Quantifying the variability of paleotemperature fluctuations on heat flow measurements

Geothermics 67 (2017) 102–113

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Quantifying the variability of paleotemperature fluctuations on heat flow measurements Asadusjjaman Suman ∗ , Duanne White Institute for Applied Ecology, University of Canberra, ACT 2601, Australia

a r t i c l e

i n f o

Article history: Received 25 June 2015 Received in revised form 22 October 2016 Accepted 6 February 2017 Available online 14 February 2017 Keywords: Borehole Paleotemperature Heat flow Geothermal exploration Tasmania

a b s t r a c t Climatically-driven surface temperature fluctuations disturb the steady state geotherm, and affect vertical heat flow measurements in shallow (<1000 m deep) boreholes. We investigated the causes of variability in reconstructed paleotemperature recorded by boreholes with a well constrained regional dataset from Tasmania, and a global synthesis. Variability between reconstructed past temperature changes is lower at local rather than regional scales. Climatic factors influence the magnitude of variability between boreholes, with non-climatic factors including topography, lithology and land use representing secondary measurable influences. The magnitude of variability means that paleotemperature corrections for heat flow will generally increase the accuracy, but not the precision of heat flow measurements. © 2017 Elsevier Ltd. All rights reserved.

1. Introduction The production of geothermal energy has increased significantly during last two decades (Lund et al., 2011) due to increasing economic drivers (Moghaddam et al., 2013), and the need to reduce fossil fuel use (Arianpoo, 2009; Calvin et al., 2005). It is particularly attractive relative to other low-carbon energy sources, as geothermal energy output is independent of weather conditions (Yan et al., 2010). Most existing geothermal generating sites have been in areas of active volcanism (Moghaddam et al., 2013), where temperatures close to the surface are naturally high. In less volcanically active areas, geothermal resources are typically found deeper in the crust. The successful economic extraction of deeper resources is strongly dependent on the depth of the resource, thus, improving knowledge of the temperature field at depth is a critical part of the geothermal exploration process. Given the high cost of drilling to the potential resource depths (often up to 5 km), early exploration by government (Gerner et al., 2012; Weber et al., 2011) or industry (Donaldson, 2008; Green Rock Energy Limited, 2006) is typically done by measuring crustal thermal properties in relatively shallow boreholes, between 300 and 1000 m depth. Using temperature gradient from this shallow tem-

∗ Corresponding author. E-mail addresses: [email protected], [email protected] (A. Suman), [email protected] (D. White). http://dx.doi.org/10.1016/j.geothermics.2017.02.005 0375-6505/© 2017 Elsevier Ltd. All rights reserved.

perature measurement and underlying rock thermal properties, heat flows are estimated at the deeper crust. This process usually assumes a steady-state geotherm, with depth-invariant heat flow. Climatically driven surface temperature variations, which we refer to as the ‘surface paleotemperature history’, can disrupt steady-state geotherm near the surface. This departure in near surface temperatures can have significant consequences in the determined heat flow values from exploration phase assessment of temperature at shallow depths. Variations in surface temperature propagate down into the crust over time via thermal diffusion (Beltrami et al., 1992; Beltrami et al., 2003; Beltrami and Mareschal, 1995). Thus, any permanent variation in surface temperature will have an effect on the sub-surface temperature and heat flow gradient. Changes in surface temperature may have occurred due to longterm climate and land use change (Buyadi et al., 2013; George et al., 2007; Gosselin and Mareschal, 2003a; Jiang and Tian, 2010). On long timescales, such changes can be substantial, with surface temperature change since the Last Glacial Maximum (LGM, ∼20,000 years ago, Yokoyama et al., 2000) potentially affecting temperatures at shallow-moderate depths (∼2000 m). Temperature deviations can be up to 10 ◦ C near the surface (decreasing exponentially with depth), and lead to underestimation of basal heat flow rates of up to 20% (Fig. 1). Smaller, but more recent temperature changes can also have a significant impact on the measurement of heat flow in the shallow crust. For example, the increase in temperature of ∼1 ◦ C since ∼1900 Common Era increases temperatures in the upper ∼150 m

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Fig. 1. Importance of paleotemperature corrections for heat flow measurements. The geotherm for three schematic, but representative surface paleotemperature histories (top left), were modelled using the error function forward model described by Beardsmore and Cull (2001), assuming a conductive thermal regime with a basal heat flow of 65 mW/m2 , a thermal conductivity of 3 W/mK. The schematic surface paleotemperature histories represent (a) no change during the last 100,000 years, (b) a 1◦ increase at 100 years before present, and (c) a representative glacial cycle. A realistic scenario in many parts of the world would combine scenarios (b) and (c). The resulting apparent heat flow measurements were calculated between different depths using two methods shown in the top right panel − (a) from the surface to the measured depth, at 100 m increments, and (b) between two depths below the surface, at an interval of 100 m. The results are shown in the bottom two panels, which indicate the ratio of the modelled heat flow that might be measured from a borehole to steady state heat flow. The models indicate that for depths between of 200–500 m, transient (paleo) surface temperature effects can result in apparent heat flow measurements that are anywhere between 0.6 to 0.9 times the actual basal heat flow values. Based on this example, the accuracy of measured heat flow values increases with depth, but does not reach better than 10% below depths of 300 m for scenario (b) and 800 m for scenario (c).

