Hydrochemistry of runoff and subsurface flow within Sahelian microdunes

Hydrochemistry of runoff and subsurface flow withinSahelian microdunes

O. RIBOLZIa , T. BARIAC

b, A. CASENAVEa, J . P. DELHOUME

a, J . DUCLOUXc & V. VALLES

d

aIRD, BP 182, Ouagadougou 01, Burkina Faso, bLaboratoire de Biogeochimie Isotopique, Universite Pierre-et-Marie-Curie, case 120,

4 place Jussieu, 75252 Paris Cedex 05, cLaboratoire Argiles, Sols et Alterations, CNRS, URA 721, 40 avenue du Recteur Pineau, 86022

Poitiers, and dLaboratoire Chimie et Environnement, Universite de Provence, case 29, 3 place Victor Hugo, 13331 Marseille Cedex 3,

France

Summary

The sandy microdune systems of the Sahel are important for biomass production, in that they trap and

store water. We have studied the movement of water over and in a dune and the chemistry of the water to

understand this aspect of the systems. We experimented with simulated rain using a field sprinkling

infiltrometer. We applied demineralized water with a chemical composition similar to that of the natural

rain on a 1-m2 plot. The plot was delimited by a metallic two-level setting: the first enabled us to collect

surface runoff, while the second measured subsurface flow. Water samples were taken at 5- to 10-minute

intervals throughout each simulation for chemical analysis (alkalinity, SO42–, F–, NO3

–, Ca2þ, Mg2þ, Naþ,

Kþ and Si). Mass balances, combined with a simple mixture model involving one tracer (chloride) and two

reservoirs (old and new waters), were calculated. The equilibrating pressures of the CO2 (pCO2) and the

saturation index with respect to specified minerals (e.g. calcite, fluorite, silicates) were also calculated by the

AQUA ion-pair model. The solute concentrations decrease in surface runoff as well as in subsurface water,

except for F– and Si in the subsurface. The pCO2 decreased to a pressure less than the atmospheric pressure.

The difference between measured concentrations and concentrations computed with the mixing model

highlighted interactions between the soil and water. The dissolution of calcite which consumes CO2, and the

cation exchanges, dominated, whereas the dissolutions of fluorite, silicates and gypsum appear secondary.

Reactive mineral stocks were quickly exhausted, especially in the surface flow.

Introduction

In the Sahelian zone of Burkina Faso, overgrazing, extension

of cultivation and increasing dry climate aggravate erosion. In

this context, sandy microdunes are islets of fertility within

degraded landscapes. They are ecological units where infiltra-

tion of water is significant (Casenave & Valentin, 1992) and

are very important for biomass production (Grouzis, 1991).

These sandy aeolian deposits are potential starting points for

the regeneration of eroded surfaces. Preservation of such units

is therefore crucial in this environment.

The aim of the present work is to understand better the

transport of water and solute in the soil of microdunes. Pre-

liminary studies by Biaou et al. (1999) showed the existence of

surface runoff and lateral subsurface fluxes through these

microdunes during storms, and Ribolzi et al. (2000a) showed

that the mixture of rain water (new water) with the water

already present in the soil (old water) affects the solute

concentrations in the flow. However, this process cannot

wholly explain the variations observed. We may wonder

whether the chemical contents are also affected by inter-

actions between soil and solution together with mixture pro-

cesses.

This kind of problem can be resolved by combining the

mass-balance method (e.g. Plummer & Black, 1980) with a

simple mixing model using environmental tracers. Tracers are

currently widely used to identify and occasionally to quantify

hydrogeochemical processes within catchments. The main

results have been obtained by using isotopic tracers of water

(O18 or H2) (e.g. Sklash & Farvolden, 1979; Ribolzi et al.,

1996). Water can also be traced by its physical (e.g.

Nakamura, 1971; Ribolzi et al., 1997) and chemical characteristics

(e.g. Hooper et al., 1990; Ribolzi et al., 2000b). In all cases, a

mixing model is used to separate the contributions of the

different types of water. However, in some cases the mixing

model used to separate the contributions of the different types

Paper given at the Michel Rieu Memorial Colloquium, 8–10 October

2001, in Paris.

