Malusis, M. and Benson, C. (2006), Lysimeters versus Water-Content Sensors for Performance Monitoring of Alternative Earthen Final Covers, Unsaturated Soils 2006, Geotechnical Special Publication No. 147, ASCE, 1, 741-752.
Lysimeters versus Water-Content Sensors for Performance Monitoring of Alternative Earthen Final Covers Michael A. Malusis1 and Craig H. Benson2 1
Department of Civil and Environmental Engineering, Bucknell University, Lewisburg, PA 17837, USA; PH: (570) 577-1683; FAX: (570) 577-3415; email:
[email protected] 2 Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA; PH: (608) 262-7242; FAX: (608) 263-2453; email:
[email protected]
Abstract This paper reviews the relative merits of two approaches for performance monitoring of alternative earthen final covers (AEFCs): (1) direct measurement of percolation via largescale pan lysimetry and (2) indirect assessment using data from water-content sensors. Large-scale lysimeters account for spatial variability and preferential flow and can resolve percolation rates on the order of 0.1 mm/yr, which is at least ten times lower than typical percolation criteria applied in practice (1-3 mm/yr). However, factors such as the capillary break effect, thermal water fluxes, and leaks can affect the accuracy of percolation rates measured with lysimeters. Lysimeters are also expensive and difficult to install. In contrast, water-content sensors are relatively inexpensive and are relatively simple to install, permitting multiple monitoring points within a cover at relatively low cost. However, demonstrating compliance using water content data is confounded by several factors, particularly spatial variability, scale effects, pedogenesis, and preferential flow. An analysis conducted using Monte Carlo simulation shows that estimates of percolation rate from water content data can vary by as much as six orders of magnitude due to spatial variability alone. For sites where a percolation criterion has been established, the best approach is to combine lysimetry and water-content sensors. Percolation rates measured with the lysimeter can be used for compliance monitoring and water-content data can be used for interpreting the lysimeter data and to assess reliability and representativeness. Introduction Alternative earthen final covers (AEFCs) are earthen covers for waste containment systems that rely on water balance principles to limit the rate of percolation into the underlying waste. An AEFC generally is used as an alternative to a conventional cover that relies on a resistive barrier (i.e., a compacted clay or geomembrane-clay composite barrier) to limit percolation. In many applications, the AEFC is required to be equivalent to the conventional cover, which usually means that the percolation rate from the AEFC must be no more than the percolation rate expected for the conventional cover.
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AEFCs function by storing water that infiltrates during periods of elevated precipitation and minimal evapotranspiration and subsequently transmitting the stored water back to the atmosphere via evapotranspiration during drier periods (Stormont and Morris 1998, Khire et al. 2000). AEFCs are effective when the cover profile has sufficient storage capacity and sufficient evapotranspirative demand exists to remove the stored water. When an AEFC performs satisfactorily, water contents within the AEFC rise and fall on a seasonal basis in response to infiltration and evapotranspiration. Provided the cover has adequate storage capacity and functions properly, percolation rates from AEFCs can be very low (< 1 mm/yr) (Albright et al. 2004). Because AEFCs are a relatively new technology, monitoring often is required to demonstrate that the cover is satisfying a percolation criterion. Two approaches currently are used to monitor the percolation rate: (i) direct measurements using large-scale pan lysimeters or flux meters and (ii) indirect measurements using water-content sensors. The objective of this paper is to assess the viability of both monitoring approaches and to discuss conditions under which the methods are applicable. Limitations of each approach are discussed and potential sources of uncertainty are identified. A combined approach is discussed that benefits from the merits of both techniques. Lysimeters Two devices are available for obtaining direct measurements of percolation: pan lysimeters and flux meters (Gee and Hillel 1988, Benson et al. 2001, Gee et al. 2002). Schematics of both are shown in Figure 1. Pan lysimeters typically are used for measuring percolation over a relatively large area, whereas flux meters are used for point measurements of percolation. Spatial variability (i.e., heterogeneity in hydraulic and vegetation properties) and preferential flow through macrofeatures (i.e., cracks, holes, etc.) are key factors that affect the rate at which percolation is transmitted, and accounting for these factors requires a measurement large enough to reliably represent the pathways conducting flow. Because the properties of engineered soils are correlated over distances of 1-3 m (Benson 1991), an area with dimensions of 10 m x 10 m or larger is needed to adequately account for spatial variability and preferential flow (Benson et al. 2001). For this reason, percolation measurements made with large-scale pan lysimeters are considered to be more representative than those with flux meters. Modern pan lysimeters that are carefully designed can be used to resolve percolation rates as low as 0.1 mm/yr (Benson et al. 2001).
