Introduction
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 (nitrates NO3-,
ammonium NH4+). In natural soils, these compounds come from
biological nitrogen fixation (BNF, ), from atmospheric
dry and wet deposition and from the
mineralization of organic matter through the bacterial and fungal
decomposition of dead matter. N cycle in the soil is dominated by microbial
transformations. Bacterial processes involve important reactive gaseous
components, e.g. NO formation through nitrification and
denitrification . A significant fraction of these compounds can be
released to the atmosphere. NO is one of the most important precursors
for tropospheric ozone, and participates in the formation of nitric acid,
participating in N deposition. NOx (NOx = NO+NO2) are also involved in the abundance
of the hydroxyl radical (OH) which determines the lifetime of some pollutants and greenhouse gases .
Atmospheric NOx is coupled to the Earth's nitrogen cycle through complex
interactions involving soil microbial activity, soil N content and N inputs
to the soil, either from anthropogenic or atmospheric origin
. The processes of NO production and
consumption in the soil have been studied through modelling, laboratory or
field studies by several authors for different types of soils and
climates ( and for European soils,
and for tropical soils as examples). The
release of NO (NO emission) to the atmosphere is the result of
production and consumption processes in the soil. In many previous studies it
was observed that the NO release equals the NO production minus the NO
consumption. Several biotic and abiotic processes in soils and plants are
responsible for the production and consumption of NO
(). Microbial nitrification and denitrification
constitute the principal processes . According to
and references therein, fluxes are regulated by
factors that include the concentration of inorganic N (NO3- and
NH4+), soil moisture, temperature, accessibility of labile C, and
physical soil properties. Most of the trace gas production and consumption
processes in soil (trace gases such as NO, N2O, CH4,
CO) are probably due to microorganisms . Oxidation of NO to nitrate has
been found to be the dominant NO consumption mechanism in some soils
( and references therein). Release rates of NO can be
much lower than the NO production rates, since NO consumption is of
similar magnitude to NO production. NO shows both high and
variable production and consumption rates in soil and consequently highly
dynamic compensation points . The concept of the compensation
concentration is based on the observation that production and consumption of
a trace gas occur simultaneously in a soil and that the consumption rate is a
function of the trace gas concentration, whereas the production rate is not
. According to , the net exchange of
NO between ecosystems and the atmosphere is globally dominated by
biogenic emissions of NO from soils. Only at exceptionally high
ambient NO concentration might direct deposition to plants constitute a
significant removal mechanism for atmospheric NO .
After NO is oxidized into NO2, the NO2 can be deposited on the
vegetation, decreasing the net emission of NO above canopy to the atmosphere.
Above-canopy emissions are calculated by introducing the canopy reduction
factor (CRF) concept, based on the leaf area index (LAI), and considering the
canopy as an absorber of NO2 ().
NO release in arid and semi-arid soils are mainly governed by pulse events, produced when first precipitations
shower long-dried soils at the beginning of the rainy season. Several studies have shown that pulse emissions of NO
contribute strongly to the total emission (), specifically
in semi-arid regions. In those regions, mineral and organic substrates tend to accumulate at the soil surface and in the
soil during the long dry season, when there is little nutrient demand, leading to an excess of mineralization during the early phases of the wet cycle .
At the global scale, NO emissions from soils have been estimated to be
approximately 21 Tg(N)yr-1 at the ground
level (below canopy), a portion of the NO2 being deposited within the
canopy. Above-canopy emissions were estimated to be 5.45
Tg(N)yr-1 by one of the first global modelling study on the
subject , and more recently up to 8.6 Tg(N)yr-1
and 10.7 Tg(N)yr-1 .
At the scale of the Sahelian region, have calculated a
0.5 ± 0.1 Tg(N)yr-1 above-canopy NOx emission,
representing 5 to 10 % of the global budget according to
or . have shown that the largest pulsed
enhancements in their model are predicted over this region during the monsoon
onset (April to June), comprising 15 to 65 % of the simulated NO2
column and increasing variability by a factor of 5. As a consequence, the
contribution of the Sahel in emitting NO is no longer considered as
negligible. Though they are of high interest for the specific mechanisms
taking place there, and for their relatively high contribution to the global
N cycle, semi-arid regions remain poorly investigated due to the remoteness
of the sites and the complexity of running long-term measurements in difficult
conditions. Modelling is therefore a precious help to describe the
environmental conditions that favour or not NO emissions. However, in the
same time, laboratory and field measurements are necessary to better
understand production and consumption processes in the soil leading to the
release of NO, and to improve modelling approaches.
In this study, we propose a modelling approach of NO emissions from soils at the yearly and
seasonal scale. The goal is to identify production and consumption processes, linked to NO release,
through organic matter decomposition and microbial dynamics in the soil, in semi-arid ecosystems. A coupled
vegetation–litter decomposition–emission modelling approach is used, which links three existing models specifically
developed for semi-arid regions, simulating respectively the growth and degradation of the vegetation
(STEP, ), the decomposition of the organic matter and microbial processes in the soil (GENDEC, ),
and the release of NO (NOFlux, ) associated with environmental variables. Modelling results are compared to
data collected in the northern Mali site of Agoufou for the years 2004 to 2008. This modelling tool has been developed for semi-arid
regions where specific processes such as pulses of emission need to be taken into account. Indeed, 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. Furthermore,
the Sahel region is a large region grazed by domestic cattle, and the role of animals in biomass management, as it is included in
our modelling approach, is seldom highlighted in regional or global models. This study presents a more detailed emission model than
those based on the approach only, and gives an insight in N production processes in the soil at the origin of the emission.
First, the Agoufou site is presented, as well as the different measurements used for model comparison. Then, the three models,
STEP, GENDEC and NOFlux, are introduced. Finally, modelling results are discussed and compared to field measurements, and limitations and uncertainties are assessed.
Data source: Agoufou site
The Agoufou study site (Mali, 15.34∘ N, 1.48∘ W) is part of the
African Monsoon Multi-disciplinary Analysis (AMMA) – Couplage de
l´Atmosphere Tropicale et du Cycle Hydrologique (CATCH) site observatory
located in the northern Mali Gourma region. This region stretches from the
loop of the Niger River southward down to the border region with Burkina Faso
. Located towards the northern limit of the area reached by
the West African Monsoon, the region experiences a single rainy season with
most precipitation falling between late June and mid-September. The rainy
season is followed by a long dry season of approximately 8 months. At the
Agoufou site, the soil is sandy, with 91.2 % of sand, 3.1 % of silt
and 4.6 % of clay in the first 5 cm. The surface pH is 6.7. The
hydrologic system is endorheic operating at short distance from dune slopes
to inter-dune depressions within small adjacent catchments. The vegetation at
Agoufou is an open woody savanna, typical of mid-Sahel sandy soil vegetation
with a herbaceous layer almost exclusively composed of annual species, and
scattered trees and shrubs with a 3.1 % crown cover .
