Key words: soil water retention, tropical soil, ISSS texture classification, Green-Ampt, wetting front, sorptivity

Transcription

1 J. Japan Soc. Hydrol. and Water Resour. Vol. 21, No.3, May 2008 pp Water retention and hydraulic properties of undisturbed tropical soils collected from many regions of Indonesia were analyzed to estimate infiltration characteristics of the soils. Soil texture was classified based on International Society of Soil Science (ISSS) classification. The van-genuchten model was used to estimate the relationship between water content and matrix potential at pf=1, pf=2, pf=2.54, pf=4.2. The 165 soil water retention data were used to optimize parameters of the model and to find the air entry value. Green-Ampt and Philip's infiltration models were applied to characterize soil infiltrability of each textural type. The Nash and Sutcliffe's efficiency was used to evaluate numerical simulation of cumulative infiltration of Green-Ampt's infiltration model compared to the results of laboratory experiments. The 165 soil samples were classified and were optimized into 10 ISSS textural types: heavy clay, sandy clay, sandy clay loam, sandy loam, sand, light clay, clay loam, loam, silty clay, and silty clay loam. The results of performance evaluation of Green-Ampt's infiltration model showed that Green-Ampt's infiltration model can describe infiltration characteristics by using soil water retention and hydraulic properties data. The tropical soils based on soil texture exhibit contrasting infiltration characteristics as indicated by infiltration rate, length of wetting front and sorptivity. The characteristics of soil infiltrability are mainly influenced by hydraulic conductivity, initial water content, and matrix potential at the wetting front. Key words: soil water retention, tropical soil, ISSS texture classification, Green-Ampt, wetting front, sorptivity Infiltration is the physical process of water entering the soil from its surface. The amount of water that infiltrates into the soil and its variation with time depend upon slope, soil structure, surface roughness, soil texture, surface cover, hydraulic conductivity and surface water content (Leonard and Andrieux, 1998). It plays important role for agricultural planning, environmental research and policy analysis such as development of plant irrigation, fertilizer and soil nutrition movement, surface and subsurface water pollution, and groundwater recharge (Netto et al., 1999; Dingman, 2002). In the tropical region such as Indonesia, soils often receive high precipitation and subject to

2 loose their top soils due to run-off and soil erosion especially when their surfaces openly exposed to the atmosphere. In this situation, soil infiltrability reduces with times because of the crust formation, or the exposure of subsoil whose relatively dense on the soil surface after its top soil is being removed by soil erosion process. Soil infiltrability which is variant of soil textures also changes with their bulk density and initial water content. These conditions become big constraints to measure infiltrability of soils in the fields. Therefore, numerical simulation models of soil infiltrability will be very important in understanding this process. Many water flow problems near the soil surface can only be solved numerically due to soil heterogeneity, non-linearity of soil physical properties, non-uniform root water uptake and rapid changing boundary conditions. Water flow in the vadose zone in term of infiltration process is predominantly vertical, and commonly can be simulated as one-dimensional flow in many applications (Romano et al., 1998). By running the one-dimensional model at various locations, horizontal variability of meteorological conditions, crop characteristics, soil properties and drainage conditions is accommodated and regional water can be determined (Bresler and Dagan, 1983; Hopmans and Stricker, 1989). Soil water retention and hydraulic data which are collected at a great number of soil physical laboratories (Rawls and Pachepsky, 2002; Hodnett and Tomasella, 2002) enhance the applicability of the some equations related to infiltrability of the soil such as Richards-Darcy's, Philip's and Green- Ampt's infiltration model (Wang et al., 1997; Romano et al., 1998; van Dam and Feddes, 2000; Braud et al., 2005; Regalado et al., 2005; Kozak and Ahuja, 2005). Most of studies concerning the application of soil water retention and hydraulic properties on simulation of the soil infiltrability have been widely conducted at subtropical region. On the contrary, the characteristics of soil infiltrability at tropical region using those data have not been extensively studied. In Indonesia, the application of soil water retention and hydraulic data on the estimation of soil infiltrability has been recently conducted on sand and silty clay textures (Saleh, 2000) and sandy clay texture (Hermantoro, 2003) using Richards- Darcy's infiltration model. The objective of present study, therefore, was to optimize soil hydraulic function using van Genuchten's equation and to estimate the infiltration characteristics of various tropical soil textures using the optimized soil hydraulic function data. The one-dimensional, downward-infiltration flow system is depicted schematically in. At fixed time t 0 after the instantaneous ponding of water depth h c at t = 0 on the top surface of the soil (Z = 0), position coordinate Z being taken as positive downward. Volumetric water content = (Z, t) for independent Z and t in general, Z 0 is the depth above which = 0 at time t (for zero

