2 it: . .. h -2 2.3.1:...5. .2: 2!. i..3...:a 5 1...: . I (’7) l‘l‘3‘w‘nzil‘n 5.1. ! 55.; .7. -3. a .3 .5: 2.1.3.1.: :3 . . ‘ 154 35 yLo..4...i. ‘ a .. £ .5; us .. .4... 3.... 3r 5‘. ‘\ 1‘ r. b: l‘\‘. 2“ ‘1':‘ll‘..$i -‘ .«islrpix.i> .. 1.3.x .511: 5 1.9L. . ‘ a 5...?» is... .41 4o 3.. 1.1.11: .1 I... 3.9 21% :3 1:52.35 t 3.. ‘1 . |\A..3 n...» lllllllllllllllllllllllllllll’IIHHIllllllhlllhlllllllll 31293 01415 3476 This is to certify that the dissertation entitled SPATIAL AND TEMPORAL SOIL WATER CONTENT CHANGES WITHIN A SLOPING LANDSCAPE presented by Martin John Rosek has been accepted towards fulfillment of the requirements for Doctoral degree in Crop and Soil Sciences Dr. James R. Crum Major professor Date October 8, 1994 MSU is an Affirmative Action/Equal Opportunity Institution 0-12771 44... W'— m LIBRARY Mlchlgan State Unlverslty PLACE It RETURN BOXtonmwombdnckoume TOAVOIDFINESMonorbdondu-M MSU IIMMWVENOMIW W1 SPATIAL AND TEMPORAL SOIL WATER CONTENT CHANGES WITHIN A SLOPING LANDSCAPE By Martin John Rosek A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Crop and Soil Sciences ] 994 es: Ne. 30'; ABSTRACT SPATIAL AND TEMPORAL SOIL WATER CONTENT CHANGES WITHIN A SLOPING LANDSCAPE. By Martin John Rosek Soil physical properties and the amount of soil water within a sloping landscape are largely determined by landscape position. This study determined temporal dynamics of soil water within a sloping landscape by (l) examining the spatial variability of selected soil properties that regulate water retention; (2) quantifying water balance by slope position; (3) determining the minimum amount of stored soil water data required to estimate the amount of soil water within a sloping landscape using geostatistics. Neutron probe access tubes were placed at two meter intervals, in two transects, across a sloping topography of Kalamazoo loam (fine-loamy, mixed, mesic, Typic Hapludalfs) and Oshtemo sandy loam (coarse-loamy, mixed mesic, Typic Hapludalfs) at Kellogg Biological Station in southwestern Michigan. Volumetric water content of the soil was monitored approximately weekly in the spring, summer and autumn of 1990 and 1991 at 15, 30, 60, 90, 120, and 150 cm by neutron attenuation. Soil samples from a 48 by 34 meter grid between the access tubes were described and sampled. Water content of each sample point was estimated with CERES Maize for two sample dates. Selected volumetric water per 150 cm soil depth values were removed from the data sets, then kriged and cokriged for m soil water at locations where data points were removed. The Ap horizon of the lower backslope, footslope, and toeslope display a greater mean thickness, percent silt and organic carbon, relative to upslope soils. The Btl horizon and control-section display a greater mean silt and clay below the middle of the backslope. The amount of soil water was least within the upper backslope position, moderate within the summit and lower backslope positions, and greatest within the footslope and toeslope positions. Cokriging estimation of stored soil water, using mm soil water per 150 cm soil depth at field capacity as the auxiliary variable, reduced the required sample distance to twice the range of spatial dependence in the direction parallel to the contour of the slope. Variogram models of the amount of soil water from large data sets could be used to predict the amount of soil water in other studies with similar landscape and soil conditions. ACKNOWLEDGMENTS I wish to express my appreciation and gratitude to: Dr. James Crum, my major advisor, for his patience and guidance throughout the research project. The graduate committee: Dr. Delbert Mokma, Dr. Fran Pierce, and Dr. James Hart. Jim Bronson, the Kellogg Biological Station farm manager, and his crew; Greg Parker, Sam Akough, and Jim Przewosniak for their help and assistance. My loving wife, Janet, and our two children, Alyssa and Jacob, who have made all my efforts worth while. My parents, Edward and Marie Rosek, for their financial support and encouragement. My mother-in-law, Alice Heim, for her financial support and the purchase of the computer that made this dissertation possible. My sister, Debbie F rechette, for her expression of faith in me during the duration of this project. iv Ch. Chg. l~.Yl-.I\JR.SR Gener TABLE OF CONTENTS List of Tables ............................................................................................... vii List of Figures ............................................................................................. viii Introduction ................................................................................................... 1 References .............................................................................................. 10 Chapter 1. Literature Review. Movement of Water On and Within Slopes ............................................. 11 Soil Water Balance ................................................................................. 15 Relationships of Soil Development and Landscape .................................. 17 Soil Spatial Variability and Geostatistics ................................................. 19 References .............................................................................................. 28 Chapter 2. Spatial Variability of Sand, Silt, and Clay Content, and Horizon Thickness of Soils Within a Sloping Landscape. Abstract ................................................................................................. 34 Introduction ........................................................................................... 35 Materials and Methods ....................................................... . .............. ....36 Results and Discussion ........................................................................... 43 Summary and Conclusions ..................................................................... 83 References ............................................................................................. 84 Chapter 3. Soil Water Balance and Geostatistical Estimation of Stored Soil Water in a Sloping Landscape. Abstract ........... . ...................................................................................... 87 Introduction ............................................................................................ 88 Materials and Methods ............................................................................ 91 Results and Discussion .......................................................................... 99 Summary and Conclusions ..................................................................... 124 References ............................................................................................. l 26 General Conclusions .................................................................................... 131 Appendices. Appendix A. Soil Profile Horizon Depth, Particle Size Distribution, Control Section Clay Content, and Soil Series Data ................................. 134 Appendix B. Soil Moisture Retention Input for CERES Maize simulation of Soil-Water Content ............................................................................. 154 vi LIST OF TABLES Table 2.1. Location of slope units at the study site ................................................... 36 Table 2.2. Descriptive statistics for variables determined in the field and laboratory ............................................................................................. 51 Table 2.3. Descriptive statistics as a function of slope position for selected soil properities ....................................................................................... 53 Table 2.4. Semivariance statistics for soil properties ................................................ 60 Table 2.5. Pooled estimation variance of block kriging ............................................. 80 Table 3.1. Average soil profile water retention (0 to 150 cm) at field capacity for each landscape position ................................................................. 102 Table 3.2. Changes in Runofl‘ Curve Numbers ........................................................ 109 Table 3.3. Changes in average field capacity volumetric moisture content for the south transect ....................................................................................... 112 Table 3.4. Changes in average field capacity volumetric moisture content for the north transect ........................................................................................ 113 Table 3.5. Statistical comparison of measured and simulated values of mm soil water per 150 cm soil ...................................................................... 118 Table 3.6. Water flux amounts for each slope position ........................................... 120 Table 3.7. Semivariance and cross-semivariance statistics ...................................... 123 Table 3.8. Statistical comparison of CERES mm soil water values of mm soil water per 150cm soil with kriged and cokriged estimates ........................... 123 Table 3.9. Pooled kriging estimation variance ........................................................ 124 vii LIST OF FIGURES Figure i. 1. General conceptual model for the LTER project ....................................... 2 Figure i.2. Location of Kellogg Biological Station (KBS) .......................................... 5 Figure i.3. Schematic diagram of the LTER with the location of the study area .......... 6 Figure i.4. Topography of the study area, looking south ............................................ 7 Figure 1.1. Components of the hillslope hydrological cycle ...................................... 12 Figure 1.2. Portion of a hypothetical grid, illustrating semivariance computation for given lags ........................................................................................ 21 Figure 1.3. Hypothetical semivariogram ................................................................... 21 Figure 1.4. Weights for kriging point P ........................... . ........................................ 24 Figure 1.5. Geometric anisotropy about a sample ..................................................... 24 Figure 2.1. Typical Kalamazoo and Oshtemo soil profiles ........................................ 37 Figure 2.2. Study site surface 3 dimentional plot ...................................................... 38 Figure 2.3. Control—section percent clay of the study site ......................................... 40 Figure 2.4. Hypothetical semivariogram ................................................................... 42 Figure 2.5. Frequency of Ap, Btl and ZBt2 horizon percent clay ............................. 44 Figure 2.6. Frequency fo conntrol-section percent clay and clay mass ...................... 45 Figure 2.7. Frequency of Ap, Btl , and 2Bt2 horizon percent sand ............................ 46 Figure 2.8. Frequency of Ap, Btl, and ZBtZ percent silt ........................................... 47 Figure 2.9. Frequency of Ap, Btl, And ZBt2 horizon thickness ................................ 48 Figure 2.10. Frequency of Btl, ZBt2, and soil profile clay mass ............................... 49 Figure 2.11. Frequency of Ap percent organic carbon and solum thickness .............. 50 viii Figure 2.12. Semivariograrns for Ap and Btl percent clay, sand, and silt; and ZBt2 percent sand and silt ........................................................................................... 57 Figure 2.13 Semivariograms for Ap percent organic carbon; Ap, Btl, ZBt2, and solum thickness .................................................................................................. 58 Figure 2.14 Semivariograms for control-section percent clay, Btl, ZBt2, control- section, and soil profile clay mass ....................................................................... 59 Figure 2.15 Block-kriged map of Ap horizon percent organic carbon ....................... 62 Figure 2.16 Block-kriged map of Ap horizon percent clay ....................................... 63 Figure 2.17 Block-kriged map of Btl horizon percent clay ...................................... 64 Figure 2.18. Block-kriged map of 2Bt2 horizon percent clay .................................... 65 Figure 2.19. Block-kriged map of control-section percent clay ................................. 66 Figure 2.20. Block-kriged map of Ap horizon percent sand ...................................... 67 Figure 2.21. Block-kriged map of Btl horizon percent sand ..................................... 68 Figure 2.22. Block-kriged map of Ap horizon percent silt ........................................ 69 Figure 2.23. Block-kriged map of Btl horizon percent silt ....................................... 70 Figure 2.24. Block-kriged map of Ap horizon thickness ........................................... 71 Figure 2.25. Block-kriged map of Btl horizon thickness .......................................... 72 Figure 2.26. Block-kriged map of 2Bt2 horizon thickness ........................................ 73 Figure 2.27. Block-kriged map of solum thickness ...................... . ............................ 74 Figure 2.28. Block-kriged map of Btl horizon clay mass ......................................... 75 Figure 2.29. Block-kriged map of 2Bt2 horizon clay mass ....................................... 76 Figure 2.30. Block-kriged map of control-section clay mass ..................................... 77 Figure 2.31. Block-kriged map of soil profile clay mass ........................................... 78 Figure 2.32. Estimation variance of Ap horizon percent clay .................................. 81 ix rig Fi; F13 F13 F11 F11 Figure 2.33. Estimation variance of Ap horizon percent silt ...................................... 82 Figure 3.1. Study site surface 3 dimentional plot ...................................................... 92 Figure 3.2. Typical Kalamazoo and Oshtemo soil profiles ........................................ 