The area is used as livestock grazing under communal access. Because of the
proximity of the Agoufou permanent pond, the grazing pressure is high during
the dry season. Agoufou can be considered as representative of Sahelian dry
savannas. A comprehensive description of the site can be found in
.
Meteorological and vegetation data
At the Agoufou site, woody and herbaceous plant density and species
composition are organized in facies following finer topography and soils
nuances or differences in land use practices and histories
. The herbaceous layer has been monitored using a two-level
stratified random sampling design, as described in . Total
and green vegetation cover (visual and digital photograph estimates in %,
), standing and litter mass (destructive measure, with
harvest, air drying and weighing) and species composition (list with visual
estimates of contribution to bulk) are assessed in 1×1 m
plots randomly sampled in each of the vegetated strata along the transect.
Aboveground green and dry masses and surface litter mass have been sampled
during several years, but only the years 2004, 2005, 2006, 2007 and 2008 are
used in this study to evaluate the performance of the model. Indeed, these
years represent contrasted meteorological conditions, with low rainfall years
(2004 and 2008) and years with normal rainfall for the region (2005, 2006,
2007). Furthermore, vegetation data are more numerous during these years,
when the AMMA experiment took place in West Africa.
A meteorological station was installed from 2002 to 2010, giving data on
rainfall, wind speed, relative humidity, air temperature and global
radiation. These data were quality checked and gap filled for the years 2004
to 2008 only. Data on soil moisture at different levels and different places
(top, middle and bottom locations of dune slope), and soil temperatures at
different levels are also available, except for year 2004. A detailed
description of the soil moisture network and methodology and of the
meteorological station is given in and .
Meteorological and vegetation data are available in the AMMA database:
http://bd.amma-catch.org/amma-catch2/main.jsf . Other
data are progressively integrated in this database.
Calculation of NO flux
NO fluxes were measured at Agoufou during summers 2004 and 2005, from
closed dynamic chambers (flowed-through non-steady-state), defined in
. A comprehensive description of the chamber device and
calculation flux theory is available in . Stainless steel
opaque chambers of 800 cm2 area (40 × 20) and 18 cm
height were used. A stainless steel frame is inserted into the ground before
the measurement which starts when adjusting the chamber on the frame, sealing
being assured by a slot filled with water. The air inlet is on one side of
the chamber, and the air outlet on the other side is connected to the analyser
with 2 m of Teflon tubing, so that the chamber is swept with an air
flow only due to the pump of the instrument. The inflow is not ozone free.
Therefore, due to chemical reactions inside the chamber, the fluxes are
underestimated. This underestimation is calculated (see below) and is small,
due to low ozone mixing ratios. The residence time of the air inside the
chamber is approximately 10 min. No significant change in air temperature
in the chamber has to be noticed during this lapse time. Pressure is assumed
to be constant throughout the flux measurement and equal to ambient pressure.
A small vent of 4 mm in diameter provided the pressure equilibrium between the
inside and outside of the chamber. As the chamber is ventilated (a
circulation of air is always assured by the small vent and the pumping), the
system is assumed to be dynamic. Stainless steel is known to be quasi-inert
to NO, as is Teflon for tubing, ensuring that NO does not
react with the walls of the chamber . This method has been
widely used in the field, as reported for example in ,
, , and
.
Several different places at the site of Agoufou were sampled. In
June–July 2004, 180 fluxes were sampled. The chambers were placed on the
soil, 90 with short vegetation inside, 90 over bare soil. In August 2005, 70
fluxes were sampled, mostly over vegetation, the whole site being covered by
vegetation in the core of the wet season. Fluxes were sampled every day
between 30 June and 12 July 2004 and between 11 and 13 August 2005, in the
morning and in the afternoon. Average values were calculated and are reported in
Table 3, with their standard deviations.
Following and , the net flux is calculated
from the slope of the increase of mixing ratio in ppb within the chamber,
assuming that this increase is linear during several minutes (no chemical or
deposition loss during that period), and that the air flow is constant. One
should note that, as long as the air flow rate is constant, it does not need
to be taken into account for the flux calculation (see below). Considering
that the mass of NO within the chamber at time t + dt is equal to the
mass of NO present at time t, plus the mass of NO entering the chamber in
the dt interval (soil flux), minus the mass of NO leaving the chamber in
that same dt interval, if the air flow is constant, only the soil flux has
to be taken into account in this mass balance:
F=dC[NO]dtVMNSRT,
where dC[NO]dt is the initial rate of increase
in NO mixing ratio calculated by linear regression
(ppbs-1), MN is the nitrogen molecular weight
(gmol-1), S=800 cm2 is the surface of the chamber,
V=18 L is the volume of the chamber,
R=0.082 cm3atmmole-1 K-1 is the gas constant,
T (K) is the air temperature in the chamber, and F is the resulting
flux in ng(N)m-2s-1 .
NO mixing ratio in the chamber was measured using a ThermoEnvironment 42 CTL analyser.
This analyser detects NO by chemiluminescence with O3. Detection limit and sensitivity is
around 0.05 ppbv, as indicated in the guide, but found a sensitivity threshold of 0.1 ppbv for
the same analyser. Flow rate in the analyser and the chamber is about 0.8 Lmin-1. Multipoint calibration was checked
before and after each field experiment with a dynamical calibration system. Considering a sensitivity threshold of 0.15 ppbv,
the minimum flux detected by this device would be 0.25 ng(N)m-2s-1.
The magnitude of ozone mixing ratios is around 20 ppb in July, NO2
mixing ratios around 2.5 ppb , and NO2 deposition
velocity was estimated to be 0.13cms-1 () at the Agoufou site. NO ambient mixing ratios
(measured at 20 cm) during the 2004 field campaign were
0.60 ± 0.57 ppb (information not available for the 2005 field
campaign). The NO flux is underestimated if neither deposition nor
conversion to NO2 through reaction with O3 is taken into
account, but the underestimation should be limited considering the low ozone
mixing ratios, low NO2 deposition velocities, and no direct radiative
radiation in the opaque chamber. estimate that even for cases
with a large absolute chemistry effect (meaning NO fluxes from soils up to
4 ng(N)m-2s-1, with NO mixing ratios above 5 ppb
and ozone mixing ratios between 15 and 20 ppb), the underestimation
due to chemical effects is less than 50 %. As a comparison,
finds a maximum underestimation of 25 %.
show that artefacts can be introduced when NO mixing ratios
are high (up to 60 ppb). In our case, NO mixing ratios are much lower
than the values indicated by these authors.
