British Columbia Mine Reclamation Symposium

Intensively monitoring cover system thermal properties with distributed temperature sensing Tallon, L.K.; O’Kane, M.A. 2013

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 1 INTENSIVELY MONITORING COVER SYSTEM THERMAL PROPERTIES WITH DISTRIBUTED TEMPERATURE SENSING  L.K. Tallon, P.Ag. M.A. O?Kane, P.Eng.  O?Kane Consultants Inc. 112-112 Research Drive Saskatoon, SK.  S7N 3R3  ABSTRACT  Reclamation soil cover systems can range in size from hundreds of square metres at the pilot scale to tens of hectares at the commercial scale. Performance of cover systems is typically conducted using point scale monitoring stations. While vertically intensive, a point-source monitoring profile is inadequate for characterizing the spatial and temporal variations of the cover that exist at the landscape scale of interest. Distributed temperature sensing is a technology that intensively measures temperature at 1 m resolutions over distances between 5,000 to 10,000 m. This paper presents a series of case studies that detail how the technology can be used to the field-scale performance of cover systems through measurement of thermal properties.    Key Words: fibre optics, distributed temperature sensing, cover system, thermal property, spatial variability   INTRODUCTION  Performance of soil cover systems is governed by the balance of water and energy within the cover (Scanlon et al., 2006). The amount of water that is allowed to travel past the cover system into the underlying waste is the net result of precipitation, runoff, percolation, storage, and evaporation. Evaporation, in turn, is governed by the partitioning of solar radiation at the soil surface and is affected by both soil water content and thermal properties (Buchan, 2001). The balance of energy at the soil surface will determine cover system temperatures at the surface and in the subsurface. Therefore, investigating temperatures and thermal properties of cover systems can provide an insight into how these systems are performing.   The current state of practice when monitoring cover systems is to intensively monitor a vertical profile in one to three locations. Point-source monitoring stations provide little information as to how the cover system is performing at the field scale, and profile monitoring does not lend itself easily to scaling to field-scale estimates (Minacapilli et al., 2009). Data from point source stations are often used as inputs into numerical models as a means of inferring field-scale performance. However, one-dimensional analyses have been found to be poor at estimating field-scale percolation and runoff (Ogorzalek et al. 2008; Bohnhoff et al. 2009; McGuire et al. 2009). A monitoring system that captures both small scale variability and large-scale trends represents the ideal (Bl?schl and Grayson, 2000). Studies on the field-scale performance of soil covers have yet to be conducted.   2 Cover system performance can be monitored, given the inherent links between soil temperatures, thermal properties, and evaporation. Distributed temperature sensing (DTS) is a novel technology that uses fibre optic cables and a strong laser source to intensively measure temperatures at high spatial and temporal resolutions. A fibre optic cable of distances up to between 5,000 to 10,000 m is interrogated with a high-powered laser reader. Temperatures of the material surrounding the cable can be determined based on the wavelengths of the backscattered light. A DTS system can precisely measure temperatures at spatial resolutions down to 1m at temporal resolutions as low as 30s, depending on the integration time of the signal. For a further discussion of the technology, the reader is referred to excellent reviews in Selker et al., (2006a, b) and Tyler et al., (2009).  There is a dearth of research investigating the spatial variability of reclamation cover systems. Soil temperatures and thermal properties are linked to performance of cover systems and represent an interesting avenue of further research. A DTS system allows for intensively measuring temperature and thermal properties over large spatial extents. The objective of this paper is to detail a series of cases studies where a DTS system has been used to investigate the performance of reclamation cover systems.   MATERIALS AND METHODS  DTS system  The DTS system used for the field experiment was an Oryx DTS-SR (Sensornet, UK). The Oryx was a robust field unit with a measurement range up to 5000 m at 1 m measurement resolution. Temperature resolution can be as fine as 0.01 ?C depending on integration time. Measurements were calibrated by passing a reserved length of cable through an area of known temperature that was independently verified using a pair of PT-100 platinum thermistors.   Power draw for the Oryx was rated at 30 W during active measurement and 0.5 W when idle. Power for the system was supplied by deep cycle batteries that were charged with a solar panel array. The solar system also powered a peripheral laptop for data logging.   