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Chemical Engineering Journal 191 (2012) 13– 21

Contents lists available at ScienceDirect

Chemical Engineering Journal

j ourna l ho mepage: www.elsev ier .com/ locate /ce j

oil water infiltration measurements using electrical impedance tomography

.A. Gutiérrez Gnecchia,∗, A. Gómez-Tagle Chávezb,1, G.M. Chávez Camposa,2,.H. Olivares Peregrinoa,2, E. Marroquin Pinedaa,2

Instituto Tecnológico de Morelia, Departamento de Ingeniería Electrónica, Avenida Tecnológico 1500, Col. Lomas de Santiaguito. C.P. 58120, Morelia, Michoacán, MexicoINIRENA, Universidad Michoacana de San Nicolás de Hidalgo, Instituto Nacional de Investigaciones Sobre los Recursos Naturales, Departamento de Ciencias de la Tierra,aboratorio de Suelos, Av. Sn. Juanito Itzícuaro SN, C.P. 58330, Morelia, Michoacán, Mexico

r t i c l e i n f o

rticle history:eceived 23 April 2008eceived in revised form 26 February 2010ccepted 16 March 2010

eywords:oil hydraulic conductivitylectrical impedance tomographyultimodal infiltrometerater transport phenomena

a b s t r a c t

Characterizing hydraulic properties of different types of soils is of great importance for agricultural andgeological studies. In situ measurement of soil hydraulic conductivity is a time-consuming task since datahas to be recorded at fixed time intervals over a period of minutes or hours depending on the type ofsoil. Moreover, it is still necessary to use real data for validating models. This paper presents the designof a multimodal infiltrometer which consists of an automated soil infiltration single-ring measurementdevice and Electrical Impedance Tomography (EIT) data acquisition system. A data logger measures theliquid level change of the 100 cm Marriotte-type column, as the water infiltrates at a rate by maintaininga small positive pressure on the water as it moves out of the infiltrometer. A phantom fitted with 16 plate-type electrodes is embedded in the measurement site underneath the water outlet for EIT measurements.The automated infiltrometer data is used for determining soil saturated hydraulic conductivity whereasthe EIT measurement system produces images of the water movement through the material below the

land surface whose water saturation fluctuates. The equipment was tested in laboratory and in situ trialson four different types of soil (silt loam, clay loam, silty clay and sandy loam) around the Cuitzeo lakewatershed (19◦58′N, 101◦08′W). The results suggest that EIT can be used to image the dynamics of waterinfiltration of different types of soils, and the automated infiltrometer can provide better information thatvisually recorded measurements. Thus, a set of images can be used for measuring drainage properties aswell. This has important implications for improving computer modelling and soil characterization.

. Introduction

Knowledge of the soil hydraulic conductivity is one of the mostmportant requirements for the correct characterization of soilroperties for irrigation and environmental services. Amongst theost common devices for measuring hydraulic conductivity are

he disc infiltrometer and the ring infiltrometer. A number of ringnfiltrometers have been patented over the last 50 years and theurrent trend is focused towards the use of automated equipment1–6]. Infiltrometers measure the rate of water infiltration by main-

aining a small positive pressure on the fluid, while the liquid exitshe container. It is common to find that the analysis of resultingata is based on a circular source flow model [7,8]. For each test,

∗ Corresponding author. Tel.: +52 4433121570x270/248;ax: +52 4433121570x211.

E-mail addresses: agnecchi@itmorelia.edu.mx, angugi98@netscape.netJ.A. Gutiérrez Gnecchi).

1 Tel.: +52 443 3 27 23 50x109; fax: +52 443 3 27 23 51.2 Tel.: +52 443 3121570x270/248.

385-8947/$ – see front matter © 2010 Elsevier B.V. All rights reserved.oi:10.1016/j.cej.2010.03.023

© 2010 Elsevier B.V. All rights reserved.

