In the sub-Saharan Sahel, energy and water cycling at the land surface is
pivotal for the regional climate, water resources and land productivity, yet it
is still very poorly documented. As a step towards a comprehensive
climatological description of surface fluxes in this area, this study
provides estimates of long-term average annual budgets and seasonal cycles
for two main land use types of the cultivated Sahelian belt: rainfed millet
crop and fallow bush. These estimates build on the combination of a 7-year
field data set from two typical plots in southwestern Niger with detailed
physically based soil–plant–atmosphere modeling, yielding a continuous,
comprehensive set of water and energy flux and storage variables over this
multiyear period. In the present case in particular, blending field data with
mechanistic modeling makes the best use of available data and knowledge for
the construction of the multivariate time series. Rather than using the
model only to gap-fill observations into a composite series, model–data
integration is generalized homogeneously over time by generating the whole
series with the entire data-constrained model simulation. Climatological
averages of all water and energy variables, with associated sampling
uncertainty, are derived at annual to sub-seasonal scales from the time
series produced. Similarities and differences in the two ecosystem behaviors
are highlighted. Mean annual evapotranspiration is found to represent
Situation of study plots:
In Africa, counterintuitive water cycle dynamics (Favreau et al., 2009;
Descroix et al., 2013) and prospects of increased water stress (Boko et al.,
2007) or decreasing yields of rainfed agriculture (Schlenker and Lobell,
2010) challenge our ability to provide reliable projections of these key
resources, especially in the densely populated, semiarid Sahel (rainfall
Despite the importance of these surface processes, quantitative knowledge of surface exchanges and ground–atmosphere interactions is still very limited in sub-Saharan Africa. Their distribution in space and time is all the more poorly documented. In the Sahelian domain of the West African monsoon, scarce field observations have generally covered only short periods of time – typically a few days to a few weeks – at a few sites (e.g., Lloyd et al., 1997; Ezzahar et al., 2009; Timouk et al., 2009). Few studies have covered a complete seasonal cycle (Wallace et al., 1991; Miller et al., 2009; Ramier et al., 2009). To our knowledge, none have been based on a period of several years that is needed to capture the strong interannual variability of Sahelian rainfall. Current adverse public security conditions throughout the Sahelian belt leave little hope that the complex type of instrumentation required (eddy covariance, scintillometry) could be significantly densified in the near future. In this context, remote sensing estimations are particularly promising for this region. However, methods are still in development, and require context-specific field evaluation and calibration (e.g., Tanguy et al., 2012; Verhoef et al., 2012; Marshall et al., 2013). This is also true for model-derived estimates, as the ability of the current generation of land surface models (LSMs) to correctly reproduce dominant land processes in Africa is still largely in question (Boone et al., 2009a). Evaluating and improving the capabilities of general-purpose LSMs for this large continental region requires substantial reliable documentation of surface energy and water cycles at different space- and timescales (Boone et al., 2009b).
When available, field estimates of surface fluxes are undoubtedly an invaluable asset. Nearly all components (radiative, conductive, turbulent) of the surface energy cycle are now more or less readily accessible to field estimation, even though this involves rather complex techniques and inhomogeneous representative scales. However, these data are associated with significant uncertainty, particularly for turbulent fluxes of sensible and latent heat. This uncertainty arises from a variety of sources such as instrumental error, departure of field conditions from underlying theory, or processing pitfalls (Foken et al., 2006; Aubinet et al., 2012). The general lack of energy balance closure that results from these estimation problems typically ranges between 10 and 35 % of the available energy (Foken, 2008). Its assignment to the various possible sources is still a matter of debate (Aubinet et al., 2012). When estimation becomes unreliable, the corresponding data must be discarded. Added to the recurrent interruption of sensitive equipment in harsh field conditions (dust, temperature, wind), this generally leads to substantial gap rates in the derived time series. For the surface water cycle, a number of components can hardly be field-measured precisely and continuously on a routine basis, e.g., overland runoff, vertical drainage, and lateral subsurface flow, or partitioning of evapotranspiration into direct soil evaporation and canopy transpiration. For all these reasons – sparse data sets, unobserved components, and uncertain data with conservation biases – it is not feasible to estimate complete and reliable water and energy balances at various timescales from field observations only, and some sort of modeling is thus necessary. Combining as many field observations as possible with physics-driven models, which integrate available knowledge of the main local water and energy cycling processes, appears to be the most reliable way to make robust quantitative estimates of surface–atmosphere exchanges, particularly in this region.