Spatial data analysis  The spatial structure of soil temperature data was analyzed using semivariograms (Isaaks & Srivastava, 1989). There are three key parameters of interest when examining semivariograms. The nugget is the extrapolated semivariance at lag = 0, and represents the measurement noise. The range is the lag at which the semivariance reaches a plateau and becomes constant. Finally, the semivariance at which the range is reached is known as the sill. A spherical model can be combined to form nested models in the case of a semivariogram that displays more than one obvious structure.    3 Thermal property estimation  The simple Amplitude method for estimating thermal diffusivity from Horton and Wierenga (1983) was chosen as it relies on the amplitude decay between two depths and only requires that temperatures at two depths to be known. Temperatures at 0 and 10 cm were used in the calculation of apparent thermal diffusivity.  CASE STUDIES  Spatial and temporal distribution of temperatures  A cover system at a northern Saskatchewan uranium mine was instrumented with a DTS system. The study area was a 1 m thick cover system over a waste rock dump. The cover system was configured as a 1 ha plateau sloping at a south-facing 2%, and a 0.5 ha north-facing 25% slope. The cover system was constructed using fine sand salvaged from a local drumlin. Average sand, silt, and clay content were 94, 5, and 1%, respectively, with an average bulk density of  1.55 g cm-3. The fibre optic cable was into a 100 m linear transect at depths of 90, 50, 10, and  0 cm. The 0 cm cable was laid directly on the surface and covered only with sufficient material as to prevent the cable from being directly exposed to the atmosphere. A typical installation schematic is shown in Figure 1.  Figure 1. Schematic of fibre optic cable installation at Site #2 in a grid pattern at multiple depths.  The cover system was monitored for a week in late August after air temperatures had reached their seasonal maximum. Distributed temperatures demonstrated the homogeneity of the cover system and responded closely to air temperatures (Figure 2).    4  Figure 2. Air temperature and cover system surface temperature (0 cm cable). Note that the plateau extends from 0 to 65 m and the slope extends from 65 to 100 m.  Cover system temperatures decreased with increasing depth at Site #1 (Figure 3). Regardless of depth, however, a similar pattern was seen where a distinct separation was found between the plateau (0 to 65 cm) and the slope (65 to 100 m).  Date8/21/11  8/23/11  8/25/11  8/27/11  Distance (m)204060800 5 10 15 20 25 30 aAir Temperature (?C)51015202530Tair 5  Figure 3. Cover system distributed temperatures as a function of distance and time. Depths correspond to a) 0 cm, b) 10 cm, c) 50 cm, and d) 90 cm.  Spatial statistics of cover system temperatures  The spatial structure of cover system temperatures can be analyzed using spatial statistics to gain further insight into the behavior of the cover system. Semivariograms were used to assess the degree of spatial variability in temperatures across the cover system (Isaaks and Srivastava, 1989). Minimum temperature Semivariograms demonstrated a consistent nested behavior at all depths (Figure 4). Spatial dependencies were initially moderate and displayed ranges of between 2 to 28 m. However, for each depth a secondary structure was found at approximately 40 m. Strong spatial dependencies for the secondary structure were found, with all depths being less than 25%. Ranges for the secondary structure extended to 97 m, suggesting strong autocorrelation at large spatial scales. Date8/21/11  8/23/11  8/25/11  8/27/11  Distance (m)204060800 5 10 15 20 25 30 Date8/21/11  8/23/11  8/25/11  8/27/11  Date8/21/11  8/23/11  8/25/11  8/27/11  Distance (m)20406080Date8/21/11  8/23/11  8/25/11  8/27/11  a bc dMissing5 ?C10 ?C15 ?C20 ?C25 ?C30 ?C 6    Figure 4. Semivariograms of minimum spatial temperature at a) 0 cm, b) 10 cm, c) 50 cm, and d) 90 cm.    Lag (m)0 20 40 60 80Semivariance (?C2 ) SemivariogramSpherical ModelLag (m)0 20 40 60 80Semivariance (?C2 ) (m)0 20 40 60 80Semivariance (?C2 ) (m)0 20 40 60 80Semivariance (?C2 ) bc d 7 Estimation of thermal properties A second site in the Athabasca oil sands region was instrumented with DTS cable. The cover system consisted of salvaged peat placed over 70 cm of sand. Fibre optic cable was installed at 10 and 15 cm, as well as a cable into the soil surface (0 cm) and another laid directly on the surface (Surface). Cable was installed across the 215 m of a 236 m transect (Figure 5).   Figure 5. Cover system profile and cable installation depth.  Thermal diffusivity (? [m2 s-1]), is a composite thermal property that describes the rate of temperature change in the soil and is the ratio of thermal conductivity (? [W m-1 K-1]) and volumetric heat capacity (Cv [J m3 K-1]). Diffusivity is a parameter of interest because it is related to the amount of water in the soil.  