14,000-YEAR CARBON ACCUMULATION DYNAMICS IN A SIBERIAN LAKE REVEAL CATCHMENT AND LAKE PRODUCTIVITY CHANGES

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Study Site

Lake Malaya Chabyda (Озеро Малая Чабыда) (61.9569 °N, 129.4091 °E) is located approximately 15 km southwest of the City of Yakutsk (Central Yakutia, Eastern Siberia). This lake is at 188 m a.s.l, has an area of 0.24 km2, and a max depth of three meters (Kumke et al., 2007). During initial surveying in July 2005, Lake Malaya Chabyda had a pH of 6.71, a conductivity of 131 (μS/cm), and a temperature of 18 ºC (Pestryakova et al., 2012). The lake catchment is 10 km2 and also includes Lake Ulakhan Chabyda (Tarasov et al., 1996), which is a lake four times larger than Lake Malaya Chabyda, approximately three km to the northwest (Figure 1). Lake Ulakhan Chabyda has an area of 2.1 km2, an average depth of 0.5 m and a maximum depth of 2.0 m. There are no surface inflows into Lake Ulakhan Chabyda (Pestryakova et al., 2012), but this lake does discharge into Lake Malaya Chabyda during times of high water (i.e. after spring melting). Lake Malaya Chabyda sits on massifs of spear-shaped dunes which have been fixed in place by vegetation growth since the onset of the Holocene. Both lakes sit on the former Lena River erosion-accumulation plain, within the central Yakutian Depression. This plain is composed of Quaternary loams overlying Cambrian limestones (Pestryakova et al., 2012).

Sediment core subsampling and dating

After XRF scanning in early 2014, subsampling of the cores for laboratory analyses began in November 2018 with a simple visual description and photography of the eight cores. One-cm-thick discrete subsamples were then taken at approximately 10 cm intervals using an inox spatula. Each subsample was split into two parts containing approximately equal amounts of material (4–10 g) and weighed. One subsample was kept in the cold room for potential future analysis. The remaining subsample was used for all subsequent analysis.
Four bulk samples and three organic vegetal macro remains were extracted from the sediment cores and sent for 14C dating to the MICADAS radiocarbon lab at AWI, Germany (Table 1). These samples were placed in glass containers and dried at 50°C and analyzed for radiocarbon dating using accelerator mass spectrometry (AMS) after using an acid treatment (method outlined in (Vyse et al., 2020). We applied Bacon in R (Blaauw and Christen, 2011) and the IntCal20 calibration curve (Reimer et al., 2020) to model the age-depth relationship.
The surface of the core represents 2013 CE (the time of core retrieval) and the linear relationship of the samples from the bottom to the top of the core shows that there is no significant reservoir effect in the lake.

X-ray fluorescence (XRF) analysis

High–resolution X–ray fluorescence (XRF) analyses were carried out with 10 mm resolution on the entire sequence using an Avaatech XRF core scanner at AWI (Bremerhaven, Germany) with a Rh X-ray tube at 10 kV (without filter, 12 s, 1.5 mA) and 30 kV (Pd-thick filer, 15 s, 1.2 mA). The sediment surface was cleaned, leveled, and covered with a 4μm ultralene foil to avoid sediment desiccation prior to XRF scanning. Individual element counts per second (CPS) were transformed using a centered log transformation (CLR) and element ratios were transformed using an additive log ratio (ALR) to account for compositional data effects and reduce effects from variations in sample density, water content, and grain size (Weltje and Tjallingii, 2008). Statistical analysis was completed using the Python programming language (Python Software Foundation, https://www.python.org/). XRF analysis of the sequence indicated 24 detectable elements and a subset of these were selected for analysis based on low element Chi-Square (2) values. 2 values are produced by the WinAxil Software to help determine the goodness of fit of the mathematical model. Provided that the 2 value does not exceed 3, it is considered acceptable. These selected elements include the major rock forming elements of Silicon (Si) (Chi2 1.4), Calcium (Ca) (Chi2 6.3), Titanium (Ti) (Chi2 1.3), Rubidium (Rb) (Chi2 0.6), Strontium (Sr) (Chi2 0.7), Zircon (Zr) (Chi2 0.6) and the redox sensitive, productivity indicating elements of Manganese (Mn) (Chi2 1.3), Iron (Fe) (Chi2 2.5), and Bromine (Br) (Chi2 0.8).