Unfortunately, it is not possible to precisely recalculate the
underestimation of NO flux in our study since NO2 mixing
ratios measured by the 42 ∘C are no longer available. However,
following and , we have calculated the
underestimation with the mean climatological ozone mixing ratio found in
, which is 20 ppb in July, with
k=5.32 10-4ppb-1s-1 (reaction rate constant at 1013 hPa and 313K, estimated pressure and temperature in the chamber), and with
the mean NO mixing ratio 0.6 ppb obtained with the field
measurements in 2004. The mean underestimation
(k.[NO].[O3] = 5.32 × 10-4 × 20 × 0.6 = 0.0064 ppbs-1) is 7.6 % of the mean slope
(0.0847 ppbs-1, obtained from Eq. 1). The mean underestimation is
therefore estimated at 7.6 %.
During summer 2004 (from 30 June to 12 July), NO daily fluxes ranged
from 2.47 to 11.35 ng(N)m-2s-1 (mean
= 6.69 ± 2.44 ng(N)m-2s-1, ).
During summer 2005 (from 11 to 13 August), NO fluxes ranged from 1.81
to 3.20 ng(N)m-2s-1 (mean
= 2.28 ± 0.79 ng(N)m-2s-1, unpublished data). In
the following simulations, NO fluxes were not measured at Agoufou
during the years 2006 to 2008. However, since NO flux data are scarce,
these field measurements from 2004 and 2005 will be helpful to give an order
of magnitude of NO emission at the beginning and during the wet
season.
Model description
Modelling approach
Biogeochemically based model of instantaneous trace gas production can be
parametrized for individual sites, describing local nitrification and
denitrification processes responsible for emission, but more generalized
models are needed for the calculation of temporally or regionally integrated
models . In that purpose, a new approach for the calculation
of biogenic NO emissions from soils has been developed by
, in order to use general environmental parameters easily
available as inputs. This approach was used at the regional scale to simulate
pulse events in the Sahel and at the yearly scale at several
Sahelian sites (). This approach has been
partly inspired by the hole-in-the-pipe (HIP) concept, developed by
, presenting the environmental parameters which control
the variation of trace-N-gases by nitrification and denitrification with
different levels of regulation, from proximal (e.g. mineralization,
immobilization, respiration, plant uptake) to distal (e.g. pH, soil porosity,
soil type,…). Using two functions based on soil N availability and soil
water content, the HIP model characterizes a large fraction of the observed
variation of NO emissions from soils .
The NO emission model will be described in the following sections. In its previous
version , the N availability in the soil was driven by the N input at the surface
(organic and livestock fertilization) and considered constant in time (a similar amount of N was injected each month).
In the new version, the N in the soil is calculated from buried litter (vegetation and faeces) decomposition and varies
in time, thanks to the coupling with the other models which provide vegetation and organic matter in a dynamic way.
The N input used to calculate the NO flux is therefore more realistic than in the previous version where it
was prescribed without any link with vegetation growth and decay. The link between vegetation, litter decomposition,
microbial dynamics in the soil and NO emission is explained in the following sections.
The on-line coupled models are presented here and used at the daily scale: the herbaceous and tree leaf masses
are simulated using the Sahelian Transpiration Evaporation and Productivity (STEP) model, the buried litter decomposition
and microbial dynamics is simulated in GENDEC, and the NO release to the atmosphere is simulated with the NOFlux model.
A schematic view of the model imbrications is given in Fig. .
Inputs for each model are detailed in Table 1.
Inputs for the models used.
STEP
Inputs
Unit
Value
Initial parameters
Conversion efficiency
g(d.m.)MJ-1
4
Initial green biomass
g(d.m.)m-2
0.8
Initial specific leaf area
cm2g-1
180
Meteorology
Precipitations
mm
Daily variation
Global radiation
MJm-2
Daily variation
Min and max air temperature
∘C
Daily variation
Relative humidity
%
Daily variation
Wind speed
ms-1
Daily variation
Soil
Thicknesses (4 layers)
cm
2; 28; 70; 200
Initial water stock (4)
mm
0.1; 1.5; 7.3; 38
Clay content (4)
4.5; 5.5; 5.2; 5.5
Sand content (4)
91.2; 91.3; 91; 92.3
pH (4)
6.7; 6.7; 6.7; 6.7
Annual vegetation
Initial dry biomass and litter
g(d.m.)m-2
10; 30
Root fraction (3)
0.75 ;0.2; 0.05
% dicotyledon
%
29.5
% C3
%
29.3
Max tree foliage mass
kgha-1
600;400
(year before and current year)
Animals
Animal categories (bovine,
%
Monthly variation
caprine, ovine, asine,
e.g. for January:
cameline, equine
0.826; 0.091; 0.055; 0.024; 0.001; 0
Animal stock (12 months)
Head number
2893; 5288; 15626 ;22537
13874; 7832; 1191; 408
3168; 2835; 2510; 3348
Grazing area
ha
5000
GENDEC
Inputs
Unit
Value
Soil temperature
∘C
From STEP
Matrix potential
MPa
From STEP
Microbial assimilation efficiency
0.6
Carbon pool
gC
From STEP
Microbial death rate
0.2
N / C (6) labile compounds,
10; 1000; 34; 8; 25; 9
holocellulose, resistant
compounds, dead and living
microbial biomass, nitrogen pool
NOFlux
Inputs
Unit
Value
Surface WFPS
%
From STEP soil moisture
Surface soil temperature
∘C
From STEP
Deep soil temperature
∘C
From STEP
Wind speed
ms-1
From meteorological forcing
pH
6.7
Sand content
%
91.2
Mineral nitrogen
g(N)m-2
From GENDEC
Schematic view of links between the STEP, GENDEC and NO emission models.
STEP
STEP is an ecosystem process model for Sahelian herbaceous vegetation. In its
current version, tree phenology (leaf mass set-up and fall) is also described
by considering six phenological types which proportions must be known. This
model is defined to be used at local or regional scale in order to simulate
the temporal variation of the main variables and processes associated with
vegetation functioning in Sahelian savannas. In this study, the model will be
used at the local scale. In previous studies, STEP has been coupled to
radiative transfer models in the optical and active/passive
microwave domain (), allowing an indirect
comparison of satellite observations and modelling results of the vegetation
growth (e.g. ). The performance of the STEP process model in
predicting herbage mass variation over time and herbage yield along a
north–south bio-climatic gradient within the Sahel was tested along a 15-year period, and gave high correlation coefficients between model and
measurements when the model was calibrated for each site .
Modifications brought to the first version of the model have been given in
. The regional-scale use of the model is illustrated in
and .
