Journal of Archaeological Science (1997) 24, 505–516

Soil Temperature and Obsidian Hydration Dating: AClarification of Variables Affecting Accuracy

M. Jones, P. J. Sheppard and D. G. Sutton

Centre for Archaeological Research, The University of Auckland

(Received 2 October 1995, revised manuscript accepted 14 May 1996)

The results of a year-long soil temperature monitoring programme are presented. They increase understanding of themagnitude, spatial scale and predictability of variation in soil temperature regimes which affect obsidian hydrationdating. It is demonstrated that current archaeological temperature estimation methods, either due to design orapplication, do not provide temperature control at a spatial resolution fine enough to control for microregionalvariation. This failure is resulting in significant and avoidable dating errors. ? 1997 Academic Press Limited

Keywords: HYDRATION, TEMPERATURE, VARIATION, SCALE, MAGNITUDE, AFFECTS, PROTOCOL.

Introduction

A ttempts to reduce errors in obsidian hydrationdating have focused on improving hydrationrim measurement and the estimation of glass

hydration rates ( Lee et al., 1974; Ambrose, 1976, 1993;Freidman & Long, 1976; Lowe, 1977; Tsong et al.,1978; Leach & Naylor, 1981; Duerden, Cohen &Ambrose, 1982; Michels, Tsong & Smith, 1983; Kondo& Matsui, 1992; Mazer et al., 1992; Stevenson et al.,1993, 1995). Comparatively little experimental workhas been carried out on soil or storage temperature(Freidman, 1976; Ambrose, 1980, 1984; Freidman &Trembour, 1983; Leach & Hamel, 1984; Stevenson,Carpenter & Scheetz, 1989), although ambient soiltemperature is one of the principal variables governingthe rate at which obsidian hydrates (Freidman &Smith, 1960). Agrometeriological and microclimato-logical research suggests that significant systematictemperature variation will occur throughout archaeo-logical deposits, at both macroregional and micro-regional scales (Chudnovskii, 1962; Baver, 1966;Campbell, 1977, 1985; Rosenberg, Blaine & Shashi,1983; Mahrer & Avissar, 1985; Davidoff, Lewis &Selim, 1986; Oke, 1987; Ayra, 1988; Davidoff & Selim,1988; Horton, 1989; Monteith & Unsworth, 1990;Stoutjesdijk & Barkman, 1991). It is known that smallerrors in temperature estimation can result in largecorresponding date errors. This paper reports an at-tempt to understand and control for this fundamentalfactor.

Obsidian Hydration Dating and TemperatureObsidian hydration dating relies upon converting ameasured hydration rim thickness into a ‘‘date’’ based

500305–4403/97/060505+12 $25.00/as960134

upon an estimated hydration rate. As the rate at whichobsidian hydrates is principally a function of theambient temperature and glass chemistry (Freidman &Smith, 1960; Ambrose, 1976), accurate temperaturecontrol is fundamental to producing accurate obsidianhydration dates. While glass chemistry is essentiallystatic throughout the lifetime of an artefact, and can becalculated to a high precision in later laboratory exper-iments, the soil temperature regime is a dynamic vari-able linked to a range of complex systems; local,regional, global and even solar. It is this dynamic thatmust be estimated in the production of any obsidianhydration date (OHD), whether externally calibrated(hydration rate calibrated via a primary dating tech-nique such as 14C) or intrinsic (hydration rate calcu-lated on the basis of glass chemistry). Standardpractice (e.g. Stevenson, Carpenter & Scheetz, 1989) isto produce an estimate of the effective storage tempera-ture throughout the lifetime of an artefact. As the rateof the hydration reaction at the surface of a piece ofobsidian is exponentially related to the ambient tem-perature the effective hydration temperature differsfrom the arithmetic mean temperature over the storageperiod. Any temperature fluctuation about the arith-metic mean results in a higher exponential mean. Thusthe EHT for any artefact reflects the exponentialtemperature history of the location, which is a functionof both the arithmetic mean and the extent of tempera-ture fluctuations about this mean. It is crucial that theestimated EHT is as accurate as possible in order tominimize dating error. Additionally important is anunderstanding of the dating error resulting from tem-perature estimation inaccuracies. This enables a realis-tic appraisal of the true accuracy limits of any datesproduced, which is a measure vital to the correctapplication and interpretation of any temporal data.

5? 1997 Academic Press Limited

506 M. Jones et al.

A convenient approach to calculating the datingerror induced by temperature uncertainties is by con-sidering the relationship between EHT and OHD. As aprimary step the relationship between hydration rate(k), glass chemistry and the EHT is usually describedwith an Arhennius type relationship which has thegeneral form:

k=Ae"E/RT (1)A=pre-exponential componentE=activation energyR=universal gas constantT=effective hydration temperature

This is then fitted into an empirically derived relation-ship relating rim thickness and age such as:

x2=kt (2)x=rim thicknessk=hydration ratet=time

An equation such as this, which relates estimateddate, glass chemistry and EHT in the appropriatemanner can be used to express the percentage datingerror resulting from uncertainty in EHT estimation. Asan example of this process two typical New Zealandobsidians have been analysed for dating error arisingfrom inaccurate EHT estimation (Table 1). In thisexample the ‘‘true’’ age of the artefacts is 1000 yearsand the ‘‘true’’ EHT is 17)C. For a series of tempera-tures above and below the ‘‘true’’ EHT the correspond-ing age has been calculated via equations (1) and (2);i.e.

tapparent=ktruettrue

kapparent(3)

where ktrue and kapparent are calculated with ‘‘true’’EHT and estimated EHT respectively.

