Comparison of Four Soil Infiltration Models on A Sandy Soil In Lafia ...

PAT December, 2011; 7 (2): 116-126 ISSN: 0794-5213 

Online copy available at 

www.patnsukjournal.net/currentissue 

Publication <strong>of</strong> Nasarawa State University, Keffi 

<strong>Comparison</strong> <strong>of</strong> <strong>Four</strong> <strong>Soil</strong> <strong>Infiltration</strong> <strong>Models</strong> on A Sandy <strong>Soil</strong> In Lafia, Southern 

Guinea Savanna Zone <strong>of</strong> Nigeria 

Ogbe, V. B. 1 , Jayeoba, O.J. 1 and Ode, S. O. 2 

1. Agronomy Department, Faculty <strong>of</strong> Agriculture, Nasarawa State University, Keffi, Shabu-Lafia Campus 

2. Department <strong>of</strong> Agricultural Engineering Technology, College <strong>of</strong> Agriculture, Lafia, Nasarawa State. 

Abstract 

Prediction <strong>of</strong> soil infiltration is a major problem due to its variability and proper selection <strong>of</strong> 

the technique used to determine the parameters <strong>of</strong> the models which depend on the local soil 

characteristics. Field experiments were conducted to assess the predictability <strong>of</strong> kostiakov (K), 

modified-Kostiakov (MK), Philip (P) and Horton’s (H) models on a sandy soil and to compare 

the measured and predicted cumulative infiltration using these models under local condition. A 

double ring infiltrometer was used to carry out three measurements each at 30m interval in 

three different strips <strong>of</strong> 100m long and 30m wide. A total <strong>of</strong> nine infiltration test was conducted 

in the field. From the values <strong>of</strong> cumulative infiltration and time interval measured, the models 

parameters were determined. Using the calibrated soil infiltration models, predictions <strong>of</strong> the 

cumulative infiltration were made for each strip. GENSTAT package was use to analyze the 

predictions. The result <strong>of</strong> the study shows that the cumulative infiltration predicted by K, MK, 

P and H infiltration models were very close to the field measurements for all the strips as 

indicated from the average values <strong>of</strong> the slope between the measured and predicted by K, MK, 

P and H models (1.064, 1.073, 1.044, 1.027) respectively and their coefficient <strong>of</strong> 

determination, r 2 (0.997, 0.994, 0.994, 0.999) respectively. The Root Mean Square Error 

(RMSE) was used to rank the models in the order H > K > P > KL with values (0.0439, 

0.0558, 0.0585, 0.0729) respectively. Horton’s models gave best fit to the measured cumulative 

infiltration, although the other models provided good overall agreement with the field 

measured cumulative infiltration depths. 

Keywords: <strong>Infiltration</strong>, Sandy <strong>Soil</strong>, Ring Infiltrometer, <strong>Infiltration</strong> <strong>Models</strong>, 

Introduction 

<strong>Soil</strong> water represents a negligible fraction <strong>of</strong> the total water on our planet, but it is 

undoubtedly one <strong>of</strong> the most important as it plays a vital role in water availability for 

plants in the various root zones <strong>of</strong> the soil (Musa and Adeoye, 2010). Rainfall or 

irrigation water is divided into two parts by infiltration. One part goes through overland 

flow and stream channels to the sea as surface run<strong>of</strong>f; the other part goes initially into 

the soil and hence, through groundwater flow into the stream or else is returned to the 

atmosphere by evaporative processes (Hickok and Osborn, 1969). 

<strong>Infiltration</strong> is the process <strong>of</strong> water movement from the ground surface into the soil and 

is an important component in the hydrological cycle (Haghaibi et. al., 2011) while soil 

water movement or percolation is the process <strong>of</strong> water flow from one point to another 

within the soil. <strong>Infiltration</strong> and percolation cannot be treated independently, because the

PAT 2011; 7 (2): 116 -126: ISSN: 0794-5213; Ogbe et al; <strong>Comparison</strong> <strong>of</strong> <strong>Four</strong> <strong>Soil</strong> <strong>Infiltration</strong> <strong>Models</strong> ….. 117 

rate <strong>of</strong> infiltration is controlled by the rate <strong>of</strong> percolation below the surface (Hsu et. al., 

2002). However, infiltration has received a great deal <strong>of</strong> attention from soil and water 

scientists because <strong>of</strong> its fundamental role in land-surface and subsurface hydrology, 

irrigation and agriculture (Mishra et. al., 2003). Through infiltration rainfall or 

irrigation water is turned into soil water and used to sustain the growth <strong>of</strong> crops, 

vegetation, replenish ground water supply to wells, springs and streams (Rawls et al., 

1993). It is key to soil and water conservation, and irrigation management because it 

determines the amount <strong>of</strong> run<strong>of</strong>f over the soil surface during rainfalls or irrigations 

(Oku and Aiyelari, 2011). 

