Soil respiration spatial and temporal variability in China between 1961 and 2014

Abstract

Soil respiration (Rs) plays an important role in terrestrial–atmospheric carbon exchange but remains one of the least studied components of the carbon cycle. How environmental changes influence Rs, and in turn, how Rs influences terrestrial carbon storage in China is unclear. Here, we estimated spatial patterns and temporal trends in Rs from 1961 to 2014 to determine the influence of the recent warming hiatus on the Rs temporal variability in China. We evaluated the relationship between Rs and a set of environmental factors and developed biome‐specific Rs models that were parameterized based on 2,862 Rs records and 18 continuous variables (e.g., air temperature, precipitation and leaf area index). Based on the biome‐specific Rs models and environmental information, we estimated the annual Rs as well as the change trend of Rs in China during the study period. The mean annual Rs from 1961 to 2014 was 3.50 (confidence interval ± 0.87) Pg·C·year−1. Rs in China increased at a rate of 0.008 Pg·C·year−2 between 1982 and 1998 (warming period), but was static between 1999 and 2014 (warming hiatus). This reduction in Rs increase rate may have an important role in enhancing terrestrial carbon sequestration. These findings confirm that temporal heterogeneity in Rs could be influenced by regional environmental changes, and further help in improving the understanding of soil carbon dynamics in China.Highlights No study in China has integrated multiple biotic and abiotic factors and analysed how soil respiration correlates with different factors across biomes. In this study, biome‐specific models, including biotic and abiotic factors, were developed to estimate soil respiration in China. Annual soil respiration in China was estimated to be 3.50 (confidence interval ± 0.87) Pg·C·year‐1. Annual soil respiration in China had greater deceleration compared to the global deceleration between 1999 and 2014.

The global contribution of roots to total soil respiration

“…In other words, if the measurement errors for R S and R H are σ 1 and σ 2 , respectively, the error for R root is roughly the sum of σ 1 and σ 2 (because R root = R S − R H ) in many conditions. In R S modelling, soil temperature, precipitation and vegetation (e.g., EVI) are usually the most important variables to predict R S (Hashimoto et al., 2015; Jian, Steele, Day, et al., 2018; Jian, Steele, Thomas, et al., 2018; Jian, Yuan, et al., 2021; Raich & Potter, 1995; Raich et al., 2002; Warner et al., 2019). Here, however, those variables explained only very limited variability of R root : R S , emphasizing the increased difficulty involved in predicting how root respiration contributes to total R S at the soil surface.…”

Soil respiration database at different time scales in forest ecosystems across China

“…alkali absorption, gas chromatography and various infrared gas analyzers. Alkali absorption method could underestimate Rs (Chen et al, 2008;Jian et al, 2020). The total samples of mean monthly Rs were 5003, which was much larger than the other database's monthly samples of 1782 in China's forest ecosystems (Jian et al, 2020;Steele and Jian, 2018).…”

“…Alkali absorption method could underestimate Rs (Chen et al, 2008;Jian et al, 2020). The total samples of mean monthly Rs were 5003, which was much larger than the other database's monthly samples of 1782 in China's forest ecosystems (Jian et al, 2020;Steele and Jian, 2018). Additionally, we extended the database with the digital software (WEBPLOTDIGITIZER) from the monthly (Shi et al, 2014;Song et al, 2014;Sun et al, 2020).…”

A compiled soil respiration dataset at different time scales for forest ecosystems across China from 2000 to 2018

“…Forest area in China ranks fifth in the world (FAO, 2020) and covers a broad climatic gradient, including coldtemperate, temperate, subtropical, and tropical zones. In China, most R s measurements began only after 2001 (Chen et al, 2010), but have rapidly increased during the last 20 years (Jian et al, 2020). Several studies have summarized annual R s in China's forest ecosystems, but with small samples (e.g., N = 50 in Zheng et al, 2010; N = 62 in Chen et al, 2008; N = 120 in Zhan et al, 2012; N = 139 in Song et al, 2014).…”

“…Yu et al (2010) established a geostatistical model with a total of 390 monthly R s data from different ecosystems in China. With 1782 monthly R s in forest ecosystems across China, Jian et al (2020) analyzed the spatial patterns and temporal trends from 1961 to 2014. However, numerous R s data are still unexploited, because they were only displayed in the form of monthly dynamics in the figures of the original papers.…”