This allows us to determine the date error arisingfrom an uncertain EHT estimate. As an example wecan see that for a typical Mayor Island obsidian(Stevenson et al., 1995) hydrating for 1000 years at anEHT of 17)C, an estimated EHT of 17·5)C correspondsto a calculated date of 941 years or a date error ofaround 6%. A more usual scenario is that the EHTwould be estimated as temperature range; for example17)C&0·4). In the case represented in Table 1 for aMayor Island obsidian this would correspond to a daterange of 1050–952 years, or a 9·8% date error range.As the hydration rate is exponentially related to the

ambient temperature it is not possible to provide anyexact one-to-one relationship for the dating signifi-cance of EHT estimation inaccuracy. This is becausethe magnitude of dating errors arising from an incor-rect EHT estimate differ between overestimation andunderestimation. As an example we can again use thefigures presented in Table 1. For a typical MayorIsland obsidian hydrating for 1000 years, a 1) under-estimation of a true EHT of 17) results in a calculateddate of 1130 years or an error of 130 years. Similarly a1) overestimation corresponds to a date of 886 years,or an error of 114 years. Thus date errors are notsymmetric over any estimated temperature range. Inaddition, date error depends on both the magnitude ofthe EHT estimation inaccuracy and the ‘‘true’’ EHT.There is a further consideration in that the exponentialtemperature response of hydration rates varies depend-ing upon glass chemistry. This means that the datingerror associated with any temperature estimation erroris specific to each particular obsidian. For example, inTable 1 a typical Great Barrier obsidian (Stevensonet al., 1995) hydrating for 1000 years at an estimatedtemperature of 17)C&0·4) generates a correspondingdate range of 1048–954 years, or a 9·4% date errorrange, in comparison to the 9·8% error range for theMayor Island obsidian. Therefore it is necessary tocalculate the dating errors associated with an EHTestimation for each artefact explicitly.

Effective Hydration Temperature EstimationClearly small levels of uncertainty in the estimation ofEHT have a large impact upon dating precision. Aserrors are additive throughout the system the datingerror introduced by EHT estimation represents a limitto dating accuracy. It is therefore imperative to ac-count for every predictable component of the soiltemperature regime.As the EHT is a function of climatological variables

it is useful to refer to the considerable quantity ofresearch available in this field (e.g. Monteith, 1975;Campbell, 1977, 1985; Hanks & Ashcroft, 1980;Rosenberg, Blaine & Shashi, 1983; Oke, 1987; Ayra,1988; Monteith & Unsworth, 1990; Stoutjesdijk &Barkman, 1991) to establish a baseline for the study ofthe archaeological soil climate.

Table 1. Apparent ages using different temperatures from the truetemperature of 17)C giving deviation from true age of 1000 years

Temperature()C)

Mayor*Island

GreatBarrier

15 1278 126515·5 1202 119216 1130 112416·6 1050 104816·8 1025 102417 1000 100017·2 976 97717·4 952 95418 886 89018·5 834 84019 785 79319·5 739 749

*Using a pre-exponential component (A) of 0·87 and gas activationenergy (E) of 85·57 kJ for the Mayor Island source; pre-exponentialcomponent (A) of 1·12 and gas activation energy of 81·00 kJ for theGreat Barrier source (Stevenson et al., 1995; Stevenson, pers.comm.).

Soil Temperature and Obsidian Hydration Dating 507

Climate systems are studied at three general spatialscales: macro- (>10 km), meso- (1–10 km) and micro-(1–10 m), with significant climatic variation operationat each of these scales (Monteith & Unsworth, 1990).This is in itself a concern as archaeologists tendto consider variation in EHT macro- and, at best,meso- (Freidman, 1976; Ambrose, 1984; Leach &Hamel, 1984) scales, with any consideration of micro-scale variation reserved almost exclusively for the caseof variation in EHT with depth (Ambrose, 1984;Stevenson, Carpenter & Scheetz, 1989; Ridings, 1991).While there are no microclimatological soil studies suit-able for directly determining subsurface EHT (as theresearchers are predominantly interested in processessuch as seed germination) there is a wide variety of datathat point to considerable variation in soil surface tem-peratures over small spatial scales (Chudnovskii, 1962;Mahrer & Avissar, 1985; Davidoff, Lewis & Selim,1986; Stoutjesdijk & Barkman, 1991). For instance,Stoutjesdijk (1977) reports a surface temperature rangeof 40)C (39–"1)) over a transect with an associated4 cm soil temperature range of 16·8)C (1·4–18·2)) atmidday in winter. While this will certainly be a short-term manifestation there is evidence to suggest thatthese sorts of short-term surface temperature fluctu-ations propagate to depths of at least 30 cm (Persaud &Chang, 1984); and this sort of variation would certainlyinfluence the EHT.Fortunately, the principal variables governing cli-

matic systems at all three spatial scales have beenestablished in previous research (see Monteith &Unsworth, 1990 for an overview). This makes it poss-ible to isolate a series of variables that are potentiallysignificant in the explanation of variation in thearchaeological soil temperature regime.

