Landscape controls and vertical variability of soil

Catena 147 (2016) 725–741

Contents lists available at ScienceDirect

Catena journal homepage: www.elsevier.com/locate/catena

Landscape controls and vertical variability of soil organic carbon storage in permafrost-affected soils of the Lena River Delta Matthias Benjamin Siewert a,⁎, Gustaf Hugelius a, Birgit Heim b, Samuel Faucherre c a b c

Department of Physical Geography, Stockholm University, SE-106 91 Stockholm, Sweden Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Telegrafenberg A43,14473 Potsdam, Germany Center for Permafrost (CENPERM), University of Copenhagen, DK-1350 Copenhagen, Denmark

a r t i c l e

i n f o

Article history: Received 1 March 2016 Received in revised form 16 July 2016 Accepted 27 July 2016 Available online xxxx Keywords: Soil organic carbon Soil taxonomy Permafrost Thematic mapping Deltas

a b s t r a c t To project the future development of the soil organic carbon (SOC) storage in permafrost environments, the spatial and vertical distribution of key soil properties and their landscape controls needs to be understood. This article reports findings from the Arctic Lena River Delta where we sampled 50 soil pedons. These were classified according to the U.S.D.A. Soil Taxonomy and fall mostly into the Gelisol soil order used for permafrost-affected soils. Soil profiles have been sampled for the active layer (mean depth 58 ± 10 cm) and the upper permafrost to one meter depth. We analyze SOC stocks and key soil properties, i.e. C%, N%, C/N, bulk density, visible ice and water content. These are compared for different landscape groupings of pedons according to geomorphology, soil and land cover and for different vertical depth increments. High vertical resolution plots are used to understand soil development. These show that SOC storage can be highly variable with depth. We recommend the treatment of permafrost-affected soils according to subdivisions into: the surface organic layer, mineral subsoil in the active layer, organic enriched cryoturbated or buried horizons and the mineral subsoil in the permafrost. The major geomorphological units of a subregion of the Lena River Delta were mapped with a land form classification using a data-fusion approach of optical satellite imagery and digital elevation data to upscale SOC storage. Landscape mean SOC storage is estimated to 19.2 ± 2.0 kg C m−2. Our results show that the geomorphological setting explains more soil variability than soil taxonomy classes or vegetation cover. The soils from the oldest, Pleistocene aged, unit of the delta store the highest amount of SOC per m2 followed by the Holocene river terrace. The Pleistocene terrace affected by thermal-degradation, the recent floodplain and bare alluvial sediments store considerably less SOC in descending order. © 2016 Elsevier B.V. All rights reserved.

1. Introduction The permafrost region has an extent of around 24% of the land area in the Northern Hemisphere (Zhang et al., 1999). This area is known for its large stocks of soil organic carbon (SOC), which have been quantified in the Northern Circumpolar Soil Carbon Database (Tarnocai et al., 2009; Hugelius et al., 2013b, 2014). The most recent estimate of SOC stocks in the circumpolar permafrost region is 1307 Pg with an uncertainty range of 1140–1476 Pg. Of this 472 ± 34 Pg are stored in the top meter and around 822 Pg are perennially frozen (Hugelius et al., 2014). The area thus stores around half the global SOC stocks (Köchy et al., 2015). Permafrost SOC remineralized following thaw is expected to be a positive feedback to global warming and models project that Northern Hemisphere terrestrial ecosystems will switch from a sink to a source of carbon (C) by the end of the 21st century (Koven et al., ⁎ Corresponding author. E-mail addresses: [email protected] (M.B. Siewert), [email protected] (G. Hugelius), [email protected] (B. Heim), [email protected] (S. Faucherre).

http://dx.doi.org/10.1016/j.catena.2016.07.048 0341-8162/© 2016 Elsevier B.V. All rights reserved.

2011). In a recent review on the vulnerability of permafrost carbon Schuur et al. (2015) concluded that potential changes in permafrost carbon pools and resulting effects on climate will be significant, equivalent to roughly one tenth of anthropogenic emissions by 2100. The release of carbon will be a gradual and prolonged emission of greenhouse gases expected to continue over centuries. They high-light the necessity of better model-data integration. This includes the explicit vertical distribution of SOC for model projections of permafrost thaw and carbon decomposition (Schuur et al., 2015). Several factors have led to the accumulation of large amounts of SOC in permafrost soils (Ping et al., 2015). The cold environment combined with frozen subsoil that impedes water drainage lead to low decomposition rates. Freeze and thaw processes promote soil mixing, called cryoturbation, which is effective in transporting recently deposited organic material to deeper soil layers where decomposition rates are low. Furthermore, large parts of the permafrost region are depositional environments and peat formation is common (Ping et al., 2015). Permafrost soils are complex; they form their own soil order in the US soil taxonomy and are further distinguished into great groups of organic soils (Histels), mineral soils affected by cryoturbation (Turbels) and other

