Bateman, Mark D.

Cresswell, Alan

Kinnaird, Tim

Nitundil, Shashank

Sanderson, David

Srivastava, Aayush

Stone, Abi

English

Luminescence signals using portable luminescence readers (port-OSL) are insightful tools for exploring sample relative age and casting additional light on sediment characteristics, used back in the laboratory or in the field. Sand-rich dryland environments are a useful testing ground for using port-OSL, owing to the likelihood of complete signal bleaching before burial. We have been exploring the extent to which it is possible to produce generalized chronologies that approximate sediment burial age in different locations and geomorphic settings. Our approach compares port-OSL signals with ages established using standard luminescence dating protocols for quartz, as a form of port-OSL first-order ‘calibration’ (e.g. Stone et al., 2015; 2019; Nitundil et al., 2022). The rationale for this work is to provide landscape-scale assessments of Quaternary environmental change in a cost-effective and time-effective manner.In this study we present samples from dunes in four regions within southern African dunes (re-measured in 2022 to provide a consistent analytical sequence and data processing approach) and from the Thar Desert in western India. This represents n=26 for the Namib Sand Sea, n=32 for the western Kalaha-ri, n=22 for the southern Kalahari, and n=44 for the Thar (and may include further regions, if samples are available before June 2023). We also present samples from palaoelake shorelines in the former megalake Makgadikgadi sequence (n=73 from Lake Ngami, n=40 from Lake Mababe and n=36 from Makgadigadi). We find the port-OSL calibrations are region specific with coherent regional groupings, and strong coefficients of correlations for the dune datasets. However, those from the lake shorelines define a weaker correlation with sample age. We explore the influence of sample composition data (such as quartz to feld-spar rations and particle size distribution) and inherent port-OSL signal brightness (using a subset of southern African dune and lake shoreline samples). <br/

