Predicting plot-scale water infiltration using the correlation between soil apparent electrical resistivity and various soil properties

Physics and Chemistry of the Earth 36 (2011) 1033–1042

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Predicting plot-scale water infiltration using the correlation between soil apparent electrical resistivity and various soil properties Vincent Chaplot a,b,⇑, Graham Jewitt b, Simon Lorentz b a

Centre IRD d’Ile de France, 32, Avenue Henri Varagnat, 93143 Bondy Cedex, France SBEEH, Institut de Recherche pour le Développement, School of Bioresources Engineering and Environmental Hydrology, Rabie Saunders Building, University of Kwazulu-Natal, Private Bag X01, Scottsville 3209, South Africa

b

a r t i c l e

i n f o

Article history: Available online 26 August 2011 Keywords: Soil degradation Digital mapping Degraded pasture Soil erosion Spatial correlations

a b s t r a c t The identification of runoff source areas is essential for Integrated Water and Resources Management (IWRM). Although direct methods for the determination of steady-state water infiltration in soils (Inf) do exist, these are tedious and time-consuming. Geophysical techniques offer an alternative, however, geophysical data are often misinterpreted, especially in terms of the inter-relationships between soil apparent electrical resistivity (Rho) and Inf and several other soil physical or chemical properties. This paper evaluates the magnitude of the extend Rho measurements might allow prediction of Inf. This study was conducted in the Kwazulu-Natal province of South Africa where surface runoff arising from the steep slopes has a large impact in land degradation. Measurements of Rho with an RM-15 resistance meter were taken within a 10  30 m plot showing similar sandy-loam Acrisols but different proportions of soil surface coverage by plants (from 0–5% to 75–100%), depth to the clayey Bw horizon (D2B), top-soil (0– 0.1 m) water content (h) and bulk density (BD). There was a low correlation between Rho and Inf obtained under controlled conditions of rainfall (30 mm h1during 45 min) at fifteen 1 m2 micro-plots (r2 = 0.30). However, the correlation with the normalized Rho (Rhon) as if D2B, h, and BD were constant over the study plot and equal to their average value, was much higher (r2 = 0.66), pointing out the need to consider the complex and multiple correlations between soil properties and Rho in an attempt to map the spatial variations of Inf. Finally, the use of Rhon as a co-kriging co-variate appeared to significantly improve the short range spatial prediction of water infiltration in soils and thus IWRM implementation. Ó 2011 Elsevier Ltd. All rights reserved.

1. Introduction Water infiltration in soil is a key component of the hydrologic cycle and plays a major role in the hydrological functioning of landscapes. If the rainfall intensity is greater than the infiltration rate, water will accumulate on the surface and runoff will commence (Horton, 1933). However, if the rainfall intensity is less than the infiltration capacity, then there is the possibility of complete water infiltration contributing to either sub-surface runoff or the recharge of water tables. While most of the components of the hydrological cycle (for example, precipitation, water storage in impoundments or runoff) are relatively easy to measure, the measurement of water infiltration in soils remains more problematic (e.g., Huat et al., 2006). The evaluation of infiltration is even more challenging since the infiltration rate decreases with time until a ⇑ Corresponding author at: IRD, UMR Bioemco c/o, School of Bioresources Engineering and Environmental Hydrology, Rabie Saunders Building, University of Kwazulu-Natal, Private Bag X01, Scottsville 3209, South Africa. Fax: +27 33 260 58 18. E-mail address: [email protected] (V. Chaplot). 1474-7065/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.pce.2011.08.017

final, constant value, known as the steady water infiltration rate (Inf), is reached. There are a few direct and indirect methods to evaluate Inf. Some of the direct methods, such as those involving the use of disk permeameters or rainfall simulation are tedious and time consuming. Moreover, they are not suitable for use on sloping lands since these require the creation of an artificial horizontal soil surface that interferes with the natural soil hydrodynamic properties. Pouring large amounts of water onto the soil surface might also induce a rapid wetting of soil aggregates, inducing aggregate breakdown which in turn will sharply decrease Inf (Mamedov et al., 2001). In addition, these observations are not spatially representative (some dm2 is the maximum surface area of the measurement) of entire surface areas especially when these exhibit large variability of infiltration such as non-cultivated areas (Sisson and Wierenga, 1981). For this reason, the use of surface areas of more than 400 cm2 is usually recommended to provide representative measurements of Inf (Haws et al., 2004). Another commonly used method for estimating Inf is that of rainfall simulation. Based on the principle of spraying water onto the infiltrating surface (Peterson and Bubenzer, 1986; Ogden

