Science China Earth Sciences最新文献

Tibetan Plateau (TP) is known as the “Third Pole” of the Earth. Any changes in land surface processes on the TP can have an unneglectable impact on regional and global climate. With the warming and wetting climate, the land surface of the TP saw a darkening trend featured by decreasing surface albedo over the past decades, primarily due to the melting of glaciers, snow, and greening vegetation. Recent studies have investigated the effects of the TP land surface darkening on the field of climate, but these assessments only address one aspect of the feedback loop. How do these darkening-induced climate changes affect the frozen ground and ecosystems on the TP? In this study, we investigated the impact of TP land surface darkening on regional frozen ground and ecosystems using the state-of-the-art land surface model ORCHIDEE-MICT. Our model results show that darkening-induced climate changes on the TP will lead to a reduction in the area of regional frozen ground by 1.1×10⁴±0.019×10⁴ km², a deepening of the regional permafrost active layer by 0.06±0.0004 m, and a decrease in the maximum freezing depth of regional seasonal frozen ground by 0.06±0.0016 m compared to the scenario without TP land surface darkening. Furthermore, the darkening-induced climate change on the TP will result in an increase in the regional leaf area index and an enhancement in the regional gross primary productivity, ultimately leading to an increase in regional terrestrial carbon stock by 0.81±0.001 PgC. This study addresses the remaining piece of the puzzle in the feedback loop of TP land surface darkening, and improves our understanding of interactions across multiple spheres on the TP. The exacerbated regional permafrost degradation and increasing regional terrestrial carbon stock induced by TP land surface darkening should be considered in the development of national ecological security barrier.

Wetland degradation has been accelerating in recent years globally. Accurate information on the geographic distribution and categories of wetlands is essential for their conservation and management. Despite being the world’s fourth largest continent, South America has limited research on wetland mapping, and there is currently no available map that provides comprehensive information on wetland distribution and categories in the region. To address this issue, we used Sentinel-1, Sentinel-2 and SRTM data, developed a sample collection method and a wetland mapping method with a collection of multi-source features such as optical features, polarization features and shape features for South American wetlands. We produced a 10-m resolution wetland map based on the Google Earth Engine (GEE) platform. Our Level-1 wetland cover map accurately captured six wetland sub-categories with an overall accuracy of 96.24% and a kappa coefficient of 0.8649, while our Level-2 water cover map included five sub-categories with an overall accuracy of 97.23% and a kappa coefficient of 0.9368. The results show that the total area of existing wetlands in South America is approximately 1,737,000 km², which is 6.8% of the total land area. Among the ten wetland categories, shallow sea had the largest area (960,527.4 km²), while aquaculture ponds had the smallest area 1513.6 km². Swamp had the second largest area (306,240.1 km²). Brazil, Argentina, Venezuela, Bolivia, and Colombia were found to have the largest wetland areas, with Brazil and Colombia having the most diverse wetland categories. This product can serve as baseline data for subsequent monitoring, management, and conservation of South American wetlands.

Land surface temperature (LST) is a key parameter reflecting the interaction between land and atmosphere. Currently, thermal infrared (TIR) quantitative remote sensing technology is the only means to obtain large-scale, high spatial resolution LST. Accurately retrieving high spatial resolution mountainous LST (MLST) plays an important role in the study of mountain climate change. The complex terrain and strong spatial heterogeneity in mountainous areas change the geometric relationship between the surface and satellite sensors, affecting the radiation received by the sensors, and rendering the assumption of planar parallelism invalid. In this study, considering the influence of complex terrain in mountainous areas on atmospheric downward radiation and the thermal radiation contribution of adjacent pixels, a mountainous TIR radiative transfer model based on the sky view factor was developed. Combining with the atmospheric radiative transfer model MODTRAN 5.2, a nonlinear generalized split-window algorithm suitable for high spatial resolution MLST retrieval was constructed and applied to Landsat-9 TIRS-2 satellite TIR remote sensing data. The analysis results indicate that neglecting the topographic and adjacency effects would lead to significant discrepancies in LST retrieval, with simulated data showing LST differences of up to 2.5 K. Furthermore, due to the lack of measured MLST in the field, the MLST accuracy obtained by this retrieval method was indirectly validated using the currently recognized highest-accuracy forward 3D radiative transfer model DART. The MLST and emissivity were input into the DART model to simulate the brightness temperature at the top of the atmosphere (TOA) of Landsat-9 band 10, and compared with the brightness temperature at TOA of Landsat-9 band 10. The RMSE (Root Mean Square Error) for the two subregions was 0.50 and 0.61 K, respectively, indicating that the method proposed can retrieve high-precision MLST.