94 Figure 3.3. Topography (relative meters) of the study site with sample point locations .................................................................................................... 97 Figure 3.4. Slope profile of the south transect, showing the location of each neutron probe access tube ..................................................................... 100 Figure 3.5. Slope profile of the north transect, showing the location of each neutron probe access tube ..................................................................... 101 Figure 3.6. Precipitation and average daily temperature for the 1990 field study period .............................................................................................. 103 Figure 3.7. Precipitation and average daily temperature for the 1991 field study period .............................................................................................. 104 Figure 3.8. Average total soil water of the south transect for 1990 ......................... 105 Figure 3.9. Average total soil water of the north transect for 1990 ......................... 106 Figure 3.10. Average total soil water of the south transect for 1991 ....................... 107 Figure 3.11. Average total soil water of the north transect for 1991 ....................... 108 Figure 3.12. Simulated total soil water of the south transect for 1990 .................... 114 Figure 3.13. Simulated total soil water of the north transect for 1990 ..................... 115 Figure 3.14. Simulated total soil water of the south transect for 1991 .................... 116 Figure 3.15. Simulated total soil water of the north transect for 1991 ..................... 117 Figure 3.16. Semivariograms and cross-semivariograms for soil water properties ......................................................................................... 122 m. an la: Slo nu: 001 We ”led INTRODUCTION In 1987 a Long Term Ecological Research (LTER) project in agroecosystems was established at the Kellogg Biological Station (KBS) in southwest Michigan. The goal of this research was to understand interactions among organisms and between organisms and their environment in agricultural ecosystems sufficiently well to manage these interactions for levels of agronomic yield that are economically and environmentally sound. The global hypothesis of the LTER project was that agronomic management based on ecological concepts can substitute for reliance on chemical subsidies in production-level agriculture (Robertson et al., 1986). Management of soil-water, on an ecological basis (i.e., cropping systems and soil management practices), can substitute for or reduce producer water inputs (irrigation) and reduce soil-water outputs. Effective crop management should incorporate landscape position based practices (i.e., variable planting and fertility rates for each slope position, contour plowing, grassed waterways) to reduce losses of water, nutrients, and pesticides, from the cropping system to groundwater and atmospheric outputs. This is illustrated in the conceptual model for the LTER project (Figure i. 1). Effective management of soil-water within agricultural landscapes, which minimize external inputs and outputs and optimize yield, requires a solid understanding of the mechanisms that regulate soil-water-landscape interactions. Soil productivity, water movement, and nutrient losses are inherently governed by landscape position. Water is the medium in which biological and chemical transformations of nutrients occur and in which different nutrient forms move and are transported in the soil profile, either to plant roots or out of the profile and eventually into the ground water Chemical subsidies Natural Inputs Management practice ‘ Landscape position Nutrient AvaHabHHy Microbial dynamics Substrate/$0M availability Macroinvertebraie activrty Ule history strategies Time oi germination/growth Herbicide resistance Herbivore pop'n shifts Predation pressure Pesticide elticiency Crop Productivity Plant competition Insect! Pathogen dynami C. N Allocation I Management practices Landscape position Groundwater. runoit, and atmospheric outputs Figure i. 1. General conceptual model for the LTER project. (Nielsen et al., 1973). To predict nutrient behavior in soil, therefore, one must first be able to predict water retention and distribution. Water distribution studies are made more complex soil by characteristics common to most soils. These characteristics complicate the prediction of water distribution under a variety of landscape conditions. Much of the literature in soil spatial variability suggests that most soil profile characteristics contain components that are spatially dependent within single map units and even individual fields (Campbell, 1977, 1978; Burgess and Webster, 1980; Gajem et al., 1981; Vieira et al., 1981; Yost et al., 1982). Since soil-water distribution is a function of soil profile characteristics, the quantification of the spatial variability of those profile characteristics should make it possible to predict the spatial variability of water distribution for a given set of soil and landscape conditions. This study makes such a quantification and comparison between soil profile and soil-water properties for the characterization of soil-water distribution. Water can be a major limiting factor for crop production in sandy soils of southwest Michigan. Since these soils are formed in glacial materials, they can be quite variable, and have significant differences in water holding capacity within the landscape. Many of the landscapes where these soils are found have complex topographies, which contributes to the differential drying patterns. These soils tend to loose water first on the more steeply sloping (backslope) positions, followed by the upper more level area (summit), while the lower portion of the landscape below the backslope (toeslope) tends to dry last (Helvey et al., 1972; Hall, 1983). Knowledge of water retention and distribution in soils can be used to more efficiently manage agricultural ecosystems. For example, the yield potential of soils within sloping landscapes varies dramatically, mainly because of differences in the ability to retain and supply plant available water. To more efficiently manage such systems, different amounts of inputs (i.e. plant population, fertilizer, pesticides, etc.) should be made to the 1 land Stud 4 the different soils, that have variable productivity's, on the various parts of the landscape. Study Site The study was conducted at the W. K. Kellogg Biological Station (KBS), in the northeast corner of Kalamazoo County, Michigan (Figure i.2). KBS is situated on the pitted Galesburg-Vicksburg outwash plain (Monahan et al., 1983). The study area is located at the east end of the north (reserve) field of the LTER (Figure i.3). A small north-south valley dissects a level plain where the valley floor dips downslope north to an outlet (Figure i.4). The valley is approximately 4.4 meters deep and fi'om 90 to 140 meters wide. The study site consisted of distinct summit, backslope, and toeslope components (Ruhe, 1960) and the research was conducted on the east side of the valley where the gradient of the backslope ranged from 4.6% to 5.6%. The backslope on the west side of the valley ranged from 8% to 10%. The dominant soils on this landscape are Kalamazoo Loam (Fine-loamy, mixed, mesic, Typic Hapludalfs) and Oshtemo Sandy Loam (Coarse-loamy, mixed, mesic, Typic Hapludalfs). Stratified sand and gravel is found from the lower profile of these soils to the contact with glacial till, which is approximately 15 meters below the surface, as indicated by well logs from the area. Both Kalamazoo and Oshtemo soils are well drained and are found on all positions of the landscape, including the toeslope. Hypotheses It is the hypothesis of this study that landscape position controls soil physical properties through soil formation (i.e. organic matter accumulation, formation of soil horizon and soil structure, and clay translocation) which in turn effects soil moisture. Fi Kalamazoo County Figure i.2. Location of Kellogg Biological Station (KB S). KBS LTER Site 1:] Study Area 100m Figure i.3. Schematic diagram of the LTER with the location of the study area. \ .~ 7 . .. \ \ qflfi--4-_- ”.m “EV m.m 0; M6 coLgo>m~m m) Figure i.4. T0pography of the study area, looking south. There in lhe 50115 8 Landscape position, in part, determines how much precipitation infiltrates into the soil profile. Infiltration of precipitation is a major factor in soil development and soil-water content. On a landscape with summit, backslope, and toeslope components, overland and lateral subsurface flow of water is most likely to be small on the summit and toeslope positions, while possibly significant on the backslope. Overland and lateral subsurface flow from the backslope is likely to become overland and subsurface flow to the toeslope, adding water to the soils in the toeslope position. Thus, in a sloping landscape where all of the soils are well drained, such as the study site, infiltration of precipitation may be lowest in the backslope soils, moderate in the summit soils, and greatest in the toeslope soils. From this the following corollaries of this study are made: 1. Soil water content is affected by landscape position in the following modes: 3. Soil water content is directly affected by landscape position due to the effect of runofl‘ and runon. b. Within a given landscape where the parent material is the same throughtout the landscape, soil-water content is indirectly affected by landscape position because the degree of soil development, erosion, and deposition effect soil water holding capacity. The more developed the soil profile (on a given landscape) the greater its water holding capacity. Soil development is dependent on the amount of precipitation and infiltration. Thus, in this sloping landscape where all the soils are well drained, soil development is greatest in the toeslope soils, moderate in the summit soils, and least in backslope soils. Therefore, for much of any given year, the amount of soil water will be greatest in the toeslope soils, moderate in the summit soils, and lowest in the backslope soils. 9 2. Soil development and the amount of soil water vary spatially on this sloping landscape. Objectives The goal of this research was to detemrine the temporal dynamics of soil water within a leping landscape. This goal was achieved by the following objectives: 1. Characterize the spatial distribution of soils within a sloping, erosional landscape at Kellogg Biological Station (KBS) in southwest Michigan. 2. Determine water balance that occurs within each slope component of the landscape of the study site. 3. Determine the minimum amount of sample data required to characterize the amount of soil water within a sloping landscape at KBS, using soil profile variability. 10 REFERENCES Burgess, TM. and R. Webster. 1980. Optimal interpolation and isarithmic mapping of soil properties. I. The semi-variogram and punctual kriging. J. Soil Sci. 3 1 :3 15-331 . Campbell, J .B. 1977. Variation of selected soil properties across a soil boundary. Soil Sci. Soc. Am. J. 41:578-582. Campbell, 1.3 1978. Spatial variation of sand content and pH within single contiguous delineations of two soil mapping units. Soil Sci. Soc. Am. J. 42:460-464. Gajem, Y.M., A.W. Warrick, and DE. Myers. 1981. Spatial dependence of physical properties of a Typic Torrifluvent soil. Soil Sci. Soc. Am. J. 45:709-715. Hall, GE 1983. Pedology and geomorphology. In Wilding, L.P., NE. Smeck, and GE Hall (eds), Pedogenesis and soil taxonomy. 1. Concepts and interactions. Elsevier, Amsterdam. pl 1 7- 140. Helvey, J.D., J .D. Hewlett, and J .E. Douglass. 1972. Predicting soil moisture in the southern Appalachians. Soil Sci. Soc. Am. Proc. 36:954-959. Monahan, G.W., G.J. Larson, D.W. Forstat, and H0. Sorensen. 1983. Selected geologic maps of Kalamazoo County, Michigan. Mich. Dept. of Nat. Res, Geol. Surv. Div., Lansing, Mich. Nielsen, D.R., J.W. Biggar, and KT. Erh. 1973. Spatial variability of field-measured soil-water properties. Hilgardia 42:215-259. Robertson, G.P., E.A. Paul, M]. King. 1986. Organisms in the agricultural landscape: Long Term Ecological Research. Unpublished. Ruhe, R.V. 1960. Elements of the soil landscape. Trans. 7th Int. Congress Soil Sci, Madison Wise, 4: 165-170. Vieira, S.R., J.L. Nielsen, and J.W. Biggar. 1981. Spatial variability of field-measured infiltration rate. Soil Sci. Soc. Am. J. 45: 1040-1048. Yost, R.S., G. Uehara, and KL. Fox. 1982. Geostatistical analysis of soil chemical properties of large land areas. I. Semi-variogriograms. Soil Sci. Soc. Am. J. 46:1028-1032. 