Reducing spatial resolution increased net primary productivity prediction of terrestrial ecosystems: A Random Forest approach.

Net primary production (NPP) is a pivotal component of the terrestrial carbon dynamic, as it directly contributes to the sequestration of atmospheric carbon by vegetation. However, significant variations and uncertainties persist in both the total amount and spatiotemporal patterns of terrestrial NPP, primarily stemming from discrepancies among datasets, modeling approaches, and spatial resolutions. In order to assess the influence of different spatial resolutions on global NPP, we employed a random forest (RF) model using a global observational dataset to predict NPP at 0.05°, 0.25°, and 0.5° resolutions. Our results showed that (1) the RF model performed satisfactorily with modeling efficiencies of 0.53-0.55 for the three respective resolutions; (2) NPP exhibited similar spatial patterns and interannual variation trends at different resolutions; (3) intriguingly, total global NPP varied greatly across different spatial resolutions, amounting 57.3 ± 3.07 for 0.05°, 61.46 ± 3.27 for 0.25°, and 66.5 ± 3.42 Pg C yr-1 for 0.5°. Such differences may be associated with the resolution transformation of the input variables when resampling from finer to coarser resolution, which significantly increased the spatial and temporal variation characteristics, particularly in regions within the southern hemisphere such as Africa, South America, and Australia. Therefore, our study introduces a new concept emphasizing the importance of selecting an appropriate spatial resolution when modeling carbon fluxes, with potential applications in establishing benchmarks for global biogeochemical models.

Controls and variability of soil respiration temperature sensitivity across China.

Understanding the temperature sensitivity (Q10) of soil respiration is critical for benchmarking the potential intensity of regional and global terrestrial soil carbon fluxes-climate feedbacks. Although field observations have demonstrated the strong spatial heterogeneity of Q10, a significant knowledge gap still exists regarding to the factors driving spatial and temporal variabilities of Q10 at regional scales. Therefore, we used a machine learning approach to predict Q10 from 1994 to 2016 with a spatial resolution of 1 km across China from 515 field observations at 5 cm soil depth using climate, soil and vegetation variables. Predicted Q10 varied from 1.54 to 4.17, with an area-weighted average of 2.52. There was no significant temporal trend for Q10 (p = 0.32), but annual vegetation production (indicated by normalized difference vegetation index, NDVI) was positively correlated to it (p < 0.01). Spatially, soil organic carbon (SOC) was the most important driving factor in 62 % of the land area across China, and varied greatly, demonstrating soil controls on the spatial pattern of Q10. These findings highlighted different environmental controls on the spatial and temporal pattern of soil respiration Q10, which should be considered to improve global biogeochemical models used to predict the spatial and temporal patterns of soil carbon fluxes to ongoing climate change.