Evaluation Of Techniques For Determination Of Saturated Hydraulic Conductivity In The Vadose Zone

Saturated hydraulic conductivity of a soil (Ks) is a measure of a soil's ability to transmit water in a water-saturated state. Infiltration, drainage, and groundwater pollution are strongly influenced by the magnitude and spatial distribution of the vadose zone field saturated soil hydraulic conductivity (Kfs). There are numerous methods of estimating K, ranging from direct measurement in the laboratory or in situ to models that use only basic soil data (e.g. soil textural classes, bulk density, Db, organic matter, OM, or porosity, E,). However, the results from different measuring techniques vary under different field conditions. In this study of Serdang Series soils found in the Universiti Putra Malaysia (UPM) campus, soil K, values were collected at different depths using three direct methods. Estimation of K, were done using six empirical models. The direct methods were in situ techniques of Guelph Permeameter (GP) and double ring infiltrometer (DRI), and constant head pcrrneameters (SCHP), a laboratory technique on intact soil cores extracted from the same site at different depths. Predictive models included models of Cosby et al. (1 984); Brakensiek et al (1984); Saxton et a1 (1986); Vereccken et al. (1990); Sabro (1992) and Amin et al. (1997). In this study of K, in the vadose zone, the focus was towards comparison of measurements in the field to those of extracted samples from the same site, but determined by laboratory testing, under controlled condition, and those estimated from empirical models. In addition, a model was developed for determining K, values based on seven basic soil properties (sand, silt, clay, Db, moisture content (MC), E and OM). The results of the comparison showed that the geometric mean of K, values obtained by the three experimental methods varied from 7.333 x 1 o - t~o 1.3 15 x 1 0-2 cm S-I (6.34 x 1 o-' m I day to 11.36 m 1 day). The GP method yielded the widest range from 7.333 x 1 0-8 to 1.654 x 1 0-3 cm s-' while the SCHP yielded the narrowest range from 4.4 x 10" to 1.3 15 x cm s-'. Geometric mean K, values were 27 to 360 times greater for the SCHP compared to the GP method and were significantly different at all depths. Measurements of Ks for the soil under consideration indicate that the DRI and GP methods provided reasonable similar values at the topsoil layer (0-1 5 cm). While the geometric mean Ks values measured by the DRI method was statistically different from those obtained by SCHP method at 0-1 5 cm depth. The laboratory technique yielded greater standard deviation (SD) at the 30 cm and 60 cni depths. Some soil cores may have more macropores than others. whereas the coefficient of variation values were greater for the GP method. The GP produced in situ calculation of Kfs in a relatively short time (25 to 90 minutes for a single measurement) compared to DRI (1 20-1 80 minutes) and SCHP (1 500-1 660 minutes).The results of the multiplc regression analysis indicated that the significant Intercorrelations limitcd the numbcr of useful functional relationships that could be der~ved from the seven variables (Textural classes, Dh, MC, E, and OM). The results of regress~on for full data set showed that only simple function based on silt content and OM gave a significant relationship with K, at 0.05 level, but only 10.5 n/o of variability in K1, was explained by those variables. There was a significant relationship between K, and the input variables at each depth. These relationships however were different at each depth. The best models found from this study at depth of 0- 15 cm, have silt. sand, E, and MC; at depths of 15-30 cm have silt. sand, and E; at depths of 30-60 cm have clay, sand, OM, and MC: and at depths of 60-90 cm silt. Db and E with values of R'= 0.57, 0.50, 0.41 and 0.74, respectively. In this study the geometric mean error ratio (GMER) and geometric standard deviation error ratio (GSDER) were used to evaluate the applicability of the selected empirical models. The results showed that model of Amin et a1 (1997) produced noticeably best results with GMER closest to 1 (0.54) and the lowest GSDER (7.64) of the models tested here. This is followed by the Jabro (1992) model with GMER (0.43) and GSDER (10.22), then Brakensiek et a1 (1984) with GMER (0.43) and GSDER (15.6). It consequently appeared, at least for this soil (Serdang Series), that of the six models compared in this study. the Amin et a1 model was the model of choice for the prediction of K,. The second best model was labro model whereas the model of Brakensiek et al. ranked third.Comparison between the methods was hampered by a number of factors. It was difficult to discriminate between spatial variables of Ks and errors related to the methods. Different sample volumes and sample numbers were used. Comparisons made between different K, measurements in the field are subject to natural soil variations that may be larger than the differences between methods. Findings of this study can be used as a guideline for application of these methods particularly to the same soil type and depth setup. The correct use of any of these methods for one of the most extensive and productive soils in Selangor (Serdang Series) could be highly beneficial to the agricultural sector.

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