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Department of Land, Air, and Water Resources, One Shields Avenue, University of California, Davis, CA 95616-8628
* Corresponding author (ThHarter{at}ucdavis.edu)
Received 26 April 2004.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSFIELD-SCALE WATER AND NITROGEN...STATISTICAL AND GEOSTATISTICAL...RESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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Abbreviations: BNF, biological N fixation • K–S, Kolmogorov–Smirnov • MB, mass balance • OM, organic matter
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSFIELD-SCALE WATER AND NITROGEN...STATISTICAL AND GEOSTATISTICAL...RESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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Driven in part by pollution prevention measures that attempt to optimize the use of fertilizers, N budgeting methods for specific crop–fertilizer application scenarios have been widely used in agronomy to determine the fate of N in soils and the potential for N leaching to groundwater (Tanji and Gupta, 1978; Frissel et al., 1981; Legg and Meisinger, 1982; Willigen and Neeteson, 1985). These methods are driven by experimental studies of N cycling processes that have almost exclusively focused on the uppermost soil horizon (0–30 cm, Paustian et al., 1990; Tindall et al., 1995; Watkins and Barraclough, 1996; Simek and Kalcik, 1998; Sharmasakkar et al., 1999) or on the root zone (0–1.8 m, Lafolie et al., 1997; Trettin et al., 1999; de Vos et al., 2000; Allaire-Leung et al., 2001; Stenger et al., 2002). Some experiments examined the annual N budget with intensive field investigations, but were not of long enough duration for a proper assessment of the effects of land management practices on groundwater quality (e.g., Paustian et al., 1990; Aronsson, 2001).
Critical gaps remain in our understanding of the influence of the vadose zone below the root zone, where it exists, on the estimation of N loading to aquifers (Ling and El-Kadi, 1998). Mechanisms involved in N transfer in the (deep) vadose zone below the root zone are rarely measured. The dearth of information about the deeper vadose zone results partly from the misconception that little chemical and biological activities occur below the root zone (i.e., below 0.3–1.8 m) (Pionke and Lowrance, 1991; Krug and Winstanley, 2002), but the vadose zone of many agricultural regions is considerably deeper and may contain appreciable amounts of organic matter (OM) or NO3 or both (Stevenson, 1986). Nitrate well below 1.8 m may be available to some plants (Smith and Cassel, 1991). Furthermore, denitrification between the root zone and the water table may significantly reduce N loading to groundwater, although this is difficult to quantify (Rees et al., 1995). Our current understanding of NO3 fate and transport below the root zone is further limited by prohibitive experimentation costs (e.g., Rees et al., 1995), by potentially long travel times through deep vadose zones, and perhaps most importantly, by a large degree of spatial variability.
Spatial variability is caused by spatially variable water and N application rates (i.e., external variability) and by spatially variable vadose zone hydraulic and chemical properties (i.e., intrinsic variability). Both may lead to highly nonuniform distribution of NO3 and other agrochemicals (Rao and Wagenet, 1985; Mohanty and Kanwar, 1994). Past studies have quantified spatial variability of NO3 by geostatistical methods, but only within the root zone of agricultural field soils (Dahiya et al., 1984; Tabor et al., 1985; White et al., 1987; van Meirvenne and Hofman, 1989; Istok et al., 1993; Cambardella et al., 1994; Hofman et al., 1994; Mohanty and Kanwar, 1994; Sharmasakkar et al., 1999; Allaire-Leung et al., 2001; Ilsemann et al., 2001; Stenger et al., 2002).
Equivalent field work on the spatial variability and storage of NO3 in the deep vadose zone (below the root zone) and analysis of its relationship to field-scale N mass balance and NO3 leaching into groundwater has, to our knowledge, not yet been attempted. The goal of our work is therefore to provide a detailed field analysis of NO3 occurrence in a deep alluvial vadose zone, its relationship to the geologic and hydraulic characteristics of the vadose zone and to fertilizer management, and to discuss the implications of our findings with respect to common interpretations of vadose zone data.