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Nomenclature BD C CEC Clay Cov Crust CV D2B DR IR IReq1 IReq2 Hor. Max Min

the 0–0.1 m soil bulk density (g cm3) soil organic carbon content (g kg1) cation exchanges capacity (cmolc kg1) the soil clay content (%) the proportion of the soil surface covered by the vegetation (%) proportion of soil surface crusting (%) coefficient of variation (%) depth to the clayey B horizon the delayed runoff (mm) the soil infiltration rate (mm h1) estimated IR using SW, Crust, and D2B (mm h1) estimated IR using Rhon and Cov (mm h1) soil horizon maximum minimum

et al., 1997), this technique is used for surface areas ranging from 1 m2 to few m2. It has been successful in the estimation of the overall infiltration in a variety of environmental contexts (e.g., Valentin, 1978; Chaplot and Le Bissonnais, 2003; Podwojewski et al., 2008). However, although the spatial representativity of Inf is improved compared to disk infiltrometers, rainfall simulation is, along with surveys under natural rainfall (e.g., Chaplot et al., 2005, 2007), one of the most costly and time consuming method. In general, all of these in situ techniques are not only costly but they can also be very destructive. Furthermore, since only point observations are available, the spatial variability of Inf, especially in the case of large areas, is not possible. In addition, studies such as that of Vieira et al. (1981) where 1280 measurements were used to map the spatial variability of infiltration of an 8800 m2 area cannot be easily reproduced. Thus alternative in situ techniques to predict the spatial variation of Inf are required. A general principle of geophysical exploration is the non-intrusive collection of data on the material under investigation. Geophysical methods are also rapid, efficient, and relatively cheap to use, thus permitting the investigation of large areas. In relatively homogeneous materials in the vadose zone, electrical resistivity depends mainly on soil water content, electrical conductivity of pore water and soil porosity (e.g., Archie, 1942; Rhoades and van Schilfgaarde, 1976). Since soil conductivity depends on the concentration and the viscosity of the water and its concentration of dissolved ions (Scollar et al., 1990) geophysics has been used to monitor flow and dissolved ions transport mechanisms. Several studies based on electrical resistivity changes over time using electrical resistivity tomography have assessed preferential flow paths such as the study of Michot et al. (2003) in cropped fields of Western France; of Descloitres et al. (2008) in sand micro-dunes of Sahel; or of Zhou et al. (2001) in a research plot of Japan. But while these relatively costly studies have successively investigated the temporal changes in soil water distribution and preferential water pathways, more is to be done on the ability of geophysical techniques to predict the rate of Inf. Due to soil apparent resistivity (Rho) depending on many soil characteristics that vary simultaneously (e.g., Friedman, 2005; Samouëlian et al., 2005), direct interpretation of Rho data in terms of Inf is expected to be unreliable. Geophysical data are indeed still often misinterpreted especially in terms of their inter-relationships with a variety of soil properties including soil clay content (Williams and Hoey, 1987);

N nitrogen content (g kg1) pH pH water Quartile1 first quartile Quartile3 third quartile Rho the apparent resistivity (X m). Rhon normalized apparent resistivity (X m) Saf find sand (50–200-lm) (g kg1) Sac coarse sand (200–2000-lm) (g kg1) StDev standard deviation Sf fine silt (2–20-lm) (g kg1) Sc coarse silt (20–50-lm) (g kg1) S base saturation (%) S/T percentage of saturation in cations SW the 0–0.1 m soil water content (%) TRI the time to runoff initiation (s)

soil water content (Kalinski and Kelly, 1993; Michot et al., 2003), soil salt or nutrient content (Marion and Babcock, 1976; Eigenberg et al., 1998; Nissen et al., 1998) and soil depth (Chery, 1995; Chaplot et al., 2001). Chaplot et al. (2010) in degraded pastures of South Africa, showed, however, that a better understanding of the multiple relationships existing between Rho and selected soil properties significantly improved spatial estimates of the fertile A horizon thickness. The present study investigates to what extent Rho data gathered under dry soil conditions together with limited information on selected soil properties might allow accurate and cost-effective prediction of the spatial variations of Inf. We tested this research question in the sloping lands of the province of Kwazulu-Natal, a region of South Africa severely affected by overgrazing with direct consequences on water infiltration in soils, runoff and soil erosion. A research plot (10  30 m) showing both degraded and non-degraded pasture for which we expected spatial variations of Inf to occur along with spatial changes of soil clay content, soil bulk density, and soil water content was selected for this study.