In this context, the purpose of this study is to propose – for the first
time to our knowledge – a description that can be representative, in a
climatological sense, of water and energy cycles for two dominant land cover
types in the cultivated Sahel, namely rainfed millet crop and fallow bush.
First-order dynamics at annual to sub-seasonal scales are analyzed here,
through estimation of long-term means. A reliable climatology is useful as a
powerful reference for a variety of purposes, including extracting the most
significant features in system dynamics, deriving anomalies, analyzing
processes and understanding system behavior, making robust comparisons
between systems or across different bioclimatic settings (globally or
regionally as expected from the AMMA-CATCH African Monsoon
Multidisciplinary Analyses – Coupling Tropical Atmosphere and Hydrological
Cycle;
This climatological description is based on the production and analysis of a multivariate series covering an unprecedented full 7-year-long period for two plots in Niger (Velluet, 2014). This continuous series was obtained by combining a unique field data set over that period (Boulain et al., 2009a; Cappelaere et al., 2009; Ramier et al., 2009) with the physically based SiSPAT (Simple Soil–Plant–Atmosphere Transfers) model (Braud et al., 1995). The study area is located in the so-called central Sahel region, which is considered the most representative of the West African monsoon rainfall regime (Lebel and Ali, 2009). Available data include local rainfall and meteorology, vegetation phenology, all surface energy cycle components, and soil moisture and temperature profiles. The SiSPAT model solves the 1-D vertical equations for coupled diffusive transfers of water and heat in a heterogeneous soil, coupled with surface and plant exchanges with the atmosphere. It has been shown (Demarty et al., 2004; Shin et al., 2012) that even in the general heterogeneous, layered case, this type of soil water model can be reliably inverted for hydrodynamic properties from soil moisture observations when the profile is predominantly draining (no underlying moisture source), which is the case in nearly all of this region. SiSPAT has already been tested successfully over a short period in this environment (Braud et al., 1997; Braud, 1998). Other GAI studies, either data-based (e.g., Miller et al., 2009; Ramier et al., 2009; Lohou et al., 2014) or model-based (e.g., Daamen, 1997; Pellarin et al., 2009; Saux-Picart et al., 2009a, b), were carried out in the study area. However, as mentioned earlier for the whole subregion, they were all limited to sub-seasonal periods or at most to one particular year. The models used were generally less detailed than in this study, with a more exploratory perspective. Deriving a reference climatology as done here requires a long-enough, complete, and reliable series. This required continuous multivariate series is provided by the strongly data-constrained 7-year model simulation, which is used in its entirety rather than only for gap-filling observations into a composite series. As the paper shows, the series allows capturing statistical population averages for the variables investigated, while minimizing the effect of possible decadal non-stationarities of the monsoon (Lebel and Ali, 2009) or of land management. As it carries the most robust features in the dynamics, analysis of mean system behavior enables a powerful comparison of the two investigated systems. These results should contribute a substantial step to documenting the dynamics of surface fluxes in the Sahel.
After a brief description of sites, data, model, and overall methodology (Sect. 2), results are presented for the climatology of a synthetic average year from annual to sub-seasonal timescales (Sect. 3). The significance of these results – as induced in particular by the study methodology – as well as information inferred on key processes are discussed in Sect. 4. As they play a key part in the methodology, implementation and evaluation steps for model–data integration (parameter estimation, model validation) are detailed separately in Appendices A and B, for better overall readability.
Equations of water and energy conservation are written as
The study area is located
At the start of the 2005 monsoon, the two plots were equipped with an
identical data acquisition setup for continuous recording of
(i) meteorology: rainfall, air pressure, temperature and humidity, wind speed
and direction, four-component radiation; (ii) high-frequency eddy covariance
for sensible and latent heat flux estimation: 3-D wind, temperature, and
vapor concentration (carbon flux also monitored but not used in this study);
(iii) soil variables: shallow ground heat flux,
2.5 m deep temperature and moisture profiles. Details of this setup are
given in Table 1. The millet plot was turned to cultivation just before
instrumentation began in 2005, while the fallow field had not been cropped
since the early 2000s. In both plots, land use remained unchanged throughout
the 7-year study period (May 2005–April 2012). Soil texture and bulk
density were analyzed from samples taken along several 2.5 m deep profiles at
different dates over the period to calibrate soil moisture sensors for
volumetric water content. Consistent particle size distributions of
Mean seasonal courses of meteorological variables in
Wankama catchment:
Description of permanent GAI-recording stations in the Wankama fallow and millet plots.