Isolating the variablesOne of the easiest ways to outline factors that mayhave a significant influence on the soil temperatureregime is to make use of an energy budget equationthat describes the balance of energy at the soil surfaceand in turn determines the subsurface soil temperatureregimes (Campbell, 1985; Monteith & Unsworth,1990). A suitable energy budget equation is:

G=Rn"H"LE (4)

Rn=net radiation balance at the soil surfaceG=soil heat fluxH=sensible heat fluxLE=latent heat flux

Factors affecting any of the terms outlined in thisrelationship have a potentially significant influence onthe archaeological EHT. While this is a simplificationof the problem in that there is a linked process of soilmatrix heat transfer, EHT variation due to this second-ary process can be approximated as a function ofmatrix thermal properties and distance from the soil

surface (Hanks & Ashcroft, 1980; Campbell, 1985;Mahrer & Avissar, 1985), simply requiring that thevariables of sample depth and small-scale matrix vari-ation be considered as potentially significant sources ofvariation. Other variables can be isolated by regardingeach of the terms in equation (3) separately.

Net radiation balancePossibly the most regular effect will be due to the netradiation balance, described by the relationship:

Rn=(1"albedo)St+åaóta4"åsóts

4 (5)

St=global short waveta=air temperaturets=soil surface temperatureåa=atmospheric emissivityås=soil emissivityó=the Stefan-Boltzmann constant

From this relationship we can see that any factorswhich affect the albedo (surface short wave reflect-ance), the global short wave per unit area, or thedifference between soil and air temperatures, have apotentially significant effect on EHTs.On a purely geometric basis it is possible to isolate

the latitude of a location, the immediate aspect andgradient of a surface as potentially significant variables.The latitude, aspect and gradient have an effect on theangle between the normal to the slope and the solarbeam (Oke, 1987), which is one of the primary controlson the direct shortwave flux (Campbell, 1985; Oke,1987; Monteith & Unsworth, 1990). In addition, due toincreased atmospheric path length at higher latitudes,solar beam attenuation can be correlated to the lati-tude (Oke, 1987; Monteith & Unsworth, 1990). Thegeneral trend that would be expected under the influ-ence of these two variables is that EHT would dropwith increasing latitude, and that slopes orientatedtowards the equator with optimum slope gradientequal to the latitude would have higher EHTs thanthose with the opposite orientation.Another important influence on the net radiation

balance is the surface vegetative cover. This governsboth the immediate surface albedo and the energy fluxto the soil surface (Monteith, 1975; Clothier et al.,1986; Stathers & Bailey, 1986; Horton, 1989; Monteith& Unsworth, 1990; Eck & Deering, 1992; Luo, Loomis& Hsiao, 1992; Mark & Ashton, 1992). The magnitudeof this influence will depend on the extent and type ofvegetative cover.At a macroscale level, the influences of climate

modifiers such as large bodies of water or raised reliefhave a potentially significant influence, regulating theatmospheric temperature and affecting the longwavebalance.

Sensible heat fluxThe sensible heat flux (H) is essentially due to con-duction and convection (Tipler, 1982; Monteith &

508 M. Jones et al.

Unsworth, 1990). As such this flux will be influencedby the driving gradient of temperature difference be-tween the surface and atmosphere. Therefore variablessuch as altitude and climate modifiers which affect airtemperatures should have an effect on the sensible heatflux. Another influence on the sensible heat flux isthe thermal capacity of the boundary atmosphere(Monteith & Unsworth, 1990; Heitor, Biga & Rosa,1991). So the variables of atmospheric humidity and airpressure are potentially significant. The other majorfactor influencing the sensible heat flux is the surfacewind speed (Monteith & Unsworth, 1990). The greaterthe average wind speed that a surface is exposed to, thegreater the flux. In this context vegetative cover alsoplays an important role in that vegetative cover tendsto reduce the effective surface wind speed thereforeminimizing the sensible heat flux.