2. Materials and methods 2.1. Description of the study site The study area was within the South African province of KwaZulu-Natal and is part of a 10 km2 watershed, located in the northern foothills of the Drakensberg Mountains in the headwaters of the Thukela basin (30,000 km2) (Fig. 1). The climate is temperate with most of the rainfall occurring in summer (October–March) as high intensity storms. At Bergville, 10 km to the east of the study area, the mean annual precipitation over the last 30 years is 684 mm, with a potential evaporation of 1600 mm and a mean annual temperature of 13 °C (Schulze, 1997). Altitudes range between 1080 and 1455 m. The relief is relatively smooth with a mean slope gradient of 15%. Slopes with shallow sandy soils are dedicated to cattle grazing (Kongo et al., 2007). Cattle play an important role in local Zulu livelihoods and since they are also an important cultural asset, there tend to be large numbers of animals. This, combined with highly acidic soils of low productivity, rapidly leads to overgrazing with dramatic consequences on soil degradation.

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Fig. 1. Location of the study plot within the Potshini catchment of South Africa. Digital Elevation Model, contour lines of relative altitude with a 0.2 m interval, and position of 1 m2 micro-plots for infiltration tests.

The study plot is a 300 m2 (10  30 m) area in a sloping land unit under pasture and stretches out along the steepest slope with a gradient of about 5% (Fig. 1) and exhibiting a gradation in the degree of soil coverage by vegetation (Fig. 3). At the northern limit of the plot, clear evidence of soil degradation with areas of bare soil and the presence of exposed roots are observed whereas downslope these surface features disappear and the soil surface is entirely covered by the vegetation. Analytical data of soil horizons computed from three soil profiles whose location is displayed in Fig. 1 are presented in Table 1. The soil is an Acrisol (WRB, 1998), brown at the soil surface (7.5 YR4/4) to reddish (5YR 4/6) at depth. The average A horizon thickness was 0.23 m with values between 0.16 and 0.30 m. The A horizon shows a fine granular structure and a sandy loam texture (53 < sand < 68% versus 17 < clay < 19%) (Table 1). The AB horizon is slightly depleted in clay (2%) while the concentration in clay particles increases to 21–22% in the Bw horizon of massive struc-

ture. The surface horizon is acidic (4.9 < pH < 5.3), with low concentrations of exchangeable cations (2.0–4.1 cmol(+) kg1) and organic carbon content (5.9–27 g kg1). 2.2. Mapping of soil apparent electrical resistivity Apparent electrical resistivity (Rho) measurement was performed just before the rainfall simulation using a resistance meter with a two-electrode ‘‘pole–pole’’ array. This is a theoretically interesting method since it permits the selection of the depth of the investigation and can be operated by a single person quickly. The RM-15 resistance meter with the MPX15 multiplexer and the multi-electrode frame PA20 Multiprobe array (Geoscan Research, Heather Brae, Chrisharben Park, Clayton, Bradford, West Yorkshire BD14 6AE, UK) were used. We selected a 1 m long beam with five electrodes separated by 0.25 m. Such a short distance between electrodes allowed the

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Table 1 Selected soil properties for three soil profiles located within the study plot (Fig. 1) from down-slope to upslope position. Location of profiles 1–3 are reported in Fig. 1. Hor.  Profile 1 A ABw Bw1 Bw2

Depth (m)

Clay (g kg1)

Sf

0–0.16 0.16–0.42 0.42–0.65 0.65–1.20

173 199 224 297

79 88 112 92

0–0.24 0.24–0.61 0.61–0.85 0.85–1.20

191 182 211 326

0–0.30 0.30–0.62 0.62–0.85 0.85–1.20

186 175 219 311

Sc

Saf

Sac

53 65 62 86

508 497 474 419

188 152 128 107

139 71 83 91

111 62 61 70

453 519 497 429

90 66 73 91

86 56 65 78

488 547 540 424

C

N

pH

CEC (cmolc kg1)

S

S/T (%)

BD (g cm3)

