Spatiotemporal investigation of near-surface CH4 and factors influencing CH4 over South, East, and Southeast Asia.

Methane (CH4) is the second most abundant greenhouse gas after CO2, which plays the most important role in global and regional climate change. To explore the long-term spatiotemporal variations of near-surface CH4, datasets were extracted from Greenhouse gases Observing SATellite (GOSAT), and the Copernicus Atmospheric Monitoring Service (CAMS) reanalyzed datasets from June 2009 to September 2020 over South, East, and Southeast Asia. The accuracy of near-surface CH4 from GOSAT and CAMS was verified against surface observatory stations available in the study region to confirm both dataset applicability and results showed significant correlations. Temporal plots revealed continuous inflation in the near-surface CH4 with a significant seasonal and monthly variation in the study region. To explore the factors affecting near-surface CH4 distribution, near-surface CH4 relationship with anthropogenic emission, NDVI data, wind speed, temperature, precipitation, soil moisture, and relative humidity were investigated. The results showed a significant contribution of anthropogenic emissions with near-surface CH4. Regression and correlation analysis showed a significant positive correlation between NDVI data and near-surface CH4 from GOSAT and CAMS, while a significant negative correlation was found between wind and near-surface CH4. In the case of temperature, soil moisture, and near-surface CH4 from GOSAT and CAMS over high CH4 regions of the study area showed a significant positive correlation. However significant negative correlations were found between precipitation and relative humidity with GOSAT and CAMS datasets over high CH4 regions in South, East, and Southeast Asia. Moreover, these climatic factors showed no significant correlation within the low near-surface CH4 areas in our study region. Our study results showed that anthropogenic emissions, NDVI data, wind speed, temperature, precipitation, soil moisture, and humidity could significantly affect the near-surface CH4 over South, East, and Southeast Asia.

Unraveling the significance of soil organic carbon-gravel parameterization: Insights into soil water and heat transport on the Tibetan Plateau.

In the Tibetan Plateau (TP) soil water and heat transfer process, soil organic carbon (SOC) and gravel content are considered as the most influential soil texture factors. However, the issues of underestimating SOC and neglecting gravel effect affected the simulation performance of CLM5.0 on soil moisture (SM) and soil temperature (ST). This paper proposed a new parameterization scheme, the organic carbon-gravel (OC-G) scheme, to simulate ST and SM from 1990 to 2018. The results showed that correlation between the simulated and observed ST or SM was higher, and the error was smaller, after the modification of the parameterization scheme. This improvement justifies the applicability of the scheme for soil hydrothermal simulations on the TP. The experiment described that ST and SM were more sensitive to changes in SOC content. And changes in gravel or SOC content had the "Same-Frequency" effect in the northeast and southeast TP. When the SOC and gravel content changed at the same time, the effects on ST and SM were a "cumulative" effect. The change directly affected the memory time of ST and SM in summer. Specifically, when the SOC content was increased, the memory time of SM increased in the northwest and decreased in the southeast. When gravel content was increased, the memory time of SM decreased in the northwest but increased in the southeast, but the memory time of ST remained largely unchanged. Changes to the abnormal duration may alter summer weather and climate in Eastern China.

Unexpected no significant soil carbon losses in the Tibetan grasslands due to rodent bioturbation.

The Tibetan grasslands store 2.5% of the Earth's soil organic carbon. Unsound management practices and climate change have resulted in widespread grassland degradation, providing open habitats for rodent activities. Rodent bioturbation loosens topsoil, reduces productivity, changes soil nutrient conditions, and consequently influences the soil organic carbon stocks of the Tibetan grasslands. However, these effects have not been quantified. Here, using meta-analysis and upscaling approaches, we found that rodent bioturbation impacts on the Tibetan grassland soil organic carbon contents were depth-dependent, with significant (P < 0.001) decreasing of 24.4% in the topsoil (0 to 10 cm) but significant (P < 0.05) increasing of 35.9% in the deeper soil layer (40 to 50 cm), and nonsignificant changes in other soil layers. The depth-dependent responses in soil organic carbon content were closely associated with rodent tunnel burrowing, foraging, excrement deposition, and mixing of the upper and deeper soil layers. Rodent bioturbation had shown nonsignificant impacts on soil bulk density, independent of soil layer. Tibetan grasslands totally lose -35.2 Tg C yr-1 (95% CI: -48.5 to -21.1 Tg C yr-1) and -32.9 Tg C yr-1 (-54.2 to -8.6 Tg C yr-1) due to rodent bioturbation in the 0 to 10 or 0 to 30 cm soil layer, while no significant net loss was found over the 0 to 90 cm layer. Our findings highlight the importance of considering depth-dependent factors to robustly quantify the net changes in the terrestrial soil organic carbon stocks resulting from disturbances such as rodent bioturbation.

