Over the past century, vegetation change has been reported at global, national, and regional scales, accompanied by significant climate change and intensified human activities. Among the regions is the rangeland of the Three-River Headwaters Region (TRHR) in China. However, which factor dominates in causing vegetation change in this region is still under considerable debate, and how would the grasslands adapt to the changing environment is largely unknown. To address these issues, we attribute growing season vegetation activity to climate change and human activities, investigate the interactions among different driving variables, and explore the dynamic relationship between vegetation activity and the driving variables. We perform Mann–Kendall trend analysis, Pearson correlation analysis, and partial correlation analysis. The results indicate that the dominant factor for vegetation growth, during the period 1995–2014, was temperature for the southeastern and southern parts of the TRHR, precipitation for the western part, and solar radiation for the northeastern part. The regulation effects of temperature on precipitation and cloud cover contributed to vegetation growth, while grazing activity and population activity offset the positive contribution of climate change. The dynamic relationship between vegetation activity and the driving variables reflected the acclimatization and adaption processes of vegetation, which needs further investigation.

This technical note presents the DualSPHysics application to spillway hydraulics, and an extensive comparison has been made between the single-water and two-phase water–air modelling approaches. It has been found that the DualSPHysics code based on the smoothed particle hydrodynamics method could provide a promising design tool for spillway flows in the engineering field. The model is first validated on a documented skimming flow and the numerical results are also compared with those computed from a smoothed spillway flow. Then the model is further validated by a laboratory experiment for a nappe flow, where the computed flow depth and velocity are validated against the physical measurements. It has been disclosed that for a skimming flow, the single- and two-phase SPH modelling approaches lead to small differences, while for a nappe flow with more serious free surface motion, the two-phase model clearly improved the simulation accuracy. After the model is fully validated, it is applied to investigate a practical spillway flow under different flow discharges, bed slopes and step numbers, covering two distinct spillway flow regimes. From the simulation results, the flow energy dissipation features are investigated with regards to the different flow rates and spillway configurations. It has been found that the energy dissipation efficiency of a spillway is proportional to the number of the steps but inversely proportional to the flow discharge and the spillway slope.

Climate warming has increased grassland productivity on the Qinghai-Tibet Plateau, while intensified grazing has brought increasing direct negative effects. To understand the effects of climate change and make sustainable management decisions, it is crucial to identify the combined effects. Here, we separate the grazing effects with a climate-driven probability model and elaborate scenario comparison, using the Normalized Difference Vegetation Index (NDVI) of the grassland on the Qinghai-Tibet Plateau. We show that grazing has positive effects on NDVI in the beginning and end of the growing season, and negative effects in the middle. Because of the positive effects, studies tend to underestimate and even ignore the grazing pressure under a warming climate. Moreover, the seasonality of grazing effects changes the NDVI-biomass relationship, influencing the assessment of climate change impacts. Therefore, the seasonality of grazing effects should be an important determinant in the response of grassland to warming in sustainability analysis.

Recent studies offer more evidence that the rate of warming is amplified with elevation, indicating thereby that high-elevation ecosystems tend to be exposed to more accelerated changes in temperature than ecosystems at lower elevations. The phenomenon of elevation-dependent warming (EDW), as one of the regional climate-change impacts, has been observed across the Tibetan Plateau. Studies have often found large-scale greening trends, but the drivers of vegetation dynamics are still not fully understood in this region, such that the local implications of vegetation change have been infrequently discussed. This study was designed to quantify and characterize the seasonal changes in vegetation across the Three Rivers Headwaters Region (TRHR), where the land cradles the headwaters of the Yangtze, the Yellow, and the Lancang (Mekong). By mapping the normalized difference vegetation index (NDVI) over the growing season from 1982 to 2015, we were able to evaluate seasonal changes in vegetation cover over time. The results show a slightly increased tendency in green vegetation cover, which could possibly be attributed to sustained warming in this region over the past three decades, whereas a decline in the green-up rate with elevation was found, indicating an inconsistent trend of vegetation greening with EDW. The cause of the green-up rate decline at high elevations could be linked to the reduced soil water availability induced by the fast increase in warming rates associated with EDW. The findings of this study have important implications for devising adaptation strategies for alpine ecosystems in a changing climate.

Solitary wave propagating over a bottom-mounted barrier is simulated using the Incompressible Smoothed Particle Hydrodynamics (ISPH) method in order to study the generation and transport of turbulence associated with flow separation around submerged structures. For an accurate capture of turbulence characteristics during the wave propagation, rather than employing the standard sub-particle scale (SPS) model, the k-ε turbulence model is coupled with the numerical scheme. The results of the numerical model are compared with experimental data, and good agreement is observed in terms of mean velocity, free surface elevation, vorticity fields and turbulent kinetic energy. The numerical model is then employed to investigate the effects of wave non-linearity and geometrical size of the submerged barrier on the flow separation; and calculate the reflection, dissipation and transmission coefficients to evaluate the importance of energy dissipation due to the generation of vortices. The results of this study show that the developed ISPH method with the k-ε turbulence closure model is capable of reproducing the velocity fields and the turbulence characteristics accurately, and thus can be used to perform predictions of comprehensive hydrodynamics of flow-structure interactions in the urban hydro-environment systems.

Accurate estimation of anthropogenic CO₂ emissions is crucial for effective climate change mitigation policies. This study aims to improve CO₂ emission estimates in China using remote sensing measurements of column-averaged dry air mole fractions of CO₂ (XCO₂) and a neural network approach. We evaluated XCO₂ anomalies derived from three background XCO₂ concentration approaches: CHN (national median), LAT (10-degree latitudinal median), and NE (N-nearest non-emission grids average). We then applied the Generalized Regression Neural Network model, combined with a partition modeling strategy using the K-means clustering algorithm, to estimate CO₂ emissions based on XCO₂ anomalies, net primary productivity, and population data. The results indicate that the NE method either outperformed or was at least comparable to the LAT method, while the CHN method performed the worst. The partition modeling strategy and inclusion of population data effectively improved CO₂ emission estimates. Specifically, increasing the number of partitions from 1 to 30 using the NE method resulted in mean absolute error (MAE) values decreasing from 0.254 to 0.122 gC/m²/day, while incorporating population data led to a decrease in MAE values between 0.036 and 0.269 gC/m²/day for different partitions. The present methods and findings offer critical insights for supporting government policy-making and target-setting.