Three different models (STEP–GENDEC–NOflux, Zhang2010, and Surfatm)
are used to simulate NO, CO2, and NH3 fluxes at the daily
scale for 2 years (2012–2013) in a semi-arid grazed ecosystem at Dahra
(15∘24′10′′ N, 15∘25′56′′ W, Senegal, Sahel). Model
results are evaluated against experimental results acquired during three
field campaigns. At the end of the dry season, when the first rains re-wet the
dry soils, the model STEP–GENDEC–NOflux simulates the sudden mineralization of
buried litter, leading to pulses in soil respiration and NO fluxes. The
contribution of wet season fluxes of NO and CO2 to the annual mean
is respectively 51 % and 57 %. NH3 fluxes are simulated by
two models: Surfatm and Zhang2010. During the wet season, air humidity and
soil moisture increase, leading to a transition between low soil
NH3 emissions (which dominate during the dry months) and large
NH3 deposition on vegetation during wet months. Results show a
great impact of the soil emission potential, a difference in the deposition
processes on the soil and the vegetation between the two models with however
a close agreement of the total fluxes. The order of magnitude of NO,
NH3, and CO2 fluxes is correctly represented by the
models, as well as the sharp transitions between seasons, specific to the
Sahel region. The role of soil moisture in flux magnitude is highlighted,
whereas the role of soil temperature is less obvious. The simultaneous
increase in NO and CO2 emissions and NH3 deposition at
the beginning of the wet season is attributed to the availability of mineral
nitrogen in the soil and also to microbial processes, which distribute the
roles between respiration (CO2 emissions), nitrification (NO
emissions), volatilization, and deposition (NH3
emission/deposition). The objectives of this study are to understand the
origin of carbon and nitrogen compounds exchanges between the soil and the
atmosphere and to quantify these exchanges on a longer timescale when only a
few measurements have been performed.
Introduction
The Sahel is one of the largest semi-arid regions in the world and it is a
transition zone between the Sahara desert in the north and the more humid
Sudanese savanna in the south. In semi-arid zones, the exchanges of trace
gases are strongly influenced by hydrologic pulses defined as temporary
increases in water inputs (Harms et al., 2012). In the West African Sahel
(between 12∘ N/18∘ N,
15∘ W/10∘ E), soil water availability strongly affects
microbial and biogeochemical processes in all ecosystem compartments (Wang et
al., 2015), which in turn determines the exchange fluxes of C and N (Austin
et al., 2004; Tagesson et al., 2015a; Shen et al., 2016). After a long dry
period (8 to 10 months in the Sahel), the first rainfall events of the wet
season cause strong pulses of CO2, N2O, NO, and
NH3 to the atmosphere (Jaeglé et al., 2004; McCalley and
Sparks, 2008; Delon et al., 2015; Shen et al., 2016; Tagesson et al., 2016b).
Anthropogenic activities have a strong impact on N and C cycling, and in
large parts of the world, deposition of N compounds has several damaging
impacts on ecosystem functions, such as changes in species biodiversity
(Bobbink et al., 2010). The Sahel is still a protected region from this N
pollution (Bobbink et al., 2010), but climate change could create an
imbalance in biogeochemical cycles of nutrients (Delgado-Baquerizo et al.,
2013).
The emission of NO from soils leads to the formation of N2O and
O3 in the troposphere. Soil NO biogenic emissions from the African
continent expressed in teragrammes of nitrogen per year are considered as the largest in the
world (Fowler et al., 2015) because of extended natural areas. The pulses of
NO from the Sahel region at the beginning of the wet season have been shown
to strongly influence the overlying N2O tropospheric column (Jaegle
et al., 2004; Hudman et al., 2012; Zörner et al., 2016), indicating the
urgent need for improved understanding of the dynamics of NO pulses from this
region. NH3 emissions lead to the formation of particles in the
atmosphere, such as ammonium nitrates (NH4NO3), whose vapour
phase dissociation further produces NH3 and HNO3 (Fowler
et al., 2015). The land–atmosphere exchange of ammonia varies in time and
space depending on environmental factors such as climatic variables, soil
energy balance, soil characteristics, and plant phenology (Flechard et al.,
2013). Emissions of these compounds involve changes in atmospheric
composition (ozone and aerosol production) and effects on climate through
greenhouse gas impacts.
The N exchange fluxes are also influenced by the soil N content, and the main
inputs of N compounds into the soil in semi-arid uncultivated regions are
biological nitrogen fixation (BNF), decomposition of organic matter (OM), and
atmospheric wet and dry deposition (Perroni-Ventura et al., 2010). Soil N
losses to the atmosphere involve N2O, NH3, and NO gaseous
emissions, whereas within the soil, N can be lost via erosion, leaching, and
denitrification. NO emissions to the atmosphere are mainly the result of
nitrification processes, which is the oxidation of NH4+ to
nitrates (NO3-) via nitrites (NO2-) through microbial
processes (Pilegaard et al., 2013; Conrad, 1996). In remote areas, where
anthropogenic emissions such as industrial or traffic pollution do not
happen, NH3 bidirectional exchanges are regulated through diverse
processes: NH3 is emitted by livestock excreta, soil, and litter
and is
regulated by the availability of NH4+ and NH3 in the
aqueous phase (NHx), by the rate of mineralization of NH4+, and
by the availability of water, which allows NHx to be dissolved, to be taken up
by organisms, and to be released through decomposition (Schlesinger et al.,
1991; Sutton et al., 2013). Additionally NH3 can be dry and wet
deposited on soil and litter (Laouali et al., 2012; Vet et al., 2014), leaf cuticles, and stomata and regulated by chemical interactions within the
canopy air space (Loubet et al., 2012). The N cycle is closely linked to the
C cycle, and it has been suggested that C–N interactions may regulate N
availability in the soil (Perroni-Ventura et al., 2010). The link between N
and C cycles in the soil, and their effects on OM decomposition, affect the
emissions of C and N compounds to the atmosphere. These cycles are
interlinked by respiration and decomposition processes in the soil, and the
balance between C and N is controlled by biological activity, mainly driven
by water availability in drylands (Delgado-Baquerizo et al., 2013). Indeed,
the decomposition of soil OM, and its efficiency, regulates the amount of
CO2 that is released to the atmosphere (Elberling et al., 2003).
Biogeochemical regional models have been applied for N compound emissions
mostly in temperate regions (Butterbach-Bahl et al., 2001, 2009), where the
spatial and temporal resolution of data is well characterized. Global
approaches have also been developed, with a simplified description of processes
and with coarse spatial resolution (Yienger and Levy, 1995; Potter et al.,
1996; Yan et al., 2005; Hudman et al., 2012). Considering the weak number of
experimental data in semi-arid regions about trace gas exchanges and their
driving parameters, one-dimensional modelling is a complementary, essential,
and alternative way of studying the annual cycle dynamics and the underlying
processes of emission and deposition. The specificity of the semi-arid
climate needs to be precisely addressed in the models used to be able to
correctly represent the pulses of emissions and the strong changes in C and N
dynamics at the transition between seasons. Improving the description of
processes in 1-D models in tropical regions is therefore a necessary step
before implementing regional modelling.
In this study, three main modelling objectives are focused on
(1) investigating the links between N and C cycles in the soil and
consecutive daily exchanges of NO, NH3, and CO2 between
the soil and the atmosphere, at the annual scale and specifically at the
transition between seasons, (2) comparing two different formalisms for
NH3 bidirectional exchange, and (3) highlighting the influences of
environmental parameters on these exchanges. Different one-dimensional
models, specifically developed or adapted for semi-arid regions, were used in
the study. As a study site, representative of the semi-arid region of the
western Sahel, we selected the Dahra field site located in the Ferlo region
of Senegal (Tagesson et al., 2015b). The one-dimensional models were applied
for the years 2012 and 2013 to simulate the land–atmosphere exchange fluxes
of CO2, NO, and NH3. Model results were compared to flux
measurements collected during three field campaigns in Dahra in July 2012
(7 d), July 2013 (8 d), and November 2013 (10 d), and presented in Delon et
al. (2017).
Materials and methodsField site
Measurements were performed at the Dahra field station, part of the Centre de
Recherches Zootechniques (CRZ), in the Sahelian region of Ferlo, Senegal
(15∘24′10′′ N, 15∘25′56′′ W). The Dahra field site
is located within the CRZ managed by the Institut Sénégalais de
Recherche Agronomique (ISRA). This site is a semi-arid savanna used as a
grazed rangeland. The Sahel is under the influence of the West African
Monsoon (cool wet southwesterly wind) and the Harmattan (hot dry
northeasterly wind) depending on the season. Rainfall is concentrated in the
core of the monsoon season, which extends from mid-July to mid-October. At
Dahra, the annual rainfall was 515 mm in 2012 and 356 mm in 2013 with an
average of 416 mm for the period 1951–2013. The annual mean air temperature
at 2 m height was 28.4 ∘C in 2012 and 28.7 ∘C in 2013,
with an average of 29 ∘C for the period 1951–2003. The most
abundant tree species are Balanites aegyptiaca and Acacia tortilis, and the herbaceous vegetation is dominated by annual C4 grasses
(e.g. Dactyloctenium aegyptium, Aristida adscensionis,
Cenchrus biflorus, and Eragrostis tremula) (Tagesson et al.,
2015a). Livestock is dominated by cows, sheep, and goats, and grazing occurs
permanently all year-round (Assouma et al., 2017). This site was previously
described in Tagesson et al. (2015b) and Delon et al. (2017).