Correspondence: O. Ribolzi. E-mail: [email protected]

Received 7 November 2001; revised version accepted 11 October 2002

European Journal of Soil Science, September 2003, 54, 531–542

# 2003 Blackwell Publishing Ltd 531

of water is difficult to apply, either because it fails to

distinguish clearly the different end members or because there

is a significant temporal variation in the rainfall during the

storm event (e.g. Bazemore et al., 1994).

Materials and methods

Site description

The area we studied is in the north of Burkina Faso, some

13 km from Dori (UTM30, WGS84, 80�980470 0E, 15�5009300N).

It is a degraded fan (general mean slope of 1%) crossed and

overgrazed by livestock. The climate is of the Sahelian type,

with a single rainy season (June–September) each year. Aver-

age annual rainfall recorded in Dori is 512mm, with a mean

maximum of 181mm in August. There is a large year-to-year

variation in rainfall (244mm minimum, 784mm maximum).

Mean annual potential evapotranspiration, calculated by the

method of Penman, is about 2396mm.

Most of the soil at this site is Solonetz (in the FAO termi-

nology) and developed from granitic and amphibolitic rocks.

It shows various types of surface characteristics caused by

water (Karambiri et al., 2003) and wind processes. Wind

deposits lead to the formation of sandy microdunes which

are almost exclusively the support of the herbaceous vegeta-

tion (Figure 1). These dunes extend about 10–100m2 in plan

and are up to 0.7m thick.

For our study we chose a dune composed of two main

horizons (Figure 2a,b). The upper 5 cm comprises a recent

deposit of loose sand with numerous macropores formed by

plant roots and soil fauna. The total porosity of this horizon

ranges between 39 and 47%. The horizon below 13 cm has a

laminated structure alternating between continuous sandy

and plasmic layers. Macroporosity is less than in the upper

horizon. It is also more compact with fewer roots. This

second horizon lies over a massive silty-sandy horizon.

Some of the physico-chemical properties of these horizons

are reported in Table 1. The vegetation (30% of the surface)

is dominated by two grasses, Cenchrus biflorus and Schoene-

feldia gracilis.

Experimental method

We used chloride as tracer. To circumvent the problem of

geochemical variation of natural rain, we used simulated

rain. In that way we could ensure a constant geochemical

signature of the rain (e.g. Turton et al., 1995).

A field sprinkling infiltrometer, designed by Asseline &

Valentin (1978), produced rain on a 1-m2 experimental plot

delimited by a metallic two-level setting (Figure 2a,c). The

first level allows surface runoff to be measured and collected

in a tank equipped with a water level recorder (accuracy of

�5%), while the second collects subsurface flow. Subsurface

Sh

rub

(50

cm)

ero

Figure 1 Photograph of the landscape showing a typical sandy microdune (dotted line) and a crusted surface of the erosion type (ero).

532 O. Ribolzi et al.

# 2003 Blackwell Publishing Ltd, European Journal of Soil Science, 54, 531–542

flow was also collected in a tank, but in this case, it was

estimated through capacity discharge (accuracy of �5%).

The hydraulic potential of the soil was measured in the two

sandy horizons with tensiometers. Moisture was measured

before and after each simulation by a direct gravimetric

method from three samples collected within 1m around the

experimental plot.

Four successive simulations were performed (Figures 3 and

4), all with demineralized water with a chemical composition

similar to that of natural rain. Three of the simulations (Sim1,

Sim2 and Sim4) were at constant intensity. The third one

(Sim3) equalled that of a typical annual frequency storm

(Chevallier et al., 1985); the rainfall event was unimodal with

a gentle initial stage, a high-intensity burst, and a tail of

decreasing intensity. Time intervals between successive sprink-

lings Sim1, Sim2, Sim3 and Sim4 are 24, 37 and 6 hours,

respectively.

The drying crust is very fragile. It was partly destroyed at

the periphery of the plot during the installation of the metal-

lic setting. Therefore it was necessary to reconstitute this

ma.

ro.

pl.