For the practical and theoretical reasons mentioned earlier (e.g., equipment
failure, temporarily unsuitable conditions), the data series include gaps of
variable lengths (10–35 % missing data). Meteorological variables, needed
for model forcing, were gap-filled by substituting the closest available
data from similar instruments deployed over the Wankama catchment
(Cappelaere et al., 2009). Eddy covariance data were processed into
half-hourly turbulent fluxes, using
A field survey of vegetation phenology was conducted at both plots every
1 or 2 weeks from June through December of all 7 years (Boulain et al.,
2009a). Of particular interest for this study are the seasonal courses of
vegetation height and leaf area index (LAI). Height was sampled from
15–30 individuals per plot and date. LAI was derived from hemispherical
photographs, following the protocol prescribed by the VALERI project
(
The SiSPAT model (Braud et al., 1995; Braud, 2000; Demarty et al., 2002) was chosen for its ability to simulate the coupled heat and water exchanges through the soil–plant–atmosphere continuum on physical bases. Model overview diagrams are provided by Fig. 1 in Demarty et al. (2004) and Fig. 6.2 in Velluet (2014). As a SVAT (soil–vegetation–atmosphere transfer) column model, it is forced at a reference level with observed meteorology (rainfall, wind speed, air temperature and humidity, atmospheric pressure, incoming short- and long-wave radiation). Two energy budgets, one for the vegetation canopy and one for the soil surface, are solved concurrently and continuously for surface–atmosphere exchanges over the diurnal cycle, with temperature and humidity at the soil surface, at the leaf surface, and at the canopy level of the atmosphere as state variables. Leaf area is prescribed as time-variable LAI, and also conditions a rainfall interception reservoir. Turbulent fluxes are expressed using a classical electrical analogy in this two-layer system, based on the computation of a bulk stomatal resistance and of three aerodynamic resistances. The bulk stomatal resistance, representing the plant physiological response to climatic and environmental conditions, is modeled in terms of incoming global radiation, vapor pressure deficit, and leaf water potential (Jarvis, 1976). The three aerodynamic resistances are determined using the Shuttleworth and Wallace (1985) wind profile parameterization inside and above the canopy. Radiation transfers in the short- and long-wave bands account for the two-layer formalism with shielding and multiple reflection effects (Taconet et al., 1986).
Seasonal course of daily LAI at
A major strength of the model is its mechanistic representation of soil thermal and hydraulic dynamics, accomplished by solving the coupled differential equations of heat and mass transfer, including vapor phase. This allows in particular to account for strong heterogeneity in the soil profile, e.g., the common presence of a surface crust in this environment or several soil horizons with contrasted thermal and hydraulic conduction and retention properties. Different parameterizations of the hydraulic conductivity and retention curves are possible. Each horizon is discretized for a numerical solution of the dynamic and continuity equations, with variable node density in relation to magnitude of state variable gradients (e.g., higher near the surface or horizon boundaries). Water is extracted by plants based on a prescribed, constant or dynamic root density profile, assuming no plant storage (Federer, 1979; Milly, 1982). The above- and below-surface model components are coupled through soil surface temperature and humidity, leaf water potential, and conservation of energy and mass at the soil and plant surfaces. A lower boundary condition needs to be assigned for both the heat and mass transfer equations at the bottom of the simulated soil column. Various boundary condition types, including Dirichlet and Neumann types, are proposed (Braud, 2000). The model is forced with meteorological data at a sub-hourly timestep to capture the diurnal cycle, and the data are linearly interpolated at the computational timestep. The timestep is adjusted automatically according to soil water pressure and temperature gradients. This enables accurate representation of process dynamics (e.g., when sharp variations occur during rain events) as well as satisfaction of numerical convergence and stability criteria.
The SiSPAT model has been previously applied to Sahelian sites near the study area (Braud et al., 1997; Braud, 1998), for relatively short simulation periods, but with encouraging results as to the model's ability to reproduce the Sahelian GAI behavior. It has also been used successfully in a variety of other complex, physics-oriented applications, such as isotopic tracing (e.g., Rothfuss et al., 2012) and remote-sensing simulations (e.g., Demarty et al., 2005).
The SiSPAT model is forced for the fallow and millet plots with their 7-year (1 May 2005–30 April 2012) time series of half-hourly meteorological variables and daily LAI. A 4 m deep soil domain is modelled to minimize possible errors in surface energy and water fluxes arising from assumed bottom conditions. These conditions are gravitational water drainage and constant temperature taken as the observed multiyear average at 2.5 m depth. To allow for vertical non-homogeneity, the soil column is divided into five horizons named H1 to H5, with depth ranges of 0–0.01, 0.01–0.20, 0.20–0.70, 0.70–1.20, and 1.20–4.00 m, respectively. The thin H1 horizon makes it possible to differentiate a surface crust – if any – from the soil proper. Separation of the latter into H2–H5 is derived from soil density profiles observed in the two fields. The five-layer soil column is discretized into a total of 194 computation nodes to ensure accurate state variable profiles. These are initialized with soil water content and temperature profiles observed on 1 May 2005, linearly interpolated over the computation domain.