Previous studies using DTS have also examined the spatial distribution of thermal diffusivity in the soil (Steele-Dunne et al., 2010; Krzeminska et al., 2011). Thermal diffusivity was estimated using the amplitude method (Horton and Wierenga, 1983).  Spatial variability of thermal diffusivity varied considerably in response to average soil water content (Figure 6). Dry soil served to homogenize the thermal properties in the soil, leading to lower variability in diffusivity across the transect. On the wet day, water was distributed in response to local controls, leading to a more variable estimate of diffusivity across the transect.       8 Figure 6. Spatial distribution of thermal diffusivity on a wet and dry day.   SUMMARY  A distributed temperature sensing (DTS) system was used to monitor soil temperatures and thermal properties at two mine reclamation cover systems. The first example detailed the spatial distribution of temperatures across the cover system through time. Distinct zones were found that corresponded to different aspects that led to differing thermal regimes. Semivariogram analysis of minimum temperatures corroborated the qualitative findings by examining the spatial structure of the temperature signal.   A second site was given as an example of the spatial estimation of thermal properties. Thermal diffusivity was estimated using temperatures measured along the transect with the DTS system. Comparison of a wet and dry day showed a marked difference in thermal diffusivity; presumably as a result of the distribution of water due to local controls.  CONCLUSION  Performance of reclamation cover systems are controlled in part by thermal properties, which are reflected in soil temperatures. Measuring temperatures and thermal properties across the entire cover system provides an opportunity to investigate the spatial variability of the systems at a range of scales. A distributed temperature sensing system is easy to deploy and provides accurate, precise, and spatially and temporally intensive measurements that allow for examining both small  9 scale variations as well as field-scale trends. An improved understanding of the spatial scales of cover system variability will contribute to the overall sustainability in the design and construction of reclamation programs.  REFERENCES  Bl?schl, G. and R. Grayson. 2000. Spatial observations and interpolation. p. 17-50. In Grayson, R. and G. Bl?schl (eds.) Spatial patterns in catchment hydrology: Observations and modeling. Cam-bridge University Press, United Kingdom.  Bohnhoff, G.L., A.D. Ogorzalek, C.H. Benson, C.D. Shackleford, and P. Apiwantragoon. 2009. Field data and water-balance predictions for a monolithic cover in a semi-arid climate.  Buchan, G.D. 2001. Soil temperature regime. In: Smith, K.A. and Mullins, C.E. (Eds). Soil and environmental analysis. Physical methods, 2nd Ed. Marcel Dekker, Inc. New York.  Horton, R. and P.J. Wierenga, 1983. Evaluation of methods for determining the apparent diffusivity of soil near the surface. Soil Sci. Soc. Am. J. 47: 25-32.  Isaaks E.H. and R.M. Srivastava. 1989. An introduction to applied geostatistics, Oxford University Press, Toronto, Canada.  Krzeminska, D.M., S.C. Steele-Dunne, T.A. Bogaard, M.M. Rutten, P. Sailhac, and Y. Geraud. 2011. High-resolution temperature observations to monitor soil thermal properties as a proxy for soil moisture condition in clay-shale landslide. Hydrol. Process. DOI: 10.1002/hyp.7980.  McGuire, P.E., B.J. Andraski, and R.E. Archibald. 2009. Case study of a full-scale evapotranspira-tion cover. J. Geotech. Geoenviron. Eng. 135(3): 316-332.  Minacapilli, M., and I.F. Blanda. 2009. High resolution remote estimation of soil surface water content by a thermal inertia approach. J. Hydrol. 379: 229-238.  Ogorzalek, A.S., Bohnhoff, G. L., Shackelford, C. D., Benson, C. H., and Apiwantragoon, P. 2008. Comparison of field data and water-balance predictions for a capillary barrier cover. J. Geotech. Geoeng. 134(4):470-486.  Selker, J.S., N. Van De Giesen, M. Westhoff, W. Luxemburg, and M. B. Parlange. 2006a. Fiber optics opens window on stream dynamics. Geophys. Res. Lett. 33. doi:10.1029/2006GL027979.  Selker, J.S., L. Th?venaz, H. Huwald, A. Mallet, W. Luxemburg, N. Van De Giesen, M. Stejskal, J. Zeman, M. Westhoff, and M. B. Parlange. 2006b. Distributed fiber-optic temperature sensing for hydrologic systems. Water Resour. Res. 42 (12). doi:10.1029/2006WR005326   10 Si, B.C., R.G. Kachanski, and W.D. Reynolds. 2007. Analysis of soil variability. p. 1163-1191. In E.G. Gregorich (ed.) Soil sampling and methods of analysis.  Steele-Dunne, S.C., M.M. Rutten, D.M. Krzeminska, M. Hausner, S.W. Tyler, J.S. Selker, T.A. Bogaard, and N.C. van de Giesen. 2010. Feasibility of soil moisture estimation using passive distributed temperature sensing. Water Resour. Res. 46: W03534, doi:10.1029/2009WR008272, 2010.  Tyler, S.W., J.S. Selker, M.B. Hausner, C.E. Hatch, T.Torgersen, C.E. Thodal, and S.G. Schladow. 2009. Environmental temperature sensing using Raman spectra DTS fiber-optic methods. Water Resour. Res. 45: W00D23, doi:10.1029/2008WR007052, 2009.       


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