Field dataHydro-meteorological data and sensible and latent heat fluxes
A range of hydro-meteorological variables are measured by a meteorological
station at the Dahra field site (Tagesson et al., 2015b). The
hydro-meteorological variables used in this study were rainfall (mm), air
temperature (∘C), relative air humidity (%), wind speed
(m s-1), air pressure (hPa) at 2 m height, soil temperature
(∘C), soil moisture (%) at 0.05, 0.10, and 0.30 m depth, and net
radiation (W m-2). Data were sampled every 30 s and stored as 15 min
averages (sum for rainfall). Data have then been 3h and daily averaged for
the purpose of this study.
Land–atmosphere exchanges of sensible and latent heat was measured for the
years 2012 and 2013 with an eddy covariance system consisting of an open-path
infrared gas analyzer (LI-7500, LI-COR Inc., Lincoln, USA) and a three-axis
sonic anemometer (Gill R3 ultrasonic anemometer, Hampshire, UK) (Tagesson et
al., 2015a). The sensors were mounted 9 m above the ground and data were
collected at a 20 Hz rate. The post-processing was performed with the
EddyPro 4.2.1 software (LI-COR Biosciences, 2012) and statistics were
calculated for 30 min periods. For a thorough description of the post-processing of sensible and latent heat fluxes, see Supplement of Tagesson et
al. (2015b).
Atmospheric NH3 concentrations using passive samplers
Atmospheric concentrations of NH3 and other compounds such as
N2O, HNO3, O3, and SO2 were measured
using passive samplers on a monthly basis, in accordance with the methodology
used within the INDAAF (International Network to study Deposition and
Atmospheric chemistry in Africa) program (https://indaaf.obs-mip.fr,
last access: 8 May 2019) driven by the
Laboratoire d'Aerologie (LA) in Toulouse. While not being actually part of
the INDAAF network, the Dahra site was equipped with the same passive sampler
devices and analyses of these samplers were performed following the INDAAF
protocol at LA.
Passive samplers were mounted under a stainless-steel holder to avoid direct
impact from wind transport and splashing from precipitation. The holder was
attached at a height of about 1.5 m above ground. All the samplers were
exposed in pairs in order to ensure the reproducibility of results. The
samplers were prepared at LA in Toulouse, installed and collected after 1
month exposure by a local investigator, and sent back to LA. Samplers
before and after exposition were stored in a fridge (4 ∘C) to
minimize possible bacterial decomposition or other chemical reactions.
Samplers were then analysed by ion chromatography (IC) to determine ammonium
and nitrate concentrations. Validation and quality control of passive
samplers according to international standards (World Meteorological
Organization report), as well as the sampling procedure and chemical analysis
of samples, have been widely detailed in Adon et al. (2010). Monthly mean
NH3 concentrations in parts per billion by volume are calculated for the period 2012
and 2013. The measurement accuracy of NH3 passive samplers,
evaluated through covariance with duplicates, and the detection limit
evaluated from field blanks were estimated respectively at 14 % and
0.7±0.2 ppb (Adon et al., 2010).
Measurements of NO, NH3, and CO2 (respiration) fluxes from
soil and soil physical parameters
NO, NH3, and CO2 fluxes were measured for 7 d in
July 2012, 8 d in July 2013, and 10 d in November 2013; these periods will
hereafter be called J12, J13, and N13 respectively. The samples were taken at
three different locations along a 500 m transect following a weak dune slope
(top, middle, and bottom) with one location per day. Each location was then
sampled every 3 d, approximately from 08:00 to 19:00 UTC for soil
fluxes, and 24 h a day for NO and NH3 concentrations. Between 15
and 20 fluxes were measured each day during the three campaigns.
NO and NH3 fluxes were measured with a manual closed dynamic Teflon
chamber (non-steady-state through-flow chamber; Pumpanen et al., 2004) with
dimensions of 200 mm width × 400 mm length × 200 mm
height. During the J12 campaign, the chamber was connected to a
Laboratoire d'Aerologie analyzer, whereas in J13 and N13, it was connected to a
Thermo Scientific 17I analyzer (ThermoFischer Scientific, MA, USA). The
calculation of fluxes is based on an equation detailed in Delon et
al. (2017), adapted from Davidson et al. (1991). The increase rate of NO and
NH3 mixing ratios used in the flux calculation equation was
estimated by a linear regression fitted to data measured for 180 to 300 s
for NO (120 s for NH3) following the installation of the chamber on the
soil, as detailed in Delon et al. (2017). Close to the Teflon chamber, soil
CO2 respiration was measured with a manual closed dynamic chamber
(SRC-1 from PP Systems, 150 mm height × 100 mm diameter) coupled
to a non-dispersive infrared CO2/H2O analyzer EGM-4 (PP Systems,
Hitchin, Hertfordshire, UK). Soil CO2 respiration was measured
within 30 cm of the location of the NO and NH3 fluxes.
Measurements were performed on bare soil to ensure only root and microbe
respiration. Results of NO, NH3, and CO2 fluxes are
presented as daily means with daily standard deviations. Along with flux
measurements, soil physical parameters were measured during the campaigns:
soil pH ranges from 5.77 to 7.43, sand content ranges between 86 % and
94 %, and clay content ranges between 4.7 % and 7.9 %. All the methods,
calculations, and results from the field campaigns are fully detailed in Delon
et al. (2017).
Modelling biogenic NO fluxes, CO2 respiration, and ammonium content
in STEP–GENDEC–NOfluxThe STEP–GENDEC model
The STEP model is presented in Appendix A, with forcing variables detailed in
Table A1, site parameters used in the initialization in Table A2, numerical
values of parameters used in the equations in Table A3, and equations,
variables, parameters, and constants used in the equations in Table A4.
STEP is an ecosystem process model for Sahelian herbaceous savannas (Mougin
et al., 1995; Tracol et al., 2006; Delon et al., 2015). It is coupled to
GENDEC, which aims at representing the interactions between litter, decomposer
microorganisms, microbial dynamics, and C and N pools (Moorhead and Reynolds,
1991). It simulates the decomposition of the organic matter and microbial
processes in the soil in arid ecosystems. Information such as the quantity of
organic matter from faecal matter from livestock and herbal masses is
transferred from STEP as inputs to GENDEC (Fig. 1).
Schematic representation of NO and CO2 flux modelling in
STEP–GENDEC–NOflux (adapted from Delon et al., 2015).
Soil temperatures are simulated from air temperature according to
Parton (1984). This model requires daily maximal and minimal air temperature,
global radiation (provided by forcing data), herbaceous aboveground biomass
(provided by the model), initial soil temperature, and soil thermal
diffusivity. Details of equations are given in Delon et al. (2015) and
Appendix A (Tables A3 and A4).
Soil moisture values are calculated following the tipping-bucket approach (Manabe,
1969): when the field capacity is reached, the excess water in the first
layer (0–2 cm) is transferred to the second layer, between 2 and 30 cm.
Two other layers are defined, between 30–100 cm and 100–300 cm. Equations
related to soil moisture calculation are detailed in Appendix A (Table A4)
and in Jarlan et al. (2008). This approach, while being simple in its
formulation, is especially useful in regions where detailed description of
the environment is not available or unknown, and where the natural
heterogeneity of the soil profile is high due to the presence of diverse
matter fragments such as buried litter, dead roots from herbaceous mass and
trees, stones, branches, and tunnels dug by insects and little mammals.
The STEP model is forced daily by rain, global radiation, air temperature,
wind speed, and relative air humidity at 2 m height. Initial parameters
specific to the Dahra site are listed in Table A1 and site parameters in
Table A2.
Respiration and biogenic NO fluxes
The quantity of carbon in the soil was calculated from the total litter input
from faecal and herbal mass, where faecal matter is obtained from the number
of livestock heads grazing at the site (Diawara, 2015; Diawara et al., 2018).
The quantity of carbon is 50 % the buried litter mass. The carbon and
nitrogen exchanges between pools and all equations are detailed in Moorhead
and Reynolds (1991) and will not be developed here. Carbon dynamics depend
on soil temperature, soil moisture, and soil nitrogen (linked to microbial
dynamics). The concentration of nitrogen in the soil is derived from the
quantity of carbon using C/N ratios.
Biogenic NO fluxes were calculated using the coupled model
STEP–GENDEC–NOflux, as detailed in Delon et al. (2015). The NOFlux model uses
an artificial neural network approach to estimate the biogenic NO emission
from soil to the atmosphere (Delon et al., 2007, 2015). The NO flux is
calculated from and depends on parameters such as soil surface temperature
and moisture, soil temperature at 30 cm depth, sand percentage, N input
(here given as a percentage of the ammonium content in the soil), wind speed,
and
soil pH. The input of N to the soil from the buried litter is provided by
STEP, and the calculation of the ammonium content in the soil coming out from
this N input is provided by GENDEC. The equations used for NO flux
calculation are reported in Appendix B, taken from Delon et al. (2015).
The main structure of the model is kept identical as in the Delon et
al. (2015) version, except for N uptake by plants, for which the present
paper proposes a formulation detailed in Appendix C. In brief, in the
previous version of the model 2 % of the NH4+ pool of the
soil was used for NO emission calculation. In the current version, the NO
emitted to the atmosphere results from 1 % of the NH4+ pool
in the soil minus the N absorbed by plants. The percentage of soil
NH4+ pool used to calculate the NO emission has been changed
from 2 % to 1 % based on Potter et al. (1996), who proposed a range
between 0.5 % and 2 %. In the present study, the 1 % value was
more adapted to fit experimental values.