STEP is driven by daily standard meteorological data obtained from site
measurements in Agoufou (precipitations, global radiation, air temperature,
relative humidity and wind speed), prepared for the years 2004 to 2008. Site-specific parameters like sand and clay percentage, pH, C3/C4 percentage,
initial green biomass, initial dry biomass and initial litter, number of soil
layers, initial water content in each layer, livestock composition (between six
different categories: cattle, sheep, goats, donkeys, horses, camels) and
livestock total load are given as input parameters (see Table 1). The
seasonal dynamics of the herbaceous layer, a major component of the Sahelian
vegetation, is represented. The processes simulated are: water fluxes in the
soil, evaporation from bare soil, transpiration of the vegetation,
photosynthesis, respiration, senescence, litter production, and litter
decomposition at the soil surface. Moreover, structural parameters such as
vegetation cover fraction fCover, LAI (leaf area index) and canopy height
are also simulated. A new development has been included in the model for the
present study: soil temperatures are simulated from air temperature according
to , discussed below. Parton et al. (1984) report a
simplified soil temperature model in a short grass steppe. This model
requires daily max and min air temperature, global radiation (provided by
forcing data), plant biomass (provided by the model), initial soil
temperature, and soil thermal diffusivity. Thermal diffusivity
(cm2s-1) is the ratio between thermal conductivity
(Wm-2K-1) and volumetric heat capacity
(1 500 000 Jm-3K-1). Thermal conductivity for each layer
i of the soil is
Cond(i)=-9.77+12.19⋅(soil moisture(i)0.0528).
Total aboveground herbaceous mass is divided into three components: aboveground green mass,
standing dead (or dry) biomass, and litter biomass. Green biomass variations are controlled by the
balance between total photosynthetic inputs expressed by the gross photosynthesis and total outputs
due to respiration losses and senescence. Dry biomass results from the senescence of green material,
minus litter production, ingestion by animals and burned biomass. Litter biomass accumulation is the
result of dead material falling down on the soil, due to trampling and to climate conditions like rain,
wind, and air temperature, minus litter burying and ingestion by animals, litter burning and litter
decomposition due to insects, small mammals and climate conditions (rain kinetic energy, soil humidity,
air temperature and wind). Green vegetation growth starts at seedling emergence with an initial above ground
biomass. The date of emergence is estimated from the number of days required for germination when the
moisture content of the soil surface layer is above wilting point. The quantity of faecal matter is calculated from the livestock total load given as input parameter.
The quantity of carbon in the soil is calculated from the total litter input
(from faecal and herbal mass), which is 50 % of the buried litter mass.
The quantity of nitrogen in the soil is derived from the quantity of carbon
using the C / N ratio. A more detailed description of STEP can be found
in , and . Information such
as the quantity of faecal and herbal masses are transferred as inputs to
GENDEC, the litter decomposition model.
GENDEC
GENDEC (for GENeral DEComposition) is a general, synthetic model, which aim
is to examine the interactions between litter, decomposer microorganisms,
microbial dynamics and C and N pools, and to explore the mechanisms of
decomposition in arid ecosystems. The decomposition of buried litter by microorganisms is the first step of the GENDEC model, giving access to the mineral
C and N pools of the ecosystem. GENDEC has been specifically developed to
reproduce these processes in semi-arid ecosystems, where inputs of organic
matter and soil moisture are low. The ultimate step, C and N mineralization,
is fed by (1) decomposition of organic matter and (2) growth, respiration
and death of microbes (microbial dynamics). The general modelling approach is
based on fundamental decomposition processes. Six pools of C and N are used
in this model, representing dead organic matter (labile materials with high N
content and rapid decomposition rate, cellulose and related materials with an
intermediate decomposition rate and very little associated N, very slowly
decomposing recalcitrant compounds with moderate levels of physically
associated N, and dead microbiota with high N content and rapid
decomposition), living microbial biomass (final pool of organic matter), and
soil N for the nitrogen submodel . The C / N ratio is
different for each of these compartments, and is set to 10, 1000, 34, 8, 25
and 9 respectively for labile compounds, holocellulose, resistant compounds,
dead microbial biomass, living microbial biomass and nitrogen pools, based on
and experimental results from the Agoufou site detailed
below. Flows between these pools are driven by empirical relationships
according to characteristics of the microbial community. Climatic parameters
such as soil moisture and soil temperature are important drivers for C and N
dynamics. The model describes the processes underlying the interactions
between C substrate, principal decomposers and nutrients that ultimately
result in mineralization. Decomposition and microbial metabolic rates
increase with increasing moisture availability (at least until saturation
leads to anaerobic conditions) and with increasing temperature (at least at
temperature below 30–40 ∘C). The dynamics of the soil N pool gives
the net mineralization. The model emphasizes the association between C and N
dynamics and microbial processes. Wetting drying events increase the turnover
of microbial processes, stimulate C mineralization, and involve a short-term
carbon dynamics since soil organic matter and nitrogen content are low.
Microbial growth and respiration are functions of total carbon available, i.e.
total C losses from the litter. Mineral N used to calculate NO release to the
atmosphere is directly linked to mineral C used to calculate the respiration
of microbes (i.e. CO2 release).
GENDEC is driven by organic matter input coming from four different boxes in
STEP: buried litter (herbs and tree leaves), trees, faecal matter, and dry
herb roots. It is also driven by soil temperature and soil water potential
calculated in STEP. Input parameters include the assimilation efficiency and
the microbial mortality rate (see Table 1). Finally, mineral nitrogen, total
quantities of C and N, respiration are obtained for each box (buried
herbaceous litter, buried leaf trees, dry roots and faecal matter). The
addition of these four contributions gives access to the total C and N in the
soil. Organic carbon is assumed to be the sole source of energy and substrate
for heterotrophic microbial growth. Organic matter mineralization driven by
heterotrophic activity of soil microorganisms releases mineral nitrogen. This
is the starting point for the calculation of nitrogen transformations in
soils . The mineral nitrogen is then used as an input in
the NOFlux model described below.
NOFlux
The NO biogenic emissions from soils is calculated with an emission
algorithm derived from a neural network . The equation is
detailed below. NO flux is a function of soil moisture, soil
temperature at two depths (5 and 20–30 cm), wind speed, soil pH,
sand percentage and fertilization rate (quantity of nitrogen given as input
to the soil):
NOfluxnorm=w24+w25tanh(S1)+w26tanh(S2)+w27tanh(S3),
where NOfluxnorm is the normalized NO flux. The normalization is used
for all inputs and output to give them the same order of magnitude and
facilitate the calculation process :
S1=w0+∑i=17wixj,normS2=w8+∑i=915wixj,normS3=w16+∑i=1723wixj,norm,
where j is 1 to 7, and x1,norm to x7,norm correspond to
the seven normalized inputs, as follows:
j=1:x1,norm=c1+c2×(surface soil temperature),j=2:x2,norm=c3+c4×(surface WFPS),j=3:x3,norm=c5+c6×(deep soil temperature),j=4:x4,norm=c7+c8×(fertilization rate),j=5:x5,norm=c9+c10×(sand percentage),j=6:x6,norm=c11+c12×pH,j=7:x7,norm=c13+x14×(wind
speed).