The University of Manchester - Institutional Repository

The University of Manchester ResearchExploring dryland dynamics with portable luminescencereadersDocument VersionAccepted author manuscriptLink to publication record in Manchester Research ExplorerCitation for published version (APA):Stone, A., Nitundil, S., Bateman, M. D., Sanderson, D., Cresswell, A., Srivastava, A., & Kinnaird, T. (2023).Exploring dryland dynamics with portable luminescence readers. Poster session presented at 17th InternationalLuminescence and Electron Spin Resonance Dating conference, Copenhagen, Denmark.Citing this paperPlease note that where the full-text provided on Manchester Research Explorer is the Author Accepted Manuscriptor Proof version this may differ from the final Published version. If citing, it is advised that you check and use thepublisher's definitive version.General rightsCopyright and moral rights for the publications made accessible in the Research Explorer are retained by theauthors and/or other copyright owners and it is a condition of accessing publications that users recognise andabide by the legal requirements associated with these rights.Takedown policyIf you believe that this document breaches copyright please refer to the University of Manchester’s TakedownProcedures [http://man.ac.uk/04Y6Bo] or contact uml.scholarlycommunications@manchester.ac.uk providingrelevant details, so we can investigate your claim.Download date:18. Jul. 2023Lum sensitivitySenExploring dryland dynamics with portable luminescence readersAbi Stone1*, Shashank Nitundil1, Mark Bateman2, David Sanderson3,Alan Cresswell3, Aayush Srivastava4, Tim Kinnaird4Affiliations, acknowledgements and references 1. University of Manchester, UK,   2. University of Sheffield, UK,   3. Scottish Universities Environmental Research Centre, UK, 4. University of St Andrews, UK (see abstract for full address details). * abi.stone@manchester.ac.uk @AbiStone Thanks: A.S. thanks the Ministry of Environment and Tourism, Namibia for research/collecting permits during sampling in the NSS and Kalahari, and numerous field assistants (Hartmut Kolb, Prof. Heather Viles, Prof. David Thomas, Prof. Christine Lane, Dr Jerome Mayaud, and to Dr Sallie Burrough and Dr Matt Telfer for access to their bulk sediment samples. Funders for those periods of fieldwork are thanked via those papers. A.S. also thanks Dr Will Fletcher for guidance with regression model statistics and to the luminescence community for productive discussions around the port-OSL reader. The Thar work by S.N. was undertaken during his MSc in Environmental Monitory Modelling and Reconstruction, who thanks Ms Komal Songara and her team for support in the field and the University of Manchester, The Manchester Geographical Association and the QRA for financial support.References: [1] Sanderson, Murphy (2010) Quat. Geochron. 5, 299-305. [2] Stone et al. (2015) Quat Geochron 30, 134-140. [3] Stone et al. (2019) Quat. Geochron 49, 57-64. [4] Nitundil et al. (2022) MSc thesis; Nitundil et al. (2023, under revision) Quat. Geochron. [5] Stone et al. (2010) PPP 288, 35-53. [6] Bristow et al. (2007) Geol. 35, 555-558. [7] Stone, Thomas (2008) Quat. Sci. Rev. 27, 1667-1681.  [8] Telfer, Thomas (2006) Geol. 34, 853-856. [9] Telfer, Thomas (2007) Quat. Sci. Rev. 26, 2617-2630. [10] Burrough, Thomas (2013) Quat. Sci. Rev. 80, 29-46. [11] Brrough et al. (2007) PPP 253, 280-299. [12] Burrough, Thomas (2008) Quat.Res. 69, 388-403. [13] Burrough, Thomas (2009) Quat. Sci. Rev. 28, 1392-1411. [14] Garzanti et al. (2012) Earth-Sci. Rev. 112, 173-189. [15] Garzanti et al. (2022) Earth-Sci. Rev. 224, 103867.  [16] Srivastava et al. (2019a) Geomorph 344, 90-98.  [17] Srivastava A. et al. (2019b) Earth Surf. Process. Landform 44, 1407-1418. ▪ Port-OSL readers provide rapid insights into relative age and cast light on sediment characteristics (see Box. 1) [1]. ▪ Is it possible to produce generalized chronologies for dryland dunes (and for one site lake shorelines)? In the: - Kalahari & Namib, southern Africa [2,3] (Fig. 1,2)- Thar Desert, India [4] (Fig. 4,5,7), & other dunefields…▪ What else do we learn about our sediments? OverviewFig.1. Sn African dune & shoreline locationsNSS = Namib Sand Sea [5,6], WK-w = western Kalahari, with an ‘east of Stampriet’ subset [7],   SK = southern Kalahari [8,9], NK-landform = on floor of Makgadigadi pans [10]. (B) shows lakes               , Mababe [12] and Makgadigadi [13].IRSL:BSL ratioDunesRegion-specific calibrationsRegions slightly separated→ reflects minerologyRegional groups, scattered NK-la→ NK-la dependence on texture ▪ Regional petrology & port-OSLDunes- NSS: feldspathoquartzose (fspq) (Q:F 3-4), rich heavy mineral suite (HMs) [14]High sensitivity, IRSL:BSL 0.26 ± 0.15. - SK: Q-rich fspq (Q:F 4-10), poor HMs, local outcrops Karoo basalt [15]Med-sensitivity, IRSL:BSL 0.12 ± 0.03. - WK-w: Q-rich fspq (Q:F 5-7), as SK [15]Low sensitivity, IRSL:BSL 0.10 ± 0.03. - WK-east: Q-rich fspq (Q:F 6-10) [15]Low sensitivity (BSL), & ↓ IRSL,  IRSL:BSL 0.04 ± 0.01.  - NK-la: Q-pure (Q:F 56), v-poor HMs [15]Variable sensitivity (BSL) & dim IRSL.Shorelines are within Q-pure NK [15](but no direct petrology)                            Highly sensitive (BSL), v-low IRSL. - Ngami: IRSL:BSL 0.002±0.001. - Mababe: IRSL:BSL 0.001±0.001- Mak: IRSL:BSL 0.001±0.000       → Reflects composition = (f) provenance, transport and weathering histories. Lake sediments also influenced by textural variation. Lake shorelinesMore scatter in calibrationsSediment textureLum sensitivityFig. 2. port-OSL signal and sediment characteristics• Calibration uses 44 samples: 17 linear, Bikaner [16], 4 linear Ghotaru (new), 23 parabolic, Jodhpur [17] (Fig. 5). • Bulk sample  characteristics influence the calibration.• altering moisture content: 24% ↓ port-OSL signal at ~0.75% moisture (Fig. 6)• carbonate-concretions ↑ port-OSL signals• organics ↓ signalsSignal size (BSL & IRSL)Sensitivity Response to applied lab-doseIRSL:BSLratioDepletionindices• Sample age (+ for BSL & IRSL)• Luminescence sensitivity (+ for BSL & IRSL)• Mineralogical variation  ( feldspar =  IRSL) • Linked to minerology/provenance, sedimentary cycles (), heating (), and may also reflect variations in sed colour/transparency & particle size• Mineralogical variation ( feldspar =  ratio) reflects sediment provenance and/or weathering• Mineralogical variation ( feldspar =  BSL index), and un-known influences from HMs • Residual/ inherited luminescence ( =  index) • Grain coatings (cleaner surface  =  index)NOTE: decay rate also shows an age (signal size) dependenceSo, what can we learn from port-OSL signal characteristics? 050010001500200025000 20 40 60IRSL (cts/s)0.E+001.E+062.E+063.E+060 20 40 60OSL (cts/s)TIme (s)SSenRD• Applied the calibration to additional samples (Fig. 4 inset) to produce dune accumulation records (Fig. 7).• All ~2 m profiles are Holocene age.• Thar 1/1 contains older sediment than 1/2 & 1/3. • Interdunes oldest.• Thar 1/1 E dune flanks younger than W flanks and crests, but 1/2 & 1/3 have younger crests.Thar1/1Thar1/2Thar1/3Fig. 7. Dune accumulation estimatesRange large for each shoreline as with NK-la→ what drives this?Existing Thar dune ages and new Wn-most samples (Fig. 4)  to build calibration (Fig. 5). Fig. 5. port-OSL calibrationcontain carbonate-concretionsFig. 4. Thar dune sitesLocation of samples used to build calibration. Schematic shows additional samples used for age-estimation. The effect of removing calcrete in Sn African lake samples.Attenuation of port-OSL signals by moisture using Thar samples [4]. = += +Can account for some of the scatter… → Oldest ages saturated → Calcrete-rich samples are brighter→ Two ‘dim’ Mababe outliersNgami[11]saturatedcontain calcreteBox 1: LakeNgamiMakgadikgadiMababeRiGeneralised chronologies? [✓]   for dunes, within region-specific boundaries[ ? ]   for lake shorelines (sediment textural and compositional variation, including calcrete,  and perhaps mixed provenances. The ugly?  (the port-OSL rationale is  to  keep it simple with  no sample preparation but…) Moisture thresholdrangein TharSome dependence of BSS (& natural signals) on texture. Fig. 3. Fig. 6. SenCan you help us to go global? Let’s measure your dune sand bulks with current thanks to Nick Lancaster, Christina Neudorf, Lotem Robbins, Joel Roskin, & Dave Thomas.NK-la(B)

Exploring dryland dynamics with portable luminescence readers

https://pure.manchester.ac.uk/ws/files/269303610/Stone_etal_2023_FabricPoster.pdf

Exploring dryland dynamics with portable luminescence readers

Abstract

Similar works

Full text

Available Versions

The University of Manchester - Institutional Repository