Recognizing that the study is site specific, we do not make a strong claim that our results can be quantitatively transferred to other sites and situations. However, the general site conditions (alluvial soils, semiarid Mediterranean climate, irrigated crops) are representative of many important agricultural regions around the globe. The fundamental conditions at the study site, namely the strong heterogeneity of the NO3 distribution, the lack of significant denitrification, and the strong control of the heterogeneous hydraulic and flow conditions on the NO3 distribution are therefore not unique to this site and provide universal insight into "real" deep vadose zones. Therefore, findings from this site provide important evidence for the fate and transport of NO3 in deep vadose zones in general. In particular, we hope that studies like the one presented here will provide a useful basis for developing guidance on the role of monitoring devices in the deep vadose zone.
In the following, we give a brief description of the site and the experimental methods. We then implement a conventional field-scale root zone water and N mass balance (MB) analysis to estimate NO3–N leaching from the root zone and to provide a predictive framework for the assessment of deep vadose zone NO3–N. A statistical analysis of the measured water content and NO3–N distribution is used to separate deterministic large-scale spatial variability that can be explained by depth, N treatment, and discrete lithofacies zonation from random smaller-scale spatial variability. For the nondeterministic residuals, we develop appropriate geostatistical models of the deep vadose zone water content and NO3–N data to estimate the total deep vadose zone NO3–N mass. In the discussion, we compare this estimate with the total NO3–N mass predicted from the MB analysis to evaluate the deep vadose zone NO3 fate and transport processes and the role of spatial variability in assessing potential NO3 leaching to groundwater.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSFIELD-SCALE WATER AND NITROGEN...STATISTICAL AND GEOSTATISTICAL...RESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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Fertilizer Treatments
Planted in 1975, the matured orchard was subject to a 12-yr fertilizer trial that began in September 1982. A complete random block design was used (Fig. 1)
with application rates of 0, 110, 195, 280, or 365 kg N ha–1 yr–1 in several replicates. Fertilizer was broadcast in September of each year at a rate of 110 kg N ha–1 to all rows except the control treatments (0 kg N ha–1 yr–1). During the following spring, the 195, 280, and 365 kg N ha–1 yr–1 treatments received additional applications at a rate of 85 kg N ha–1 (or 75 lb ac–1) once, twice, and three times, respectively, to achieve the desired annual fertilization rate. In the first year, (NH4)2SO4 was applied. To prevent soil acidification, NH4NO3 (33.5% N content) and Ca(NO3)2 (15.5% N content) were used throughout the remainder of the study. There was no application of fertilizer in 1995. In September 1996, 110 kg N ha–1 was applied throughout the entire orchard including the control plots in the usual broadcast application method. Vadose zone water quality analysis was not part of the original project's scope.
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Yield and Plant Nitrogen Uptake
The nectarine orchard blossoms in mid- to late February immediately before leafing out. Fruit ripening is completed by July. Using standard methods, crop yield (fruit weight) and leaf N concentrations were measured in 1983 through 1985 and in 1991 through 1994. Fruit N concentrations were only measured in 1983 (Johnson et al., 1995).
Vadose Zone Sampling
In 1997, three subplots with the 0, 110, and 365 kg N ha–1 yr–1 treatments were selected for sampling (Fig. 1). For convenience, the three subplots are named "control," "standard," and "high," respectively, throughout the text. Between July and October 1997, 60 undisturbed continuous soil cores were drilled with a Geoprobe Systems (Salina, KS) direct-push drilling rig to a depth of 15.8 m (52 ft), including 18 cores from each subplot (Fig. 1). Cores were obtained in approximately 1.2-m sections (4 ft), their sedimentologic characteristics were described, and then the cores were sampled. More than 1000 soil samples of 22.5 cm long and 4 cm in diameter were taken at 30- to 60-cm intervals depending on stratification. Subsamples were prepared and preserved for later analysis. During the drilling phase, groundwater was detected at approximately 16 m below the ground surface.