Soil respiration is the sum of autotrophic (root only) and heterotrophic
respiration. The autotrophic respiration in STEP is calculated from growth
and maintenance respirations of roots and shoots (Mougin et al., 1995),
following equations reported in Table A4. Autotrophic respiration depends on
root depth soil moisture and soil temperature (2–30 cm) and root biomass,
whose dynamics are simulated by STEP. The heterotrophic respiration is
calculated in GENDEC from the growth and death of soil microbes in the soil
depending on the available litter C (given by STEP). Microbial respiration
ρ in grammes of carbon per day is calculated as in Eq. (1).
ρ=(1-ε)Ca
Microbial growth in grammes of carbon per day is γ=ε Ca, where
ε is the assimilation efficiency (unitless) and Ca is total C
available in grammes of carbon per day, i.e. total C losses from four different litter
inputs, buried litter, litter from trees, faecal matter, and dry roots.
Microbial death is driven by the death of the living microbe mass, and the
change in water potential during drying–wetting cycles (change between -1.5
and -0.01 MPa in the layer 2–30 cm). These calculations are described in
Moorhead and Reynolds (1991) and Delon et al. (2015) and are not reported in
detail in this study. A schematic view of STEP–GENDEC–NOFlux is presented in
Fig. 1. Simulated variables and corresponding measurements used for
validation are summarized in Table 1.
Summary of different models used in the study, with the variables
simulated and compared to measurements. All simulated and measured variables
were daily averaged for the purpose of the study.
Model (resolution)Simulated and measured variables (units)Methods used for measured variables (resolution and reference)Surfatm (3 h)NH3 bidirectional fluxes (ngN m-2 s-1)Closed dynamic chamber (15–20 fluxes a day, Delon et al., 2017)Soil surface temperature (∘C)Campbell 107 probe (15 min, Tagesson et al., 2015a)Sensible and latent heat fluxes (W m-2)Eddy covariance (15 min, Tagesson et al., 2015a)Zhang2010 (3 h)NH3 bidirectional fluxes (ngN m-2 s-1)Closed dynamic chamber (15–20 fluxes a day, Delon et al., 2017)STEP (day)NO biogenic fluxes (ngN m-2 s-1)Closed dynamic chamber (15–20 fluxes a day, Delon et al., 2017)CO2 respiration fluxes (ngN m-2 s-1)Closed dynamic chamber (15–20 fluxes a day, Delon et al., 2017)Ammonium content (%)Laboratory analysis (six samples per campaign, Delon et al., 2017)Soil temperature at two depths: 0–2and 2–30 cm (∘C)Campbell 107 probe at two depths: 5 and 10 cm (15 min, Tagesson et al., 2015a)Soil moisture at two depths: 0–2and 2–30cm (%)HH2 Delta probe at two depths: 5 and 10 cm (15 min, Tagesson et al., 2015a)Modelling NH3 fluxes
The net NH3 flux between the surface and the atmosphere depends on
the concentration difference χcp-CNH3 , where
CNH3 is the ambient NH3 concentration in
microgrammes per cubic metre, and χcp is the concentration of the
canopy compensation point in microgrammes per cubic metre. The canopy compensation
point concentration is the atmospheric NH3 concentration in the
canopy for which the fluxes between the soil, the stomatal cavities, and the
air inside the canopy switch from emission to deposition, or vice versa
(Farquhar et al., 1980; Wichink Kruit et al., 2007). The canopy compensation
point concentration takes into account the stomatal and soil layers. The soil
compensation point concentration, χg, in parts per billion has been
calculated from the emission potential Γg (unitless) as a
function of soil surface temperature Tg in kelvin according to
Wentworth et al. (2014):
χg=13587×Γg×e-(10396/Tg)×109.
A large Γg indicates that the soil has a high propensity to
emit NH3, considering that the potential emission of NH3
depends on the availability of ammonium in the soil and on the pH.
Γg=[NH4+]/[H+] concentrations were measured
in the field and are available in Delon et al. (2017).
Two different models designed to simulate land–atmosphere NH3
bidirectional exchange are used in this study and described below.
Inferential method (Zhang et al., 2010)
An inferential method was used to calculate the bidirectional exchange of
NH3. The overall flux FNH3 (µg m-2 s-1) is calculated as
FNH3=(χcp-CNH3)×Vd,
with Vd= 1/(Ra+Rb+Rc),
where Vd (m s-1) is the deposition velocity, determined by
using the big-leaf dry deposition model of Zhang et al. (2003).
Ra (s m-1) and Rb (s m-1) are the
aerodynamic and quasi-laminar resistances respectively, and Rc
(s m-1) is the total resistance to deposition resulting from component
terms such as stomatal, mesophyll, and non-stomatal/external/cuticular and soil resistances (Flechard et al.,
2013, and references therein). CNH3 (µg m-3) is
determined at the monthly scale from passive sampler measurements. The
χcp term (µg m-3) is calculated following the
two-layer Zhang et al. (2010) model, hereafter referred to as Zhang2010. This
model gives access to an extensive literature review on compensation point
concentrations and emission potential values classified for 26 different land
use classes (LUCs). Compensation point concentrations are calculated in the
model and vary with canopy type, nitrogen content, and meteorological
conditions. This model was adapted by Adon et al. (2013) for the specificity
of semi-arid ecosystems such as leaf area index (LAI) or type of vegetation,
assuming a ground emission potential of 400 (unitless), considered a low-end value for non-fertilized ecosystems according to Massad et al. (2010) and
based on Delon et al. (2017) experimental results, and a stomatal emission
potential of 100 (unitless) based on Massad et al. (2010) for grass, and on
the study of Adon et al. (2013) for similar ecosystems as the one found in
Dahra. Considering the bidirectional nature of NH3 exchange,
emission occurs if the canopy compensation point concentration is superior to
the ambient concentration (Nemitz et al., 2001). Emission fluxes are noted as
positive. Meteorological forcing required for the simulation is 3 h-averaged
wind speed, net radiation, pressure, relative humidity, air temperature at 2 m
height, surface temperature at 5 cm depth, and rainfall. The equations used
in this model are extensively described in Zhang et al. (2003, 2010), and
will not be detailed here.
The Surfatm model
The Surface-Atmosphere (Surfatm) model combines an energy budget model
(following Choudhury and Monteith, 1988) and a pollutant exchange model
(following Nemitz et al., 2001), which allows distinction between the soil
and the plant exchange processes. As in Zhang2010, the scheme is based on the
traditional resistance analogy describing the bidirectional transport of
NH3 governed by a set of resistances Ra,
Rb, and
Rc (Hansen et al., 2017, and references therein) already described
in the preceding paragraph. Surfatm includes a diffusive resistance term from
the topsoil layer to the soil surface. Surfatm represents a comprehensive
approach to study pollutant exchanges and their link with plant and soil
functioning. The NH3 exchange is directly coupled to the energy
budget, which determines the leaf and surface temperatures, the humidity of
the canopy, and the resistances in the layers above the soil and in the soil
itself. This model has been comprehensively described in Personne et
al. (2009) and more recently in Hansen et al. (2017).
The model is forced every 3 h by net radiation, deep soil temperature
(30 cm), air temperature, relative humidity, wind speed, rainfall, and
atmospheric NH3 concentration with monthly values from passive
sampler measurements repeated every 3 h. Forcing also includes values of
leaf area index (LAI, measured), canopy height Zh (estimated),
roughness length Z0 (0.13 Zh), displacement height D
(0.7 Zh), stomatal emission potential (constant), ground
emission potential (derived from measurements during field campaigns,
constant the rest of the time), and measurement height Zref
(2 m). LAI was measured according to the methodology developed in Mougin et
al. (2014). Data from Dahra were measured monthly during the wet season and
were not published (Mougin, personal communication). Linear interpolation was
performed between these monthly estimates, and values for the dry season were
found in Adon et al. (2013), for an equivalent semi-arid ecosystem in Mali,
derived from MODIS (Moderate-Resolution Imaging Spectroradiometer)
measurements. The ground emission potential has been set to 400 (unitless),
and the stomatal emission potential has been set to 100 (unitless) as in the
simulation based on Zhang2010, except during field campaign periods, where
the ground emission potential was derived from experimental values (700 in
J12 and J13 and 2000 in N13). In Table 2, constant input parameters are
listed. Some of them were adapted to semi-arid conditions to get the best fit
between measured and simulated fluxes, specified in Table 2.
Input parameters for the Surfatm model. Ranges refer to Hansen et
al. (2017). All measured parameters refer to Delon et al. (2017).
Description of parameters in SurfatmValue in this study (range)SourcesTime step3 hCharacteristic length of leaves0.03 m (0.03–0.5)Minimum valueTotal soil depth0.92 mSoil density1500 kg m-3Radiation attenuation coefficient in the canopy0.7 (0.5–0.8)EstimatedWind attenuation coefficient in the canopy2.3 (1.5–5)EstimatedInitial soil moisture0.09 kg(H2O) kg(soil)-1MeasuredDry soil moisture0.02 kg(H2O) kg(soil)-1MeasuredField capacity0.14 kg(H2O) kg(soil)-1MeasuredWilting point0.02 kg(H2O) kg(soil)-1MeasuredThermal conductivity of wet soil layers2.5 W m-1 K-1 (1.6–2.2)EstimatedThermal conductivity of dry soil layers1.5 W m-1 K-1 (0.2–0.3)EstimatedDepth of temperature measurements0.3 mMeasuredSoil porosity0.45 (0.25–0.4)Estimated specifically for semi-arid ecosystemsSoil tortuosity2.5 (2–4)Estimated specifically for semi-arid ecosystems
The main difference between Surfatm and Zhang2010 is the presence of a SVAT
(surface vegetation atmosphere transfer) model in Surfatm (Personne et al.,
2009), allowing for energy budget consideration and accurate restitution of
surface temperature and moisture. Simulated variables and corresponding
measurements used for validation are summarized in Table 1.