Vegetation, surface, and soil parameters in SiSPAT model
(Braud, 2000), with values either calibrated (parameter groups C and D; see
Appendix A for group definitions) or non-calibrated (parameter groups A and B).
The right column shows prior values or ranges obtained from the literature:
Continued.
Estimated mean annual
SiSPAT involves a rather large set of input parameters defining soil,
vegetation, and surface properties (Table 2). Regarding soil properties, and
based on previous experiences with the model for these Sahelian ecosystems
(Braud et al., 1997; Braud, 1998), the water retention and conductivity
curves for each horizon are parameterized using the van Genuchten (1980) –
with the Burdine (1953) condition – and the Brooks and Corey (1964) models,
respectively. This leads to six hydrodynamic parameters (
The extensive validation of the simulated series (Appendix B) permits derivation, from these entire multivariate series directly, of climatological averages for the water and energy fluxes at both plots for annual to sub-seasonal (running monthly with a view to daily) timescales (Sect. 3). Despite the moderate sample size, sampling-induced uncertainty on estimated means is quite small. Combined with the high model skill, this small statistical uncertainty suggests that robust climatological features can be inferred from the analysis. The significance of these results, as governed by the data and model used and by the way these two sources of information are blended in the study methodology, is discussed in Sect. 4.1.
Over the 7-year period, rainfall shows interannual variability in amount and
timing in line with that reported for the Wankama catchment over the longer
1992–2006 period (Ramier et al., 2009), suggesting that our study period is
representative of the general conditions prevailing in this area.
Specifically, annual rainfall (values in legend of Fig. 3) ranges from
350 to 580 mm yr
Simulated variables are analyzed in their distribution at annual, semiannual, seasonal, and sub-seasonal scales over the study period, with the aim of estimating an average year for each site from this 7-year sample. Since climatological differences in forcing fluxes (rainfall, incoming short- and long-wave radiation) between the two sites are all very small, these specific variables are not duplicated in the following.
The two pie charts in Fig. 4a display the distribution of the interannual
mean water balance into its component parts for the fallow and the millet
systems. It can be seen that (i) direct soil evaporation is
the largest component for both systems, and for the fallow particularly
(60 % of total rainfall against 52 % for the millet field); (ii) canopy
transpiration is the second largest in both cases, albeit lower in the
fallow (25 %) than in the millet field (31 %); (iii) these two
evaporative components result in quite similar total evapotranspiration for
the two systems that largely dominates the water balance (85 and
82 %, respectively); (iv) runoff ranks next in magnitude for both systems,
but is substantially larger for the fallow (15 % against 10 % for the
millet field), (v) drainage (
Estimated
Because, at this largest timescale, differences between the two systems are much less substantial for the energy balance, a similar decomposition – in this case of total global radiation – is presented only for the average of the two systems (Fig. 4b). It shows that net long-wave radiation is the main component (40 % of global radiation), closely followed by reflected solar radiation (32 %). Sensible heat ranks next (17 %), followed by latent heat (12 %). Soil heat flux is negligible at this scale of integration. When compared to a global-averaged continental energy budget (Trenberth et al., 2009), all components are found larger at the study site, including latent heat. Regarding radiative losses, reflected shortwave is closer to net long-wave loss than it is globally. As for turbulent losses, sensible heat is greater than latent heat, contrary to globe averages.
Figure 5 displays in more detail the climatological water and energy
balances for both systems, at the annual scale and for two 6-month periods
corresponding to the monsoon (May–October) and dry (November–April) seasons. Elemental components are also grouped by type: liquid versus
atmospheric vapor fluxes for water (Fig. 5a) and radiative versus turbulent for
energy (Fig. 5b). Estimated annual means are shown with standard estimation
errors and sample ranges. It can be seen that sampling uncertainty of
estimated means is very small for all energy variables (max. standard error
of 2.8 W m
Estimated mean seasonal courses of water cycle
components, for fallow (solid lines) and millet (dashed lines) plots:
Results suggest that annual-scale differences between ecosystems – even
though small for the energy balance – are statistically significant for
most elemental components. Exceptions are turbulent (latent or sensible)
heat fluxes, and also aggregated liquid fluxes. Hence, when switching
ecosystems, tradeoffs occur at the annual scale between runoff and drainage
(
Estimated mean seasonal courses of energy cycle
components, for fallow (solid lines) and millet (dashed lines) plots:
We are interested here in the general pattern of variation of daily
variables over an average year, as can be derived from the 7-year sample.