Vadose Zone Textures
The entire vadose zone at the site consists of unconsolidated sediments deposited on a stream-dominated alluvial fan. The textural groups range from clay and clayey paleosol hardpans to a wide range of silt and sand, including occasional coarse sand and gravel sediments. Coarse-grained materials are believed to represent channel deposits embedded within finer-grained floodplain and levee deposits. Sandy loam is the most common textural unit in the profile while clay was the least (48 and 8% of the vertical profile length, respectively). Ten major stratigraphic units were identified based on texture, color, and cementation and are referred to as "lithofacies." They exhibit vertically varying thicknesses, yet are laterally continuous across the experimental site. The measured saturated hydraulic conductivity data, best described by a lognormal distribution, indicate high hydraulic variability at the local scale (10–2–10–1 cm, for details see Minasny et al., 2004).
Soil Water Content and Nitrate
Gravimetric water content 
dw (g g–1) was determined using measured values of oven-dried (105°C for 24 h) 1.25-cm (1/2-in)-long samples. Bulk density was measured on 119 core samples and varied from 1.3 to 1.9 g cm–3, with an average of 1.6 g cm–3. However, core samples, particularly finer-textured samples, were subject to variable compression during coring. Therefore, a more representative constant bulk density 
b of 1.45 g cm–3 (Hausenbuiller, 1985) and the measured values of dw were used to compute volumetric water content (m3 m–3). Regardless of the specific number used for bulk density, the potential error introduced is small (<10%) compared with the large range of observed NO3 concentrations (see below).
Nitrate concentration was measured in 0.5 M K2SO4 soil extractions (5/1 ratio, 1-h reciprocal shaking) prepared from 809 core subsamples sieved through a 1-mm screen (Horwath and Paul 1994). Soil extracts were analyzed by automated flow-injection colorimetry following the methods of the USEPA 353.2 (Wendt, 1999). At each subplot, the smallest horizontal sampling interval varied between 1.2 to 3 m (10 and 4 ft, respectively, Fig. 1). The average vertical sampling interval was approximately 0.6 m (2 ft). Two hundred twenty-four sample concentrations were below the limit of detection (LOD = 10–3 mg L–1 = 10–3 g m–3) and recorded as zero. Measured values were converted to aqueous concentrations NO3–Naq in units of grams per cubic meter (or equivalently µg mL–1), using measured water content data. Both, and NO3–Naq measurements contribute to the N mass estimate and are therefore both included in the statistical analysis. Results of core analyses for and NO3–Naq are summarized in Table 1.
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FIELD-SCALE WATER AND NITROGEN FLUXES: NITROGEN BUDGET |
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TOPABSTRACTINTRODUCTIONMETHODSFIELD-SCALE WATER AND NITROGEN...STATISTICAL AND GEOSTATISTICAL...RESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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 | [1] |
N is the change in the total amount of N (organic and inorganic) stored in the root zone. Nleaching is equivalent to the long-term potentially leachable N (LPLN) described in Meisinger and Randall (1991).
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N 
0 (Meisinger and Randall, 1991). Warm, semiarid climate conditions with irrigation further accelerate the time required for a system to reach quasi-steady-state conditions with respect to annual N storage changes. The 12-yr duration of the fertilizer treatment was considered sufficient to assume that annual N storage changes were negligible (N = 0).
Nitrogen Inputs
Annual N inputs included fertilizer applications, and N received from irrigation, precipitation, dry deposition, and nonsymbiotic N2 fixation. Table 2 lists average annual N inputs and margin of errors (95% confidence intervals) in the N mass balance analysis (e.g., Berthouex and Brown, 1994). Average NO3–N concentration in irrigation water was 4 g m–3 (Harter et al., 1999). Long-term average annual irrigation N input is therefore 70 kg N ha–1 yr–1. A margin of error of ±10% primarily accounts for the lack of precise irrigation flow measurements and also for measurement errors of the NO3–N concentration.