Statistic analysis
The R software (http://www.R-project.org, last access: 8 May 2019) was used to provide results of simple and
multiple linear regression analysis. The cor.test() function was used to test a single
correlation coefficient R, i.e. a test for association between paired
samples, using one of Pearson's product moment correlation coefficients. The
p value is used to determine the significance of the correlation. If the
p value is less than 0.05, the correlation is considered non-significant.
The lm()
test was used for stepwise multiple regression analysis. The adjusted
R squared (i.e. normalized multiple R squared, R2), determines how
well the model fits to the data. Again, the p value is calculated, and has to
be less than 0.05 to give confidence in the significance of the determination
coefficient R2. These tests are used in the following paragraphs (i) to
determine if the models are precise enough to correctly represent
environmental variables like soil moisture, soil temperature, and latent and
sensible heat fluxes at the annual scale and to represent measured fluxes of
NO, NH3, and CO2 for some periods (ii) to verify if
environmental drivers, taken individually or in groups, explain the
NO/NH3/CO2 simulated fluxes and to what extent and (iii) to
compare the two models used for NH3 flux modelling.
(a) Volumetric soil moisture simulated by STEP in the first layer
(0–2 cm) in black and soil moisture measured at 5 cm in blue, as a percentage, at
a daily scale. (b) Volumetric soil moisture simulated by STEP in the second
layer (2–30 cm) in black, soil moisture measured at 5 cm as a blue solid line,
measured at 10 cm as a blue dotted line, as a percentage, at a daily scale.
ResultsSoil moisture, soil temperature, and land–atmosphere heat fluxes
Soil moisture simulated by STEP in the surface layer (Fig. 2a) is limited at
11 % during the wet season. This value corresponds to the field capacity
calculated by STEP. The soil moisture modelling follows the tipping-bucket
approach; i.e. when the field capacity is reached, the excess water is
transferred to the second layer, between 2 and 30 cm. Experimental values
measured at 5 and 10 cm are better represented by the model in this second
layer (Fig. 2b). Linear regression gives a R2 of 0.74 (resp. 0.81), a
slope of 0.98 (resp. 1.05), and an offset of 0.34 (resp. 0.32) between STEP
soil moisture in the 0–2 cm (resp. 2–30 cm) layer and experimental soil
moisture at 5 cm. R2 is 0.77, slope is 0.93, and offset is 0.84 between
STEP soil moisture in the 2–30 cm layer and experimental soil moisture at
10 cm. The temporal dynamics given by STEP, the filling of the surface
layer, and the maximum and minimum values are comparable to the data. However,
the drying of the layers is sharper in the model than in measurements at the
end of the wet season, leading to an underestimation of the model compared to
measurements until December each year.
As a comparison, linear correlation between STEP H (STEP LE) and EC H
(EC LE) gives R2 of 0.4 (0.7), for both years of simulation
(Fig. 3a and b). The significant correlation between Surfatm and EC latent
heat fluxes indicates that the stomatal, aerodynamic, and soil resistances are
correctly characterized in the model, giving confidence in the further
realistic parameterization of NH3 fluxes, despite missing values in
intermediate fluxes, due to the criteria applied by the post-processing (see
Supplement of Tagesson et al., 2015b).
(a) Daily modelled latent heat flux in Surfatm vs. daily measured
latent heat flux, in watts per square metre; (b) daily modelled sensible heat flux in Surfatm
vs. daily measured sensible heat flux, in watts per square metre. The thick black line is for the
linear regression, and the dashed black line is the 1:1 line. Available measured EC
data are more numerous for H than for LE due to the criteria applied by the
post-processing (see Supplement of Tagesson et al., 2015b).
Surfatm soil surface temperature is very close to measured soil surface
temperature (Fig. 4a, R2=0.70, p<0.001 in 2012–2013). Mean
annual values were 35.8 and 34.2 ∘C respectively for surface Surfatm
and measured soil surface temperatures in 2012 and 32.4 and 33.8 ∘C
in 2013. STEP surface temperatures (0–2 cm layer) present mean values of
32.0 ∘C in 2012 and 32.6 ∘C in 2013. Linear regression
between STEP surface temperature and measured surface temperature (Fig. 4b) gives
a R2 of 0.7 (p<0.001) for 2012–2013. Slopes and offsets are
indicated in the figures.
(a) Modelled daily surface temperature in Surfatm vs. measured daily
temperature at 5 cm depth; (b) modelled daily surface temperature in STEP
(0–2 cm layer) vs. measured daily temperature at 5 cm depth. The thick black line
is for the linear regression, and the dashed black line is the 1:1 line.
Biogenic NO fluxes from soil and ammonium content
In J12, average NO fluxes are 5.1±2.8 and 5.7±3.1 ngN m-2 s-1 for modelled and measured fluxes respectively.
In J13, average NO fluxes are 10.3±3.3 and 5.1±2.1 ngN m-2 s-1 for modelled and measured fluxes respectively.
In N13, average NO fluxes are 2.2±0.3 and 4.0±2.2 ngN m-2 s-1 for modelled and measured fluxes respectively.
Emission fluxes are noted as positive.
In Fig. 5, the model represents the daily fluxes for 2012 and 2013 and is
compared to measurements. The model is comprised within the standard
deviation of the measurements in J12 and N13 but overestimates fluxes in J13.
Figure 6 reports nine points of measured ammonium from Delon et al. (2017),
showing an overestimation of released N during the J13 wet season and an
underestimation at the end of the wet season (as N13).
Daily NO flux simulated by STEP–GENDEC–NOFlux (ngN m-2 s-1, black line) and daily averaged NO flux measurements during the three field campaigns (red triangles). Error bars in red give the standard
deviation for measurements at the daily scale. Rain is represented by the
blue line in millimetres in the bottom panel. The upper panels show a focus on each field
campaign.
Daily ammonium simulated by STEP–GENDEC (%, black line) and
daily averaged ammonium measurement (red squares) during the field campaigns.
Error bars in red give the standard deviation at the daily scale for
measurements. The upper panel is a focus of J12.
Modelled dry and wet season NO fluxes are respectively 2.5±2.5 and
6.2±4.1 ngN m-2 s-1 for both 2012 and 2013, and the
simulation gives a mean flux of 3.6±2.9 ngN m-2 s-1 for the
entire study period. Wet season fluxes represent 51 % of the annual mean,
even though it only lasts 3 to 4 months. Simulated NO fluxes are significantly
correlated with measured soil moisture at 5 cm depth (R2=0.42,
p<0.001, slope = 0.65, offset = 0.69) and 10 cm depth
(R2=0.43, p<0.001, slope = 0.72, offset = 0.33) for
both years, but not directly with soil temperature. A multiple linear
regression model involving soil moisture at 5 cm depth, soil temperature at
5 and 30 cm depth, and wind speed to explain simulated NO fluxes leads to a
R2 of 0.43 (p<0.001). These parameters have been shown as
important drivers of NO emissions in several previous studies, such as Homyak
et al. (2016), Medinets et al. (2015), or Delon et al. (2007). Indeed, as
detailed in Appendix B, NO fluxes in STEP–GENDEC–NOflux are calculated by an
equation derived from an artificial neural network (ANN) algorithm, trained
with data from temperate and tropical ecosystems, taking into account these
four
parameters, together with sand percentage, soil pH, and N input.
Soil CO2 respiration
Soil respiration includes soil heterotrophic respiration, which refers to
the decomposition of dead soil organic matter (SOM) by soil microbes, and
root respiration, including all respiratory processes occurring in the
rhizosphere (Xu et al., 2016). The simulated respiration of aboveground
biomass is not included as in measured data.
In J13, the average measured flux is 2.6±0.6 gC m-2 d-1,
and the average modelled flux is 1.9±0.4 gC m-2 d-1. The
correlation between the two data sets is not significant. In N13, the average
measured flux is 0.78±0.11 gC m-2 d-1, and the average
modelled flux is 0.18±0.02 gC m-2 d-1. The two data sets
are not correlated. November fluxes are less important than July fluxes, as
illustrated by both the model and the measurements (Fig. 7), and as
previously shown with eddy covariance data (Tagesson et al., 2015a).
Simulated respiration fluxes are in the range of measured fluxes in J13, but
appear to underestimate measured fluxes in N13 (Fig. 7). The simulated
autotrophic respiration (roots + aboveground biomass) is shown, together
with the heterotrophic (microbes) respiration, to check for a possible role
of aboveground biomass in comparison with measurements (Fig. 8). As expected,
the heterotrophic respiration is higher than the autotrophic respiration
before and after the growth of the vegetation, i.e. at the beginning and end
of the wet season in 2012, or during precipitation dry spells (e.g. in J13).
At the end of the wet season, the late peaks of simulated heterotrophic
respiration are linked to late rain events because autotrophic respiration is
no more effective when vegetation is not growing anymore. Adding the
autotrophic respiration to the heterotrophic respiration does not help to
better fit the measured respiration in N13.
