Abstract
The CO2 efflux from forest soil (FCO2) is one of the largest components of the global carbon cycle. Accurate estimation of FCO2 can help us better understand the carbon cycle in forested areas and precisely predict future climate change. However, the scarcity of field-measured FCO2 data in the subtropical forested area greatly limits our understanding of FCO2 dynamics at regional and global scales. This study used an automatic cavity ring-down spectrophotometer (CRDS) analyzer to measure FCO2 in a typical subtropical forest of southern China in the dry season. We found that the measured FCO2 at two experimental areas experienced similar temporal trends in the dry season and reached the minima around December, whereas the mean FCO2 differed apparently across the two areas (9.05 vs. 5.03 g C m−2 day−1) during the dry season. Moreover, we found that both abiotic (soil temperature and moisture) and biotic (vegetation productivity) factors are significantly and positively correlated, respectively, with the FCO2 variation during the study period. Furthermore, a machine-learning random forest model (RF model) that incorporates remote sensing data is developed and used to predict the FCO2 pattern in the subtropical forest, and the topographic effects on spatiotemporal patterns of FCO2 were further investigated. The model evaluation indicated that the proposed model illustrated high prediction accuracy for the training and testing dataset. Based on the proposed model, the spatiotemporal patterns of FCO2 in the forested watershed that encloses the two monitoring sites were mapped. Results showed that the spatial distribution of FCO2 is obviously affected by topography: the high FCO2 values mainly occur in relatively high altitudinal areas, in slopes of 10–25°, and in sunny slopes. The results emphasized that future studies should consider topographical effects when simulating FCO2 in subtropical forests. Overall, our study unraveled the spatiotemporal variations of FCO2 and their driving factors in a subtropical forest of southern China in the dry season, and demonstrated that the proposed RF model in combination with remote sensing data can be a useful tool for predicting FCO2 in forested areas, particularly in subtropical and tropical forest ecosystems.
Highlights
The increasing trend of greenhouse gases (GHGs) in the atmosphere over the past 100 years has been reported by the Intergovernmental Panel on Climate Change (IPCC) [1].As one of the largest sources of GHGs between the terrestrial ecosystems and the atmosphere [2], the soil CO2 efflux (FCO2 ), composed of belowground autotrophic respiration and heterotrophic respiration, is the major carbon source to the atmosphere [3,4,5] and plays a vital role in the global carbon cycle and biogeochemical processes [6,7]
The obvious seasonal variations and similar temporal trends of the Forest Soil CO2 Efflux (FCO2) are observed at the Chenhe Dong (CHD) and GY study areas (Figure 2)
Our results found that FCO2 showed a decreasing trend in the dry season (Figure 2), which is consistent with the previous studies in the similar subtropical forests of Guangdong province, southern China [20,24,79]
Summary
The increasing trend of greenhouse gases (GHGs) in the atmosphere over the past 100 years has been reported by the Intergovernmental Panel on Climate Change (IPCC) [1].As one of the largest sources of GHGs between the terrestrial ecosystems and the atmosphere [2], the soil CO2 efflux (FCO2 ), composed of belowground autotrophic respiration (i.e., plant roots and rhizosphere microbial respiration) and heterotrophic respiration (including soil microbial and animal respiration), is the major carbon source to the atmosphere [3,4,5] and plays a vital role in the global carbon cycle and biogeochemical processes [6,7]. The topographic effects of subtropical forest soil CO2 flux over large scales can not be well explored based on the in-situ studies [16] To compensate for these disadvantages, based on the nature of the study site, empirical models are widely developed and used to estimate the FCO2 [8]. The parameterization of empirical models is often based on site-based observations; parameters in models, are characterized by non-linear changes over time and space, and the initial parameters are spatially constrained Both the chamber methods and empirical models are limited in providing sufficient information about FCO2 and in capturing the spatial variations of FCO2 at a large spatial scale
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