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Nitrogen Plant Uptake and Transformations
In the first year of the experiment, there were no significant differences in yield or average fruit weight among the subplots. In the second and third years, the control subplot dropped off in both yield and fruit size, but then remained at about the same level for the duration of the experiment. The 7-yr average yield was 36, 51, and 48 t ha–1 for the control, standard, and high subplots, respectively, indicating a negative response of the high subplot to overfertilization. Nitrogen content in dry fruit measured in 1983 was 0.71, 1.51, and 2.05% for the control, standard, and high subplots, respectively. Nitrogen uptake estimates are based on 7-yr average annual crop yield and the dry matter fruit N content measured in 1983:
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Nitrogen in tree leaves is considered to be completely recycled into the root zone. Losses due to soil erosion and surface runoff are negligible since the ground surface at the orchard is flat and the basin irrigation system (surface flooding) generates no surface water return flow.
Denitrification and NH3 losses were estimated from previous experimental studies (Meisinger and Randall, 1991) that took into account various controlling site conditions (e.g., irrigation, drainage, climate, soil OM, and pH). The observed range of 6 to 20% N loss from denitrification is consistent with the experimental findings of Dowdell and Webster (1984), who reported N loss of 2 to 19% during a long-term N balance study, but lower than the 15 to 30% loss estimates reported by Allison (1966) and Hauck (1981). We adopted an average N loss of 10% of Ninputs with the error margins equal to the range of reported loss percentages (2–30%, Table 2). Neutral to slightly acidic soil pH conditions at the site (not shown here) keep NH3 volatilization at a minimum (Paustian et al., 1990). Average volatilization losses are approximately 10% with error margins equivalent to those of denitrification (Table 2) (Meisinger and Randall, 1991).
Water Mass Balance and Deep Vadose Zone Nitrogen
The average annual water budget, as illustrated in Fig. 2, is (Martin et al., 1991)
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For predictive purposes, we applied a commonly used simple one-dimensional uniform steady-state flow concept. Average NO3–N concentration (g m–3) in the 14-m-deep vadose zone was obtained by multiplying annual NO3–N leaching loss from the root zone with the recharge rate R. The total amount of NO3–N (kg ha–1) contained within the deep vadose zone was computed by multiplying the annual NO3–N leaching loss, Nleaching, with the average time of travel, 
, through the deep vadose zone, where = 14 m x avg/R, and avg is the average reported field capacity (25%) for the dominant soil texture (Martin et al., 1991).
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STATISTICAL AND GEOSTATISTICAL ANALYSIS |
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TOPABSTRACTINTRODUCTIONMETHODSFIELD-SCALE WATER AND NITROGEN...STATISTICAL AND GEOSTATISTICAL...RESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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and NO3–Naq. Factors considered included depth, N treatments, and lithofacies distribution.
Depth-dependent trends were determined using a separate regression analysis of the and logtransformed NO3–Naq for each subplot treatment. After removing trends, a Kolmogorov–Smirnov (K–S) test (e.g., Davis, 1986; Olea, 1999) was used to test normality of the and logtransformed NO3–Naq distribution. The effects of subplot treatment (three groups: control, standard, and high) and lithofacies (10 sample groups, one for each lithofacies), and their interactions (30 groups) on and lnNO3–Naq, were measured by a sigma-restricted ANOVA with effective hypothesis decomposition (Hocking, 1985) to account for the unbalanced design (unequal number of samples between groups). Homogeneity of variance was assumed if the ratio of the largest to smallest group standard deviation did not exceed 3. Where significant effects were observed (p < 0.05), Newman-Keuls and Duncan's multiple range tests were performed for post-hoc pair-wise comparison of means. Nitrate-N samples below the LOD were not included in the significance analysis. To check for potential bias from exclusion of nondetects, a nonparametric Kruskal–Wallis ANOVA was performed on the bimodally distributed dataset with nondetect samples recorded at one-half the LOD concentration (see below). A Kruskal–Wallis test was also performed to test for significant effects of subplot and vertical location on the probability of nondetects (using an indicator variable of 1 for "non-detects" and 0 for "detects"). All statistical analyses were performed with the Statistica software (Statsoft, 2002).
