###### Abstract  
Gross primary productivity (GPP), the primary driver of carbon accumulation, governs the sequestration of atmospheric CO\({}_{2}\) into biomass. However, GPP cannot be measured directly, as photosynthesis and respiration are simultaneous. At canopy level in plot-scale studies, GPP can be estimated through the closed chamber-based measurements of net ecosystem exchange (NEE) and ecosystem respiration (Reco). This technique is cost-effective and widely used in small-scale studies with short vegetation, but measurements are periodic-based and require temporal interpolations. The rectangular hyperbolic model (RH) was the basis of this study, developing two temperature-dependent factors following a linear and exponential shift in GPP when the temperature oscillates from the optimum for vegetation performance. Additionally, a water table depth (WTD)-dependent model and an exponential model were tested. In the peak season, modified RH models showed the best performance, while for the rest of the year, the best model varied for each subplot. The statistical results demonstrate the limitations of assuming the light-use efficiency as a fixed shape mechanism (using only one model). Therefore, a multi-model approach with the best performance model selected for each period is proposed to improve GPP estimations for peatlands. [PERSON], [PERSON], [PERSON], and [PERSON] (2020) 22 December 2020 27 January 2020  
One of the most widespread techniques to measure short-vegetation CO2 fluxes (NEE and Reco) at canopy level are closed dynamic chambers, a non-expensive and easy-to-operate technique used for over a century [5]. In the chamber technique, opaque and transparent chambers are used to measure Reco and NEE fluxes, respectively [6]. The main advantages of chamber systems are their relatively low costs and suitability for use on small plots with short and heterogenous vegetation, enabling determination of plot-specific fluxes. However, they have disadvantages, primarily related to their labour-intensive nature (particularly when using manual chambers) and their limited spatial (restricted to plot-scale) and temporal (dependent on measurement frequency) resolutions [5]. The spatial limitation can also be an advantage, as the chamber's technique is frequently employed in experimental studies where subareas are manipulated with induced WTD, precipitation and/or temperature changes. When manual chambers are used instead of automatic systems, the gap-filling of fluxes between campaigns is needed [7]. Multiple environmental variables, such as soil and air temperatures, water table depth (WTD), or photosynthetically active radiation (PAR), and their relationship with GPP are employed as gap-filling approaches [8; 9]. Consequently, a modelled GPP can be obtained with using the same timestamp as the variable used for its estimation, with PAR being the most commonly used for this purpose. Light-response models are based on the relationship between the photon flux density and the photosynthetic rate as described by the light-response photosynthetic curve. Numerous equations have been developed to explain this connection. Among them, the most commonly employed are the rectangular hyperbolic (RH), the non-rectangular hyperbolic (NRH), and the exponential (EXP) models [10]. The accuracy of these models was recently evaluated in drip-irrigated maize regions, where an improved version of the RH model, including the leaf area index (LAI), demonstrated the best performance [10]. Similarly, an overview of the strategies for fluxes time-integration in peatlands has been compiled as a review by [PERSON] et al. [8]. Therefore, comparing modelling strategies for either fluxes or gap-filling in CO2 exchange is not a new concept. However, our objective was to develop a new approach to identify the most suitable model for peatlands.