Weights w and normalization coefficients c are given in Table 2:
WFPS=Water Filled Pore Space.
The resulting NO flux is obtained after de-normalization of NOfluxnorm
(NOFlux = x15+x16×NOfluxnorm).
A CRF is applied to the NO flux. The CRF ranges between 1 (no reduction
because no vegetation, LAI = 0) and 0.83 (LAI = 1.8 at the most,
). Considering the CRF applied, the maximum quantity of N
re-deposited above canopy during the wet months when LAI is at its maximum is
negligible compared to the total N input from faecal, herbal and root masses.
Indeed, in the data used in this work, the proportion of N re-deposition
compared to total N input ranges between 0.24 and 1.5 %, depending on the
year.
Weights and normalization factors for the calculation of NO flux.
w0
0.561651794427011
w14
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9.205080
In this study, we use a new approach to calculate the fertilization rate
(this input was given as constant in the previous versions of the algorithm).
This approach is based on the one of , who have developed an
extended version of the CASA (Carnegie Ames Stanford) model, where potential
emission of total nitrogen trace gases
(NT=NO+NO2+N2O+N2) at the soil
surface is treated as a given percentage (2 %) of gross mineralized
nitrogen at any given time step (this corresponds to the definition of the
emission factor). This version of the simple conceptual model is not designed
to distinguish between nitrification and denitrification as sources of N
gases. In order to adapt this approach to our own study, we made the
assumption that the sandy soil texture in Agoufou favours predominantly
aerobic conditions and subsequently nitrification processes (). Furthermore, the WFPS remains below 20 %
(volumetric soil moisture below 10 %), and according to
the total oxidized N emitted would be composed of 95 to
100 % of NO.
In the present work, we have adapted the concept developed in CASA in a
different way: the fertilization rate (i.e. N entering the soil and further
available for NO emission) is 2 % (same percentage as in
) of the mineral N content in the soil (which depends both on
N input and N content). The mineral N is obtained from STEP–GENDEC
calculations. The main difference between the approach and the
one of this study is that the NO emission is now modulated by
additional parameters such as pH and wind speed, as well as soil moisture and
temperature which have an impact on both mineralization and emission. When
soil moisture is too low, microbial respiration is blocked in the model,
microbial dynamics is frozen, and mineralization is stopped. If the value of
mineral N is 0, a minimum value of 0.01 gm-2 is applied as a
first guess in the NOFlux model to avoid null values of NO emission.
Indeed, very little is known about mineral N dynamics and subsequent
NO emission at low soil moisture, but experimental studies show low
emission even during the dry season .
The principal advantage of this NO parametrization is to depend on different factors at two
levels. The first level concerns climatic impacts and environmental parameters, such as precipitations,
soil texture and pH, temperatures, wind speed, and the second level concerns intrinsic processes of N turnover
in the soil, through the organic matter degradation from vegetation and livestock, and the microbial dynamics.
The majority of the first-level variables are easily available on site or/and from atmospheric model reanalysis
and global databases; the second level is a sophistication of the model, making it possible to add biotic processes in this parametrization of NO emission.
Results and discussion
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 (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). In this section, we discuss the reliability of the simulated variables, in order to assess the robustness of the simulated NO flux.
Soil moisture
Soil moisture has a strong influence on NO emission from soils, particularly
in hot and dry regions, at the global, regional or local scale
(). This variable needs to be
well reproduced by the model in order to calculate reliable NO
release. Volumetric soil moisture is calculated by STEP at different soil
layers, using a tipping bucket approach. Figure shows the
volumetric soil moisture calculated by STEP between 0 and 2 cm from
2004 to 2008, compared to the volumetric soil moisture measured at Agoufou at
5 cm depth in 2005, 2006, 2007 and 2008. From 2006 to 2008, these
measurements are actually an average of three data sets from soil moisture probes
operating at the top, middle and bottom locations of dune slopes. In 2005,
only bottom slope data were available.
Soil moisture calculated by STEP at the surface layer (0–2 cm) in blue, mean soil moisture measured at 5 cm in pink, for years 2004 to 2008 at Agoufou.
The comparison between STEP and measurements in Fig. is not
direct, because depths are not exactly equivalent. Indeed, it is in general
quite difficult to have in situ soil moisture measurements in the very first
soil centimetres especially over sandy soils. Despite this, the comparison
gives satisfying results from 2005 to 2008. In the surface layer, the
measurements reach 10 to 12 % during summers and show lower values during
the dry season than those calculated by STEP. A threshold at 8 % is
observed on the STEP plot. This value corresponds to the field capacity
calculated by STEP. In reality, this theoretical value may be overstepped
during short periods of time after a rainfall event, and water is not
systematically transferred to the layer underneath. In the model, when the
field capacity is reached, the excess water is transferred to the second
layer, between 2 and 30 cm. The higher soil moisture peaks observed
in the measurements as compared to STEP may be also due to the deeper soil
depth at which the measurements are taken. For all years, the model is
consistent and correctly reproduces the temporal dynamics – the increase and
decrease of the soil moisture are well in phase, and the filling and emptying
of the surface layer is reasonably well represented. The determination
coefficient between model and measurements R2 is 0.70 for the considered
period (5 years).
Soil temperature
Soil temperature is also an important variable for modelling NO emissions
from soils. In tropical regions, emissions are mostly driven by soil
moisture, but temperature influence has to be taken into account, especially
during the dry season when soil moisture is very low (). Figure shows the soil temperature calculated in
the two first STEP layers and compared to measurements at 5 and 30 cm
at Agoufou from 2005 to 2008. Temperatures at both levels are needed in the
NOFlux model. The seasonal cycle is well reproduced by the model, with some
missing high-frequency variations due to rain events during the wet season.
The determination coefficient R2 between the simulated and measured
temperatures in the surface layer is 0.86, and 0.82 in the 30 cm
layer, showing a good representation of temperature at both levels in the
model.
(a) Soil temperature measured at 5 cm at the low slope station (in pink), soil temperature simulated at the surface layer in blue;
(b) soil temperature measured at 30 cm at the low slope station (in pink), soil temperature simulated at the second layer (2–30 cm) in blue, for years 2004 to 2008 at Agoufou.