18 c

m

ve.

dry

1 m

1 m

Erosion crust

PVC tube

Metallic two-level settingHerbaceous cover

30 cm

Loose sands

Plasmic lamina

Aeoliandeposits

Silty-sandy horizon

Tanks(buried)

(a)

(b) (c)

Figure 2 Diagram of the soil profile and the border setting (a), a photograph of the soil profile showing plant roots (ro.), biological macropores (ma.)

and a plasmic layer (pl.) (b), and a photograph showing the soil surface of the plot (just before Sim2) with tufts of desiccated grass (ve.) and spots of

drying crust (dry) (c).

Table 1 Some physical and chemical properties of the soil near the experimental plot (see Figure 2)

Particle-size distribution Exchangeable cations

Clay Sif Sic Saf Sac OM N Ca2þ Mg2þ Kþ Naþ CEC

Depth /cm /% /g kg�1 pH (H2O) /mmolc kg�1

0–5 3 2 3 67 26 2 0.1 6.8 9.5 4.5 1.7 0.1 18.3

5–13 6 1 3 38 51 2 0.2 6.7 13.5 5.4 1.0 0.2 23.4

13–15 18 3 4 40 33 4 0.2 6.3 26.6 17.4 1.1 0.7 49.8

Clay< 2�m; Sif, fine silt 2–20�m; Sic, coarse silt 20–50�m; Saf, fine sand 50–200�m; Sac, coarse sand 200–2000�m; OM, organic matter; CEC,

cation exchange capacity.

Hydrochemistry of Sahelian microdunes 533


drying crust with preliminary simulated rain (Sim1), fol-

lowed by a desiccation phase. Sim1 was made with intense

rainfall (almost 120mmhour�1) to accelerate the reorganiza-

tion of the soil surface (Figure 3). This complete cycle (rain

þ desiccation) totally reconstructed the drying crust, as

shown on the photograph in Figure 2(c), taken 23 hours

after Sim1.

Water sampling and analyses

Samples of water were taken at 5- to 10-minute intervals

throughout each simulation for chemical analysis. A cumu-

lated sample of rain was collected at the end of each applica-

tion in a rain gauge near the plot.

Temperature, electrical conductivity (accuracy of �0.5%

of the measured value) and pH were measured in the field.

After microfiltration at 0.2 �m, samples collected during the

second application (Sim2) were stored in the dark in poly-

ethylene flasks. In the laboratory total calcium, magnesium,

sodium, potassium, silicon and sulphur concentrations were

measured by inductively coupled plasma spectrometry.

Chloride and nitrate ions were analysed by automated

colorimetry, and fluoride was determined by an ion-specific

electrode. All these chemical compounds were determined

by the CIRAD-AMI-AGROMIE laboratory at Montpellier

(France), certified by AFAQ (ISO 9002). Total alkalinity

(Alk) was measured by the method of Gran (1952) by the

IRD laboratory at Ouagadougou (Burkina Faso). Detection

limits and analytical accuracies reported in Table 2 were

determined following the standard French Normative proced-

ure NFX-06-044 of 1984.

Mass-balance and uncertainty calculations

Decomposing a flow from tracer data is equivalent to solving,

at each outlet sampling time, a system of n mixing equations

with n unknown variables (the various contributions to the

flow). Assume that two types of water contribute to flow

0

100

200

300

0 10 20 30 40

Suc

tion

/hP

a

0

20

40

60

0 10 20 30 40 50 60

Time /minutes

Sim2

0

40

80

120

0 10 20 30 40 50 60

0

100

200

300

0 10 20 30 40 50 60

Sim1

0

40

80

120

0 10 20 30 40

Inte

nsity

/mm

hou

r –1

0

20

40

60

0 10 20 30 40

Time /minutes

EC

/µS

cm

–1

Subsurface flow Surface runoff

5 cm deep 15 cm deep

Rain Surface runoffInfiltration Subsurface flow

Figure 3 Variation in the time of intensities,

suctions and electrical conductivity (EC)

during the first two simulations with

demineralized water (Sim1 and Sim2).



discharge: old water (pre-event water) and new water (event

water). Also assume that in each type of water there is a tracer

of constant concentration but significantly different from that

in the other. Then solving the system of equations leads to the

mixing ratio (Rm):

Rm ¼Ct

c;outlet � Cc;new

Cc;old � Cc;new; ð1Þ

where Ctc,outlet is the concentration of the conservative species

c at the outlet at time t, Cc,new is the concentration of the

conservative species in the new water, and Cc,old is the con-

centration of the conservative species in the old water. The

quantity Rm, which ranges from 0 to 1, gives the fraction of old

water.

