Natural wetlands are among the most important sources of atmospheric methane and thus important for better understanding the long-term temporal variations in the atmospheric methane concentration. During the last 60 years, wetlands have experienced extensive conversion and impacts from climate warming which might result in complicated temporal and spatial variations in the changes of the wetland methane emissions. In this paper, we present a modeling framework, integrating CH4MOD _wetland , TOPMODEL, and TEM models, to analyze the temporal and spatial variations in CH ₄ emissions from natural wetlands (including inland marshes/swamps, coastal wetlands, lakes, and rivers) in China. Our analysis revealed a total increase of 25.5 %, averaging 0.52 g m ⁻² per decade, in the national CH ₄ fluxes from 1950 to 2010, which was mainly induced by climate warming. Larger CH ₄ flux increases occurred in northeastern, northern, and northwestern China, where there have been higher temperature rises. However, decreases in precipitation due to climate warming offset the increment of CH ₄ fluxes in these regions. The CH ₄ fluxes from the wetland on the Qinghai–Tibet Plateau exhibited the lowest CH ₄ increase (0.17 g m ⁻² per decade). Although climate warming has accelerated CH ₄ fluxes, the total amount of national CH ₄ emissions decreased by approximately 2.35 Tg (1.91–2.81 Tg), i.e., from 4.50 Tg in the early 1950s to 2.15 Tg in the late 2000s, due to the wetland loss totalling 17.0 million ha. Of this reduction, 0.26 Tg (0.24–0.28 Tg) was derived from lakes and rivers, 0.16 Tg (0.13–0.20 Tg) from coastal wetlands, and 1.92 Tg (1.54–2.33 Tg) from inland wetlands. Spatially, northeastern China contributed the most to the total reduction, with a loss of 1.68 Tg. The wetland CH ₄ emissions reduced by more than half in most regions in China except for the Qinghai–Tibet Plateau, where the CH ₄ decrease was only 23.3 %.

Wetlands are the largest and most uncertain natural sources of atmospheric methane (CH ₄ ). Several process-based models have been developed to quantify the magnitude and estimate spatial and temporal variations in CH ₄ emissions from global wetlands. Reliable models are required to estimate global wetland CH ₄ emissions. This study aimed to test two process-based models, CH4MOD _wetland and Terrestrial Ecosystem Model (TEM), against the CH ₄ flux measurements of marsh, swamp, peatland and coastal wetland sites across the world; specifically, model accuracy and generality were evaluated for different wetland types and in different continents, and then the global CH ₄ emissions from 2000 to 2010 were estimated. Both models showed similar high correlations with the observed seasonal/annual total CH ₄ emissions, and the regression of the observed versus computed total seasonal/annual CH ₄ emissions resulted in R ² values of 0.81 and 0.68 for CH4MOD _wetland and TEM, respectively. The CH4MOD _wetland produced accurate predictions for marshes, peatlands, swamps and coastal wetlands, with model efficiency (EF) values of 0.22, 0.52, 0.13 and 0.72, respectively. TEM produced good predictions for peatlands and swamps, with EF values of 0.69 and 0.74, respectively, but it could not accurately simulate marshes and coastal wetlands (EF <0). There was a good correlation between the simulated CH ₄ fluxes and the observed values on most continents. However, CH4MOD _wetland showed no correlation with the observed values in South America and Africa. TEM showed no correlation with the observations in Europe. The global CH ₄ emissions for the period 2000–2010 were estimated to be 105.31 ± 2.72 Tg yr ⁻¹ by CH4MOD _wetland and 134.31 ± 0.84 Tg yr ⁻¹ by TEM. Both models simulated a similar spatial distribution of CH ₄ emissions globally and on different continents. Marshes contribute 36 %–39 % of global CH ₄ emissions. Lakes/rivers and swamps are the second and third greatest contributors, respectively. Other wetland types account for only approximately 20 % of global emissions. Based on the model applicability, if we use the more accurate model, i.e., the one that performs best as evidenced by a higher model efficiency and a lower model bias, to estimate each continent and wetland type, we obtain a new assessment of 116.99–124.74 Tg yr ⁻¹ for the global CH ₄ emissions for the period 2000–2010. Our results imply that performance at a global scale may conceal model uncertainty. Efforts should be made to improve model accuracy for different wetland types and regions, particularly hotspot regions, to reduce the uncertainty in global assessments.