Grain size analysis

All subsequent analyses took place after the extracted subsamples had been freeze–dried until completely dry (approximately 48 hours). Grain size analysis was conducted on 16 samples that were chosen to span the entire sequence at relatively regular intervals. The samples were first treated for five weeks with H2O2 (0.88 M) in order to isolate clastic material. After treatment, seven samples were eliminated from the analysis because the remaining inorganic sediment fraction was too low for detection by the laser grain size analyzer. The remaining samples were homogenized using an elution shaker for 24 h and then analyzed using a Malvern Mastersizer 3000 laser. Standard statistical parameters (mean, median, mode, sorting, skewness, and kurtosis) were determined using GRADISTAT 9.1 (Blott and Pye, 2001).

Dry bulk density, sedimentation and organic carbon accumulation calculations

Total organic carbon concentrations (see below) were used to determine the organic vs. mineral matter content (OM vs. MM) in each sample, assuming that bulk OM contains about 50% of organic carbon (Pribyl, 2010). OM and MM concentrations were used to derive average particle densities, based on values of 1.25 g cm-3 and 2.65 g cm-3 for OM and MM, respectively (Avnimelech et al., 2001). Dry bulk density (DBD, in g cm-3) values were then inferred by multiplying particle densities by porosity values, which had been calculated using wet and dry weights (thus the water content before and after sediment drying by freeze drying). Sedimentation rate (SR, in cm a-1) was calculated using the R function “accrate.depth”, which estimated mean sedimentation rate derived from the age-depth model at 0.5 cm increments downcore. All iterations at each depth from the bacon modelling output where then used in a student t-test to calculate the 95% confidence range and the p-values for SR at each 0.5 cm increment. Sediment mass accumulation rate (MAR, in g cm-2 a-1) was obtained by multiplying DBD by SR. Finally, the organic carbon accumulation rate (OCAR, in g OC m-2 a-1) was inferred as the adjusted product of MAR and the total organic carbon concentration. OCAR and MAR uncertainties were calculated from the 95% uncertainty ranges of SR.