of most boreholes, leading to reduced temperature gradients and heat flows above this depth (Fig. 2). Any model that extrapolates temperature gradients (or applies heat flow measurements) from the upper 150 m to deeper parts of the crust without correcting for paleotemperature would underestimate temperatures at depth. The error in the extrapolation increases with depth, and can lead to differences of >10 ◦ C at resource depths of 3–5 km. So, accurate correction of even small surface paleotemperature effects is important for estimation of temperature resources at depth. Despite the clear need, heat flow measurements are not always routinely corrected for surface paleotemperature histories. Corrections have been conducted in some areas of the Northern Hemisphere, especially in regions with consistent lithology i.e. in Canadian Shield (Mareschal, 1999; Guillou-Frottier et al., 1995). However, this issue is less well addressed on a global basis, par-

ticularly where the crustal thermal characteristics are less well constrained, and for the longer-term changes since the Last Glacial Maximum. This is largely due to uncertainties in past temperature histories, and uncertainties in the factors that influence how the surface temperature changes propagate into the crust. Factors that have been suggested to influence temperature propagation include topographic (e.g. flat vs steep slopes), geologic (e.g. different lithologies, groundwater movement, erosion, sedimentation), land use and spatial and temporal climatic variations. There is limited empirical evidence to quantify the complex influence of these factors. Average surface temperature changes during the past few centuries have been compiled globally (Beltrami, 2002; Huang et al., 2000; Pollack and Huang, 2000), and at a continental scale in areas such as Canada (Mareschal, 1999), India (Roy et al., 2002; Roy and Chapman, 2012) and Australia

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Fig. 2. Effect of ground surface temperature variation in estimation of temperature at depth from shallow measurements at Runnymede in Tasmania (147.56◦ E, 42.63◦ S). Runnymede borehole is located in consistent Jurassic Dolerite with mean thermal conductivity 2.37 ± 0.21 Wm−1 K−1 . Here, we assume that apparent recent ground surface warming of ∼1.5 ◦ C has increased the absolute temperature and reduced the temperature gradient of the upper 150 m of the crust. Using the projected temperature gradients/heat flow measurements from only the upper 150 m (which are most affected by recent warming) under estimates temperature at 300 m depth by ∼2 ◦ C (double headed arrow), or by >60 ◦ C at 5 km. Unless accounted for, the surface temperature changes may mislead an economic to uneconomic geothermal resource zone.

(Pollack et al., 2006). In each of these studies, there is significant variation between boreholes in the magnitude and timing of paleotemperature changes from model inversions of the borehole temperature measurement, which we refer to as the ‘reconstructed paleotemperature’. Further, there is a wide variety of suggested causes for variability, ranging from land use change (Akkiraju and Roy, 2011; Gosselin and Mareschal, 2003a,b) to regional climate variability (Beltrami et al., 2003; Roy et al., 2002; Roy and Chapman, 2012). However, the distribution and relative importance of these influences have not been rigorously assessed, and there is little understanding of the temporal and spatial scales across which these factors may affect crustal temperatures from an empirical perspective. Thus, further study is required before corrections for surface paleotemperature histories can be widely applied to heat flow measurements. In this study, we aim to improve understanding of the factors that cause variability in the influence of surface paleotemperature changes on heat flow. We investigate whether or not the magnitude and variability in reconstructed paleotemperatures correlates with topographic, geologic, and climatic factors from a density spatial array of boreholes in Tasmania, Australia. We also determine whether or not commonly measured parameters can be used to select for boreholes where paleotemperature corrections can produce more reliable heat flow measurements. We then combine this new data with existing datasets from India and Canada to identify how each of these factors are responsible for the spatial variation at local, regional and national scales. 2. Study area North East Tasmania, Australia was selected for investigation due to the availability of a good spatially distributed borehole data set. It is also an area with significant recent changes in surface temperature. An array of 36 boreholes (Table 1) from 189 m to 302 m depth was drilled for heat flow measurements by KuTH Energy in

2007–2008 (Fig. 3). Average air temperatures in the region have increased by ∼1.9 ◦ C since 1900 in northern coastal area and about 1.3 ◦ C in the south near Hobart (Bureau of Meteorology, 2014). Topography varies considerably across the region and ranges from flat to undulating coastal and inlands plains, to steep granite and dolerite ranges and peaks. Elevation varies from sea level to a maximum 1573 m at Ben Lomond (Holgate, 2011; McIntosh et al., 2009; Seymour et al., 2011). Land cover is characterized by Eucalypt forest, alpine heathlands, cool temperate rainforest and moorlands. The north-eastern highlands are covered by forest, and silviculture plantations are harvested on a production cycle of 80–100 years (Forestry Tasmania, 2007). The geology of north-east Tasmania is well known from several geological and geophysical studies, both at the local and continental scale (Black et al., 2004; McDougall, 2008; Seymour et al., 2007b). The basement rocks are comprised of deformed low-grade metasediments of the Ordovician-Devonian Mathinna Supergroup. Intruded into the basement is a range of highly fractionated highheat producing Devonian-aged granites and granophyres (Burrett and Martin, 1989). Most of the basement in this region is concealed by thick sequences of gently dipping Permian-Triassic, Parmeener Supergroup sediments, bedded quartzose sandstones, mudstones and siltstones, which have been intruded by Jurassic dolerite. These are overlain in the valleys by weathered, bedded Paleogene sedimentary deposits, consisting of moderately consolidated clays, silts and quartz sands (McDougall, 2008; Seymour et al., 2007a). The boreholes investigated in this study were drilled mostly through Permian-Triassic sedimentary rocks and Jurassic dolerite. Groundwater flow in the region is largely controlled by the regional topography and tectonic fracture networks. Areas of high hydraulic head occur in the uplands in the centre of the study area, decreasing toward the coast and the Tasmanian midlands (Hocking et al., 2009). Hydraulic conductivities vary from 0.1–1 m/day in dolerites, through 1–2 m/day in the Mathinna Supergroup, 1–5 m/day in the Tertiary sediments to 5–10 m/day

