## 1 Introduction  
The carbon cycle of terrestrial ecosystems is the core of the material and energy cycles of these ecosystems and is the key process driving ecosystem change [1]. Hence, the quantitative assessment of the carbon budget is an important task in the study of ecosystems and global climate change [2, 3]. Net ecosystem exchange (NEE) refers to the change in ecosystem carbon storage caused by plant photosynthesis, the carbon storage in the canopy air, and the carbon emissions from both biotic and abiotic respiration. It mainly reflects the carbon budget of an ecosystem and determines the transition process of the \"Sink-source\" or \"Source-sink\" of a surface ecosystem from the perspective of flux observation [4]. As an important part of the Earth's ecosystem, terrestrial ecosystems are not only the foundation of human survival and development but also play an important role in the global carbon cycle [5]. An accurate assessment of the carbon budget of terrestrial ecosystems not only provides a theoretical basis for understanding and predicting global climate change but also provides a scientific basis for the government to make relevant decisions to reduce greenhouse gas emissions and promote social sustainable development. The Qinghai-Tibet Plateau ecosystems grow in extremely harsh environments characterized by a low annual average temperature and low rainfall, a high solar radiation and wind speed, a limited availability of soil nutrients and water, and short growing seasons. Therefore, these alpine ecosystems are extremely fragile and sensitive to climate change. Accurately measuring and estimating the carbon flux of the alpine ecosystems in the Qinghai-Tibet Plateau under the background of climate change is crucial for the study of the ecosystem carbon balance. With the development of energy exchange and mass transfer and the urgent demand for carbon emission reduction, the observation of the NEE has been paid more attention by scholars through research. The vorticity covariance method is one of the most effective methods for measuring the carbon budget between the atmosphere and various ecosystems since the 1980s [6; 7]. It has been widely used to measure the exchange of matter and energy between the atmosphere and the Earth's surface [6; 7]. However, in the course of observation, due to various reasons (rainfall, instrument failure, human error operation, etc.) and due to part of the observation data going missing, the CO\({}_{2}\) flux data measured by the flux towers have a high rate of deletion [8]. Nearly 17-50% of the observed data is deleted in a year [8], which brings difficulties to the application of the flux tower data. How to establish an effective and reasonable data interpolation method to form the complete and reliable data integration of CO\({}_{2}\) flux data is a problem to be solved. Currently, FLUXNET and the European flux network of Carbon Europe use the marginal distribution sampling (MDS) of the mean interpolation method for the interpolation of missing flux data and apply it to the FLUXNET 2015 dataset [9]. OzFlux, the Australian National Ecosystem Research Network, employs an artificial neural network algorithm to interpolate missing flux data [10; 11]. However, up to now, the interpolation methods for missing flux data have not been unified among flux networks, and most of them are for the net ecosystem exchange. For example, [PERSON] [12] systematically discussed the effectiveness of 15 data interpolation methods in the calculation of net ecosystem exchange annual accumulation using 10 sets of observational data from 6 forest sites. The results showed that the selection of an interpolation method has a significant influence on the calculation of annual NEE cumulants and that the uncertainty is about \(\pm\)25 g\(\cdot\)C\(\cdot\)m\({}^{-2}\)\(\cdot\)y\({}^{-1}\).