Daycent Model Development and Testing Using High Frequency N2o Sampling Data

Monday, 15 December 2014: 4:30 PM
Stephen J Del Grosso1, William J Parton2, Emma Suddick3, Peter Grace4, Peter Thorburn5, Massimiliano De Antoni Migliorati4, Clemens Scheer4, Melannie Hartman2, Rebecca L Phillips6 and Kathleen E Savage7, (1)USDA ARS SPNR, Fort Collins, CO, United States, (2)Colorado State Univ, Fort Collins, CO, United States, (3)Woods Hole Research Center, Falmoutn, MA, United States, (4)Queensland University of Technology, Brisbane, Australia, (5)CSIRO Ecosystem Sciences Precinct, Brisbane, Austria, (6)Landcare Research, Hamilton, New Zealand, (7)Woods Hole Research Center, Falmouth, MA, United States
Gaseous nitrogen losses from cropped and grazed soils are an important, but poorly constrained component of the N budget. Most observational data sets of N gas fluxes are for N2O measured using ground based chambers at small spatial and temporal resolution and interpolation and extrapolation are used to estimate annual emissions at the plot scale. These plot level data are then used to develop and test models of varying complexity, ranging from empirical models based mainly on N application rates to process based models that account for how vegetation, land management and environmental factors interact to control N gas losses. This is problematic for two reasons; first, N gas emissions are highly variable in space and time so annual plot level emission estimates based on infrequent sampling are suspect and second, testing of models that predict daily fluxes is limited because models often predict spikes in emissions on days when gas fluxes were not measured. Recent high temporal resolution N2O observations help solve these problems. We show that the latest version of the DayCent biogeochemical model, which includes a varying N2O product ratio for nitrification, has improved accuracy for simulated N2O emissions from continuous chamber measurements for cotton and wheat systems in Australia, and frequent measurements for alfalfa and grass pastures in North Dakota. Model developers are currently working on implementing a NH3 volatilization sub model. This highlights the need for field data with more complete N gas (N2O, NH3, NOx) data to perform further model development and more rigorous testing.