Table 1 Geographic, Geologic and topographic factors of Tasmanian boreholes with maximum temperature change. *Boreholes with an unacceptable model-data misfit. MG = Mathinna Group mudstones and siltstones, PSG = Parmeener Super Group upper (mudstones, siltstone) and lower (tillites), JD = Jurassic Dolerite, TC = Tertiary Conglomerate. Borehole Depth, m

Elevation, m

Km from coast

Ground Cover

Slope

Aspect

Relief

MGA94 Northing

MGA94 Easting

Lithology

Thermal conductivity range

Borehole Fractures (no./m)

dT past 500 yrs

Bangor Beaconsfield Ben Lomond Bluestone Tier Cambridge Charlton Elizabeth Epping Fingal Frankford Kingston Lake Leake Lemont Lisle Macquarie Marion Bay Mt Nicholas Murdunna* Native Hut Nunamara Oatlands* Perth Rheban Rocherlea Runnymede Snow Hill Sorell Swan Temple Bar Tiberias Tooms* Tower Hill Tunbridge Westbury Weymouth Woodsdale

251 249 277 253 236 251 299 289 251 249 236 302 246 247 221 250 248 245 249 236 249 253 241 250 248 280 248 189 296 253 263 253 252 252 250 252

204 90 694 353 43 242 439 215 577 289 287 475 333 307 295 81 398 139 378 727 526 200 79 49 247 749 50 126 353 437 414 584 252 233 102 365

19.8 14.5 59.5 10.8 2.0 42.3 44.5 74.0 17.0 37.5 59.5 21.2 36.2 24.0 67.5 2.0 19.5 4.5 25.3 47.0 52.3 60.8 3.5 39.5 17.4 26.3 1.0 20.0 57.3 38.0 16.0 33.0 57.3 55.8 4.3 22.0

cut forest forest forest forest grass grass forest grass forest forest grass forest forest forest grass grass forest forest forest forest grass grass grass grass grass forest grass forest forest grass forest forest grass forest forest forest

5.0 5.6 1.1 4.4 4.5 1.0 2.2 1.7 6.1 1.0 4.5 2.5 4.4 1.3 5.6 0.3 7.0 8.0 5.0 7.3 5.0 3.1 3.0 5.0 2.7 3.7 1.7 3.5 5.2 0.5 0.3 2.2 2.5 4.2 4.7 1.7

173 14 21 297 61 34 100 95 87 146 314 174 295 28 33 132 335 344 87 151 23 114 165 273 224 158 90 134 289 266 8 2 134 305 318 70

135 92 318 205 246 65 199 75 337 151 88 147 151 468 234 200 471 161 249 410 115 91 226 178 85 162 81 207 162 119 177 178 27 61 52 146

5440427 5439884 5402059 5300093 5261742 5339821 5356701 5382606 5380292 5416602 5383093 5338586 5322898 5437495 5359621 5260030 5401440 5242021 5284634 5415737 5319896 5399080 5279380 5420496 5280238 5358389 5260122 5359271 5403592 5301300 5319894 5399699 5339428 5396730 5457196 5296499

508572 489244 546613 571901 534378 545174 549501 533251 589312 490171 547791 568510 547437 528218 526048 568645 587962 573413 530061 528262 531347 513500 573260 509171 546175 572873 550181 588108 530426 531690 567354 573964 529875 485940 508409 552007

MG JD/PSG MG JD TC JD JD JD JD/uPSG JD/uPSG JD JD JD MG JD/uPSG Tb, JD, uPSG lPSG/MG JD uPSG/JD JD/lPSG JD JD JD JD JD JD lPSG/JD JD JD uPSG JD MG JD, uPSG, JD JD MG uPSG/JD

2.06–3.77 2.28–2.34 3.87–4.41 2.07–2.18 1.93–2.22 2.23–2.44 1.99–2.27 1.87–2.18 0.68–2.53 2.17–3.26 1.88–1.97 1.96–2.18 2.09–2.31 2.32–4.8 2.44–4.96 2.02–2.93 1.85–4.8 2.13–2.42 2.19–4.48 2.17–2.47 1.96–3.18 2.07–2.41 2.05–2.23 1.97–2.25 2.17–2.63 1.99–2.25 2.2–3.76 1.98–2.13 2.28–2.49 1.7–4.5 1.8–2.07 4.06–5.23 1.83–2.39 2.07–2.21 2.95–4.02 2.28–4.54

