Introduction

Hydraulic properties of soil are one of the most important agents controlling infiltration, runoff rate, pesticides leaching in agricultural lands, and migration of pollutants from contaminated sites to groundwater. Therefore, there is a need for good water management practices in order to solve water-related problems such as irrigation and erosion issues. In this regards, it is essential to predict water infiltration rate into soil, to model water and solute transport processes, and to evaluate soil physical quality (Hillel 1998; Bagarello and Iovino 2004; Kutılek 2004; Machiwal et al. 2006).

Infiltration is the term applied to the process of water entry into the soil. This process is the movement of water into the soil from the surface by downward or gravitational flow. The rate at which it occurs is known as the infiltration rate, which continues to decrease and asymptotically approaches the saturated hydraulic conductivity. This rate of infiltration is termed the steady state infiltrability. It is approximately the same as the field saturated hydraulic conductivity of the surface soil. Infiltration rate mainly depends on soil properties, such as; initial moisture content, hydraulic conductivity, soil texture, porosity, swelling degree of soil colloids, organic matter content, and chemical properties, as well (Saxton et al. 1986; Bagarello and Iovino 2004; Chowdary et al. 2006).

Soil comprises water, air, and solid particles with varied directions and shapes (Prieksat et al. 1994). Connectivity has a great impact on liquid and its movement characteristics in solid particles. The array forms, sizes, and shapes of solid ingredients and interstices constitute soil structure and determine the series of characteristics and diversification of soil physical properties (Lin et al. 1999). Therefore, the infiltration process is closely associated with soil particle size distribution as well as soil pore size distribution (Boix 1997; Hwang 2004).

Soil particle size distribution (PSD) is the most widely used characteristic for soil processes modeling (Le Bissonnais and Arrouays 1997; Diaz-Zorita et al. 2007). In such models, PSD data afford a means of computing the water reserve that is usable by plants, determining fertility groups and estimating the soil’s vulnerability to compaction, subsurface drain clogging, and ditch and watercourse bank instability, and also its vulnerability to the upward migration of coarse fragments as a result of human action (Dominguez et al. 2001; Hwang and Powers 2003). The PSD data can help users locate filling materials and estimate a soil’s ability to tolerate the passage of vehicles, its frost susceptibility, or its tendency to settle (Bittelli et al. 1999; Hwang et al. 2002). However, soil samples have ably different PSD and therefore all kind of soils demonstrate different infiltration rates (Campbell and Shiozawa 1992; Bittelli et al. 1999).

In general, soil PSD is defined as primary (PPSD) and secondary (SPSD) which the former is related to soil texture and the later is attributed to aggregates. The main particle fractions of clay, silt, and sand do not provide complete information on soil PPSD. A more accurate description of texture is obtained by defining a PPSD function (Bittelli et al. 1999). In spite of such aspects, using this factor is partially beneficial and applicable. Determination of the soil PPSD by sieving (Kemper and Rosenau 1986), hydrometer and pipette (Gee and Bauder 1986) methods, or by laser diffraction (Hernadi et al. 2008; Bah et al. 2009) is mainly due to extraction of the size of irregularly shaped particles (Eshel et al. 2004). The soil PSD is useful for quantifying soil properties because it is related to soil pore space and water movement. Pore size distribution of soil depends on its PSD therefore, soils including larger particles have greater pores indicating higher infiltration rates. In other word, estimating hydraulic properties from particle size data is preferred when studying soil infiltration of different particle sizes.

Textures of various weight proportions of the primary separates particles of soil produce different PPSD. Particle size distributions are often rendered as cumulative functions, either as number of particles larger than a certain diameter, or as mass smaller than a certain diameter and are often showed by the geometric mean particle-size diameter (d g ; Bittelli et al. 1999; Shirazi et al. 2001). The D g parameter changes a sample description from a conventional texture triangle and separate limits to a new system (Shirazi et al. 2001). The transformation has two distinct steps, the first defines the PPSD and the second mathematically integrates the PPSD to calculate the statistics d g (Shirazi et al. 2001).