Mass-balance calculations quantify the mass of element

and associated mineral or gas phase that has reacted along a

flow path. The mass-balance calculation used along flow

paths A and B relies on Rm (Plummer & Black, 1980).

From the Rm of the successive surface or subsurface samples

collected during the simulated rain, concentrations of

Table 2 Detection limits and analytical inaccuracies determined fol-

lowing the standard procedure NFX-06-044 (in �M)

Analytical uncertainty Detection limit

Chloride � 20%, <30; �3%, >30 3

Sulphate � 10%, <10; �3%, >10 2

Nitrate � 20%, <20; �3%, >20 1

Fluoride � 5%, <50; �1%, >50 1

Alkalinity � 3%, <20; �1%, >20 1

Calcium � 8%, <25; �3%, >25 1

Magnesium � 10%, <40; �1%, >40 2

Sodium � 14%, <40; �3%, >40 9

Potassium � 13%, <30; �3%, >30 10

Silicon � 8%, <50;� 3%, >50 2

0

20

40

60

0 10 20 30 40 50

Time /minutes

EC

/µS

cm

–1

Sim3

0

40

80

120

0 10 20 30 40 50

Inte

nsity

/mm

hou

r–1

Sim4

0

40

80

120

0 10 20 30 40

0

100

200

300

0 10 20 30 400

100

200

300

0 10 20 30 40 50

Suc

tion

/hP

a

0

20

40

60

0 10 20 30 40

Time /minutes

Rain Surface runoffInfiltration Subsurface flow

5 cm deep 15 cm deep

Subsurface flow Surface runoff

Figure 4 Variation in the time of intensities,

suctions and electrical conductivity (EC) during

the last two simulations with demineralized

water (Sim3 and Sim4).



alkalinity, SO42–, F–, Ca2þ, Mg2þ, Naþ, Kþ and Si were

estimated by

Eti;outlet ¼ Rm Ci;old þ ð1� RmÞCi;new; ð2Þ

and

�ti ¼ Ct

i;outlet � Eti;outlet; ð3Þ

where Eti,outlet is the predicted concentration of a compound

i at the outlet at time t, based on conservative mixing, and

Cti,outlet is the measured concentration. The mass-balance

model assumed that the differences ð�tiÞ between the observed

and the predicted concentrations, Equation (3), can be attrib-

uted to geochemical reactions. These differences are termed

reaction-derived quantities of a compound. Negative reaction-

derived quantities are taken to represent a loss of element from

solution due to mineral precipitation, ion exchange for the

absorbing ion, or loss of CO2; positive ones are taken to repre-

sent dissolution of minerals, ion exchange for the desorbing

ion, or gain of CO2.

A previous study with 18O as tracer demonstrated that sub-

surface water is exclusively composed of old water at the

beginning of the flow (Ribolzi et al., 2000a). Consequently,

here we supposed that the signature of old water is that of

the first sample collected in the subsurface flow. The composi-

tion of new water is that of the accumulated rain sample

collected in the rain gauge at the end of the simulation.

We adopted the approach proposed by Genereux (1998) to

quantify the uncertainties of Rm and �ti . Each known param-

eter Ctc,outlet, Cc,new, Cc,old, C

ti,outlet, Ci,new and Ci,new of Equa-

tions (1), (2) and (3) was assigned an average error calculated

with analytical inaccuracies (Table 2).

Saturation index of the solutions

Field measurements of pH, temperature, and the concentra-

tions of alkalinity, Cl–, SO42–, F–, Ca2þ, Mg2þ, Naþ, Kþ and

Si were input into the AQUA software ion-pair model. This

model is derived from the GYPSOL program of Valles (1987),

based on the Debye–Huckel law including the deviation func-

tion of Scatchard. This geochemical program accounts for

speciation within the aqueous phase, and evaluated the equi-

librating pressures of the CO2 (pCO2). The pCO2 was estimated

from the pH measured in the field and the carbonate alkalinity

of each solution (Bourrie, 1976).