Figure 1. Schematics of (a) a pan lysimeter and (b) a flux meter. Adapted from Benson et al. (2001) and Gee et al. (2002).
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Although pan lysimeters provide a simple large-scale measurement of percolation, they also have several drawbacks that affect the accuracy with which percolation can be measured. The three most significant technical drawbacks are (i) the capillary break effect at the interface between the drainage material at the base of the cover and the overlying cover soil, (ii) the barrier to vapor diffusion caused by the collection pan, and (iii) the possibility of leaks or a malfunction that prevents water from being collected. Lysimeters also are difficult and costly to install, which limits the number of points that can be monitored. Capillary Break Effect The capillary break effect is caused by the contrast in hydraulic properties at the interface between the base of the cover and the drainage layer. The capillary break formed at this interface impedes the flow of water into the drainage collection system until the soil immediately above the interface is nearly saturated. This impedance can result in underestimation of the percolation rate relative to that which might occur in the actual cover. An example of the capillary break effect is illustrated by field data obtained from a lysimeter at the Rocky Mountain Arsenal (RMA) that was used to evaluate one of four AEFC designs proposed for the site. The cover profile consisted of a 1.07-m-thick layer of clayey sand with a field capacity (defined as the water content at a matric suction head of 3.3 m) ranging from 15 to 30 % (average field capacity ≈ 23 %) (TTFW 2005b). Percolation from the base of the cover was collected in a geocomposite drainage net (GDN) underlain by a geomembrane (GM). The lysimeter was similar to that shown in Figure 1a, except there was no root barrier, interim cover soil, or sidewalls. Moderate-frequency (MHz range) time domain reflectometry (TDR) sensors were installed within the perimeter of the lysimeter at six different depths to record temporal and spatial variations in volumetric water content. Water contents measured during the Spring and Summer of 1999 are shown in Figure 2. Near the base of the cover, the water contents exceed the average field capacity in May and June. However, no percolation was recorded in May or June 1999, or for the remainder of the summer. The absence of percolation when the water content at the base is above field capacity is indicative of a capillary barrier effect. Laboratory tests were conducted by Tetra Tech FW, Inc. (TTFW 2005a) to determine if a similar capillary barrier effect could be reproduced in the laboratory using borrow soil that exhibits similar texture and has been approved for full-scale AEFC construction at RMA. Soil water characteristics curves (SWCCs) were measured for the GDN and the borrow soil and column tests were conducted that simulated infiltration into the cover profile. The SWCC data are shown in Figure 3 along with smooth curves corresponding to the van Genuchten function (van Genuchten 1980). The suction (ψb) and water content (θb) corresponding to imminent breakthrough across the capillary break, as defined by Khire et al. (2000), are also shown in Figure 3. According to the theory in Khire et al. (2000), breakthrough across the interface between the cover soil and GDN will occur when the suction drops below 0.01 m. At this suction, the water content of the cover soil at the interface will be approximately 43%, which is nearly the same water content reached at the base of the test cover in June 1999 (Figure 2). Thus, percolation into the GDN of the lysimeter underlying the test cover probably was imminent in June 1999. If the capillary break had not been present, the additional water stored at the base of the cover (i.e., ≥ 22 mm for θfc ≤ 30%) may have been transmitted as percolation.