<strong>Infiltration</strong> characteristics <strong>of</strong> the soil are quantified when field infiltration data are fitted 

mathematically to infiltration models (Oku and Aiyelari, 2011). Despite advances in the 

estimation from soil physical properties, surface irrigation practitioners still use 

empirical infiltration models. However, not all models are applicable in all soils. 

Several studies have been conducted to establish models parameters, validate models or 

compare models efficiencies and applicability for different soil conditions. Igbadun and 

Idris (2007) investigated the capacity <strong>of</strong> Kostiakov’s, Philip’s, Kostiakov-Lewis’ and 

modified Kostiakov infiltration models to describe water infiltration into a 

hydromorphic soil <strong>of</strong> the flood plain in Zango village, Samaru, Zaria, Nigeria. They 

reported that although the four models provided good overall agreement, Kostiakov’s 

and modified Kostiakov model were found to provide best fit. Oku and Aiyelari (2011) 

deduced that Philip’s model was more suitable than Kostiakov’s model from a study <strong>of</strong> 

the infiltration capacity <strong>of</strong> the soil (Inceptisols) <strong>of</strong> the humid forest in southern Nigeria 

using the two models. Musa and Adeoye (2010) in their study to adapt infiltration 

equations to the soil <strong>of</strong> the permanent site farm <strong>of</strong> the Federal University <strong>of</strong> 

Technology, Minna, in the Guinea Savannah Zone <strong>of</strong> Nigeria stated that Kostiakov’s 

model showed a better performance over those <strong>of</strong> Philip’s and Horton’s models. 

Mbagwu (1997) reported that Philip’s model would always fail to predict measured 

infiltrations when the assumptions <strong>of</strong> the model are not met during the infiltration 

process. However, the need for continuous in-depth and field specific study <strong>of</strong> the 

applicability <strong>of</strong> infiltration equations cannot be over emphasized since models 

parameters and performance vary for different soils and with time. 

In the study reported herein, the performance <strong>of</strong> four infiltration models (Kostiakov, 

modified Kostiakov, Philip and Horton) in a sandy soil was evaluated. The general 

objective <strong>of</strong> the study was to test the competence <strong>of</strong> the four models to predict water 

infiltration in the soil type. The specific objectives are to estimate the models 

parameters and to compare the cumulative infiltration depths estimated by the models 

with those measured in the field.


Materials and Methods 

Study Area 

The experiment was carried out at the Research and Teaching Farm <strong>of</strong> the College <strong>of</strong> 

Agriculture, Lafia (08 o 35’N, 08 o 33’E), located in the Guinea Savanna Zone <strong>of</strong> North 

Central Nigeria at an attitude <strong>of</strong> 177m above sea level. The mean monthly maximum 

and minimum temperature range between 35.06 0 C – 36.40 0 C and 20.16 o C – 20.50 0 C 

respectively while the mean monthly relative humidity and rainfall are 74.67% and 

168.90mm respectively. 

Field Measurements 

Field experiment was carried out at the College <strong>of</strong> Agriculture experimental farm, Lafia. 

The portion <strong>of</strong> the field used was 100m long by 90m wide. The field was divided into 

three strips <strong>of</strong> 100m by 30m. Three points at 30m interval the length was marked out 

and infiltration test carried out at those points for each strip. <strong>Soil</strong> samples were also 

collected from the adjacent area <strong>of</strong> the marked points at 0-15 cm and 15-30 cm depths 

for soil analysis. <strong>Infiltration</strong> measurement was carried out using a double ring 

infiltrometer. The infiltrometer was driven into the soil to a depth <strong>of</strong> 10cm and a 

measuring tape was fixed inside the inner cylinder from where readings were taken. 