Comprehensive assessment of nitrous oxide emissions and mitigation potentials across European peatlands.

European natural peatlands have undergone long-term anthropogenic drainage activities that have severely decreased their functions, such as carbon sequestration. Recent rewetting has been conducted to restore the ecosystem services of peatlands and mitigate the emissions of potent greenhouse gases such as nitrous oxide (N2O). However, the magnitudes and spatial patterns of annual N2O fluxes and their mitigation potentials across European peatlands remain unknown. Here, we synthesized 492 annual N2O flux data points from 77 in situ studies across European peatlands and found that the soil annual N2O fluxes varied extensively from -1.08 to 33.40 kg N2O-N ha-1 yr-1; these results were significantly and interactively (P < 0.05) affected by the peatland status, climatic regime and nutrient supply type. Drainage significantly (P < 0.05) stimulated soil N2O emissions from natural minerotrophic rather than ombrotrophic peatlands, regardless of the climatic regime. Similarly, rewetting significantly (P < 0.05) reduced soil N2O emissions from drained minerotrophic rather than ombrotrophic peatlands, demonstrating that the high N2O emissions were driven by a simultaneous decline in the water table depth and increase in the soil nitrogen (N) availability. Magnitudes of the increases or decreases in N2O emissions due to drainage or rewetting were also significantly influenced by the land-use and drainage history before rewetting and in the years following drainage/rewetting, respectively. The estimated annual mean N2O emission total was found to be 90.42 (95% confidence interval: 64.49-122.57) Gg N2O-N in 2020 from European peatlands. Scenario analysis showed that drained peatlands should be rewetted expeditiously; postponing rewetting would cause larger emissions from continued N2O emissions from drained peatlands. Fully rewetting the drained peatlands used for forestry and peat extraction and partially rewetting those used for agriculture and grassland comprise a strategy for mitigating drained peatland N2O emissions without compromising food security.

Land Surface Model and Particle Swarm Optimization Algorithm Based on the Model-Optimization Method for Improving Soil Moisture Simulation in a Semi-Arid Region.

Improving the capability of land-surface process models to simulate soil moisture assists in better understanding the atmosphere-land interaction. In semi-arid regions, due to limited near-surface observational data and large errors in large-scale parameters obtained by the remote sensing method, there exist uncertainties in land surface parameters, which can cause large offsets between the simulated results of land-surface process models and the observational data for the soil moisture. In this study, observational data from the Semi-Arid Climate Observatory and Laboratory (SACOL) station in the semi-arid loess plateau of China were divided into three datasets: summer, autumn, and summer-autumn. By combing the particle swarm optimization (PSO) algorithm and the land-surface process model SHAW (Simultaneous Heat and Water), the soil and vegetation parameters that are related to the soil moisture but difficult to obtain by observations are optimized using three datasets. On this basis, the SHAW model was run with the optimized parameters to simulate the characteristics of the land-surface process in the semi-arid loess plateau. Simultaneously, the default SHAW model was run with the same atmospheric forcing as a comparison test. Simulation results revealed the following: parameters optimized by the particle swarm optimization algorithm in all simulation tests improved simulations of the soil moisture and latent heat flux; differences between simulated results and observational data are clearly reduced, but simulation tests involving the adoption of optimized parameters cannot simultaneously improve the simulation results for the net radiation, sensible heat flux, and soil temperature. Optimized soil and vegetation parameters based on different datasets have the same order of magnitude but are not identical; soil parameters only vary to a small degree, but the variation range of vegetation parameters is large.