Latent heat fluxThe latent heat flux (LE) is essentially governed byrates of water phase transformation and transpiration.Obviously surface vegetation cover will have a signifi-cant influence on this flux, with increased vegetativecover increasing the latent heat flux through evapo-transpiration. Other variables that have been shown tohave an influence on LE are air pressure, atmospherichumidity, air temperature, and wind speed (Monteith,1975; Lindroth & Halldin, 1986; Monteith &Unsworth, 1990; Luo, Loomis & Hsiao, 1992; Mark &Ashton, 1992).In light of this brief examination we can outline 10

variables that have a potentially significant influenceon the archaeological EHT. For archaeological pur-poses it makes sense to group these into two spatialscales microregional (1–10 m) and macroregional(>1 km) as this puts the variables into an intra-site/intersite framework suitable for archaeological use.The potentially important macroregional variables

are:

- Air pressure and humidity- Latitude- Proximity to climate modifiers- Altitude

The potentially important microscale variables are:

- Vegetative cover- Aspect- Slope gradient- Depth- Exposure to wind- Matrix properties

While many other factors such as atmospheric tur-bidity also have an influence on the surface energybudget, the factors outlined represent a baseline for thestudy of soil climates projected into the past.The interesting point to note is that approximately

two-thirds of the variables that are potentially signifi-

cant in the explanation of variation in EHT operate ata microscale level. Unfortunately few of the currentprocedures for estimating effective hydration tempera-ture account for microscale temperature variation, andeven where this is possible the focus is almost inevi-tably on providing macro- or mesoscale measures.Previous efforts directed at accounting for microscalevariation have focused almost exclusively uponvariation due to depth (Ambrose, 1984; Stevenson,Carpenter & Scheetz, 1989; Ridings, 1991). While thisfocus may be valid where all other potential microscalevariables are constant, in situations where there isvariation in surface aspect, gradient, vegetative cover,exposure or a heterogeneous soil matrix, significantunmeasured variation in soil temperature regimes mayexist.It is apparent from the preceding discussion that

microscale temperature variation is potentially signifi-cant in terms of producing accurate OHDs. Given thatthis is the case it would seem that an effective protocolfor estimating site EHTs should be able to providecontrol for any variation in the microscale variationsoutlined previously. In light of this it is useful toexamine the mainstream approaches to estimatingEHTs and outline to what extent each can account forany potential microscale variation in EHTs.

Archaeological EHT estimationOne of the most common approaches to estimating anEHT involves the use of temperature cells (Ambrose,1980) such as those used in the study presented in thispaper. This either consists of large-scale regionalstudies such as those conducted by Leach & Hamel(1984) in New Zealand, or smaller scale approacheswhere cells are placed in a few sites of interest. Invari-ably the effort is directed at providing a ‘‘temperature’’for any site, ignoring intra-site or microregionalvariation. While this process can be extended to ac-count for microscale variation, this is not a standardpractice. One of the few examples of this approachbeing used to control for microscale variation is pre-sented by Ambrose (1984). In this study Ambrosemakes use of hydration cells to provide controls forvariation in soil temperature regimes with depth. It ispossible to design a survey making use of hydrationcells to account for variation introduced by anynumber of variables.Other techniques involve the use of air temperature

data in some form of integration equation. A commonapproach to this is via a reworking of Lee’s (1969;equation (12), p. 430) temperature integration equa-tion, where long-term regional air temperature data isused to provide estimates of subsurface soil tempera-tures (Stevenson, Carpenter & Scheetz, 1989).

Te=Ta+1.2316+0.1607RT

1.0645(6)

Soil Temperature and Obsidian Hydration Dating 509

whereTe=exponential mean (use as an estimate of EHT)Ta=arithmetic meanRT=annual temperature range monthly maximumminus monthly minimum

This approach has been critiqued by Stevenson,Carpenter & Scheetz (1989), who point out that thisestimation does not allow for changes in temperaturewith depth. An additional problem with this approachis that as a general regional temperature is generatedfor the zone around which the long-term air tempera-tures have been recorded there can be no accommoda-tion of any potential variation introduced by the typeof microregional variation outlined in this paper. Fur-ther, as the exponential chemical temperature responseused in the derivation of this empirical relationship isalmost certainly different from that of the obsidianconversion to perlite, there has to be some doubt as towhether the exponential mean derived in this relation-ship actually estimates an EHT with any more ac-curacy than an arithmetic mean. Additionally thisrelationship was not intended to produce soil tempera-ture data on the basis of air temperatures and anyattempt to use it in this manner is bound to be errorladen as soil and air temperature regimes are quitedifferent (Chudnovskii, 1962; Baver, 1966; Campbell,1977, 1985; Rosenberg, Blaine & Shashi, 1983; Mahrer& Avissar, 1985; Davidoff, Lewis & Selim, 1986; Oke,1987; Ayra, 1988; Davidoff & Selim, 1988; Horton,1989; Monteith & Unsworth, 1990; Stoutjesdijk &Barkman, 1991).A third method is that described by Freidman

(1976), where hourly soil temperatures are measuredfor a period of at least 24 h and the hydration rate,averaged over this period, is used as a yearly measure.This method is limited in terms of accuracy and spatialresolution unless a large-scale monitoring programmemeasuring temperatures at many locations within a siteover a long-term period (at least 12 months) is carriedout.As an alternative, Stevenson, Carpenter & Scheetz