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Figure 2. Water content profiles from lysimeter at RMA. Base of the cover is at 1.07 m.
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Figure 3. SWCCs for RMA geocomposite drainage net (GDN) and cover soil.
The column testing apparatus used by Tetra Tech FW, Inc. was similar to that used by Stormont and Anderson (1998). A schematic of the column is shown in Figure 4a. The column consisted of a 202-mm-diameter plastic cylinder containing a 500-mm-thick layer of clayey sand underlain by the GDN and a layer of gravel (TTFW 2005a). Tensiometers were installed in the fine soil at depth increments of 100 mm, except for the lowermost tensiometer, which was installed at a depth of 490 mm (10 mm above the interface between the cover soil and the overlying GDN). Inflow into the column was controlled by a peristaltic pump and outflow was measured with a tipping bucket. Data collected from the column test are shown in Figure 4b. Breakthrough occurs when the suction at the lowermost tensiometer is between 0.013 and 0.020 m, which corresponds to suctions ranging between 0.003-0.010 m at the interface between the fine soil and the GDN. This suction is in close agreement with ψb estimated using the theory in Khire et al. (2000) and the data in Figure 3. Capillary break effects caused by the presence of a lysimeter are negligible if the soil overlying the drainage layer is coarse-grained or if a similar capillary break is expected to occur at the base of the cover profile in areas outside the lysimeter. Cases where this might occur include covers underlain by a coarse-grained biota barrier (e.g., crushed rock) or covers placed on municipal solid waste, which has air entry suction on the order of 0.01-0.03 m (Benson and Wang 1998). If a capillary break will not exist outside the boundaries of the lysimeter, some means of minimizing the capillary effect is needed to more appropriately simulate the lower boundary in the cover. One approach is to place a geosynthetic root barrier and a layer of fine-textured interim cover soil between the base of the cover and the surface of the drainage layer, as shown in Figure 1a. The root barrier prevents roots from entering the interim cover soil, and therefore precludes root water uptake from the interim soil layer. Consequently, once the interim cover soil is wetted for the first time, the soil will remain wet due to the capillary effect from below and the lack of root-water uptake. Sensors such as TDR probes may be used to verify that the root barrier and interim cover soil are functioning as intended. However, inclusion of an interim cover soil layer adds complexity to the lysimeter design.
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Figure 4. (a) Schematic of column test conducted by Tetra Tech FW, Inc. for evaluating capillary barrier effect and (b) data collected from column test (graphed using data from TTFW 2005a). Another approach is to use an automated equilibrium tension lysimeter (AETL), where the water status at the base of the profile is continuously adjusted so conditions inside and outside the lysimeter are the same (Masarik et al. 2004). However, AETLs have only been applied in agricultural applications in humid regions. Thus, their effectiveness is unknown in drier climates (where AEFCs are common) and in cover monitoring programs, where very low percolation rates need to be measured. Vapor Barrier Effect The barrier to thermally driven vapor flow caused by the base of the lysimeter results in a conservative error in percolation rate. Water flowing downward in response to thermal gradients in the summer months is blocked at the bottom of the lysimeter and collected in the lysimeter pan, whereas water that flows upward in the winter is blocked but not collected. In effect, the lysimeter pan behaves as a hydraulic rectifier, and too much water can be collected in the lysimeter. Cumulative Thermal Water Flux (mm)
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Figure 5. Computed thermal and measured percolation lysimeters in Marina Sacramento, CA, USA during summer months of 2001-2004.
