## 1 Introduction  
Currently global forests store approximately 30% of total anthropogenic CO\({}_{2}\) emissions [1], however, the potential of forests to act as a carbon sink in the future is uncertain due to possible saturation effect [2], or negative impact of changed environmental conditions on forest productivity [3]. The eddy covariance (EC) technique is a widely used, state-of-the-art method, and it has become a standard in the estimation and monitoring of high frequency (typically half-hourly) carbon and water fluxes within terrestrial ecosystems [4]. Long-term data series of net ecosystem carbon exchange (_NEE_) or net ecosystem productivity (_NEP_, \(NEP=-NEE\)), in combination with meteorological and biometric measurements, provide invaluable information on the response of forest ecosystem to environmental conditions and climate change [5; 6; 7; 8]. EC data are essential for calibration of process-based models like Biome-BGC and its variants [9; 10] and they are also used for calibration and testing of products derived from remote sensing with sensors, such as Moderate Resolution Imaging Spectroradiometer (MODIS) [11]. Unfortunately, the complexity and high maintenance costs of EC systems have limited the number of sites with measurements spanning a decade or longer. For example, there are 101 forest sites in the global network of micrometeorological tower sites (FLUXNET) 2015 database, out of which 44 datasets are ten or more years long, and only 13 of those are datasets for sites classified as Deciduous Broadleaf Forests [12]. The EC method is far from perfect. EC measurements often result with bad data (e.g., due to instrument malfunctions, ambient conditions not satisfying the EC method or precipitation) and even when data are good a series of corrections during the post-processing is needed [13] (see Section 2). Independent measurements (e.g., biometric, soil respiration, etc.) of the ecosystem fluxes within the footprint of the EC tower are highly needed for the understanding of the ecosystem being measured, as well as for the validation of the EC results. The above and belowground plant growth, or net primary productivity (_NPP_) [14] of a forest stand, is typically assessed through biometric measurements. _NPP_ can be estimated from sequential measurement of trees attributes (diameter at breast height--_DBH_, tree height, etc.) and the use of specific allometric equations and parameters (volume equations, height curves, basic wood densities, root-to-shoot ratios). However, some components of the forest _NPP_ are rather difficult and/or costly to measure (e.g., fine roots production, herbivory losses, and changes in the non-structural carbohydrate reserves) and are not frequently assessed [15; 16]. On the other hand, _NPP_ cannot be directly measured with EC [14; 17]. Additional data processing (e.g., flux partitioning) and measurements/assumptions (e.g., on the share of heterotrophic in the total ecosystem respiration) have to be made in order to estimate _NPP_ from EC. Consequently, there are many studies comparing _NEP_ (i.e., \(-NEE\)) from flux measurements with _NEP_ from biometric measurements, where biometric _NEP_ is calculated as a difference between biometric _NPP_ and measured or estimated heterotrophic respiration [18; 19; 20; 21]. On the other hand, some studies provide the comparison of EC and biometric _NPP_ estimates as well [6; 22; 23]. Comparison of biometric and EC _NEP_ estimates often show poor agreement on a year-to-year basis [18; 20; 22; 24]. The inter-annual variability of forest productivity is a result of a direct response of trees to occurring meteorological conditions [25], but it is also affected by the postponed response due to the carry-over effect [26; 27]. The postponed response in plants is often explained by the presence of carbon reserve pools in the form of non-structural carbohydrates (NSCs). This mechanism has evolved to help trees to cope with adverse effects, such as climate extremes and dampens the visible signal in trees growth [28; 29; 30; 31].