(1989) proposed a model that accounted for variationin effective hydration temperature with depth. Thismodel is essentially a simplified form of the analyticsolution to the Fourier-Biot heat flow equation. Thepractical application of this model as presented suffersin that temperatures are not described at an acceptablespatial resolution. In their application Stevenson,Carpenter & Scheetz (1989) made use of meteorologi-cal data from a station 60 km away to define theirinput parameters, and also used general measures todefine the soil diffusivity at the site. Obviously thisspatial resolution is insufficient to control for anypotential microscale variation in EHT. Additionalproblems arise in that this approach assumes that thesoil matrix is homogeneous (Chudnovskii, 1962). Thisis very rarely the case in any archaeological context. Asdifferent soils can have very different thermal proper-

ties (Campbell, 1985) the assumption that an archaeo-logical soil matrix is homogeneous will almost certainlyproduce inaccurate results.These approaches are primarily used in conjunction

with intrinsic hydration rate determinations. In con-trast, externally calibrated rate determinations wherethe rate is calculated by correlating a measured rimthickness with a date provided by another technique,do not require EHT as an input variable. This does notmean that the use of externally calibrated rate determi-nations are free from any potential problems arisingfrom microscale variations in EHT. Unless methodsare developed to adjust the rate according to micro-scale climatic variation, the rate is only applicable tothe exact location dated by the primary technique. Thiswould render OHD practically redundant, as an exter-nal date would be required for every context to bedated by OHD.As can be appreciated in the light of the preceding

discussion, very few of the EHT estimation techniquescurrently control for potential microscale temperaturevariations. Thus it is important to determine to whatextent microscale climatic variation actually occurs inarchaeological deposits and to what extent it is predict-able. In part the answer can be taken from a tempera-ture survey recently conducted in New Zealand.

SurveyA year-long temperature survey of the northern half ofthe North Island of New Zealand (Figure 1) wasconducted as a component of a major research pro-gramme to develop archaeologically useful obsidianhydration dating in New Zealand (Jones et al., 1995).The goals of this survey were to add to the availableEHT data for the region (Leach & Hamel, 1984) and toinvestigate microscale variation in EHT.The survey region comprises the northern half of the

North Island of New Zealand, with a latitude range of34–38) in a general SSE–NNW orientation. The cli-mate is moderate with abundant year-round rainfall.The environmental contexts vary considerably, rangingfrom the sand dune complexes of the northern penin-sula through to heavily vegetated ranges (<600 maltitude), and flood plains. This range of latitude andenvironments suggested that considerable regional cli-mate variation was likely, requiring extensive EHTdata to make use of OHD.In total, 200 zeolite temperature cells (Ambrose,

1980; Michels, Tsong & Smith, 1983; Trembour, Smith& Freidman, 1988; Stevenson, Carpenter & Scheetz,1989) were placed at 60 locations throughout the studyarea (Figure 2) in a two-part experiment (Jones et al.,1995). In the first component, cells were placed in eachlocation at a 30 cm depth with near identical surfaceconditions of year-round short grass cover and mini-mal surface gradient. The intention was to try tominimize potential microscale variation among the

510 M. Jones et al.

locations (due to aspect, depth, surface vegetativecover and exposure to wind) while incorporating therange of potential macroregional variation throughoutthe study area (due to latitude, general climate zoneand proximity to climate modifiers). The second part ofthe experimental design consisted of multiple cell place-ments at five of the locations (Table 2) to test themagnitude and predictability of variation at the micro-scale level. The microscale variables incorporated intothe experiment (depth, aspect (north versus south),vegetative cover, exposure to wind, and variation inmatrix properties) were isolated through considerationof the relevant physics (as presented earlier). Thisdesign allows the degree and predictability of variationat both macro- and microregional scales to be com-pared and analysed in terms of significance to OHD.The cells were placed in two 6-month sets to generate

the 12-month figures. This was for three reasons.Firstly, placing the cells in 6-month sets meant that weavoided any risk of the cells becoming saturated andany consequent change in their absorption function; italso meant that variations between the winter andsummer months could be analysed; and finally itallowed us to be sure that any trends observed were notmerely artefacts of inaccuracies in the operation of thecells. That is, if matched pairs of cells located in whatare potentially different microclimates return consist-ent relative results over both recording periods thenany recorded temperature difference can be interpretedas the result of differing microclimates. For example, ifboth the summer and winter cells on a northern slopeare warmer than the associated cells on an adjacentsouthern slope we can be more certain that the

20050 0

N

Study area

50 100 150Kilometres

Figure 1. Location of survey area.

