Validation of DNDC for 22 long-term N2O field emission measurements

Twenty-two long-term measurements of direct N2O emissions from soils in an intensive agricultural area were used for the validation of the process-based DNDC model (version 8.3P). Model simulations were evaluated for temporal patterns of N2O. NH4+, NO3- and water-filled pore space (WFPS) and total N2O emissions. Several soil and crop input parameter adjustments to the model were evaluated but only the recalculation of the WFPS at wilting point and at field capacity, using pedotransfer functions, resulted in a clear improvement of the simulated variables (WFPS in all cases, N2O in some cases). Therefore, only this adjustment was made to DNDC 8.3P. This change, however, resulted for some cases (both cropland and grassland) in retardation of nitrate leaching and to a lesser extent of NH4+ to the deeper soil layers. The goodness of fit of the simulated temporal pattern of N2O varied considerably between sites. The total simulated N2O emissions from cropland showed a good agreement with the measurements, although there was a systematic overestimation of 7.4 kg N2O-N ha(-1). Grassland soils, in contrast, gave a low agreement between total simulated and measured N2O losses. On the basis of all measured data a regional emission factor of 3.16 with a 95% confidence interval of -0.89 to 7.21 could be calculated. DNDC simulations resulted in an emission factor of 6.49 with a 95% confidence interval of 4.04-8.93. The overall outcome of the N2O emission measurements and DNDC simulations were compared with several empirical regression models, which may be applicable for a temperate climate system. All of the tested regression models showed reliable results up to a N2O emission of 10 kg N2O-N ha(-1). Higher emissions, however, were systematically underestimated. Though DNDC both under- and overestimated specific sites, the general agreement, over the whole range between measurements and simulations of total N2O losses (simulations = 0.82 x meas. + 6.2), was better than for the different regression models. (c) 2007 Elsevier Ltd. All rights reserved.