15.8 1.4 1.1 3.5 5.3 3.6 1.5 1.8 4 2.8 2 1.6 5 7.2 10 4.9 7 3 3.6 5.7 8.1 1 5.6 1.2 1.3 1 2.8 1.5 3 10 12 10 4.2 2.9 9.5 2.4

1.73 1.79 1.69 1.61 1.84 2.41 0.84 0.96 0.73 1.3 0.55 1.59 1.48 1.04 1.44 2.23 1.06 6.05 3.66 1.46 4.1 1.83 1.84 0.29 1.95 1.6 1.03 1.91 0.72 1.67 0.82 0.82 1.1 0.77 0.24 1.21

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Hole Name

105

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for a given depth. For the relatively shallow boreholes in this study, inversion yields temperature changes over the last ∼500 years. The perturbation at depth z, T(z), due to temperature changes at the earth surface, can be written considering thermal conductivity variations, as the superimposition of the equilibrium temperature and the perturbation Tt (z) induced by temporal surface temperature condition (Beltrami et al., 1992).

T (z) = T0 + q0 R (z) + Tt (z)

Fig. 3. Study area in Tasmania with quality of borehole based on model misfit between modelled and measured temperature profile. High quality long term climate sites shown in stars.

in the Parmeener Supergroup (Bacon and Latinovic, 2003). Most porosity and permeability in these units is through secondary fractures, with the exception of sandstones in the upper Parmeeneer supergroup, which has retained some primary porosity. Thus, flow rates, and the influence of groundwater on the geotherm may vary substantially within regions or lithology types. 3. Methods Boreholes were drilled between 29/10/2007 and 28/11/2008. Drilling time of each boreholes are varies between 2 to 7days. The first 60–100 m were drilled using a reverse circulation TH 62 percussion drill rig, followed by diamond coring with a G & K 1000 rig to ∼300 m (Holgate, 2012a). Subsurface temperatures were logged 2–3 months after drilling. Temperature data presented here are from the second of two logging runs and are considered to be equilibrated (Hot Dry Rocks Pty Ltd., 2008), and provide an accurate representation of the natural thermal conditions of the crust. Temperatures were measured every metre down hole using a calibrated thermistor with a precision of 0.001 ◦ C (Holgate, 2011). Thermal conductivity was measured from 3 to 5 core samples in each borehole by divided bar instrument at a standard temperature of 30 ◦ C to a precision of ±2 ◦ C (Holgate, 2012a). Paleotemperature was reconstructed on each hole by inversion of the temperature-depth profiles. Inversion of borehole temperature-depth profile is a process that changes a temperature vs. depth profile at a given time into a temperature vs. time profile

(1)

Where T0 is the equilibrium surface temperature, q0 is the surface heat flow density and R (z) is the thermal resistance to depth z. The influence of heat production is small and can be ignored. Eq. (1) can be transformed as unknown linear equation and can be solved by singular value decomposition (SVD) (Beltrami and Mareschal, 1995; Mareschal and Beltrami, 1992; Menke, 1989). Using the SVD model, paleotemperature reconstructed during the past 500 years for all 36 boreholes. Boreholes are categorized as high or low quality (Table 1) based on the misfit between measured downhole temperature data and modelled data. Three ‘low quality’ holes produced a poor model fit with measured temperature profile, and with model-data divergence of an average of more than 0.1◦ across the borehole. These low quality holes were possibly influenced by ground water movement or errors in temperature logging. We report these low quality boreholes in the data and discussion on borehole variability, given the relevance to the drivers of uncertainty in these data. However, to enable consistency with the literature, we excluded these data when calculating the average and standard deviation of the reconstructed temperature history, as they are clearly not representative of the conductive thermal regime. We compiled a variety of commonly measured geologic, topographic and climatic factors that may have influenced reconstructed paleotemperatures. Borehole elevations were determined via GPS measurements on site. Other geographic factors such distance from coast, slope aspect and relief were measured from the Shuttle Radar Topography Mission (SRTM) 30 m resolution digital elevation model (Kidd et al., 2015; Rabus et al., 2003; Zyl, 2001) using ARCGIS (version 9.3.1) software. Slope and aspect were measured from the nearest tile (i.e. across 60 m) while relief was assessed from a radius of 2000 m. Land use, and the change in land use since 1982 was assessed from Landsat, Google Earth satellite imagery and records of logging history obtained from Forestry Tasmania. Core samples and drill chips were used to determine the borehole lithology. The number of factures per metre in core samples was used as a proxy of hydraulic conductivity and the potential for groundwater flow to disturb the geotherm for each hole. Metrological data from long-term air temperature sites were used to assess local climate variations for the past ∼100 years (Bureau of Meteorology, 2014). Previously published data were used to determine whether reconstructed temperatures become more or less variable with increasing spatial scale. We chose to interrogate records from India (Akkiraju and Roy, 2011; Roy et al., 2002; Roy and Chapman, 2012) and Canada (Beltrami et al., 1992; Beltrami et al., 2003; Gosselin and Mareschal, 2003a,b), as published records from these regions provide detailed location information as well as site characteristics that could be used to identify the drivers of variability. For boreholes in these areas, we used the SRTM and satellite imagery data to determine if the geographic and land use factors analysed in Tasmania were significant in producing variability in the reconstructed paleotemperature history in other regions. We also used this data to determine the effect of scale on the variability of ground surface temperatures at local, regional and national scales.