Daily root and microbe respiration in milligrammes of carbon per
square metre per day
simulated by STEP–GENDEC (black line), and daily averaged soil respiration
measurements (red squares) during two field campaigns. Error bars in red give
the standard deviation at the daily scale. The upper panels show a focus of J13
and N13 field campaigns.
Daily autotrophic (roots + green vegetation, black line) and
daily heterotrophic (microbes, grey dashed line) respiration (mgC m-2 d-1) and rain (blue line, mm). Averaged daily measurements of soil
respiration as red squares, with standard deviation.
Average dry and wet season simulated soil respiration are respectively
0.3±0.7 and 1.0±0.4 gC m-2 d-1, while the annual mean is
0.5±0.7 gC m-2 d-1. This annual mean is below global
estimates for grassland (2.2 gC m-2 d-1) and deserts partially
vegetated (1.0 gC m-2 d-1; Xu et al., 2016). The wet season has
the largest contribution (57 %) to the annual respiration budget (with
wet seasons of 114 and 81 d in 2012 and 2013 respectively).
Simulated daily respiration from microbes and roots is significantly
correlated with measured soil moisture at 5 cm depth with R2=0.50,
p<0.001, slope = 0.17, offset = 0.26 and 10 cm depth
with R2=0.5, p<0.001, slope = 0.19, offset =-0.37
for both years, whereas soil field-measured respiration shows a lower
correlation with surface soil moisture, with R2=0.4, p=0.09,
slope = 0.03, offset =-0.07 in J13 and R2=0.3, p=0.1,
slope = 0.02, offset =-0.02 in N13.
NH3 bidirectional exchange
NH3 fluxes were simulated by two different models: Surfatm
(Personne et al., 2009) and Zhang2010 (Fig. 9). The same ambient
concentrations deduced from in situ measurements are prescribed in both
models. Average fluxes are reported in Table 2. In J12, simulated fluxes are
not significantly correlated with measured data. In J13, Surfatm and
measurement fluxes are not significantly correlated (R2=0.2p=0.2).
In N13, Surfatm and measured fluxes are not significantly correlated
(R2=0.2, p=0.2), and Zhang2010 and measured fluxes are significantly
correlated (R2=0.5, p=0.01, slope = 1.5, offset =-3.8).
Daily NH3 flux (ngN m-2 s-1) simulated by
Surfatm (black line) and Zhang2010 (grey dashed line) and daily averaged
NH3 flux measurements during three field campaigns (red triangles).
Error bars in red stand for standard deviation at the daily scale. Air
humidity as a percentage (blue line). DS: dry season; WS: west season.
Figure 9 shows alternative changes between low NH3 emission and low
deposition. This switch occurs during the dry seasons (from mid-October to
the end of June). Indeed, monthly averaged compensation point and ambient
concentration values are quite similar during the dry seasons. Compensation
point concentration averaged during the 2012 and 2013 dry seasons is 3.8±1.5 ppb, and averaged ambient concentration is 4.3±1.5 ppb for the
same period. If the 2012 and 2013 dry seasons are considered separately, the
values of the means remain the same. Low deposition dominates when air
humidity is sufficiently high, roughly above 25 % (before and after the
wet season), whereas low emission dominates when air humidity is low
(< 25 %).
The net dry and wet season fluxes reported in Table 3 are in a similar range
as NH3 fluxes calculated by Adon et al. (2013) using Zhang2010 at
comparable Sahelian sites in Mali and Niger. NH3 fluxes ranged
between -3.2 and 0.9 ngN m-2 s-1 during the dry season and
between -14.6 and -6.0 ngN m-2 s-1 during the wet season.
Averaged NH3 fluxes for measurements and the Surfatm and Zhang2010
models during specific periods. Measurements are available during the three field campaigns and not at the annual or seasonal scale.
Figure 10 shows the partition between the different contributions of soil and
vegetation to the NH3 fluxes in Surfatm and Zhang2010. During the
wet season, the contributions of vegetation and soil in Surfatm (Zhang2010)
are -6.3±3.7 (-0.8±0.36 ngN m-2 s-1) and 2.0±1.9 ngN m-2 s-1
(-7.3±3.0 ngN m-2 s-1) respectively for both years. During
the dry season, vegetation (i.e. stomata + cuticles) and soil
contributions are low: -0.9±1.7 and 0.7±0.6 ngN m-2 s-1 respectively in Surfatm and -0.4±0.5 and
-0.5±2.3 ngN m-2 s-1 in Zhang2010, as reported in Table 4.
In N13, at the end of the wet season, the soil contribution is 2.9±0.7 ngN m-2 s-1 in Surfatm, whereas it is -2.6±0.8 ngN m-2 s-1 in Zhang2010.
Contributions of vegetation and soil to the total NH3 flux in
Surfatm and Zhang2010, wet season mean, dry season mean, and annual mean, for
both years of simulation.
In Fig. 10a, the total net flux above the canopy in Surfatm results from an
emission flux from the soil and a deposition flux onto the vegetation via
stomata and cuticles, especially during the wet season. Conversely, the
total flux in Zhang2010 in Fig. 10b results from a strong deposition flux on
the soil and a very low deposition flux onto the vegetation. This is
explained by a strong contribution of deposition on cuticles in Surfatm
(Fig. 10c) whereas it is close to zero in Zhang2010 (Fig. 10d). In Surfatm,
emission from stomata also occurs but it is largely offset by the deposition
on leaf surfaces, which leads to a deposition flux onto vegetation (Sutton et
al., 1995). In Surfatm, the deposition on cuticles is effective until the end
of the wet season, whereas deposition through stomata lasts until the
vegetation is completely dry, i.e. approximately 2 months after the end of
the wet season. On the basis of the different averages for each contributing
flux in Table 4, we estimate that the soil is a net source of NH3
during the wet season, while the vegetation is a net sink in Surfatm, and the
soil is a net sink in Zhang2010.
Daily NH3 flux (ngN m-2 s-1)
partitioned between soil and vegetation. The black line is for total net flux
(Ftot), the grey dashed line is for soil flux (Fsol), and the blue line is for
vegetation flux (Fveg) for Surfatm in (a) and for Zhang2010 in (b). The red line
is for stomatal flux (Fstom) and the green line is for cuticular flux (Fcut) for
Surfatm in (c) and for Zhang2010 in (d).
DiscussionNH3 exchangesRelevance of monthly NH3 concentration input vs. daily NH3 flux
outputs
In the two models, CNH3 used as input data arises from passive
sampler measurements, integrated at the monthly scale (see Sect. 2.2.2).
Output fluxes are provided at a 3 h timescale, averaged at the daily scale
for the purpose of this study. The relevance of using monthly NH3
concentrations instead of concentrations with finer resolution in time has
already been approached in the literature. Riddick et al. (2014, 2016) have
used ALPHA samplers to measure NH3 concentrations at the scale of
the week and/or the month. They have noticed that time-averaged NH3
fluxes from these samplers provided estimated fluxes similar to those
calculated from online sampling. In the case of passive sampling
concentration measurements, meteorological and area sources of uncertainty
can still be accounted for in the flux calculation. Riddick et al. (2014)
conclude that active and passive sampling strategies give similar results,
which support the use of low-cost passive sampling measurements at remote
locations where it is often logistically hard to deploy expensive active
sampling methods for flux measurements. These statements have been confirmed
in Loubet et al. (2018), and provide a valuable reason to use monthly
concentrations as inputs in the present study.
NH3 deposition flux variation
Dahra is a grazed savanna where the main source of NH3 emission to
the atmosphere is the volatilization of livestock excreta (Delon et al.,
2012); the excreta quantity and quality is at a maximum at the end of the wet
season, (Hiernaux et al., 1998; Hiernaux and Turner, 2002; Schlecht and
Hiernaux, 2004) because animals are better fed. In August, a strong leaching
of the atmosphere occurs, which decreases the NH3 atmospheric
concentration (not shown here), compared to July concentration, and the
deposition flux decreases as well. Indeed, if the concentration decreases
from July to August whereas the canopy compensation point remains stable, the
flux will decrease as shown by Eq. (3).
August is the month with the maximum ammonium wet deposition, which leads to
a strong leaching of the atmosphere and explains the decrease in the
NH3 concentration (Laouali et al., 2012).
Role of soil moisture and soil temperature in NH3
fluxes
A significant correlation is found between Zhang2010 fluxes and measured soil
moisture at 5 cm depth (R2=0.6, p<0.01, slope =-1.2,
offset = 2.1) for 2012–2013. Surfatm fluxes and measured soil moisture
at 5 cm depth are also significantly correlated with R2=0.3,
p<0.01, slope =-0.7, offset = 1.7 for 2012–2013, and
this correlation is higher if only the dry season is considered (0.7 and 0.5
respectively). A weak but significant correlation is found between Surfatm
fluxes and soil surface temperature (R2=0.2, p<0.001,
slope = 0.14, offset = 33.9) for both wet seasons, whereas it is not
found with Zhang2010 fluxes. An explanation may be that the NH3
exchange in Surfatm is directly coupled with the energy balance via the
surface temperature (Personne et al., 2009). A stepwise multiple linear
regression analysis was performed between Zhang2010 fluxes and NH3
ambient concentrations, air humidity, wind speed, and soil surface temperature
and moisture, for both years of simulation. The model selection was performed by
adding each variable step by step, i.e. the best combination was chosen with
the best associated significant R2 (p<0.05). The resulting
model gives a R2 of 0.9 (p<0.001), showing a large
interdependence of the above-cited parameters on NH3 fluxes, whereas
the correlation between NH3 fluxes and each individual parameter is
not significant. While the isolated soil temperature effect is not
demonstrated, these complex interactions between influencing parameters
suggest that the contribution of soil temperature to NH3 fluxes,
together with other environmental parameters, becomes relevant.