5.9 2.4 1.0 0

0.6 0.2 0.1 0

4.9 4.9 4.9 5.3

2.0 1.98 2.47 3.31

2.28 2.12 2.06 1.83

100 91 83 55

1.33 1.35 1.34 1.38

106 166 149 84

14.7 4.5 2.4 0.1

1.4 0.4 0.2 0

5.2 4.9 4.9 5.0

4.10 2.32 1.95 3.59

4.27 2.09 2.05 2.28

100 90 89 63

1.31 1.34 1.32 1.37

150 158 104 95

26.8 6.2 2.3 0.1

2.1 0.5 0.2 0

5.3 5.5 5.2 5.1

4.08 3.06 2.23 3.45

4.28 3.08 1.87 2.05

100 90 83 59

1.31 1.32 1.23 1.37

Profile 2 A ABw Bw1 Bw2 Profile 3 A ABw Bw1 Bw2

 Hor.: soil horizon; Clay: clay content (<2 lm); Sf: fine silt (2–20-lm); Sc: coarse silt (20–50-lm); Saf: find sand (50–200-lm); Sac: coarse sand (200–2000-lm); C: soil organic carbon content; N: nitrogen content; pH: pH water; CEC: cation exchanges capacity; S: base saturation; S/T: percentage of saturation in cations; BD: soil bulk density.

depth of investigation to be between 0.25 and 0.5 m which was a suitable depth of investigation for Inf studies. The operating frequency was set to 35 Hz. Moreover, a 1 mA current and a 100 V output were selected, giving a maximum logged resolution of 0.1 X m. A total of 2400 measurements were made on a 0.25  0.5 m grid all over the plot. Data were gathered at dawn on 1st July, just before the start of the rainfall simulation. The survey of the 300 m2 area took approximately 40 min.

2.3. The evaluation of soil characteristics The soil characteristics under study were 0–0.1 m soil water content (h), 0–0.1 m soil clay content (Clay), and 0–0.1 m soil bulk density (BD) that had previously been shown to have a significant correlation with Rho. Additional information on the depth to the Bw horizon (D2B) was recorded since differences in the clay content that may be associated with differences in the permeability have been observed between the surface A horizons and the Bw horizon. Determinations of h, BD, and D2B were performed just after the geophysical investigation and before the rainfall simulation. The sampling was performed within the 300 m2 plot and at 2 m outside at the nodes of a 2  2 m grid. Soil samples consisted of undisturbed soil cores (250 ml). One replicate per node was oven dried at 105 °C for 24 h for h and BD determination while a composite sample was collected and sieved to obtain the clay fractions (<2 lm; AFNOR, 1983). Based on the three soil profile observations, the soils are characterized by a superposition of brown sandy loamy A on a very reddish and clayey Bw. We used this color contrast to determine the depth to the Bw horizon at the 90 data points.

cially the range of soil surface coverage by vegetation from 5 to 95%. Artificial rains were applied under dry soil conditions from the 1st to the 5th of July. We selected the middle of the dry season (mid-July) as being under steady-state conditions of soil water content and soil surface conditions. Another reason for simulating rainfall during the dry season was to avoid possible soil water content drifts due to natural rains during the rainfall simulation experiment. At each micro-plot a 30 mm h1 rainfall intensity was applied for 30 min, resulting in a total rainfall amount of 15 mm. This rainfall event is typical of rainfall in the region with a return period of 1–3 years (Schulze, 1997). The rainfall was produced using the rainfall simulator manufactured by Capelec (1995) and based on the initial rainfall simulator built in the 1970s by ORSTOM and whose description can be found in Valentin (1978). The simulator comprised an oscillating nozzle (Teejet SS 6560) located 3.5-m above the plot. A valve and a pressure gauge to set water pressure were located at the same height as the nozzle, allowing precise control of water pressure and consequently the constancy of rain intensity and raindrops kinetic energy. At a water pressure of 40 kPa the estimated kinetic energy of the rain was 25 J m2 m1. Before applying the rainfall to the soil surface, the rainfall intensity was set with an error of less than 5% by covering the 1 m2 plots with an impervious surface. During the rain simulations, runoff was continuously recorded from the beginning of the rain to the end of runoff using graduated tubes and at a 1-s time resolution. The steady-state water infiltration in the soil (Inf) corresponded to the infiltration plateau reached about 15–20 min after the start of the rain. We used here infiltration data published by Podwojewski et al. (2011).

2.5. Statistical and geostatistical analysis 2.4. The evaluation of water infiltration in soils The evaluation of water infiltration in soils (Inf) was performed by using artificial rainfall at 15 micro-plots of 1 m2 installed within the 300 m2 area. The micro-plots were installed immediately after the geophysical and soil surveys. Their location was set to avoid the soil disturbance caused by soil coring and to encompass the different soil surface conditions observed within the plot, espe-

Descriptive statistics (minimum; maximum; average; median, standard deviation; coefficient of variation; skewness; and kurtosis) were computed for Rho, h, BD, Clay, D2B, and Inf data and the relationship between these variables was quantified using standard Pearson r-coefficients (use of STATISTICA software, StatSoft, Inc., 1996). A value of Rho, h, BD, Clay, D2B was assigned to each 1 m2 micro-plot by averaging the four closest neighbors. Since