11 CHAPTER 1 LITERATURE REVIEW MOVEMENT OF WATER ON AND WITHIN SLOPES The distribution of water in soils is governed by a complex set of interrelated factors. After climatic effects, the major controlling factors are determined by soil and vegetative properties, topographic characteristics such as slope form and gradient, and positional attributes such as relative height and distance from the slope base (Gerrard, 1981). Water can move across, through, and be stored in soil in a variety of ways as illustrated in Figure 1.1. Of those in Figure 1.1, infiltration, Horton overland flow, saturated overland flow, saturated throughflow, return flow, pipe flow, and deep seepage are the major pathways for water movement on and within slopes. Infiltration is simply the process of water entering the soil. In general,capacity for infiltration displays a rapid initial rate which drops quickly to some constant value. This decrease in infiltration capacity occurs primarily for two reasons. First, as soil- water content increases, wetting causes a reduction of the hydraulic gradient near the surface. Second, reduction of soil pore diameter by clay mineral swelling upon wetting, combined with the washing of fines into soil pores, impede infiltration (Gerrard, 1981). Infiltration rate is determined by external factors such as rainfall intensity and duration and rain drop size, and soil characteristics such as texture, structure, slope, profile depth, type and proportion of clay minerals, vegetation, and land-use (Gerrard, 1981). When water moves through soil, it displaces water previously retained in the soil pores. Water moves into and within soil under the influence of gravitational and capillary forces, the latter being due to attractive molecular forces between soil particles and water which yields very slow water movement (Baver, 193 7). At low soil moisture, most water movement is due to Humus Unsaturated Soil Saturated Water table ]Bcdrock Precipitation (gross rainfall) P Horton overland flow q,, Channel precipitation PC Saturated overland flow q, Precipitation intensity 1' Return flow q, Evapotranspiration e, Pipe flow I Canopy interception loss at Pipe storage 7' Interception and canopy storage I Unsaturated throughflow mu Stemflow and drip 5 Saturated throughflow m, Litter flow / Soil-moisture storage M Litter interception loss 8, Seepage into bedrock so Litter storage L lntertlow in bedrock a Evaporation e Aeration zone storage A Depression storage RP Deep seepage d Detention storage R7 Baseflow b Infiltration f Groundwater storage 8 Figure 1.1. Components of the hillslope hydrological cycle. (from Chorley, 1978) l3 capillarity and takes place in micropores. When soil moisture is between field capacity (soil-moisture content where excess water has drained) and saturation, most water movement is due to gravitational pull, and takes place in macropores. When the ability of the soil to intake water is not surpassed, the amount of water infiltrated is contingent upon the rainfall rate, and is called flux controlled infiltration. Ifthe rainfall rate exceeds the infiltration rate, ponded, or profile-controlled infiltration occurs. When the infiltration capacity of the soil is surpassed, surface ponding occurs and this ponded water moves downslope as surface flow or Horton overland flow (Horton, 1935). This surface flow rarely occurs as a uniform sheet of water and most of the water travels downslope in lateral concentrations. It is generally accepted that these lateral concentrations possess characteristics of sheet flow (Emmett, 1970). At a critical distance downslope overland flow becomes deep enough to cause sheer stress which can dislodge and move surface soil particles causing erosion to occur as rills. Cook (1946) presented a sequence of events that occurs with overland water flow on slopes as follows: a. A thin water layer forms on the surface and downslope surface flow begins; b. The flowing water gathers in surface depressions. c; When firll these depressions overflow; d. Overland flow enters microchannels which coalesce to form rivulets which discharge into gullies; e. Along each microchannel, lateral inflow from the land surface takes place. Horton overland flow happens relatively instantaneously over a basin only if the basin is small and has homogeneous soil, soil moisture, rain interception, and infiltration conditions. Also important, is the lateral downslope movement of water (throughflow) within soil layers (Freeze, 1972, 1974). In soils where there is a discontinuous decrease of hydraulic conductivity with depth, saturation may build up from the base of a soil horizon within which saturated throughflow may occur downslope (Chorley, 1978). Temporary zones of saturation above the groundwater surface have been noted by Burt and Butcher (1985). In this type of water flow soil physical properties and depth l4 assume a large role (Hoover and Hursh, 1943). In coarse-textured soils, vertical flow dominates, while in fine textured soils there is resistance to vertical flow, initiating saturated throughflow. Of major importance is soil structure, especially in fine- textured soils or soil layers, where fissures, cracks, and channels replace textural voids as the main avenues of water flow. The effect of cracks and channels on water movement is enhanced if they penetrate different soil horizons, and lithologic discontinuities (Gerrard, 1981). Differing permeability's greatly enhance lateral downslope throughflow. Differences in soil horizons, dense layers, weathered and unweathered bedrock cause hydraulic discontinuities. Early in a storm event, a saturated wedge forms at the slope base, and throughflow begins. This saturated throughflow increases as the saturated layer becomes thicker, intersecting the ground surface, initiating return flow (Dunne, 1978: Gerrard, 1981), causing saturated overland flow (Kirkby and Chorley, 1967; Kirkby, 1969; Calver et al., 1972; Chorley, 1978). This surface flow is supplemented by direct precipitation onto the saturated area. As the storm continues the saturated wedge increases upslope and the amount of saturated overland flow also increases. Zaslavsky and Sinai (1977, 1981a, b, c) and Sinai et a1. (1981) noted four forms of lateral throughflow (saturated and unsaturated) are induced by rainfall and infiltration: 1. Splash of rain drops lead to larger splash downhill and result in a net lateral flow. 2. Lateral flow is formed in a transition layer between the soil and the air due to its nonuniforrnity, anisotropy, and slope. 3. When the layers of a soil are slanted, lateral flow occurs. This occurs in many alluvial deposits, gemetically formed A and B horizons, and soil layers formed by cultivation. 4. Lateral flow will form in the unsaturated zone immediately above a sloped phreatic surface. 15 These forms of lateral flow start almost immediately after the beginning of the rainfall, but may lag for an increasingly longer time after the rain at increasingly greater soil depths. The lateral flux component is proportional to the slope of the surface or to the slope of the soil layers. As a result, water will accumulate in concave parts of the landscape and diverge from convex positions. Piping provides a macropore network for the quick transmission of throughflow water. Pipe networks are usually discontinuous and may discharge onto the same slope segment as the pipe inlet (Gilman and Newson, 1980). Beasley (1976) noted subsurface flow ofien began shortly after rainfall began even when there was neither saturation at the point of outflow nor high antecedent soil moisture and attributed this phenomena to interconnected channels through the soil formed by decayed roots and animal burrows. Soils most likely to produce pipes are peaty surface horizons and impermeable layers at shallow depth, loamy soils on a steep slope (Gerrard, 1981), and highly dispersive soil layers with a high exchangeable sodium percentage (Omodt et al., 1975). Eluviation in highly dispersive soils forms pipes by allowing the fine earth fraction to be removed progressively through the soil matrix. This is indicated by the deposition of clay and silt where the pipes emerge (Gerrard, 1981). Desiccation creates cracks that act as pipes in cohesive clay soils (Parker, 1964). SOIL WATER BALANCE Water in soils is either in a flowing or stored state. Soil retention storage relies on the concept of field capacity, the amount of water that a soil can permanently hold against the downward pull of gravity (Horton, 1933). Conversely, soil-detention storage consists of soil-water in excess of field capacity which is slowly draining through large, non-capillary pores (Fletcher, 1952: Hoover, 1962). 16 In general, the concept of soil water balance, as outlined by Rouse (1970) is that the change in stored soil water (dS) is the difference between input water (Iw) to the soil and output water fiom the soil (Ow) or; d8 = Iw - Ow. Separating the input and output water into components, the following equation is obtained: dS=P+Ro-Rf-ET-D where the change in soil profile water content, dS, is the result of the input of precipitation, P, and surface and subsurface runon, Ro, minus surface and subsurface runoff, Rf, evapotranspiration, ET, and drainage from the profile, D. Since soil profile drainage is much more difficult to measure than the change in soil moisture content, the above equation can be rearranged to estimate drainage as follows: D=P+Ro-Rf-dS-ET. To determine ET, potential evapotranspiration (PET) may be estimated. There have been several methods developed to estimate PET, among which the Thomthwaite (1948), Penman (1948), and Priestley and Taylor (1972) methods are most widely accepted. Once PET is estimated, it is incorporated into the water balance equation as follows: in periods where D > 0, and AET = PET; D=P+Ro-Rf-dS-PET: in periods where D = 0, and ET 3 PET; ET = P + Ro -Rf- dS. Many computer models have been developed to simulate hydrologic processes associated with water balance in the vadose zone (Baire et al., 1972; M012 and Remsorr, 1971; Parkes and O'Callaghan, 1980; Francis and Pidgeon, 1982; Belmans et al., 1983; Jones and Kiniry, 1986; Workman et al., 1990). Conceptually, mathematical computer models of soil-water balance have been developed using either a parametric 17 or deterministic approach. Each model has strengths and weaknesses, depending on what the user needs the model for and what hydrologic circumstances the model is used to simulate. RELATIONSHIPS OF SOIL DEVELOPMENT AND LANDSCAPE The distribution of water on slopes has a substantial influence on the properties of soils, and water movement integrates soils existing on different parts of the landscape (Gerrard, 1981). This gives rise to the catina concept, first proposed by Milne (1935a, b). A catena is considered to be the interlocking of soils on the landscape. The catena concept has been use interchangeably with the toposequence concept, which relates morphologic changes (especially color) with relative elevation, and thus to water table depth and fluctuation. But catena is also a process-response concept. Not only do the soils of a catena differ morphologically, but differ as a result of erosion, transport and deposition of sediments, as well as leaching, translocation (vertically and laterally), of chemical and particulate matter in the soil (Hall, 1983). Thus, the processes of each soil member of a catena are related to every other soil member, and are continuously . adjusting to the environmental changes of the landscape (Dan and Yaalon, 1964). Moving water is the principal cause of movement of material overland and within sloping soils. The distribution of water and water movement are the primary reasons that different soils are found on a landscape composed of the same parent material (Hall, 1983). Slope gradient and length are very important in regards to movement of water and materials in water on landscapes. As slope gradient increases, flow velocity of surface runofi' increases, and the force of the overland flow increases exponentially (Zingg, 1940). Amemiya (1970) noted that as a given straight slope element increases in length, the flow velocity and volume of water that reach the lower portion is greater. 18 The ability to erode increases as a firnction of increased water flow volume (Wischrneier, 1975). As discussed previously, within a landscape, flowlines of water exist both on the surface and within the soil. These flowlines run straight only where the soil is homogeneous and the contour lines of a slope are parallel. Concave downslope contour lines (coves and headslopes) make for convergent flowlines, and convex downslope contour lines (spurs and noseslopes) make for divergent flowlines (Hall, 1983) There are significant differences in water movement and erosion between convex, concave and straight portions of a slope (Acton, 1965; Gerrard, 1981; King et al., 1983). Water velocity and soil erosion increase on convex segments as slope steepness increases downslope (Meyer and Kramer, 1969). The opposite happens on concave slopes as water velocity and soil erosion decrease with gentler downslope angles. The amount of water that flows over and through (vertically and laterally) soils effects the depth and morphology of the soil. Water that vertically percolates through the soil can leach salts, elements, and oxides in soil-solution as well as translocate silts and clays, which leads to development and deepening of the soil profile. Overland water flow causes erosion which removes material from sloping areas and deposits them on gentler areas downslope, causing thinner profiles on the slopes and thicker profiles in the depressions downslope. Lateral translocation downslope of silts and clays as well as salts and oxides and hydroxides of A1, Fe, Mn, and Si in throughflow water has also been observed (Glazovskaya, 1968; Huggett, 1976). Much research has attempted to relate processes to the occurrence of soils on specific landscape positions (Acton, 1965; Beckett, 1968; Ruhe and Walker, 1968; Walker and Ruhe, 1968; Malo et al., 1974; Huggett, 1975; Conacher and Dalrymple, 1977; Davidson, 1977; Krikby, 1977; King et al., 1983). Ruhe (1960) developed the 19 most widely used system for describing landscape units. These include the summit, shoulder, backslope, footslope, and toeslope. On summits, water movement in soils is predominantly vertical, except near the transition to the shoulder. Solum thickness is dependent upon soil permeability and amount and frequency of rainfall. As a result, the summit is a very stable element of the landscape (Hall, 1983). Shoulder positions are almost always convex. This increases surface runoff and decreases infiltration, resulting in a highly erosional and unstable surface with thin soil profiles. Lateral mass movement downslope of surface material (soil creep) may occur. Lateral throughflow may also be an important process within this position (Hall, 1983). Lateral transport of water and material (both surface and subsurface) is very important on and within the backslope position. Soil creep may also occur. If the backslope is relatively smooth, surface transport of material will be uniform. The soils of the backslope are thinner than the other hillslope elements except the shoulder. Footslopes are almost always concave, resulting in increased