Measurement and modelling of the dynamics of NH3 surface-atmosphere exchange over the Amazonian rainforest

Local and regional modelling of NH3 surface exchange is required to quantify nitrogen deposition to, and emissions from, the biosphere. However, measurements and model parameterisations for many remote ecosystems - such as tropical rainforest - remain sparse. Using 1 month of hourly measurements of NH3 fluxes and meteorological parameters over a remote Amazon rainforest site (Amazon Tall Tower Observatory, ATTO), six model parameterisations based on a bidirectional, single-layer canopy compensation point resistance model were developed to simulate observations of NH3 surface exchange. Canopy resistance was linked to either relative humidity at the canopy level (RHz0'), vapour pressure deficit, or a parameter value based on leaf wetness measurements. The ratio of apoplastic NH4+ to H+ concentration, Gamma(s), during this campaign was inferred to be 38.5 +/- 15.8. The parameterisation that reproduced the observed net exchange of NH3 most accurately was the model that used a cuticular resistance (R-w) parameterisation based on leaf wetness measurements and a value of Gamma(s) = 50 (Pearson correlation r = 0.71). Conversely, the model that performed the worst at replicating measured NH3 fluxes used an R-w value modelled using RHz0' and the inferred value of Gamma(s) = 38.5 (r = 0.45). The results indicate that a single-layer canopy compensation point model is appropriate for simulating NH3 fluxes from tropical rainforest during the Amazonian dry season and confirmed that a direct measurement of (a non-binary) leaf wetness parameter improves the ability to estimate R-w. Current inferential methods for determining Gamma(s) were noted as having difficulties in the humid conditions present at a rainforest site.

Automated summary

This study developed six model parameterisations for simulating NH3 surface exchange based on hourly measurements at a remote Amazon rainforest site. The model using cuticular resistance parameterisation based on leaf wetness measurements and a value of Gamma(s) = 50 was most accurate, while the model using RHz0' and the inferred value of Gamma(s) = 38.5 performed the worst. Current methods for determining Gamma(s) were found to have difficulties in the humid conditions of a rainforest site.