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 15N in the N2O molecule (site preference, SP) are powerful instruments to identify dominant N turnover processes, and to attribute N2O 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 15N‐N2O and, to our knowledge for the first time, SP, to constrain simulated N fluxes by LandscapeDNDC. LandscapeDNDC successfully simulated N2O emission, soil nitrate, and ammonium, as well as soil environmental conditions of an intensively managed grassland site in Switzerland. Accordingly, the dynamics of 15N‐N2O and SP of soil N2O fluxes as simulated by SIMONE agreed well with measurements, though 15N‐N2O was on average underestimated and SP overestimated (root‐mean‐square error [RMSE] of 8.4‰ and 7.3‰, respectively). Although 15N‐N2O 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 N2O 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.

What can we learn from N2O isotope data? – Analytics, processes and modelling

“…Evaluation of N 2 O sources can be obtained by the introduction of natural abundance isotope data into process‐based biogeochemical models to reconcile measured and modelled N isotopic compositions 43,101 . First attempts of including N isotope ratios into N cycling models comprised the incorporation of soil δ 15 N values into the DAYCENT process‐based model to determine gaseous nitrogen losses from soil 101 .…”

“…More recently, N 2 O isotopic signatures have also been integrated as additional model parameters, for example into the Landscape DNDC model (SIMONE – Stable Isotope Model for Nutrient cyclEs), helping to reduce uncertainty in the estimates of ecosystems N fluxes 43 . So far SIMONE/LandscapeDNDC has demonstrated its capacity to constrain the dynamics of the N 2 O isotopic composition ( δ 15 N bulk and δ 15 N SP ) and precursors ( δ 15 N NO3‐ and δ 15 N NH4+ ) within a few European fertilized grasslands 43 . The model outputs have been interpreted jointly with dual isotope plots that suggested some model shortcomings, e.g.…”

“…At local scales, the interpretation of N 2 O isotope data can be significantly improved if supported by process‐ and location‐specific information regarding substrates and isotope fractionation effects, 42 as well as complementary use of 15 N‐labelling approaches to reduce the uncertainties in process partitioning through cross‐validations between the two approaches. At broader spatiotemporal scales, a combination of natural abundance measurements and modelling approaches 43,105 will allow the spatial extrapolation of N 2 O source and sink information obtained from individual studies to the regional or even global perspective. We expect that, in the future, with the advanced development of analytical methods, a better understanding of processes and mechanisms, and further extension of data‐analysis approaches, N 2 O isotope techniques will be more and more effective in reliably identifying N 2 O sources and sinks, providing important, and probably more accurate constraints on N 2 O budgets for the development of effective mitigation strategies.…”

“…In order to access the complex source information contained in the isotopic composition of N 2 O, a number of mathematical data analyses and modelling approaches have been utilized 23,42,43 . For example, given the higher δ 15 N bulk and δ 18 O in marine compared with continental N 2 O sources, 44 Snider et al 23 applied a Bayesian isotope mixing model to partition the global contribution of N 2 O emitted from these sources to tropospheric N 2 O. Isotopocule measurements of N 2 O from individual laboratory and field studies are often limited by spatial and temporal coverage, thus requiring upscaling to obtain regional information of N 2 O emission sources.…”

Denitrification Is the Main Nitrous Oxide Source Process in Grassland Soils According to Quasi‐Continuous Isotopocule Analysis and Biogeochemical Modeling

“…First steps in this direction have been made for the CLM-CN model (Houlton et al, 2015), the DAYCENT model (Bai & Houlton, 2009), and the nonequilibrium stable isotope simulator NESIS (Rastetter et al, 2005). Recently, Denk et al (2019) developed the "Stable Isotope Model for Nutrient cycles" model (SIMONE), which uses fluxes between ecosystem N pools (soil organic N, mineral N, plants, and microbes) calculated by biogeochemical models and literature isotope effects to calculate the isotopic composition of soil N pools and N 2 O emissions.…”