Analyzing the changes in carbon storage in terrestrial ecosystems caused by land use changes is a crucial part of exploring the carbon cycle. In addition, enhancing carbon storage in terrestrial ecosystems is an effective and environmentally friendly measure to sequester anthropogenic carbon emissions, which is significant for achieving carbon neutrality and curbing global climate change. This paper uses land use data and carbon density tables with the InVEST model to obtain a carbon storage distribution map of China. It further applies land use response elasticity coefficients, Theil index multi-stage nested decomposition, and spatial autocorrelation analysis to examine the spatial-temporal patterns, causes of changes, and evolution characteristics of carbon storage in terrestrial ecosystems from 1980 to 2020. The results show that the temporal changes in China’s carbon storage generally present an inverted S-curve, with an initial rapid decline followed by a slower decrease. Spatially, it features high levels in the northeast, low levels in the northwest, and a uniform distribution in the central and southern regions. The disturbance of land use type changes on terrestrial ecosystem carbon storage has been effectively mitigated. The significant reduction in grassland area in the Southwest region is the main source of carbon storage loss during the study period, and the encroachment of construction land on arable land in large urban agglomerations is one of the important causes of carbon storage loss. The Theil index multi-stage nested decomposition results indicate that the overall difference in carbon storage in China has decreased, while differences among cities within provinces and among counties within cities have increased. The influence of natural factors on the distribution of carbon storage is weakening, whereas the impact of human activities is becoming more profound, enhancing its influence on the spatial distribution of carbon storage in China. From 1980 to 2000, the carbon density in coastal metropolises generally showed a declining trend. From 2000 to 2020, the carbon density in the central urban areas of eastern coastal city clusters gradually showed an upward trend and continued to expand outward, revealing to some extent the “Environmental Kuznets Curve” characteristic in the development process of urban carbon storage. Therefore, in future ecological construction, the government should fully consider the impact of land management planning on carbon storage in different regions, promote the efficient use and standardized management of land, and strive to cross the “Environmental Kuznets Curve” inflection point of carbon storage as soon as possible.

Soil carbon sequestration is listed by the United Nations Framework Convention on Climate Change as one of the key ways to achieve long-term “carbon neutrality” in the context of global warming. Soil carbon sequestration is a complex biogeochemical process that involves plants, microbes, and rock minerals at its core. Yet, its regulation mechanisms and promotion pathways remain unclear. This paper reviews recent progress in the related domestic and international research and provides an overview of the key processes and mechanisms of soil carbon sequestration. The main pathways for enhancing soil carbon sequestration (including plant inputs, mineral protection, microbial transformation, and rock weathering) are summarized. The paper also discusses and synthesizes how advanced biogeochemical methods and technologies may be employed to explore soil carbon sequestration mechanisms and potentials. The overall aim of this review is to improve our understanding of soil carbon sequestration as a nature-based solution to combatting climate change from the biogeochemistry perspective, and to highlight the role of fundamental research in Earth Sciences in helping to achieve China’s carbon neutrality goals.