the constant infiltration rate is used for calculating the hydraulicconductivity, K, for a given tension, H. The saturated hydraulic con-ductivity can then be obtained by using a number of methods suchas the Maulem–Van Genuchten [9,10], the Wu1 [11] and Vander-vaere [12] methods using transient phase data and the Wu2 methodusing steady-state data [11]. Although the use of infiltrometers isa common practice, modifications and new designs are continu-ously reported, that intend to reduce the effect of systematic errors[13], improve accuracy [14] and aid mathematical modelling [8].Soil hydraulic conductivity depends on a number of factors such assoil content and texture [15,16], vegetation root hardness [17,18],soil preparation [19,20], chemical content [21] and soil temper-ature and weather conditions [22,23]. Given the nature of themeasurement technique (i.e. water through a soil sample) Elec-trical Impedance Tomography (EIT) could also be used to measurethe dynamics of water transport through a soil sample [24]. In orderto gather as much information on the infiltration process a num-

ber of multimodal sensors have also been reported [25,26]. Still,knowledge of the dynamics of water transport is still scarce anda number of questions arise after examining experimental results[27]. Therefore, the authors propose the use of EIT to measure the

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ynamics of water transport through a soil sample, together withn automated ring infiltrometer, which could prove useful for soilharacterization and mathematical modelling.

. Apparatus and EIT equipment

.1. Infiltrometer

Fig. 1 shows the schematic diagram of the single-ring infiltrom-ter built for this work.

The container is a 1-m long Perspex pipe. The transparent naturef the container allows the comparison of visual measurementsith those obtained with the pressure transducer. The 100 cm

ontainer is filled with water up to the 85 cm mark. A 10 kPa dif-erential transducer (MPX2010DP) measures the change of heightf the water level as liquid exits the container. The differentialrray also intends to reduce the errors associated with the bub-ling effect which occurs when the water leaves the container. Aalve is used for starting/stopping the experiment. The infiltrom-ter uses a plastic pipe, instead of the usual ring array, where theing is directly attached to the container, to allow the investigationf soil hydraulic conductivity properties around the location of thenfiltrometer, without moving the container. The ring can then benserted in the soil at different depths ranging from 5 to 15 cm.n EL-USB3 data logger captures the data corresponding to pres-ure changes, when the liquid is allowed to exit the infiltrometer.he data logger accepts a voltage range from 0 to 30 V; a low-

ower (<700 nA) DC–DC converter and signal conditioning circuitas built to provide the 30 V to feed the instrumentation amplifier

o adjust the output signal range from 0 to 30 V corresponding to aater column height of 0–100 cm (Fig. 2).

ig. 1. (A) Field installation and (B) Schematic diagram of the single-ring infiltrometer anB.iii) Graduated scale, (B.iv) Differential pressure transducer. (B.v) Stand. (B.vi) Plastic pi

Fig. 2. Block diagram of the pressure tran

ineering Journal 191 (2012) 13– 21

2.2. EIT data acquisition system

Fig. 3 shows a simplified schematic diagram of the EIT dataacquisition system.

The data acquisition card (Data Translation DT series) controlsall the tasks involved in the acquisition of EIT measurements.The card is programmed to generate a 24-point/cycle analoguesinewave, which in turn feeds a band pass filter. Given the max-imum output update rate of the card, it is possible to generatesinewave excitation signals from DC up to 48 kHz. The resultingsignal drives the voltage-to-current converter. An analogue multi-plexer array selects the current injection electrode pair as well asthe electrode measurement pair. The multiplexer array also permitsthe use of the adjacent-electrode and opposite-electrode currentinjection techniques. A differential amplifier stage provides ampli-fication and offset correction before the measured signal is digitizedthrough the analogue input. The digital output signals control theVCCS gain, electrode selection (current injection and voltage mea-surement), and the differential amplifier’s gain.

2.3. Phantom

The EIT electrode array consists of 16 plate-type electrodeslocated around the periphery of a PVC pipe (Fig. 4).

The stainless steel electrodes are attached equidistantly aroundthe periphery of 5′′ diameter PVC pipe. The current excitation andvoltage measurement signals are carried by coaxial cables. The use

of a PVC pipe constitutes a robust, low-cost implementation of theEIT sensor array, which can be used in laboratory trials as well as forfield experimentation. Since the infiltration measurement processintends to disturb the soil location as little as possible, the sensor

d EIT measurement setup. (B.i) The reservoir is a perspex pipe. (B.ii) Tension tubing.pe. (B.vii) Valve, (B.viii) Ring. (B.ix) EIT phantom with 16 electrodes.

sducer signal conditioning circuit.

