Author(s): Shuai Wang; Shujing Qin; Lei Chen; Pan Liu; Jiekai Zou; Chenhao Fu; Lu Zhang
Linked Author(s): Lei Chen
Keywords: Ecosystem respiration; CO2 flux partitioning; Dual-source module; Environmental factors; Carbon cycle
Abstract: Empirical models for estimating ecosystem respiration (ER) are widely used in CO2 flux partitioning algorithms that partition the net ecosystem CO2 exchange (NEE) into gross primary productivity (GPP) and ER due to advantages of its simple structure and readily available of input data. However, empirical ER models remain limited due to single-source conceptualization that does not discriminate different responses of aboveground respiration (AGR) and belowground respiration (BGR) to environmental factors (i. e., temperature and/or soil moisture). In this study, a dual-source module with only one parameter α was proposed and incorporated into six widely used empirical ER models to enhance the model capabilities. Long-term flux measurements of six typical terrestrial ecosystems and soil chamber respiration data at two flux sites were collected to evaluate respiration models. Results showed that integration of the proposed dual-source module can significantly improve the performance of all the empirical models in all types of ecosystems with a mean R2 improvement of 0.10±0.16. The site years with relative increased R2 (ΔR2) larger than 10% ranges from 6% to 79% amongst different models. Further validation between soil respiration and estimated BGR showed good correlations (r > 0.7) and demonstrated that proposed method can provide robust estimate of above/below ground respiration. Calibrated α of the proposed dual-source module varies amongst ecosystem types. Further analysis indicates variation of α is largely influenced by ratio of aboveground and belowground biomass and annual average moisture conditions. Our findings highlight the critical need for partitioning the empirical ER models into dual-source for developing empirical CO2 flux partitioning algorithms and support the dual-source module as an effective means to enhance the understanding of carbon budget and global carbon cycles with changing climate.
Year: 2024