## 1 Introduction
NEE is a key variable to determine the carbon balance of an ecosystem and therefore, understanding it responses to environmental change, management, and site characteristics is essential [33; 34; 35]. Over seasonal and interannual time scales, NEE in managed grasslands can vary with the frequency, timing and duration of management practices. Moving and grazing removes photosynthesizing biomass and can thereby temporarily but substantially reduce GPP [36; 18; 37]. To develop a better understanding of ecosystem processes, or predict the response of ecosystem to climate change and management practices, various process-based (mechanistic) vegetation models have been developed [38; 39; 40]. They vary in complexity and can operate at different temporal and spatial scales. They are being used widely to simulate ecosystem carbon and nitrogen dynamics. The reliability of these models highly depend on the quantity and quality of the data used for their calibration [41; 42; 43]. Model parameters calibration aims to constrain the uncertainty in model parameter space and optimize the model output of ecosystem-atmosphere CO2 exchange [44]. Measured CO2 fluxes were used to constrain model simulation through parameter calibration. Once parameterized, these models allow to simulate separately the constituents of NEE (soil, microbial, plant, and animal respiration and gross primary production) that cannot be measured directly by EC, or to interpolate and extrapolate CO2 fluxes in time and space. However, insufficient knowledge about underlying processes (e.g., all the processes leading to observed carbon and water fluxes and flows that existsin the real world but are not or are only poorly understood and modeled) as well as parameters and initial conditions uncertainties can lead to bias and uncertainty in model simulations [45]. Also, because interannual responses are usually less well captured by models than daily and seasonal dynamics [46; 47; 48], the availability and quality of long-term datasets are crucial to improve model performances [49]. Process-based simulation models are therefore required to gain insight of processes and interactions between managed grasslands C dynamics, climate change, and management practices, in combination with experimental observations, especially for long-term analyses [50; 51]. CenW (carbon, energy, nutrients, and water) is a process-based model, running at a daily time step. It was originally developed to simulate the carbon balance of forests over time [52; 53; 54]. The soil organic matter module of the model was derived from the CENTURY model [55], which was originally developed for grasslands (more details are given in Section 2.2). Recently, CenW was successfully parameterized and used to simulate carbon and water fluxes of an intensively grazed dairy pasture in New Zealand [56], and to test effects of different climate and management practices on soil carbon stocks and milk production [57]. In this study, we used the CenW model to simulate the seasonal and interannual variability of carbon dioxide and water fluxes of two differently managed grassland fields located in France. The two selected paddocks are part of the Agroecosystem Biogeochemical Cycles and Biodiversity (ACBB) long-term national research infrastructure. They are located only 200 meters apart and are equipped with eddy covariance (EC) flux towers. These sites were either regularly moved or grazed, and they received different fertilizer doses applied following different application patterns. Within the framework of this study, we focused on the differences between carbon and water fluxes between grasslands under moving and grazing managements. Flux data from the paired paddocks were used to parameterize and validate the CenW model.