J.A. Gutiérrez Gnecchi et al. / Chemical Engineering Journal 191 (2012) 13– 21 15

Fig. 3. Block diagram of the EIT

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ig. 4. Schematic diagram of the EIT sensor showing electrode location and geom-try.

rray can be buried and left unattended in remote locations withittle concern for theft and destruction as far as cost is concerned.urying the sensor array, intends to allow the soil to settle, andttain the properties of the surroundings after a period of time.

.4. Image reconstruction and display software

Throughout this work, the adjacent-electrode current injection

ethod was used, yielding 104 measurements for a 16-electrode

rray per data frame. Images are reconstructed using the weightedensitivity back-projection algorithm (Fig. 5A) as described else-here [28].

ig. 5. Flow diagram of the data acquisition, image reconstruction and enhancement algnd reconstruction and (C) choices for image enhancement and display.

data acquisition system.

Three different FEM mesh sizes can be selected for image recon-struction: 104, 464 and 1016 triangular elements (Fig. 6).

The graphical output of the qualitative reconstruction algorithmprovides a crude, limited-resolution interpretation of the recon-structed image (Fig. 7A).

The reconstructed data (Fig. 7B) correspond to the values locatedat the centroid of each triangular element. Therefore, it is com-mon practice to use some post-reconstruction image processingtechnique in order to improve the appearance of the reconstructedimages for image analysis and feature extraction. In the simplestof cases which might involve a binary image or an image of excep-tional contrast, it is possible to isolate an object of interest merelyby measuring the difference between the object’s intensity and thatof the background. However, most EIT images preclude the use ofsuch a simple scheme for a number of reasons. It is very often thecase that the background intensity level is not constant but is spa-tially variant. In addition, the edges of the different conductivityregions are not continuous, that is, their contrast varies accordingto the conductivity distribution. Alternatively, using image smooth-ing (Fig. 7C) and contour tracing procedures (Fig. 7D) can provide

additional information on the overall intensity of the image, andwould facilitate extracting features of particular interest. A discreteapproximation to the conductivity distribution can be obtained byinterpolating the data in a predefined grid. For the two-dimensional

orithm. (A) Initialization acquisition of reference frame, (B) data frame acquisition

16 J.A. Gutiérrez Gnecchi et al. / Chemical Engineering Journal 191 (2012) 13– 21

Fig. 6. The program provides three different mesh configurations for EIT image reconstruction: (A) 104 triangular elements and 69 nodes, (B) 464 triangular elements and265 nodes, and (C) 1016 triangular elements and 541 nodes.

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ig. 7. Reconstructed image post-processing procedure. (A) The results from the Eriangular element. Further processing is required to enhance the quality of data ob

ase, the task is to find intermediate points where the bilinearnterpolators represent the distances from the location of inter-olated value to the four neighbouring points. The radial basisunction network (RBF) [29,30] is an alternative approach, basedn the assumption that an arbitrary function can be approximateds the linear superposition of a set of localized basis functions. TheBF network originated from techniques for exact interpolationetween data points in high dimensional spaces, which consist of

inear superposition of basis functions, one for each data point [31].here are many possible forms for the basis functions; the Gaussianunction is a common choice. In order to ensure a smooth intercon-ection of neighbouring points various heuristic methods can besed. The fastest and most straight forward approach consists ofhoosing the centres of the Gaussian functions to be equal to someubset of the input vectors. The interest is to minimize the compu-ational time involved and can be done by generating a grid, andsing the intersection points as the centres of the basis functions.ow, the width of the Gaussian function is set by choosing to bequal and to be given by the average maximum distance betweenhe basis functions centres, thus ensuring that the Gaussians over-ap up to some degree and give a smooth representation of theistribution of the input data. This approach is very fast since thesealues have to be calculated just once [32]. Alternative methods cane used to obtain the basis function parameters, such as the K-meanlgorithm to obtain the centres. However, it is an iterative processnd even though it may converge within a few iterations, a sin-le back substitution solution is preferred. The RBF algorithm used

n this work for EIT interpolation is derived from a method usedor on-line analysis of dynamic images of hydrocyclone [32] andontrol of a LARCODEMS [33] pilot-scale plant. The image recon-truction software allows the display of the reconstructed images

able 1hemical properties of the water used for EIT experiments.

pH Conductivity (mS/cm) Anion (mg/l)

CO32− HCO3

− Cl2

6.37 216.1 0 67.1 47

ge reconstruction algorithm correspond to (B) data located at the centroid of each by means of (C) interpolation and (D) contour tracing procedures.

directly (raw data), bilinear interpolation or RBF interpolation forcomparison (Fig. 5C).

