Peatlands are major natural carbon pool in terrestrial ecosystems globally and are essential to a variety of fields, including global ecology, hydrology, and ecosystem services. Under the context of climate change, the management and conservation of peatlands has become a topic of international concern. Nevertheless, few studies have yet systematized the overall international dynamics of existing peatland research. In this study, based on an approach integrating bibliometrics and a literature review, we systematically analyzed peatland research from a literature perspective. Alongside traditional bibliometric analyses (e.g., number of publications, research impact, and hot areas), recent top keywords in peatland research were found, including 'oil palm', 'tropical peatland', 'permafrost', and so on. Furthermore, six hot topics of peatland research were identified: (1) peatland development and the impacts and degradations, (2) the history of peatland development and factors of formation, (3) chemical element contaminants in peatlands, (4) tropical peatlands, (5) peat adsorption and its humic acids, and (6) the influence of peatland conservation on the ecosystem. In addition, this review found that the adverse consequences of peatland degradation in the context of climate change merit greater attention, that peatland-mapping techniques suitable for all regions are lacking, that a unified global assessment of carbon stocks in peatlands urgently needs to be established, spanning all countries, and that a reliable system for assessing peatland-ecosystem services needs to be implemented expeditiously. In this study, we argued that enhanced integration in research will bridge knowledge gaps and facilitate the systematic synthesis of peatlands as complex systems, which is an imperative need.

The Tibetan Plateau (TP) is distributed with large areas of permafrost, which have received increasing attention as the climate warms. Accurately modeling the extent of permafrost and permafrost changes is now an important challenge for climate change research and climate modeling in this region. Uncertainty in land use and land cover (LULC), which is important information characterizing surface conditions, directly affects the accuracy of the simulation of permafrost changes in land surface models. In order to investigate the effect of LULC uncertainty on permafrost simulation, we conducted simulation experiments on the TP using the Community Land Model, version 5 (CLM5) with five high-resolution LULC products in this study. Firstly, we evaluated the simulation results using shallow soil temperature data and deep borehole data at several sites. The results show that the model performs well in simulating shallow soil temperatures and deep soil temperature profiles. The effect of different land use products on the shallow soil temperature and deep soil temperature contours is not obvious due to the small differences in land use products at these sites. Although there is little difference in the simulating results of different land use products when compared to the permafrost distribution map, the differences are noticeable for the simulation of the active layer. Land cover had a greater impact on soil temperature simulations in regions with greater land use inconsistency, such as at the junction of bare soil and grassland in the northwestern part of the TP, as well as in the southeast region with complex topography. The main way in which this effect occurs is that land cover affects the net surface radiation, which in turn causes differences in soil temperature simulations. In addition, we discuss other factors affecting permafrost simulation results and point out that increasing the model plant function types as well as carefully selecting LULC products is one of the most important ways to improve the simulation performance of land-surface models in permafrost regions.

Vegetation dynamics in Qinghai-Tibet Plateau (QTP) have been debated in recent decades. Most studies suggest that wetter and warmer climatic conditions would release low temperature constraints and stimulate alpine vegetation growth. Other studies suggest that climate warming might inhibit vegetation growth by increasing soil moisture depletion in the southern QTP. Most of previous studies have relied on vegetation indices derived from satellite observations to retrieve large-scale vegetation changes, and the uncertainty of vegetation indices makes it difficult to accurately characterize the vegetation trends on the QTP. Here, we developed a deep learning algorithm in the Google Earth Engine (GEE) platform to accurately map the land use/cover change (LUCC) on the QTP, and then infer vegetation gain and loss and their drivers during the period 1988-2018. The vegetation on the QTP experienced rapid greening, which was dominated by transitions from bareland to alpine grassland (27.45 x 104 km2) and from alpine grassland to alpine meadow (17.43 x 104 km2) during 1988-2018. Furthermore, although human activities influence vegetation succession at the local scale, the dominant influ-encing factors affecting vegetation greening on the QTP are precipitation (q -statistic = 23.87 %) and temperature (q-statistic = 11.01 %). A 30-year time series analysis clarified the specific dynamics of vegetation on the QTP, thus contributing to the understanding of the response mechanisms of alpine vegetation under climate change and providing a reference for the formulating of reasonable ecological protection policies and human develop-ment strategies.