In these cases, a detailed characterization of infiltrate properties based on limit size of soil particles (passed percentage of particle of soil from sieves) are very important for better understanding of their behavior towards PPSD and d g . For example, in the International Soil Science Society system the boundary for sand fraction is different to the United States Department of Agriculture system. Because relationships between texture and other hydraulic properties are affected by these differences, the size definitions of the three main particle fractions of clay, silt, and sand, used as diagnostic characteristics in most classification schemes, are rather arbitrary, and they do not provide complete information on the soil PPSD and hydraulic properties (Bittelli et al. 1999; Shirazi et al. 2001). Infiltration in soil is a quite complex issue; therefore, in the present study, we wish to investigate the PPSD to show the most effective size on infiltration rate and also to determine the degree of relationship between infiltration rates and selected soil properties.

Material and methods

Soil sampling and preparation

Because infiltration rate is controlled by soil properties especially particle size distribution, soil samples were collected from different soils. Totally, 15 sites were selected near the city of Kerman and Jiroft, in south eastern Iran in arid and semiarid regions (Fig. 1). The mean annual temperature in the regions is 15–25°C and the mean annual precipitation is 140–180 mm, respectively. The soils have aridic moisture and thermic temperature regimes, dominantly. The main dominant plant species are four major vegetation types: evergreen broad leaved, warm coniferous plants, tropical shrubs, and hassock.

Fig. 1
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Location of 15 sites on Kerman province map

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Measurement of infiltration rates and soil properties

At each site, three points were selected as replications to determine mean infiltration rate. Infiltration rate was measured using double-ring method with constant water head of 5 cm (Bouwer 1986). This method is useful that offers a simple, fast, and convenient means of determining soil hydraulic properties based on in situ infiltration measurements at the soil surface. Double ring has also been widely used for estimation of field-saturated hydraulic conductivity under pounded conditions without much disturbance to the soil surface at the measurement site (Bouwer 1986). Three double-ring tests were carried out at each site and duration of each experiment was based on the time to reach the steady state.

Beside each point, a soil sample was collected to analyze soil physical and chemical properties. Collected soil samples, after determination of primary moisture and bulk density, were air-dried, gently crushed, and passed through a 2-mm mesh. Sieved samples were stored in unused polythene bags, were labeled appropriately, and were prepared to laboratory analysis of selected physical and chemical properties. Bulk density was determined using core samplers (Grossman and Reinsch 2002). Saturation paste of the soils was prepared and the pH and EC were measured. Organic carbon was determined using Nelson and Sommers (1982) method. Soluble Ca+2 and Mg+2 were measured by titration method. Soluble Na+ and K+ were obtained by a flame photometer. Gypsum content of the soils was calculated using the method of Berigari and Al-Any (1994).

Prior to particle size analysis, all soil samples were dispersed in sodium hexametaphosphate solution and shaken for 24 h to disperse aggregates. Sedimentation techniques based on Stoke’s law were used to obtain particles finer than 2 mm diameters (Bittelli et al. 1999). The distribution of particles was determined by hydrometer method (Gee and Bauder 1986). To obtain other size classes finer than 2 mm, not obtained from hydrometer method, sieve analysis was done consequently. Sieve analysis consists of shaking the soil sample through a set of sieves that have progressively smaller openings (4.76, 2, 1, 0.5, 0.25, and 0.149 mm). The data of sieve analysis were expressed in terms of the percentage of the total weight of soil that passed through different sieves. The results of mechanical analyses (sieve and hydrometer analyses) were generally presented by semilogarithmic plots known as particle size distribution curves. The diameter in the particle size distribution curve corresponding to % finer particles was defined as the effective size or D n . The obtained data were analyzed using correlation and regression analysis in order to estimate the relationship between infiltration rates and PPSD using SPSS software.

Results and discussion

The measured values of some physical properties of studied soils from 15 sites are shown in Table 1. Because the soils were selected mainly based on diverse properties, the coefficient of variations (CV%) is relatively high that is satisfactory. As it is clear from the results of the physical properties (Table 1), the primary particles including clay, silt, and sand have the CV% higher than 50%, indicating adequate diversity in the soils texture. It was also found that the soils had a proper distribution regarding the organic matter. Table 2 shows some chemical properties of the soils. High quantities of CV percent for soluble cations, as well as EC reveal diverse conditions of salinity and electrolyte concentrations. In addition, the amount of gypsum in the soils was negligible, while the quantity of CaCO3 was higher in comparison. In addition to the final IR, initial and saturated moisture contents and hydraulic conductivity were measured (Table 3). The range of initial moisture percentage varied from 2.1% to 18.5%, while the content of saturated moisture changed between 26.4% and 55.6%. As shown in Table 3, the range of hydraulic conductivity values varied from 1.8 to 22.4 cm h−1, indicating the importance of particle size distribution effect on hydraulic properties. This is due to the fact that both pore size distribution and hydraulic conductivity are influenced by PSD.

