FIELD EVALUATION OF PEDOTRANSFER FUNCTIONS TO ESTIMATE SATURATED SOIL HYDRAULIC CONDUCTIVITY

Transcription

1 Proceedings of the South Dakota Academy of Science, Vol. 83 (2004) 197 FIELD EVALUATION OF PEDOTRANSFER FUNCTIONS TO ESTIMATE SATURATED SOIL HYDRAULIC CONDUCTIVITY Darrell W. DeBoer Department of Agricultural and Biosystems Engineering South Dakota State University Brookings, SD R. G. TeKroney Ground Water and Drainage Group U. S. Bureau of Reclamation Denver, CO ABSTRACT Obtaining accurate saturated hydraulic conductivity values for drainage system design purposes is time consuming and expensive. Bureau of Reclamation drainage engineers collected hundreds of soil characteristic data sets for the Lake Plain area of South Dakota. Pedotransfer functions based on a regression relationship of chemical and physical parameters were assessed for an ability to estimate saturated field conductivity values. Two functions explained just 18 and 28 % of the variation in measured conductivity values. One of the functions explained only 13 % of the variation in an independent data set. Keywords Pedotransfer function, hydraulic conductivity, field, measured, estimated INTRODUCTION Accurate measurements or estimations of saturated soil hydraulic conductivities are critical for the optimum design of subsurface drainage systems. Drain spacing relationships are of marginal value without the availability of reliable conductivity data. Estimation of field hydraulic conductivity values for soils found in the Lake Plain area of the James River Valley (Figure 1) is the subject of this paper. An irrigation project, designated as the Oahe Unit, was proposed for the Lake Plain area (Bureau of Reclamation, 1973). One unique feature of the project was the simultaneous installation of a subsurface drainage system and a water distribution system before actual water delivery to the project area. Soils in the Lake Plain area were derived mainly from silty lacustrine (lakebed) sediments. Project lands were designated for areas where sediments varied from 3 to 12 m in thickness overlying a glacial till. Pre-delivery installation of the drainage system

2 198 Proceedings of the South Dakota Academy of Science, Vol. 83 (2004) Figure 1. Field locations for Data Sets A and B within the boundary of the Lake Plain area. was deemed necessary because drainage construction costs were anticipated to be prohibitive after the establishment of a water table due to unstable coarse silt materials and very fine sand located about 2 m below the soil surface. In response to the need for pre-water delivery installation of a subsurface drainage system, the Bureau of Reclamation assigned a team of drainage engineers to assess the drainage characteristics of Lake Plain soils. Since direct in-place measurements of saturated hydraulic conductivity is a laborious and expensive process, the Bureau initiated a special program to evaluate indirect methods for conductivity estimation based on physical and chemical soil parameters (Bureau of Reclamation, 1974). The objective of this paper is to assess the feasibility of estimating field saturated hydraulic conductivity values from a pedotransfer function based on physical and chemical soil parameter values of Oahe Unit soils. PREVIOUS WORK Indirect methods of soil hydraulic conductivity estimation have been used for many years with initial approaches based on utilizing physical properties to estimate conductivity. Baver (1939) found a correlation between pore-size distribution and hydraulic conductivity. Aronovici (1946) established a correlation

3 Proceedings of the South Dakota Academy of Science, Vol. 83 (2004) 199 between percent sand and hydraulic conductivity for silt loam to sandy soils in the Imperial Valley of California. Incorporation of detailed field description of soil structure along with soil pores and texture was used as the basis for placing soils into seven permeability classes ranging from very slow (less than 0.03 m/d) to very rapid (6.0 m/d or more) for soils at 182 locations in the USA (O Neal, 1952). A more recent study by Suleiman and Ritchie (2001) dealing with the use of effective soil porosity (total porosity minus field capacity) to estimate saturated hydraulic conductivity shows a great deal of promise. Soil and water chemistry can also significantly impact the ability of water to move through soils. For example, Quirk and Schofield (1955) found that the proper ratio of electrolyte concentration of irrigation water to percent exchangeable sodium of soil was vital for stable soil conductivity. Hillel (1998) presented a summary of how solute concentrations can significantly affect soil hydraulic conductivity. The terminology pedotransfer function was presented by Bouma (1989) as translating data we have into what we need. Pedotransfer functions can be defined as predictive functions of certain soil properties from other easily, routinely, or cheaply measured properties (McBratney et al., 2002). Many pedotransfer functions have been developed to predict given soil properties for a geographical region. Functions for predicting soil hydraulic properties have been given by Rawls et al. (1991) and Wosten et al. (2001). Saxton et al. (1986) used a multiple regression approach to develop an empirical relationship for the prediction of saturated hydraulic conductivity. STUDY PROCEDURES Bureau of Reclamation drainage engineers conducted the field drainage investigations (Bureau of Reclamation, 1974). Drill crews initiated the field process by digging pilot bore holes that were logged and used to delineate areas where the subsoils appeared to be uniform for in-place hydraulic conductivity tests. One criterion used in the selection of a test site was that it must contain at least a 0.75 m horizon of uniform soil because the conductivity tests were conducted with a 0.6 m test zone and required a minimum of 0.15 m of the same uniform soil below the bottom of the test zone. Tests were conducted in soils below the crop root zone at depths from 1.5 to 5.0 m. Most of the field conductivity tests were conducted where water table or saturated soil conditions were not present. In this case, the shallow well pump-in test (constant head) was used to obtain saturated hydraulic conductivities from 10-cm diameter holes (Bureau of Reclamation, 1978). When a water table was present, single auger pump-out tests were used. Hence, the data sets used in this paper should be considered as pump-in data sets. Five centimeter diameter undisturbed soil cores were collected at each of the test sites. A portion of each core was used for physical and chemical analyses in the laboratory. All laboratory analyses were conducted by Bureau of Reclamation personnel in accord with standard analytical practices. A summary of the physical and chemical parameters used to describe 1963, 1971, 1972, 1973, and