The Qilian Mountains (QMs), located in the northeast part of the Qinghai-Tibetan Plateau in China, have a fragile ecological environment, complex and sensitive climate, and diverse land-cover types. It plays an important role in the Qinghai-Tibetan Plateau Ecological Barrier and Northern Sand Control Belt in China's two screens and three belts ecological security strategy. Based on land use data of 1980, 1990, 1995, 2000, 2005, 2010, 2015, and 2020, we utilized GIS technology, land use dynamic degree, and land use transition matrixes to analyze the spatial and temporal evolution of land use in the QMs from 1980 to 2020. The results showed the following: (1) From 1980 to 2020, grassland, forest land, and unused land were the main land-use types in the QMs, and the proportion of construction land accounted for only 0.31% of all land-use types. (2) The single land use dynamic degree showed that the dynamic degree of construction land was the highest and the fastest change rate from 2010 to 2015. The comprehensive land use dynamic degree showed that the intensity of land-use change was relatively drastic in the three time periods of 1990-1995, 1995-2000, and 2015-2020. (3) The land-use types in the study area switched infrequently during 2000-2005, 2005-2010, and 2010-2015. (4) The main transition directions of land-use types were grassland and unused land to other land-use types. These changes altered the spatial distributions of different land-use types. The study is critical for understanding the spatial and temporal change patterns of land-use change in the QMs and providing guidance for the optimization of land use in the study area and the improvement of regional eco-environmental protection.

Boreal forest and wetland have important influences on the development and protection of the ecosystem-dominated Xing'an permafrost. However, the responses of different ecosystems to climate change and the impacts on the underlying permafrost are still unclear. Here, based on the multi-period land use/land cover (LULC) data and long-time series of air temperature, combined with the ordinary least squares (OLS) and ordinary kriging (OK) methods, the effects of land use and cover change (LUCC) on the distribution of mean annual air temperature (MAAT) and permafrost in Northeast China were analyzed. From 1980s to 2010s, MAAT showed an upward trend (0.025 degrees C per yr) and extents of permafrost showed a decreasing trend (-3668 km(2)yr(-1)) in Northeast China. Permafrost degradation mainly occurred in forested land and grassland, with areal reductions of 4.0106 x 10(4) and 3.8754 x 10(4) km(2), respectively. The transformation of LULC aggravates the degradation of permafrost. The conversions of forested land and grassland to cultivated land and forested land to grassland resulted in the shrinkage of permafrost extent by 6233 km(2) from 1980s to 2010s . Our results confirm the significant impacts of LUCC on the Xing'an permafrost resulting in its degradation. Additionally, they can provide a scientific basis for ecological environment protection and restoration and sustainable development of boreal forest and wetland ecosystems in permafrost regions of Northeast China.

To explore the impact of climate change on snow cover and spring soil moisture (SM) in areas with seasonally frozen soil, snow cover parameters and spring SM for different land use types in Northeast China are extracted based on remote sensing snow cover and SM products. Snow cover parameters include snow days (SD), first day of snow cover (FSD), last day of snow cover (LSD), maximum snow depth (MSD) and average snow depth (ASD). The spatiotemporal variations and correlations between snow cover parameters and spring SM for different land use types are analyzed. The results showed that the average spring SM for different land use types was ordered woodland > farmland > grassland, with obvious woodland and farmland increases. Woodlands had many SD and large snow depths (MSD and ASD) that eventually decreased. Farmland SM increased significantly in spring, which aided crop development. The decrease in grassland spring SM was not obvious, but the snow cover parameters of certain areas decreased notably. Snow cover significantly impacted farmland SM, and correlation coefficients were highest between all snow cover parameters and SM in spring. The correlations between grassland snow cover parameters and SM in April were higher than those in May, but woodland snow cover parameters and spring SM were not correlated. Among the five snow parameters, FSD had the lowest correlation with spring SM, and SD had the greatest impact on SM. These results show the significant relationship between snow cover and SM and reveal relevant patterns. As future climate warming may introduce drought risk to woodland and grassland areas, advance preparations should be made. Farmland areas will continue to maintain appropriate SM, which is beneficial for agricultural development.

Lucerne (Medicago sativa L.) is one of the most successfully introduced species for revegetation on the Loess Plateau of China and provides important ecosystem services. However, the driving mechanism of soil organic carbon (SOC) and total nitrogen (TN) in lucerne grasslands remains unclear. This study explored the controlling factors of SOC and TN in lucerne grasslands in the semiarid Loess Plateau. A total of 112 quadrats were employed in 28 lucerne fields. Vegetation characteristics, topographic factors, and soil properties at a 0-20 cm depth were measured in each quadrat. The SOC and TN contents increased with altitude and showed positive correlations with species richness, aboveground biomass of native plants, soil moisture, soil inorganic nitrogen, total soil phosphorus (P), and C:P and N:P ratios. Variations in SOC and TN contents were mainly attributed to soil resources, followed by the interaction of topography, vegetation and soil. Soil P, soil moisture, altitude, and native plant species were the main factors controlling SOC and TN contents in these lucerne grasslands. Results suggest that specific abiotic (soil P and moisture) and biotic (plant species diversity) factors controlled SOC and TN in semiarid lucerne grasslands. These factors should be included in SOC and TN evaluation models to predict the future terrestrial ecosystem carbon and nitrogen dynamics.