3 air entry value), and 0 Z Z 0 is the region of constant water content 0. At t = 0; the uniform soil had been at constant initial water content n throughout its semiinfinite extent Z 0. Corresponding to the flow column in, schematic graphs (profiles) of water content against depth Z at fixed time t are shown in. The profile for a general soil is indicated by the solid heavy curve that is constant at 0 for 0 Z Z 0 (water content changes little as tension increase up to a point of inflection). This more or less distinct point represents to the tension at which significant volume of air begins to appear in the soil pores and is called air entry tension. As tension increases beyond its air entry value, the water content begins to decrease rapidly and then more gradually (from 0 at Z 0 to n at Zn). At very high tensions, the curve again becomes nearly vertical reflecting a residual water content ( n after Zn). Misra et al. (2003) catagorized Green-Ampt and Philip's infiltration model as the mathematical solutions to physically based theories of infiltration. Green-Ampt assumed a piston-type water content profile ( ) with a well-defined wetting front. The piston-type profile assumes the soil is saturated at a volumetric water content of 0 (except for entrapped air) down to the wetting front. At the wetting front, the water content drops abruptly to an antecedent value of n, which is the initial water content. The soil-water pressure head at the wetting front is assumed to be h f (negative). Soil-water pressure head at the surface, h 0, is assumed to be equal to the depth of the ponded water. Corresponding to, the Green-Ampt profile (heavy broken line) remains at 0 for 0 Z L but drops in abrupt, stepwise manner to n at Z = L and remains at n for Z L. Using the assumption described above, the Green-Ampt's infiltration equation takes the form (Hillel, 1980): di h0 h f + Z i = = K S (1) dt Z where i (t) is the infiltration rate at time t, I (t) is the cumulative infiltration at time t, and is equal to Z ( 0 n ), K S is the hydraulic conductivity corresponding to the surface water content (the saturated hydraulic conductivity), and Z is the length of wetting front. The mathematical and physical analysis of the Philip's infiltration model (Philip, 1957) separated the infiltration process into two components - that was caused by a sorptivity factor and was influenced by gravity. Sorptivity is the rate at which water will be drawn into a soil in the absence of gravity; it comprises the combined effects of adsorption at surfaces of soil particles and capillarity in soil pores. The gravity factor is due to the impact of pores on the flow of water through soil under the influence of gravity. The Philip's model takes the form of a power series but in practice an adequate description is given by the two-parameter equation: S p i () t = t K (2) p 2 where i is infiltration rate, Sp is sorptivity, t is time and Kp is a gravity factor related to hydraulic conductivity. Sorptivity indicates the capacity of a soil to absorb water and is the dominant parameter governing the early stages of ilfiltration. As the time increases, the parameter Kp becomes important in governing the infiltration rate. Soil texture was classified based on International Society of Soil Science (ISSS) classification using distribution of sand, silt, and clay fractions. The classification was conducted by using the triangle textural references as shown in. The infiltration experiment was conducted on standard sand and loam (2 mm-sieved) soil types (Setiawan, 1992), and silty clay soil type (2 mmsieved) (Askari et al., 2006).