Geostatistical Analysis of Water Content and Nitrate Data
After trends were removed and appropriate variable transformations were done based on the results of the statistical analysis, geostatistical analysis was used (i) to quantify the amount of spatial variability in the and NO3–N distributions unexplained by depth, treatment, and lithofacies location; (ii) to characterize differences in the NO3–N distribution among the three different fertilizer treatments; and (iii) to quantify the field-scale N loading rate to groundwater from those local-scale measurements. The correlation coefficient between NO3–Naq and was –0.11; hence, and NO3–Naq were considered uncorrelated for purposes of the geostatistical analysis. Due to a large number of nondetect NO3 concentrations, two sets of experimental NO3–Naq semivariograms were computed for the complete dataset and for the dataset that excluded nondetects.
Directional (horizontal and vertical), nested spherical semivariograms were fitted to the observed semivariograms by initially using a manual calibration followed by a least square optimization process (e.g., Davis, 1986; Olea, 1999). Directional semivariograms were constructed with appropriate lag intervals that were assigned according to average horizontal and vertical sampling scheme (Fig. 1).
Ordinary block kriging (Deutsch and Journel, 1992) was applied to estimate average volumetric block values of lnNO3–Naq and residual (i.e., trend-removed) from their point measurements. The kriging domain size for each subplot was x = 24 m, y = 3 m, and z = 16 m (80, 10, and 53 ft, respectively), discretized into blocks with x = 0.75 m, y = 0.3 m, and z = 0.15 m.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSFIELD-SCALE WATER AND NITROGEN...STATISTICAL AND GEOSTATISTICAL...RESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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avg = 25%) and the mean travel time to groundwater, , is 3.2 (2.8–3.8) yr (Fig. 2). At = 3.2 yr, field-scale N concentration in the leachate (and recharge) is predicted to be 5, 9, and 25 g m–3 for the control, standard, and high subplots, respectively. Corresponding deep vadose zone N storage is predicted to be 180, 300, and 880 kg N ha–1, respectively. For 1997, the deep vadose zone storage can be computed by considering that all subplots were subject to the "control" leaching rate in 1995 (no fertilizer application) and to the "standard" leaching rate in 1996 (uniform standard fertilizer application). Then, the predicted deep vadose zone N storage at the time of drilling in 1997 is 220, 260, and 480 kg N ha–1. The wide confidence intervals for the deep vadose zone N storage, summarized in Table 2, reflect potential errors in both the recharge and the LPLN computation.
Statistical Analysis of Water Content and Nitrate Distribution
Water Content
Measured water content data follow an approximately symmetric, normal distribution (Table 1). Within all subplots water content is characterized by a significant linear increase with depth. Separate linear trend models were fit to each subplot dataset, as illustrated in Fig. 3
. Trend residuals are shown to be normally distributed with K–S differences insignificant at the p = 0.1 significance level. Trends are essentially identical between the three subplots.
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Nitrate
The NO3–Naq distribution is highly skewed (Fig. 4a)
and lognormal (significance level, p < 0.05) after excluding nondetect samples (Fig. 4b). Resulting sample means (estimated from the moments of the logtransformed data, Table 1) are 5.2, 3.3, and 7.4 g m–3 for the control, standard, and high subplots, respectively. Detectable NO3–Naq concentrations range from 0.04 to 129.72 g m–3 (Table 1). Of the samples with detectable concentrations, 21% measure <1 g m–3 and 10% exceed the maximum contamination level for drinking water (10 g m–3). Significant differences exist between subplots. In the high subplot, the fraction of low NO3–Naq measurements is less than one-third of the fraction observed in the other two subplots. On the other hand, most of the high concentration samples, exceeding 10 g m–3, are found in the high subplot, while only a small fraction (8%) of these are found below the root zone of the control plot.
