Abstract
Abstract. This work is an attempt to provide seasonal variation of biogenic NO emission fluxes in a Sahelian rangeland in Mali (Agoufou, 15.34° N, 1.48° W) for years 2004, 2005, 2006, 2007 and 2008. Indeed, NO is one of the most important precursors for tropospheric ozone, and previous studies have shown that arid areas potentially display significant NO emissions (due to both biotic and abiotic processes). Previous campaigns in the Sahel suggest that the contribution of this region in emitting NO is no longer considered as negligible. However, very few data are available in this region, therefore this study focuses on model development. The link between NO production in the soil and NO release to the atmosphere is investigated in this modelling study, by taking into account vegetation litter production and degradation, microbial processes in the soil, emission fluxes, and environmental variables influencing these processes, using a coupled vegetation–litter decomposition–emission model. This model includes the Sahelian Transpiration Evaporation and Productivity (STEP) model for the simulation of herbaceous, tree leaf and faecal masses, the GENDEC model (GENeral DEComposition) for the simulation of the buried litter decomposition and microbial dynamics, and the NO emission model (NOFlux) for the simulation of the NO release to the atmosphere. Physical parameters (soil moisture and temperature, wind speed, sand percentage) which affect substrate diffusion and oxygen supply in the soil and influence the microbial activity, and biogeochemical parameters (pH and fertilization rate related to N content) are necessary to simulate the NO flux. The reliability of the simulated parameters is checked, in order to assess the robustness of the simulated NO flux. Simulated yearly average of NO flux ranges from 2.09 to 3.04 ng(N) m−2 s−1 (0.66 to 0.96 kg(N) ha−1 yr−1), and wet season average ranges from 3.36 to 5.48 ng(N) m−2 s−1 (1.06 to 1.73 kg(N) ha−1 yr−1). These results are of the same order as previous measurements made in several sites where the vegetation and the soil are comparable to the ones in Agoufou. This coupled vegetation–litter decomposition–emission model could be generalized at the scale of the Sahel region, and provide information where few data are available.
Highlights
In the continental biosphere, most of the N cycle is accomplished through internal processes such as mineralization/assimilation, because N is mostly assimilated in the biosphere from its mineral form
Several parameters, included in the NO emission model, play an important role in modulating emission. These parameters can be classified in two categories: physical parameters which affect substrate diffusion and oxygen supply in the soil and influence the microbial activity (Skopp et al, 1990), and biogeochemical parameters
Simulations are performed at the site of Agoufou (Mali), with a coupled vegetation–litter decomposition–NO emission model, for years 2004, 2005, 2006, 2007 and 2008
Summary
Most of the N cycle is accomplished through internal processes such as mineralization/assimilation, because N is mostly assimilated in the biosphere from its mineral form (nitrates NO−3 , ammonium NH+4 ). A coupled vegetation–litter decomposition–emission modelling approach is used, which links three existing models developed for semi-arid regions, simulating respectively the growth and degradation of the vegetation (STEP, Mougin et al, 1995), the decomposition of the organic matter and microbial processes in the soil (GENDEC, Moorhead and Reynolds, 1991), and the release of NO (NOFlux, Delon et al, 2007) associated with environmental variables. Pulses are usually underestimated by global-scale modelling, and the specificity of a model developed for semi-arid regions helps to provide magnitudes of NO fluxes In our study, these emissions are related to their biogeochemical origin, to the quantity of biomass, to the quantity of livestock which drives the quantity of organic matter and the N pool and N turnover in the soil. Modelling results are discussed and compared to field measurements, and limitations and uncertainties are assessed
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