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Fig. 4. Reduced temperature from a linear steady state geotherm with depth in the 36 boreholes used to reconstruct paleotemperature. Reduced temperatures are calculated by subtracting the background (steady state) thermal regime from the measured temperature. Three boreholes-Murdunna, Tooms and Oatlands are low quality based on model misfit shows high variability in reduced temperature.

4. Results The Tasmanian boreholes produced a generally consistent pattern of temperature divergence from a steady state geotherm (Fig. 4), indicating a consistent influence of paleotemperature across this region. Most maintained a linear temperature change with depth between 100 and 300 m below surface. Above 100 m, most holes had a considerable reduction in the thermal gradient, consistent with a recent increase in surface temperature. Where boreholes diverge from this trend (e.g. Rocherlea, Mt Nicholas) it is usually due to decreases in thermal conductivity in the upper part of the borehole. When inverted to reconstructed paleotemperature, the modelled time-temperature profiles from the 33 boreholes with high quality data are examined produced a mean temperature history consistent with the meteorological record (Fig. 5). Most boreholes provided evidence of limited temperature change from 500 to 100 years ago, and substantive change during the last 100 years (i.e.

during the ∼20th Century), with an average increase of 1.2 ◦ C. However, there was considerable variation in the magnitude of the 20th Century temperature increase, with the standard deviation across the 33 boreholes of 0.6 ◦ C. Topographic, geological and land use factors did not appear to strongly influence the reconstructed paleotemperature changes recorded in the boreholes (Fig. 6). There was little trend in the mean or variability of the 20th Century temperature changes with topographic factors such as altitude, aspect, or relief. Similarly, there was no significant difference in the variability or average 20th Century temperature change between boreholes drilled in either of the two major lithologies present in the study region (dolerite or bedded sedimentary rocks), or a mix of these rock types (Fig. 7a). Reconstructed paleotemperature changes were slightly higher in the grassland/farming regions compared to forest areas (Fig. 7b), but again this was not significant. The variability of maximum temperature change was slightly higher in the forest areas compared to grassland, which we attribute to the influence of multi-decadal

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Fig. 5. Comparison of temperature changes in last century from meteorology and borehole temperature data. The 9 years running mean of the temperature changes in last century measured from five meteorological stations (Low Head, Launceston, Hobart Ellserlie Road, Cape Bruny Island and Eddystone Point) in eastern Tasmania is consistent with the timing and magnitude of the average temperature change reconstructed from the 33 boreholes with high quality data in this region.

oscillations in ground temperature produced by clear-felling and subsequent regrowth. Geographic and climatic factors did influence the magnitude of reconstructed paleotemperature change. Reconstructed paleotemperatures were higher around the coast compared to midland Tasmania (Fig. 6b, and also to the north with maximum temperature change in northern Tasmania. This trend from ground surface temperature warming matches the meteorological data, with increased magnitudes of warming from the coastal sites (Low Head, Eddystone Point) compared to sites further inland (Launceston, BoM site inland of Hobart) (Bureau of Meteorology, 2014) since 1900. In general based on an analysis of the published data, the magnitude of variability in reconstructed 20th Century ground surface temperature changes from hole to hole, is lower at small spatial scale (Fig. 8). However, this is not the case for all available sites. For instance, in the analysis of data from India, the magnitude of 20th Century temperature change was highest at a spatial scale of >1 km–<10 km and lower at larger spatial scales, although this result is only just significant at the 95% confidence level. Given the average global scale shows trends towards higher magnitude at larger spatial scales, we suggest the result in India may not be representative of the broader trend. The global dataset also indicates there is limited predictive power with regard to either the magnitude, or variability of 20th Century temperature changes for topographic or land use variables (data not shown). In India and the broader Australian dataset none of the topographic variables (slope, aspect, relief, elevation) produced linear correlations (R2 ) greater than 0.01. Similarly, in Canada, most variables produced correlations <0.01. Altitude had a slightly significant correlation (0.05), but due to the limited altitude range (300–500 m) we consider this correlation is more likely to have been produced by chance. There were no significant differences between the magnitude of the 20th Century temperature increases and land cover class in any of the datasets studied, although in most cases the types of land cover present in each area were restricted − i.e. cropland and cropland mosaic dominated the land cover type in India, and open shrublands were the dominant type in Australia, and there were insufficient numbers of boreholes in other land cover types to determine if land cover produced a significant effect.

