Trends in vegetation productivity related to climate change in China's Pearl River Delta

Protected areas mitigate the reduction in habitat suitability for swans under climate change: A case study in the Yellow River Basin

Changes in migratory birds' habitats are important indicators of the health of global ecosystems. However, the habitat dynamics of the flagship swan species in China's major river basins and the adaptive strategies these species employ to respond to climate change have remained unclear. Using citizen science data, survey data, and species distribution models, we explored how three future climate scenarios for 2040-2060 affect habitat suitability for migratory swans in the Yellow River Basin. We also evaluated the role of protected areas (PAs) in mitigating the negative impacts of climate change. We found that (1) under current climate conditions, the Mute Swan (Cygnus olor), the Bewick's Swan (Cygnus columbianus bewickii), and the Whooper Swan (Cygnus cygnus) occupy substantial suitable habitats, with the Whooper Swan having the most extensive range. (2) the Mute Swan and the Bewick's Swan were predicted to experience the largest habitat loss under the high-emission scenario, while the Whooper Swan would benefit from climate change by gaining suitable habitat, especially under the medium-emission scenario. (3) PAs were most effective in mitigating the adverse effects of climate change on habitat suitability for the Mute Swan, followed by the Whooper Swan, with limited mitigating effect for the Bewick's Swan. These findings highlight the need for species-specific conservation strategies and the critical role of PAs in preserving habitat suitability under climate change.

Embedding nature-based solutions into the social cost of carbon

China, the world's largest CO₂ emitter, is making every effort to transition to a low-carbon economy and fulfill its part of a concerted global commitment to combating climate change. In tandem with decarbonizing energy and industries, feasible supplementary measures are urgently needed to help remove anthropogenic CO₂ from the atmosphere. A burgeoning literature has emphasized the CO₂ removal capability of land re-naturalization (such as afforestation and wetland restoration), thereby regarding cognate land-use conversions as Nature-based Solutions (NbS) and potential climate policy options. However, little empirical evidence exists concerning the effectiveness of different land re-naturalization pathways (such as converting wetlands to forests or agricultural lands to grasslands), and it also remains unclear how NbS alternatives (i.e., land-use conversions resulting in negative CO₂ emission) and non-NbS options (i.e., land-use conversions resulting in positive CO₂ emission) could affect the social cost of carbon (SCC), a conventional measurement for prescribing carbon mitigation approaches. This study aims to fill in this knowledge gap via embedding NbS into the dynamic integrated climate-economics (DICE) model to quantify their impacts on the SCC. Using the Pearl River Delta region (south China) as a case study for the temporal horizon during 2000-2020, we find that both positive and negative CO₂ fluxes have been brought by different natural/semi-natural land conversions, affecting the SCC correspondingly. A total of 7 out of 17 types of land-use conversions could be identified as feasible NbS interventions, including forest restoration, forest-to-wetland, grassland-to-forest, grassland-to-wetland, grassland-to-cropland, cropland-to-forest, and cropland-to-wetland conversions, which could reduce the SCC values (comparing 2020 base-year with 2000 base-year) by 0.0132, 0.0009, 0.0033, 0.0030, 0.0001, 0.0082, and 0.0001 (USD/tCO₂), respectively. While the SCC is mainly determined by energy and industrial structure, the overall effect of NbS is larger than the sum of land urbanization and non-NbS land-use conversions. Via embedding the real-world inter-dynamics of land-use conversions into the SCC quantification, this study presents a pioneer assessment of the impacts of NbS on the SCC in an integrated framework, sheds important insights into the effectiveness of NbS, and offers practical implications for policy-makers to devise comprehensive policies covering all feasible CO₂ abatement options.

Spatiotemporal patterns and driving forces of remotely sensed urban agglomeration heat islands in South China

Rapid urbanization and increasing population have widely caused the urban heat island effect. Due to the decreasing distance between cities, there is an urgent need to reevaluate regional heat island intensity (RHII) in an urban agglomeration scale by considering all cities together instead of from conventional single city perspective. Using cropland land surface temperature as the reference temperature, we assessed the diurnal and seasonal RHII variations in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) urban agglomeration in South China. The boosted regression trees (BRT) method was then used to analyze the relative influence and marginal effect of possible drivers to disentangle their underlying driving mechanisms. Results showed that the daytime RHII spatial patterns averaged over the period 2003-2017 illustrated higher intensity and greater heterogeneity than their nighttime counterparts, especially for the stronger RHII in the central GBA around the estuary area. Seasonal dynamics of daytime RHII displayed a generally descending trend from summer to winter, but the opposite for night. BRT analyses indicated that at both annual and seasonal scales, vegetation fraction and background temperature had a dominant influence on RHII in daytime and nighttime, respectively. RHII variations were also considerably attributed to other drivers for different seasons. For daytime RHII, the other influential drivers included anthropogenic heat emissions and precipitation in summer, anthropogenic heat emissions and terrain in the transition season, and temperature and albedo in winter. For nighttime RHII, anthropogenic heat emissions for all seasons, vegetation activities for summer and the transition season, and precipitation for winter also had important contributions. The marginal effects detected the different nonlinear responses of diurnal and seasonal RHII to potential drivers, suggesting contrasting driving mechanisms. Results of this study highlight more targeted and informed strategies for RHII mitigation in the GBA and provide helpful insights into RHI evaluation in other urban agglomerations.

Spatial Heterogeneity and Complexity of the Impact of Extreme Climate on Vegetation in China

The impact of extreme climate on natural ecosystems and socioeconomic systems is more serious than that of the climate’s mean state. Based on the data of 1698 meteorological stations in China from 2001 to 2018, this study calculated the 27 extreme climate indices of the Expert Team on Climate Change Detection and Indices (ETCCDI). Through correlation analysis and collinearity diagnostics, we selected two representative extreme temperature indices and three extreme precipitation indices. The spatial scale of the impact of extreme climate on Normalized Difference Vegetation Index (NDVI) in China during the growing season from 2001 to 2018 was quantitatively analyzed, and the complexity of the dominant factors in different regions was discussed via clustering analysis. The research results show that extreme climate indices have a scale effect on vegetation. There are spatial heterogeneities in the impacts of different extreme climate indices on vegetation, and these impacts varied between the local, regional and national scales. The relationship between the maximum length of a dry spell (CDD) and NDVI was the most spatially nonstationary, and mostly occurred on the local scale, while the effect of annual total precipitation when the daily precipitation amount was more than the 95th percentile (R95pTOT) showed the greatest spatial stability, and mainly manifested at the national scale. Under the current extreme climate conditions, extreme precipitation promotes vegetation growth, while the influence of extreme temperature is more complicated. As regards intensity and range, the impact of extreme climate on NDVI in China over the past 18 years can be categorized into five types: the humidity-promoting type, the cold-promoting and drought-inhibiting compound type, the drought-inhibiting type, the heat-promoting and drought-inhibiting compound type, and the heat-promoting and humidity-promoting compound type. Drought is the greatest threat to vegetation associated with extreme climate in China.