Readings were then taken at intervals to determine the amount <strong>of</strong> water infiltrated 

during the time interval with an average infiltration head <strong>of</strong> 5cm maintained. The 

infiltration rate and the cumulative infiltration were then calculated. 

Moisture content was determined by gravimetric method. The soil texture <strong>of</strong> the site 

was determined by mechanical analysis method. The United States Department <strong>of</strong> 

Agriculture (USDA) Textural Classification Triangle was used to classify the soil based 

on the results obtained from the analysis. 

<strong>Infiltration</strong> <strong>Models</strong> Evaluated 

Several models including Kostiakov (K), modified Kostiakov (MK), Philip (P) and 

Horton’s (H) models have been developed for monitoring infiltration process. 

Kostiakov and Modified Kostiakov equations 

Kostiakov (1932) and Lewis (1938) independently proposed a simple empirical 

infiltration equation based on curve fitting data. It relates infiltration to time as a power 

function as presented by Eq: (1) 

Z = kt a (1) 

where, Z = cumulative infiltration (cm/hr), t = time from start <strong>of</strong> infiltration (min), k, a 

= constants that depends on the soil and initial conditions. A better representation <strong>of</strong> the


depth infiltrated over a long period <strong>of</strong> time is given by the modified Kostiakov equation 

expressed as 

Z = kt a + f 0 t (2) 

in which f 0 is the long term steady or basic infiltration rate in units <strong>of</strong> volume per unit 

length per unit time and width and other parameters as previously defined. The 

Kostiakov and modified Kostiakov tend to be the most preferred infiltration models 

used in surface irrigation application because <strong>of</strong> their simplicity, capability <strong>of</strong> fitting 

most infiltration data and probably because it is less restrictive as to the mode <strong>of</strong> water 

application than some other models. 

Philip’s equation 

Philip (1957b) proposed that by truncating his series solution for infiltration from a 

pounded surface after the first two terms, a concise infiltration rate equation could be 

obtained which would be useful for small times. The resulting equation is 

Z = St 0.5 + At (3) 

Where, S = Sorptivity, A = Parameter related to saturated hydraulic conductivity, other 

parameters as previously defined. Parameter ‘A’ is considered roughly identical to 

steady infiltration. Parameter ‘S’ is sensitive to antecedent soil moisture condition and 

relatively less sensitive to the parameter ‘A’ (Al-Azawi, 1985). 

Horton equation 

Horton (1940) proposed an infiltration equation derived from work and energy 

principles as 

z = f + (f 0 - f) e -βt (4) 

where, f is the steady state infiltration rate, f 0 is the infiltration rate at time, t = 0, β is the 

infiltration decay factor, other parameters as previously defined. The rate <strong>of</strong> decrease <strong>of</strong> 

infiltration rate (z) to steady state infiltration (f) is determined by β. Horton (1940) also 

attributed this decrease primarily to factors operating at the soil surface rather than to 

flow processes within the soil. 

Integrating Eq: (6) gives the cumulative infiltration <strong>of</strong> the Horton model and expressed 

as 

Z = ft + (f0 - f)β -1 (1 – e -βt ) (5) 

Estimation <strong>of</strong> <strong>Infiltration</strong> Characteristics 

Optimal values <strong>of</strong> the parameters <strong>of</strong> the four models were estimated using regression 

module <strong>of</strong> the GENSTAT package. To estimate the Kostiakov model parameters, the 

cumulative infiltration and time data were subjected to a non-linear regression analysis 

(z = kt a ) to find the parameters k and a. Using the final infiltration rate, the cumulative


infiltration and time data were subjected to a non-linear regression analysis (z – ft = kt a ) 

to determine parameters k and a for the modified Kostiakov model. The Philip’s model 

parameters were estimated by subjecting the cumulative infiltration and time data to a 

linear regression analysis (zt -0.5 = S + At 0.5 ) to find the parameters S and A. Horton’s 

model parameters were estimated by subjecting the infiltration rate and time data to a 

semi non-linear regression analysis 

to determine 

parameters f o and β. 

Statistical Analysis 

The accuracy <strong>of</strong> the different equations for predicting the cumulative infiltration were 

evaluated by comparing the observed values <strong>of</strong> measurement on the field and the 

predicted values based on the fitted equation. The data were then subjected to a linear 

regression analysis and the t-paired test using the GENSTAT. 

