## 4 Discussion
### Empirical Models of GPP, Re, and NEP  
Empirical models of GPP, Re, and NEP captured their basic features, which were in agreement with other studies. In addition, these models describe dynamic and multiple-direction interactions in energy and especially quantify the relationships between energy (PAR) and atmospheric GLPs (CO\({}_{2}\) and other GLPs). The complex and interacted CO\({}_{2}\) processes in the atmosphere, biosphere, and land were described using PAR and its related components (e.g., diffuse PAR). It is an advantage that these empirical models can simplify CO\({}_{2}\) processes and capture the main properties of CO\({}_{2}\) without spending much time on specific processes. In the future, to obtain more accurate simulations of seasonal NEP, more studies are still needed, such as the improvement of measured PAR data quality and the timely calibration of PAR sensors. It should be emphasized that the energy interactions between photons, CO\({}_{2}\) molecules, and other GLP quantum obey the statistical law. An inherent relation exists between E, T, and RH. Compared to T and RH, water vapor and/or E (as a representative of T and RH) play more vital and objective roles in the multiple processes and mechanisms (e.g., chloroplasts absorb the incoming PAR to convert CO\({}_{2}\) and H\({}_{2}\)O and decompose water into [H] and O\({}_{2}\)) in the atmosphere-vegetation-land system, along with its transfer between the atmosphere, vegetation, and land system. Water vapor is also a vital participant in chemical and photochemical reactions (CPRs), associated with OH, BVOCs, and solar radiation [65; 70]. Additionally, the key driving roles of T in GPP and BVOC emissions can be reflected by the water vapor factor (in the sensitivity tests in references [40; 70]) to some extent. The most important issue is that (1) water is a dominant driving factor in plant growth and carbon production (e.g., GPP), which was displayed by a sensitivity analysis of GPP [40]; and (2) water vapor represents air temperature and humidity objectively [109]. The large variation ranges of the estimated monthly sums of Re and NEP, compared to their observations for periods from 2013-2016 [41] and 2003-2017, may reveal some problems in data processing. For example, spike detection, threshold (u\({}^{*}\)) determination of nocturnal friction velocity (u\({}^{*}\)), and gap filling had evident effects on annual NEE estimates [102]. Using the u\({}^{*}\)\({}_{\rm c}\) criterion, \(>\)50% of night data are removed [102]. A large quantity of flux data removal may cause small changes in the variation range in observed Re and NEP. The ratios of the measured annual sum Re/GPP and NEE/GPP were 0.764 and 0.236, respectively, from 2013-2016 [40] and 0.744 and 0.256, respectively from 2003-2017, indicating the relative magnitudes of Re and NEP to GPP. Thus, more studies on appreciated data-processing methods are needed to provide high-quality flux data with more relatively reliable maxima (positive and negative). Under all sky conditions from 2003-2017, the simulated and observed annual sums of GPP, Re, and NEE showed an increasing carbon sink (Section 3.1.5), which was in line with and contributed to a carbon sink from 1980 to 2019 in China [110]. [PERSON] et al. (2013) reported that the carbon density and carbon storage in Taihe County (Qianyanzhou located in Taihe County) increased from 1985-2003 [111]. From 2003-2017, the estimated and observed annual sums increased by 1.04% and 0.93%, respectively, for GPP, 4.57% and 1.06% for Re, and 0.18% and 0.01% for NEE, i.e., the estimates of GPP, Re, and NEE showed larger increase rates than the observations, revealing that the simulated GPP, Re, and NEE have larger enhanced carbon sink under realistic sky conditions than the measurements.