3. EIT measurements of clay soil samples

There are three basic water content measurement methods:gravimetric, nucleonic and electromagnetic techniques [34]. One ofthe preferred electromagnetic sensing methods since the 1970s, forin situ volumetric soil water content measurements, is TDR (TimeDomain Reflectometry) [35,36]. In addition, capacitance methodshave gained popularity because they also permit obtaining mea-surements in situ [37]. The TDR method uses a waveguide (i.e.electrodes buried in the test site) to propagate a short-durationelectromagnetic pulse, in the gigahertz band. The propagationproperties of the pulse are affected by the dielectric (Ka) sur-rounding the waveguide. Since there is a considerable differencebetween the dielectric properties of air (Ka = 1), soil particles andminerals (Ka = 2–4) and water (Ka ≈ 80), pulse propagation changescan be directly related to the water content of the sample wherethe electrodes are inserted: the higher the dielectric constant,the slower the propagation time [35]. Capacitance methods alsoexploit the changes of soil dielectric properties as a function ofthe water content. Alternatively, low-frequency (<100 MHz) soilimpedance measurements can also be used for soil water con-tent measurements. At low frequencies, soil temperature andconductivity properties greatly influence the resulting impedancemeasurements, and require, at least, of some form of temperature

compensation [38].

Alternatively, qualitative EIT could be used for providing imagerelated to the dynamics of the infiltration process. Although qual-itative EIT does not provide the exact conductivity values of the

Cation (mg/l)

− SO42− Na+ K+ Ca+ Mg+

.92 19.2 8.39 2.8 42 39.6

J.A. Gutiérrez Gnecchi et al. / Chemical Engineering Journal 191 (2012) 13– 21 17

Fig. 8. Sequence of images of the phantom filled with clay up to 1 in. above the electrode array. Water was poured into the container at 1 in. from electrodes 5 and 6. Resultsare presented after 60 s (Image 1), 120 s (Image 2), 180 s (Image 3) and 240 s (Image 4).

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ple using the gravimetric method). The EIT system was adjustedto produce a 1 mA, 25 kHz current excitation signal. 50 cycles permeasurement were obtained at 1 MSPS (millions of samples persecond). One litre of water was poured at 1′′ from the periphery

ig. 9. Comparison between the bilinear and RBF interpolation procedures. Resultnd (B) 50 × 50 grid size (resolution: 2% of space). Result of the RBF interpolation pr

ample, the resulting images can potentially be of use for measur-ng the hydraulic conductivity properties of soil since the methodelies in the changes of conductivity with respect to a base (ref-rence) image. Hence the rate of change of pixel intensity overross-section can be used to visualize and measure the dynamicsf the water infiltration process. It has been shown that EIT can besed to provide images of water propagation through sandy soilince highly porous media can allow water to penetrate the samplend thus provide a conductive path for the excitation current [39].owever, the use of electromagnetic sensing methods for measur-

ng the soil water content of clay samples could be a difficult task toccomplish, even for TDR: tightly compressed particles impede theransport of water evenly through the sample; cracks in the samplean reduce the contact area between the electrodes and the sample.herefore, a laboratory experiment was conducted to determine ifn EIT system, which operates at a lower frequency than the TDRethod (namely 25 kHz), can provide useful information on theater infiltration process in clay-type soil samples. Table 1 shows

he chemical properties of the water used for the experiments.

The phantom was filled with fine sandy clay loam soil (67% clay,

6% loam, 17% sand) up to 1′′ above the electrode array. The soilample was tightly compressed. The initial humidity content of theample was measured to be 18.1% (data obtained from a 200 g sam-

bilinear interpolation procedure using (A) 30 × 30 grid (resolution: 3.3% of space)re using (C) 30 × 30 grid and (D) 50 × 50 grid.