Results and Discussion 

<strong>Soil</strong> Properties 

The result <strong>of</strong> analysis <strong>of</strong> soil physical properties <strong>of</strong> the study area is presented in table 1. 

The results showed that the texture <strong>of</strong> the field surface (0 - 15) cm and the sub-surface 

(15 - 30) cm depths for the three sampled strips were predominantly sandy, having sand 

fraction ranging from 88 – 92 %. The average bulk densities are 1.54g/cm 3 and 

1.55g/cm 3 with average porosities <strong>of</strong> 42.01% and 41.63% at the 0 – 15cm and 15 – 

30cm depth respectively. The average initial antecedent moisture contents are 4.43% 

and 4.97% at the surface and subsurface depths respectively. 

Table 1: Average soil properties at different depths. 

Strip Depth (cm) Clay (%) Silt (%) Sand (%) Texture .B.D (g/cm 3 ) Porosity (%) MC(%) 

A 0 – 15 8.6 3.4 88 Sandy 1.46 44.90 4.70 

B 

C 

15 – 30 

0 – 15 

15 – 30 

0 – 15 

15 – 30 

6.6 

8.6 

6.6 

10.6 

6.6 

3.4 

1.4 

1.4 

1.4 

1.4 

90 

90 

92 

88 

92 

MC - Moisture content. B.D – Bulk Density 

Sandy 





1.43 

1.74 

1.75 

1.41 

1.46 

46.03 

34.33 

33.96 

46.79 

44.90 

5.30 

4.20 

5.20 

4.40 

4.40 

<strong>Infiltration</strong> Parameters 

The average infiltration parameters <strong>of</strong> the four infiltration equations for the three strips 

are shown in Table 2.


Table 2: Estimated values <strong>of</strong> the models parameters for the three strips. 

Stirp Kostiakov Modified Kostiakov Philip Horton 

label k a k a f S A f c f 0 β 

A 1.3868 0.781 1.652 0.494 0.333 1.851 0.307 20 59.92 -1.998 

B 1.5596 0.784 1.722 0.546 0.350 2.138 0.345 21 67.53 -1.696 

C 1.5171 0.785 1.734 0.529 0.350 2.131 0.333 21 70.50 -1.968 

The average values <strong>of</strong> the time exponent <strong>of</strong> Kostiakov equation was observed to range 

between 0.781 and 0.785 which were in accordance to the theory <strong>of</strong> infiltration that puts 

the value to be positive and always less than unity. However, most observed values lies 

between 0.2 and 0.9 (Blair and Reddell, 1983; Serralheiro, 1988). The values <strong>of</strong> these 

parameters do not possess any specific physical meaning; however they reflect the 

effect <strong>of</strong> soil physical properties <strong>of</strong> influence on infiltration as well as antecedent soil 

moisture content and surface conditions (Zerihun and Sanchez, 2003). The equations 

describe the measured infiltration curve and given the soil and initial water condition, 

allow prediction <strong>of</strong> an infiltration curve using the same constants developed for those 

conditions. 

Tables 3a – c show the models predicted cumulative infiltration for strips A to C 

respectively, and the measured cumulative infiltration used for comparison. The models 

predicted values closely agree with that measured from the field for all the strips as seen 

in the tables. However, Kostiakov, modified Kostiakov and Philip’s models averagely 

over predicted at the initial stages up to the 11 th minute, under predicted at the middle to 

the 108 th minute period and then over predicted towards the end respectively. Horton’s 

model, however over predicted slightly all through the period for all the strips. 

Table 3a: Cumulative infiltration (cm) predicted by the models compared with 

measured values for strip A. 

Time (Min) Measured Kostiakov Modified Kostiakov Philip Horton 

3 2.9 3.3 3.8 4.1 2.9 

6 5.5 5.6 6.0 6.4 5.6 

11 9.5 9.0 9.1 9.5 9.6 

16 13.0 12.1 11.8 12.3 13.7 

26 19.0 17.7 16.9 17.4 20.1 

36 24.2 22.8 21.7 22.2 26.0 

46 29.0 27.6 26.3 26.7 31.1 

60 35.0 33.9 32.5 32.8 37.3 

74 40.6 40.0 38.5 38.6 42.9 

88 46.0 45.8 44.4 44.4 48.3 

108 53.1 53.7 52.7 52.4 55.4 

128 59.9 61.3 60.8 60.2 62.3 

148 66.7 68.7 68.8 68.0 69.2


168 73.5 75.9 76.7 75.6 75.9 

188 80.3 82.8 84.6 83.1 82.5 

208 87.1 89.6 92.3 90.6 89.4 

Table 3b: Cumulative infiltration (cm) predicted by the models compared with measured values 

for strip B. 