The program also calculates the saturation index (SM) of the

solutions with respect to specified minerals, M. This index is

defined as

SM ¼ logQ

KTM

� �; ð4Þ

where Q is the ion activity product and KTM is the equilibrium

constant for the mineral dissolution reaction at water tempera-

ture, T. It indicates oversaturation (positive values) and

undersaturation (negative values).

Results and discussion

Hydrodynamic behaviour

Surface runoff occurred after a 5-minute lag in the case of

constant intensity simulations (Sim2 and Sim4), and after

10minutes in the case of the variable intensity simulation

(Sim3). In the first instance (with rain falling at

90mmhour�1), surface runoff increased rapidly during the

first 5–10minutes, then remained constant at around

75mmhour�1 (Figures 3 and 4). Note that the volumetric

water content increases from 10 to 21% (saturation point)

and 15 to 21%, respectively, during simulations 2 and 4.

With regard to Sim3 (daily rainfall of annual frequency), the

lag time was longer on account of the less intense rain at the

beginning of the simulation. Then, surface runoff intensity

changed with changes in the intensity of the rain (Figure 4).

In this case, mean soil moisture increases from 12 to 18%.

To understand hydrodynamic behaviour of our plot, the

chloride ion is useful because it is a conservative tracer that

enters the soil with the rain. In simulation Sim2 the chloride

content of the surface runoff appeared somewhat larger than

that of the rain (Table 3). This discrepancy would have been

much larger if chloride minerals had been present in the soil,

because the solubility of such minerals is important. Therefore,

this discrepancy could be explained better by the mixing of the

rain (new water) with a small proportion of water already

present in the soil before the rain (old water). The concentra-

tion of chloride in this old water is greater than in the new

water because the evaporation phase before Sim2 has

increased the chloride concentration in the soil. Consequently,

we have decided to use chloride as a tracer to calculate Rm

(Figure 5).

Thus, the values of Rm indicate that surface runoff was not

strictly due to excess rain. The occurrence of old water in this

flow can be explained by the surface roughness of the plot. The

surface consists of micromounds made up of wind deposits

trapped in tufts of grass (Figure 2a,b). The latter allow for

infiltration and preferential storage of water close to the sur-

face. During rain, a proportion of this water may flow out at

the basis of the micromound, and thus become part of the

surface runoff.

Subsurface flow represents 0, 7.0, 2.7 and 5.5% of total flow

(surfaceþ subsurface) in simulations 1, 2, 3 and 4, respectively.

This proportion varied during the simulations 2, 3 and 4. In

Sim2 the contribution of subsurface flow was 30% at the early

stage of the simulation, then decreased quickly and became

constant at about 5.5%. In the third simulation (variable rain

intensity), subsurface flow occurred 15minutes after the begin-

ning of the simulation (Figure 4), that is 10minutes after the

complete saturation of the first horizon with water (suction at

5 cm close to 0). When the soil surface was saturated with

water, 36mmhour�1 rain was not intense enough to release

subsurface flow (Figure 4). The latter occurred 5minutes after

rain falling at 120mmhour�1, and persisted for the intensities



of 90 and 45mmhour�1. Given that subsurface flow stops at

the intensity of 36mmhour�1, it follows that the threshold at

which this phenomenon occurred ranges between 36 and

45mmhour�1.

Surface runoff and subsurface flow started simultaneously

during Sim2 and Sim4 (constant rainfall intensity), that is

5minutes after the beginning of each application (Figures 3

and 4). Subsurface flow increased during the first 10minutes,

and then it stabilized at close to 5mmhour�1. The rapid

response of this flow is typical of a piston effect. This process

has already been explained by using artificial isotopic and

chemical tracers (Ribolzi et al., 2000a): the new water infil-

trates the soil, saturates the upper horizon, and displaces the

old water there, either vertically or laterally. Old and new

waters become intermixed during the simulation: initially the

flow contains almost 100% of old water, but the proportion of

new water keeps increasing.