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Because of the large number of nondetects, replacing nondetects with a default value of –4.605 (one-half of the LOD) leads to a bimodal lnNO3–Naq distribution; the LOD for NO3–Naq is significantly lower than the extent of the left tail of the lnNO3–Naq distribution in Fig. 4b. In other words, the number of nondetects (224) far exceeds the (small) number that would be expected if the fitted lognormal distribution of Fig. 4b is considered to be censored to the left. Because of the large number of nondetects, the first two sample moments of lnNO3–Naq strongly depend on the concentration specified for nondetects (here one-half of LOD). Effectively then, the mean and variance become a measure of the mid-point and spread between the mode of the detect group and the level specified for the nondetect group (Table 1).
Geostatistical Analysis of Water Content and Nitrate Distribution
Separate water content and NO3 semivariograms were computed for each subplot. Data density was not sufficient to derive well-structured separate semivariogram models for individual lithofacies within each subplot. But by applying a thin vertical bandwidth (<15 cm) in the search window, horizontal semivariograms were computed for data pairs containing only points within the same lithofacies (e.g., Deutsch and Journel, 1992).
Water Content
Semivariograms of the water content trend residuals exhibit not only a significant geometric anisotropy (unequal range), but also a strong zonal anisotropy (unequal sill) throughout all subplots. The sill in the horizontal direction (Fig. 6a–6c)
is significantly smaller, while the range in the horizontal direction is significantly longer than in the vertical direction (Fig. 6d–6f).
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, shown in Fig. 7
, is much less variable than the distribution of NO3–Naq (see below). Due to the large horizontal correlation scale, water content distribution is fairly uniform, with predominantly horizontal layering (e.g., Hills et al., 1991). Although all three subplots exhibit a similar trend of soil moisture distribution, the depth profiles of water content data shown in Fig. 7b suggest that the deeper portion of the control and high subplots is wetter than that of the standard subplot.
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These observations raise several issues to be discussed in the following section: What are the potential errors contributing to the difference between predicted and measured deep vadose zone N? How representative and significant is the amount of observed spatial variability of water content and NO3? What does the observed spatial variability of water content and NO3 indicate with respect to the spatial distribution of water flux and the expected fate of transport?
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSFIELD-SCALE WATER AND NITROGEN...STATISTICAL AND GEOSTATISTICAL...RESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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The computed error margins for recharge, LPLN, and the resulting deep vadose zone storage (Table 2) are on the same order as the 30% of actual leaching losses suggested by Meisinger and Randall (1991). Although large, neither these errors nor those from the kriging analysis can explain the observed difference between MB predicted and measured deep vadose zone N.
If the differences were assumed to be primarily caused by denitrification (Bar-Yosef and Kafkafi, 1972; Aronsson, 2001) under predominantly uniform vertical flow conditions, the amount of N loss in the deep vadose zone should be on the order of one hundred to several hundred kilograms per hectare within one leaching cycle (i.e., 3.2 yr) with much higher denitrification rates under the high subplot than the other two subplots. Most of this denitrification would have had to occur in the shallowest zone because no significant depth-dependent decrease in NO3–N was observed below the root zone and because several relatively high NO3–N concentrations were measured even at depth. However, denitrification rates of more than 55 to 60 kg ha–1 yr–1 are unlikely, given the low organic C content of the root zone and its relatively coarse texture (e.g., Rolston et al., 1982; Aronsson, 2001; Sanchez et al., 2001; Krug and Winstanley, 2002). This is also consistent with the lack of significant vertical trends in the N isotope fractionation observed at the site (Harter et al., 2004).
While the denitrification processes in the deep vadose zone may be locally significant (Harter et al., 2004), other explanations, namely the role of heterogeneity and flow nonuniformity (not considered in the MB model) must be considered to explain the large discrepancy between field measured and MB estimated deep vadose zone N content. The site stratigraphy and hydraulic properties are highly variable both between facies and within facies (Minasny et al., 2004). The significant degree of layering observed at the site is typical of the alluvial fan architecture in the region, which contains laterally extensive hardpans and floodplain deposits intercalated between higher permeable sediments of varying texture representing channel and overbank deposits (Page and LeBlanc, 1969; Weismann et al., 1999). While flow paths of NO3 are thought to be predominantly vertical within one layer, the stratigraphic layering may contribute to lateral flows (Iqbal, 2000) leading to both, preferential flow pattern and potentially significant NO3 exchange between subplots.
