###### Abstract
Temperature controls numerous metabolic processes linked to photosynthesis, e.g., an increase in temperature can stimulate enzyme activity and accelerate the photosynthesis rate or indirectly increase vapour pressure deficit (VPD), thereby reducing the photosynthetic rate [11; 12]. Therefore, classical approaches (RH, EXP, NRH) that do not consider the stress caused by too low or too high of a temperature in vegetation may overestimate GPP fluxes during frozen or heatwave periods. This paper presents two temperature-based factors that can improve the RH model during overheated periods over summer heatwaves or during frost events in spring and winter when plants are under stress. Empirical observations revealed a shift in the light curve during winter and early spring, related to the low, near-zero-degree temperatures typical for frost events. Additionally, when temperatures exceeded 28 \({}^{\circ}\)C, non-fitting values were observed in the classical RH model. Based on these observations, we developed a temperature factor to detect temperature-related changes over shorter time spans instead of daily intervals. The usual assumptions include a fixed-shape type of the light-use efficiency curves independently of the vegetation stage, status, or distribution of species abundances when applying it to heterogeneous vegetation covers. In this research, we intend to present the limitations of these assumptions, demonstrating how, depending on the conditions of each period, a different model may perform better, and there is a need to combine them to accomplish the best possible results. We conducted a comparison of the accuracy of classical models and the improved RH model that includes temperature factors against a water-table-depth-based GPP model in a peatland ecosystem, where WTD and temper ature significantly influence its dynamics, to demonstrate how the performances of the models change within the year. Moreover, beyond the classical performance analysis and oscillations in yearly cumulative fluxes, we estimated the growing seasons for each model to compare them with the classical thermal growing season, providing deeper insides into implications of modifying the modelling strategy. Henceforth, the novelty of this work lies not only in the creation of a multi-model framework and gathering the best performances of each model, but also in the development of two new parameters to enhance GPP estimations in peatlands, along with an analysis of the implications of these models for phenology estimations. No two peatlands are identical; each is unique in its vegetation, water table regimes, and climate, which are the main factors defining peatland carbon flux dynamics and the shape of the photosynthetic light-response curve. Different models are expected to show the best performance in each different site. For instance, the presence or dominance of Sphagnum mosses is known for shifting the RH fit when growing in full light (open peatlands) as the photoinhibition plays a major role in lowering GPP values [8]. The results presented in this study will also be affected by the site dependency; thus, the extrapolation of the conclusions for different peatlands and sites is not their intended application. The algorithm should be applied in each case study to determine the best approach. However, no considerable variances in their trend are expected. GPP is the most significant carbon flux in the terrestrial system; thus, improving its estimation is essential for understanding the global dynamics and reducing uncertainty [13].