Abstract

Forest thinning is an effective measure to improve carbon sequestration (CS) to adapt to and mitigate global warming. However, reducing forest cover could negatively affect other ecosystem services such as soil conservation (SC), making it difficult to effectively implement thinning strategies. This problem is exacerbated by a lack of previous research. Accordingly, after assessing thinning for two typical trees (Robinia pseudoacacia L. (RP) and Quercus L. (QL)) in Shaanxi Province, we propose a forest management framework that provides detailed thinning strategies and improves CS using a process-based biogeochemical model (Biome-BGCMuSo) by considering future climate change and SC constraints. After calibration for Biome-BGCMuSo, the average root mean squared error and percentage bias for the simulated results and observed data of RP and QL decreased (39.2–54.5 % and 66.0–86.9 %) and the correlation coefficient and Nash–Sutcliffe efficiency improved (11.6–12.3 % and 114.7–153.0 %,) respectively. Comparison between current (2001–2020) and future (2081–2100) conditions predicted overall future CS increase, and future SC would display either increasing or decreasing trends in different climatic scenarios and subregions. Partial correlation analysis showed precipitation as the dominant factor positively influencing SC, while temperature, precipitation, and CO2 concentration were the dominant factors positively influencing CS. Based on the proposed framework, RP and QL should be thinned three times every 7–11 and 3–11 years during the non-growing season with intensity ranges of 2–27 % and 1–9 %, respectively. Although the average CS during 2023–2100 with thinning increased slightly (0.1–2.6 %) compared with that without thinning, a notable potential to increase CS (0.2–7.7 %) was observed for thinning during a period at the end of this century predicted to experience the most warming. The proposed framework facilitates maximizing CS and maintaining SC. As the framework is based on a process-based biogeochemical model, it could be applicable to other regions.

Full Text
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