infiltration and deposition of material. Thickness of soil profiles vary, but tend to increase downslope. Toeslope positions are constructional in nature and thus unstable. Alluvial material from up valley and/or the adjacent footslopes are frequently being deposited on this position, resulting in very thick A horizons. SOIL SPATIAL VARIABILITY AND GEOSTATISTICS Soil variability consists of systematic and random components (Trangmar et al., 1985). Wilding and Drees (1983) describe systematic soil variation as a gradual or distinct change in soil properties as a firnction of landform, geomorphic elements and soil forming factors, and/or man induced management. Observed differences in soil properties that cannot be related to known causes is termed "random" variation. Systematic soil forming processes and landscape element position determine soil occurrence. As such, classical statistics may not be appropriate to analyze soil :‘V and 20 properties, which vary spatially. The theory of regionalized variables (Matheron, 1971), the basis for geostatistics, takes into account both systematic and random variation of spatially distributed variables. Interpolation of spatially dependent variables was first developed by Krige (1966) to estimate gold content of ore deposits in the South Afiican mining industry. Krige's procedures were expanded by Matheron (1971) into the theory of regionalized variables which forms the basis of techniques for estimation of spatially dependent variables. These techniques are known as geostatistics. Geostatistics are founded on three concepts; regionalized variables, random functions, and stationarity. If a set of a measured property of individuals is characterized by some probability distribution law, then it is a random variable (2). Examples of random variables in soil research include percent clay, pH, organic matter content, and bulk density. If the value of the random variable is dependent on the position (x) it was sampled, it is a regionalized variable and its location can be used in statistical analysis. If an infinite set of random variables are considered with their sample locations, then the regionalized variable becomes a member of an infinite set of random variables for all locations within the considered region (Trangmar et al., 1985). Such a set is called a random function Z(x). If the expected value of the random function Z(x) is the same in all locations in the considered region, then it has first-order stationary. Expressed statistically E[Z(x)] = m = mean and E[Z(x)-Z(x+h)] = 0 where h is the vector of separation between sample positions, termed the lag (Figure 1.2) (Trangmar et al., 1985). For all observation pairs separated by lag h, the intrinsic hypothesis requires the variance of the increment Z(x)-Z(x+h) be finite and independent of position within the ill- teed ., ”It: II in f °// i n éure I 3 21 I I I I I I I Lag 1 .§o Lag 1.4 .eeeeeeee.eeeeele- I LagZD Figure 1.2. Portion of a hypothetical grid, illustrating semivariance computation for given lags. (from Johnson, 1990) it sm -— ................ 1k”- 15$: Explained _ Variance I" 1.0 Nugget Variance " Range — T I r j t 1 1 T O 2 4 6 8 Figure 1.3. Hypothetical semivariogram. Numbers refer to lags. (from Johnson, 1990) considered r Tris defib‘ their dlilde‘ mIiSIiC “h ’ 01.39”” 13: music hi?‘ I 5" M I 1313371 3110.3 there there 3 The plot ( Emir?” ' (S. a; ‘N large). the r. 13) The sill of reiatit e1} c attabie ex tit Fitch ' es the Si .rianance 22 considered region: VAR[Z(x)-Z(x-h)]=E[Z(x)-Z(x-h)]2 =21(h) This describes the variance of the difference between pairs of observations, which when divided by two, yields a per-observation variance known as the semivariance statistic t(h). The semivariance is a measure of the average similarity between points of a given lag distance apart. The more alike the measurements of the points are, the smaller the semivariance (Burgess and Webster, 1980a). Semivariance provides the basis for kriging and cokriging techniques, which are used for unbiased, optimal interpolation between known points of data (Burgess and Webster, 1980a). When the intrinsic hypothesis is assumed, the semivariance for a lag h distance between all observations separated by the lag is: t(h)=l/2Nh2 [Z(X)—Z(x+h)]2 where there are Nh sample observations separated by lag h. The plot of semivariance versus lag distance h is known as the semivariogram. The semivariogram consists of four basic parts; the sill, the range of spatial dependence (range), the nugget variance (nugget), and the structural or explained variance (Figure 1.3). The sill approximates the sample variance of classical statistics, and is the region of relatively constant semivariance. The range is the lag distance over which the variable exhibits spatial dependence, and is defined by the value at which the curve reaches the sill. The nugget is the y-intercept value of the semivariogram. Ideally, the semivariance at zero lag is zero, but often is not. The nugget represents unexplained or random variance, which is caused by sampling error, or variability which cannot be detected by the sampling scale used (Trangmar et al., 1985). The explained variance is the portion of the total variation correlated with distance. A continuous increase in semivariance without an apparent sill or range indicates a broader regional trend and nonstationarity, thus not allowing for definition of a spatial trainee. An of spatial de; determined a' Plotted se 52/21131le for 1 fitting obsen anode! are i. 19%) Iliere are 5.9% and a being the e- Where Z] ‘ Z(xii I 23 variance. An absence of spatial structure in the semivariogram indicates either a lack of spatial dependence between sample values, or that the spatial structure cannot be determined at the sampling scale used (Trangmar et al., 1985). Plotted semivariance points must be fitted to a mathematical model to produce statistics for kriging procedures. There exists no set mathematical procedure for fitting observed semivariograms (Webster, 1985). In general, the criteria for choosing a model are high correlation coeflieient, small nugget, and a large range (Johnson, 1990) There are two basic kriging procedures; simple point estimation, or punctual kriging, and average estimates for discrete areas, or block kriging. In punctual kriging, the estimated value of the regionalized variable 2 at location x is: n Z'(XO)=ZLiZ(Xi) where Z'(xo) = kriged estimate Z(xi) = sample value Li = weight applied to sample value Z(xi) n = number of neighboring samples used in interpolation The kriged estimate for point P in Figure 1.4 is calculated by multiplying each sample value in the estimation neighborhood by its concomitant weight (Li), then sum the results for all sample locations in the neighborhood. The fact that near sample points carry much more weight than far samples (Figure 1.4) means that kriging is essentially a local estimation procedure (Webster, 1985). The configuration of sample points near the estimation point can modify the effect of distance on kriging. The efi‘ect of distant samples tend to be dampened by samples near the estimation point, and lone points have more weight than individual points found in clusters. Soil properties are anisotropic if they do not vary in a similar manner in all directions. The effect of anisotropy can be seen in Figure 1.4. Variance is greatest in 24 0.032 0.006 - 0.001 0.095 0.130 0.076 0.019 0.019 0.009 0.251. 0.107 -0.002. 0.005 g 0.036 0.079 0 Figure 1.4. Weights for kriging point P. (from Webster, 1985) ’2 Figure 1.5. Geometric anisotropy about a sample. (from Journel and Huijbregts, 1978) the 100 e1 ‘11 the mon 11 115 \ersa lfa pro; applies to 21 occurs aroo Limce h 11 111311011 [1; decadence. Hul'bt’fi‘fl S. 25 the lower left to top right direction, and least in a perpendicular direction, resulting in the most weight being placed on sample points in the direction of least variability and vis versa. If a property varies the same in all directions, it is isotropic, and one semivariogram applies to all parts of the study region where a circular range of spatial dependence (h) occurs around each sample location. If the magnitude for variance of a property is at a distance h in one direction, and a distance kh in another direction for an equivalent variation, then the property varies anisotropically where an ellipsoidal range of spatial dependence, elongated in the direction of minimum variance, occurs (Journel and Huijbregts, 1978) (Figure 1.5). The anisotropy ratio k is a measure of the magnitude of directional differences in variation, and is calculated by dividing the explained variance by the range in the direction of greatest variation by the explained variance by the range perpendicular to it (Trangmar et al., 1985). In the calculation of estimates, the only difference between punctual and block kriging is how the weighting coefficients are determined. An average semivariance between sample points and all points in a block is calculated for the determination of sample point weight (Trangmar et al., 1985). Block kriging has the effect of smoothing local discontinuities, which is desirable when the investigator is more interested in regional patterns than local detail. The spatial distribution of a variable can be closely related to that of other variables affected by the same spatial process. Such properties are co-regionalized and are spatially dependent on each other (Trangmar et al., 1985). Cokriging uses two or more correlated random variables simultaneously in such a manner that the spatial information fiom each parameter aids in the estimation process. If a correlation exists between the two random variables then one method of improving the sampling efficiency is to increase the sampling of the covariable with respect to the under sampled (primary) variable. Using cokriging, the spatial information of the covariable (Simsfetted 1‘ quahh' ofthe E roost e...cien line to 6.1.99?” liftosmw 9‘ a The co—re‘gi 21311211103311: :lll’hitil there .\'1' hi is Smi's'afiograo mtaszred Lt te‘ ‘ionship hr The prime. ot the cox’ana Pit kriged p 26 is transferred to the primary through the cross-correlation firnction, thus improving the quality of the estimates of the primary variable (Yates and Warrick, 1987). Cokriging is most efficiently used where one variable may not have been sampled sufficiently (due to experimental difficulties, high costs, ect.) to provide enough estimates (Trangmar et al., 1985). The co-regionalization of variables 21 and Z2 is described by a cross- semivariogram: n 112(h)=(2N(h))'1Z[Z1(xi)-Zl(xi+h)][ZZ(xi)-22(xi+h)] where N(h) is the number of pairs of variables separated by vector h. The cross- semivariogram is calculated using only the locations where both variables are measured. Unlike semivariances, cross-semivariances can be negative if the relationship between the primary and covariables are negative (Trangmar et al.,1985). The primary variable is calculated as the weighted average of the observed values of the covariable and primary variable that occur in the estimation neighborhood at each kriged point: n1 n2 Z'2(xo)=ZLiZ 1 (X1 )+ZL222(X2) where L1 and L2 are the weights associated with Z1 and 22, and n] and n2 are the number of neighbors of Z] and Z2 involved in estimating Z'2 at each xo location (Trangmar et al.,1985). The above equations can be extended to include additional covariables. Kriging has been used to evaluate soil variability for soil delineation of map units (Burgess and Webster, 1980a, b; Webster and Burgess, 1980; McBratney and Webster, 1983; Ovalles and Collins, 1988; Di et al., 1989; Webster and Oliver, 1989; McBratney et al., 1991). Kriging has also been used to spatially quantify soil chemical properties (Yost et al., 1982; Samra et al., 1988), soil structural properties (Reinert, 27 1990), and soil hydraulic properties (Vieira et al., 1981; Van Kuilenberg et al., 1982; Russo and Bresler, 1982). Yates and Warrick (1987) and Mulla (1988) used soil surface temperature with sand content and penetrometer resistance, respectively, as covariables to cokrig for soil water content. Nash et al. (1992) cokriged for vegetative cover using soil moisture as the covariable. Stein et a1. (1988) used mean high water table as the covariable to estimate soil moisture deficit. me: 28 REFERENCES Acton, OF. 1965. The relationship of pattern and gradient of slopes to soil type. Can. J. Soil Sci. 45:96-10]. Amemiya, M. 1970. Land and water management for minimizing sediment. In Willrich, TL. and GE. Smith (Eds), Agricultural practices and water quality. Iowa State University Press, Ames, Iowa. p. 35-45. Baier, W., D.Z. Chaput, A. 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Optimal interpolation and isarithmic mapping of soil properties. III. Changing drifi and universal kriging. Webster, R., and MA. Oliver. 1989. Optimal interpolation and isarithmic mapping of soil properties. VI. Disjunctive kriging and mapping the conditional probability. J. Soil Sci. 40:497-512. Wilding, LP, and LR. Drees. 1983. Spatial variability and pedology. In Pedogenesis and soil taxonomy. 1. Concepts and interactions. Elsevier, New York. Wischmeier, WH. 1975. Cropland erosion and sedimentation. In Control of water pollution from cropland. Vol. II. An overview. Agric. Res Service and Environmental Protection Agency, Washington, DC Workman, S.R., R.W. Skaggs, J .E. Parsons, and J. Rice. 1990. DRAINMOD User's manual. The North Carolina State University Press. Yates, SR, and AW. Wanick. 1987. Estimating soil water content using cokriging. Soil Sci. Soc. Am. J. 51:23-30. Yost, R.S., G. Uehara, and R.L. Fox. 1982. Geostatistical analysis of soil chemical properties of large land areas. 11. Kriging. Soil Sci. Soc. Am. J. 46:1033-1037. Zaslavsky, D., and G. Sinai. 1977. Surface hydrology. ICW, Wageningen, The Netherlands, Note 1017. Zaslavsky, D., and G. Sinai. 1981a. Surface hydrology: 1. Explanation of phenomena. J. Hydraul. Div. Am. Soc. Civ. Eng. 10721-16. Zaslavsky, D., and G. Sinai. 