Grassland ecosystems play an important role in subsistence agriculture and the global carbon cycle. However, the global spatio-temporal patterns and environmental controls of grassland biomass are not well quantified and understood. The goal of this study was to estimate the spatial and temporal patterns of the global grassland biomass and analyze their driving forces using field measurements, Normalized Difference Vegetation Index (NDVI) time series from satellite data, climate reanalysis data, and a satellite-based statistical model. Results showed that the NDVI-based biomass carbon model developed from this study explained 60% of the variance across 38 sites globally. The global carbon stock in grassland aboveground live biomass was 1.05 Pg·C, averaged from 1982 to 2006, and increased at a rate of 2.43 Tg·C·y⁻¹ during this period. Temporal change of the global biomass was significantly and positively correlated with temperature and precipitation. The distribution of biomass carbon density followed the precipitation gradient. The dynamics of regional grassland biomass showed various trends largely determined by regional climate variability, disturbances, and management practices (such as grazing for meat production). The methods and results from this study can be used to monitor the dynamics of grassland aboveground biomass and evaluate grassland susceptibility to climate variability and change, disturbances, and management.

Terrestrial gross primary production (GPP) is the largest global CO₂ flux and determines other ecosystem carbon cycle variables. Light use efficiency (LUE) models may have the most potential to adequately address the spatial and temporal dynamics of GPP, but recent studies have shown large model differences in GPP simulations. In this study, we investigated the GPP differences in the spatial and temporal patterns derived from seven widely used LUE models at the global scale. The result shows that the global annual GPP estimates over the period 2000–2010 varied from 95.10 to 139.71 Pg C∙yr⁻¹ among models. The spatial and temporal variation of global GPP differs substantially between models, due to different model structures and dominant environmental drivers. In almost all models, water availability dominates the interannual variability of GPP over large vegetated areas. Solar radiation and air temperature are not the primary controlling factors for interannual variability of global GPP estimates for most models. The disagreement among the current LUE models highlights the need for further model improvement to quantify the global carbon cycle.

Background: The allocation of photosynthate among the parts of plants (e.g., leaves, wood tissues and roots) strongly regulates their growth, and this conditions the terrestrial carbon cycle. Recent studies have shown that atmospheric CO₂ and climate change dominate the changes in carbon allocation in plants, but the magnitude and mechanism of its effects remain unclear. Methods: The Community Atmosphere Biosphere Land Exchange (CABLE) model can accurately simulate the responses of carbon allocation to environmental changes. This study quantifies the contributions of four environmental factors—atmospheric CO₂, temperature, precipitation, and radiation—on resource availability and carbon allocation from 1979 to 2014 by using the CABLE model. Results: The results of the CABLE model showed that rising CO₂ significantly reduced carbon allocation to the leaves of plants at a global scale, but the other three environmental factors exhibited contrasting effects that dominated the rise in carbon allocation to the leaves. The increased precipitation and CO₂ significantly reduced the light availability and increased carbon allocation to the wooden parts of plants. By contrast, the rising temperature reduced the water availability, resulting in a decrease in carbon allocation to the wooden parts. All four environmental factors consistently exhibited negative effects on carbon allocation to the roots, with rising precipitation causing the largest reduction in carbon allocation to them. Moreover, except for CO₂, the effects of the other three environmental factors were heterogeneous owing to their variable interactions in different regions. Conclusions: The CABLE model can accurately represent the mechanisms of response of resource availability and carbon allocation to environmental changes. Our study highlights the substantial environmental regulation of global carbon allocation. The responses of carbon allocation to global environmental changes need to be extensively studied through ecosystem models based on different hypotheses.

Evapotranspiration (ET) is a core parameter of the hydrology and carbon cycles, and its accurate estimation is crucial for water resource management. Satellite-based ET products provide an effective means for large-scale monitoring. However, due to limitations in the spatial and temporal resolution, the use of these products at regional and field scales is limited. In this study, daily ET in the Baoding Plain—a key groundwater resource recharge area—was estimated at a 500 m spatial resolution using the Bayesian Model Averaging (BMA) method. The model was driven by a synthesis of remote sensing datasets, reanalysis products, and interpolated data from meteorological stations. Validation results from in-situ observations indicated that the BMA ET had better performance (R=0.83, RMSE=1.25 mm/d) than each model in the BMA scheme. The spatiotemporal analysis revealed that the average annual BMA ET in the Baoding Plain was 683 mm/year from 2000 to 2019. Seasonal and monthly variations in the BMA ET captured the irrigation and water consumption patterns of the local crop rotation systems. A significant increasing trend of BMA ET (2.40 mm/year²) was observed in the Baoding Plain over the study period. At the regional scale, ET over more than 50% of the plain exhibited a significant positive trend. Further analysis identified water availability, solar radiation, and temperature as the primary drivers of ET variation. The BMA ET product generated in this study is characterized by high spatiotemporal resolution and accuracy. This reliable, high-resolution dataset offers valuable support for precision agricultural water management and hydrological studies, including groundwater investigations, in this predominantly agricultural region.