This hypothesis is confirmed by the observations presented

above. Thus, the chloride content decreased rapidly through-

out the simulation (Table 3). By the end of the simulation, a

small fraction of old water remained in the subsurface flow as

shown by the chloride concentrations in it, which does not

quite reach that of the new water (Table 3). This discrepancy is

probably due to the persistence of a small fraction of immobile

old water in the soil (Angulo-Jaramillo et al., 1996).

Biogeochemical processes

As concluded previously, runoff and subsurface flows both

contain a small proportion of old water varying throughout

simulations. However, this mixing process alone cannot

entirely explain all the chemical variations observed in

those flows. As show by Ribolzi et al. (2000a) the interac-

tions between soil and solution have to be taken into account

too. Therefore we decided to study in detail those interac-

tions using the mass-balance approach of Plummer & Black

(1980).

The waters collected were much less concentrated, their cat-

ionic charge ranging between 22 and 271�M. The subsurface

waters were dominated by Ca2þ and alkalinity with a significant

content in monovalent cations (Kþ and Naþ) (Table 3), whereas

the surface waters were dominated by Kþ and alkalinity.

Table 3 Chemical composition of waters sampled during the second simulation (Sim2)

Time Si Cl– SO42– NO3

– Alkalinity F– Ca2þ Mg2þ Naþ Kþ

Type of water /minutes pH /�M

Demineralized water 0 7.6 39 11 " " 17 " 3 2 10 1

Surface runoff 4.0 6.2 61 29 5 1 127 4 12 11 13 110

9.0 6.4 – 15 3 " 88 2 5 6 15 6

19.9 7.3 56 18 2 " 42 " 5 6 12 4

25.0 7.8 46 16 2 " 50 " 3 4 12 4

34.8 7.2 45 16 2 " 37 " 6 3 12 3

54.1 7.4 48 16 3 " 30 " 6 4 10 2

Subsurface flow 8.7 6.5 95 115 21 6 335 3 109 67 81 14

12.7 6.7 108 89 16 1 326 5 103 62 77 12

22.3 6.9 175 56 8 " 240 10 74 41 75 7

33.9 6.9 165 23 5 " 174 13 48 30 45 5

58.6 6.9 202 35 3 " 143 15 36 24 45 5

", below detection limit; –, not determined.

0

0.1

0.2

0.3

0 10 20 30 40 50 60

Surface runoff

0

0.2

0.4

0.6

0.8

1.0

0 10 20 30 40 50 60

Time /minutes

Subsurface flowMix

ing

ratio

, Rm

Figure 5 Variations in time of mixing ratio, Rm, in surface runoff and

subsurface flow based on chloride concentration. Error bars

determined by the method of Genereux (1998).



Thermodynamic calculations show them to be undersatur-

ated with respect to minerals such as calcite, fluorite, gypsum

and amorphous silica (Figure 6), and in equilibrium with regard

to silicates such as potassium feldspar and phyllosilicates.

During the experimentation, the concentrations of solutes

generally decreased, in both the surface and the subsurface

(Table 3). This decrease could be the consequence of progres-

sive dilution of the old water by the new water. In the same

way, the pCO2 decreased steadily, in both the surface and the

subsurface, reaching values less than that of the atmosphere

(Figure 6). Nevertheless, the increased concentrations of silica

and fluoride in the subsurface emphasize the existence of

chemical interactions between the solid phases of soil and its

solution. It is therefore necessary to distinguish between the

mixture and the reactive processes during the transfer of

solutes.

Calculations of reaction-derived quantities, with Equations

(2) and (3) and chloride as tracer, show that solutes have

different behaviours (Figures 7 and 8). At the surface, the

soil releases elements, especially at the beginning of the flow,

then quickly the balance is nil and the transfer of solute

becomes non-reactive. Below the surface, interactions between

soil and water modify the concentrations of the various ele-

ments significantly until the end of the flow. Here the trans-

port is reactive. Fluoride is the only exception (Figure 7). The

calculations show a clear and long-lasting release of this ion by

the soil in subsurface. At the surface, the release was less and

shorter-lived. This mechanism can be the result of the dissolu-

tion of fluorite, already observed in the Sahelian soils

(Barbiero et al., 1995). The undersaturation of the waters in

relation to this mineral confirms this hypothesis (Figure 6).