Figures 6a and 7a display the estimated interannual mean seasonal courses of
water and energy budget components, respectively. A 30-day running averaging
was applied to filter out high-frequency components and obtain a more
robust estimate of the low-frequency-dominated population's mean seasonal
cycle (the value of this filtering is further discussed in Sect. 4.1.3). The
sample-induced standard estimation error is shown as a confidence interval
for each variable. It can be seen that the sample of years enables deriving
interannual mean cycles with low statistical uncertainty, especially for
most energy variables. Water cycle variables show somewhat larger relative
uncertainties, with the noticeable exception of millet transpiration for
which statistical uncertainty is very small (
The rainfall signal displays the slightly skewed bell shape, with a slow
rise and sharp tail, that is typical of Sahelian rainfall seasonality
(Fig. 6a). It is even strikingly close to the 1990–2007 mean seasonal cycle
obtained for a 5
Overall, both seasonal soil evaporation and runoff follow rather homothetic
general courses relative to the rainfall bell, yet smoother for evaporation.
Maxima are at 2.8 and 2.4 mm day
As transpiration is strongly buffered by the whole soil/vegetation system,
it displays a very smooth course (Fig. 6a), lagged relative to rainfall by
about 1 month for the fallow and 1.5 months for the millet system, and
peaking around 1.5 mm day
Drainage from the millet plot at 4 m depth starts later than all other fluxes
(around the beginning of September), peaks in October with limited intensity
(maximum: 0.3 mm day
Estimated mean seasonal courses of
Due in particular to intertropical latitude and concomitance of the
astronomical summer with the cloudier monsoon season, global radiation shows
only limited seasonality (230–275 W m
As the monsoon sets in, consumption by latent heat of a major part of the
net shortwave energy (more than half at monsoon peak, even for the
less-consuming millet plot) carves a corresponding hollow in the courses of
both net long-wave and sensible heat (Fig. 7a) through the lowering of surface
temperature. These hollows are modulated in their amplitude and timing by
other atmospheric controls, such as air humidity for net long-wave radiation
(making LW
The energy cycle dynamics is overall sharper and more pronounced for the
fallow plot, generally displaying a somewhat earlier timing. For example,
like latent heat, net radiation is higher (lower net long-wave) in the fallow
until late September, and vice versa until the next monsoon
(
To our knowledge, this study is the first attempt to put forward a climatological view of GAI energy and water fluxes in the Sahel environment. While only two sites are considered in this study, a fallow bush field and a rainfed millet field, these are quite representative of dominant ecosystems in the Sahelian agricultural context. This not only applies to southwestern Niger but also to a very significant part of the whole sub-Saharan Sahelian belt. Variations obviously exist within this huge domain, depending e.g. on geology, monsoon specifics, population, and agricultural practices; however, the first regional flux–site intercomparisons (Merbold et al., 2009; Sjöström et al., 2011, 2013; Lohou et al., 2014) evidenced strong similarities over the Sahelian domain, relative to the other ecoclimatic domains of tropical Africa. Hence, it is believed that the new results obtained at these two sites can serve as a useful reference well beyond the study area.
Previous studies (e.g., Braud, 1998; Verhoef et al., 1999; Miller et al., 2009; Ramier et al., 2009; Saux-Picart et al., 2009a) provided specific experimental and/or modeling results for surface fluxes in such ecosystems over much shorter periods, i.e., at scales ranging from a single event to at most an annual cycle. For instance, Miller et al. (2009) made a detailed field analysis of the surface energy balance at sub-seasonal to seasonal scales, based on a 1-year record at a Niamey fallow site, i.e., in conditions very similar to ours. However, in light of the 7-year series studied here, it appears that the quite dry observation year (375 mm) at their site produced substantial flux anomalies, e.g., comparable latent and sensible heat fluxes at the heart of the rainy season. Such results could be misleading if they were considered alone. Conversely, the season analyzed by Ramier et al. (2009) was unusually wet (580 mm). This underlines the need for multiyear series to derive major features of surface response to variable monsoonal forcing. The unprecedented length of our study period for this region is a step in that direction. Seven years is probably a lower limit for producing robust results. However, it seems a reasonable length in light of the rather small statistical uncertainty on estimated variables. Comparison of rainfall statistics for the 7-year period (interannual mean and variability, seasonal distribution) with longer records for the catchment (Ramier et al., 2009) or for the area (Lebel and Ali, 2009) suggests that our study period is quite representative of current monsoon conditions in the central Sahel. Accounting for non-stationarities in climate or in the hydro-ecosystem (land cover, soil) or for land management variability (e.g., crop/fallow rotation, cultivation practices, animal grazing/manuring) is another challenge facing the long-term observatory in the Wankama catchment (Cappelaere et al., 2009). Now that a seemingly robust model has been developed for these ecosystems, it will be interesting to investigate additional years as more meteorological and phenological forcing data become available.