4 The apparatus used in infiltration experiment of standard sand and loam had 20 cm of diameter and 50 cm of soil column length consisted of 10 pieces of 5 cm soil rings. The length of soil column was lengthened because there was 40 cm of noncontinues macropore. During the infiltration, pressure head profile was measured by using a pressure transducer. Output terminal of the pressure transducer were connected to data logger. A marriote tube was applied to supply water into the soil surface indicating cumulative volume of infiltration and a weighting balance was used in order to measure draining water from the bottom of the soil matrix. The weighing balance was connected to a second personal computer. The apparatus used in infiltration experiment of silty clay adopted those in infiltration experiment of standard sand and loam although was more simplified and was manually operated. The apparatus as shown in had 5 cm of diameter and 25 cm of length consisting of 5 pieces of 5-cm soil rings. A marriote tube was applied to supply water into the soil surface indicating cumulative volume of infiltration. A weighting balance was used in order to measure the change of soil column weight during the water was infiltrated. Inspite of the apparatus being used were dissimilar, the measuring procedures were commonly not different. The soil sample would be manually compacted as uniformly as possible into soil ring started from lower part of the soil column. Equation (3) would be used to calculate weight of the soil needed to be compacted into the fixed volume of the soil ring in order to get the expected dry bulk density. The compaction should be done gently enough in order not to destroy soil aggregate. W ( W + ) ρ V s = 1 b (3) where Ws is soil weight, W is mass wetness, b is dry bulk density, and V is volume of the container. The physical characteristics and soil water retention data for simulation refered to the data presented by Askari et al. (2006), Saleh (2000) and Setiawan (1992). The 165 data of soil physical and hydraulic properties consisted of percentage (%) of sand, silt, and clay fractions; bulk density; organic matter (carbon organic); saturated water content, water content at pf 1, pf 2, pf 2.54, pf 4.2; and saturated hydraulic conductivity which are collected from many regions of Indonesia such as

5 Flores, Kotawaringin Barat, Samarinda, Kutai, dan Gorontalo (Hikmatullah and Sulaeman, 2006) were used to optimize the parameters of soil hydraulic function of van Genuchten (van Genuchten, 1980): θs θr θ( ψ) = θr + (4) ( 1+ ( α ψ ) ) n m where ( ) is effective soil water content, s and r are the saturated and residual water content, is matrix potential, and, n and m are empirical parameters. Modification of in Eq. (4) to be 1/ (Setiawan, 1992) will give a parameter that is called air-entry value ( ae ). Empirical parameters of van Genuchten's soil hydraulic function are computed from measured retention data points by employing non-linear regression techniques, with constrains 0, n 1, and 0 m 1 (Pereira and Allen, 1999). Solver Add-In on Microsoft Excel were used to optimized the parameters. The infiltration characteristics using the Green- Ampt's infiltration model were divided into infiltration rate and length of wetting front. The infiltration rate was calculated by Eq.(1). Meanwhile, the length of wetting front was calculated by the Eq. (5) below (as the result of integration of Eq. (1)): K ( ) (5) ( ) ( ) S Z t = Z h0 hf Ln 1+ θ 0 θn h0 hf The Eq. (5) has 7 variables which are K S, 0, n, t, Z, h 0, and h f as we had previously explained concerning infiltration model. K S and 0 were obtained from saturated hydraulic conductivity and saturated soil water content data respectively. n was assumed to be equal to residual soil water content ( r) because there were no data of soil water content when soil sample was taken. Xie et al. (2004) stated that if no initial water content is obtained, it is assumed to have initial water content equal to the residual water content. h 0 was 0 cm H 2 O with the assumption that the soil surface was in saturated condition without ponded water, and h f was obtained from air entry value resulted from the optimization of soil hydraulic function. Another variable, Z, was determined by employing Newton-Raphson method (Burden and Faires, 1993) on the Eq. (5). Another infiltration characteristic that is sorptivity, Sp, was calculated using the following equation (Philip, 1969 in Angelaki et al., 2004): S (6) Several statistical measures are available for evaluating the performance of a model. These include correlation coefficient, relative error, standard error, volume error, coefficient of efficiency (Hsu et al., 1995 in Mishra et al., 2003), among others. The Nash and Sutcliffe efficiency (Nash and Sutcliffe, 1970 in Mishra et al., 2003) was one of the most frequently used criteria. This criterion is analogous to the coefficient of determination and is expressed in percentage form as: D Efficiency = (7) D0 where D 1 is the sum of the squares of deviations between computed and observed data: D (8) 1 = Y0 Y and D 0 is the initial variance which is the sum of the squares of deviations of the observed data about the observed mean, expressed as: D ( Y ) 2 = Y 0 0 ( θ θ )( h h ) 2 p = 2K S 0 n 0 2 (9) where Y 0 is the observed data, Ŷ and Y stand for computed data and mean of the observed data, respectively. The efficiency varies on a scale of 0 to 100. It can also assume a negative value if D 1 D 0, implying that the variance in the observed and computed infiltration values is greater than the model variance. In such a case, the mean of the observed data fits better than the model. The efficiency of 100 implies that the computed values are in perfect agreement with the observed data. f