5. Discussion 5.1. Comparisons and causes of variability in reconstructed paleotemperature changes The variability in the magnitude of 20th Century reconstructed paleotemperature form our Tasmanian dataset was similar to other datasets at the regional (100–1000 km) to continental or subcontinental (1000+ km) scales. At a regional scale, the standard deviation in reconstructed 20th Century surface temperatures from boreholes ranges from 0.6 ◦ C in Australia (Tasmania, this study), 0.7 ◦ C in Canada (Gosselin and Mareschal, 2003a,b), to 0.9–1.4 ◦ C in India (Roy and Chapman, 2012). At a continental scale the standard deviation is more consistent, varying from 1.1 ◦ C in India (Roy et al., 2002) to 1.2 ◦ C in Australia (Pollack et al., 2006). Most regions observed long-wavelength spatial variation in 20th Century borehole temperature increases that are consistent with air temperature increases. In India, the greatest changes in air temperatures and borehole temperatures were recorded in the northern interior, with the remainder of the country averaging about half of this temperature (Roy et al., 2002). Similarly, in North America, the largest 20th Century air temperature and borehole changes have been observed in the north (Skinner and Majorowicz, 1999), although the strong north to south gradient observed in air temperature increases within Canada is obscured (Beltrami et al., 2003; Majorowicz et al., 2005), perhaps due to the influence of permafrost. It is clear that in Tasmania and the wider dataset, spatial variation in past air temperatures is a key influence on borehole temperature patterns, and must be considered when correcting for the effect of surface paleotemperature histories on heat flow. Differences in land cover have the potential to produce differences in ground surface warming, and studies on local scales around the globe have demonstrated this effect (Gosselin and Mareschal, 2003a; Roy et al., 2002; Roy and Chapman, 2012). Large-scale changes in land cover have been implicated causing variability in 20th Century borehole temperature increases at a regional scale in Canada during the past 200 years (Skinner and Majorowicz, 1999), albeit partly through influencing regional climates. However, in other regions the relationships are less clear. In Tasmania, there was limited influence of land cover type on the magnitude of 20th Century ground surface warming, probably due to the limited change in land cover observed in past few hundred years (Fensham,

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(b)

6.00

Max. temp. change in last 500 years

Max. temp. change in last 500 years

(a) y = -0.0003x + 1.6768 R² = 0.0022

4.00

2.00

0.00 0

200

400

600

6.00

y = -0.0102x + 1.9141 R² = 0.0383

4.00

2.00

0.00 0

800

Elevation of borehole, m (Surface)

20 40 60 Distance from coast, km

80

(d)

6.00

Max. temp. change in last 500 years

Max. temp. change in last 500 years

(c) y = 0.1415x + 1.0927 R² = 0.07

4.00

2.00

6.00

y = -0.0003x + 1.6414 R² = 0.001

4.00

2.00

0.00

0.00

0

2

Max. temp. change in last 500 years

(e)

4 6 Slope, degree

8

10

6.00

y = -0.0003x + 1.6403 R² = 0.0007

4.00

2.00

200 300 Relief, m

400

y = 6E-06x - 1.541 R² = 0.0219 4.00

2.00

520000 560000 Easting, m

400

4.00

2.00

5300000 5400000 Northing, m

5500000

(h)

6.00

0.00 480000

300

y = -1E-05x + 53.107 R² = 0.2785

500

Max. temp. change in last 500 years

(g)

100

200 Aspect, degree

6.00

0.00 5200000

0.00 0

100

(f) Max. temp. change in last 500 years

0

Max. temp. change in last 500 years

109

600000

6.00 y = -0.0128x + 1.6528 R² = 0.0018

4.00

2.00

0.00 0

5 10 15 Borehole Fractures (number/m)

20

Fig. 6. Relationship between the 20th Century temperature changes reconstructed from boreholes and (a) Elevation (b) Distance from coast (c) Slope (d) Aspect (e) Relief (f) Northing and (g) Easting (h) Borehole fractures. All trends shown are linear.

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(a)

(b) 2

2.0 standard deviation mean

Max. temp change (°C)

Max. temp change (°C)

standard deviation mean 1.5

1

0.5

0

1.5

1.0

0.5

0.0 dolerite

mixed lithology

sediments

grassland

forest

Fig. 7. Relationship between maximum temperature change and (a) lithology (b) land cover. Error bars represent standard errors, with a 1␴ confidence interval for both the variability (i.e. standard deviation) and the mean.

Tasmaina

Difference in 20th Century temperature change (oC)

2

Australia

Canada

India

World

1.8 1.6 1.4 1.2 1 0.8

0.6 0.4 0.2 0 0-1

1 - 10

10 - 100 Km between boreholes

100 - 1000

>1000

Fig. 8. Effect of spatial scale on the variability of 20th Century ground surface temperate histories reconstructed from boreholes. Note the slightly reduced variability at small spatial scales. Error bars represent standard errors, with a 2␴ confidence interval.

1989). Similarly, borehole temperature datasets from Australia and India are broadly from single land use types, and do not provide information on the importance of land use on temperature propagation into the crust. The significance of land use and land cover change on borehole temperatures on large spatial scales remains relatively poorly quantified, but current data suggests it may not be a significant first-order factor in many regions. At a regional to continental scale, topographic factors had only a secondary influence on the magnitude and variability of 20th Century ground warming. Many studies have rejected borehole records from steeper areas for providing unreliable data (e.g. 10◦ slope Gosselin and Mareschal, 2003b). Sites with steeper slopes near large lakes appear to have high variability in recorded 20th Century ground surface temperature histories (Guillou-Frottier et al., 1998). However, in Tasmania, India or Canada there was no trend between slope and reconstructed 20th Century temperature change for the relativley low slope (<5◦ ) sites where boreholes were drilled. With the exception of very steep sites, topography does not appear to be a strong controlling factor for the magnitude or variability in reconstructed paleotemperature at a regional or continental scale. The drivers of variability in reconstructed paleotemperature between boreholes appears different at local scales (boreholes <10 km apart) compared to the regional and continental scales. The