Biogeochemical analysis

Total carbon (TC), total organic carbon (TOC), and total nitrogen (TN) analyses were completed after the freeze–dried subsamples were ground in a Pulverisette 5 (Fritsch) planetary mill at 3000 rpm for 7 minutes. TC and TN were measured in a carbon–nitrogen–sulphur analyzer (Vario EL III, Elementar). Five mg of sample material were encapsulated in tin (Sn) capsules together with 10 mg of tungsten–(VI)–oxide. The tungsten–(VI)–oxide ensures complete oxidation of the sample during the measurement process. Duplicate capsules were prepared and measured for each subsample. Blanks and calibration standards were placed every 15 samples to ensure analytical accuracy (< ± 0.1 wt%). Between each sample spatula was cleaned with KIMTECK fuzz-free tissues and isopropyl.
Analysis of TOC began by removing the inorganic carbon fraction by placing each subsample in a warm hydrochloric acid solution (1.3 molar) for at least three hours and then transferring the sample to a drying oven. The TC measured for each subsample in the previous analysis was used to determine the amount of sample required for the TOC analysis. The appropriate amount of sample was weighted in a ceramic crucible and analyzed using the Vario Max C, Elementar. The TOC/TN ratio was converted to the TOC/TNatomic ratio by multiplying the TOC/TN ratio by 1.167 (atomic weight of carbon and nitrogen) (Meyers and Arbor, 2001). Total inorganic carbon (TIC) analysis was completed using a Vario SoilTOC cube elemental analyzer after combustion at 400 ºC (TOC) and 900 ºC (TIC) (Elementar Corp., Germany).
Calculation of δ13C was completed twice for a subset of samples using two different methodologies. The analysis completed at the AWI Potsdam ISOLAB Facility removed carbonate by treating the samples with hydrogen chloride (12 M HCl) for three hours at 97 °C, then adding purified water and decanting and washed three times. Once the chloride content was below 500 parts per million (ppm), the samples were filtered over a glass microfiber (Whatman Grade GF/B, nominal particle retention of 1.0 μm). The residual sample was dried overnight in a drying cabinet at 50 °C. The dry samples were manually ground for homogenization and weighted into tin capsules and analyzed using a ThermoFisher Scientific Delta–V–Advantage gas mass spectrometer equipped with a FLASH elemental analyzer EA 2000 and a CONFLO IV gas mixing system. In this system, the sample is combusted at 1020 °C in O2 atmosphere so that the OC is quantitatively transferred to CO2, after which the isotope ratio is determined relative to a laboratory standard of known isotopic composition. Capsules for control and calibration were run in between. The isotope composition is given in permil (‰) relative to Vienna Pee Dee Belemnite (VPDB).
The analysis of a small subset of samples which took place at Laboratoire des sciences du climat et de l’environnement Isotopic Laboratory for methodological comparison underwent a slightly different treatment, as follows. The sediment underwent a soft leaching process to remove carbonate using pre-combusted glass beakers, HCl 0.6N at room temperature, ultra-pure water and drying at 50 °C. The samples were then crushed in a pre-combusted glass mortar for homogenization prior to carbon content and δ13 C analysis. The handling and chemical procedures are common precautions employed with low-carbon-content sediments. Analysis was performed online using a continuous flow EA-IRMS coupling, that is, a Fisons Instrument NA 1500 Element Analyzer coupled to a ThermoFinigan Delta+XP Isotope-Ratio Mass Spectrometer. Two in-house standards (oxalic acid, δ13C =−19.3% and GCL, _13C =−26.7 %) were inserted every five samples. Each in-house standard was regularly checked against international standards. The measurements were at least triplicated for representativeness. The external reproducibility of the analysis was better than 0.1 %, typically 0.06 %. Extreme values were checked twice.

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General Stratigraphy

The composite sequence is separated into three broad stratigraphic units based on the sedimentological and biogeochemical analyses (Figure 4) and the cluster analysis (Figure 6). Due to an existing talik (area of unfrozen ground surrounded by permafrost) below Lake Malaya Chabyda (Bakulina et al., 2000), the entire sequence was unfrozen. From bottom to top, stratigraphic zones are described as follows:
● Unit 1 (663-584 cm) (14.1 cal kBP–12.3 cal kBP). The bottom 80 cm of the sequence consists of dark brown massive (i.e., not laminated) clayey silt. The lower section (663–619 cm) is notably dry and has a ‘crumbly’ texture. A small gypsum aggregate particle was found within this unit (623 cm) and identified using a binocular microscope. The upper section (636–584 cm) appears less dry and displays mm-scale, cal BP)Depth (cm)Unit 1Unit 2Unit 3OCAR ( gOC m2/yr) 50100 sections of lighter grayish brown.
● Unit 2 (584–376 cm) (12.3 cal kBP–9.0 cal kBP). This unit is composed of dark brown, laminated, clayey silt with interstratified light brown to white laminations. These laminations are well-defined horizontal layers of non–calcareous sediment (based on room temperature HCl (10%) test), each one being approximately 1 cm thick and continuous across the width of the sediment cores. The light-colored laminations are notably visible at three sections of the unit, i.e., between 550 and 505 cm depth, and near 450 and 435 cm depth. Above 400 cm depth, the core transitions to homogenous light colored clay characteristic of Unit 3.
● Unit 3 (376-0 cm) (9.0 cal kBP–CE 2013). This unit is uniformly lighter brown, silty, homogenous (non-laminated) clay. Traces of oxidation were observed between ~ 265 and 225 cm depth.