Aboveground and litter vegetation
The temporal variation of the green living biomass, dry standing biomass (or
standing straw), surface litter and buried litter is simulated and compared
to measurements at Agoufou (except for buried litter because no measurements
are available). Green biomass begins to increase between 20 and 25 June for
years 2004 and 2005, and between 10 and 15 July for years 2006 to 2008, when
the surface soil moisture is above the wilting point during 5 consecutive
days (green in Fig. ). In 2004, 2005, 2006, 2007 and 2008
respectively, the cumulative rain is 191, 418, 376, 286 and 227 mm. The
maximum of green biomass simulated by STEP is 80, 205, 192, 161 and
104 g(d.m.)m-2 (d.m. = dry matter), when the respective
measurements give 47, 224, 174, 150 and 82 g(d.m.)m-2 in 2004,
2005, 2006, 2007 and 2008. In 2004, two distinct growing phases are
simulated, corroborated by measurements. Indeed, the first green biomass
growth is interrupted due to a lack of rainfall, and starts again later in
the season. The maximum simulated green biomass value seems to be slightly
late in 2006 and 2007, and early in 2008, compared to measurements, whereas
the seedling emergence is correctly simulated for these years. In 2008,
the quantity of precipitation is lower, but the soil moisture is sufficient
to trigger seedling emergence in the model. Overall, simulations and
measurements are in good agreement with R2 = 0.72 for green biomass
for the 5 years.
Green biomass in green, dry biomass in light blue, surface litter in red, buried litter in dark blue (line for the model, dots for measurements),
in g(d.m.)m-2. Standard deviations are indicated for the measurements. Rain in blue-grey in mm, for years 2004 to 2008 at Agoufou.
The change over time of the herbaceous standing mass is driven by mechanical
and biological degradation, influenced, among other causes, by livestock
grazing. Forage consumption and trampling by livestock have major effects on
herbage offtake, decay and decomposition including seed dispersal
. The STEP model allows the drying from green to dry standing
biomass, and the degradation of the dry biomass by livestock. The minimum
value for the initialization of dry standing biomass in 2004 is
10 g(d.m.)m-2. The increase of the senescent aboveground biomass
at the end of the wet season is well reproduced by the simulation (light blue
in Fig. ). R2 between simulations and measurements is 0.56 for
dry standing biomass for the 5 years of simulation. The maximum of dry
standing biomass is underestimated in 2006 and 2008 and well reproduced in
2004 (despite a particular feature) and 2007. No measurements were available
for year 2005.
The minimum value for the initialization of the surface litter in 2004 (red
in Fig. ) is 30 g(d.m.)m-2. The maximum value is
encountered in December–January (end of November in 2004). Litter decay is
sharper in the measurements than in the simulation, with minimum occurring in
the middle of the wet season. R2 between simulations and measurements is
0.5 for litter for the 5 years of simulation.
The evolution of simulated buried litter (dark blue in Fig. is closely linked to
that of surface litter. The first days of rain induce a sharp decrease of buried litter, which is rapidly
decomposed. The minimum is observed in September (August in 2004), when it begins to increase again with the
surface litter accumulation. That accumulation feeds the C and N pools, and is the N resource for soil mineral N and N losses to the atmosphere.
The evaluation of the model in terms of vegetation dynamics, quantity and production of surface litter seems to
be reliable, despite time lags in some cases. Therefore, the quantity of organic matter (via the buried litter)
likely to be degraded and to produce N in the soil can be considered as correctly reproduced by the model.
N content in the soil
The N content calculated by the model has been compared to N content analysis
made on 35 different soil samples in Agoufou (sampled in July 2004). Results
from soil samples give a mean total N content of
0.20 ± 0.14 gkg-1 (or 0.02 %), with a mean C / N
ratio of 9.80 ± 1.11. have mentioned 0.011 % of
total N in sandy soils of the Gourma region, where Agoufou is situated.
However, the few studies performed on arid or semi-arid soils showed that
high microbial metabolism and high turnover rates of little nutrients might
be major explanatory factors of the observed NO fluxes
( and references therein). In the model, the total soil
N content is the sum of mineral N, organic N and microbial N
(Fig. ). To convert the model output in g(N)m-2 to
percentage, we assume a density of 1500 kgm-3 for the soil,
according to , and we apply this value to the first 2 cm
of the soil (first layer in the model). Yearly means range from 0.016 %
in 2004, 0.023 % in 2005, 0.030 % in 2006 to 0.035 % in 2007 and
2008. To compare directly to measurements, we have calculated the average for
the 15 first days of July in the model, and obtained 0.020 % in 2004,
0.022 % in 2005, 0.031 % in 2006 to 0.037 % in 2007 and 2008,
values that are representative of the beginning of the wet season, and very
close to measurements at the same dates. It is rather difficult to find
direct comparisons to ensure the model initializations in terms of N content
and N dynamics in the region of the Sahel. However, some studies like
and show from incubation results that after
the long dry season the soil contains approximately 14 kg(N)ha-1
(approximately 0.005 %, lower than our results) in the first 10 cm, and the nitrogen flush after the intensive first rains is about
7 to 8 kg(N)ha-1. Our results in the next section imply the same kind of emissions at the beginning of the wet season,
which gives confidence on the robustness of the N dynamics reproduced by
GENDEC.
Simulated buried litter in g(d.m.)m-2 in dark blue and
total N content in the soil in %, for years 2004 to 2008 at Agoufou.
NO emission from soil to the atmosphere
Seasonal and yearly cycle of NO emissions
As explained above, the quantity of N due to the decomposition of buried
litter and faecal biomass and to the microbial turnover is calculated in
GENDEC, and therefore used to parametrize the NO emissions from soil
(see Fig. for 2004 to 2008). Yearly and wet season (from 1 June to
30 September) averages are reported in Table 3. The largest emission (up to
24 ng(N)m-2s-1) occurs at the beginning of the wet season.
Indeed, as shown in Fig. , the first rains induce a sharp increase
in soil surface moisture until saturation, followed by a subsequent drying
out of the surface layer until the following rain event. The organic matter,
brought by the buried litter, is suddenly decomposed when the soil moisture
is sufficient, and produces a NO pulse to the atmosphere. This kind of
process has already been highlighted by e.g. in a rain
forest where a huge amount of litter that had accumulated during the
long-lasting dry period of the year before was intensively decomposed with
the onset of rainfall.
Comparison of experimental and simulated NO fluxes (daily scale) during various wet and
dry seasons in dry savanna sites. The model used is indicated in parentheses. No model specified means experimental data. For yearly means, values are also indicated in kg(N)ha-1yr-1 in parentheses.
