Chinese cities show different trend toward carbon peak.

Understanding the disparities in carbon emission trend among cities is critical for achieving carbon peak goal. However, the status and trends of carbon peaking and reduction in various city types are still unclear. Therefore, this study classified 315 Chinese cities according to their economic and industrial structure by SOM-K-means, aiming to evaluate the trends and dynamic drivers of carbon peaking progress in different city types. The findings reveal a decline in carbon emissions in 110 cities (34.9 %) since 2020. Notably, all city types show potential for carbon reduction and achieving carbon peaking. Specifically, resource-based cities and high-end service cities have the most effect on reducing emissions, with 48.4 % and 42.1 % of the cities declining in carbon emissions. Energy-based and heavy industrial cities face heightened pressure to reduce carbon emissions. Additionally, in high-end service cities, energy efficiency and investment intensity contribute to emission reduction, while industrial structure adjustment decrease carbon emissions in resource-based cities. Furthermore, enhancing energy efficiency effects and R&D intensity are effective ways to significantly reduce carbon emissions in heavy industrial cities. We conclude that differentiating carbon reduction pathways for different cities should constitute be a breakthrough in achieving the goal of carbon peaking. These insights provide recommendations for cities that have yet to reach their carbon peak for both China and other developing countries.

Assessing urban wetlands dynamics in Wuhan and Nanchang, China.

Urban wetlands play a crucial role in sustainable social development. However, current research mainly focuses on specific wetland types, and fine extraction of urban wetlands remains a challenge. This study proposes a fine extraction framework based on hierarchical decision trees and shape features for urban wetlands, using Sentinel-2 remote sensing data to obtain detailed wetland data of Wuhan and Nanchang from 2016 to 2022. Our framework applies random forests to classify land cover, extracts urban fine wetlands by hierarchical decision trees and shape features, and assesses the dynamics of wetlands in the two cities. We also analyzed and discussed the characteristics of urban wetlands in the two cities. The results show that wetland accuracies of Wuhan and Nanchang are greater than 84.5 % and 82.9 %, respectively. The wetland areas of Wuhan in 2016, 2019, and 2022 are 1969.4 km2, 1713.8 km2, and 1681.1 km2, while those in Nanchang are 1405.9 km2, 1361.6 km2, and 766.9 km2. Inland wetlands are the main wetland types in both regions, with lake wetlands accounting for the highest proportion (over 40 %). The urban wetlands in the two cities exhibit different spatial and temporal evolution patterns, with varying change trends of wetland area and the structural proportions of fine wetlands. Besides, Wuhan's urban wetlands are primarily located in the south, while Nanchang's urban wetlands are concentrated in the east, exhibiting higher spatial and temporal dynamics. Analysis suggests that the reduced urban wetlands from 2016 to 2022 are related to fluctuating decreasing precipitation, growing population, and gross domestic product (GDP). Our study provides support for the conservation of urban wetland resources in Wuhan and Nanchang and highlights the need for targeted management strategies.

Vegetation as the catalyst for water circulation on global terrestrial ecosystem.

Global climate change is expected to further intensify the global water cycle, leading to more rapid evaporation and more intense precipitation. At the same time, the growth and expansion of natural vegetation caused by climate change and human activities create potential conflicts between ecosystems and humans over available water resources. Clarifying how terrestrial ecosystem evapotranspiration responds to global precipitation and vegetation facilitates a better understanding of and prediction for the responses of global ecosystem energy, water, and carbon budgets under climate change. Relying on the spatial and temporal distribution of evapotranspiration, precipitation, and solar-induced chlorophyll fluorescence (SIF) from remote sensing platforms, we decouple the interaction mechanism of evapotranspiration, precipitation, and vegetation in linear and nonlinear scenarios using correlation and partial correlation analysis, multiple linear regression analysis, and binning. Major conclusions are as follows: (1) As a natural catalyst of the global water cycle, vegetation plays a crucial role in regulating the relationship between climate change and the water­carbon-energy cycle. (2) Vegetation, a key parameter affecting the water cycle, participates in the entire water cycle process. (3) The increase in vegetation productivity and photosynthesis plays a dominant role in promoting evapotranspiration in vegetated areas, while the increase in precipitation dominates the promotion of evapotranspiration in non-vegetated areas.

Assessing the impact of drought-land cover change on global vegetation greenness and productivity.