4 200 Proceedings of the South Dakota Academy of Science, Vol. 83 (2004) 1974 field soil data sets is presented in Table 1. These data sets were amalgamated into two unique data sets where Data Set A (sodium was expressed as sodium percent) contained the 1963, 1971, and 1972 data while the 1973 and 1974 data were placed into Data Set B (sodium expressed as exchangeable sodium percent). The two data sets were collected from two different areas within the Lake Plain boundaries (Figure 1). Table 1 soil parameters were used as independent variables in a step-wise multiple regression analysis to determine which, if any, of the parameters can be used to estimate field conductivities. Soil textural classifications were in accord with USDA recommendations. Soil structure and consistency evaluations were made in the field. Table 1. Physical and chemical parameters used to describe field soils. Chemical parameters Electrical conductivity of saturation extract ph of saturation extract Sodium percent or exchangeable sodium percent of saturation extract Physical parameters Sand, silt, and clay percent Soil structure Class (Ex: Coarse, medium, fine) Type (Ex: Platty, laminated, crumb) Grade (Ex: Weak, moderate, strong) Consistency (Ex: Loose, friable, very firm) Sample depth Soil texture (Ex: silt, silt loam, sandy loam) FINDINGS A step-wise multiple regression analysis of values in Data Set A was conducted using 372 measured hydraulic conductivities and associated physical and chemical parameters. Five independent parameters, in order of importance (percent sand, coarse structure class, structure consistency, percent sodium, and electrical conductivity) made significant (5 % level) contributions to the reduction of sum of squares, which resulted in the following predictive relationship. K=0.6096*( *S+0.250*CS *C+0.159*Na *EC) (1) where K = Saturated hydraulic conductivity (m/d) (range: to 1.77 m/d) S = Sand (%) (range: 0.3 to 47.0 %) CS = Coarse structure (if absent = 1, if present =2) C = Structure consistency (range: 1 = loose to 3 = friable to 5 = very firm) Na = Sodium (%) (range: 0.0 to 24.0%) EC = Electrical conductivity (mmho/cm) (range: 0.16 to 15.0 mmho/cm) The value was used to convert conductivity units from in/h to m/d. Hydraulic conductivity increased with an increase in percent sand and sodium

5 Proceedings of the South Dakota Academy of Science, Vol. 83 (2004) 201 and when a coarse soil class was present. A decrease in hydraulic conductivity was caused by an increase in electrical conductivity and a change in soil consistency from loose to very firm. However, these five parameters could only explain 18 % of the variation in measured conductivity values. The standard error of estimate for equation (1) was m/d. A graph showing the relationship between estimated conductivity values, based on equation (1), and measured conductivity values is presented in Figure 2. Data points tend to follow a horizontal trend line rather than the line of equality (1:1). Figure 2. Estimated hydraulic conductivity values derived from regression equation (1) versus measured conductivity values using the combined 1963, 1971 and 1972 data sets (Data Set A). Results of step-wise multiple regression analysis of Data Set B, containing 539 measured hydraulic conductivities, were similar to the results summarized in equation (1). Again five independent parameters, in order of importance (percent sand, structure consistency, electrical conductivity, Sbk structure type, and exchangeable sodium percentage), made statistically significant contributions to the reduction of sum of squares as presented in the following relationship. K=0.6096*( *S 0.168*C *EC 0.970*Sbk *ESP) (2) where K = Hydraulic conductivity (m/d) (range: to 3.17m/d) S = Sand (%) (range: 1.0 to 71.0 %) C = Structure consistency (range: 1 = loose to 3 = friable to 5 = very firm) EC = Electrical conductivity (mmho/cm) (range: 0.4 to 19.5 mmho/cm) Sbk = Sub angular blocky structure type (if absent = 1, if present =2) ESP = Exchangeable sodium percent (%) (range: 0.0 to 24.5 %)

