Model testing for nitrous oxide (N2O) fluxes from Amazonian cattle pastures. (October 2016) - Meurer, Katharina H.E.; Franko, Uwe; Spott, Oliver; Stange, C. Florian; Jungkunst, Hermann F.
Abstract: Process-oriented models have become important tools in terms of quantification of environmental changes, for filling measurement gaps, and building of future scenarios. It is especially important to couple model application directly with measurements for remote areas, such as Southern Amazonia, where direct measurements are difficult to perform continuously throughout the year. Processes and resulting matter fluxes may show combinations of steady and sudden reactions to external changes. The potent greenhouse gas nitrous oxide (N2 O) is known for its sensitivity to e.g. precipitation events, resulting in intense but short-term peak events (hot moments). These peaks have to be captured for sound balancing. However, prediction of the effect of rainfall events on N2 O peaks is not trivial, even for areas under distinct wet and dry seasons. In this study, we used process-oriented models in both a pre-and post-measurement manner in order to (a) determine important periods for N2 O-N emissions under Amazonian conditions and (b) calibrate the models to Brazilian pastures based on measured data of environment conditions (soil moisture and Corg ) and measured N2 O-N fluxes. During the measurement period (early wet season), observed emissions from three cattle pastures did not react to precipitation events, as proposed by the models. Here both process understanding and models have to be improved by long-term data in high resolution in order to prove or disprove a lacking of N2 O-N peaks. We strongly recommend the application of models as planning tools for field campaigns, but we still suggest model combinations and simultaneous usage. Highlights: Models were used to identify hot moments for N2 O-N in Southern Amazonia. Observed emissions from pastures were low and did not react to rainfall events. Calibration and validation primarily aimed to reproduce environmental conditions. Strong need for further process understanding and highly resolved measurements.