Fig. 10. Typical water infiltration process measurement (transient and steady-state).

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8 J.A. Gutiérrez Gnecchi et al. / Chemic

etween electrodes 5 and 6 (located at 3 and 4 o’ clock positions).IT images were reconstructed using the modulus of the measuredoltage signals. Fig. 8 shows a sequence of images obtained at 60,20, 180 and 240 s after the experiment started.

The sequence of images clearly shows that the EIT system can

rovide information on the infiltration process. In addition, a com-arison was made between the bilinear and RBF interpolationrocedures to determine which method can provide better images.ig. 9 shows images of the clay soil sample enhanced by the bilinear

ig. 11. Summary of the results for clay-type soil measurements. (A) Comparison of soil hmages of the infiltration process at (B.i) 440 s, (B.ii) 880 s, (B.iii) 1320 s, (B.iv) 1760 s, (B.vo the transient phase.

ineering Journal 191 (2012) 13– 21

and RBF interpolation procedures with 30 × 30 and 50 × 50 inter-polation grids. The results indicate that using the RBF method witha 50 × 50 interpolation grid can provide better images than thebilinear method.

4. In situ experimental data

In general, the infiltration process can be divided in two phases:transient and steady states (Fig. 10).

ydraulic conductivity measurements from visual and automatic measurements. (B)) 2200 s, (B.vi) 2640 s, (B.vii) 3080 s, (B.viii) 3520 s and (B.ix) 3960 s, corresponding

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The EIT measurement system was used to produce images dur-ng the transient phase. Once the hydraulic conductivity processas reached the steady state, the soil in the field of vision of theIT system is saturated. Calculation of the hydraulic conductivityequires measured data during the steady state. Infiltration dataan now be calculated using the Wu2 method [11], so that a linearquation:

= At + c = afKst + c (1)

an be fitted to infiltration data, and the saturated hydraulic con-uctivity, Ks, can be calculated from:

s = A

af= A

a(((H + (1/˛))/(Indepth + (r/2)) + 1)(2)

here A is the slope, c is the intercept from the linear regressionf measured data, a and b are dimensionless constants (a = 0.9084,

= 0.1682), H is the ponded depth in the ring, Indepth is the insertionepth of the ring, r is the ring radius = Ks/ϕm and ϕm is the matricux potential.

After the laboratory trials were completed, the equipmentas used for measuring the hydraulic conductivity of 4 differ-

nt test locations around the Cuitzeo Lake watershed (19◦58′N,01◦08′W): silt loam (Umécuaro, 19◦31′6′′N, 101◦14′15′′W), clay

oam (Cointzio, 19◦38′6′′N, 101◦16′55′′W), silty clay (Valle detécuaro, 19◦36′12′′N, 101◦11′34′′W), and sandy loam (Volcán de

orullo, 18◦56′49′′N, 101◦43′23′′W).Fig. 11 shows the results of the experiments in clay-type soil

n a corn field located near Atécuaro, Michoacán, México (19◦36′N,01◦11′W). The field is located at the end of a 5◦ slope. The soil con-

ents were 67% clay, 17% lime and 16% sand. The initial humidityas 19.1%, measured with a CS616 TDR from Campbell Scien-

ific, and the soil temperature was 16.5 ◦C. The data logger wasdjusted to take 1 sample per second. The EIT system was adjusted

ig. 12. Summary of the results for sandy soil measurements. (A) Comparison of soil hyrojection of the images obtained, corresponding to the infiltration process at (B.i) 120 s,ater going through the soil.

ineering Journal 191 (2012) 13– 21 19

to produce a 2.5 mA, 25 kHz current excitation signal. 50 cycles permeasurement were obtained at 1 MSPS (millions of samples persecond). Visual observations (water column height change) wererecorded every 2 min for comparison (Fig. 11A).