Vegetation dynamics in Qinghai-Tibet Plateau (QTP) have been debated in recent decades. Most studies suggest that wetter and warmer climatic conditions would release low temperature constraints and stimulate alpine vegetation growth. Other studies suggest that climate warming might inhibit vegetation growth by increasing soil moisture depletion in the southern QTP. Most of previous studies have relied on vegetation indices derived from satellite observations to retrieve large-scale vegetation changes, and the uncertainty of vegetation indices makes it difficult to accurately characterize the vegetation trends on the QTP. Here, we developed a deep learning algorithm in the Google Earth Engine (GEE) platform to accurately map the land use/cover change (LUCC) on the QTP, and then infer vegetation gain and loss and their drivers during the period 1988-2018. The vegetation on the QTP experienced rapid greening, which was dominated by transitions from bareland to alpine grassland (27.45 x 104 km2) and from alpine grassland to alpine meadow (17.43 x 104 km2) during 1988-2018. Furthermore, although human activities influence vegetation succession at the local scale, the dominant influ-encing factors affecting vegetation greening on the QTP are precipitation (q -statistic = 23.87 %) and temperature (q-statistic = 11.01 %). A 30-year time series analysis clarified the specific dynamics of vegetation on the QTP, thus contributing to the understanding of the response mechanisms of alpine vegetation under climate change and providing a reference for the formulating of reasonable ecological protection policies and human develop-ment strategies.

The Amazon Basin is at the center of an intensifying discourse about deforestation, land-use, and global change. To date, climate research in the Basin has overwhelmingly focused on the cycling and storage of carbon (C) and its implications for global climate. Missing, however, is a more comprehensive consideration of other significant biophysical climate feedbacks [i.e., CH4, N2O, black carbon, biogenic volatile organic compounds (BV0Cs), aerosols, evapotranspiration, and albedo] and their dynamic responses to both localized (fire, land-use change, infrastructure development, and storms) and global (warming, drying, and some related to El Nino or to warming in the tropical Atlantic) changes. Here, we synthesize the current understanding of (1) sources and fluxes of all major forcing agents, (2) the demonstrated or expected impact of global and local changes on each agent, and (3) the nature, extent, and drivers of anthropogenic change in the Basin. We highlight the large uncertainty in flux magnitude and responses, and their corresponding direct and indirect effects on the regional and global climate system. Despite uncertainty in their responses to change, we conclude that current warming from non-CO2 agents (especially CH4 and N2O) in the Amazon Basin largely offsets- and most likely exceeds-the climate service provided by atmospheric CO2 uptake. We also find that the majority of anthropogenic impacts act to increase the radiative forcing potential of the Basin. Given the large contribution of less-recognized agents (e.g., Amazonian trees alone emit similar to 3.5% of all global CH4), a continuing focus on a single metric (i.e., C uptake and storage) is incompatible with genuine efforts to understand and manage the biogeochemistry of climate in a rapidly changing Amazon Basin.

Despite the fact that winter lasts for a third of the year in the temperate grasslands, winter processes in these ecosystems have been inadequately represented in global climate change studies. While climate change increases the snow depth in the Mongolian Plateau, grasslands in this region are also simultaneously facing high pressure from land use changes, such as grazing, mowing, and agricultural cultivation. To elucidate how these changes affect the grasslands' winter nitrogen (N) budget, we manipulated snow depth under different land use practices and conducted a(15)NH(4)(15)NO(3)-labeling experiment. The change in(15)N recovery during winter time was assessed by measuring the(15)N/N-14 ratio of root, litter, and soils (0-5 cm and 5-20 cm). Soil microbial biomass carbon and N as well as N2O emission were also measured. Compared with ambient snow, the deepened snow treatment reduced total(15)N recovery on average by 21.7% and 19.2% during the first and second winter, respectively. The decrease in(15)N recovery was primarily attributed to deepened snow increasing the soil temperature and thus microbial biomass. The higher microbial activity under deepened snow then subsequently resulted in higher gaseous N loss. The N2O emission under deepened snow (0.144 kg N ha(-1)) was 6.26 times than that of under ambient snow (0.023 kg N ha(-1)) during the period of snow cover and spring thaw. Although deepened snow reduced soil(15)N recovery, the surface soil N concentration remained unchanged after five years' deepened snow treatment because deepened snow reduced soil N loss via wind erosion by 86%.