3 3.2 3.7 4.2 4.7 3.3 

6 6.1 6.4 6.7 7.3 6.4 

11 10.5 10.2 10.2 10.9 11.0 

16 14.5 13.7 13.4 14.1 15.8 

26 21.7 20.1 19.3 19.9 23.2 

36 28.1 25.9 24.8 25.3 30.1 

46 33.7 31.4 30.0 30.4 36.2 

60 40.7 38.7 37.1 37.3 43.4 

74 47.3 45.6 44.0 43.9 49.9 

88 53.3 52.2 50.7 50.4 56.0 

108 61.0 61.3 60.0 59.5 63.9 

128 68.4 70.0 69.2 68.4 71.4 

148 75.4 78.5 78.2 77.1 78.9 

168 82.4 86.6 87.1 85.7 86.0 

188 89.4 94.6 95.8 94.2 93.0 

208 96.4 102.4 104.6 102.6 100.2 

Table 3c: Cumulative infiltration (cm) predicted by the models compared with 

measured values for strip C. 


3 3.1 3.6 4.2 4.7 3.4 

6 5.9 6.2 6.6 7.2 6.6 

11 10.2 10.0 10.0 10.7 11.3 

16 14.2 13.4 13.1 13.9 16.0 

26 21.5 19.6 18.8 19.5 23.4 

36 28.0 25.3 24.1 24.8 30.0 

46 33.7 30.6 29.2 29.8 35.8 

60 40.5 37.7 36.1 36.5 42.6 

74 46.5 44.5 42.8 43.0 48.8 

88 52.1 51.0 49.3 49.3 54.6 

108 59.4 59.9 58.4 58.1 62.2 

128 66.3 68.4 67.4 66.7 69.5 

148 73.2 76.7 76.2 75.2 76.8 

168 80.1 84.7 84.9 83.6 83.9 

188 87.0 92.5 93.5 91.8 90.8 

208 93.9 100.2 102.0 100.0 98.0


Prediction <strong>of</strong> Cumulative <strong>Infiltration</strong> by the Model 

Predictions were made for each <strong>of</strong> the strips using all the four models and the predicted 

cumulative infiltration were compared with the measured cumulative infiltration. The 

cumulative infiltration values predicted using the infiltration models and those 

measured were plotted against each other and fitted with a linear equation with zero 

intercept to verify the validity <strong>of</strong> each prediction. The slope <strong>of</strong> the line <strong>of</strong> best fit and its 

coefficient <strong>of</strong> determination (R 2 ) for each model and strip are given in tables 4a – c 

respectively. To check the discrepancies between the predicted and the measured 

values, paired t-test and Root Mean Square Error (RMSE) were used. Values <strong>of</strong> the t- 

test and RMSE for each model and strip are also presented in tables 4a – c respectively. 

The result <strong>of</strong> the slope between the predicted and measured values shows that the 

models satisfactorily predicted the cumulative infiltration for all the strips with values 

ranging from 1.027 to 1.080 and the coefficient <strong>of</strong> determination values lay between 

0.991 and 0.999, which are very high, further shows good predictability <strong>of</strong> the models 

for all the strips. 

Statistical results indicates that all the models used satisfactorily predicted the 

cumulative infiltration given that the t-calculated values for all the models in each strip 

are less than the t-table value (2.131). Therefore, it can be concluded that the fieldmeasured 

cumulative infiltration do not differ from those predicted by the models since 

the observed difference can be accounted for by experimental error. However, RMSE 

was used to determine the accuracy <strong>of</strong> prediction <strong>of</strong> the cumulative infiltration in the 

order K > P > H > KL for strip A and H > K > P > KL for strips B and C respectively. 

Table 4a: Values <strong>of</strong> the performance indices between the predicted and measured 

cumulative infiltration for strip A. 