Spatial Variability of Water Content and Nitrate
Water Content
The geometric and zonal anisotropy (Fig. 6) are the result of the highly stratified conditions and strong horizontal layering of water content across the site, which is also evident in the kriged water content map (Fig. 7a, 7b). Such "layering" of moisture content can be the result of either layered strata with significant textural differences and also of transiency in the water flux. The significant contribution of textural layering to the water content distribution suggests that textural differences at the site are the main cause of the water content differences with depth. Similar phenomena have been observed in other field experiments and in numerical studies of vadose flow through heterogeneous media (e.g., Hills et al., 1991; Polmann et al., 1991).
Nitrate
Lognormal NO3–Naq distributions found at this deep vadose zone site are not unlike those reported in other studies focusing on the root zone (e.g., Tabor et al., 1985; Sharmasarkar et al., 1999; Ilsemann et al., 2001). However, CVs for each subplot treatment (Table 1) are significantly higher than those measured elsewhere, where reported CVs typically range from 20 to 50% and in few cases are as high as 70 to 100% (e.g., Mohanty and Kanwar, 1994; Sharmsarkar et al., 1999; Ilsemann et al., 2001). In part, the higher observed variability of NO3–Naq may be attributed to the small sample size (3.5-cm diameter by 7.5-cm length) relative to other typical soil samples (3.2-cm diameter by 30-cm length). It may also be a result of the fact that practically all samples are taken at depths well below the mechanical impact zone of agricultural practices. It cannot be attributed to lithofacies control, since no large concentration contrasts were observed between most lithofacies.
The significantly larger mean NO3–Naq of the high subplot indicates that higher than standard fertilizer treatment indeed affects NO3 transport to groundwater. However, the difference must be interpreted carefully in light of the high degree of spatial variability. Some of the key patterns in NO3–Naq distribution are also due to other boundary effects:
Nitrate semivariograms exhibit a statistically significant spatial continuity as postulated in theoretical stochastic models of solute transport through the vadose zone (e.g., Harter and Yeh, 1996). The observed geometric anisotropy may be caused by nonuniform N applications (extrinsic variability). Possibly, such strong geometric anisotropy may also be the result of highly heterogeneous vadose flow processes (see below). While it is difficult to further facilitate the comparison of the results of spatial correlation that we observed for NO3 with those reported by previous studies, it is noteworthy to mention that some studies observed a finite range of spatial dependence (e.g., van Meirvenne and Hofman, 1989). Other studies found monotonically increasing semivariance with increasing lag distances (e.g., Tabor et al., 1985). In many studies with large sampling distances (10–500 m), a pure nugget effect (no spatial correlation) is observed (e.g., Hofman et al., 1994; Ilsemann et al., 2001; Stenger et al., 2002). This is consistent with our finding that correlation scales of core-measured NO3–Naq extend to a few meters at most.
Spatial Variability of Nitrogen Flux: Interpretation in the Context of Heterogeneous Flow Fields
The probability distributions of and NO3–Naq are consistent not only with root zone field studies but also with results obtained from stochastic models of flow and transport in heterogeneous porous media. Harter and Yeh (1998) and Harter and Zhang (1999) demonstrated that spatially variable soil properties lead to approximately normal distributed moisture distributions while the resulting vadose moisture velocity distribution is highly skewed (lognormal), which then leads to a skewed concentration distribution. Like its marginal probability distribution, the kriged NO3–Naq distribution pattern at the experimental site is also surprisingly similar to that found in other experimental studies (e.g., Hills et al., 1991; Roth et al., 1991) and to that postulated in numerical models (e.g., Harter and Yeh, 1996; Ünlü et al., 1990; Tompson and Gelhar, 1990) of transport in highly heterogeneous hydraulic conductivity fields. We observed zones with individual plumes apparently moving laterally in some locations and downward in others, high concentration variability, and large zones with negligible NO3 concentrations.