average magnitude of variability is slightly smaller at local scales in the Canadian (Gosselin and Mareschal, 2003a), Australian (Pollack et al., 2006) and global datasets. This may be a function of the spatial autocorrelation in climate (Arnfield, 2003; Jones and Trewin, 2000, 2002), which produces smaller differences in temperature change for boreholes located closer together. However, non-climatic factors clearly influence variability at the local scale. Studies that have chosen sites that are not affected by non-climatic perturbation (Akkiraju and Roy, 2011) have produced standard deviations as low as ±0.1 ◦ C. For example, clusters of boreholes in the Pipe Mine area in Canada recorded standard deviations in 20th Century warming of ±0.5 (Thompson Belt), ±0.2 (Moak Lake) and ±0.1 ◦ C (Mystery Lake) (Gosselin and Mareschal, 2003a). Conversely, sites with significant differences in the non-climatic factors, such as local land use change, have produced differences of >2 ◦ C between boreholes located within a few kilometers, and a standard deviation of 0.9 ◦ C (Gosselin and Mareschal, 2003a). Thus, boreholes located close together will, on average, have similar responses to paleotemperature changes, although the variability at this scale from region to region is high. The time of borehole measurement is also a significant factor. One of the best tests of this factor is a study completed by the Geological Survey of Canada in 1980. Temperature inversions

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from 15 boreholes from North Western Ontario indicate temperatures increased by 0.77 ± 0.16 (1␴ standard error) ◦ C (Gosselin and Mareschal, 2003b) during the last 150–200 years. However, from the same area 18 boreholes were studied by GEOTOP-IPGP in 2000 and temperature increase was 1.28 ± 0.18 ◦ C (Gosselin and Mareschal, 2003b), which indicates a statistically significant increase in temperature change (independent sample t-test, p = 0.04). The influence on borehole temperature measurement year on variability is less well constrained. During the same period, the standard deviation of the temperature increase expanded slightly from 0.6 to 0.8, but the difference between these two values was insignificant (p = 0.3). 5.2. Implications for correction of heat flow measurements The Tasmanian and global datasets indicate that site to site variability in surface paleotemperature histories, and/or their propagation down into the geotherm is significant for heat flow measurements. Variations in the magnitude of climatic change are responsible for some of this variability. However, the driver(s) of the majority of variability from site to site remains unquantified, either due to unidentified cause(s) or because present techniques for quantifying the modulating factors (e.g. groundwater flow, land changes) are not sufficiently well measured or understood. The unquantified variability from site to site has substantive implications for correcting heat flow measurements from boreholes. Obtaining a precise paleotemperature history for an individual borehole will be challenging (Gosnold et al., 2011). Obtaining accurate reconstructed paleotemperatures from the geotherm will require a number of borehole measurements to be averaged. In most areas, the limited spatial density of accurately measured borehole temperatures means that only regional averages will be accurate enough for paleotemperature corrections. However, correcting an individual borehole with the regional average reconstructed paleotemperature history will still result in a substantial uncertainty. This is because the variance from site to site means the standard deviation (rather than the standard error) in the regional dataset will provide the best measurement of the uncertainty for individual records. In the case of the corrections for the 20th Century temperature increase, this deviation is typically of ∼1 ◦ C at the surface, or the same order of magnitude as the reconstructed paleotemperature change. The limited number of deep borehole temperature measurements will mean that correction for LGM paleotemperature changes will be even more challenging. In some cases this problem may be alleviated by the use of independent paleotemperature records (e.g. from Meteorological records, isotopes in ice cores, biological proxies etc.), but in most areas these are widely scattered, so typically provide only regional or continental level reconstructions. The difference in the magnitude and causes of variability at local to regional scales will mean that the precision of reconstructed paleotemperature corrections for heat flow measurements will likely be influenced by the density of borehole records. In areas with a limited density of accurately measured borehole temperatures, the regional paleoclimate trends obtained will have a relatively high standard error, resulting in imprecise paleotemperature corrections. In areas where a high spatial density of boreholes are available, the reduced site to site variability in the local borehole cluster will allow more precise (local) reconstructed paleotemperature records to be generated, resulting in a smaller uncertainty in the paleotemperature correction. In the case of the 20th Century temperature changes (Fig. 8), the uncertainty varies from 0.2 to 1 ◦ C, and is dependent on the region. Paleotemperature corrections for regional-average heat flows can be more precise than those for a single borehole. In the regional case, the heat flow measurements are conducted at the same scale