As for Zhang2010 fluxes, a stepwise multiple linear regression analysis is
run between Surfatm NH3 fluxes and NH3 concentrations,
air humidity, wind speed, soil surface temperature, and latent heat fluxes.
R2 is 0.6 with p<0.001. The nested influences of
environmental parameters in Surfatm are highlighted. These interactions
become more complex with the energy balance effect, but may be more accurate
in representing the partition between surface and plant contributions.
Contribution of soil and vegetation to the net NH3
flux
In Surfatm, during the wet season, deposition on the vegetation through
stomata and cuticles dominates the exchange. Indeed, during rain events, the
cuticular resistance becomes small and cuticular deposition dominates
despite an increase in soil emission. This increase is due to an increase in
the deposition velocity of NH3, after the humidity response of
the surface, and a decrease in the canopy compensation point, sensitive to
the surface wetness (Wichink-Kruit et al., 2007). In Zhang2010, despite the
difference in magnitude, cuticular deposition increases as well during the
wet season, but is dominated by deposition on the soil.
During the dry season, aboveground herbaceous dry biomass stands for a few
months after the end of the wet season when the soil becomes bare, and the
vegetation effect is negligible in both models. At the end of wet season 2013,
the soil contribution to the total flux increases significantly in Surfatm
due to the increase in the ground emission potential prescribed at 2000
(instead of 400 for the rest of the year, to be consistent with measurements
noted in Delon et al., 2017).
Surfatm versus Zhang2010 NH3 bidirectional
models
The two models are based on the same two-layer model approach developed in
Nemitz et al. (2001). In the two models, the ground emission potential and
the NH3 ambient concentrations are prescribed. The comparison of
modelled and measured flux values in Fig. 9 shows differences, especially for
results predicted by Zhang2010. This is partly because in Surfatm the ground
emission potential varies with time and was specifically modified for the
field campaign periods, whereas this parameter does not vary in Zhang2010.
The lack of variability of the ground emission potential in Zhang2010
highlights the sensitivity of fluxes to this specific parameter for 1-D
modelling in semi-arid soils. The abrupt transitions between seasons need a
certain flexibility of the ground emission potential to represent the changes
in flux direction.
In Surfatm, the temperatures (above and in the soil) are calculated through
the sensible heat flux; the humidity and evaporation at the soil surface are
calculated through the latent heat flux. The resistances needed for the
compensation point concentration and for the flux calculation are deduced
from the energy budget. This allows us to simultaneously take into account the
role of temperature and humidity of the soil. In Zhang2010, the
Ra, Rb, and Rc resistances are calculated
directly from the meteorological forcing, and the soil resistance is
prescribed. Again, the flexibility of this parameter is more adapted than
fixed values for 1-D modelling, and this may lead to completely different
repartitions of the fluxes between the soil and the vegetation, as shown in
Fig. 10. This difference in flux repartition highlights the importance of the
choice in the type of soil and/or vegetation for the simulations.
However, the close correlation between both models (R2=0.5, p<0.01, slope = 0.6, offset = 0.4) indicates a similar representation
of the net flux in each model and emphasizes clear changes at the transition
between seasons.
Effect of soil moisture, soil temperature, and soil characteristics on
exchange processes
For most of the biomes the temperature strongly governs soil respiration
through metabolism of plants and microbes (Lloyd and Taylor, 1994; Reichstein
et al., 2005; Tagesson and Lindroth, 2007). However, in our results we found
no significant correlation between soil surface temperature and trace gas
fluxes. This confirms that in the semi-arid tropical savannas, physiological
activity is not limited by temperature (Archibald et al., 2009; Hanan et al.,
1998, 2011; Tagesson et al., 2016a, 2015a). Instead, soil moisture
variability overrides temperature effects as also underlined by Jia et
al. (2006). Indeed, for low soil moisture conditions, slight changes in soil
moisture may have a primordial effect, while temperature effect on microbial
activities is not observable (Liu et al., 2009). This may explain why soil
temperature and NO, CO2, and NH3 fluxes are not correlated
at the annual scale (dominated by dry months) as mentioned in the preceding
paragraphs. Due to higher soil moisture in wet seasons (8.1±2.7 %
vs. 3.2±1.5 % in dry seasons), soil temperature effect becomes
visible, elevated temperatures may increase microbial activity, and changes
in soil temperature may have an influence on N turnover and N exchanges with
the atmosphere (Bai et al., 2013).
The over- or underestimations of NO emissions in the model in Fig. 5 may be
explained by the ammonium content shown in Fig. 6. Released N is
overestimated during the J13 wet season and underestimated at the end of the
wet season (as N13), when the presence of standing straw may lead to N
emissions in addition to soil emissions, not accounted for in the model
because litter is not yet buried. The slight underestimation of modelled soil
moisture (Fig. 2) at the end of the wet season may also explain why modelled
fluxes of NO (Fig. 5) and CO2 (Fig. 7) are lower than measured
fluxes. Furthermore, the model over-predicts the death rate of microbes and
subsequently underestimates the CO2 respired, whereas microbes and
residues of root respiration persist in the field despite low soil moisture.
The large spatial heterogeneity in measurements may be explained by
variations in soil pH and texture and by the presence of livestock and the
short-term history of the Dahra site, i.e. how livestock have trampled,
grazed, and deposited manure during the different seasons and at different
places. This spatial variation is evidently not represented in the 1-D model,
where unique soil pH and soil texture are given, as well as a unique input of
organic fertilization by livestock excreta.
During the dry season, substrates become less available for microorganisms,
and their diffusion is affected by low-soil-moisture conditions (Xu et al.,
2016). The microbial activity slows down gradually and stays low during the
dry season (Wang et al., 2015; Borken and Matzner, 2009). De Bruin et
al. (1989) have experimentally shown that drying did not kill the microbial
biomass during alternating wet–dry conditions at a Sahelian site. It is
therefore likely that the transition from activity to dormancy or death at
the end of the wet season is too abrupt in the STEP–GENDEC–NOFlux model,
leading to smaller NO and CO2 fluxes than the still rather large
measured fluxes. Furthermore, the two first layers of the soil in the model
dry up more sharply than what measurements indicate, and the lower modelled
soil moisture has an effect on modelled fluxes.
During the wet season, and just before and after, the link between soil or
leaf wetness related to air humidity and NH3 dry deposition is
straightforward, as NH3 is highly soluble in water. Water droplets,
and thin water films formed by deliquescent particles on leaf surfaces
increase NH3 dry deposition (Flechard and Fowler, 1998). This
process is easily reproduced by the two models used in this study, as shown
in Fig. 9 where a net NH3 dry deposition flux is observed during
the wet season.
With wet season NO fluxes being more than 2 times higher than dry season
fluxes, results emphasize the influence of pulse emissions in that season
This increase at the onset of the wet season over the Sahel, due to the
drastic change in soil moisture, has been previously highlighted by satellite
measurements of the N2O column, by Vinken et al. (2014), Hudman et
al. (2012), Jaegle et al. (2004), and Zörner et al. (2016). After the
pulses of NO at the beginning of the wet season (Fig. 5), emissions decrease
most likely because the available soil mineral N is used by plants during the
growing phase of roots and green biomass, especially in 2013, and is less
available for the production of NO to be released to the atmosphere (Homyak
et al., 2014; Meixner and Fenn, 2004; Krul et al., 1982). During the wet
season, NO emissions to the atmosphere in the model are reduced by 18 %
due to plant uptake (compared to NO emissions when plant uptake is not taken
into account). Indeed, N uptake by plants is enhanced when transpiration
increases during the wet season (Appendix C).
Coupled processes of NO, CO2, and NH3
emissions
Larger CO2 and NO fluxes were seen at the beginning of the wet
season (Figs. 5 and 7), compared to the core of the wet season and to the dry
season. This can be explained by the rapid response of the soil decomposers
to the increase in soil moisture leading to a rapid decomposition of the
litter buried during the preceding dry season and a rapid increase in
ammonium as shown in Fig. 6. A pool of enzymes remains in the soil during the
dry season and ensures decomposition with the first rains even when
microorganism population is not yet fully developed. Austin et al. (2004)
have stated that as microbial substrates decompose rapidly, microbes will be
sufficiently supplied for growth and respiration, involving CO2
emissions, and the excess N will therefore be mineralized. Indeed, the
NH4+ dynamics control nitrification and volatilization
processes (Schlesinger and Peterjohn, 1991; McCalley et al., 2011). The
NH4+ pool may be depleted via nitrification, involving NO
emissions, and in parallel volatilized, involving concomitant NH3
emissions. Conversely, a major depletion of the NH4+ pool via
nitrification may favour deposition of NH3 if NH4+ is
no longer available in the soil to be volatilized.
During the dry season, as the microbial activity is reduced to its lower
limit, the N retention mechanism in microbial biomass does not work anymore,
N retention is linked to the mineralization of organic C caused by
heterotrophic microbial activity and allows N to be available for plants, and
mineral N may accumulate in the soil during this time (Perroni-Ventura et
al., 2010; Austin et al., 2004). Therefore, N loss should neither occur via
NH3 volatilization during that period, nor via NO emission.
Furthermore, the very low soil moisture and air humidity do not stimulate
NH3 deposition on bare soil or vegetation, if present, during the
dry season, knowing that NH3 is very sensitive to ambient humidity.