1981b. Surface hydrology: 3. Causes of lateral flow. J. Hydraul. Div., Am. Soc. Civ. Eng. 107:37-52. Zaslavsky, D., and G. Sinai. 1981c. Surface hydrology: 5. In surface transient flow. J. Hydraul. Div., Am. Soc. Civ. Eng. 107165-93. Zingg, AW. 1940. Degree and length of slope as it affects soil loss in runoff. Agric. Engr. 21:59-64. 34 CHAPTER 2 SPATIAL VARIABILITY OF SAND, SILT, AND CLAY CONTENT, AND HORIZON THICKNESS OF SOILS WITHIN A SLOPING LANDSCAPE ABSTRACT Spatial studies of soil properties have been primarily conducted on relatively level landscapes. This study determined the spatial relationships of selected important soil morphological properties within a sloping landscape. Soil profiles of Kalamazoo loam (fine-loamy, mixed, mesic, Typic Hapludalfs), from a 52 by 34 meter area with 4.25 by 4.00 meter grid cells on a sloping landscape (backslopes range fi'om 4.6 to 5.6 %) at Kellogg Biological Station in southwest Michigan were described and sampled. Statistical and geostatistical analyses were performed on percent organic carbon of the Ap horizon, percent sand, silt, clay, and thickness (cm) of the Ap, Btl, and 2Bt2 horizons, and amount of clay (kg) in the Btl , ZBt2, control-section, and soil profile. The range of spatial dependence for the soil properties varied from 6.4 to 26.1 meters. All soil properties, except the 2Bt2 horizon properties, exhibit anisotropy associated with slope direction. The Ap horizon of the lower backslope, footslope, and toeslope displayed greater mean thickness, percent silt and organic carbon, relative to the summit and upper backslope position Ap horizons. The Btl horizon and control- section displayed a greater mean percent silt and clay below the middle of the backslope. Solum thickness increased downslope from the upper backslope to the toeslope. These results indicate erosional and depositional processes may have afl‘ected the morphology of the Ap and Btl horizons. Differential glacio-fluvial sorting of material at each position of the landscape and water movement on and through the landscape likely caused the difi‘erences in soil profile morphology at different positions of the landscape. 35 INTRODUCTION Topography plays a major role in processes that create soil variability within a landscape (Gerrard, 1981; Jenny, 1941; Ruhe, 1960). In order to understand how topography controls the distribution of soil properties within a landscape, the spatial relationships of these variables need to be examined. An increased knowledge of spatial variability of soil properties can enhance interpretation of soils and lead to a better understanding of the complex topographical-soil relationships. Soil forming processes do not behave uniformly but vary with hillslope component (Hall, 1983; Ruhe, 1960). Soil thickness and particle-size distribution are highly related to slope position (Acton, 1965; King et al., 1983; Merrnut et al., 1983; Miller et al., 1988). Soil thickness and particle size distribution effect water holding capacity, nutrient supply, and rooting depth. Hanna et al. (1982) found water content to be greatest in the backslope and footslope position soils, and least in the summit and shoulder position soils. Geostatistics is a very useful approach to study spatial variability of soils (Trangmar et al., 1985; Webster, 1985). Although many studies involve spatial variability of soil physical properties on relatively level landscapes (Campbell, 1977, 1978; Burgess and Webster, 1980; Gajem et al., 1981; Vieira et al., 1981) there have been few spatial studies conducted on sloping landscapes (Miller et al., 1988). It is the hypothesis of this study that landscape position controls soil formation (i.e. organic matter accumulation, formation of soil horizons and soil structure, and clay translocation) which effects soil morphological properties. The objective of this study was to characterize the spatial variability of selected important soil morphological properties within a sloping landscape of southwest Michigan. 36 MATERIALS AND METHODS Study Site The study was conducted at the WK. Kellogg Biological Station (KBS) in southwest Michigan. KBS is situated on a pitted outwash plain and the major soils found there are Kalamazoo Loam (F ine-loamy, mixed, mesic, Typic Hapludalfs) and Oshtemo Sandy Loam (Coarse-loamy, mixed, mesic, Typic Hapludalfs) (Figure 2.1). , Both soils are well drained. A typical Kalamazoo soil profile from the study site has a loam Ap horizon, from O to 20 cm, a loam E horizon , from 20 to 25 cm, a clay loam Btl horizon, fi'om 25 to 56 cm, a sandy loam ZBt2 horizon, from 56 to 112 cm, and a 3E/Bt horizon of sand and loamy sand lamellae, from 112 to 150 cm. A typical Oshtemo soil profile from the study site has a sandy loam Ap horizon, from O to 18 cm, a sandy loam Btl horizon from 18 to 56 cm, and a 2E/Bt horizon of sand and loamy sand lamellae, from 56 to 150 cm. Both soils probably formed in glacio-fluvial outwash in which the parent material becomes coarser with depth. The area was deglaciated approximately 14,000 b.p. (Wayne and Zumberge, 1965). The study site was located on a west facing slope with distinct summit, backslope (4.6% to 5.6% slopes), and toeslope components (Ruhe, 1960) (Figure 2.2). There is no distinct shoulder component to this landscape, as the upper portion of the backslope is linear, except near the transition to the summit. The location of the slope units are given in Table 2.1. Table 2.1. Location of slope units at the study site. Slope Position Position east of west border of site Width of slope position ---Meters--- Meters Summit 27.6 to 34.0 4.0 Upper Backslope 19.1 to 27.6 2.4 Lower Backslope 6.4 to 19.1 12.7 Footslope 4.0 to 6.4 8.5 Toeslope 0.0 to 4.0 6.4 37 Os htemo Depth Kalamazoo Depth Texture cm Texture cm SL LS LS LS L5 L5 Btl ZE/Bt I r I I I 0t0l00t010¢0fl 00fl 00‘0fm01000m0100 . 0000000000000000000000 00000000r000000000000. 00000000000000000000000 :0000000000000000000000. 0000500000000000 00000 v0000000000000000000000. 0 00000000000000 00000 .0000000000000000000000. 000 0000000000000 00000 v000000000000000000000 0000. fie... ..D....... ..... ........... .000 00. .000000000L v00f0. 0000000000. ....... A .%.~..... v00000ne 0 00000000,. 0.000000000000000000000 v00000000000000000 0000. 0000000000000000 00000 - 000000 0 0 0000000 0 00000. W0000000000000 00 0000000 00 00000 00 00 0000. 000000000 00 00 00000 700 000000000 000’. 00005000 000 000000 10000000 000 0 000. .wkannnn. 0 .56 SL S LS 150 -. -.. -.:-'.-::.-:-°.-. 0 I I :-'::-‘.-:-.-. . I O I. I‘I '0 0 0 000000000000 0 . 0 00 0 0 0 00 00 0 ...... .............................. 1.6%.". . O 0 Z ZBtZ 3E/Bt 112 150 Figure 2.1. Typical Kalamazoo and Oshtemo soil profiles. 38 .81 332586 m cont?“ 8?. 32m N N 223m :EE:w N am .828.qu .695 u m: .Kofixoam 5.33 n ma 636.com u E 63308. n 8. //// /'// //// //’/ / /’/ ///\b‘* 3613‘ 6% 6‘3 '6 '1 £4 91 If '2 ac; gene/3 (W) A 34 me“ size. using a 1 were Iocated elerarr'ons “ e convened to ' Heefbmg Inst aken using a were samPIed made for a rot standard pr0C< rhe base of the ofthe sampies organic carbon rnuréne organic In order to < the control ~Sec1 20?] I. \- ; fichonzon. 39 A 34 meter by 68 meter rectangular grid of sampling points was established on the site, using a 4.25 meter (east-west) by 4 meter (north-south) grid cell. Grid points were located and flagged using a WILD Distomat and Theodolite (Total Station), and elevations were recorded for topographic map production. The raw survey data were converted to coordinate data using the WILDSOFT computer program (Wild Heerbrug Instruments, Inc., 1987). Soil descriptions and samples at each point were taken using a Giddings hydraulic probe mounted on a pickup truck. Additionally, soils were sampled at 2 meter intervals on the north and south boarders of the site. This made for a total of 180 soil profiles sampled. Soil profiles were described using standard procedures (Soil Survey Staff, 1984) and sampled to a depth of 150 cm, or to the base of the ZBtZ horizon if it was deeper than 150 cm. Percent sand, silt, and clay, of the samples were determined by a hydrometer method (Grigal, 1973). The percent organic carbon of the Ap horizon samples were determined colormetrically by a routine organic matter method (Graham, 1948). In order to delineate the Kalamazoo and Oshtemo soils on the landscape, a map of the control-section clay content, the average clay content of the upper 50 cm of the argillic horizon, or the entire argillic horizon if less than 50 cm thick (Soil Survey Staff, 1975), was generated for the study site (Figure 2.3) using geostatistical methods (Trangmar et al., 1985; Webster, 1985). The 18% clay line separates Kalamazoo soils which are classified as fine-loamy, from Oshtemo soils, which are classified as coarse- loamy. Most of the Oshtemo soils occur in the north 12 meters of the site. Statistical Approach In order to perform statistical analyses on a single sample population where topography alone regulates the major trends, the soil samples from the north 16 meters were eliminated from firrther examination (Figure 2.3), leaving 135 soil profiles for statistical analyses. South - North (m) ‘Q _. p l l 32 West - East (m) Figure 2.3. Control-section percent clay of the study site. ll! 0 136‘ l“ 1‘. 4:3, 4_ but] ‘ ' I GI 41 Statistical analyses were performed on the percent organic carbon of the Ap horizon; percent sand, silt, and clay, of the Ap, Btl, and 2Bt2 horizons; percent clay in the control-section; thickness of the Btl, 2Bt2, and solum (the surface to the base of the 2Bt2 horizon in this study); and amount of clay (kg) in the Btl, ZBt2, control- section, and soil profile (0 to 150 cm). These variables were chosen for study because of there relationship to soil development and water holding capacity. Each variable was first subjected to classical statistical analysis to obtain the mean, variance, standard deviation (SD), and coefficient of variation (CV) of each horizon. ‘ Mean and SD of the variables within each landscape position were calculated. A one- way ANOVA was performed on each variable for all five landscape positions to determine if the means of the variable were significantly different, and least significant difference (LSD) values were obtained to determine which means of the variable were significantly different. The degree of spatial variability for each variable was determined by geostatistical methods (Trangmar et al., 1985; Webster, 1985). A semivariogram for each property to ascertain the degree of spatial variability between neighboring observations, and a appropriate model was fit to the semivariogram. The semivariance is a measure of the average similarity between points of a given lag distance apart. The more alike the measurements of the points are, the smaller the semivariance (Burgess and Webster, 1980). The plot of semivariance versus lag distance, 11, is known as the semivariogram. The semivariogram consists of four basic parts; the sill, the range of spatial dependence (range), the nugget variance (nugget) and the explained variance (Figure 2.4). The sill approximates the sample variance, and is the region of relatively constant semivariance. The range is the distance over which the variable exhibits spatial dependence, and is defined by the value at which the curve reaches the sill. The nugget is the y-intercept value of the semivariogram. The nugget represents unexplained or random variance, which is caused by sampling error, or variability of 42 .EEmoEZEom 32552;: .vN Samra m3 owcam 3:523 wa= Z .l 353...; vein—aum l Em 43 the variable which cannot be detected by the sampling scale used (Trangmar et al., 1985). The explained variance is the portion of the total variation correlated with distance. Plotted semivariance points must be fitted to a mathematical model to produce statistics for kriging procedures. There exists no set mathematical procedure for fitting observed semivariograms (Webster, 1985). In general, the criteria for choosing a model are high correlation coefficient, small nugget, and a large range. Semivariance data in this study were fitted to exponential, spherical, linear, and gaussian models. Semivariance provides the basis for kriging and cokriging techniques, which are used for unbiased, optimal interpolation between known points of data (Burgess and Webster, 1980). Semivariograms and block kriging interpolation data for each variable were calculated using GEOPACK (Yates and Yates, 1989). Each property was block-kriged with the statistics generated by the chosen semivariogram model. A width of two meters was chosen for the block size, which defined 486 blocks within the study area. A maximum search radius of 62. 13 meters was used, and the 16 nearest data points ("neighbors") were used for kriging. The generated block kriged data for each variable was gridded in SURFER (Golden Software, 1989). The gridded estimates were used to produce maps of the variables with SURFER. RESULTS AND DISCUSSION The distributions for the studied variables of the south 52 meters of the site , under visual inspection, were approximately normal (Figures 2.5 to 2.11) except for the amount of sand and silt in the 2Bt2 horizon, which were eliminated from geostatistical analysis. The descriptive statistics for the soil properties studied are given in Table 2.2. Johnson (1990) found similar mean, SD, and CV values for percent clay of the Btl (25.6, 4.5, and 17.6 %, respectively) and 2Bt2 (10.0, 3.0, and 30.0 %, Ap Clay (°/o) n «m u N a s u .4. 5 \\\\\\\\\\\\\\\\\\\\ u § 2 2 § u ) \\\\\. m \\\\\\\\\ 4 y m/o; .\\\\\\\\\\\\\\\\\\\\\\\\\\\\\. ...,_. w m/m \\\\\\\\\\\\\\\\\\a s§\\\\\\\\\\\\\\\\\a 2m H \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\S 3W W. m \\\\\\\\\\\\\\\\\\\\. mm w \\\\\\\\\\\\\\\\\\\\\ a m .. xx.....\....._....._w w... \\\\\\\\m\\\\\\\\\\\\\\\\\ m P H“ SW2 6 P w 2 \\ \ 2 1 \\\\\\\\\ m B Sn 2 \\\\\\\\\.m <§\\\ m a m \\ a . m .. m .. 6 m w m. w m w m 5 lo a w. a w w m s w m w m a m ...... m 5 o 3:302“. 