Constraining N cycling in the ecosystem model LandscapeDNDC with the stable isotope model SIMONE.

Abstract

The isotopic composition (ic) of soil nitrogen (N) and, more recently, the intramolecular distribution of 15 N in the N2 O molecule (site preference, SP) are powerful instruments to identify dominant N turnover processes, and to attribute N2 O emissions to their source processes. Despite the process information contained in the ic of N species and the associated potential for model validation, the implementation of isotopes in ecosystem models has lagged behind. To foster the validation of ecosystem models based on the ic of N species, we developed the stable isotope model for nutrient cycles (SIMONE). SIMONE uses fluxes between ecosystem N pools (soil organic N, mineral N, plants, microbes) calculated by biogeochemical models, and literature isotope effects for these processes to calculate the ic of N species. Here, we present the concept of SIMONE, apply it to simulations of the biogeochemical model LandscapeDNDC, and assess the capability of 15 N-N2 O and, to our knowledge for the first time, SP, to constrain simulated N fluxes by LandscapeDNDC. LandscapeDNDC successfully simulated N2 O emission, soil nitrate, and ammonium, as well as soil environmental conditions of an intensively managed grassland site in Switzerland. Accordingly, the dynamics of 15 N-N2 O and SP of soil N2 O fluxes as simulated by SIMONE agreed well with measurements, though 15 N-N2 O was on average underestimated and SP overestimated (root-mean-square error [RMSE] of 8.4‰ and 7.3‰, respectively). Although 15 N-N2 O could not constrain the N cycling process descriptions of LandscapeDNDC, the overestimation of SP indicated an overestimation of simulated nitrification rates by 10-59% at low water content, suggesting the revision of the corresponding model parameterization. Our findings show that N isotope modeling in combination with only recently available high- frequency measurements of the N2 O ic are promising tools to identify and address weaknesses in N cycling of ecosystem models. This will finally contribute to augmenting the development of model-based strategies for mitigating N pollution.