It was suggested in the introduction that the study's objectives could not be met with field data alone. This section further examines the need for and merits of the model–data integration performed. As mentioned, field data limitations include (i) not all variables of interest being monitored (e.g., evapotranspiration partitioning, runoff, drainage), (ii) substantial, unevenly distributed gap rates (one-fourth to one-third of turbulent fluxes observations missing here after data filtering, depending on variable and site, and 11 to 18 % for other energy fluxes), and (iii) measurement representativity and accuracy issues, including scale discrepancies.
“Black-box” gap-filling techniques do exist, but they boil down to very basic data modeling, with crude hypotheses, which themselves may induce considerable errors and biases. Even when more elaborate modeling is achieved as it is here, observational shortcomings as well as likely statistical biases in deriving a climatology from a heterogeneous series of gap-filled observations severely question the basic gap-filling approach. Using instead the physics-based model simulation for the whole reference series, provided it is constrained by successful calibration/validation with dense and diverse observations through the whole simulation period, better integrates all sources of information into a homogeneous, coherent series. Specifically, it allows to (i) make all available field information work together (across variable types and over time) instead of separately, (ii) constrain them with physical principles for regularization rules to find the best compromise and make the most sense of all these different types of information/knowledge, and (iii) produce output variables at a consistent plot scale, obeying known physics, and as compatible as possible with the whole data set.
Attempting to match a long and diverse set of observations – all high-resolution surface energy fluxes, soil moisture/temperature profiles – with a rather complex model, could be seen as quite a challenge. Results show that this is feasible for the two ecosystems and the variable forcing conditions (Appendix B), with parameters assigned in part from prior knowledge from the field and the literature, and in part from split-sample calibration/validation (2 and 5 years, respectively). This was performed with a heuristic parameter adjustment method, based on expertise with the model, the data, and the field properties and processes (Appendix A). In the authors' judgement, the compromise achieved in integrating the various data and regularization constraints is about the best possible. Some parameter equivalence does exist; however, because of the strong conditioning by the wide range of control variables and simulation conditions, including those for validation, this should not unduly affect the simulated trajectories. In this study, model application is restricted in time to the observation period, which avoids extrapolating to weakly conditioned situations. However, the calibrated model is thought to have the potential for reliable simulations well outside the observed conditions. Regarding unobserved fluxes, the fact that they may often occur separately in time (runoff during rainstorms, evaporation in the early rainy season, transpiration during dry spells and in the early dry season) makes calibration/validation of their main controlling parameters, and hence their simulation, all the more reliable.
These key methodological issues are further discussed in Velluet et al. (2014).
Strictly speaking, because of the 30-day filtering applied to the simulated
time series, the mean seasonal cycles produced (Figs. 6–8) pertain to
moving monthly quantities. However, the very smooth variations to be
expected for the population's mean cycles should imply low sensitivity of
the latter to a time resolution below 1 month. Hence, it is believed that the
estimated seasonal courses of Figs. 6–8 provide rather good climatological
estimates for finer timescales as well, down to daily resolution. For this finest resolution, only the
peaks (highs and lows) would be expected to be
slightly smoothed out (underestimated maxima, overestimated minima). To get
an idea of the possible differences between the population's daily and
running monthly mean seasonal cycles, we can simulate their relationship by
applying a 30-day filter to the estimated seasonal signals of Figs. 6a and 7a:
this reduces the seasonal standard deviation of water cycle variables by
only 2 % (for soil evaporation) to 5 % (for runoff), and by 1.5 % (net
long-wave or latent heat) to 3 % (sensible heat) for all energy variables
but global radiation and ground flux (
Finally, as only the systems' mean behaviors are documented here, variability around climatological means is not reflected. For instance, dry spells commonly occurring within the Sahel monsoon are not shown explicitly but only through their effects on the mean seasonal signals. Thus, it should be kept in mind that, at any timescale (daily to annual), some of the features highlighted by this first-order analysis may not hold at all times, and that they can even turn out to be the opposite under certain circumstances.
This discussion focuses on water cycle processes, as they were shown to also largely condition the other GAI processes in this environment (Sect. 3.2.2).