3:302“. accuses“. Percont Cl08y Figure 2.5. Frequency of Ap, Btl, and 2Bt2 horizon percent clay. 45 Control Section Clay (°/o) 15 18 21 24 27 3O 33 36 39 Percent Clay Control Section Clay (kg) Sm .\\\\\\\\\.m \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\Sm \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\.m Kg Clay Figure 2.6. Frequency of control-section percent clay and clay mass. S\\\\\\\ o 5 \\ \\..\ 1 \f, 5 \.\.. 2 ‘\\ 1 1 i1 ‘ I d 1 Jr 5 o 5 o 5 O 5 o 3 3 2 2 4| 1| 5:03.02... A h 00v \nr.:-:~vaus i (v Ap Sand (%) 25 '30 Percent Sand Bt1 Sand (%) 111111111 2Bt Sand (%) SS” \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\Sm S\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\.u 5560657075 Percent Sa Figure 2.7. Frequency of Ap, Btl, and ZBt2 horizon percent sand. 47 Ap Silt (%) R.W. S u S 7 SS\\\\\\\\\\\\\\\ m n \/W S\\\\\\\\\\\\\\\\\\ a t \/W \S 5 SSSS\\\\\\\\\\\\\\\S a w... m\ .. . . .. S: - . t w ml \\\\\\\\x 4 . .. . . S\\\\\\\\\\\\\\\\\\\\S m. m m §§ m m m \ . . . \ m \SSSSSSSSSSSSSSSS - m. SSSSSSSSSSSSSSSS .. SSSSS M S\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\S m S\\\\S\S\\\\\\\\\\\\\\\ 1 Scene!“— >oceao£u 3:302“. Percent Silt Figure 2.8. Frequency of Ap, Btl, and 2Bt2 percent silt. 48 Ap Thickness \ 28 22 19 60 o mu m o o 4 3 2 1| 5:03.600“. Horizon Thickness (cm) Bt1 Thickness \\\\\\\\\\\\\\\.\\ \\\\\V\\\\\\\\\\\\\\\\\\\\\\\\\\\\ .\\\\\\\\\\\\ \\\\\\\\\\\\\\\\\r\\\\\\\\ \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\. \\\\\\\\\\\\\\\S '6'12 36 42 48 54 6O 66 30 rckness (cm) 28t Thickness Horizon Th 4 2 8 1 O 4 q 5 o 5 O . O .r O 3 2 2 0| 1 35:60.... 126 108 S SSS‘SS 90 S.\\\\\\\\\\\\S S S S \\ 72 S\\\\\\\\\\\\\\\\\S SSSS\\\\\\\\\\\\\\\\\\\\\\\\. a. SS\\S\\\\\\.\\\\\\\\\\\\\\. .\\S.\\\\\\\\\\\\\\\\ u 63 61 99 117 45 Horizon Thickneea (cm) Figure 2.9. Frequency of Ap, Btl, and 2Bt2 horizon thickness. 49 Bt1 Clay Mass .\\.\\\\\\\\\.M S\\\\\\.SSSS\\\.\\\\\\\\\\\\\. .\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\. S w. \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\ SSS.SSSS\Q\\\\\\\\SS 210 280 350 420 140 245 315 365 Kg Clay 28t Clay Mass 175 105 210 240 270 300 330 SSSSS SSSSSSS m SSSSSSSSSS m SSSSSSSSSSSS m SSSSSSSS m S\\\\\Sw O30 4O >05:er Kg Clay Clay Mass (0 to 150 cm) Kg Clay Figure 2.10. Frequency of Btl, 2Bt2, and soil profile clay mass. 50 Organic Carbon (%) 1.35 S SSSSSSSSSSSS m. SSSSSSSSSSSSS m 5 2. 1 Organic Carbon (96) Solum Thickness To base of 28t2 horizon 80 100 120 140 160 180 60 90 1 10 130 150 170 Solum Thickness (cm) 70 Figure 2.11. Frequency of Ap percent organic carbon and solum thickness. Table 2.2. Descriptive statistics for variables determined in the field and laboratory. V_ariable Min. Max. Mean Vaaiance SD CV (%) O.C. (%) Ap 0.65 1.29 0.93 0.017 0.13 13.8 Clay (%) Ap 18.0 27.0 21.8 3.1 1.8 8.1 Btl 0.0 39.0 29.9 22.4 4.7 15.7 ZBtZ 0.0 26.0 13.8 13.4 3.7 26.8 Control Sec. 15.0 39.0 24.2 22.4 4.7 19.6 Sand (%) Ap 25.0 65.0 45.9 38.4 6.2 13.5 Btl 27.0 79.0 55.2 102.4 10.2 18.4 ZBt2 57.0 94.0 84.1 28.6 5.4 6.4 Silt (%) Ap 13.0 55.0 32.2 40.2 6.4 19.7 Btl 1.0 34.0 14.8 47.7 6.9 46.9 ZBt2 0.0 7.0 1.9 2.9 1.7 91.6 Thick. (cm) Ap 13.0 33.0 22.6 15.8 4.0 17.6 Btl 0.0 63.0 30.9 112.8 10.7 34.5 ZBt2 0.0 120.0 54.7 431.7 20.9 38.1 Solum 69.0 173.0 113.3 550.5 23.5 20.8 Clay (kg in m2 x m depth ) Btl 0.0 405.4 155.4 3834.0 62.2 40.0 ZBt2 0.0 326.0 123.4 2665.9 51.8 42.0 Control Sec. 120.1 321.7 199.0 1524.1 39.2 19.7 0 to 150 cm 275.8 633.9 405.2 5023.4 71.1 17.6 52 respectively) horizons on a relatively level landscape at KBS. Johnson also found similar means for control-section clay (20.4%) and Btl thickness (34.1 cm), but greater SD and CV values (6.0 and 29.9 percent for control-section clay content and 18.3 and 53.7 percent for Btl thickness) for these variables. The mean ZBt2 thickness of Johnson's study was less (43.1 cm) while the SD and CV values were substantially greater (28.1 and 65.9 %). The greater SD and CV values of these variables in Johnson's study could be due to the larger study area (440 by 140 meters) and sample grid cells (20 by 20 meters). Also, Johnson's study had both Kalamazoo and Oshtemo soils in the data set, and exhibited a bimodal population, which may contribute to the greater SD and CV values of control-section clay content. The descriptive statistics for the soil properties as a firnction of landscape position are given in Table 2.3. There is no significant difference (or = 0.05) between the means of all the slope positions for the ZBtZ thickness and 2Bt2 amount of clay (kg), therefore LSD values were not calculated for those variables. Organic carbon displayed a small but steady increase fi'om the upper backslope to the toeslope soils. Ovalles and Collins (1986) and Miller et al. (1988) found a similar trend from the summit to backslope soils and the shoulder to the toeslope soils, respectively. The summit and upper backslope position Ap horizons have significantly less O.C. than do those of the footslope and toeslope positions. The lower backslope position Ap horizons have significantly less O.C. than those of the toeslope positions. This may reflect the greater amount of water which enters the soils on the lower portions of the landscape via surface and subsurface run-on from upslope and throughflow (Rosek and Crum, 1994). The mean percent sand in the Ap horizon of the toeslope position is significantly less than in Ap horizons of upslope positions. The percent sand in the Btl horizon of the lower backslope, footslope, and toeslope positions are significantly lower than the Btl horizon of the upper backslope position. The ZBtZ horizon of the upper backslope had significantly more sand than the 2Bt2 horizon of the footslope 2.1. 71—...2 _.: .7. are-5.077.... :: :.::.r.. I. .1? £7..-. :..:.::: 2‘ ?= r..v:?.~::r. .0»~:\s~ alifis ~ l ex ‘2. . . . . . . . s I 53 .2820? a he.“ 26:38 :83qu moocoaobmc ESEcmmm 888%: u can A. d as .1. ages a .820 u a a Sagan .. as *3: 2m 23:. 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The trend of percent sand to decrease downslope could be due to glacio-fluvial sorting of the parent material at the time of deposition. This trend in the Ap horizon could also be due to erosion, where the silt fraction is transported downslope, increasing the proportion of sand in the summit and backslope soils and decreasing the proportion of sand in the toeslope soils. The trend of percent silt of the Ap, Btl, and 2Bt2 horizon, with landscape position, is to increase downslope. The toeslope position Ap horizons have significantly more silt than Ap horizons upslope of the footslope position. The Btl horizon of the summit and upper backslope have significantly less silt than the Btl horizons of downslope positions. The 2Bt2 horizon of the upper backslope position had significantly less silt than of the toeslope position. This is further indication that differential glacio-fluvial sorting occurred at deposition. Also, there is evidence of erosion and transportation of the silt fi'action, which is the most highly erodible fi'action (Hjulstrom, 193 9), from the soils of the summit and upper backslope positions to the footslope and toeslope. There are statistically different means of Ap percent clay, but the differences are small, and the significance is most likely due, in part, to the low CV (8.1 %) of this variable. The mean percent clay of the Btl horizon of the upper backslope position is significantly less than mean percent clay of downslope positions. The summit position mean percent clay of the Btl horizon is significantly less than the lower backslope Btl mean percent clay. The mean percent clay of the Btl horizon is least in the upper backslope position, greatest in the lower backslope, footslope, and toeslope positions, with the summit mean clay percent between the upper backslope and lower slope positions. The mean percent clay of the ZBt2 horizon and control-section of the upper backslope position is significantly less than the footslope and toeslope, and lower backslope and toeslope positions, respectively. The percent clay in the argillic horizon of this landscape appears to decrease from the summit position to the upper backslope 55 position and increase downslope. Malo et al. (1974) found a similar pattern of percent clay in the solum of a toposequence within a glacial till plain of North Dakota (the shoulder position of their study is analogous to the upper backslope position of this study). The Ap horizon of the footslope and toeslope positions is significantly thicker than the Ap horizon thickness of the upslope positions. This is further evidence that erosion and transport from the upper backslope to the footslope and toeslope positions has taken place. The Btl horizon of the toeslope horizon is significantly thicker than the upper backslope Btl. There are no significant differences in ZBtZ horizon thickness associated with landscape position. The mean solum thickness decreases fi'om the summit to the backslope positions, and then increases downslope. The toeslope position mean solum thickness is significantly greater than the upper and lower backslope solum thickness. The greater solum thickness of the footslope and toeslope positions is due to a thicker Ap horizon, the presence of an B horizon in most soil profiles (Appendix 1), and a thicker Btl horizon. King et al. (1983) noted solum thickness increases greatly below the point downslope where the slope becomes concave. In this study, the slope becomes concave at the backslope-footslope juncture. The trend in Btl and solum thickness may be due to water moving from the upper backslope position via overland flow and subsurface lateral downslope throughflow of water to the footslope and toeslope soils (Rosek and Crum, 1994), increasing the amount of leaching and translocation of clay in the footslope and toeslope soils. This is indicated by the presence of E horizons in the footslope and toeslope positions. The mean amount of clay (kg) in the Btl, control-section, and soil profile (0 to 150 cm) decreases from the summit to the upper backslope, and then increases downslope, with the footslope position having slightly less clay in the Btl and control-section than the lower backslope. The mean amount of clay in the Btl horizon of the toeslope 56 position is significantly greater than in the upper backslope position. The mean amount of clay in the control-section of the upper backslope is significantly less than the lower backslope and toeslope positions. The mean amount of clay from O to 150 cm in the upper backslope position is significantly less than in the downslope positions. The mean amount of clay from O to 150 cm in the summit position is significantly less than in the toeslope position. The trends of the mean amount of clay as a fiinction of landscape position noted above are probably due to both differential glacio-fluvial sorting which may have occurred between the landscape positions at deposition, and pedogenic clay translocation. Water moving from the backslope via overland flow and subsurface downslope lateral throughflow could transport clay to the soils downslope. Glazovskaya (1968) observed lateral subsurface translocation of silt and clay in throughflow water. Overland flow fi'om upslope can also transport clay, which can infiltrate into the soils of the lower backslope. The semivariograms for the soil properties are presented in Figures 2.12 to 2.14. The spatial dependence of the soil properties are summarized in Table 2.4. The "explained" variation (that portion of the total variation correlated with distance) ranged from 27.7 to 100 percent. The "unexplained" variation is attributable to the nugget variance. The range of spatial dependence values were adequately large to provide for valid block kriging within the 4.25 by 4.00 meter grid cells. All variables except the ZBt2 horizon properties exhibited significant cast-west anisotropy (Journel and Huijbregts, 1978). This is expected since the slope components are aligned in the north-south direction, and the greater amount of variability should occur up/down slope. The absence of anisotropy for the 2Bt2 horizon variables indicate these processes affecting these variables are acting somewhat independent of landscape position. Fewer significant differences among the 2Bt2 variables occur than for other soil layers (Table 2.3). Miller et al.(1988) found Ap percent sand, silt, clay, silt + very Somlvurlanco (96 Clay) " 2 Semivariance (96 Sand) " 2 Semivariance (96 Sllt) " 2 57 Percent Clay 25 20' 15" 10‘ o 5 1? 115 2'0 25 so Lag (meters) Percent Sand 140 120q 100* 80‘ o 5 1'0 1'5 2'0 25 30 Lag (meters) Percent Silt 70 501 401 30‘ 205 10‘ o 3 1'0 1'5 20 25 30 Lag (meters) [-Ap x811 A280] Figure 2.12. Semivariograms for Ap and Btl percent clay, sand, and silt; and ZBt2 percent sand and silt. 58 Ap Organic Carbon (%) 0.022 ; 0.02‘ 0.018" 0.016“ 0.014‘1 0.0121 0.01- 0.0082 0.006‘ 0.004 0.002‘ Semivariance (96 0C) " 2 o 5 1'0 15 {o 25 30 Lag (meters) Thickness of Soil Layer 700 630- 560- 490‘ 420- . 350« 2801 21 o‘ 140‘ 70- __¥__‘l_ 4.; A A o r fs—fi—iv T 0 1'0 1'5 2'0 25 30 Lag (meters) L+ Ap - an A 2&1 Solum Thickness To base of 2Bt2 horizon Semivariance (cm) " 2 700 630% + 5601 + 490‘ '0' 420‘ 350‘ 280‘ 210‘ 140J 70" Semlverlence (cm) " 2 I o 5 1 o 75 2‘0 25 30 Leg (meters) Figure 2.13. Semivariograms for Ap percent organic carbon; Ap, Btl, ZBt2, and solum thickness. 59 Control Section Percent Clay 24 20‘ 16‘ 121 Semlverlance (96 Clay) " 2 0 5 1'0 1'5 20 2‘5 30 Leg (meters) Clay Mass (kg) 400 35° " + + ‘ 4- 300« t 250- " ' 200‘ 150‘ ‘ — L A a Semivariance (kg) " 2 100‘ 50‘ o 3 1'0 1'5 2‘0 25 30 Leg (meters) ‘0' Btl I 28t A Control Section 1 Clay Mass (kg) 0-150 cm 5000 ‘ 4000‘ 3000‘ 2000‘ Semivariance (1:9)" 2 v 1000‘ o 5 1'0 15 20 25 30 Leg (meters) Figure 2.14. Serrrivariograms for control-section percent clay, Btl, ZBt2, control-section, and soil profile clay mass. 11..AI' 111'.- . . .0 2-5 :0: C 32:35.... as flieh - . 4 ad . _ Ember”. 03:3— ,.:C. v 0.25.25» 2.? >a 0:07.. . 7.50: 330%. Z. s DU fihldflho~fl~ -AUZ ~A V‘ ’0.U§‘zhh-~?. FVr'-§-§.§ \.-).