Background: China’s terrestrial ecosystems play a pronounced role in the global carbon cycle. Here we combine spatially-explicit information on vegetation, soil, topography, climate and land use change with a process-based biogeochemistry model to quantify the responses of terrestrial carbon cycle in China during the 20th century. Results: At a century scale, China’s terrestrial ecosystems have acted as a carbon sink averaging at 96 Tg C yr^− 1, with large inter-annual and decadal variabilities. The regional sink has been enhanced due to the rising temperature and CO₂ concentration, with a slight increase trend in carbon sink strength along with the enhanced net primary production in the century. The areas characterized by C source are simulated to extend in the west and north of the Hu Huanyong line, while the eastern and southern regions increase their area and intensity of C sink, particularly in the late 20th century. Forest ecosystems dominate the C sink in China and are responsible for about 64% of the total sink. On the century scale, the increase in carbon sinks in China’s terrestrial ecosystems is mainly contributed by rising CO₂. Afforestation and reforestation promote an increase in terrestrial carbon uptake in China from 1950s. Although climate change has generally contributed to the increase of carbon sinks in terrestrial ecosystems in China, the positive effect of climate change has been diminishing in the last decades of the 20th century. Conclusion: This study focuses on the impacts of climate, CO₂ and land use change on the carbon cycle, and presents the potential trends of terrestrial ecosystem carbon balance in China at a century scale. While a slight increase in carbon sink strength benefits from the enhanced vegetation carbon uptake in China’s terrestrial ecosystems during the 20th century, the increase trend may diminish or even change to a decrease trend under future climate change.

Accurate detection of wind profiles within the troposphere is essential for atmospheric dynamics research and extreme weather forecasting. Coherent Doppler wind lidar (CDWL) is widely regarded as the most suitable technique for high spatial and temporal resolution wind profile detection. However, since coherent detection relies heavily on the concentration of aerosol particles, which cause Mie scattering, the received backscattering lidar signal exhibits significantly low intensity at high altitudes. As a result, conventional methods, such as spectral centroid estimator, often fail to produce credible and accurate wind retrieval results in these regions. To address this issue, we introduce a deep learning model for lidar wind profile retrieval, which integrates Transformer architecture with the Kolmogorov-Arnold network. Our model is trained solely on targets derived from the traditional wind retrieval algorithm and utilizes radiosonde measurements as the ground truth for test results evaluation. Experimental results demonstrate that our model not only extends the maximum vertical wind profile detection range but also produces more accurate results, exhibiting a level of precision that surpasses the labeled targets. This phenomenon, which we refer to as super-accuracy, is explored by investigating the potential underlying factors that contribute to this intriguing occurrence. In addition, we compare the performance of our method with other state-of-the-art (SOTA) models, highlighting its superior effectiveness and capability in high-resolution wind retrieval. In the future, our proposed model could potentially be extended to other remote sensing spectrum signal processing applications (e.g., microwave Doppler lidar), thereby establishing a benchmark for future research and driving the advancement of deep learning models in this domain.

As climate change intensifies, extreme drought events have become more frequent, and investigating the mechanisms of watershed drought has become highly significant for basin water resource management. This study utilizes the WRF-Hydro model in conjunction with standardized drought indices, including the standardized precipitation index (SPI), standardized soil moisture index (SSMI), and Standardized Streamflow Index (SSFI), to comprehensively investigate the spatiotemporal characteristics of drought in the Huai River Basin, China, from 2012 to 2018. The simulation performance of the WRF-Hydro model was evaluated by comparing model outputs with reanalysis data at the regional scale and site observational data at the site scale, respectively. Our results demonstrate that the model showed a correlation coefficient of 0.74, a bias of −0.29, and a root mean square error of 2.66% when compared with reanalysis data in the 0–10 cm soil layer. Against the six observational sites, the model achieved a maximum correlation coefficient of 0.81, a minimum bias of −0.54, and a minimum root mean square error of 3.12%. The simulation results at both regional and site scales demonstrate that the model achieves high accuracy in simulating soil moisture in this basin. The analysis of SPI, SSMI, and SSFI from 2012 to 2018 shows that the summer months rarely experience drought, and droughts predominantly occurred in December, January, and February in the Huai River Basin. Moreover, we found that the drought characteristics in this basin have significant seasonal and interannual variability and spatial heterogeneity. On the one hand, the middle and southern parts of the basin experience more frequent and severe agricultural droughts compared to the northern regions. On the other hand, we identified a time–lag relationship among meteorological, agricultural, and hydrological droughts, uncovering interactions and propagation mechanisms across different drought types in this basin. Finally, we concluded that the WRF-Hydro model can provide highly accurate soil moisture simulation results and can be used to assess the spatiotemporal variations in regional drought events and the propagation mechanisms between different types of droughts.