Table 1 Some physical properties of the studied soils
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Table 2 Some chemical properties of the studied soils
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Table 3 Initial and saturated moisture content in relation with hydraulic conductivity for 15 sites
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Figure 2 shows the average infiltration rates for the 15 sites. The range of IR for studied soils was varied from 1.6 to 30.66 cm h−1. Accordingly, the least IR was observed in sites 2, 9, 10, 13, and 15, while sites 5, 7, 4, and 8 found to have the highest rates of infiltration. The comparison of means for final infiltration rates was done. A high significant difference (p < 0.05) was observed among the sites, indicating that the final infiltration rates vary in different sites. To determine if these differences can be attributed to PPSD, the analysis of simple correlation was carried out on data.

Fig. 2
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Comparison of means of final infiltration rates for 15 sites (alpha = 0.05)

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Table 4 shows simple correlation results between all of D n and final infiltration rates. Significant correlation (p < 0.05) was found between most of the D n values and infiltration rates. The results indicated that the final infiltration rates vary because of the differences in D n (Table 4) in the studied soils. In other words, D n has significant positive effect on the final infiltration rate. Among all fractions, D 30, D 40, and D 60 showed higher relationships with IR than the others. These diameters are attributed to the average of particle sizes, which (for D 30, D 40, and D 60) are about 0.05, 0.08, and 0.16 mm, respectively. This finding implies that the highest primary particles which control the final IR are placed in sand fraction.

Table 4 Simple correlation analysis of final infiltration rates with all of Dn values
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To investigate the influence of D n values on final IR, some representative sites were compared. Based on Fig. 2, sites 2 and 5 showed the least and the highest final infiltration rates, respectively. The differences between sites 2 and 5 in their IR may be partly assigned to the differences in their D n . Therefore, D 30, D 40, and D 60 values as well as soil textures for the sites is presented in Table 5. As shown in Fig. 3, site 5 has a higher IR compared to site 2, which is due to higher D n values. This means that the PPSD has meaningful influence on infiltration rate which has been found by some other researchers (Romkens et al. 1995; Dominguez et al. 2001; Hwang et al. 2002; Barthes et al. 2008). The results indicated that the percent of sand for site 5 is 5.8 times higher than site 2, which caused to 31 times increase in final IR. Reversely, clay and silt contents showed negative effects on final IR. The relationship between measured IR and D n indicated that high average of IR was usually observed under the maximum D n . These results may be attributed to such factors as high total porosity and unclosed effective void (Kutılek 2004; Lipiec et al. 2006). The variation of IR in these sites is apparently due to variation in soils properties (Katsvairo et al. 2002).

Table 5 Comparison between sites 2 and 5 in respect of Dn and soil textures
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Fig. 3
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Changes in infiltration rates with time for sites 2 and 5

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Each fractions of soils texture showed a different behavior with regard to IR. Table 6 shows simple correlation results of final infiltration rates with selected soil properties. The correlations of IR with clay, silt, and sand contents were −0.551, −0.705, and 0.706, respectively. The negative role of clay and silt fractions is probably due to their small sizes, which caused blocking in the effective pores (Oster and Shainberg 2001; Bronick and Lal 2005; Carpenter and Chong 2010) especially the particles stay apart from aggregating (Curtin et al. 1994; Halliwell et al. 2001; Lado et al. 2004; Ruize-Vera and Wu 2006). Also, another fraction of texture, sand particles, probably because of their big sizes, caused to high IR (Romkens et al. 1995; Hwang and Powers 2003).

Table 6 Simple correlation analysis of final infiltration rates with selected soil properties
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Simple correlation values of final infiltration rates with initial moisture and also saturated moisture contents are shown in Table 6. The initial moisture had no meaningful influence on final IR, however saturated moisture showed a negative significant effect (R 2 = −0.563). In other word, those soils with higher saturated moisture contents have lower infiltration rates. This is probably attributed to clay effect that is to say increasing clay content, total porosity would be increased, while the frequency of macropores reduces. In addition, the results show that there is no significant relationship between final IR and hydraulic conductivity (Table 6). This is partly due to the fact that measurement of hydraulic conductivity was carried out in laboratory condition using disturbed soil samples. In other word, pore size distribution and subsequent hydraulic behaviors are highly affected by soil disturbance.