NH3, NO, and CO2 fluxes are affected by the same biotic
and abiotic factors, including amount of soil organic C, N quantity and
availability, soil oxygen content, soil texture, soil pH, soil microbial
communities, hydro-meteorological conditions, amount of above- and below-ground biomass, species composition, and land use (Xu et al., 2016; Pilegaard
et al., 2013; Chen et al., 2013).
At the end of the wet season, the increase in the senescent aboveground
biomass increases the quantity of litter, which leads to an input of new
organic matter to the soil and therefore a new pool of mineral N available
for the production of NO and NH3 to be released to the atmosphere,
at a time when herbaceous species would no longer benefit from it. This
process has been highlighted in Delon et al. (2015) in a similar dry savanna
in Mali. Furthermore, NO and NH3 emissions are suspected to come
from the litter itself, as shown in temperate forests by Gritsch et
al. (2016), where NO litter emissions increase with increasing moisture.
In the STEP–GENDEC–NOFlux model respiration and soil NO fluxes were
significantly correlated (R2=0.6, p<0.001, slope = 0.2,
offset =-0.2), but not directly in the measurements, due to the
spatial variability of the site. The microbial activity is not efficient
enough in the model when the soil moisture is low, whereas in measurements,
as for NO fluxes, this microbial activity seems to remain at a residual level
leading to a release of both NO and CO2 to the atmosphere (Delon et
al., 2017). A lagged relationship may somehow be displayed in measurements if
measured NO fluxes are shifted by 1 d (i.e. CO2 is in advance) in
J13, then R2=0.6, p=0.03, slope = 62.4, and offset =-2.5
(R2=0.2 if not shifted), highlighting a lag between CO2 and NO
emission processes. If the same lag is applied in model predictions, then
R2=0.6, p<0.001, slope = 3.3, and offset = 2.0, showing
that soil respiration and nitrification processes (causing NO release) are
closely linked by microbial processes through soil microorganisms that
trigger soil respiration and decomposition of soil organic matter (Xu et al.,
2008; Ford et al., 2007). This 1 d lag however has to be considered an
open question. The exact lag duration should be studied more thoroughly, but
highlights the close relationship between processes of nitrification
and respiration anyway.
Conclusions
This study has shown that NH3, NO, and CO2 exchanges between the
soil and the atmosphere are driven by the same microbial processes in the
soil, presupposing that moisture is sufficient to engage them, and taking
into account the very specific climatic conditions of the Sahel region.
Indeed, low soil and air water content are a limiting factor in semi-arid
regions in N cycling between the surface and the atmosphere, whereas
processes of N exchange rates are enhanced when water content of the
exchange zone, where microbial processes occur, becomes more important. The
role of soil moisture involved in N and C cycles is remarkable and obvious
in initiating microbial and physiological processes. Conversely, the
role of soil temperature is not as obvious because its amplitude of
variation is weak compared to soil moisture. Temperature effects are
strongly alleviated when soil moisture is low in the dry season, and become
again an influencing parameter in the wet season for N exchange. CO2
respiration fluxes in this study are not influenced by soil temperature
variations, overridden by soil moisture variation at the seasonal and annual
scale. NH3 bidirectional fluxes, simulated by two different models,
have shown a high sensitivity to the ground emission potential. The
possibility of adjusting this parameter to field measurements has greatly
improved the capacity of the Surfatm model to fit the observation results.
The understanding of underlying mechanisms, coupling biogeochemical,
ecological, and physico-chemical process approaches, are very important for
an improved knowledge of C and N cycling in semi-arid regions. The
contrasted ecosystem conditions due to drastic changes in water availability
have important non-linear impacts on the biogeochemical N cycle and
ecosystem respiration. This affects atmospheric chemistry and climate,
indicating a strong role of coupled surface processes within the Earth
system. If changes in precipitation regimes occur due to climate change, the
reduction of precipitation regimes may affect regions not considered as semi-arid until now and drive them to semi-arid climates involving exchange
processes such as those described in this study. Additionally, an increase
in demographic pressure leading to increases in livestock density and
changes in land uses will cause changes in soil physical and chemical
properties, vegetation type, and management, important factors affecting N
and C exchanges between natural terrestrial ecosystems and the atmosphere.
Code availability
The Surfatm model is available from Erwan Personne
(erwan.personne@agroparistech.fr) on request. STEP–GENDEC–NOflux is available
from Eric Mougin (eric.mougin@ get.omp.eu) on request. Zhang2010 is available
from Leiming Zhang (leiming.zhang@ec.gc.ca) on request.
Data availability
Data used in this study are not publicly available. They
are available upon request from Claire Delon (claire.delon@aero.obs-mip.fr)
for modelling outputs and measurements and in Delon et al. (2017) for
measurements. Data from the meteorological station in Dahra are available from Torbern Tagesson (torbern.tagesson@ign.ku.dk) and Rasmus
Fensholt (rf@geo.ku.dk) upon request.
Details on STEP formulations
Daily climatic data of the Dahra station used for the forcing of the
STEP–GENDEC–NOFlux model.
VariableSymbolUnitSourceRainfallPmmDahra meteorological stationMaximum air temperature, minimum air temperatureTamax, Tamin∘CDahra meteorological stationIncident global radiationRgloMJ m-2Dahra meteorological stationMean relative air humidityHr%Dahra meteorological stationWind speedwsm s-1Dahra meteorological station
Site parameters necessary for initialization of the STEP–GENDEC–NOFlux
model.
ParameterSymbolUnitValueSourceLatitudelat∘15∘24′10′′ N,GPS measurementLongitudelong∘15∘25′56′′ WGPS measurementSoil depthSdm3MeasurementNumber of soil layersNi–4Thickness of layer ieicm2/28/70/200Sand content of layer iSandi%89/89/91/91Delon et al. (2017)Clay content of layer iClayi%7.9/7.9/7.4/5; 5Delon et al. (2017)pH value of layer ipHi–6.4/6.4/6.4/6.4Delon et al. (2017)Initial water content of layer iShumimm0.4/8/10/38Field measurementInitial soil temperature of layer iTsi∘C23.5/23.9/28/30Field measurementRun-off(on) coefficientCRuiss–0Endorheic siteSoil albedoωs–0.45Station scale, satelliteInitial dry massBMs0g m-210Delon et al. (2015)Initial litter massBMl0g m-230Delon et al. (2015)C3/ C4 herb proportionC3C4%43/67Field measurementDicotyledon. contributionDicot%43Field measurementRoot mass proportion of layer i (layers 2 to 4)Root%75/20/5Mougin et al. (1995)Initial soil carbon contentCsgC m-250Unpublished dataInitial soil N contentNsgN m-23Unpublished data
Model parameters used to run the STEP–GENDEC–NOFlux model.
ParameterSymbolUnitValue [range]SourceVegetation albedoωv–0.2Station measurement, satelliteCanopy extinction coefficient for green vegetationkc–0.475Mougin et al. (2014)PAR extinction coefficientkfAPAR–0.581Mougin et al. (2014)Maximum conversion efficiencyεmaxgDM MJ-14 [4–8]Scaling parameterInitial aboveground green massBMg0g m-20.8 [0.1, 3]Scaling parameterSpecific plant area at emergenceSLAg0m2 g-10.018 [0.01–0.03]Scaling parameterSlope of the relation SLA(t)kSLA–0.028Unpublished data (Mougin)Specific plant area for dry massSLAdm2 g-10.0144Unpublished data (Mougin)Shoot maintenance respiration costmcs(–)0.015Breman and de Ridder (1991)Root maintenance respiration costmcr(–)0.01Breman and de Ridder (1991)Shoot growth conversion efficiencyYG(–)0.75McCree (1970)Root growth conversion efficiencyYGr(–)0.8Bachelet et al. (1989)Green mass senescence ratesd-10.00191Mougin et al. (1995)Live root senescence ratesrd-10.00072Nouvellon (2000)Optimal temperature for photosynthesisTmax∘C38Penning de Vries and Djitèye (1982)Leaf water potential for 50 % stomatal closureψ1/2MPa0.6Rambal and Cornet (1982)Shape parametern(–)5Rambal and Cornet (1982)Minimum stomatal resistancers,mind m-1100Körner et al. (1979)Parameters of the canopy height curvea, b, c(–)-0.0000024, 0.0055, 0.047Mougin et al. (1995)Infiltration time constantKicm d-11200/120/120/80Casenave and Valentin (1989)Parameters of the soil water resistance equationas, bs(–)4140, 805Camilloand Gurney (1986)Parameters of the soil characteristicretention curveai, bi(–)3.95/5.42/6.97/9.80Modified from Cornet (1981)2.93/2.71/2.59/2.43Field capacityFCim3 m-30.093/0.093/0.086/0.081PrescribedPsychrometric constantγBar C-10.00066Monteith (1995)Allocation factorafactor(–)0.5 [0,1]Mougin et al. (1995)
Equations, variables, parameters, and constants used in STEP.
Variables are in italics. DM: dry matter.