\-§IU.’ \\ 0‘ ‘\R‘: .2‘ - 1 . y 1 . 60 00.0 0.00 00.0 0.0 0.0000 0.0000 0.80... 800.80 00.0 .00 00.0 000 0.000. 0.00 .8880 828.80 00.. 00 00.0 00. 0.0000 00.0 .8880 0.0.0 00.. 0.0. .00 0.00 0.0000 0.0000 800.0 :m 02.80 a x "a 0. 00.0 0.0 00.0 0... 00.0 0.00 00.000 00.000 .8880 80.00 00.. 0.0 00.0 00. 2.000 00.0 .8880 050 00.0 0.0 00.0 0.00 0.00. 00.00 .8880 :m 00.. .00 00.0 0.00 00.... 00.0 .80: 0< 0.880.020 00.0 .00 00.0 000 00.00 00.00 .80.). :m 00.. 0.: 00.0 000 00.00 000. 8.8000 00. 0.00:0 00.0 00. 00.0 0.00 0000. 00.00 8.8000 :m .2 0.00 00.0 .00 00.00 0.0. .80... 0.0 0.80080 00.0 .0 00.0 0.00 00.00 0...: 800.0 828.80 00.. 0.0 00.0 00. 00.0. 00.0 8.880 0.0.0 0.0 0.0. 00.0 0.00 00.00 00.0 .8020. :m 00.. .0. 00.0 0.00 .00 0.0 .8880 0.0. 02008.0 00.0 0.0. 00.0 0.0.. 0000.0 0000.0 .8000. 0000 .00 .8 0 0.00 «wok/numwm 2:3 @000 005035 00.8.05 3880.5. 0223 .too 00.8.0.5 Em 00322 .0022 3.502.. 03:00.05 :00 .80 00.00380 3§t0>wcom .vN 030,—. 61 fine sand, organic carbon, and thickness to be isotropic for soils on a sloping landscape. Johnson (1990) found a similar nugget and sill (3.7 and 19.0 (% clay)2) of the Btl clay content for a relatively level landscape at KBS, though the range of spatial dependence (22.9 meters) is more than twice that of this study. Johnson found little spatial dependence for the 2Bt2 clay content. Johnson found the Btl and 2Bt2 thickness (cm) exhibited substantially greater sill (315.9 and 837.2 cm2) and range (11.7 and 27.5 meters) values than those of this study. The nugget value in J ohnson's study for the Btl thickness was much lower (0.0 cmz), while much higher for the ZBt2 thickness (125.6 cm2). The greater range of spatial dependencies Johnson found were most likely due to the level landscape and larger sample grid cells (20 by 20 meters) of that study. Reinert (1990) found the range of spatial dependence for Ap bulk density, porosity, and saturated hydraulic conductivity was from 10.2 to 43.2 meters on a relatively level landscape at KBS. All ranges of the soil properties were greater than the width of the footslope and toeslope positions that were sampled, and most ranges were greater than the width of the portion of the summit position that was sampled. Maps of the soil properties for the south 52 meters of the site, generated by kriging, are presented in Figures 2.15 to 2.31. The kriged estimation maps indicate a progressive downslope increase in Ap percent organic carbon, percent clay in the Btl and control-section, percent silt in the Ap and Btl, thickness of the Ap, and amount of clay (kg) in the Btl, control-section, and soil profile from O to 150 cm. These variables decrease from the summit to lower backslope in the south 12 to 16 meters of the site. Kriged estimation maps of Ap and Btl percent sand (Figures 2.20 and 2.21) indicate a progressive downslope decrease, except the south 12 to 16 meters, where percent sand increases from the summit to the upper backslope. Most of the increase in Ap percent organic carbon, silt, and thickness (cm), and decrease in Ap percent silt, occurs at the toeslope, footslope, and lower backslope positions. This indicates st (m) 3.. r. I 4o 32 24 63 as: 15 West - East (m) Figure 2.16. Block-kriged map of Ap horizon percent clay. South - North (m) :0 .. 64 32 O 4. :5 £82 - 58m 2 flaw. \ 3 . West - East (m) Figure 2.17. Block-kriged map of Btl horizon percent clay. :0 48 4o 32 24 16 (I) O 8 16 24 32 1 l \l/ l I 1 AV <95 $530“ b4» 48 > N /1 (CC/v \ 8 (8‘ 27 4O — ”3 -i ‘33, W (0’ 23 ‘Q —1 25 “3 ‘3 \. 32 “i - /\\\3 % 2/\/ o " 2 - N \ A 0 Q 8 Q /\/ 2 <\/ - 24 — W.\ ”L ‘ 7’6 J W” f l\) 26 l\) A O —‘ on 03 (‘3) O\—/ 16 — 03 — 9 0 6‘ —— C? \ \ V/‘T \ 8 — W “L 0 ’l/ ’27 (V / / r99 f NW 0 —\ 1 /\1 1 m 11% O 8 16 24 32 West - East (m) Figure 2.19. Block-kriged map of control—section percent clay. South - North (m) .r. 67 Lo k» 448 / ,9— 4O C57 X is}: \:// 8 West - East (m) Figure 2.20. Block-kriged map of Ap horizon percent sand. South - North (m) :0 68 :5 5.02 - £00m West - East (m) Block-kriged map of Btl horizon percent sand. Figure 2.21. I» ~rzu 69 gcmsspfi West - East (m) Figure 2.22. Block-kriged map of Ap horizon percent silt. South - North (m) (n 71 South - North (m) West - East (m) Figure 2.24. Block-kriged map of Ap horizon thickness. J O W F gu k-kriged ma y g» @Qflf/ to Io @ O 1 6 2 4 3 2 p of Btl horizon thickness. U 5 Y! 73 :5 £82 - 5.8m yo 0 _ ?\\7\\\\\7/\7 2 4 4 3 West - East (m) Figure 2.26. Block-kriged map of ZBt2 horizon thickness. (Al (I! 74 2 3 :5 5.32 - 5:8 8 West - East (m) Figure 2.27. Block-kriged map of solum thickness. 48 4O 32 24 16 8 O 75 48 4o 32 \ 24 O ’20 16 — m J [)0 Q Q 9 8’ O 8 790W/// «Q /\ .2 -—*O m. ,/ M‘W? f0 0 8 16 24 32 West - East (m) Figure 2.28. Block-kriged map of Btl hon'zon clay mass. South - North (m) I. A» 76 :5 5.52 - 5.3m West - East (m) Figure 2.29. Block-kriged map of 2Bt2 horizon clay mass. U U 77 Figure 2.30 West - East (m) . Block-kriged map of control-section clay mass. South - North (m) 78 80: M West - East (m) Figure 2.31. Block-kriged map of soil profile clay mass. South - North (m) 79 deposition of Ap material via overland flow from upslope may be significant, increasing the Ap thickness and amount of organic carbon and silt at the lower backslope, footslope, and toeslope positions relative to the upper backslope and summit positions. Most of the increase in Btl percent clay and decrease of Btl percent sand occurs at the middle of the backslope. This may be due to glacio-fluvial sorting of material differentially, above and below the middle of the backslope at deposition; and/or increased infiltration of water at the middle of the backslope and downslope via overland flow and subsurface lateral downslope throughflow fiom upslope, which could increase the amount of clay translocated to the Btl of the lower portion of this landscape. In the north 36 to 42 meters of the site that was studied, clay content decreased downslope. Since the summit portion of the study site is near the transition to the backslope, surface and subsurface lateral downslope movement of water may be occurring (Hall, 1983), causing reduced leaching and clay translocation. Incorporation of the Btl into the Ap of the upper backslope (no E horizons exist in the upper backslope position soils, Appendix 1), could have increased the clay percentage of Ap horizon at that position. Increased wetting and drying cycles may have contributed to the upper backslope Btl having a greater clay content than the summit. Hall (1983) noted summit soils have less distinct horizonation, and thicker and more continuous cutans occur on ped surfaces of backslope than summit argillic horizons. The 2Bt2 horizon thickness and amount of clay (kg), and solum thickness did not vary with depth (Figures 2.26, 2.27, and 2.29). In the center and north portions of the site, there appear to be "fimnels" for preferential water flow, as indicated by relatively deep solum depths in irregular circular patterns on the backslope (Figures 2.26 and 2.27). This is also expressed with a greater amount of clay in the 2Bt2 horizon (Figure 2.29). Mokma and Doolittle (1993) observed the common occurrence of "filnnels" expressed in soil profile segments of southwest Michigan. Ta Clay “(2) (Tong/f 0 [0 I 80 The pooled block estimation variances of the kriged soil properties are given in Table 2.5. The kriging estimation variances display either a repetitive spatial pattern, which is characteristic of grid sampling, as shown by the estimation variance of Ap clay content in Figure 2.32; or a relatively smooth spatial pattern where the variance is much greater at the edges and corners of the field, where less variable data is available for kriging, as shown by the estimation variance of Ap silt content in Figure 2.33. The south border of the site has much lower estimation variances (Figures 2.32 and 2.33) backslope at deposition; and/or increased infiltration of water at the middle of the backslope and downslope via overland flow and subsurface lateral downslope because the 2 meter sample spacing was much closer than the 4.25 meters for the rest of the kriged portion of the site. Table 2.5. Pooled estimation variance of block kriging. Property Pooled Estimation Variance O.C. (%)2 0.0021 Clay (%)2 Ap 0.33 Btl 2.70 2Bt2 3.07 Control Sec. 3 .37 Sand (%)2 Ap 2.01 Btl 2.81 Silt (%)2 Ap 2.01 Btl 2.40 Thick (cm)2 Ap 1.59 Btl 25.30 ZBt2 85.68 Solum 19.31 Clay (kg)2 Btl 395.04 2Bt2 513.00 Control Sec. 60.95 0 to 150 cm 679.73 (Z C/oy)2 81 ‘2?! o;- 53:; «W5 '” ‘ '5. I - .,-.. {“23“ ‘gll ' ‘ '0 ”O .zt‘fi“ “a"? ‘ . ‘ “‘3‘ .3; :IA‘;‘Q:.:(’,‘ 1" d 0!“. “O O} V \\“\\\\‘9“. 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Estimation variance of Ap horizon percent clay \“‘\‘\ “ kg“ , \ “\\\\\“\\ - \\\“ii\\\“‘\\\\\\:%s\xi’ . \\\\“\ ‘\\\\\\\ ‘\\\\1\ _ “‘\‘\\\\“\\\\:\\“\\\ \\\ \ “‘13“ “‘ 1 ‘5? “er, .‘i‘\\\\\“\\\\“ \“\\\\\\\‘ \\\\\\“ \ \\\\\\\\\\\ 2 31’ l) (I 82 3 M n 3.0 ' '3'”: ‘33“..."’Ol’l’l’lllllflIIIIIII1IllIIlIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII I “ ‘3‘3.3.'.'¢,"440:’lllllnr4ImuII/I/ImmnuII/Il/”lull/lull 414/525255‘5‘127117 “‘3‘3“‘.‘.....":III’IZZ:2151;127:115$01,111::leIIZIIIIIIIIIIIIIIIlllll’ll’llllllllllll ‘. . . 0' llllll’ II II III] ”IIIIIIIIIIIIIIIIIIIII’IIIIII’IIIII “":g:ss:g$:\’:3253:1404, Illll’lll Illttxggzzz ll IIIIII I'IZ:Illzilllltzlllzzcllllzjllll \\ ‘\\ ‘\ ‘\ ‘\‘\ \ \\“\\ \\\‘\\ \‘ \‘ \\ \‘ \‘ \\‘:\“\“:\‘:‘ \\\:\\:\\‘:\‘:\‘3 3| \\ ‘\‘ \“\“ ‘:‘ \“\\‘ \‘ \\‘8\\ ‘ §\ ‘\ ‘\\‘\\“\ ‘\“\\‘\\ \\ \\ My «‘6‘ ‘ \“\\‘ \\ \‘\“ \‘ \\ \‘ \‘ \‘9 \“ \\ ‘\\ \‘ \\ \‘ \\\\‘ \\‘\“ 0\ \“\\‘ \‘ \“\“ ‘ \\ ‘\ ‘\\ ‘\ \\ . \\‘ \\ ‘\ \\“l‘\ ~m:\“‘\\\‘\\‘“\\\\ ‘ l") ‘«\'.'.|“‘\‘\\‘ MK“ ‘3“? .8 "“\‘:\“ k “in“ . U? N Q) 9 .1t ts‘ c'3n p“r '20" he“ pr .anceo an . nv . atlo Est‘m 3. 2.3 . re F13” IIIIIIIIIIIIII I III,” ”Ill/III [751%, u , 83 SUMMARY AND CONCLUSIONS The soil properties of a sloping landscape were shown to be spatially dependent. Only the ZBt2 horizon soil properties did not display significant anisotropy. The results of the analyses of the soil variables indicate soil morphology is, in part, a function of landscape position. Three processes which may have or are occurring within this landscape probably cause differences in soil morphology at each landscape position. Depositional processes at the lower backslope, footslope, and toeslope may contribute to thicker Ap horizons with more silt and less sand from the lower backslope downslope, relative to the summit and upper backslope position soils. Difi‘erential glacio-fluvial sorting of material at deposition may, in part, be responsible for the greater percent silt and clay in the soils below the middle of the backslope than the soils upslope. Overland flow and subsurface lateral downslope throughflow of water may be contributing to greater solum thickness and percent clay in the Btl horizon below the middle of the backslope by increasing the amount leaching and clay translocation that occurs at these positions. Overland flow and subsurface lateral downslope thoughflow from the upper backslope can carry clay to downslope, where it can be incorporated into the soils upon infiltration. Evidence of preferential water flow occuring within the study area is expressed morphologically by deep, circular flannel shaped soil profiles found within various parts of the landscape. Ac Burg Camp! Campbc' Galen i 84 REFERENCES Acton, DP. 1965. The relationship of pattern and gradient of slopes to soil type. Can. J. Soil Sci. 45:96-10]. Burgess, T.M., and R. Webster. 1980. Optimal interpolation and isarithmic mapping of soil properties. I. The semivariogram and punctual kriging. J. Soil Sci. 31:315-331. Campbell, J .B. 1977. Variation of selected soil properties across a soil boundary. Soil Sci. Soc. Am. J. 41:578-582. Campbell, J .B. 1978. Spatial variation of sand content and pH within single contiguous delilneations of two soil mapping units. Soil Sci. Soc. Am. J. 41:460-464. ‘ Gajem, Y.M., A.W. Warrick, and DE. Myers. 1981. Spatial dependence of physical properties of a Typic Torrifluvent soil. Soil Sci. Soc. Am. J. 45:709-715. Gerrard, A]. 1981. Soils and landforms. George Allen and Unwin. London, p. 219. Glazovskaya, MA. 1968. Geochemical landscapes and types of geochemical soil sequences. Trans. 9th Int. Congress Soil Sci, Adelaide, 4:303-312. Golden Sofiware, Inc. 1989. SURFER Computer Program, version 4.0. Golden Colorado. Graham, ER. 1948. Determination of soil organic matter by means of a photoelectric colorimeter. Soil Sci. 65: 181-183. Grigal, DP. 1973. Note on the hydrometer method of particle-size analysis. Minnesota Forestry Research Notes, No. 245. Univ. of Minnesota, St. Paul. 4 p. Hall, GP. 1983. Pedology and geomorphology. In L.P. Wilding, NE. Smeck, and GP. Hall, (eds), Pedogenesis and Soil Taxonomy. 1. Concepts and Interactions. Elsevier, Amsterdam. p. 117-140. Hj l Jem John Journ King 31310, L Mennur. Miller‘ M an MOkma E SOU ‘71 Oldies. F1: in n( Reine”. D I Haplt RM. M 1.. 301} u Ruhea R ‘ Mad}. 85 Hanna, A.Y., PW. Harlan, and D.T. Lewis. 1982. Soil available water as influenced by landscape position and aspect. Agron. J. 74:999-1004. Hjulstrom, F. 1939. Transportation of detritus by moving water. In P.D. Trask, (Ed.), Recent marine sediments. Am. Assoc. Petrol. Geol. p. 5-31. Jenny, H. 1941. Factors of soil formation. McGraw-Hill, New York. Johnson, B.K. 1990. Nitrate leaching potential as affected by the spatial variability of Bt horizon morphology. MS. Thesis. Michigan State University, E. Lansing. Journel, AG, and Ch]. Huijbregts. 1978. Mining geostatistics. Acad. Press. New York. 600 p. King, G.J., D.F. Acton, and R.J. St. Amaud. 1983. Soil-landscape analysis in relation to soil distribution and mapping at a site within the Weyburn Association. Can. J. Soil Sci. 63:657-670. Malo, D.D., B.K. Worcester, D.K. Cassal, and K.D. Matzdorf. 1974. Soil-landscape relationships in a closed drainage basin. Soil Sci. Soc. Am. Proc. 38:813-818. Mermut, A.R., D.F. Acton, and W.D. Eilers. 