Drought-land cover change (D-LCC) is considered to be an important stress factor that affects vegetation greenness and productivity (VG&P) in global terrestrial ecosystems. Understanding the effects of D-LCC on VG&P benefits the development of terrestrial ecosystem models and the prediction of ecosystem evolution. However, till today, the mechanism remains underexploited. In this study, based on the Theil-Sen median estimator and Mann-Kendall test, Hurst exponent evaluation and rescaled range analysis (R/S), Pearson and Partial correlation coefficient analyses, we explore the spatiotemporal distribution characteristics and future trends of Leaf area index (LAI), Net primary productivity (NPP), Solar-induced chlorophyll fluorescence (SIF), Standardized precipitation evapotranspiration index (SPEI), Soil moisture (SM), Land cover type (LC), and the impact mechanism of D-LCC on global VG&P. Our results provide four major insights. First, three independent satellite observations consistently indicate that the world is experiencing an increasing trend of VG&P: LAI (17.69 %), NPP (20.32 %) and SIF (16.46 %). Nonetheless, productivity-reducing trends are unfolding in some tropical regions, notably the Amazon rainforest and the Congo basin. Second, from 2001 to 2020, the frequency, severity, duration, and scope of global droughts have been increasing. Third, the impact of land cover change on global VG&P is region-dependent. Finally, our results indicate that the continuous growth of VG&P in the global vegetation area is likely to become more difficult to maintain.

Assessment of the importance of increasing temperature and decreasing soil moisture on global ecosystem productivity using solar-induced chlorophyll fluorescence.

The accurate assessment of the global gross primary productivity (GPP) of vegetation is the key to estimating the global carbon cycle. Temperature (Ts) and soil moisture (SM) are essential for vegetation growth. It is acknowledged that the global Ts has shown an increasing trend, yet SM has shown a decreasing trend. However, the importance of SM and Ts changes on the productivity of global ecosystems remains unclear, as SM and Ts are strongly coupled through soil-atmosphere interactions. Using solar-induced chlorophyll fluorescence (SIF) as a proxy for GPP and by decoupling SM and Ts changes, our investigation shows Ts plays a more important role in SIF in 60% of the vegetation areas. Overall, increased Ts promotes SIF by mitigating the resistance from SM's reduction. However, the importance of SM and Ts varies, given different vegetation types. The results show that in the humid zone, the variation of Ts plays a more important role in SIF, but in the arid and semi-arid zones, the variation of SM plays a more important role; in the semi-humid zone, the disparity in the importance of SM and Ts is difficult to unravel. In addition, our results suggest that SIF is very sensitive to aridity gradients in arid and semi-arid ecosystems. By decoupling the intertwined SM-Ts impact on SIF, our study provides essential evidence that benefits future investigation on the factors the influence ecosystem productivity at regional or global scales.

Enhanced drought detection and monitoring using sun-induced chlorophyll fluorescence over Hulun Buir Grassland, China.

Drought is one of the most damaging events in the grassland ecosystem. The detection and monitoring of drought are very important to maintain the balance of the grassland ecosystem. The potential of Sun-induced Chlorophyll Fluorescence (SIF) for drought detection and monitoring were explored in this study. Based on significant negative anomalies of self-calibrating Palmer drought severity index (scPDSI), precipitation (PPT), soil moisture (SM)，surface water storage (SWS), and a significant positive anomaly of land surface temperature (LST), a severe drought event was accurately detected from June to August in 2016 over Hulun Buir Grassland. The far-red SIF was decomposed into its mechanical parts such as SIF, absorbed photosynthetically active radiation (APAR), normalized by APAR (SIFyield), physiological SIF emission yield (SIFpey), and total emitted SIF (SIFte), which were more sensitive to drought than the vegetation indices (VIs), including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), modified soil adjusted vegetation index (MSAVI2), and near-infrared reflectance of vegetation (NIRV). SIF and NIRV represented the SIF indicators and the VIs, respectively, which were most affected by drought, with a decrease of -2.67% and 4.19% in June, 50.93% and 31.76% in July, and 55.58% and 39.44% in August. The correlations between anomalies of SIF indicators, VIs, and anomalies of LST, wind speed (WS) were a strong negative correlation, indicating that their reduction was caused by the anomalies of LST and WS. Moreover, the SIF indicators had a shorter lag time in response to meteorological drought than VIs. Besides, the correlations between SIF-based drought indices such as drought fluorescence monitoring index (DFMI), SIF health index (SHI), and SM were - 0.709 and - 0.783 (P < 0.01), respectively, higher than the conventional drought indices. Moreover, DFMI and SHI could reflect the changes of SM in advance, while the conventional drought indices mostly lagged behind the changes of SM. This study shows that SIF can enhance drought detection, and the SIF-based drought index can be well suitable for drought monitoring.