This dissolution also releases calcium. In the same way, the

release of sulphate may be attributed to the dissolution of

traces of gypsum contained in the aerosols from the northern

Sahara.

At the surface, the soil releases a small quantity of potas-

sium and no sodium, whereas in the subsurface the water is

enriched with these two elements by the soil (Figures 7 and

8). The presence of Naþ and Kþ can hardly be attributed to

the dissolution of the feldspars which would be much too

slow in relation to the duration of the experiment. More-

over, the Kþ was released before the Naþ, and that is not

compatible with the solubilities of the potassic and sodic

feldspars. The ionic exchanges, usually considered to be

rapid, must be taken into consideration, and this implies

the adsorption of divalent ions, especially calcium, in

equivalent quantity.

Nevertheless, the fixation of the calcium resulting in the

ionic exchanges and in the dissolution of fluorite and of

gypsum does not correspond to the balance observed for this

element. The discrepancy can be attributed to the dissolution

of calcite. As it consumes CO2, this dissolution explains the

very small pCO2 in the surface runoff (Figure 6). It also

explains the alkalinity enrichment of the subsurface waters.

Moreover, the dissolution of calcite is possible because of the

undersaturation of the water with respect to the mineral

(Figure 6). Carbonate coats some of the grit (size> 2mm) in

the sandy deposit. Traces of calcite are also present in the soil

matrix, but they are undetectable by X-ray analyses. The

carbonate comes from the nearby erosion surface and in dust

from the Sahara.

–16 –12 –8 –4

2log(F–)

–5.5

–5.0

–4.5

–4.0

log(Ca2+)

+

+ +

+

o o o

o o

+

Fluorite

–4.0 –3.5 –3.0 –2.5

log(pCO2)

8

10

12

14

log(Ca2+) + 2pH + log(H2O)

+ +

+

+

+ +

o o

o o o

Atmosphere Calcite

oSurface runoff Subsurface flow

(b)

(a)

Figure 6 Saturation diagrams with respect to (a) fluorite and (b)

calcite.



The release of a large quantity of silica suggests the dissolu-

tion of siliceous minerals. It can neither be quartz, because the

waters are oversaturated (Figure 6), nor feldspars for the

kinetic reasons mentioned above. It is probably caused by

the dissolution of phyllosilicates, but the large excess of the

silica balance cannot be justified by it. The dissolution of

amorphous silica must be considered.

The quantification of the interactions between the solutes

and the soil matrix allows us to classify these mechanisms

according to their importance (Table 4). The dissolution of

both amorphous silica and calcite appear to be the major

mechanisms. The ionic exchanges Ca2þ with the monovalent

cations Naþ and Kþ and the dissolution of silicates are of

secondary importance. The dissolution of fluorite, although

clear, is a phenomenon of third importance, as is the dissolu-

tion of sulphate-rich aerosols. In both cases, the quantities of

fluorite and gypsum dissolved are small and quickly

exhausted.

Conclusions

The analyses of alkalinity, Cl–, SO42–, F–, Ca2þ, Mg2þ, Naþ,

Kþ and Si allow us to describe the major hydrogeochemical

processes that occurred inside microdune soils during rain.

Our main results can be summarized in three points.

1 The contribution of subsurface flow to the total water flow

(surface plus subsurface) was of secondary importance (about

5%). However, the proportion of solutes transported by the

subsurface flow was far from negligible (more than 20%).

30

20

10

∆Sili

con

/µM

0

0 10 20 30 40 50 60–10

10

5

0

∆Sul

phat

e /µ

M

0 10 20 30 40 50 60–5

120

80

40∆A

lkal

inity

/µM

0

0 10 20 30 40 50 60–40

6

3

0

∆Flu

orid

e /µ

M

0 10 20 30 40 50 60–3

20

10

0

∆Cal

cium

/µM

–10

0 10 20 30 40 50 60–20

20

0

10

–10

∆Mag

nesi

um /µ

M0 10 20 30 40 50 60

–20

5

10

0

–5

∆Sod

ium

/µM

–10

0 10 20

Time /minutes Time /minutes

30 40 50 60–15

100

40

60

80

20

0

∆Pot

assi

um /µ

M

0 10 20 30 40 50 60

Figure 7 Variations in time of reaction-

derived quantities, �i, Equation (3), of the

major chemical compounds in surface runoff.