6 202 Proceedings of the South Dakota Academy of Science, Vol. 83 (2004) Hydraulic conductivity increased with an increase in percent sand and electrical conductivity. It decreased with an increase in percent exchangeable sodium, when a Sbk structure type was present, and with a change in soil consistency from loose to very firm. These five parameters accounted for 28 % of the variation in measured conductivity values and produced a standard error of estimate for equation (2) of m/d. However, the arithmetic signs for the electrical conductivity and sodium related parameters are different in the two relationships. A graph showing the relationship between estimated conductivity values, based on equation (2) and measured conductivity values is presented in Figure 3. Data values tended to cluster around the line of equality (1:1) for measured values of less than 1.0 m/d but then followed a horizontal trend line for the few values greater than 1.0 m/d. Figure 3. Estimated hydraulic conductivity values derived from regression equation (2) versus measured conductivity values using the combined 1973 and 1974 data sets (Data Set B). The ultimate test of a proposed estimation relationship is to evaluate its performance against an independent data set. Since the data sets are for two different regions within the Lake Plain area and since two different sodium parameters were used to assess the impact of sodium on conductivity in the developed predictive relationships, we do not have an unbiased avenue for the evaluation of a predictive relationship. However, if we consider that the sodium parameter in relationship (2) was responsible for reducing the total sum of squares by less than 1 of the 28 % (reduction becomes 27 %) associated with the relationship, removal of the sodium parameter from relationship (2) should provide some

7 Proceedings of the South Dakota Academy of Science, Vol. 83 (2004) 203 insight regarding the utility of using the relationship for estimation purposes. Relationship (2) without the exchangeable sodium percent parameter was then used to make conductivity predictions using Data Set A soil parameters. The results were far from satisfactory as the output of relationship (2) was able to describe only 13 % of the sum of squares of the measured Data Set A conductivity values. It appears that the chemical and physical parameters used in this study cannot be used to accurately predict saturated hydraulic conductivity of Lake Plain soils. SUMMARY AND DISCUSSION Two independent data sets composed of measured saturated soil hydraulic conductivities and physical and chemical soil parameters were used to evaluate an indirect method for estimating saturated hydraulic conductivity in the field. One data set was comprised of 372 data elements (collected in Spink County) and the second set contained 539 elements (collected in Brown County). Both sets were confined to the boundaries of the Lake Plain area of James River valley in South Dakota, and all measurements were made below the crop root zone. Percent sand and parameters related to soil structure were the most statistically significant soil parameters for the prediction of saturated hydraulic conductivity. However, no satisfactory predictive relationships were developed for the subsoils of the Lake Plain area. Recent literature indicates that inclusion of accurate descriptions of soil morphological characteristics is critical for the development of improved pedotransfer functions (McKenzie et al., 1991 and Lin et al. 1999). Techniques for proper quantification of soil structure information are still being developed. ACKNOWLEDGEMENTS This work was supported by the South Dakota Agricultural Experiment Station and approved as Journal Article No LITERATURE CITED Aronovici, V.S The mechanical analysis as an index of subsoil permeability, Soil Sci. Soc. Am. Proc. 11: Baver, L. D Soil permeability in relation to non-capillary porosity. Soil Sci. Soc. of Am. Proc. 3: Bouma, J Using soil survey data for quantitative land evaluation. Advances in Soil Science 9: Bureau of Reclamation Final Environmental Impact Statement Initial Stage - Oahe Unit. US Dept. of The Interior, Denver, CO Bureau of Reclamation Unpublished Oahe Unit hydraulic conductivity data. US Dept. of The Interior, Denver, CO Bureau of Reclamation Drainage Manual. US Govt. Printing Office, Washington, DC.

8 204 Proceedings of the South Dakota Academy of Science, Vol. 83 (2004) Hillel, D Environmental soil physics. Academic Press, San Diego. 771 pp. Lin, H. S., McInnes, K. J., Wilding, L. P. and Hallmark, C. T Effects of soil morphology on hydraulic properties: II. Hydraulic pedotransfer functions. Soil Sci. Soc. Am. J. 63: McBratney, A. B., Minasny, B, Cattle, S. R. and Vervoort, R. W From pedotransfer functions to soil interference systems. Geoderma 109: McKenzie, N. J., Smettem, K. R. J. and Ringrose-Voase, A. J Evaluation of methods for inferring air and water properties of soils from field morphology. Aust. J. Soil Res. 29: O Neal, A. M A key for evaluating soil permeability by means of certain field clues. Soil Sci. Soc. Of Am. Proc. 16: Quirk, J. P. and Schofield, R. K The effect of electrolyte concentration on soil permeability. J. Soil Sci. 6: Rawls, W. J., Gish, T. J. and Brakensiek, D. L Estimating soil water retention from soil physical properties and characteristics. Adv. in Soil Sci. 16: Saxton, K. E., Rawls, W. J., Romberger, J. S. and Papendick, R. I Estimating generalized soil water characteristics from soil texture. Soil Sci. Soc. Of Am. Jl. 50: Suleiman, A.A. and Ritchie, J.T Estimating saturated hydraulic conductivity from soil porosity. Trans ASAE 44(2): Wosten, J. H. M., Pachepsky, Y. A. and Rawls, W. J Pedotransfer functions: bridging gap between available basic soil data and missing soil hydraulic characteristics. Jl. of Hydrology 251: