Publication Date:
2015-07-08
Description:
In this paper a new process-based, weather-driven model for ammonia (NH3) emission from a urine patch has been developed and its sensitivity to various factors assessed. This model, the GAG model (Generation of Ammonia from Grazing) was developed as a part of a suite of weather-driven NH3 exchange models, as a necessary basis for assessing the effects of climate change on NH3 related atmospheric processes. GAG is capable of simulating the TAN (Total Ammoniacal Nitrogen) content, pH and the water content of the soil under a urine patch. To calculate the TAN budget, GAG takes into account urea hydrolysis as a TAN input and NH3 volatilization as a loss. In the water budget, in addition to the water content of urine, precipitation and evaporation are also considered. In the pH module we assumed that the main regulating processes are the dissociation and dissolution equilibria related to the two products of urea hydrolysis: ammonium and bicarbonate. Finally, in the NH3 exchange flux calculation we adapted a canopy compensation point model that accounts for exchange with soil pores and stomata as well as deposition to the leaf surface. We validated our model against measurements, and carried out a sensitivity analysis. The validation showed that the simulated parameters (NH3 exchange flux, soil pH, TAN budget and water budget) are well captured by the model (r 〉 0.5 for every parameter at p 〈 0.01 significance level). We found that process-based modelling of pH is necessary to reproduce the temporal development of NH3 emission. In addition, our results suggested that more sophisticated simulation of CO2 emission in the model could potentially improve the modelling of pH. The sensitivity analysis highlighted the vital role of temperature in NH3 exchange; however, presumably due to the TAN limitation, the GAG model currently provides only a modest overall temperature dependence in total NH3 emission compared with the values in the literature. Since all the input parameters can be obtained for study at larger scales, GAG is potentially suitable for larger scale application, such as in regional atmospheric and ecosystem models.
Print ISSN:
1810-6277
Electronic ISSN:
1810-6285
Topics:
Biology
,
Geosciences
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