Benefiting from the rapid development of environmental DNA (eDNA) technologies, sedimentary DNA (sedDNA) emerges as a promising tool for monitoring plant compositions in remote regions. The Tibetan Plateau (TP), renowned for its harsh environment and numerous ponds and lakes, presents a potentially demanding region for the application of sedDNA on vegetation investigations. Here, we used the g and h universal primers for the P6 loop region of the chloroplast trnL (UAA) intron to amplify plant DNA in surface sediments from 59 ponds and small lakes on the southwestern TP. The applicability and limitations of using plant DNA metabarcoding for modern vegetation monitoring and palaeo-vegetation reconstructions have been assessed by comparing sedDNA, pollen, and vegetation survey data. Our results showed that plant DNA metabarcoding recorded 186 terrestrial taxa, of which 30.1% can be identified at the species level. The plant sedDNA approach can effectively disclose the dominant plant taxa (including Asteraceae, Cyperaceae and Poaceae) and significant vegetation assemblages in the vicinity of the investigated sites. The number of taxa and taxonomic resolution of plant sedDNA exceeded that of pollen analysis (75 taxa detected, 5.3% can be identified at species level). Unlike pollen that retains a broad spectrum of regional plant signals (including Pinus and Artemisia), plant sedDNA mirrors very local plants, underscoring its utility in local vegetation monitoring and reconstructions. To conclude, plant DNA metabarcoding of (small) lake sediments warrant increased attention in the future for local vegetation monitoring and reconstructions on the TP.

The importance of carbonate weathering carbon sinks (CCSs) is almost equal to that of vegetation photosynthesis in the global carbon cycle. However, CCSs have become controversial in formulating carbon neutral policies to deal with global climate problems in various countries, since the carbonate dissolution is reversible. In order to address these controversies, we reviewed recent advances in understanding CCSs and examined the outstanding controversies surrounding them. We have analyzed the five controversies, revealing the existence of CCSs, quantifying their magnitude, clarifying their spatiotemporal pattern, and documenting how they have increased and how they evolved under the background of global change. By addressing these five controversies, we help to bring clarity to the role of CCSs in the carbon cycle of global terrestrial ecosystems.

The response of agricultural productivity anomalies to drought stress plays a crucial role in the carbon cycle within terrestrial ecosystems and in ensuring food security. However, detailed analysis of how global agricultural productivity anomalies response to drought stress, particularly within irrigated and rainfed agricultural systems, remains insufficient. In this study, the impact of drought stress on agricultural productivity anomalies during the growing season (zcNDVI^S), across both irrigated and rainfed agriculture, were analyzed using a suite of hydro-climatic variables. Specifically, the investigation utilized the multi-scalar Standardized Precipitation Evapotranspiration Index (SPEI), the Multivariate ENSO Index (MEI), and the Madden-Julian Oscillation (MJO). Meanwhile, the relationships between hydroclimatic variables and zcNDVI^S were analyzed at one, two, three and four months before the ending of growing season (EOS). Results showed that (1) the percentages of significant (p<0.1) drying trends varied across the globe from 8.30% to 13.42%, 6.50% to 14.63%, 6.52% to 14.23%, and 6.47% to 14.95% at one-, two-, three-, and four-month lead times before EOS, respectively, during 2001–2020, which represented by the multiscalar SPEI. This observation highlights that most regions across the globe tend to be arid, which could significantly impact agricultural productivity; (2) the global mean correlation coefficients (rmax) for SPEI-1, SPEI-3, SPEI-6, SPEI-12 (indicating SPEI at 1-, 3-, 6-, and 12-month lags), MEI, and MJO with zcNDVI^S ranged between 0.24–0.25, 0.27–0.28, 0.25–0.26, 0.21–0.22, −0.02–0.01 and 0.06–0.11, respectively, across both irrigated and rainfed agriculture system from 2001 to 2020. Agricultural productivity anomalies demonstrated a significant correlation with drought stress. The strongest correlations were noted for SPEI-3 and SPEI-6, suggesting a delayed response of crops to drought conditions. This indicates that agriculture ecosystem experiences prolonged disturbances due to abiotic drought stress; and (3) the percentages of regions that showed significant correlations (p<0.1) between zcNDVI^S and drought indices (SPEI-1, SPEI-3, SPEI-6, and SPEI-12), as well as climate indices (MEI and MJO) ranged as follows: 14.77%–20.27%, 21.51%–32.55%, 22.60%–35.68%, 21.89%–35.16%, 7.93%–11.20% and 9.44%–17.94%. Quantitatively identifying how zcNDVI^S spatially responds to hydro-climatic variables can help us better understand the impact of drought on agricultural productivity anomalies worldwide.