Improving ecosystem respiration estimates for CO2 flux partitioning by discriminating water and temperature controls on above- and below-ground sources

Abstract

Empirical models for estimating ecosystem respiration (ER) are widely used in CO2 flux partitioning algorithms that partition net ecosystem CO2 exchange (NEE) into gross primary productivity (GPP) and ER due to advantages of simple structures. However, empirical ER models remain limited due to single-source conceptualization that doesn’t 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 ER models to enhance model capabilities. Long-term flux measurements of six typical terrestrial ecosystems and soil chamber respiration data at two sites were collected to evaluate models. Results showed that integration of the dual-source module can significantly improve the performance of empirical models in selected ecosystems with mean R2 improvement of 0.10 ± 0.16. The site years with relative increased R2 (ΔR2) larger than 10 % range 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/belowground respiration. Calibrated α varies amongst ecosystem types. Further analysis indicates variation of α is largely influenced by ratio of above/belowground biomass and annual average moisture conditions. Our findings highlight the critical need for partitioning ER models into dual-source for developing CO2 flux partitioning algorithms and support the approach as an effective means to enhance the understanding of global carbon cycles with changing climate.

Keywords

• Ecosystem respiration
• CO2 flux partitioning
• Dual-source module
• Environmental factors
• Carbon cycle