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• We propose a new forest prediction model and use actual deforestation data for optimization and verification. • Use the XGBoost method to reconstruct deforestation intensity data. • Deforestation hotspots in China are mainly concentrated in the southern and southeastern regions. • Our model provides certain reference information for forest disaster prevention and forest management. Forests are important for the global carbon cycle, hydrothermal balance, and climate change. Human activities can exert a significant impact on forest ecosystems, thereby having the potential to alter their physical and chemical properties and thus affecting carbon, water, and heat budgets, and climate change. The historical reconstruction of the disturbance of global forests can help us understand the processes and patterns of human activities and global change. In this paper, we construct a deforestation prediction model using a Spearman correlation coefficient and implement the XGBoost method, using Python 3.6, for the reconstruction of deforestation intensity data from 2000 to 2019. Secondly, the selection of the driver indicators is done by using extreme difference regularization to unify the magnitude, and the potential deforestation area risk index is calculated in the form of equal weights. Finally, the actual deforestation data were used for optimization and validation. The model shows that the deforestation hotspots are mainly concentrated in the southern and southeastern regions of China and that there are large differences in deforestation in different provinces. In the future, the fine spatial and temporal patterns of deforestation in China during the historical period can be quantitatively reconstructed, which can provide some reference information for forest disaster prevention and forest management in China. [ABSTRACT FROM AUTHOR]

Abstract: Urban land development substantially alters the terrestrial carbon cycle, particularly the net primary productivity (NPP), from local to global scales. However, limited attempts have been undertaken to elucidate the differences in NPP between pre- and post-urban land development in China. In this paper, the terrestrial NPP after urbanization in China was assessed by using the Carnegie-Ames-Stanford approach (CASA), toward which a calibration was conducted for adapting this model on the fine-scale application. In addition, a method of neighborhood proxy was applied to acquire the NPP in the absence of urban land development, assuming that non-urban lands can represent their nearby urban lands before they were transformed. Our analyses indicate that urban land development had overall negative effects on terrestrial NPP. They reduced the NPP at an accelerating rate of 0.31×10−3 PgCyear−1, approximately 5.88% of the annual reduction during the period of 2000–2006 in China. Furthermore, these effects of NPP variations exhibited obvious differences in the amounts and spatial distributions. However, the NPP showed a slight increase around some regions that experienced rapid urbanization, as well as the arid regions in northwest China. These were probably caused by the effects of Urban Heat Island (UHI) and Urban Rain Island (URI), an introduction of faster growing exotics, various resource augmentations and so on. [Copyright &y& Elsevier]

Abstract: Understanding the sensitivity of soil respiration to temperature change and its impacting factors is an important base for accurately evaluating the response of terrestrial carbon balance to future climatic change, and thus has received much recent attention. In this study, we synthesized 161 field measurement data from 52 published papers to quantify temperature sensitivity of soil respiration in different Chinese ecosystems and its relationship with climate factors, such as temperature and precipitation. The results show that the observed Q 10 value (the factor by which respiration rates increase for a 10°C increase in temperature) is strongly dependent on the soil temperature measurement depth. Generally, Q 10 significantly increased with the depth (0cm, 5cm, and 10cm) of soil temperature measuring point. Different ecosystem types also exhibit different Q 10 values. In response to soil temperature at the depth of 5cm, alpine meadow and tundra has the largest Q 10 value with magnitude of 3.05±1.06, while the Q 10 value of evergreen broadleaf forests is approximately half that amount (Q 10 =1.81±0.43). Spatial correlation analysis also shows that the Q 10 value of forest ecosystems is significantly and negatively correlated with mean annual temperature (R =−0.51, P <0.001) and mean annual precipitation (R =−0.5, P <0.001). This result not only implies that the temperature sensitivity of soil respiration will decline under continued global warming, but also suggests that such acclimation of soil respiration to warming should be taken into account in forecasting future terrestrial carbon cycle and its feedback to climate system. [Copyright &y& Elsevier]