Lake Malaya Chabyda carbon accumulation rates

Total organic carbon concentration (TOC) is a crucial proxy for understanding the abundance of OM in sediments, including the proportion of OM that evaded remineralization during the sedimentation process. The concentration of OM in sediment is generally equivalent to twice the recorded TOC value (Meyers, 2003). Therefore, TOC values can suggest initial production of biomass as well as subsequent levels of degradation. Moreover, since TOC concentrations are expressed in % weight, therefore influenced by mineral/clastic matter inputs (artificially diluted or concentrated), it can be useful to infer organic carbon (OC) accumulation rates (expressed as mg OC cm-2 a-1 or g OC m-2 a-1) when reliable age-depth model and estimations of sediment dry bulk density for each sample are available (Meyers, 2003).
High TOC values, high TOC/TNatomic ratios, and low 13C generally reflect OM which have not undergone significant decomposition under anaerobic conditions (Schirrmeister et al., 2011; Ulrich et al., 2019). Although Unit 1 showed the highest TOC/TNatomic ratio (20), it also had a lower weight percent of carbon (<20 percent C) compared to Unit 2 and Unit 3 (greater than 30 percent C for both units). Unit 1 can therefore be classified as a mineral sediment (<20 % C). Mineral sediments lose 6–13 % on average of their OC within one decade after exposure due to thawing or other processes (Schirrmeister et al., 2011). Moreover, the inferred OCARs for Unit 1 indeed represent low values compared to Unit 2, with an average of ~100 g OC m-2 a-1 (ranging from 24 to 105 g OC m-2 a-1; Figure 5).
These low values are nevertheless higher than reported rates for temperate latitudes, such as the Great Lakes in North America (e.g., Meyers, 2003), and much higher than several arctic/subarctic sites, such as northern Québec (Ferland et al., 2012, 2014), Finland (Pajunen, 2000), Greenland (Anderson et al., 2009; Sobek et al., 2014), as well as southeastern and northeastern Siberia (Martin et al., 1998; Vyse et al., 2021).
Unit 2 and Unit 3 have greater than 20 % carbon and are therefore classified generally as organic sediments. Unit 2, in particular, exhibited significant layering, which suggest a lack of bioturbation and enhanced preservation of OM and/or seasonal changes in sedimentation processes. Organic deposits, including deposits that are aquatic in origin (i.e. fluvial, alluvial, and lacustrine), typically exhibit decade-long losses of 17% – 34% of their OC after exposure by thawing or other processes (Schirrmeister et al., 2011). Some studies suggest that input of ancient carbon into aquatic systems may augment or even galvanize remineralization of modern dissolved OC (Vonk and Gustafsson, 2013; Mann et al., 2015; Strauss et al., 2017). This effect is likely due, in part, to low levels of carbon decomposition during deposition (i.e. colder conditions) and before thawing (Vonk et al., 2013a). A significant portion of the Lake Malaya Chabyda sediment core is classified as organic sediment, which is predicted to lose comparatively high percentages of their OC upon potential exposure. Jongejans et al. (2021) found that although the OC content of the Yukechi Yedoma ice complex sediments was relatively low, there was substantial greenhouse gas release upon thawing. These findings point to OM quality and decomposition history and more important drivers of greenhouse gas release than OM content alone (Jongejans et al., 2021). Although a lake currently exists, proxy evidence discussed above suggest that Lake Malaya Chabyda did experience high levels of evaporation, which might have brought the lake close to desiccation in the past. Changing temperature and precipitation regimes, lower precipitation and higher temperatures for example, might make drying out more likely in the future for this relatively small and shallow lake. In this case, it is important to consider OM quality and possible future greenhouse gas release. Furthermore, inferred OCARs for Unit 2 show a strong increase from the base (42 g OC m-2 a-1) to the top (76 g OC m-2 a-1) of this unit, in accordance with developing lacustrine conditions and enhanced biological productivity from algae (i.e., mostly autochthonous source of OM).