flux for and the
An example of this effect is shown in Figure 5 where computed cumulative thermally-driven water fluxes are compared with percolation measured in lysimeters for AEFCs in Marina, CA, USA and Sacramento, CA, USA as part of USEPA’s Alternative Cover Assessment Program (ACAP). Soil temperatures for the thermal flux calculations were measured during the summer months of 2001 through 2004. The method in Globus and Gee (1995) was used to compute the thermal water fluxes. At both sites, hydraulically driven flows are expected to be negligible during the summer months because precipitation is nil and the soilwater storage is depleted. Nevertheless,
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percolation trickled into the lysimeter during the summer months at both sites, as shown in Figure 5. The computed cumulative thermal water fluxes shown in Figure 5 agree reasonably well with the percolation data, suggesting that the percolation collected in the lysimeter during the summer months was largely due to thermal effects. More importantly, the thermal fluxes can be as large as 1 mm over a three-month period, which is large enough to cause non-compliance at sites where the maximum percolation rate is 1 mm/yr or less. Leaks and Malfunctions The potential for leaks or malfunctions exists whenever flows are being monitored. In lysimeters, these effects can be evaluated by including a test pipe in the lysimeter and incorporating regular lysimeter tests in the monitoring program (Benson et al. 2001). The inlet to the test pipe is above ground and the outlet is in the lysimeter sump. A known quantity of water is introduced into the test pipe, followed by monitoring of the discharge from the percolation collection system. Water-Content Sensors Water-content sensors have been used for qualitative and quantitative monitoring of AEFCs. Qualitative applications consist of monitoring the temporal variation in soil-water storage or water content over time to verify that storage and removal of water are occurring, or verifying that a wetting front is not reaching the base of the cover. Quantitative applications include comparisons against a “threshold” water content or soil-water storage above which cover performance is considered unsatisfactory, or using water-content data to calculate percolation for comparison against a percolation criterion. Both of these quantitative approaches are similar, because Darcy’s law provides the linkage between water content and percolation. Hydraulic properties of the cover soil are needed to calculate percolation rates from water content data or to define a threshold water content using Darcy’s law. Hydraulic properties typically are measured in the laboratory and include the SWCC (i.e., van Genuchten parameters) and the saturated hydraulic conductivity. A theoretical model then is used to define the unsaturated hydraulic conductivity function. These computations are straightforward and simple but are prone to appreciable error due to factors such as spatial variability, scale effects, pedogenesis, preferential flow, and inaccuracies in the hydraulic conductivity model. Ambiguities in the hydraulic gradient and drift in instrument calibrations also confound the computations and lead to uncertainty. Spatial Variability The following example illustrates how spatial variability in soil properties can confound a compliance assessment based on water-content measurements. Consider a monolithic cover where the hydraulic properties of the cover soil are free of spatial variability and are known with certainty. Assume that the saturated hydraulic conductivity of the cover soil is 10-5 cm/s, the porosity is 0.45, the residual water content is zero, and the van Genuchten parameters α and n are 2.2 m-1 and 1.3. Field capacity for these conditions corresponds to a water content of 0.24. The percolation rate corresponding to field capacity of this cover soil is 1.3 mm/yr if a unit downward gradient condition is assumed (i.e., the worst case condition).
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Although the cover soils in this example were assumed to be free of variability, actual cover soils exhibit considerable variability in hydraulic properties due to spatial variations in characteristics such as density, placement method, and soil composition (Gurdal et al. 2003). To illustrate the effect of spatial variability, the cover soils cited in the above example were assumed to exhibit low, moderate, or high spatial variability using statistical data from ACAP reported by Gurdal et al. (2003). Monte Carlo simulation was used to compute percolation rates by applying Darcy’s law and assuming unit gradient downward flow. The saturated hydraulic conductivity (Ks) and van Genuchten’s α parameter were assumed to be lognormally distributed, and van Genuchten’s n parameter was assumed to be normally distributed. In all cases, the residual water content was assumed to be zero, the saturated water content was fixed at 0.45, and all parameters were assumed to be uncorrelated. The input parameters used for the simulations are given in Table 1. For each Monte Carlo realization, the percolation rate was computed for a water content of 0.24 (i.e., the water content corresponding to field capacity with no spatial variability). A uniformly distributed error of ±0.02 in water content was included to account for the uncertainty associated with water contents measured with conventional TDR probes (Benson and Bosscher 1999, Gee and Ward 1999). Table 1. Hydraulic properties used in Monte Carlo simulation. Data from Alternative Cover Assessment Program Parameter Low Typical High Variability Variability Variability Standard deviation in lnKs 0.12 1.35 4.32 0.05 0.67 2.20 Standard deviation in α Standard deviation in n 0.01 0.18 1.24 1 RMA = Rocky Mountain Arsenal (computed using data from TTFW 2005b).