Runoff values for the two sites are compatible with results from previous
field plot studies in the area (e.g., Peugeot et al., 1997; Estèves and
Lapetite, 2003). They show high variability, with annual runoff spanning a
range of
These differences in rain infiltration capacity between the two plots appear to be one cause for the consistently higher soil water storage obtained for the millet field, but not the only one. The other one – even more important, as hypothesized by Ramier et al. (2009) – appears to be lower evapotranspiration from the millet field, at least until late September (Fig. 7a). On average, these two factors account for, respectively, about one-third and two-thirds of the difference in 0–4 m soil storage up to that date. Direct soil evaporation dominates in this evapotranspiration contrast; however, both soil evaporation and rainy season plant transpiration are lower in the millet field despite generally higher soil moisture. Hence, it appears that a combination of factors leads to higher soil water content in the millet field all along the wet season (and hence throughout the whole average year, Fig. 8).
Consequences of this higher water storage are that when the end of the rainy season approaches, drainage can start to occur at the 4 m depth in the
millet field – at least in sufficiently wet years – as well as shrub
regrowth that sustains transpiration into the dry season. This is not the
case for the fallow. Even though drainage amounts to a modest fraction of
the plot water balance, the average 31 mm yr
During most of the year, evapotranspiration appears to be water-limited, with the latent heat flux being tightly connected to variations in soil water and rainfall. Only at the monsoon peak (August–beginning of September) does the evaporative fraction (Fig. 7b) or the ratio to reference evapotranspiration (Allen et al., 1998; not shown) approach 1, suggesting that evapotranspiration becomes then more energy-limited. Both ratios peak higher for the fallow, despite lower total soil moisture.
On average over the study period, estimated transpiration amounts to
The increase in transpiration in the late monsoon when soil evaporation declines (Fig. 6a; especially for the millet system where soil moisture is still high) is interpreted partly as reflecting a relaxed competition for energy between the two processes. Note that the climatic water demand, as expressed by reference evapotranspiration, does not rise again after its monsoon low until the winter solstice. A corollary phenomenon, with soil evaporation bursts that appear to depress plant transpiration, is noticeable at smaller timescales, just after rain events. In the following days, transpiration recovers as evaporation declines (also reported by Braud et al., 1997), suggesting that evaporation extinction – for lack of shallow soil moisture – makes energy available for more plant transpiration.
Our results temper the Miller et al. (2009) suggestion that the seasonal
course of evapotranspiration is driven primarily by the contribution of
plants to atmospheric moisture in this environment. They also temper the
hypothesized benefit that plants could draw during a growing season from
subsurface moisture accumulated during the previous rainy season: while this
does happen in our simulations for the millet field vegetation in the months
just after the rainy season (
Partly due to the late wet season/early dry season shrub regrowth in the millet field, the general picture of higher evapotranspiration from a fallow ecosystem than from a millet field (Gash et al., 1997; Ramier et al., 2009) is also somewhat moderated by our results. In this study, this is true on average during most of the rainy season (Fig. 6) – despite generally lower soil moisture – but not in the late September–January period, making annual totals turn out very similar (fallow slightly above). Also, when considering interannual variability, rankings may revert both annually and/or at some periods of the wet season, likely in relation with higher short-timescale variability in transpiration for the fallow. This larger variability can be traced both to the lower and more variable soil storage (Fig. 8b) that makes fallow vegetation more exposed to rainfall shortage, and to the higher LAI variability (Fig. 3) reflecting higher ecosystem sensitivity to environmental conditions (Boulain et al., 2009a) and exposure to external factors such as pasturing.
Finally, our results also suggest that these contrasts in wet season evapotranspiration between the two ecosystems originate at least partly from differences in generation of direct soil evaporation, which is clearly enhanced in the fallow field. Hence, higher rainy season evapotranspiration from the fallow may not (only) be related to plant physiological effects on transpiration, but maybe more importantly to the physics of direct soil–atmosphere exchanges within these two ecosystems (e.g., differences in convective “shield” effect, cf. Tuzet et al., 1997, or in shallow soil properties). Whether this conclusion can be generalized requires further analysis.