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as the reconstructed paleotemperatures used for correction, and likely make use of multiple boreholes. Thus, the uncertainty in the reconstructed paleotemperautres can be derived from the standard error of the combined average, rather than the standard deviation. For a regional dataset of the 33 high quality boreholes in Tasmania, the uncertainty in the reconstructed paleotemperature history would be only ∼20% of that for an individual hole. Variability of the influence of surface paleotemperature changes on the geotherm from borehole to borehole means that in some situations the most precise method for obtaining steady state heat flow measurements may be to identify spatial zones where surface paleotemperature changes have had little effect on modern vertical heat flows. The temporal pattern of global temperature changes during the last glacial cycle (∼100,000 years) indicate that in most locations, heat flow measurements below ∼800 m depth are likely to be within 10% of the steady state value (Fig. 1), although this depth will be affected by the timing of the temperature changes and the thermal properties of the bedrock. 5.3. Implications for geothermal exploration Knowledge of temperature changes during the last 100,000 years, and how they propagate into the crust allows predictive heat flow models to produce more accurate assessments of resource depths of a few kilometers or greater. As described above, the influence of surface paleotemperature on heat flow measurements is most significant for the shallow (<500 m deep) boreholes most commonly used to assess heat flow on a regional scale. Our results indicate it is feasible to produce accurate reconstructions of paleotemperature, and its influence on heat flow at a regional scale. Thus, surface paleotemperature corrections from widely spaced, shallow boreholes can assist in determining which regions are most prospective for geothermal power or heat generation. Correcting for surface paleotemperature at tenement scale will be more challenging. The variability in reconstructed paleotemperature means that accurate reconstructions of paleotemperature and their influence on assessments of temperatures at depth for regions with only a few boreholes will be difficult. Assessing the geothermal prospectivity of a tenement or site may be better achieved by obtaining heat flow measurements from depths where surface paleotemperature effects are minimized – i.e. greater than ∼800 m in most areas. Thus, depending on the inhomogeneity of heat production across a prospective region, drilling a few deeper holes may provide more accurate predictions of temperature at depth than drilling many shallow ones, particularly if there is substantive spatial variability in the surface paleotemperature history. Using our Tasmanian example as a case study, we looked at how the heat flow measurements from the Lemont borehole could be corrected for the effect of surface paleotemperature changes. Here, the upper 100 m displays evidence of a warming, consistent with the regional average warming of ∼1.2 ◦ C. Applying the correction straightens out the upper part of the temperature profile (Fig. 9a), providing more consistent heat flow measurements down the profile (Fig. 9b) with an average of about 120 mW/m2 . However, the variability in the regional dataset (standard deviation of 0.6 ◦ C) means that the parts of the hole that have been corrected the most (i.e. the top) have limited precision, with 1␴ uncertainties of >20 mW/m2 at the surface. This uncertainty improves with depth, but is still significant at the bottom of the hole. Correcting the record for the changes in surface paleotemperature since the LGM (Annan and Hargreaves, 2015; Clark et al., 2009; Hughes et al., 2013; Yokoyama et al., 2000) provide better accuracy, but again less precision due to the uncertainty in the post-LGM temperature history. Applying these corrections significantly improves on the modelled temperature at resource depth. Previous estimates for the

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Fig. 9. Variation of measured temperature from steady state (9a, left) and heat flow measurement (9b, right)- at Lemont borehole corrected for 20th Century (20C), and both 20th Century and Last Glacial Maximum (LGM) variations in temperature. The horizontal error bars in both 9a and 9b represents the uncertainty resulting from the paleotemperature correction (red, LGM and 20C temperature changes, blue, 20C changes only), estimated by the standard deviation of the regional reconstructed paleotemperature histories derived from 33 boreholes in northeast Tasmania. The high-resolution noise in 9b is likely the result of complex variations in thermal conductivity that could not be accounted for in the heat flow calculations. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

heat flow at Lemont were 105 mW/m2 (Holgate, 2012b). Projecting this heat flow to a 5 km resource depth results in a temperature of 185 ◦ C, assuming a thermal conductivity of 3 W/mK. Using our revised heat flow of 120 ± 10 mW/m2 results in a 5 km temperature of 210 ± 17 ◦ C. This difference could very well change a previously considered un-economic geothermal resource zone to an economic resource zone. Further refinement in precision would require drilling in the local area to either increase the depth of measurement, or reduce the uncertainty in the local paleoclimate correction. 6. Conclusions The paper presents a synoptic view of spatial and temporal variability of the propagation of past surface temperature changes into the crust, and its implication to heat flow measurement. The general trend of temperature variability is low at local scale and high at national scale. We identify the magnitude of surface air temperature changes as significant in producing spatial variability in borehole temperature gradients, but acknowledge that the driver of most of the variability remains unconstrained. Past temperature variability has substantive implications for correcting heat flow measurements from shallow (<300 m) boreholes. In our Tasmanian data set, like most regions around the world, the uncertainty in the regional reconstructed paleotemperature history is ∼50–100% of the measured reconstructed paleotemperature change. Thus, this approach results in significant uncertainties for the correction for individual borehole heat flow measurements – i.e. reconstructed paleotemperature corrections improve the accuracy, but not the precision of heat flow measurements from a single borehole. However, if a dense array (n > 5) of boreholes is available in a small geographic area, more precise reconstructed paleotemperature histories, and corrections can be achieved. Acknowledgments The research is funded by the Australian Government Research Training Program Scholarship and Murray-Darling Basin Future Collaborative Research Network and is part of “Predicting the response of water quality and groundwater dependent ecosystems to climate change and land management practices: an integrated

modelling approach” project. We acknowledge J.C. Mareschal, (Geotop, University of Quebec, Canada), for his support in base model for inversion and KUTh Energy Ltd., Australia for borehole temperature and geological data. We also express our sincere thanks to reviewers for their useful suggestions that helped to improve this manuscript.

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