Table 2. Microscale test results

Location Depth AspectVegetationcover

EHT)C Location Depth Aspect

Vegetationcover

EHT)C

Haratua’s Paa (A17) Paeroa (D6)A17a 10 cm Nil Grass 17·9 D6a 10 cm Nil Grass 17·0A17b 30 cm Nil Grass 17·6 D6b 30 cm Nil Grass 15·1A18 30 cm Nil Grass 17·7 D6c 60 cm Nil Grass 15·2A19 30 cm North Grass 18·1 D6d 30 cm North Grass 0A20 30 cm North Grass 17·9 D6e 30 cm South Long grass 13·9A21 30 cm South Grass 17·1 D6f 30 cm Nil Mature trees 14·2A22 30 cm South Short shrubs 15·5 Matakana (D9)A23 30 cm Nil Grass 17·4 D9a 10 cm Nil Grass 15·7Tramvalley road (B1) D9b 30 cm Nil Grass 15·5B1 30 cm North Grass 15·9 D9c 60 cm Nil Grass 15·0B2 10 cm Nil Grass 16·0 D9d 30 cm Nil Grass 15·7B2 30 cm Nil Grass 15·1 D9e 30 cm North Short scrub 15·5B2 60 cm Nil Grass 15·3 D9f 30 cm South Grass 13·3B3 10 cm Nil Scrub 14·7 Optio (D17)B3 30 cm Nil Scrub 14·5 D17a 10 cm Nil Grass 17·6B3 60 cm Nil Scrub 14·3 D17b 30 cm Nil Grass 18·0B4 30 cm Nil Single pine 14·9 D17c 60 cm Nil Grass 17·1Paired comparison 5 m separation: 90 mile beach D17d 30 cm Nil Pohutakawa 15·4A4 30 cm Nil Nil 19·8 D17e 30 cm South Grass 14·9A5 30 cm Nil Nil 20·2 D17f 30 cm Nil Grass 15·3Paired comparison 3 m separation: Paeroa D17g 30 cm North Grass 0D5a 30 cm Nil Grass 16·3 D17h 30 cm Nil Grass 14·9D5b 30 cm Nil Grass 16·0

observed difference in recorded temperatures is due toa systematic difference.An additional benefit in recording over two 6-month

periods is that the calculation described by Ambrose(1984) can be carried out on the results to producearithmetic average temperatures and associated tem-perature ranges over the recording period as opposedto the exponential mean temperatures generated by thecells. In this manner the cell results can be used toprovide exponential mean temperatures more suitablefor use with obsidian hydration. This is necessary asthe exponential temperature response of cell wateruptake differs from that of the chemical conversion ofobsidian to perlite (Ambrose, 1984). In presenting

these results this correction has not been carried out aswe are purely interested in establishing at what spatialscale significant variation in soil temperature regimesoccurs, and which climatic variables govern this vari-ation. As the cell uptake is an analogue of the hydra-tion of obsidian, the results will provide the necessarydata. The exponential mean temperature producedfrom ‘‘raw’’ cell temperatures is not strictly suitable foruse as an EHT (though this is a common practice) asthe exponential mean temperature generated from thethermal cells will in general be lower than that neededfor use with OHD (due to the different exponentialtemperature response of the respective reactions).However, the ‘‘raw’’ cell results will allow an effective

1000 50

N

A25

A15

A14

A8A9 A11

A13

A6

A7A13

A12

A4

A17

A24

A3

A2

A1 E1C6

D19D20

A16

A26

A27

A28 B7

B1

B5C7

C1

C5D1

C2D2

C3

C4C5

D4D11

D17

D13D21

D6D9

D8

D10

B8

B6 D18

D15

D12

D3

A5

Figure 2. Temperature cell locations in survey area.

Soil Temperature and Obsidian Hydration Dating 511

512 M. Jones et al.

analysis of the question in point. The use of the ‘‘raw’’cell results as EHTs in the calculation of date rangesassociated with any temperature variations observedrepresents a conservative measure. This is because thecell exponential mean temperatures are lower than theexponential means corrected for the temperature re-sponse of the obsidian to perlite conversion. Thusworking with the marginally lower temperatures de-rived from the cells will mean that any reported dateerrors associated with variations in temperature resultswill be smaller in magnitude than would actually be thecase and therefore represent a conservative estimate ofthe degree of significant temperature variation takingplace.In addition to these experiments, four cells were

hydrated in the laboratory for 12 months to test theinherent variation in cell function in order to determineat what degree of accuracy any inferences could bemade. The results produced a .. of 0·08) which fits inwell with the nominal accuracy of &0·1)C suggestedby the supplier (W. Ambrose, TH Cells, Canberra,Australia).

ResultsThe temperatures reported in this paper are the rawexponential mean temperatures derived from the zeo-lite hydration cells. The temperatures were calculatedaccording to a formula relating weight gain over therecording period to the exponential mean temperature(Ambrose, 1980, pers. comm.)

Te=A

lnSÄW

ÄtD"B (7)

A and B=constants specific to each set of cellsÄW=cell weight gain during recording periodÄt=duration of recording period

The 12-month temperatures recorded were producedby averaging the results from each 6-month set. So forlocation A1, for instance, the 12-month reported tem-perature is simply the average of the temperatureresults for set 1 and set 2, i.e. the exponential mean forthe 12 months.