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the operator took only 40 min to map the plot, we assumed that no significant variation of soil temperature and soil water content occurred from the beginning to the end of the survey. The spatial variations of Rho and of the soil properties within the 300 m2 research plot was obtained through interpolation between the observed data points. For the variables that were sampled intensively (n P 90 as for Rho, h, BD, Clay and D2B), the interpolation was performed using ordinary kriging (OK), a geostatistical method commonly used in the literature and that has been shown to perform better for soil parameters than other available methods (e.g., McBratney et al., 2003). OK predicts a value of a defined variable (var) at unknown locations (x0) such as grid nodes within the system domain (D) using information available elsewhere in D (x1, x2, . . ., xn). This is generally carried out by expressing var(xi) {where var(x): x2D} as a linear combination of the data var(x1), var(x2), . . ., var(xn), such that:

v arðxo Þ ¼

n X

ki v arðxi Þ

ð1Þ

i¼1

where ki is the kriging weight of the parameter value at var(xi) for n nearby sample points to be used in estimation. The optimal weight (ki) is calculated such that the estimation of var(x0) is unbiased and the sum of squares of error is minimized. Interpolations were performed using the GS+7.0 version of Gamma Design Software (2007). For Inf that was sampled at 15 data points only, the use of OK was not recommended (Matheron, 1965). We thus used the inverse distance weighting function with three neighbors of ArcMap9.1 (ESRI, 2004). Finally, a principal components analysis (PCA) evaluated the interrelationships between the variables. The PCA converts the actual variables into the so-called factors, or principal components (PCs), which are linear combinations of the actual variables, not correlated with each other (i.e., they are orthogonal) and together explaining the total variance of the data. Based on these linear combinations the projectors function (Table Ortho-normal option) of the ADE4 software (Chessel et al., 2004) was used to recalculate Rho as though h, BD, Clay, D2B were constant over the study plot and equal to their average values, a procedure successfully tested by Chaplot et al. (2010) to remove the undesirable ‘‘noise’’ in Rho data in an attempt to predict the spatial variations of the A horizon thickness.

tribution of Rho was highly peaked compared to the normal distribution (kurtosis = 6) and Rho data exhibited a standard deviation of 337 X m and a coefficient of variation (CV) of 21% which was relatively low. Greater Rho values were observed within a zone of about 5 m width lying parallel to the northern limit of the plot, but discordant with the elevation contour lines (Fig. 2). Some zones of much lower resistivity (<1000 X m) appeared within this area. Rho gradually decreased down-slope, reaching 950–1600 X m in the vicinity of the southern limit of the plot. However, some limited areas of higher Rho occurred in two areas in the middle of the plot. Plot-scale water infiltration (Inf) ranged between 1.6 and 30 mm h1 with an average at 8.7 ± 3.0 mm h1 and a CV being at 107% (Table 2). Inf gradually increased from 1 < Inf < 11 mm h1 at the northern part of the plot to the southern part where Inf exceeded 20 mm h1 (Fig. 3A). Another area of greater Inf (>20 mm h1) was observed at the mid-plot position close to the eastern plot border. Soils exhibited a high proportion of soil surface crusting (Crust) with an average proportion of 47.8%. The standard error (SE) was ±4.9 and Crust values were between 5 and 90% (Table 2). Greater Crust values occurred upslope the plot while lower values occurred downslope (Fig. 4A). The average depth to the Bw horizon (D2B) was 0.23 ± 0.24 m while the proportion of soil surface crusting averaged 47.8 ± 4.86%. High D2B values were observed both at downslope and middle positions (Fig. 4B). Soil bulk density (BD) varied between 1.20 and 1.53 g cm3 (Table 2). The median BD was found to be 1.38 g cm3 and the mean was at 1.36 g cm3 with a standard error (SE) of ±0.28 g cm3. Fifty percent of BD values were between 1.38 and 1.40 g cm3 and BD distribution was not skewed hence passed the normality test. From Fig. 4C there was no evident trend in the spatial distribution of BD data. The average soil water content (h) in the 0–0.1 m layer was 5.15 ± 0.95% with values between 3.76 and 7.19%. The median h was 5.14% with a first quartile at 4.42% and a third one at 5.44% (Table 3), lower h values being found in the northern part of the plot at the place of low soil surface coverage and high crust occurrence while lower h values occurred in the south of the plot with lower crust proportion (Fig. 4D). Finally, top-soil (0–0.1 m) clay content (Clay) averaged 15.7 ± 1.9% with values between 10.5% at the pit located upslope the plot and 21.7% at the downslope pit.