EquationsParameters, variables, constantsUnitSourceSoil TemperatureTsmax= Tamax+ (Er + 0.35Tamax) × Eb Tsmin= Tamin+ 0.006 BMg - 1.82 Er = 24.07(1-exp(-0.000038Rglo) Eb = exp(-0.0048 BMg) - 0.13Tsmax(min): max(min) soil temperature Tamax(min): max(min) air temperature Rglo: global radiation BMg: above-ground green mass∘C ∘C kJ m-2 gDM m-2Parton et al. (1984)Carbon budgetVcft =1-exp(-kcLAI)Vcft: total vegetation cover fraction LAI: leaf area index kc: canopy extinction coefficient for green vegetation (Table A3)m2 m-2 m2 m-2 (–)Mougin et al. (2014)Vcfg = Vcft(LAIg / LAI) Vcfd = Vcft(LAId / LAI) LAIg = SLAg × BMg LAId = SLAd × BMd LAI = LAIg + LAIdVcfg: green vegetation cover fraction Vcfd: dry vegetation cover fraction LAIg: green LAI LAId: dry LAI LAI: total LAI BMd: above-ground dry massm2 m-2 m2 m-2 m2 m-2 m2 m-2 m2 m-2 m2 m-2Mougin et al. (2014) Mougin et al. (1995)SLAg = SLAg0exp(-kSLAt) SLAg: specific green leaf area SLAd: specific plant area for dry mass (Table A3) kSLA: constant slope (Table A3) SLAg0: scaling parameter (Table A3) t: time m-2 kg-1 m-2 kg-1 (–) m2 kg-1 sMougin et al. (1995)Water budgetif P<5I=P; if P>5I=P+ CRuiss (2P-10)P: precipitation I: infiltration CRuiss: run-off coefficientmm d-1 mm d-1 (–)Hiernaux (1984)dW1/dt=I-E1-D11: first soil layer, i=2 to 4 Wi: water content in layer imm d-1 mm d-1Manabe (1969)dWi/ dt=Di-1-Ei- Tri-DiEi: evaporation in layer iDi: drainage in layer i Tri: transpiration in layer imm d-1 mm d-1 mm d-1if Wi> FCDi= (Di-1- FCi) / Aki with Aki=ei/KiFCi: field capacity in layer i (Table 3) Aki: time constant ei: layer depth (Table A3) Ki: infiltration time constant (Table A3)mm d-1 d-1 cm cm d-1Ψs,i=aiWi-biΨs,i: soil water potential in layer iWi: water content in layer iai: retention curve parameter bi: retention curve parameter MPaWs,i=0.332–7.251 × 10-4 (Sandi)+ 0.1276log10(Clayi)Ws,i: soil water content at saturation in layer i Sandi: sand content of layer i (Table A2) Clayi: clay content of layer i (Table A2)m3 m-3 % %Saxton et al. (1986)
Continued.
EquationsParameters, variables, constantsUnitSourceSoil TemperatureE= Vcfd(sA+ρCpD/ras) /λ(s+γ(1+rss/ras)) Tr = Vcfg(sA+ρCpD/rac) / (λ(s+γ(1+rsc/rac)) s=4098es/ (237 + Ta)2rss=as (Wsat-W1) -bsWsat= 0.332–7.251 × 10-4Sand1+0.1276log(Clay1) E: evaporation Tr: transpiration D: water vapour deficit, deduced from eses: vapour pressure at saturation s: saturating vapour slope A: available energy (Rn–G) Cp: specific heat air capacity (Table A3) ras: soil aerodynamic resistance rss: soil surface resistance rac: aerodynamic resistance λ: vaporization latent heat γ: psychrometric constant (Table A3) ρ: volumic air mass as: parameter (Table A3) bs: parameter (Table A3) Wsat: soil water content at saturation W1: soil water content of layer 1mm d-1 mm d-1 Bar Bar Bar K-1 MJ d-1 MJ kg-1 C-1 d m-1 d m-1 d m-1 MJ m-3 bar C-1 kg m-3 (–) (–) mm d-1 mm d-1Monteith (1965) Camillo and Gurney (1986)rsc=rsmin (1+(ψ/ψ1/2)n)rsc: canopy stomatal resistance rsmin: minimum stomatal resistance ψ1/2: leaf water potential for 50 % stomatal closure ψ: leaf water potential n: shape factor (Table 3)d m-1 d m-1 MPa MPa (–)Rambal and Cornet (1982)hc=aBMg2+bBMg +chc: canopy height a, b, c: parameters (Table A3) mMougin et al. (1995)Growth model (shoots and roots)dBMg / dt=α1afactor PSN +α2BMg dBMr / dt=α3(1-afactor)PSN +α4BMr α1=0.75(1-e-ag) / ag, α2=e-ag, α3=0.8(1-e-ad) / ad, α4=e-ad ag = 0.01125 × 2(Ta/10-2) ad = 0.0008 × 2(Ts1/10-2) PSN = 0.466Rglo×εi×f(Ψ) ×f(T)εmax BMr / BMg =1.2/ (2+0.01 BMg) f(T)= 1–0.0389(Tmax-Ta) f(Ψ) =rsmin/rscεi= 0.187log(1+9.808LAIg)afactor: allocation factor BMr: root mass PSN: photosynthesis εmax: maximum conversion efficiency (Table A3) Tmax: optimal temperature for photosynthesis (Table A3) Ta: air temperature Ts1: soil temperature layer 1(–) gDM m-2 gDM m-2 gDM MJ-1∘C ∘C ∘CMougin et al. (1995)Respiration (shoots and roots)Rm=msYG BMg ms= mcs (2.0**(Ts/10-2))Rm: shoot respiration ms: shoot maintenance mcs: shoot maintenance respiration cost (Table A3) YG: shoot growth conversion efficiency (Table A3) Ts: soil surface temperatureg DM m-2 (–) (–) (–) ∘CMcCree (1970)Rg= (1- YG)aPSNRg: shoot growthg DM m-2Thornley and Cannell (2000)
Continued.
EquationsParameters, variables, constantsUnitSourceSoil TemperatureRmr=mrYGr BMr mr= mcr (2.0**(Ts /10-2))Rmr: root respiration YGr: root growth conversion efficiency (Table A3) mr: root maintenance mcr: root maintenance respiration cost (Table A3)g DM m-2 (–) (–) (–)Rgr= (1- YGr)[(1-a)PSNRgr: root growthg DM m-2SenescenceBMd = s BMg BMrd =srBMrs: green mass senescence rate (Table A3) sr: dry mass senescence rate (Table A3) BMrd: dry root massd-1 d-1 g DM m-2Equations used in NOflux for NO flux calculation from ANN
parameterization
NOFlux=c15+c16×NOfluxnorminkgNha-1d-1,NOfluxnorm=w24+w25tanh(S1)+w26tanh(S2)+w27tanh(S3),
where NOfluxnorm is the normalized NO flux.
S1=w0+∑i=17wixj,norm,S2=w8+∑i=915wixj,norm,S3=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+c14×(wind
speed).
Soil surface temperature is in degrees Celsius, surface WFPS
as a percentage, deep soil temperature in degrees Celsius,
fertilization rate in kilogrammes of nitrogen per hectare per day, sand
percentage as a percentage, pH unitless, and wind speed in metres per second.
Weights w and normalization coefficients c are given in Table B1.
Weights and coefficients for ANN calculation of NO
flux.
w00.561w141.611c1-2.454w1-0.439w150.134c20.143w2-0.435w16-0.213c3-4.609w30.501w170.901c40.116w4-0.785w18-5.188c5-2.717w5-0.283w191.231c60.163w60.132w20-2.624c7-0.364w7-0.008w21-0.278c85.577w8-1.621w220.413c9-1.535w90.638w23-0.560c100.055w103.885w240.599c11-25.55w11-0.943w25-1.239c123.158w12-0.862w26-1.413c13-1.183w13-2.680w27-1.206c140.614c153.403c169.205Nitrogen uptake by plants
In STEP the seasonal dynamics of the herbaceous layer are a major component
of the Sahelian vegetation, and are represented through the simulation of the
following processes: 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. Faecal
matter deposition and decomposition are also included from the livestock
total load given as an input parameter.
The N uptake by plants (absorption of mineral N by plant roots) is
calculated by the product of the soil water absorption by roots, with the
mineral N concentration in the soil water. In the STEP model, daily root
absorption is equal to the daily transpiration, which depends on climatic
conditions (global radiation, air temperature, wind velocity, and air
relative humidity), soil water potential (water content in soil layers), and
hydric potential of the plant, which controls its stomatal aperture (and then
the transpiration). Transpiration is calculated with the Penman–Monteith
equation (Monteith, 1965), in which the stomatal resistance depends on the plant
hydric potential, itself depending on the soil moisture and climatic
conditions. For equivalent climatic conditions, a dry soil involves a high
potential, a closure of stomata, and a reduction of the transpiration. Conversely, a humid soil involves a low potential, open stomata, and
large transpiration. The plant hydric potential is calculated daily with
transpiration equivalent to root absorption, which itself is calculated from
the difference between soil and plant potentials (Mougin et al., 1995).
Author contributions
CD, CGL, and DS planned and designed the research. EP and BL developed the
Surfatm model, EM, CD, and VLD developed the STEP–GENDEC–NOflux model, MA
provided model results with the Zhang2010 model, and RF and TT provided data from
the Dahra meteorological station. All authors participated in the writing of
the paper.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
This study was financed by the French CNRS-INSU (Centre National de la
Recherche Scientifique – institute National des Sciences de l'Univers),
through the LEFE -CHAT comity (Les Enveloppes Fluides et l'Environnement –
Chimie Atmosphérique). The authors thank the IRD (Institut de Recherche
et de développement) local support for logistical help in Senegal, and
the Centre de Recherches Zootechniques (CRZ) de Dahra of the Institut
Sénégalais de Recherches Agricoles (ISRA) for their logistical help
during the field campaigns. TT was funded by SNSB (Dnr95/16).
Review statement
This paper was edited by Jens-Arne Subke and reviewed by two anonymous referees.
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