Table of contents :

CHAPTER 1 GENERAL INTRODUCTION
1 PERMAFROST DEFINITION AND DISTRIBUTION
2 COMMON FEATURES OF PERMAFROST LANDSCAPES
3 ANTHROPOGENIC CLIMATE CHANGE IN THE ARCTIC AND SUB-ARCTIC
4 PERMAFROST AND LOCAL AND GLOBAL CARBON CYCLES
5 REFERENCES
CHAPTER 2 14,000-YEAR CARBON ACCUMULATION DYNAMICS IN A SIBERIAN LAKE REVEAL CATCHMENT AND LAKE PRODUCTIVITY CHANGES
1 INTRODUCTION
2 STUDY SITE
3 METHODS
3.1 Field Sampling
3.2 Sediment core subsampling and dating
3.3 X-ray fluorescence (XRF) analysis
3.4 Grain size analysis
3.5 Dry bulk density, sedimentation and organic carbon accumulation calculations
3.6 Biogeochemical analysis
3.7 Statistical Analysis
4 RESULTS
4.1 Chronology and sedimentation rates
4.2 General Stratigraphy
4.3 Grain-size distribution
4.4 Biogeochemistry
4.5 Inorganic elemental composition
4.6 PCA Analysis
5 DISCUSSION
5.1 Multiproxy-inferred paleolimnological history
5.2 Lake Malaya Chabyda carbon accumulation rates
5.3 Connections between the lake environment, permafrost dynamics, and climatic conditions
6 CONCLUSIONS
7 REFERENCES
8 ACKNOWLEDGEMENTS
CHAPTER 3 SEASONAL PATTERNS IN GREENHOUSE GAS EMISSIONS FROM THERMOKARST LAKES IN CENTRAL YAKUTIA (EASTERN SIBERIA)
1 INTRODUCTION
2 STUDY SITE
2.1 Lake types
3 METHODS
3.1 Physicochemical characteristics of lake water
3.2 Dissolved greenhouse gas measurements
3.3 Statistical analysis
4 RESULTS
4.1 Seasonal conditions
4.2 Physicochemical characterization of lake water
4.2.1 Broad trends and seasonal averages.
4.2.2 Seasonal Profiles.
4.2.3 Dissolved greenhouse gas concentrations
4.2.4 Diffusive greenhouse gas fluxes
5 DISCUSSION
5.1 Developmental stage as a driving factor on lake greenhouse gas concentrations and fluxes
5.2 Seasonal variations in greenhouse gas concentrations
5.3 Diffusive greenhouse gas fluxes: comparison across high-latitude regions
6 CONCLUSIONS
7 REFERENCES
8 ACKNOWLEDGMENTS
CHAPTER 4 AUTOMATED IDENTIFICATION OF THERMOKARST LAKES USING MACHINE LEARNING IN THE PERMAFROST LANDSCAPE OF CENTRAL YAKUTIA (EASTERN SIBERIA)
1 INTRODUCTION
2 STUDY SITE
3 METHODS
3.1. Image Data
3.2. Defining lake boundaries and lake types
3.3. General Workflow
3.3.1. Machine learning model
3.3.2. Fine tuning and training
3.3.3. Accuracy assessment of initial model
3.3.4. Ensembling
3.3.5. Comparison of total surface area for prediction and corrected shapefiles
3.4. Surface area change analysis
3.4.1. South study site
3.4.2. Center study site
3.7. Temperature and precipitation
4 RESULTS AND DISCUSSION
4.1. Changes in temperature and precipitation since 1900
4.2. Spatial distribution of lake types
4.3. Lake surface area change: South study site
4.4. Lake surface area change: Center study site
5 CONCLUSIONS
6 REFERENCES

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