RMA Data1 3.4 1.4 0.19
Percolation rates computed using this approach are summarized in Table 2. When spatial variability is low, the calculated percolation rates range between 0.15 and 1.8 mm/yr, which is reasonably close to the percolation rate obtained without considering spatial variability (1.3 mm/yr). However, when typical (i.e., moderate) variability exists, the calculated percolation rate corresponding to a water content of 0.24 can range between 0.001 and 242 mm/yr (approximately five orders of magnitude). Based on this level of variability, a water content of 0.24 would correspond to an actual percolation rate in excess of 1.3 mm/yr only 35 % of the time. In practical terms, this means that percolation can, at best, be predicted with a potential error of as much as five orders of magnitude and a false positive rate of 65 % for cover soils that exhibit typical variability. An even larger range of percolation rates (up to six orders of magnitude) is possible for a cover with high variability. Based on the results of this analysis, percolation rates computed based on water content data can be highly unreliable. Computations of percolation rate also were made using statistical parameters describing the variability in the hydraulic properties of cover soils anticipated for use in the full-scale AEFCs at the Rocky Mountain Arsenal (RMA). These parameters were computed based on hydraulic properties measured on more than 40 samples that met the requirements for construction of full-scale covers at RMA. The statistical parameters used as input are summarized in Table 1. The original test data are summarized in TTFW (2005b). Percolation rates computed using the statistical data from RMA are summarized in Table 2.
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For the level of spatial variability expected in the full-scale covers at RMA, percolation rates ranging between 0.0001 and 14,400 mm/yr can be computed for a water content of 0.24. Table 2. Effect of variability in hydraulic properties on percolation at field capacity. Flux Under Unit Gradient (mm/yr) Percolation Statistic RMA Low Typical High Variability Variability Variability Conditions Min. Percolation (mm/yr) 0.15 0.001 0.0001 0.0001 Max. Percolation (mm/yr) 1.8 242 350 14,400 % Exceeding 1.3 mm/yr 2 35 43 30 % Below 1.3 mm/yr 98 65 57 70
Scale Effects Laboratory specimens typically are too small to adequately represent the network of pores controlling flow in the field. As a result, differences may exist between hydraulic properties measured in the laboratory on small specimens and hydraulic properties operative at field scale. Because specimen size is a key factor contributing to the difference in hydraulic properties, this phenomenon is referred to as the scale effect.
