Laurens Ganzeveld
Soils are an important source of reactive
nitrogen (NOx) due to the microbial production of nitric oxide (NO) in the nitrification and (chemo-)denitrification process and the subsequent chemical
transformation of the emitted NO. Nitrification,
denitrification and chemodenitrification depend on biogeochemical and physical
properties of the soil, e.g., microbial species, soil texture, soil water, pH,
redox-potential and nutrient status [e.g., Conrad, 1996]. Soil emission fluxes are also tightly linked to land use
management through the impact of the application of natural and synthetic
fertilizers, tillage, irrigation, compaction, planting and harvesting [e.g., Frolking
et al., 1998]. The actual flux of NOx into the
atmosphere over vegetation depends on the interactions between turbulent
transport, chemistry and the subsequent uptake of the reaction products within
the canopy [e.g., Ganzeveld et al., 2002].
Soil NO emissions control NOx
budgets in remote and rural areas, while the fossil fuel source of 20-25 TgN yr-1
[e.g., Delmas et al., 1997; and references therein] dominates the NOx
budget in industrialized areas. Estimates of the soil NO sources range between
9.7 TgN yr-1 Potter et al. [1996] and 21 TgN yr-1 Davidson
and Kingerlee [1997] whereas estimates, that include the role of canopy
deposition, are about 50% smaller [Yienger
and Levy, 1995, Ganzeveld et al., 2002]. The inventory by Yienger
and Levy [1995], which is available to the modeling community through the
GEIA site, is based on an empirical model that accounts for different biomes,
pulsing, which is the enhancement of the emissions through rainfall, and the
effect of the canopy uptake. Their NO soil emission flux of 11 TgN yr-1
(below canopy, and consequently their atmospheric source estimate is 5.5 TgN yr-1) is slightly larger compared
to estimate of 9.7 TgN yr-1 by Potter et al. [1996], who used
an ecosystem modeling approach (CASA) by integrating remote sensing, climate,
vegetation and soil datasets. Monthly mean global distributions at a 1 degree
grid resolution of the NO emission fluxes (and other gases) simulated with the
CASA model can be downloaded from the NASA Ames ftp server: http://geo.arc.nasa.gov/sge/casa/data.html.
The inventory by Davidson and Kingerlee [1997] is a purely measurement based source inventory, based on major global biomes, with an estimated total global soil NO emission of 21 TgN yr-1, with major contributions from temperate and tropical cultivated land, chaparral/thorn forest and tropical savannah/woodland. The model-based estimates of the soil NO emission flux by Yienger and Levy [1995] and Potter et al. [1996], and the measurement-based estimates by Davidson [1991] and Davidson and Kingerlee [1997], disagree by a factor of 2 overall. This discrepancy suggests that the role of some important soil emission control factors such as fertilization and pulsing might not be realistically represented in the models [Hutchinson et al., 1997]. An essential difference between the model- and measurement based inventories is that the model based inventories can provide a temporally resolved soil-biogenic NOx flux. This depends on to what extent the role of parameters that control the temporal variability, e.g., soil moisture, temperature and fertilizer application, has been included in the model and, in addition, what the temporal resolution of these input parameters is.
Another useful database that provides a
global agricultural NO (and N2O and NH3) emission
inventory at a 0.5 degree spatial resolution [FAO/IFA, 2001; Bouwman et al., 2002] is accessible at: http://arch.rivm.nl/ieweb/ieweb/index.html.
This dataset is based on statistical analyses of 846 N2O and 99 emission measurements in
agricultural fields used to describe the role of the major drivers that control
the emissions.
As mentioned before, the processes that are
mainly controlling the production of soil NO are influenced by soil
environmental conditions such as soil temperature, moisture, fertility,
vegetation cover, fire and land use management. For example, a 10 °C rise in soil temperature produces a 2-5 fold increase in
NO emission rates [Williams and Fehsenfeld, 1991; Valente and
Thornton, 1993]. Short-term changes in soil moisture after a rainfall event
can influence soil behaviour such that production of NO can revert to NO
consumption [Davidson, 1991] or it can result in large pulse of NO [Davidson,
1992; Meixner et al., 1999]. One of the specific land-use practices, biomass burning,
results in a temporary reduction in the plant and microbial sink of soil
inorganic nitrogen and thereby favouring the conditions for NO production
[e.g., Verchot et al., 1999]. These examples of the sensitivity of
soil-biogenic NOx fluxes to the specific
control factors, and the fact that the actual emission flux is determined by a
complex interplay between all the production and destruction processes,
complicates a prediction of the impact of climate change and land cover and
land use changes on the emissions. A straightforward extrapolation of these
sensitivities, like the temperature induced emission increase, is not expected
to result in a realistic prediction of the changes in the global soil-biogenic
NOx emissions due to climate
change. For example, there might be a short-term increase in the soil NO
emissions due to a temperature change but the question arises if this
temperature effect would last on the long-term due to a faster depletion of the
substrates that are involved in the microbiological processes.
A promising approach to assess the impact of
anticipated future climate and land use and land cover conditions on
soil-biogenic NOx emissions is the use of
process-based soil N emission models, like the CASA model [Potter et al.,
1996], CENTURY/DAYCENT [e.g., Parton et al., 2001; Kirkman et al.,
2002] and DNDC [e.g, Li et al., 2000]: http://www.dndc.sr.unh.edu. These models consider to some
extent the short- and long-term variability in biogeochemical processes and
emission fluxes as a function of the controlling environmental parameters like
temperature, soil moisture and vegetation dynamics. These models provide a tool
to assess the impact of climate change and land cover
and land use changes on soil-biogenic NOx emissions through
integrating the dependence of the emissions on all the controlling parameters
involved on short- as well as long timescales. Moreover, the process models can
be used to identify the key parameters that are mainly responsible for the changes
in the soil-biogenic NOx emissions and that should be focus in such an assessment.
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