T12months=Tperiod1+Tperiod2

2(8)

The overall results for the study area (Figure 3,Table 2) show that there is a temperature range be-tween locations of approximately 4·7)C, with a meantemperature for the overall study region of 17·5)C. Ifthe associated dating error is calculated for theseresults with the mean as the estimated true temperature

then we can see that there is a range in the resultingdates of 58% (+33·3–"24·7%) which is quite clearlyunacceptable. Apparently the temperature variationover an area as large as our study area needs control ata much finer spatial resolution than is provided byusing a single regional temperature.The results of the five microscale experimental sites

(Table 2) show that there is spatial temperature vari-ation of between 1·7 and 3·1)C. These ranges corre-spond to associated dating errors of between 19 and38% which is again clearly unacceptable. In fact, thedegree of microscale variation is of the same order ofmagnitude as the variation over the entire study area,with microscale variation accounting for 21% of thetotal variation observed.Having demonstrated that significant variation oc-

curs at macro- and most importantly microregionalscales, it becomes important to determine the predict-ability of this variation. Once the variables that governsignificant systematic variation in the EHT are isolatedit becomes possible to develop EHT estimation proto-cols that minimize associated dating error.In interpreting our results it is not proposed to give

a quantitative analysis, but rather to show qualitativelythat the temperature results are influenced by thefactors previously outlined, and follow predictabletrends.The results of the macroregional study (Figure 3)

follow the general trends outlined earlier. It is ex-pected that as latitude increases EHT would drop.This trend has been demonstrated in other studies(Aldridge, 1982; Aldridge & Cook, 1983), and modelshave been produced which predict annual arithmeticmean soil temperatures with a high degree of accu-racy purely on the basis of latitude change (e.g.Aldridge & Cook, 1983). As has been pointed outpreviously, arithmetic temperatures are not directlysuitable for use in OHD work as different degrees oftemperature variation around any arithmetic meanwill result in quite different exponential means (andthus EHTs) (Ambrose, 1984). So while a generaltrend with latitude is apparent in the results of oursurvey it does not follow the exact pattern outlinedfor arithmetic means. This is presumably as thediurnal and seasonal variations in temperature willvary between locations due to macroscale variablessuch as altitude and climate modifiers, thus the expo-nential means deviate from the pattern expected forarithmetic means.The effects of climate modifiers can be seen at sites

such as B1. This site is situated in a valley at the foot ofthe Waitakere ranges on the edge of a temperate rainforest. The temperatures for all of the cells placed atthis site (eight in all) are 2–3) lower than sites at similarlatitudes and altitudes, yet further from the influence ofthe ranges (B5, B6, B7). This same effect can be seen onthe Hauraki plains in the comparison of Thames (D21)and Paeroa (D6). Both sites are situated near anextensive range, and as expected the inland site

(Paeroa, D6) removed from the moderating influenceof the ocean is colder.While the results seem to suggest that macroregional

variation does follow systematic trends, it is probablymore important that the microregional variation beunderstood, as even a very precise understanding ofmacroregional variation alone is insufficient in termsof dating accuracy.Given that standard archaeological EHT estimation

procedures focus on macroregional EHT variation, theresults of the microregional study are probably of themost interest at this stage. The results from the five testsites demonstrate that the variation closely followsexpected trends.

At all locations there is variation with aspect, thenorthern orientations proving warmer as expected. Theaspect variable tested was immediate surface aspect,with the assumption that as this governs the surface/solar beam angle there should be a correspondingdifference in the surface net radiation balance. As theprimary focus of this survey was to establish if signifi-cant variation was present, the only aspect variationtested was of north versus south, though there isevidence to suggest that there should be a differencebetween eastern and western aspect as well (Mahrer &Avissar, 1985). Aspect variance was tested at all fivetest sites with data on the full aspect range availablefrom only three sites (site D6 had no aspect data

1000 50

N

17.1

18.7

20.2

18.917.8 17.9

17.9

19.5

18.019.1

17.919.8

17.7

16.9

17.5

18.4

18.2

17.015.8

16.217.1

17.1

17.7

16.9

16.317.5

15.9

18.616.7

15.8

16.015.7

16.717.1

17.2

18.0 16.0

16.3

16.1

17.9

14.316.7

15.1

16.5

15.5

15.2

Figure 3. Regional temperature survey results.

Soil Temperature and Obsidian Hydration Dating 513

514 M. Jones et al.

recovered, site D17 only had aspect data for thenorthern orientation).The degree of small-scale vegetative cover also

proved to be a significant variable, demonstrating thatthe EHT will vary through time in response to thedegree of vegetative cover. This suggests that it willoften be incorrect to assume a constant temperaturehistory for any location. This variable was tested atfour sites, over a range of different vegetative covers. Inall cases the reference cells were under a cover of shortgrass. Low scrub cover (B3, A22), medium densitymature tree stands (D6) and individual mature treeshading (B4, D17d) proved to have a significantinfluence on the EHT.Small-scale variation in soil properties also proves to