The conceptual framework of lognormally distributed flow rates (e.g., Harter and Yeh, 1996) is in fundamental contrast to the uniform flow conditions assumed in the LPLN estimates of N mass in the deep vadose zone. Under the conditions of lognormal flow rates (i.e., strongly heterogeneous flux rates), quasipreferential flow paths exist (Polmann et al., 1991; Russo et al., 1994; Harter et al., 1996; Harter and Yeh, 1996), creating a flow pattern not unlike that in soils with a relatively low permeable matrix and a highly permeable macropore structure (Roth et al., 1991). Under such heterogeneous flux conditions, the majority of the pore space is occupied by regions with slow velocities (including stagnant zones that do not contribute significantly to active flow). Nitrate in those low flow regions can have tortuous flow paths, long travel times, and be subject to local denitrification, particularly in the shallow vadose zone after storm events (Pionke and Lowrance, 1991; Ryden and Lund, 1980; Xu et al., 1998; MacQuarrie and Sudicky, 2001). Largest flux contrasts between preferential flow paths and stagnant flow zones would be observed in coarse-textured material because of its low capillary potential. This is consistent with the fact that the largest amount of NO3–Naq nondetects at the site occurred in the sand lithofacies S located in the center of the vadose zone profile.
Theoretical models indicate that the relatively high flow zones are of only limited spatial extent (e.g., Fig. 9 in Harter et al., 1996), but their high flux rates lead to rapid NO3 transfer through the vadose zone. Such effects of textural heterogeneity on flow nonuniformity are further enhanced by potentially unstable infiltration into the sandy loam root zone, which were documented for this site in Wang et al. (2003). Even stronger instabilities and fingering may occur at and below the interface of fine-textured lithofacies overlying coarse-textured lithofacies (Glass et al., 1988), such as in the deeper sand lithofacies S, which had relatively low water content and a high ratio of NO3–Naq nondetects.
The combined evidence of textural heterogeneity, lithofacies contrasts, hydraulic heterogeneity (Minasny et al., 2004), and spatial variability of water content and NO3–Naq strongly suggests three major processes controlling the fate and transport of NO3 in the vadose zone:
These processes would explain both the large number of nondetects and the overall low N mass remaining in the deep vadose zone. The rapid NO3–Naq transport in localized flux channels significantly reduces the amount of N stored in the deep vadose zone, strongly limiting the role of denitrification. Our results suggest that the lack of N stored below the root zone should not automatically be interpreted as significant N attenuation due to denitrification (or other unquantified losses within the root zone). We point out that the conceptual framework of heterogeneous flow (as opposed to uniform deep vadose zone flow) also suggests the simultaneous occurrence of significantly older water next to very young water within the vadose zone. Hence, the NO3 distribution at the site (Fig. 9a) represents as much average conditions during the long-term fertilizer treatment (in lower flux regions) as it represents only the most recent two N applications (in 1994, 1996), the latter of which was uniform across all treatments (in the localized high flux regions). This would explain the relative similarity in measured total N levels between subplots.
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CONCLUSIONS |
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TOPABSTRACTINTRODUCTIONMETHODSFIELD-SCALE WATER AND NITROGEN...STATISTICAL AND GEOSTATISTICAL...RESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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The results are consistent with, albeit not a direct proof of, theoretical work on the effects of soil and sediment heterogeneity on vadose flow and transport. The extensive deep vadose zone sampling campaign presented here provides the first extensive dataset to confirm the applicability of stochastic concepts of unstable flow to predicting solute flux in the deep vadose zone. Ongoing work to substantiate the role of heterogeneity and denitrification will include a detailed, site-specific modeling analysis.
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ACKNOWLEDGMENTS |
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMETHODSFIELD-SCALE WATER AND NITROGEN...STATISTICAL AND GEOSTATISTICAL...RESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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