The purpose of this work is to build upon a unique, multiyear record of local water and energy observations for two typical plots in southwest Niger in order to propose, for the first time, a climatology of these processes in the Sahel region. The methodology relies on the development of a detailed, physically based GAI column model that is finely calibrated/validated against this important data set. It provides a time- and depth-continuous series of all water and energy variables involved, over a full 7-year period. This includes unobserved variables, most notably direct soil evaporation, plant transpiration, runoff, and drainage. The model, forced with observed meteorology and phenology, is calibrated against 2 years of data and evaluated against the full 7 years, showing very good skill in reproducing the whole observation record. For instance, the model is able to reproduce faithfully the observation of larger evapotranspiration in the fallow than in the millet plot during most of the rainy season despite lower soil moisture. The variety of monsoon conditions encountered and of evaluation variables used – covering the full surface energy balance (short- and long-wave radiation, turbulent fluxes, soil heat flux) and 2.5 m deep soil moisture and temperature profiles – offers a comprehensive set of constraints that ensures a reliable model trajectory.
The time series simulated for all water and energy variables are analyzed statistically at several timescales: annual and seasonal aggregates, and seasonal cycles of running monthly to daily values. A detailed documentation of climatological mean water and energy cycling, with sample-related uncertainty, is thus produced. From this analysis, new insights are derived on the interplay between processes that corroborate, refine, or question some ideas proposed so far in the literature. Uncertainty sources other than time sampling are not considered quantitatively in this study, as this requires elaborate assumptions to be made for all possible error sources, which is an upcoming step in this project.
With evapotranspiration/latent heat representing over 80 % of the mean annual water budget and nearly half the energy budget in peak monsoon, the case for studying these two strongly coupled cycles jointly, and for resolving this coupling explicitly, is thus clearly strengthened for the Sahel region. The atmospheric vapor flux is shown to be dominated by direct soil evaporation during most of the monsoon season in the average year. Plant transpiration becomes dominant only in the last part of the wet season (from the second half of September) and continuing into the beginning of the dry season.
Differences between the two land cover types are substantial for most
components of the water budget. For instance, differences in estimated
annual mean runoff (
These qualitative and quantitative results should prove useful as a reference
field information for various purposes such as evaluating and improving
land surface models and remote sensing algorithms in the framework of the
current ALMIP-2 project AMMA Land Model Intercomparison Project – Phase 2.
For groups A and B, assignment is completely independent of model operation.
Group A consists of soil parameters derived from field measurements only,
either directly, for texture and residual water content
Group C consists of additional vegetation parameters (total plant
resistance, minimum stomatal resistance, vegetation albedo, shortwave
interception parameter, and root density profile) that are also assigned
from values in the literature; however, unlike group B they are slightly
adjusted in the final stage of parameter assignment once group D parameters are
calibrated. This enables fine tuning for some specific stages of the
seasonal cycle (e.g., late monsoon, early dry season), when these parameters
are most important. Root profiles are considered invariant for the fallow
but seasonally dynamic for the millet system. Finally, group D consists of
soil parameters that cannot be ascribed prior values with sufficient
accuracy, with respect to model sensitivity to these values, and are thus
calibrated within prior ranges (Table 2). These are four hydrodynamic
parameters –
Assigned and calibrated parameter values are listed in Table 2. Dry and wet
soil albedo values for the two plots are in good agreement with qualitative
field indicators such as soil color and surface roughness. Soil hydrodynamic
and thermal parameters in the H2–H5 horizons are consistent with the sandy
texture, and exhibit moderate heterogeneity with depth and between sites,
especially for the H3–H5 horizons. Among the van Genuchten–Burdine retention
parameters and relative to prior ranges,
Statistics of model skill at half-hourly resolution (root mean square error
RMSE, bias, correlation
Overall, model skill appears positively related with the field-estimation
precision that can be expected for each variable. Upwelling shortwave
radiation is always very well simulated, with RMSEs on the order of
10 W m
Simulated vs. field-estimated daily energy fluxes of
Observed and simulated courses of total water storage in the
0–2.5 m soil layer at
Soil water storage in the different horizons, as estimated from
corresponding point measurements (from 0 to 2.5 m), is also very well
reproduced (Table B1). This is especially true for the upper horizons
showing significant dynamics, i.e., H1–H3 for the fallow (NSE
Scores for soil temperatures show that these are very well reproduced at the
millet site (NSE
Finally, the high correlation values obtained at half-hourly timescale for both the energy fluxes and the shallow soil temperatures suggest that, in addition to event, seasonal, and interannual dynamics, the phasing of diurnal cycles is also very well represented by the model.
Model skill scores against observed half-hourly surface fluxes
and soil moisture and temperature.
Each cell shows first the score for the whole study period (May 2005–April 2012;
plain characters), then the
score for the calibration period only (May 2006–April 2008; values in italics). RMSE is
root mean square error; NSE, Nash–Sutcliffe efficiency;
The first author's Ph.D. was financed by a student research grant from
SIBAGHE Doctoral School at University Montpellier 2
(