be a significant variable. This can be seen in theMatakana test site (D9) where two cells (D9b, D9d)were placed at 30 cm depth 2 m apart on a flat piece ofgrassed land. The matrix was assumed to differ be-tween the two locations on the basis of crop colour,and this was subsequently shown to be true when thecells were removed. One cell was located in a light,sandy soil, and the other in a rich, dark, organic soil.There was a 0·2)C consistent variation due to thisfactor, which represents an approximate 2% datingerror. An additional four paired cell experiments werecarried out to test this type of variation (location D5,A4/5, A17b/18, A19/20) with ranges of 0·3, 0·4, 0·1 and0·2) respectively. With these four tests there was nosurface evidence to suggest that there should be anymatrix variation. The tests were simply carried out todetermine the degree of variation likely to be inducedby small-scale matrix variation. The result returning adifference of 0·1 (A17b/18) is at the accuracy limit ofthe cells so probably represents no real difference inEHT. As we do not have any data on the thermalproperties of the soil positions in which the cells wereplaced we cannot predict the direction of the variationin any of these locations. As all these paired exper-iments were conducted with the pairs separated bydistances of less than 3 m we can assume that anydifference in measured temperature is due to variancein matrix thermal properties.The effect of depth on EHT has been demonstrated

in other studies (Ambrose, 1984; Stevenson, Carpenter& Scheetz, 1989; Ridings, 1991), and is seen again inthe present study. At five sites cells were placed at aseries of depths in a column. At sites B2, D6, D9 andD17 cells were placed in profile at 10, 30 and 60 cmdepths. Additionally at site B2 a second identicalprofile (B3) was placed 5 m from the first under lowscrub cover to examine the combined effects of depthand vegetative cover. At sites A17 and D17 only twocells were placed in profile at 10 and 30 cm depths. Asexpected there is a trend towards decreasing EHT withincreasing depth. This trend is also exhibited undervegetative cover, though the magnitude of the vari-ation is damped in comparison to the open profile. Theeffect of this variable was not as pronounced as we had

originally expected, with the EHTs at 10 and 30 cmdepth showing marginal systematic variation.The final variable tested in the microregional survey

was the effect of exposure to wind. This experimentwas carried out at Opito Bay on the CoromandelPeninsula (location D17, Table 2) where cells wereplaced in a sheltered low-lying area (D17 a–d), and onan adjacent exposed headland (D17 e–h). As can beseen, the effect of exposure is quite marked with theexposed pa (hillfort) temperatures falling considerablybelow the sheltered temperatures. As we have no dataon the actual relative annual wind speeds for the twolocations we can only assume that this variation is dueto increased exposure, though as an added test anexposure comparison was carried out on the pa itself.A cell (D17f) was placed in a round pit about 1·5 mdeep and 3 m in diameter, and immediately adjacent tothe pit a second cell (D17h) was placed in an exposedposition. The assumption was that the increased expo-sure of the cell D17h would result in a lower EHT. Asexpected, the cell in the sheltered location returned ahigher EHT reading, consistent over both recordingperiods.

ConclusionThe primary result of this study is that significantsystematic variation in EHT occurs at the micro-regional level. This sort of variation accounted forapproximately 21% of the total variation in EHT in theareas surveyed, corresponding to associated datingerrors of up to 38%. As current techniques andmethods do not adequately make allowance for thisvariation it is imperative that methods and associatedprotocol are developed to do so. This is important forboth externally calibrated and intrinsic hydration ratesas the same process will introduce error into both rates.It seems that it is important to develop measures of

EHT variation for each location or site to be dated. Itis probably not so vital to concentrate on the macro- ormesoscale variation as any effective approach to ac-count for the microscale variation will allow for thesescales by default. The variables isolated as contributingsignificant and predictable variation in EHT at themicroregional scale are:

- Aspect- Degree and type of vegetative cover- Depth- Exposure- Variation in soil properties

As the analysis has been purely qualitative, thesefactors have not been presented in any rank order ofeffect. It would be expected that the influence of eachof the variables would be dependent on the actualcontext.Since the variation observed followed general trends

expected on the basis of associated research, the

Soil Temperature and Obsidian Hydration Dating 515

obvious next step is to extend these results into predic-tive models based upon the appropriate physics. This isone focus of our current research (Sutton & Sheppard,1994; Stevenson et al., 1995; Sheppard et al., n.d.).Using this approach it should be possible to minimizethe errors resulting from this type of variation. Inaddition to developing stand-alone predictive models,other approaches can be developed to account for thisvariation. Protocols could usefully centre around pro-viding empirical measures of intra-site variation from asystematic survey of the site to be dated. This sort ofsurvey can be easily conducted through the use ofthermal hydration cells such as those used in oursurvey. Whatever approach is adopted it is apparentthat greater attention must be paid to the question ofintra-site EHT variation if archaeologists wish tomaximize OHD accuracy.

AcknowledgementsThe authors would like to thank the FoRST for majorfunding (UOA 315, UOA 511), Auckland UniversityResearch Committee, the Anthropology Departmentof the University of Auckland for continuing supportand Dr R. T. Wallace and Wal Ambrose for construc-tive advice. Those who helped with fieldwork includeVick and Ann Hensley of Houhora, the Clan Mcleodof Swanson, Catriona, Kris and Gareth.

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