Site name
NO flux (ng(N)m-2s-1)
Period
Reference
Banizoumbou
6.09 ± 2.63
Wet season 1992
South Africa
5.4-7.9
Wet season 1993
Chihuahuan desert
2.41
Watered soils 1993
Agoufou
6.69 ± 2.44
Wet season 2004
, This work
Agoufou
2.28 ± 0.79
Wet season 2005
This work
Agoufou (STEP)
3.36 ± 3.17
Wet season 2004
This work
Agoufou (STEP)
4.88 ± 4.28
Wet season 2005
This work
Agoufou (STEP)
4.95 ± 4.82
Wet season 2006
This work
Agoufou (ISBA)
7.99 ± 3.61
Wet season 2006
Agoufou (STEP)
4.91 ± 4.53
Wet season 2007
This work
Agoufou (STEP)
5.48 ± 5.23
Wet season 2008
This work
Agoufou (STEP)
1.46 ± 0.51
Dry season 2004
This work
Agoufou (STEP)
1.67 ± 0.59
Dry season 2005
This work
Agoufou (STEP)
1.62 ± 0.64
Dry season 2006
This work
Agoufou (STEP)
1.56 ± 0.51
Dry season 2007
This work
Agoufou (STEP)
1.80 ± 1.13
Dry season 2008
This work
Agoufou (STEP)
2.09 ± 2.06
Year 2004
This work
(0.66 ± 0.65)
Agoufou (STEP)
2.73 ± 2.92
Year 2005
This work
(0.86 ± 0.92)
Agoufou (STEP)
2.73 ± 3.23
Year 2006
This work
(0.86 ± 1.02)
Agoufou (STEP)
2.69 ± 3.08
Year 2007
This work
(0.85 ± 0.97)
Agoufou (STEP)
3.04 ± 3.58
Year 2008
This work
(0.96 ± 1.13)
Simulated NO flux in kg(N)ha-1yr-1 and rain in mm, for years 2004 to 2008 at Agoufou.
After these peaks, in the core of the rain season, though additional rainfall
events, less N is released to the atmosphere and NO emissions
decrease. Figure shows the mineral N behaviour together with the
rainfall amount. First rains and rains after a short break lead to a sharp
increase in mineral N content (in gm-2), whereas frequent rains
in the core of the wet season do not stimulate mineral N in the same
proportion. Furthermore, have shown that when the soil
moisture is sufficient to allow the growth of the vegetation, the mineral N
in the soil is taken by the plants (not numerically represented in this
version of the model), in competition with the assimilation by microbes, and
therefore less released to the atmosphere.
Simulated mineral N in gm-2 and rain in mm for years 2004 to 2008 at Agoufou.
After the end of the wet season, when the maximum of green biomass is reached, the vegetation
starts to dry out while still standing, then falls on the surface and begins to decompose since
the soil surface moisture is still relatively high. The vegetation decomposition at that moment is
responsible for the peak of emission observed in the late wet/early dry season. Afterwards, the soil
surface moisture decreases, leading to low NO emissions. During the dry season, emissions are
reduced, but still present, and mostly driven by surface temperature. Actually, the influence of
temperature also exists during the wet season at a diurnal time step (as already highlighted by
for different data), but is dominated by soil moisture effect. This temperature effect is better described in the next section.
Despite the strong dependance of NO fluxes on soil moisture, the total rainfall amount in a year
does not appear to be essential for the amplitude of the fluxes. Indeed, though 2008 rainfall amount is
less important than in 2005, 2006 and 2007, the annual and wet season NO flux averages are larger in 2008.
This is not the case in 2004, and could be attributed to the fact that 2008 wet season begins earlier and ends later
than in the other years, allowing more efficient and more frequent pulse events.
Some wet season measurements performed at Agoufou in July 2004 and August
2005 are reported in Table 3. The model underestimates the measurements in
2004 (10 days in July), and overestimates them in 2005 (3 days in August).
However, modelled fluxes are in the range of measured fluxes. In Table 3,
they are compared to other measurements made in other types of semi-arid
ecosystems, and to wet season measurements in Banizoumbou (Niger), situated
in a dry savanna site and presenting the same type of vegetation
. Data from this study are also compared to other simulations
done with the ISBA model (Interactions Soil Biosphere Atmosphere) used to
simulate emission and deposition N fluxes in dry savannas in a previous study
( and ), where the N input (fertilization rate)
was constant in time, and equivalent to 5.5 kg(N)ha-1yr-1
(17.4 ng(N)m-2s-1).
These results show that the coupled STEP–GENDEC–NO model gives fluxes in the
order of known experimental or simulated data, while not exactly equivalent
to measurements at the Agoufou site in 2004 and 2005. Several studies have
shown different ranges of NO fluxes, but always with a strong link to
soil moisture, especially in tropical regions where distinct dry and wet
seasons exist, and where large pulses of NO emissions occur at the onset of
the rainy season (, , ,
, , as examples
in tropical and semi-arid regions). As a comparison,
found average fluxes of 57 ng(N)m-2s-1 in tropical forests
soils at the transition between dry and wet season, where the quantity of
decomposed litter is far greater than in dry savanna sites of the Sahel, and
where the nutrient content of the soil is far larger, since semi-arid soils
are generally nutrient poor.
The ratios of fluxes from wet to dry seasons in this study are 2.3, 2.9, 3.0, 3.1 and 3.0 respectively for the
years 2004, 2005, 2006, 2007, 2008, in the (lower) range of what has been reported in the literature ,
but showing undoubtedly the difference between the two periods in terms of emission.
Sensitivity tests
Several sensitivity tests have been performed in the NOFlux model to
highlight the effects of soil temperature, soil humidity and mineral N
content on the NO flux to the atmosphere. The sensitivity of NO
emission to deep soil temperature and wind speed will not be shown here,
because their influence on NO emission is less important. In the first
example (Fig. ), soil moisture is set successively to a low
(2 %) and a high (10 %) value, associated respectively with a low
(0.01 gm-2) and a high (0.1 gm-2) value of mineral
N content in the soil. The associated high and low values of mineral N with
soil moisture have been chosen according to realistic outputs given by the
GENDEC model (see Fig. ), and corresponding to dry and wet season
quantities. The results are shown for year 2006 only, to lighten the figures,
because 2006 is a standard year in terms of pluviometry, and the same
conclusions would appear anyway for the other years. When soil moisture is
low and constant (associated with low and constant mineral N content),
NO fluxes are only driven by soil temperature at high (diurnal)
frequency. Pulses usually linked to soil moisture variation do not occur and
the mean value of the flux remains low. When soil moisture is high
(associated with a high value of mineral N content), the mean value of the flux
is larger, directly resulting from high mineral N content. The seasonal cycle
of fluxes is not correlated to the seasonal cycle of soil temperature, as
already found by – low-frequency variation – whereas
their diurnal cycle are correlated, in accordance with previous studies. As
an example, have stated that soil temperature fluctuations
can explain short-term variations of NO fluxes.
Sensitivity test. In dark blue: reference NO flux in
kg(N)ha-1yr-1; in yellow: NO flux with H = 2 % and
mineral N = 0.01gm-2; in pink: NO flux with H = 10 % and
mineral N = 0.1gm-2; in light blue: surface temperature in
∘C, for year 2006.
In the second example (Fig. ), soil moisture and mineral N content
are not forced; soil surface and bottom temperatures are set successively to
both a low (33 and 32 ∘C respectively) and a high (48 and
47 ∘C respectively) value, for the year 2006. These temperatures
correspond to possible values encountered during the dry and wet seasons. At
the beginning of the year, during the dry season, the soil moisture is low,
and fluxes are constant if soil temperature is constant. During both seasons,
the lowest NO fluxes are found for the highest values of soil
temperature, even if differences are reduced between mean annual fluxes (1.08
vs. 0.69 kg(N)ha-1yr-1, i.e. 3.42 vs.
