| 摘 要： |
Ecosystem respiration (RE) plays a critical role in terrestrial carbon cycles, and quantification of RE is important for understanding the interaction between climate change and carbon dynamics. We used a multi-level attention network, Geoman, to identify the relative importance of environmental factors and to simulate spatiotemporal changes in RE in northern China's grasslands during 2001-2015, based on 18 flux sites and multi-source spatial data. Results indicate that Geoman performed well (R-2 = 0.87, RMSE = 0.39 g C m(-2) d(-1), MAE = 0.28 g C m(-2) d(-1)), and that grassland type and soil texture are the two most important environmental variables for RE estimation. RE in alpine grasslands showed a decreasing gradient from southeast to northwest, and that of temperate grasslands showed a decreasing gradient from northeast to southwest. This can be explained by the enhanced vegetation index (EVI), and soil factors including soil organic carbon density and soil texture. RE in northern China's grasslands showed a significant increase (1.81 g C m(-2) yr(-1)) during 2001-2015. The increase rate of RE in alpine grassland (2.36 g C m(-2) yr(-1)) was greater than that in temperate grassland (1.28 g C m(-2) yr(-1)). Temperature and EVI contributed to the interannual change of RE in alpine grassland, and precipitation and EVI were the main contributors in temperate grassland. This study provides a key reference for the application of advanced deep learning models in carbon cycle simulation, to reduce uncertainties and improve understanding of the effects of biotic and climatic factors on spatiotemporal changes in RE. |
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