Climate-LUCC synergy drives soil respiration dynamics in China: a biome-specific machine learning approach

Abstract Soil respiration (RS) releases CO2 through autotrophic and heterotrophic respiration, representing the second largest carbon flux in terrestrial ecosystems after photosynthesis. It plays a pivotal role in global carbon cycling and climate feedback. China’s climate shifted from a warming hiatus (2001–2010) to accelerated warming (2010–2019), coupled with ongoing land use/cover change (LUCC), jointly drives the spatiotemporal dynamics of RS. However, the relative contributions and underlying mechanisms of these factors remain underexplored. In this study, biome-specific machine learning models (R2 = 0.69–0.82) were developed to estimate RS at a 1 km spatial resolution across China from 2001 to 2019. Results indicate that the long-term average annual RS across China’s vegetated areas is 4.24 ± 0.02 Pg C year−1. Interannual variability shifted from relative stability during 2001–2010 (−5.58 Tg C year−1; −0.08 g C m−2 year−1, P = 0.77) to a significant increase (36.29 Tg C year−1; 0.52 g C m−2 year−1, P < 0.05) during 2010–2019. Climate and LUCC together accounted for 61.7% of interannual RS variability, with moisture as the primary driver (29.6% of variance). Large-scale ecological engineering projects, while effective in enhancing carbon sequestration, also promote RS, potentially offsetting some carbon storage gains. The long-term time-series dataset obtained in this study not only supports research on the mechanisms influencing RS but also provides benchmark data for improving terrestrial ecosystem carbon cycle models. These findings highlight RS’ critical role in China’s carbon budget and its sensitivity to climatic and anthropogenic drivers.