<strong>Models</strong> Kostiakov Modified Kostiakov Philip Horton 

R 2 0.999 0.996 0.997 0.999 

Slope 1.042 1.061 1.036 1.027 

t-test -0.7731 -0.1591 0.1334 -7.1677 

RMSE 0.0356 0.0604 0.0450 0.0474 

Table 4b: Values <strong>of</strong> the performance indices between the predicted and measured 

cumulative infiltration for strip B. 


R 2 0.997 0.993 0.993 0.999 

Slope 1.065 1.076 1.048 1.037 

t-test -0.8512 -0.2176 0.0333 -7.5251 

RMSE 0.0585 0.0779 0.0635 0.0562


Table 4c: Values <strong>of</strong> the performance indices between the predicted and measured 

cumulative infiltration for strip C. 


R 2 0.996 0.991 0.992 0.999 

Slope 1.073 1.080 1.051 1.036 

t-test -0.7174 -0.0640 0.0636 -8.4622 

RMSE 0.0667 0.0845 0.0681 0.0586 

The pooled analyzes <strong>of</strong> the entire infiltration test <strong>of</strong> the field shows that the four models 

satisfactorily predicted the cumulative infiltration <strong>of</strong> the field as shown by the values <strong>of</strong> 

the slope <strong>of</strong> the line <strong>of</strong> best fit, coefficient <strong>of</strong> determination (R 2 ) and t-paired test in 

table 5. In terms <strong>of</strong> accuracy, the infiltration models predicted the cumulative 

infiltration in the order H > K > P > KL owing to discrepancies between the measured 

and predicted. 

Table 5: Average values <strong>of</strong> the performance indices between the predicted and 

measured cumulative infiltration for the field. 


R 2 0.997 0.994 0.994 0.999 

Slope 1.064 1.073 1.044 1.027 

t-test -0.8351 -0.2136 0.06647 -7.3372 

RMSE 0.0558 0.0729 0.0585 0.0439 

The result <strong>of</strong> this study agrees with the findings <strong>of</strong> Al-Azawi (1985), who evaluated six 

infiltration models on a relatively homogenous, coarse-textured soil. He found that 

Horton’s model gave the most satisfactory result; Kostiakov model gave a very good 

representation <strong>of</strong> the infiltration while modified Kostiakov, Green-Ampt, Philip and 

Holtan-Overton performed in that order respectively. Berndtsson (1987) in his study 

“Application <strong>of</strong> <strong>Infiltration</strong> Equation to a Catchment with Large Spatial Variability in 

<strong>Infiltration</strong>” compared two commonly used infiltration equations on a heavy calcareous 

clay soil. The result showed that the Horton equation displayed a slightly better fit to 

observed infiltration as compared with Philip’s equation. Hsu et. al. (2002) evaluated 

three models (Horton, Philip and Green-Ampt) for three soil types to assess the models 

based on Richard’s equation. Result demonstrated that all three equations provided 

similar fits to the numerical results, but the Horton model differed most as compared to 

the other two models in terms <strong>of</strong> infiltration rate. The cumulative infiltrations were


though not distinguishable from each other. This also agrees with the findings <strong>of</strong> Mishra 

et. al. (2003) who ranked Horton models third and fourth after Singh-Yu, and Holtan 

and Huggins-Monke in the first and second positions respectively out <strong>of</strong> sixteen models 

which included Green-Ampt, Kostiakov, modified Kostiakov, Philip, overton 

infiltration equations. 

Conclusion 

Considering the four infiltration models evaluated, the Horton’s models gave best fit to 

the measured cumulative infiltration, although the other models provided good overall 

agreement with the field measured cumulative infiltration depths and are therefore 

capable <strong>of</strong> simulating infiltration under the field conditions encountered in the present 

study. Consequently, the application <strong>of</strong> these equations under verified field conditions 

leads to the determination <strong>of</strong> the appropriate infiltration characteristics for the equations 

that would optimize infiltration simulation, irrigation performance and minimize water 

wastage. 

References 

Al-Azawi, S. A. 1985. Experimental evaluation <strong>of</strong> infiltration models. Journal <strong>of</strong> 

Hydrology. 24(2): 77 – 88 

Berndtsson, R. 1987. Application <strong>of</strong> infiltration equations to a catchment with large 

spatial variability in infiltration. Hydrological Science Journal. 32(3): 399 – 413 

Blair, A.W and Reddell, D.L. 1983. Evaluation <strong>of</strong> Empirical <strong>Infiltration</strong> Equation for 

Blocked Furrow Infiltrometer. Chicago, ASAE Winter Meeting, paper 83-2521. 