Percolation Rate (mm/yr)
An example of a scale effect is shown in Figure 6, which shows annual percolation rates measured in lysimeters by ACAP as a function of the annual peak soil-water storage normalized by the soil water storage corresponding to field capacity. Soil-water storage was obtained by spatially integrating water 100 contents measured with moderate frequency TDR probes installed in the 80 lysimeters. Soil-water storage at field capacity was computed using the water 60 content at field capacity and the thickness of each layer in the profile. Field capacity 40 was defined as the water content at a matric suction head of 3.3 m and was 20 obtained from SWCCs measured in the laboratory on undisturbed specimens 0 collected during construction. In principle, 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 non-zero percolation rates should Peak Soil Water Storage , S /S p c Field Capacity Storage correspond to cases where the ratio of peak soil-water storage to field capacity storage Figure 6. Annual percolation rates versus (Sp/Sc) exceeds 1.0. However, percolation peak soil-water storage normalized by is transmitted when Sp/Sc is as low as 0.69 storage at field capacity for sites in the because, in some cases, the storage Alternative Cover Assessment Program capacity operative in the field is lower than that computed from laboratory data. (adapted from Albright et al. 2004). Scale effects confound performance assessments based on water content monitoring because the magnitude of scale effects is not known a priori. As a result, an appropriate threshold water content or storage capacity cannot be defined reliably. An exception would be if largescale testing is conducted to define field-scale hydraulic properties, but such tests are not
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common and the appropriate size needed to represent field-scale conditions has not been determined for AEFCs. The potential for error is apparent when the data in Figure 6 are considered. For example, a performance requirement stipulating that Sp (computed using water content data) must be less than Sc could correspond to percolation rates either as low as zero or more than 50 mm/yr. Pedogenesis Time-dependent changes in cover soil properties caused by pedogenesis (changes in soil structure due to processes such as weathering and biota intrusion) confound quantitative assessments based on water-content measurements. For example, data collected by ACAP show that, within five years from the end of construction, the saturated hydraulic conductivity of cover soils can increase by a factor of more than 1000 and van Genuchten’s α parameter can increase by a factor of 100 (Benson et al. 2005). Changes of this magnitude can have a large effect on interpretations based on water contents unless a new threshold water content is regularly defined in accordance with the level of pedogenesis that has occurred. An appropriate method to redefine this threshold water content over time is unclear. However, at a minimum, periodic collection and re-testing of large-scale undisturbed samples from the cover soils would be necessary to evaluate potential changes in hydraulic properties. Preferential Flow One of the most important deficiencies in monitoring cover performance with watercontent sensors is the inability to detect preferential flow. Data from water-content sensors are characteristic of conditions within the soil matrix at the location of the sensor and not along cracks, fissures, or macropores. Moreover, even if sensors could detect preferential flow, placement of sensors along preferential flow paths is nearly impossible since locations of these paths are not known a priori. Consequently, water content data can provide a false impression regarding the effectiveness of a cover. Khire et al. (1997) provide an example of preferential flow in a 0.8-m-thick monolithic cover instrumented with water-content sensors and a lysimeter. Data collected during the winter of 1995 (Figure 7) show that percolation was transmitted even though the water-content sensors suggested that water had not passed through the cover. Pulses of percolation transmitted through preferential flow paths were regularly collected in the lysimeter shortly after precipitation events (Figure 7a), but two months before the deepest sensors indicated that water was reaching the base of the cover (Figure 7b). Combined Approach Any performance monitoring program for an environmental technology should be developed in conjunction with an appropriate set of data quality objectives that define the type, quality, and quantity of data needed to make reliable inferences regarding performance of the technology (USEPA 2000). An AEFC is a technology that is intended to perform equivalently to (or better than) a prescribed conventional cover. Thus, data must be collected to reliably demonstrate that an AEFC is providing equivalent performance throughout the intended service life. If equivalent performance is defined in terms of a maximum percolation rate, the monitoring method must be able to provide a reasonably accurate assessment of percolation rate. Given the uncertainties associated with
