial year of this study. Anthropogenic CO2 emissions in the study area contributed by + 0.42 W m-2 and +0.32 W m-2 to the global RF, relative to CO2 levels of 1750 and 2000, respectively. We also evaluated correlations between global mean atmospheric temperature and net, anthropogenic and natural RFs. We found that the combined (net) RF caused by CO2 emissions accounted for 30.3% of global mean temperature variations in 2000-2016.
Wetland ecosystems are crucial to the global carbon cycle. In this study, the Zhalong Wetland was investigated. Based on remote sensing and meteorological observation data from 1975-2018 and the downscaled fifth phase of the coupled model intercomparison project (CMIP5) climate projection dataset from 1961-2100, the parameters of a net primary productivity (NPP) climatic potential productivity model were adjusted, and the simulation ability of the CMIP5 coupled models was evaluated. On this basis, we analysed the spatial and temporal variations of land cover types and landscape transformation processes in the Zhalong Nature Reserve over the past 44 years. We also evaluated the influence of climate change on the NPP of the vegetation, microbial heterotrophic respiration (Rh), and net ecosystem productivity (NEP) of the Zhalong Wetland and predicted the carbon sequestration potential of the Zhalong Wetland from 2019-2029 under the representative concentration pathways (RCP) 4.5 and RCP 8.5 scenarios. Our results indicate the following: (1) Herbaceous bog was the primary land cover type of the Zhalong Nature Reserve, occupying an average area of 1168.02 ± 224.05 km 2, equivalent to 51.84% of the total reserve area. (2) Since 1975, the Zhalong Nature Reserve has undergone a dry-wet-dry transformation process. Excluding several wet periods during the mid-1980s to early 1990s, the reserve has remained a dry habitat, with particularly severe conditions from 2000 onwards. (3) The 1975-2018 mean NPP, Rh, and NEP values of the Zhalong Wetland were 500.21±52.76, 337.59±10.80, and 162.62±45.56 gC·m-2·a-1, respectively, and an evaluation of the carbon balance indicated that the reserve served as a carbon sink. (4) From 1975-2018, NPP showed a significant linear increase, Rh showed a highly significant linear increase, while the increase in the carbon absorption rate was smaller than the increase in the carbon release rate. (5) Variations in NPP and NEP were precipitation-driven, with the correlations of NPP and NEP with annual precipitation and summer precipitation being highly significantly positive (P < 0.001); variations in Rh were temperature-driven, with the correlations of Rh with the average annual, summer, and autumn temperatures being highly significantly positive (P < 0.001). The interaction of precipitation and temperature enhances the impact on NPP, Rh and NEP. (6) Under the RCP 4.5 and RCP 8.5 scenarios, the predicted carbon sequestration by the Zhalong Wetland from 2019-2029 was 2.421 (± 0.225) × 1011 gC·a-1 and 2.407 (± 0.382) × 1011 gC·a-1, respectively, which were both lower than the mean carbon sequestration during the last 44 years (2.467 (± 0.950) × 1011 gC·a-1). Future climate change may negatively contribute to the carbon sequestration potential of the Zhalong Wetland. The results of the present study are significant for enhancing the abilities of integrated eco-meteorological monitoring, evaluation, and early warning systems for wetlands.
Arbuscular mycorrhizal fungi (AMF) are universally mutualistic symbionts that colonize the fine roots of most vascular plants. However, the biogeographical patterns and driving factors of AMF diversity of plant roots in grasslands are not well investigated. In this study, we used high-throughput sequencing techniques and bioinformatics to evaluate the AMF richness of 333 individual plant roots in 21 natural grassland ecosystems in northern China, including the Loess Plateau (LP), the Mongolian Plateau (MP), and the Tibetan Plateau (TP). The AMF richness showed a significant parabolic trend with increasing longitude. In regional situations, the AMF richness in the grasslands of the MP (60.4 ± 1.47) was significantly higher than those of the LP (46.4 ± 1.43) and TP (44.3 ± 1.64). Plant traits (including plant families, genera, and functional groups) explained the most variation in the AMF richness across China’s grasslands, followed by energy and water; soil properties had the least effects. The results showed the biogeographical patterns of the AMF richness and the underlying dominant factors, providing synthetic data compilation and analyses in the AMF diversity in China’s grasslands.
Land use/cover change (LUCC) is a major factor affecting net primary production (NPP). According to the LUCC of the Loess Plateau from 2005 to 2015, the LUCC patterns in 2025 in three scenarios were predicted by using the Future Land Use Simulation (FLUS) model. Furthermore, taking the average NPP of various land use/cover types in 16 years as the reference scale, the changes in NPP in multi-scenario simulations are predicted and analyzed, and the impact of different land use/cover transfers on NPP is quantified. The results are as follows: (1) The land use/cover changes greatly in the baseline and fast development scenarios, and changes relatively little in the ecological protection scenarios. (2) The changes in NPP in different scenarios reflected the significant difference in the ecological protection effect. All the three scenarios promote an NPP increase, but the ecological protection scenario can promote NPP increases the most. (3) The changes in NPP caused by LUCC in the three scenarios reflected the significant difference in the various land use/cover types protection effect. Analyzing and predicting NPP changes in multi-scenario LUCC simulations in the future can provide a theoretical basis for decision makers to judge the future changes in ecological environments and ecological protection effects against different policy backgrounds.