2.19 ng(N)m-2s-1 for T=33 and T=48 ∘C
respectively) despite a large temperature difference (15 ∘C).
Temperature effect on NO emissions has been studied in other
circumstances, and is still under debate still no clear conclusion could be
reached. Contrasting results have been found in tropical and temperate regions:
most studies have shown that NO emissions increase with increasing
temperature as reported for example in ,
and ; other studies do not
find any clear tendency (), while
find a linear relationship during only certain periods
of the year in a tropical rain forest. Temperature effect in our study is
moderate in the dry season, and almost not visible in the wet season.
Sensitivity test. In dark blue: reference NO flux in
kg(N)ha-1yr-1; in yellow: NO flux with
T=48 ∘C; in pink: NO flux with T=33 ∘C; in
light blue: surface moisture in %, for year 2006.
In addition, soil pH effects have also been tested (not shown here) within a reasonable range
of pH from 6.1 to 8. Pulse effects and modulation by soil temperature present the same feature as
in the reference case, with a slight decrease of the base level when pH increases. and
have also found the same kind of variation, with decreasing emissions while increasing pH in tropical soils.
Sensitivity tests of the NO emission model used in this study have already been explored in
for the elaboration of the model. The most straightforward conclusion from these tests
is that soil moisture is the main driver for NO fluxes in the particular conditions of semi-arid
soils (with immediate effect on soil N content), modulated by soil temperature effect (mostly visible during the dry season) and adjusted by soil pH and wind effects.
Limitations and uncertainties
Estimating NO fluxes in semi-arid regions through modelling studies
remains a difficult exercise, considering the scarcity of data. Uncertainties
in the calculation of NO fluxes in the model are related to
uncertainties on the main drivers of NO emission, i.e. soil moisture,
and mineral N. Furthermore, the mineral N concentration in the soil is also
driven by soil moisture. The uncertainty on the NO flux has been
estimated at around 20 % when calculated with the present algorithm
. Despite the scarcity of validation flux measurements, and of
data on N cycle in the soil, this work gives results that can be added to the
existing knowledge on emission processes. Simulated fluxes are of the order
of magnitude of previous measurements performed in the same semi-arid region.
As mentioned in , a model based on regression parameters
between NO emissions and nitrogen cycling in the ecosystem will have
only order of magnitude prediction accuracy. The temporal variation of the
quantity of live and dry biomass (straw and litter) has been accurately
compared to measurements, but the case is different for the seasonal cycles
of the N pools in the soil. Comparisons have been made with the available
experimental data at a given time, but do not give access to the whole yearly
cycle. The mineral N concentration in the soil used as input in the
calculation of NO fluxes is set to zero by the model during the dry
season because the respiration of microbes is blocked when soil moisture is
too low. In this work it was set to a non-zero value to avoid null NO
fluxes. This value should be moderated and readjusted according to
experimental results of available nitrogen in the soil during the dry season.
While the STEP model was initially designed for 1-D simulations in well-documented study sites, it has also been recently used at the regional scale
in the Gourma region to produce maps of vegetation biomass by
, and in the Sahelian belt (12∘ N–20∘ N;
20∘ W–35∘ E) by and , to
estimate the amount of dust emissions in that region. The NO flux
model has also been applied in the region of Niamey, Niger
to reproduce NO pulses at the beginning of the wet season, and their
impact on ozone formation during the AMMA field campaign in 2006.
Furthermore, it has been used at the regional scale in the Sahel
and in West Africa to calculate NO
release to the atmosphere. Concerning the GENDEC model, it has been
successfully applied for situations very different from those upon which it
was based (). In other words,
we can seriously consider using this coupled STEP–GENDEC–NOflux model in the
Sahelian belt by making approximations, concerning for example biomass,
livestock, N and C pools. Considering the need for information in this region
of the world, it would be conceivable to simulate such processes of emissions
at a larger scale. The challenge is worth taking on, knowing that NO
emissions participate at a larger scale to the production of tropospheric
ozone.
Conclusions
The present work is an attempt to estimate NO fluxes in the semi-arid
region of the Sahel. 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. The vegetation model STEP correctly
reproduces the temporal dynamics of soil moisture in the first layer of the
model (layer involved in the soil N cycle), as well as the increase and
decrease, and the filling and emptying of the surface layer. The temperature
in the first two layers of the model are also in accordance with
measurements. The green and dry biomass calculated by STEP show a correct
feature when compared to measurements in terms of vegetation growth,
vegetation quantity, decay and production of litter, despite slight time lags
in the peak of green biomass for the years 2006 and 2008. The quantity of N is
calculated in GENDEC, and is directly derived from organic matter and C
contents, both quantities calculated from litter degradation and microbial
dynamics in the soil. Sahelian soils are usually considered as N poor, and
the comparison of the N content in the soil calculated in GENDEC (around
0.2–0.3 %) is well in accordance with experimental values, and with the
few references found in the literature. The coupling between the three models
is successful, and well adapted to the specific functioning of semi-arid
ecosystems, where mechanistic models have usually not been tested. The
biomass management in the Sahel is also driven by the presence of livestock,
which provides faecal biomass and buries surface litter by trampling.
The quantity of N in the soil is the consequence of the presence of
vegetation and livestock at the surface, and mineral N constitutes the N pool
available for N release to the atmosphere, in the form of NO (and
other compounds). The NO flux calculated by the model is of course
highly dependent on soil moisture, as well as on mineral nitrogen, and a
2 % fraction of this pool is used as input to calculate the NO
release, modulated by the effect of environmental parameters such as wind
speed, soil moisture and temperature, pH and sand percentage. Simulated
NO emissions during the wet season are of the same order as previous
measurements made at several sites where the vegetation and the soil are
comparable to the ones in Agoufou. Measurements during the dry season are
even scarcer in the literature than during the wet season, which complicates
even more the validation of the modelling results. However, the annual budget
of emissions is mostly dominated by emissions occurring during the wet season,
as already highlighted in different studies in semi-arid regions.
This modelling study has been strengthened at each step of the calculation process by comparison with
experimental values. It would be necessary to obtain more measurements through field campaigns, especially
for the N content in the soil, the grazing pressure, the soil N uptake by plants, and the concentrations
and fluxes of N compounds in the atmosphere. Taking into account the difficulty of organizing field campaigns
in these remote regions, modelling is an essential tool to link N cycles both in the soil and in the atmosphere,
and to understand specific processes involved in semi-arid regions. This study is a step forward in the
representation of biogenic NO release to the atmosphere in semi-arid regions, where processes of emissions
are usually adapted from temperate regions, and not specifically designed for semi-arid ecosystems.