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Figure 7. Data from Khire et al. (1997) showing (a) close correspondence between precipitation and percolation measured with a lysimeter during January-March 1995 and (b) water-content data suggesting that water did not reach the base of the cover until late February 1995. percolation rates inferred from lysimeters and water content sensors, lysimetry is more suitable for monitoring percolation rate with less potential for decision error. However, water-content sensors can play a valuable role, because water-content data can be used to satisfy other objectives such as ensuring reliability or assessing the impact of corrective actions on hydrologic processes that affect percolation (e.g., storage water storage or root water uptake). Moreover, secondary data are essential for testing hypotheses regarding mechanisms that may be responsible for non-compliance of an AEFC (e.g., excessive percolation rates). Also, multiple nests of water-content sensors can be installed inside and outside a lysimeter to assess boundary effects and to obtain a quality understanding of sitewide spatial variations. An example is shown in Figure 8, where data from water-content sensors were used to understand why percolation rates transmitted by an AEFC in Sacramento (cited previously in the discussion of Figure 5) were much higher than expected. The AEFC was designed to transmit no more than 3 mm/yr of percolation. However, during Water Years 2001-02 (July 1, 2001 to June 30, 2002) and 2003-04 (July 1, 2003 to June 30, 2004), approximately 100 mm of percolation was transmitted (Figure 8a). The reason for the high percolation rate became evident when the water-content data were evaluated, either as soil-water storage (Figure 8a) or water contents at various depths (Figure 8b). During the Summers of 2001 and 2003, water stored during the pervious winter was not completely removed (Figure 8a). As a result, the cover had inadequate soil water storage capacity the following winters, which resulted in the two large percolation events. The data in Figure 8b provide some clues regarding the unexpected behavior. Water contents within the upper 600 mm of the cover decreased during spring and summer 2001, but not to the extent that had occurred in 2000 or in 2002, and very little depletion in water content occurred at depths greater than 600 mm in 2001. This suggests that the vegetation was not functioning as intended in 2001, with the portion of the root zone deeper than 600 mm being nearly inactive. The water-content data from summer 2003 show a different phenomenon. The entire depth of the root zone was active in summer 2003, but water removal ceased when the water content reached approximately 0.15, whereas water was removed until the water content reached 0.10-0.12 during the summers of 2001 or 2003. Reasons for this unexpected behavior currently are being studied.
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Figure 8. Temporal record between July 1999 and July 2004 for an AEFC in Sacramento, CA, USA: (a) soil-water storage and percolation; (b) water contents at four depths. Summary and Conclusions Lysimetry and water-content sensors are two methods commonly used for performance monitoring of alternative earthen final covers (AEFCs). The data presented in this paper illustrate that lysimetry appears to be the best practical means currently available to monitor percolation transmitted from final covers provided that the lysimeter is sufficiently large, is properly designed to minimize the impacts of capillary breaks, and is tested regularly to ensure leakage is not occurring. Inferences regarding percolation rates can be made with water content data, but these inferences are prone to considerable uncertainty due to spatial variability of soil properties, scale effects, and pedogenesis. However, water-content sensors are useful as a secondary monitoring tool to verify the reliability of lysimeters, evaluate potential causes of excessive percolation rates, and assess the effectiveness of corrective action on hydrologic processes that affect percolation. Acknowledgments Financial support for Dr. Malusis’ work on this paper was provided by the College of Engineering at Bucknell University. Financial support for Dr. Benson’s work on this paper was provided by USEPA’s Alternative Cover Assessment Program and from the National Science Foundation through Grant No. No. CMS-0437306. The opinions and recommendations provided in this paper are solely those of the authors and are not necessarily consistent with the policies of USEPA or NSF. The authors also are grateful to Lou Greer, Glendon Gee, William Albright, and Jorge Zornberg for their assistance and thoughtful comments on monitoring strategies for landfill final covers. The graphs in Figures 4 and 7, as well as the associated computations, were prepared by Preecha Apiwantragoon. References Albright, W. H, Benson, C. H., Gee, G. W., Roesler, A. C., Abichou, T., Apiwantragoon, P., Lyles, B. F., and Rock, S. A. (2004), Field Water Balance of Landfill Covers, J. of Env. Qual., 33(6), 2317-2332.