3. Results 3.1. Spatial variations of soil apparent electrical resistivity, plot-scale water infiltration and selected soil properties Rho data exhibited an average of 1576 X m with a standard error (SE) of ±18.4 X m and a CV of 21% which was of similar order than this of h, Clay, and D2B (Table 2). Rho data were positively skewed (Fig. 2) as illustrated by a median of 1548 X m which was lower than the average value (1624 X m). Moreover, the dis-

3.2. Relationship between soil apparent electrical resistivity, plot-scale water infiltration and selected soil properties Table 3 shows the level of correlation between the selected soil properties and the apparent resistivity. The most significant correlation occurred between Crust and D2B (r = 0.78), the depth to the Bw horizon decreasing as the proportion of crusts on the soil surface increased. Greatest pearson r coefficients occurred between soil water content (h) and soil bulk density (BD) (r = 0.70);

Table 2 General statistics for BD, the 0–0.1 m soil bulk density (g cm3); h, the 0–0.1 m soil water content (%); Clay, the 0–0.1 m soil clay content (%); D2B, depth to the clayey B horizon; Crust, the proportion of soil surface crusting; Inf, the steady-state water infiltration rate (mm h1), and; Rho, the soil apparent resistivity (X m). Statistics for BD, h, D2B, and Crust were computed from the 90 observations, 15 data points were used for the Inf, and 2500 data points for Rho.

BD h Clay D2B Crust Inf Rho

Min

Quartile1

Median

Quartile3

Max

Mean

StDev

SE

CV

1.20 3.76 10.5 0.15 5 1.6 1046

1.32 4.42 12.6 0.19 40 3.7 1358

1.38 5.14 15.0 0.22 42 4.7 1548

1.4 5.44 18.8 0.275 58 8.15 1650

1.53 7.19 21.7 0.34 90 30 3864

1.36 5.15 15.7 0.23 47.8 8.7 1576

0.08 0.9 3.6 0.06 23.6 9.3 337

0.28 0.95 1.90 0.24 4.86 3.05 18.36

6 17 23 25 49 107 21

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Fig. 2. Soil apparent resistivity (Rho) using a spacing between electrodes of 0.25 m. 2500 data points interpolated using ordinary kriging (OK).

Fig. 3. spatial distribution of the observed water infiltration in soils (Inf) (A) versus the estimated Inf using the normalized Rho (Rhon, B) as if D2B, Crust, Clay, h, and BD were constant over the study plot and equal to their average value. Data interpolated at the 15 micro-plots using inverse distance weighting.

between D2B, and Inf and Rho with r of respectively 0.66 and 0.66. Other significant correlations with Inf occurred for Crust (r = 0.58), the infiltration of water in soils decreasing as crusting increase, while for Rho, significant correlations occurred as well with soil clay content (r = 0.57), Crust (r = 0.55), and h (r = 0.51) (Table 3). Rho increased as D2B, Clay, and h decreased but decreased as the Crust increased and Inf increased as D2B increased and Crust decreased. The pearson r coefficient between Rho and Inf was 0.55 which was also significant. From Fig. 5A it ap-

pears that Rho data exhibited a high spot scattering at lower water infiltration in soils. Hence, Rho ranged between 1200 and 2450 X m at Inf lower than 10 mm h1 whereas the Rho amplitude decreased sharply to about 100 X m at Inf = 30 mm h1. Using Rho data to estimate Inf would, however, result in high prediction errors. We hypothesize here that discrimination between the respective contributions of the different soil properties on the level of Rho might allow improved Inf spatial prediction.

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Fig. 4. Spatial variations of the proportion of soil surface crusting, the depth to the clayey Bw clayey horizon; the 0–0.1 m soil bulk density; and the 0–0.1 m soil water content. Data points interpolated from 96 data points using ordinary kriging. Position of the 15 observations of water infiltration in soils.

Table 3 Correlation between the soil properties and the apparent resistivity (Rho) and the normalized Rho (Rhon).

BD h Clay D2B Crust Inf Rho Rhon *

BD

h

Clay

D2B

Crust

Inf

1 0.70* 0.58* 0.61 0.39 0.39 0.23 0.05

1 0.59* 0.58 0.65* 0.36 0.51* 0.04

1 0.41 0.19 0.07 0.57* 0.03

1 0.78* 0.66* 0.66* 0.15

1 0.58* 0.55* 0.25

1 0.55* 0.81*

Marked correlations significant at p < 0.05.