The EIT images show the propagation of the water through thesoil sample during the transient phase. The ring infiltrometer wasoperated without a rain simulator so that the water beings propa-gating through the sample towards the centre of the phantom. Asthe experiment progresses, the water occupies the measurementvolume (Fig. 11B.ix). Fine particles compressed tightly allow littleroom for the water to propagate through the sample, and thus ittakes up to 2 h to characterize the sample. In contrast, coarse parti-cles form a more porous media which facilitates the propagation ofwater through the sample. Fig. 12 shows the images obtained from asandy loam test site (Volcán de Jorullo, 18◦56′49′′N, 101◦43′23′′W).The initial humidity was 16.1%, measured with a CS616 TDR fromCampbell Scientific, and the soil temperature was 18.5 ◦C. The datalogger was adjusted to take 1 sample per second. The EIT systemwas adjusted to produce a 1 mA, 25 kHz current excitation signal. 50cycles per measurement were obtained at 1 MSPS (millions of sam-ples per second). Visual observations (water column height change)were recorded every 24 s for comparison (Fig. 12A).

Images of the infiltration process were obtained (Fig. 12B.i–vi)during the first 10 min of the experiment. After 10 min the elec-trode plane region is saturated with water as the infiltration processapproaches steady state. Nevertheless, a projection of the imagesequence can be used to build a 3D representation of the watermigration process through the sample, during the infiltration tran-sient state (Fig. 12B.vi). After testing the multimodal measurement

system on different soils, the results suggest that EIT can provideuseful images of the infiltration process for the four types of soilstested from sandy soil to clay-type soil. Table 2 shows a summaryof the hydraulic conductivity values obtained for the four test sites.

draulic conductivity measurements from visual and automatic measurements. (B) (B.ii) 240 s, (B.iii) 360 s, (B.iv) 480 s and (B.v) 600 s; (B.vi) 3D reconstruction of the

20 J.A. Gutiérrez Gnecchi et al. / Chemical Engineering Journal 191 (2012) 13– 21

Table 2Summary of the hydraulic conductivity values obtained for the four test sites.

Test site

Silty clayAtecuaro

Clay loamCointzio

Silt loamUmecuaro

SandyloamJorullo

Hydraulic conductivity (mm/h), Ks

Data logger5.81 107.04 49.65 2282.2

Standard deviationData logger

1.67 45.26 21.72 429.98

Hydraulic conductivity (mm/h), Ks

Visual observations12.14 155.56 68.31 1671.82

Standard deviationVisual observations

3.44 47.05 42.85 793.56

Number of trials, N 7 7 5 5Average initial moisture content �i 0.295 0.047 0.114 0.026Average final moisture content �f 0.489 0.406 0.742 0.287Apparent density (g/cm3) 1.317 1.04 0.704 1.18Real density (g/cm3) 2.224 2.4 1.942 2.71

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The automated infiltrometer can produce more reliable infor-ation than that obtained using visual measurements. For instance

he standard deviation obtained from automatic data is smallerompared to visual observation.

. Conclusions and future work

One of the concerns with using low frequency signals for mea-uring soil humidity is the effect of many soil characteristics (bulkonductivity, salt content, temperature, type of soil, etc.). In thisork, it was shown that qualitative EIT can provide images of the

nfiltration process, even if a low frequency excitation signal is usedompared to that of TDR, for different types of soils (from sandy soilsn agreement with previously reported work by other authors, up tolay-type soils). Since infiltration is a slow process, a large numberf EIT measurements can be obtained to improve the SNR (Signal-o-Noise Ratio) before image reconstruction. The qualitative naturef the image reconstruction method, based on a reference image,ontributes to obtaining considerable good images so as to elu-idate the dynamics of the infiltration process. In addition, theBF interpolation procedure, calculates each point using the entire

mage, which contributes to obtaining smooth-looking images forhe analyst, and a large amount of data for further image process-ng. The information derived from EIT data can have importantmplications for 3D modelling of water migration through soil,or developing optimal irrigation strategies, environmental ser-ices and maximization of crop produce. The results also suggesthat other processes involving the propagation of liquid through aorous media can benefit from the use of multimodal EIT measure-ent systems Current work is focused towards developing a truly

ortable EIT-infiltrometer with a sensor array that permits mea-urement of soil hydraulic conductivity, and 3D modelling of thenfiltration process.

cknowledgements

The authors acknowledge the financial support of CONACYTConsejo Nacional De Ciencia y Tecnología, México) for carryingut this work. In addition, the authors acknowledge the support ofNIFAP (Instituto Nacional de Investigaciones Forestales, Agrícolas

Pecuarias) for providing access to the experimental field.

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[

[

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