?ngstr?m-Prescott equation (AP) is the algorithm recommended by the Food and Agriculture Organization (FAO) of the United Nations for calculating the surface solar radiation (Rs) to support the estimation of crop evapotranspiration. Thus, the as and bs coefficients in the AP are vital. This study aims to obtain coefficients as and bs in the AP, which are optimized for China’s comprehensive agricultural divisions. The average monthly solar radiation and relative sunshine duration data at 121 stations from 1957-2016 were collected. Using data from 1957 to 2010, we calculated the monthly as and bs coefficients for each subregion by least-squares regression. Then, taking the observation values of Rs from 2011 to 2016 as the true values, we estimated and compared the relative accuracy of Rs calculated using the regression values of coefficients as and bs and that calculated with the FAO recommended coefficients. The monthly coefficients, as and bs, of each subregion are significantly different, both temporally and spatially, from the FAO recommended coefficients. The relative error range (0-54%) of Rs calculated via the regression values of the as and bs coefficients is better than the relative error range (0-77%) of Rs calculated using the FAO suggested coefficients. The station-mean relative error was reduced by 1% to 6%. However, the regression values of the as and bs coefficients performed worse in certain months and agricultural subregions during verification. Therefore, we selected the as and bs coefficients with the minimum Rs estimation error as the final coefficients and constructed a coefficient recommendation table for 36 agricultural production and management subregions in China. These coefficient recommendations enrich the case study of coefficient calibration for the AP in China and can improve the accuracy of calculating Rs and crop evapotranspiration based on existing data.
Evaluation of tourism resources is necessary for tourism regionalization and planning and for the development of tourism destinations. Furthermore, the scientific evaluation of the status of existing tourism resources is important for optimally combining and rationally developing regional tourism resources. In this study, a conceptual model for estimating the ontological value of tourism resources was developed and an evaluation indicator system was designed for the ontological value. On the basis of the quantitative and spatial characteristics of regional tourism resources, six indicators were constructed: quantitative density, richness, dominance, combination, aggregation, and accessibility. Furthermore, spatial differentiation characteristics of the ontological value indicators of county-level tourism resources on Hainan Island were analyzed, and the ontological value of the tourism resources was comprehensively evaluated and ranked by using a fuzzy clustering evaluation method. Finally, the evaluation results were verified on the basis of the quantity, quality, and accessibility of regional tourism resources by using an expert scoring method. The results showed that the test results were consistent with the inferences drawn from the ontological value, indicating that the evaluation indicator system is scientific and reliable and that it is an effective alternative to existing evaluation indexes of regional tourism resources, which are inconsistent. The fuzzy clustering evaluation method overcomes the subjectivity in the evaluation process and is practical for the quantitative evaluation of regional tourism resources. The evaluation indicator system for regional tourism resources designed in this study can provide a reference for the evaluation of the tourism resource development value on a regional scale, and the evaluation results can facilitate informed policymaking for the rational development of regional tourism resources.
The coronavirus disease 2019 (COVID-19) pandemic continues to threaten lives and the economy around the world. Estimating the risk of COVID-19 can help in predicting spreading trends, identifying risk areas, and making public health decisions. In this study, we proposed a comparative risk assessment method to estimate comprehensive and dynamic COVID-19 risks by considering the pandemic severity and the healthcare system pressure and then employing the z-order curve and fractal theory. We took the COVID-19 cases from January 19-March 10, 2020 in China as our research object. The results and analysis revealed that (1) the proposed method demonstrated its feasibility to assess and illustrate pandemic risk; (2) the temporal patterns of the daily relative risk indices of 31 provinces were clustered into four groups (high-value, fluctuating-increase, inverted U-shaped, and low-stable); (3) the spatial distribution of the relative pandemic risk indicated a significant circular pattern centered on Hubei Province; and (4) healthcare system capacity is the key to reducing relative pandemic risk, and cases imported from abroad should be given more attention. The methods and results of this study will provide a methodological basis and practical guidance for regional pandemic risk assessment and public health decision-making.
Coronavirus disease 2019 (COVID-19) is continuing to spread globally and still poses a great threat to human health. Since its outbreak, it has had catastrophic effects on human society. A visual method of analyzing COVID-19 case information using spatio-temporal objects with multi-granularity is proposed based on the officially provided case information. This analysis reveals the spread of the epidemic, from the perspective of spatio-temporal objects, to provide references for related research and the formulation of epidemic prevention and control measures. The case information is abstracted, descripted, represented, and analyzed in the form of spatio-temporal objects through the construction of spatio-temporal case objects, multi-level visual expressions, and spatial correlation analysis. The rationality of the method is verified through visualization scenarios of case information statistics for China, Henan cases, and cases related to Shulan. The results show that the proposed method is helpful in the research and judgment of the development trend of the epidemic, the discovery of the transmission law, and the spatial traceability of the cases. It has a good portability and good expansion performance, so it can be used for the visual analysis of case information for other regions and can help users quickly discover the potential knowledge this information contains.