1983. Estimation of soil erosion and deposition by a landscape analysis technique on clay soils in southwestern Saskatchewan. Can. J. Soil Sci. 63:727-739. Miller, M.P., M.J. Singer, and DR. Nielsen. 1988. Spatial variability of wheat yield and soil properties on complex hills. Soil Sci. Soc. Am. J. 52: 1 133-1 141. Mokma, BL, and J .A. Doolittle. 1993. Mapping soils and soil properties in southwest Michigan using ground-penetrating radar. Soil Surv. Horiz. 34: 13- 22. Ovalles, FA, and ME. Collins. 1986. Soil-landscape relationships and soil variability in north central Florida. Soil Sci. Soc. Am. J. 50:401-408. Reinert, DJ. 1990. Soil structural form and stability induced by tillage in a Typic Hapludalf. Ph.D. Dissertation. Michigan State University. E. Lansing. Rosek, M.J., and J .R. Crum. 1994. Soil-water balance and geostatistical estimation of soil water content within a sloping landscape. (in preparation). Ruhe, R.V. 1960. Elements of the soil landscape. Trans. 7th Int. Congress Soil Sci., Madison Wisc. 42165-170. So Tra V'iei We)? Webs: lilLDs Yates, 5 86 Soil Survey Staff. 1975. Soil taxonomy. USDA-SCS Agric Handb. 436. US. Govt. Printing Office, Washington, DC. Soil Survey Staff. 1984. Soil survey manual. USDA - SCS, US. Govt. Printing Office, Washington, DC. Trangmar, 33., RS. Yost, and G. Uehara. 1985. Application of geostatistics to spatial studies of soil properties. Advances in Agronomy, 38:45-93. Vieira, S.R., J .L. Nielsen, and J .W. Biggar. 1981. Spatial variability of field-measured infiltration rate. Soil Sci. Soc. Am. J. 45:1040-1048. Wayne, W.J., and J .H. Zumberge. 1965. Pleistocene geology of Indiana and Michigan, pp. 63-84. In HE. Wright Jr. and D.G. Frey (ed), The Quaternary of the United States. Princeton University Press. Princeton, NJ. Webster, R. 1985. Quantitative spatial analysis of soil in the field. Advances in Soil Science, 3: 1-70. WILDsofi Surveying System Software, Version 1.3. Wild Heerbrug Instruments, Inc. Norcross, GA. 1987. Yates, SR, and M.V. Yates. 1989. GEOPACK: Geostatistics for waste management. U.S. Salinity Laboratory. Riverside, CA 92501. 87 CHAPTER 3 SOIL WATER BALANCE AND GEOSTATISTICAL ESTIMATION OF STORED SOIL WATER WITHIN A SLOPING LANDSCAPE ABSTRACT The amount of stored water available for crop use in a particular soil of a sloping landscape is in part determined by landscape position. This study was conducted to estimate water balance that occurs in soil within each slope position of the landscape, and determine the minimum data set required to geostatistically estimate stored soil water on a sloping landscape with a backslope gradient of 4.6 to 5.6%. Two transects of 18 neutron probe access tubes, at 2 meter intervals, were established on a sloping topography of Kalamazoo loam (fine-loamy, mixed, mesic, Typic Hapludalfs) and Oshtemo sandy loam (coarse-loamy, mixed, mesic, Typic Hapludalfs). Volumetric water content of the soil was monitored approximately weekly in the spring, summer, and autumn of 1990 and 1991 at 15, 30, 60, 90, 120, and 150 cm by neutron attenuation. Water balance was quantified for the summit, upper backslope, lower backslope, and footslope-toeslope positions of each access tube transect. The CERES Maize model was used to estimate potential evapotranspiration and surface and subsurface runoff at each position of the landscape. Soil samples fi'om a 48 by 34 meter grid, with a 4.25 by 4.00 meter grid density, between the access tube transects were used in geostatistical estimation of stored soil water within the landscape. Water content of each sample point was estimated with CERES Maize for August 6 and October 17, 1991 Selected stored soil water (mm per 150 cm soil depth) sample locations were removed from the data sets, and then kriged and cokriged the data points. Semivariograrns and cross-semivariograms for the entire data set were used to estimate stored soil water (mm per 150 cm soil depth). The amount of soil water per J6. W 56:21. Can 197. SlOpj 1969 Sim-u}; Tb. “300: 88 150 cm soil depth throughout the study period was least within the upper backslope position, moderate within the summit and lower backslope positions, and greatest within the footslope-toeslope position, which was at or near field capacity throughout the study period. Cokriging estimation of stored soil water, using the m of soil water per 150 cm soil depth at field capacity as the auxiliary variable, reduced the required sample distance to 44 meters in the direction parallel to the contour of the slope. INTRODUCTION Water content in soils of sloping landscapes is controlled by elements that are both external and internal to the soil-landscape system (Gerrard, 1981). External factors include rainfall duration and intensity. Internal factors are determined by soil physical properties such as soil texture and structure, amount and type of vegetation, and topographic properties such as slope form and angle, and positional properties such as relative height and distance from the base of the slope. Stored soil water is an important source of plant available water for crop production on well drained soils of southwest Michigan. When fertility, growing season, and management practices are sufiicient, the ability of a soil to produce crops can be limited by the capacity of the soil to supply and store water (Leeper et al., 1974). The amount of stored water available for crop use in a particular soil of a sloping landscape is influenced by it's position on the landscape (F ranzrneier et al., 1969; Hanna et al., 1982). Therefore, crop management practices and soil-moisture simulation models should take slope position into account. The use of changes in stored soil water content to estimate actual evapotranspiration and drainage is well established (Hall and Heaven, 1970; Rouse, 1970; Day et a1, 1978; Mc Gowan and Williams, 1980a, b, c; Francis and Pidgeon, 89 1982a, b; Maule and Chanasyk, 1987). In sloping landscapes where surface and subsurface runoff may be significant, the water balance equation is as follows: D=P+Ro-Rf-dS-ET. where drainage of water fi'om the soil profile (D) is the result of the inputs of precipitation (P) and surface and subsurface run-on fi'om upslope (Ro) minus surface and subsurface runoff (Rt), change in soil water content (dS), and evapotranspiration, (ET). To determine ET, the amount of water that has been removed from the soil by evaporation and transpiring plants during the time period in question, potential evapotranspiration (PET) must be estimated. Several methods have been developed to estimate PET, among which the Thomthwaite (1948), Penman (1948), and Priestley and Taylor (1972) methods are widely accepted. Once PET is estimated, it is incorporated into the water balance equation as follows: D > 0 if: PET
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Table 3.2 Changes in Runoff Curve Numbers.
Transect Topographic Original Runoff Runoff Curve
Position Curve Number Number Used in
Simulation
South Summit 75.92 75.92
Upper Backslope 86.4 9504*
Lower Backslope 86.4 7592*
Footslope-Toeslope 75.92 75.92
North Summit 75.92 75.92
Upper Backslope 86.4 9504*
Lower Backslope 86.4 7592*
Footslope-Toeslope 7 5.92 75.92
Opposite Backslope 89.64 9828*
* Changed Runoff Curve Number
the footslope and toeslope positions was the greatest of all positions, and did not vary
as widely as the m soil water of the other positions. In fact, the footslope and
toeslope soils were at or near field capacity most of the year due to the large input of
water from the backslopes east and west of the toeslope. The lowest mm soil water
per 150 cm soil depth occurred in the upper backslope positions, which was well
below field capacity during the growing season in 1991. The mm soil water per 150
cm soil depth of the summit and lower backslope positions were between the
footslope-toeslope and upper backslope mm soil water. The mm soil water per 150
cm soil depth of the summit and lower backslope of the south transect were similar (as
was water retention at field capacity), and both were somewhat below field capacity
during the growing season. The greater mm soil water in the footslope-toeslope and
lower backslope positions may be due to runoff and subsurface lateral downslope
movement of water, or throughflow (Hoover and Hursh, 1943; Chorley, 1978;
Gerrard, 1981; Burt and Butcher, 1985; Daniels and Hamer, 1992), from the summit
We
sub
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Were
110
and upper backslope positions. Throughflow may occur in the Kalamazoo soil
because as percent clay increases from the Ap to Btl horizon, the hydraulic
conductivity may decrease (Daniels and Hammer, 1992). Water may accumulate
above the Btl horizon during periods of high infiltration, and flow laterally downslope.
In their studies of soil water content in sloping landscapes, Bhargava et al. (1976) and
Hanna et al. (1982) found the amount of stored soil to be greatest in the backslope and
footslope position soils, and least in the summit and shoulder position soils.
Soil Water Balance
In order to obtain estimated values of potential evapotranspiration, surface and
subsurface runoff and run-on, the CERES Maize computer model (version 2.1) was
used to simulate these parameters for each slope position of both transects. The
model was first used to simulate soil water conditions for each slope position, except
toeslopes. It was assumed that if simulated volumetric moisture content (VMC)
values of each soil layer for each slope position were close to the average soil layer
content from the access tube transects, then the runoff estimates obtained from
CERES were probably valid. Since the slope gradient of the summit position is similar
(0 to 2%) both toward the west and north (Figure 3.3), it is difficult to determine
where surface and subsurface runoff from the summit position moves. Therefore,
runoff from the summit position was not added to water entering the upper backslope
position. Changes to the inputs were necessary to make CERES estimates similar to
field measured values. First, since the VMC values of the lower backslope soil layers
were similar to those of the summit soil layers, it was assumed that overland plus
subsurface throughflow from the upper backslope soils to the lower backslope soils
equaled runoff plus subsurface throughflow from the lower backslope soils to the
toeslope soils. Therefore, the Runoff Curve Numbers of the lower backslope soils
were changed to be the same values as the summit soils, and runoff fi'om the upper
lll
backslope was not added to precipitation. Second, since field VMC values still did not
compare well with CERES VMC estimates, the Runoff Curve Numbers were
determined for Hydrologic Condition C soils (clay loam textures) instead of
Hydrologic Condition B soils (loam and sandy loam textures) (Table 3.2). Also, field
capacity soil moisture input was modified (Tables 3.3 and 3.4), until CERES VMC
estimates were as close to field VMC values. Next, overland flow plus subsurface
throughflow from the backslope soils to the toeslope soils was estimated as follows.
Since there is a hillslope (backslope = 8 % to 10 %) to the west, which converges with
the toeslope of the study site, the runoff from this backslope was simulated and
combined with the simulated runoff values from the upper backslope of the study site
to obtain estimated values for water flow to the toeslope. Soil profile data of the
upper backslope of the north transect was used as CERES input for simulating runoff
from the backslope opposite the study site because both slope positions have similar
soil profiles. The toeslope soils of each transect
were then simulated for VMC by adding runoff fiom the study site upper backslope
and the opposite backslope to precipitation. Simulated VMC values for the toeslope
soils of both the south and north transects were similar to field data only afier runoff
from the backslopes were simulated using Hydrologic Condition C soil values for the
backslopes. Simulated mm soil water per 150 cm soil estimates are presented in
Figures 3.12, 3.13, 3.14, and 3.15.
Statistical assessment of the simulation accuracy of CERES to estimate mm soil
water per 150 cm soil is summarized in Table 3.5. The Md values are all small except
for the 1990 toeslope positions, which are moderately overestimated. The RMSE
values are low compared to those reported by Jabro et al. (1994) using Br- simulation
models. Their RMSE values ranged from 14 to 51 percent. The r values for 1991
were higher than those of 1990. This might be expected since CERES was developed
as a corn grth model. All Md values were not statistically different from zero at the
112
Table 3.3 Changes in average field capacity volumetric moisture content for the south
transect.
Position Depth (cm) Field capacity Field capacity
volumetric volumetric
moisture moisture used
in simulation
Summit 0-21 0.259 0.259
21-45 0.280 0.307
45-69 0.222 0.199
69-107 0.152 0.130
107-145 0.119 0.100
145-150 0.097 0.080
Upper Backslope 0-25 0.253 0.257
25-44 0.250 0.274
44-77 0.131 0.100
77-101 0.080 0.070
101-125 0.093 0.070
125-150 0.082 0.070
Lower Backslope 0-23 0.265 0.265
23-58 0.283 0.296
58-83 0.181 0.150
83-108 0.128 0.110
108-129 0.123 0.105
129-150 0.120 0.095
Footslope-Toeslope 0-26 0.280 0.287
26-44 0.292 0.267
44-63 0.281 0.286
63-100 0.207 0.181
100-125 0.181 0.165
125-150 0.169 0.150
113
Table 3.4. Changes in average field capacity volumetric moisture content for the north
transect.
Position Depth (cm) Field Capacity Field Capacity used
Moisture Conent in simulation
Summit 0-16 0.237 0.243
16-36 0.223 0.257
36-55 0.180 0.154
55-84 0.101 0.075
84-117 0.094 0.075
117-150 0.092 0.075
Upper Backslope 0-15 0.169 0.184
15-46 0.146 0.130
46-76 0.130 0.111
76-106 0.134 0.111
106-128 0.099 0.073
128-150 0.100 0.073
Lower Backslope 0-20 0.213 0.216
20-30 0.188 0.216
30-61 0.151 0.121
61-92 0.174 0.173
92-124 0.135 0.115
124-150 0.180 0.170
Footslope-Toeslope 0-20 0.282 0.254
20-30 0.26 0.268
30-61 0.293 0.289
61-92 0.222 0.190
92-124 0.213 0.148
124-150 0.237 0.225
114
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