Such discrimination was performed using multivariate analysis. Here we used a principal component analysis (PCA) generated using soil properties of D2B, Crust, Clay, h, and BD as primary variables (Fig. 5). The first PCA axis explained 45% of the total variance. This axis showed a trend associated with D2B, BD, Crust and h. Positive values on this axis corresponded to higher h and D2B while negative values corresponded to high Crust and BD values. The second PCA axis that explained 22% of the variability of data opposed clayey soils to sandy soils. Inf. displayed in Fig. 5 as secondary variable highly correlated to axis one which can be interpreted as a soil degradation axis. The PCA matrix that quantifies the interrelationships between the variables under study was used to recalculate Rho as if D2B, Crust, Clay, h, and BD were constant over the

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A

2400

B

2200

1800 r²=0.30

1600

Inf=1705.9-14.18×Rhon

Rhon (ohm.m)

Rho (ohm.m)

2000

r²=0.65

1400 1200 1000 0

5

10

15

20

25

30

Inf (mmh-1)

35

0

5

10

15

20

25

30

35

Inf (mmh-1)

Fig. 5. Observed water infiltration in soils at the 15 micro-plots versus observed Rho (A) and the normalized Rho (Rhon, B) as if D2B, Crust, Clay, h, and BD were constant over the study plot and equal to their average value.

study plot and equal to their average value (use of the projectors function, Table Ortho-normal option, of the ADE4 software, Chessel et al., 2004). The recalculated or normalized Rho (Rhon) exhibited a much lower spot scattering than that of Rho and the r coefficient between Rhon and Inf of 0.81 was highly significant (Table 3) and a much lower spot scattering around the fitting regression (Fig. 5B). If it was assumed that the selected soil properties were constant over the study plot then there was a correlation between Rho and Inf thus making possible the accurate mapping of Inf. Indeed, the interpolated map of estimated Inf using Rhon and the fitting equation found in Fig. 5B showed a similar trend to that of the observed Inf map (Fig. 5A) but displayed greater mapping details.

4. Discussion The average steady-state water infiltration in soils (Inf) within the research plot was 8.7 mm h1. This was close to values commonly found for inter-tropical grassland soils from the tropics (Valentin and Bresson, 1992). But surprisingly, Inf estimated from 1 m2 micro-plots was highly variable on the study plot as exhibited by values between 1.6 and 30 mm h1 and a CV of 107% and this was quite a surprising result on an area of only 300 m2. Van Es et al. (1991), for example, found a coefficient of variation of 69% for Inf in semi-arid range-land soils but on a much larger area of 2.2 ha. Haws et al. (2004) using 1 m2 plots similar to those used in the present study found water infiltration values between 7.9 and 108.5 mm h1, while Seyfried (1991) studying 10  3 m plots with shrubs separated by bare inter-space areas, found that infiltration near the shrubs was about three times greater than that of the adjacent inter-space areas. This is typically what has been found in this study with the lowest Inf occurring under bare and crusted soil surfaces while the highest Inf corresponded to higher proportions of soil surface coverage by the grass. Inf values of less than 5 mm h1 characterized soils showing both high crusting proportion together with features of soil interrill erosion such as exposed roots which was consistent with observations made by Casenave and Valentin (1992) where Inf under such soil surface conditions has been shown to be between 0 and 4 mm h1. Such high spatial variations of Inf might thus be related to these of soil surface crusting as suggested by a significant Pearson r coefficient

between these two variables. Greater Inf values for more vegetated soils with lower proportion of crusts are consistent with the fact that biological features such as stems highly favor infiltration (e.g., Rachman et al., 2004). Inf was shown to significant increase as the thickness to the clayey Bw horizon increased. This interesting result might be explained by the greater permeability of the surface A horizon than the below Bw horizon of lower organic matter content and higher clay content and bulk density (WRB, 1998) thus favoring both vertical and lateral sub-surface water fluxes. Under bare soils, water pathways might be entirely or partially filled by sediments close to the soil surface thus limiting their infiltration efficiency (Swartzendruber and Hogarth, 1991). Soil apparent resistivity (Rho) significantly correlated with soil water content, soil clay content, and this, in keeping with the literature, was expected (e.g., Rhoades and Corwin, 1990; Samouëlian et al., 2005). Electrical current in soils is mainly electrolytic, i.e., based on the displacement of ions in pore-water, thus electrical current in soils depends on the amount of water in the pores and on its quality. In most studies concerning the water content, the electrical conductivity of the solution is assumed to remain relatively constant to be neglected against its variation related to water content variation (Fukue et al., 1999; Michot et al., 2003; McCarter, 1984). Moreover, Samouëlian et al. (2005) and Butler et al. (1994) indicated that greater soil porosity results in higher electrical resistivity, this principle having been used by Robain et al. (1996) to discriminate between soil horizons with high and low porosity. Our findings, i.e., a positive correlation between Rho and the thickness of surface porous horizons are consistent with this. Finally, the correlations between soil resistivity and soil salinity not assessed here because of low content of salts in soils would have to be investigated at sites with higher salt concentration (Rhoades and van Schilfgaarde, 1976; Rhoades and Corwin, 1990). A similar statement can be made for soil temperature, considered in the present study to have remained constant during the duration of the survey. The existence at the study site of multiple correlations between, on the one hand, Rho and, on the other hand, several soil properties, confirmed the difficulty of using geophysical methods to directly record the variations of a single soil property (Corwin and Lesch, 2003) and, in this case, to accurately predict Inf. Here we