Error bars determined using the method of

Genereux (1998).



Thus, subsurface water is always more concentrated than

runoff.

2 Both surface and subsurface chemical concentrations

decrease during rainfall simulation.

3 This decreasing trend is explained by soil–solution interac-

tions and by the mixing of old water (pre-event water) with

new water (demineralized event water).

225

150

75

∆Sili

con

/µM

0

0 10 20 30 40 50 60–75

20

10

0

–10

–20

∆Sul

phat

e /µ

M

0 10 20 30 40 50 60

200

100

∆Alk

alin

ity /µ

M

0

0 10 20 30 40 50 60–100

20

15

10

5

0

∆Flu

orid

e /µ

M

0 10 20 30 40 50 60

100

50

∆Cal

cium

/µM

0

0 10 20 30 40 50 60–50

50

100

0

∆Mag

nesi

um /µ

M

0 10 20 30 40 50 60–50

80

40

0

∆Sod

ium

/µM

0 10Time /minutes Time /minutes

20 30 40 50 60–40

75

50

25

–25

–50

0

∆Pot

assi

um /µ

M

0 10 20 30 40 50 60

Figure 8 Variations in time of reaction-derived

quantities, �i, Equation (3), of the major

chemical compounds in subsurface flow.

Error bars determined using the method of

Genereux (1998).

Table 4 Total amount of dissolved minerals (amorphous silica, calcite, fluorite and gypsum) and exchanged cations within surface runoff and

subsurface flow during the second simulation (Sim2)

Amorphous silica Calcite Fluorite Gypsum Exchanged Ca2þ

Type of water /�mol

Surface runoff 729 717 25 516 1691

Subsurface flow 1594 616 83 29 338



The results, though obtained in controlled experimental

conditions, accord with those of Biaou et al. (1999) for runoff

water at the outlet of a small catchment within the same study

area. The concentrations and tendencies were similar.

The mass-balance method highlighted points related to bio-

geochemical processes.

1 The chemical reactivity of the surface runoff is significantly

different from that of the subsurface flow.

2 Microdune soils appear to be reactive, with kinetic processes

generally rapid, as in the case of ionic exchanges.

3 Dissolution reactions are limited in intensity and in time

because the mineral stocks were exhausted during the simu-

lated rainfall. These reactions probably mobilize either efflor-

escences or less reactive minerals included in impermeable

aggregates.

4 The exchanges between calcium and monovalent cations on

the clay–humus complex are evidently important.

Our results contribute to a better understanding of the

abiotic relationships in pastoral Sahelian environments.

They emphasize particularly the contribution of runoff and

subsurface flow to the lateral redistribution of nutrients

within sandy aeolian deposits. These lateral transfers take

place from the heart of the microdunes, covered by vegetation

and enriched in organic matter, to the recent sandy deposits

around. This process improves the fertility of the peripheral

surfaces of the microdunes, and thus could contribute to the

regeneration of the soil if it were protected from overgra-

zing.

Finally, our data could be used to evaluate the predictive

capability of an existing transport model either using a tracer

in a water flow model or using a combined water flow reactive

transport model.

Acknowledgements

This work was supported by the Unite de Recherche 049

‘ECU’ of the Institut de Recherche pour le Developpement

(IRD), and the INSU Programme National Sol Erosion

(PNSE) project no 99/44. We acknowledge with gratitude the

Institut National de l’Environnement et de la Recherche

Agricole (INERA) of Burkina Faso for providing access to

the site. We also thank the team of the hydrological laboratory

of IRD in Ouagadougou, with a special mention for Barry

Moussa and Tou Boureima for their help in the field. Finally,

we thank the referees and the Editor for their constructive

contributions.

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Hydrochemistry of runoff and subsurface flow within Sahelian microdunes

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