6 According to the classification of International Society of Soil Science (ISSS), the 165 soil samples were classified into 10 textural types ( ). As seeing in, light clay and heavy clay are predominant (51 %). The rest (49 %) are divided into sandy clay, sandy clay loam, sandy loam, sand, clay loam, loam, silty clay, and silty clay loam. Based on the existing data, the other two textural types which are loamy sand and silty loam, are not available. The parameters of soil hydraulic function of each textural type was computed from measured retention data points by employing non-linear regression techniques (least square error) with constrains the saturated water content equals to water content at total pores, the residual water content equals to water content at pf 4.2, 0, n 1, and m =1-1/n. Besides the least square error criteria, we should also consider the soil bulk density data as another selection criteria of the optimized data. This is useful in order to reduce the effect of variability of soil structure in the field. van Genuchten's soil hydraulic function was able to produce best-fitting for all soil textural types with coefficients of determination (R 2 ) in the range of to ( ). Soil textures dominated by sand fraction 50 % which are clay loam, sandy clay, sandy clay loam, sandy loam, loam and sand have saturated hydraulic conductivity which is equal to the addition of sand fraction value except for loam which has high saturated hydraulic conductivity due to the highest silt fraction among others. There is interesting phenomena that saturated hydraulic conductivity of clayey-soil which are heavy clay and light clay are higher than remaining soils except for sandy loam, loam and sand. These phenomena are caused not only by pore size and its distribution but also by the highest soil organic matter of clayey-soil. In addition, it is might be strongly influenced by montmorillonitic mineral content of clayey soil. Scanning electron microscrope observation indicated that the montmorillonitic soil had thicker crust comprising either small particle with a very developed washed-in zone underneath or large ones with fine material between them (Wakindiki and Ben-Hur, 2002). Sandy clay has the lowest residual water content among others. In contrast, silty clay has the highest residual water content among others. It is clearly indicated by silt and clay (fine mineral)

7 fraction content although low soil organic matter of silty clay. Silty clay loam has the highest saturated soil water content among others due to particle size distribution of this soil is dominated by silt and clay and also due to high soil organic matter content. In contrast, clay loam has the lowest saturated water content among others. Mitchell (1993) stated that the smaller soil particles performed, the larger contact area increased among its particles. As a result, the higher micropores with steady structure occurred. Thus, this soil has water content relatively higher than one which is composed by larger particle (Saxton and Rawls, 2006). The occurrence of higher soil organic matters not only will strengthen the soil aggregate but also enhance soil capacity in holding and storing water. This is because soil organic matter minimizes soil compaction, provides pores, and is able to store a quantity of water which corresponds to a multiple of the organic matter's weight (Emerson, 1995). shows the comparison between observed and computed cumulative infiltration of silty clay, standard sand, and loam soil textures as the result of laboratory experiment. Generally,