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show that the soil apparent resistivity normalized by D2B, Crust, h, and BD, correlated significantly with Inf. Using Rhon in classical regressions appeared effective in the identification of the spatial trend in Inf. The generated map correlated with the map interpolated from the 15 data observations of Inf while allowing greater details in the short range variations. 5. Conclusions This study examined the correlation between near surface ground electrical resistivity and water infiltration in soils for improved Integrated Water and Resources Management (IWRM). Soil apparent resistivity (Rho) data obtained with a portable resistivitymeter in a 300 m2 study field plot were compared with soil steadystate infiltration rate (Inf) obtained at fifteen 1 m2 micro-plots using simulated rainfall events. The greatest correlations were found between Rho and soil water content, soil clay content, soil bulk density, and depth to the Bw clayey horizon while little correlation was found with Inf. The use of the soil apparent resistivity normalized by the soil water content, soil clay content, soil bulk density, and depth to the Bw clayey horizon appeared to be an accurate and cost-effective method to predict the spatial variations of Inf. But since Rho might vary both spatially and temporally as might the soil water content, mapping Inf at other sites would require new statistical relationships to be established. The potential value of the empirical relationship between the easily obtainable soil apparent resistivity and the soil properties for the mapping of water infiltration and for IWRM implementation seems, however, limited since soil apparent resistivity is time dependent, and is, for instance, highly sensitive to the recent history of rainfall and soil temperature, thus requiring new statistical relationships to be established. But this might be overcome by integrating such a procedure to existing mathematical function of numerical modeling inversions such as those based on moment–method modeling (e.g., Tabbagh, 1985) or finite-difference approximation (e.g., Oldenburg and Li, 1994). Acknowledgments The authors are grateful to the Farmer Support Group of UKZN for facilitating research at the Potshini catchment and to the Potshini community for allowing access to the research site. This study was supported by the South African/French cooperation (PROTEA program) and the Water research Commission of South Africa. References AFNOR X31-107. 1983. Analyse granulométrique par sédimentation. In Qualité des sols, AFNOR, Paris, pp. 357–371. Archie, G.E., 1942. The electrical resistivity log as an aid in determining some reservoir characteristics. Trans. Am. Inst. Min. Metall. Pet. Eng. 146, 54–62. Butler, J., Roper, T.J., Clark, A.J., 1994. Investigation of badger sets using soil resistivity measurements. Zool. Soc. Lond. 232, 409–418. Capelec. 1995. Simulateur de pluie. 126, rue Emile Baudot – Le Millénaire34000 MONTPELLIER – France. Casenave, A., Valentin, C., 1992. A runoff capability classification system based on surface features criteria in semi-arid areas of West Africa. J. Hydrol. 130, 231– 249. Chaplot, V., Le Bissonnais, Y., 2003. Runoff features for interrill erosion at different rainfall intensities, slope lengths and gradients in an agricultural loessial hillslope. Soil Sci. Soc. Am. J. 67, 844–851. Chaplot, V., Walter, C., Curmi, P., Hollier-Larousse, A., 2001. Mapping field-scale hydromorphic horizons using Radio-MT electrical resistivity. Geoderma 102, 61–74. Chaplot, V., Rumpel, C., Valentin, C., 2005. Water erosion impact on soil and carbon redistributions within the Mekong basin. Global Biogeochem. Cy. 19, GB4004, doi:10.1029/2005GB002493. Chaplot, V., Khampaseuth, X., Valentin, C., Le Bisonnais, Y., 2007. Interrill erosion in the sloping lands of northern Laos submitted to shifting cultivation. Earth Surf. Proc. Land. 32 (3), 415–428.

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