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Benson, C. (1991), Predicting Excursions Beyond Regulatory Thresholds of Hydraulic Conductivity Using Quality Control Measurements, Proc. First Canadian Conf. on Environ. Geotechnics, Canadian Geotech. Society,, 447-454. Benson, C., Abichou, T., Albright, W., Gee, G., and Roesler, A. (2001), Field Evaluation of Alternative Earthen Final Covers, International J. of Phytoremediation, 3(1), 1-21. Benson, C. and Wang, X. (1998), Soil Water Characteristic Curves for Solid Waste, Environmental Geotechnics Report 98-13, Dept. of Civil and Environmental Engineering, University of Wisconsin-Madison. Benson, C. and Bosscher, P. (1999), Time-Domain Reflectometry in Geotechnics: A Review, Nondestructive and Automated Testing for Soil and Rock Properties, STP 1350, ASTM, W. Marr and C. Fairhurst, Eds., 113-136. Benson, C., Bohnhoff, G., Ogorzalek, A., Shackelford, C., Apiwantragoon, P., and Albright, W. (2005), Field Data and Model Predictions for an Alternative Cover, Waste Containment and Remediation, GSP No. 142, A. Alshawabkeh et al., eds., ASCE, Reston, VA, 1-12. Gee, G. and Hillel, D. (1988), Groundwater Recharge in Arid Regions: Review and Critique of Estimation Methods, J. of Hydrological Processes, 2, 255-266. Gee, G. and Ward, A. (1999), Innovations in Two-Phase Measurements of Soil Hydraulic Properties, in Proc. International Workshop on Characterization and Measurement of the Hydraulic Properties of Unsaturated Porous Media, M. Th. Van Genuchten, F. J. Leij, and L. Wu (Eds.), U. C. Riverside Press, Riverside, CA, 241-269. Gee, G., Ward, A., Caldwell, T., and Ritter, J. (2002), A Vadose Zone Water Fluxmeter with Divergence Control, Water Resources Research, 38(8), 16:1-7. Globus, A. and Gee, G. (1995), Method to Estimate Diffusivity and Hydraulic Conductivity of Moderately Dry Soil, Soil Sci. Soc. Am. J., 59, 684-689. Gurdal, T., Benson, C., and Albright, W. (2003), Hydrologic Properties of Final Cover Soils from the Alternative Cover Assessment Program, Geo Engineering Report 03-02, Geo Engineering Program, University of Wisconsin-Madison. Khire, M., Benson, C., and Bosscher, P. (1997), Water Balance Modeling of Earthen Landfill Covers, J. of Geotech. and Geoenvironmental Eng., 123(8), 744-754. Khire, M., Benson, C., and Bosscher, P. (2000), Capillary Barriers: Design Variables and Water Balance, J. of Geotech. and Geoenvironmental Eng., 126(8), 695-708. Masarik, K., Norman, J., Brye, K., and Baker, J. (2004), Improvements to Measuring Water Flux in the Vadose Zone, J. of Env. Qual., 33, 1152-1158. Stormont, J. and Morris, C. (1998), Method to Estimate Water Storage Capacity of Capillary Barriers, J. of Geotech. and Geoenviron. Eng., 124(4), 297-302. Stormont, J. and Anderson, C. (1998), Capillary Barrier Effect from Underlying Coarser Layer, J. of Geotech. and Geoenvir. Eng., 125(8), 641-648. TTFW (2005a), Final Capillary Break Test Report, Prepared by Tetra Tech FW, Inc., Rocky Mountain Arsenal, Commerce City, CO, USA. TTFW (2005b), Final Summary Report for Acceptance Zone Development and Density Requirements for RCRA-Equivalent Cover Soils, Prepared by Tetra Tech FW, Inc., Rocky Mountain Arsenal, Commerce City, CO, USA. USEPA (2000), Guidance for the Data Quality Objectives Process, Report EPA QA/G-4, United States Environmental Protection Agency, Washington, DC. van Genuchten, M. Th. (1980), A Closed Form Equation for Predicting the Hydraulic Conductivity of Unsaturated Soils, Soil Sci. Amer. J., 44, 892-898.
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