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Herrmann, Nicole; Boom, Arnoud; Carr, Andrew S; Chase, Brian M; West, Adam G; Zabel, Matthias; Schefuß, Enno (2017): n-Alkane deuterium analysis of soil samples from South Africa. PANGAEA, https://doi.org/10.1594/PANGAEA.875109, Supplement to: Herrmann, N et al. (2017): Hydrogen isotope fractionation of leaf wax n -alkanes in southern African soils. Organic Geochemistry, 109, 1-13, https://doi.org/10.1016/j.orggeochem.2017.03.008

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Abstract:
The hydrogen isotope composition of plant leaf wax (dDwax) has been found to record the isotope composition of precipitation (dDp). Hence, dDwax is increasingly used for palaeohydrological reconstruction. It is, however, also affected by secondary factors, such as vegetation type, evapotranspiration and environmental conditions, complicating its direct application as a quantitative palaeohydrological proxy. Here, we present dDwax data from soils along vegetation gradients and climatic transects in southern Africa to investigate the impact of different environmental factors on dDwax. We found that dDwax correlated significantly with annual dDp (obtained from the interpolated Online Isotopes in Precipitation Calculator data set) throughout eastern and central South Africa, where the majority of the mean annual precipitation falls during the summer. We found evidence for the effect of evapotranspiration on dDwax, while vegetation change was of minor importance. In contrast, we found that δDwax did not correlate with annual dDp in western and southwestern South Africa, where most of the annual precipitation falls during winter. Wide microclimatic variability in this topographically variable region, including distinct vegetation communities and high vegetation diversity between biomes as well as a potential influence of summer rain in some locals, likely compromised identification of a clear relationship between dDwax and dDp in this region. Our findings have implications for palaeoenvironmental investigations using dDwax in southern Africa. In the summer rain dominated eastern and central region, dDwax should serve well as a qualitative palaeohydrological recorder. In contrast, the processes influencing dDwax in the winter rain- dominated western and southwestern South Africa remain unclear and, pending further analyses, potentially constrain its use as palaeohydrological proxy in this region.
Coverage:
Median Latitude: -30.524256 * Median Longitude: 21.353498 * South-bound Latitude: -33.086267 * West-bound Longitude: 18.263583 * North-bound Latitude: -27.009600 * East-bound Longitude: 28.591150
Minimum HEIGHT above ground: 0.125 m * Maximum HEIGHT above ground: 0.125 m
Event(s):
CNT2-3 * Latitude: -30.413033 * Longitude: 18.782583 * Comment: Soil samples
CNT3-1 * Latitude: -30.170067 * Longitude: 18.953533 * Comment: Soil samples
CNT5-3 * Latitude: -29.521933 * Longitude: 19.437750 * Comment: Soil samples
Parameter(s):
#NameShort NameUnitPrincipal InvestigatorMethodComment
1Event labelEventHerrmann, Nicole
2Latitude of eventLatitudeHerrmann, Nicole
3Longitude of eventLongitudeHerrmann, Nicole
4HEIGHT above groundHeightmHerrmann, NicoleGeocode
5Height aboveground, minimumHeight minmHerrmann, Nicole
6Height aboveground, maximumHeight maxmHerrmann, Nicole
7BiomeBiomeHerrmann, Nicole
8n-Alkane C29 per unit sediment massC29/sedµg/gHerrmann, NicoleGas chromatography - Flame Ionization Detection (GC-FID)dry soil
9n-Alkane C31 per unit sediment massC31/sedµg/gHerrmann, NicoleGas chromatography - Flame Ionization Detection (GC-FID)dry soil
10n-Alkane C29, δDC29 δD‰ SMOWHerrmann, NicoleGas chromatography - Isotope ratio mass spectrometer (GC-IRMS)vs. VSMOW
11n-Alkane C29, dD, standard deviationC29 dD std dev±Herrmann, Nicole
12n-Alkane C31, δDC31 δD‰ SMOWHerrmann, NicoleGas chromatography - Isotope ratio mass spectrometer (GC-IRMS)vs. VSMOW
13n-Alkane C31, dD, standard deviationC31 dD std dev±Herrmann, Nicole
14n-Alkane C29,C31, δDC29,C31 δD‰ SMOWHerrmann, NicoledD weighted mean n-Alkane C29 and C31, vs. VSMOW
15Standard deviationStd dev±Herrmann, Nicolen-Alkane C29 + C31 dD weighted mean
Size:
758 data points

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