Relationship between measured IR and other soil properties (Table 6) indicated that organic matter and equivalent calcium carbonates have statistically significant negative effects on IR. This result is similar to those obtained by Nejadhashemi et al. (2000), Lado et al. (2004), Garcia-Orenes et al. (2005). The negative roles of the aforementioned properties might be because of their separate presence in the pores, as well as their small amounts and newly formed accumulations in these soils. These were effective on infiltration; there is no need to mention that they separate, because of the presence of some dispersing factors, such as high soluble Na+ and SAR (Ruize-Vera and Wu 2006; Carpenter and Chong 2010).

To quantify the influence of different soil properties on final IR, simple regression using stepwise method was applied. Equation 1 shows the negative effect of saturated moisture as one of the moisture characteristics on IR. Moreover, Eq. 2 presents the effect of soil texture on final IR, implying obstruction influence of silt particles on permeability that discussed above. The effect of studied fractions on final infiltration rate indicated that among different D n values, D 40 have highest influence which was confirmed in Table 4. These equations prove that PSD is the most important agent controlling final IR. This is logical as the soils had low amounts of organic carbon due to low plant cover, so particle size distribution is affected by the primary agents.

$$ IR = 30.012 - 0.544{\text{SP}}\% \quad {R^2} = 0.317 $$
(1)
$$ IR = 20.42 - 0.33{\text{Silt}}\% \quad {R^2} = 0.48 $$
(2)
$$ IR = 1.741 + 97.953{D_{{40}}}\quad {R^2} = 0.56 $$
(3)

To have a better view on the effect of major primary fractions of clay, silt, and sand, those sites had the highest and least amounts of mentioned fractions were plotted against. As shown in Figs. 4, 5, and 6 changes in infiltration rates with time regarding to clay, silt, and sand is deeply affected. The differences between infiltration rates in two contrasted cases (maximum and minimum values of primary fractions) demonstrated clearly the effect of clay, silt, and sand contents. In general, for all soils similar results were obtained on the effect of PPSD on final IR. However, some exceptions were observed that showed a reverse influence of primary particles. Saxton et al. (1986) stated that the influence of texture on infiltration rates dominates other factors. This is, however, not consistent completely with the findings of this study, for example site 6 had higher D n values than that of site 13, but the former had a lower IR (Figs. 7 and 8). This study revealed that even with the coarser primary particles of a given soil, other factors may interplay to cause a significant difference in infiltration rates. The same result was found by Osuji et al. (2010). However, in the studied soils which are all located in arid and semiarid regions, primary PSD plays an important role in permeability.

Fig. 4
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Changes in infiltration rates with time regarding to clay content for sites 2 and 6

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Fig. 5
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Changes in infiltration rates with time regarding to silt content for sites 5 and 9

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Fig. 6
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Changes in infiltration rates with time regarding to sand content for sites 9 and 14

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Fig. 7
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Comparison between sites 6 and 13 in respect of D n values

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Fig. 8
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Comparison between sites 6 and 13 in respect of time changes of infiltration rate

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Conclusion

PPSD affects the infiltration rate of studied soils. However, the correlation analysis of sand, silt, clay, and some D n revealed that the PPSD is considered as the most important factor which results in significant differences in the infiltration rates. The range of IR for studied soils was varied from 1.6 to 30.66 cm h−1. A high significant difference (p < 0.05) was observed among the sites, indicating that the final infiltration rates vary in different sites. Significant correlation (p < 0.05) was found between most of the D n values and infiltration rates. Among all fractions, D 30, D 40, and D 60 showed higher relationships with IR than the others. These diameters are attributed to the average of particle sizes, which (for D 30, D 400 and D 60) are about 0.05, 0.08, and 0.16 mm, respectively. The results indicated that increasing the percent of sand have intensified influence on increasing the final IR. Reversely, clay and silt contents showed negative effects on final IR. The correlations of IR with clay, silt, and sand contents were −0.553, −0.705, and 0.706, respectively. In general, for all soils, similar results were obtained on the effect of PPSD on final IR. However, some exceptions were observed that showed a reverse influence of primary particles. Finally, it is revealed that the role of texture was important, especially in behavior of infiltration, runoff, and production capability.