8 sand has the highest cumulative infiltration followed by loam and silty clay respectively. The value of efficiency of each soil textures indicated that numerical simulation of Green-Ampt's infiltration model agreed well with measured data. These good agreements between experimental and numerical results confirmed that Green-Ampt's infiltration model can describe infiltration characteristics using soil water retention and hydraulic properties. Optimized soil hydraulic function (as shown in ) is the input data for the estimation of infiltration characteristic of each soil texture by using Green-Ampt's and Philip's infiltration model. The infiltration characteristic derived from both models using the optimized soil hydraulic function are infiltration rate, length of the wetting front, and sorptivity. The three infiltration characteristics were using the same data as shown in. shows time dependence of infiltration rate in 10 soil textures of tropical soil. Sand has the highest initial infiltration rate among others followed by loam, sandy loam, heavy clay, light clay, sandy clay, sandy clay loam, silty clay, silty clay loam, and clay loam respectively. The same pattern in final infiltration rate is also showed by the same order. In one side, these phenomena are equal to the decreasing of saturated hydraulic conductivity especially for sand, loam, sandy loam, silty clay, silty clay loam and clay loam respectively. In another side, infiltration rate pattern of heavy clay, light clay, sandy clay and sandy clay loam are also influenced by a significant difference between matrix potential in the entry surface and matrix potential in the wetting front ( ). Subramanya (1984) stated that the distribution and pore size of soil will directly influence its infiltration rate. A loose, permeable, sandy soil will have a larger infiltration capacity than a tight, clayey soil. The existing of soil organic matter when water flows under the soil, the soil pores will not be covered by clay particles or damaged soil aggregate. shows results of numerical simulation indicating advances of length of the wetting front in the soil matrix. Since the water infiltrates through the soil surface only, the length of the wetting front vertically downward as the time increases. With the same time elapsed, sand has the biggest Z value and is followed by sandy loam, loam, heavy clay, light clay, silty clay, sandy clay loam, sandy clay, clay loam, and silty clay loam respectively. The addition pattern of Z value is different with infiltration rate caused not only by the influence of wetness increment between saturated water content and the initial soil water content but also the difference between matrix potential in the entry surface and matrix potential in the wetting front ( ).

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11 shows sorptivity characteristics as a result of antecedent soil water content changes. Generally, the sorptivity value is decreasing along with the increasing of antecedent soil water content. On the other hand, the effects of adsorption at surfaces of soil particles and capillarity in soil pores are decreasing along with the increasing of antecedent soil water content. As the water content approaches saturation, sorptivity tends to zero and the infiltration rate becomes equal to the field saturated hydraulic conductivity. As the time increases, gravity factor becomes important in governing the infiltration rate. This implies that the steady infiltration rate reached after a long time should be largely independent of the antecedent water content (Philip, 1957). Although the sorptivity is decreasing along with the increasing of the antecedent soil water content, the response for each soil texture is different. It is seen clearly that the texture of sand, loam, sandy loam, heavy clay and light clay give drastically decreasing sorptivity value among others. The drastic changes are equal to saturated hydraulic conductivity, wetness increment between saturated water content and the initial soil water content, and difference between matrix potential in the entry surface and matrix potential in the wetting front. Furthermore, the value of soil sorptivity can be used to indicate the origin and the environmental condition of soil in the field. Materechera et al. (1993) stated clearly that soil from planted treatments had higher sorptivities than soil which had not been planted due to biopores left by the roots. Parameters of van Genuchten's soil hydraulic function of tropical soil were optimized for 10 ISSS soil textures which are heavy clay, sandy clay, sandy clay loam, sandy loam, sand, light clay, clay loam, loam, silty clay, and silty clay loam. Most of them are dominated by sand mineral fraction. The results of performance evaluation of Green-Ampt's infiltration model using standard sand, loam, and silty clay soil textures showed that Green-Ampt's infiltration model can describe infiltration characteristics using soil water retention and hydraulic properties data. The tropical soils based on soil texture exhibit contrasting infiltration characteristics as indicated by infiltration rate, length of wetting front and sorptivity, in which the characteristics of soil

12 infiltrability are mainly influenced by hydraulic conductivity, initial water content, and matrix potential at the wetting front. This study can be used to estimate soil infiltrability in a field scale with previously known its soil properties. However it still needs to consider inhomogeneous of initial water content in the soil profiles. The authors would like to thank Mr. Trisnadi and Mr. Rudiyanto of Department of Agricultural Engineering, Bogor Agricultural University for their assistance to achieve laboratory experiment and numerical simulation of infiltration. The data of soil physical and hydraulics properties were provided by Mr. Yiyi Sulaeman of Indonesian Center for Agricultural Land Resources Research and Development. We are also grateful to anonymous reviewer for their help to improve the quality of this paper. This study has been supported by Directorate General of Higher Education of Ministry of Education of Indonesia through The Postgraduate Program Scholarship (BPPS). Á B Í

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