Regional-scale analysis of carbon and water cycles on managed grassland systems

Predicting regional and global carbon (C) and water dynamics on grasslands has become of major interest, as grasslands are one of the most widespread vegetation types worldwide, providing a number of ecosystem services (such as forage production and C storage). The present study is a contribution to a regional-scale analysis of the C and water cycles on managed grasslands. The mechanistic biogeochemical model PaSim (Pasture Simulation model) was evaluated at 12 grassland sites in Europe. A new parameterization was obtained on a common set of eco-physiological parameters, which represented an improvement of previous parameterization schemes (essentially obtained via calibration at specific sites). We found that C and water fluxes estimated with the parameter set are in good agreement with observations. The model with the new parameters estimated that European grassland are a sink of C with 213 g C m−2 yr−1, which is close to the observed net ecosystem exchange (NEE) flux of the studied sites (185 g C m−2 yr−1 on average). The estimated yearly average gross primary productivity (GPP) and ecosystem respiration (RECO) for all of the study sites are 1220 and 1006 g C m−2 yr−1, respectively, in agreement with observed average GPP (1230 g C m−2 yr−1) and RECO (1046 g C m−2 yr−1). For both variables aggregated on a weekly basis, the root mean square error (RMSE) was ∼5-16 g C week−1 across the study sites, while the goodness of fit (R2) was ∼0.4-0.9. For evapotranspiration (ET), the average value of simulated ET (415 mm yr−1) for all sites and years is close to the average value of the observed ET (451 mm yr−1) by flux towers (on a weekly basis, RMSE∼2-8 mm week−1; R2 = 0.3-0.9). However, further model development is needed to better represent soil water dynamics under dry conditions and soil temperature in winter. A quantification of the uncertainties introduced by spatially generalized parameter values in C and water exchange estimates is also necessary. In addition, some uncertainties in the input management data call for the need to improve the quality of the observational system.