Haghiabi, A. H., A bedi-Koupai, J.,Heidarpour, M. and Mohammadzadeh-Habili, J. 

2011. A new method for estimating the parameters <strong>of</strong> Kostiakov and modified 

Kostiakov infiltration equations. World Applied Sciences Journal. 15(1): 129 – 

135 

Hickok, R. B. and Osborn, H.B. 1969. Some limitations on estimates <strong>of</strong> infiltration as a 

basis for predicting watershed run<strong>of</strong>f. Trans. <strong>of</strong> the ASAE, 12 (6): 59- 62 

Horton, R. E. (1940). An approach towards a physical infiltration capacity. <strong>Soil</strong> Science 

Society <strong>of</strong> America Proceedings, Vol. 5, pp 399-417. 

Hsu, S. M., Ni, C. F. and Hung, P. F. 2002. Assessment <strong>of</strong> three infiltration formulas 

based on model fitting on Richards equation. Journal <strong>of</strong> hydrologic Engineering. 

7(5): 373 – 379 

Igbadun, H. E and Idris, U. D. 2007. Performance evaluation <strong>of</strong> infiltration models in a 

hydromorphic soil. Nigeria Journal <strong>of</strong> <strong>Soil</strong> and Environmental Research, Vol. 7 : 

53 – 59


Kostiakov, A. N. 1932. On the Dynamics <strong>of</strong> the Coefficient <strong>of</strong> Water Percolation in <strong>Soil</strong> 

and on the Necessity <strong>of</strong> Studying it from the Dynamic Point <strong>of</strong> View for Purpose 

<strong>of</strong> Amelioration. Trans. 6 th Comm. Int. <strong>Soil</strong> Science Society, Moscow, part A, 

17 

Lewis, M. R. 1937. The rate <strong>of</strong> infiltration <strong>of</strong> water in irrigation practice. Transactions 

<strong>of</strong> the American Geophysical Union 18: 361-368. 

Mbagwu, J. S. C.1997. Quasi-steady infiltration rates <strong>of</strong> highly permeable tropical moist 

savannah soils in relation to land use and pore size distribution. <strong>Soil</strong> Technology 

11: 185 – 195 

Mishra, S. K., Tyagi, J. V. and Singh, V. P. 2003. <strong>Comparison</strong> <strong>of</strong> infiltration models. 

Journal <strong>of</strong> Hydrological Processes. 17: 2629 – 2652 

Musa, J. J. and Adeoye, P. A. 2010. Adaptability <strong>of</strong> infiltration equations to the soils <strong>of</strong> 

the Permanent Site Farm <strong>of</strong> the Federal University <strong>of</strong> Technology, Minna, in the 

Guinea Savannah Zone <strong>of</strong> Nigeria. Au. Journal <strong>of</strong> Technology. 14(2): 147 – 155 

Oku, E. and Aiyelari, A. 2011. Predictability <strong>of</strong> Philip and Kostiakov infiltration model 

under inceptisols in the Humid Forest Zone, Nigeria. Kasetsart Journal (Natural 

Science), 45: 594 -602 

Philip, J. R. 1957b. The theory <strong>of</strong> infiltration: 4. Sorptivity and algebraic infiltration 

equations. <strong>Soil</strong> Science 84: 257-264. 

Rawls, W. J., L. R. Ahuja, D. L. Brakensiek, A. Shirmohammadi. 1993. <strong>Infiltration</strong> and 

soil water movement. In Handbook <strong>of</strong> Hydrology. McGraw-Hill, Inc. 

Serralheiro, R.P. 1988. A Study <strong>of</strong> Furrow Irrigation on a Luvisoil, Evora. Doctoral 

Dissertation Presented to the University <strong>of</strong> Evora. 

Zerihum, D. and Sanchez, C.A. 2003. A Draft Procedure for Development <strong>of</strong> 

Management Curves for Basins Irrigation on the Coarse Texture <strong>Soil</strong> <strong>of</strong> the 

Yuma MESA. www.ag.arizona.edu/aes/yac/researchdawit. Date: 15/10/2011

Comparison of Four Soil Infiltration Models on A Sandy Soil In Lafia ...

Create successful ePaper yourself

Delete template?

Save as template?