726

M.B. Siewert et al. / Catena 147 (2016) 725–741

mineral soils (Orthels) (Soil Survey Staff, 1999). Permafrost is defined as earth material that remains at or below 0 °C for two or more consecutive years (Van Everdingen, 1998). These soils are usually treated as a two layer system with the active layer thawing in summer and refreezing in winter, and the permafrost that remains frozen over long periods of time. However, a permafrost transition zone (PFTZ) can be identified that acts as a buffer between the active layer and the permafrost. This zone changes between the active layer and the permafrost at subdecadal to centennial time scales (Shur et al., 2005). The PFTZ has a strong influence on the cryological and pedological properties of the soil. The upper-most part of this zone is called the transient layer. It is typically located within the first meter of soil and is particularly rich in ice and cryoturbated SOC. Due to the high ice content and the increased energy demand of ice phase-change, it has an increased resistance to thaw. However, if thaw occurs, the accumulated ground ice will disappear, which may cause soil subsidence (Bockheim and Hinkel, 2005; Shur et al., 2005). Carbon storage in permafrost affected soils is commonly presented in vertical subdivisions of 0–30 cm and 0–100 cm as reference intervals, e.g. the Northern Circumpolar Soil Carbon Database (NCSCD) (Hugelius et al., 2013b). Another common subdivision is the organic layer, the active layer and the permafrost down to 100 cm depth, e.g. Ping et al. (2008). Sometimes a subdivision of cryoturbated soil horizons is further distinguished, e.g. by Hugelius et al. (2010, 2011), Palmtag et al. (2015) and Siewert et al. (2015). Others present a subdivision according to soil horizons, e.g. Ping et al. (1997), Gundelwein et al. (2007) and Bockheim and Hinkel (2007). The effect of sampling intervals and whether SOC should be inventoried according to soil horizons or according to depth intervals has been discussed for soils outside the permafrost area (Palmer et al., 2002; Grüneberg et al., 2010; Wiesmeier et al., 2012), and by Mishra et al. (2013) for the permafrost region. Thematic mapping is often used to upscale SOC and other soil properties from soil pedons to landscape scale. It is based on the premise that there is an empirical connection between the grouping of point measurements and mapping classes (Hugelius, 2012). Soil classification systems like the US soil taxonomy system (Soil Survey Staff, 1999) are sophisticated and detailed systems with a long tradition. However, at circumpolar scale the resolution of soil maps is low and the map qualities are variable (Hugelius et al., 2013a). To improve this situation more knowledge is needed on the spatial distribution of soil properties and on how soils can be grouped at the landscape level to maximize the information output of classifications. Such information is a prerequisite for improved thematic mapping using a land cover classification (LCC) or a land form classification (LFC) and, as more pedon data becomes available, for future digital soil mapping efforts. With the availability of highspatial resolution satellite imagery, a new degree of precision in thematic maps is possible, but also a new set of problems arises. With very high-spatial resolution, the range of spectral values for a land cover class increases, the uniqueness of the multispectral characteristics per pixel in that class decreases. As a result pixel-based classification methods become problematic as they generate too many extracted classes and become noise prone (Wulder et al., 2004; Siewert et al., 2015). Therefore, new classification methods for high-spatial resolution satellite imagery need to be investigated. The Lena River Delta has been a key site for research of Arctic lowland permafrost for many years with ongoing long-term monitoring programs on Samoylov Island in the central delta (Boike et al., 2013). An overview description of soil development in the Russian tundra zone is available in (Desyatkin 2008). Tundra soils of northern Yakutsk

have been documented by Karavaeva (1969) and a description of soils in the Lena River Delta is available in Russian (Desyatkin and Teterina 1991). More recent pedological investigations in the Lena River Delta have focused on Samoylov, where soils have been mapped for the entire island and consequently updated over the years (Pfeiffer et al., 2000, 2002; Sanders et al., 2010; Zubrzycki et al., 2013). Sanders et al. (2010) described detailed reference profiles for individual geomorphological units on the island and focused on nitrogen (N) cycling. Zubrzycki et al. (2013) quantified SOC and total N stocks for the Holocene terrace and the floodplain of Samoylov Island in the central Lena Delta and upscaled the results for the Holocene terrace and the floodplains over the entire Lena River Delta using optical satellite imagery. Further pedons from different areas in the delta and upstream along the Lena River have been presented by Ulrich et al. (2009), Zubrzycki et al. (2012) and Zubrzycki (2013). The motivation for this research is threefold: first, we aim to complement and expand earlier research on soils in the Lena River Delta. Second, we aim to better understand landscape and vertical differentiation of these soils to get insights needed for quantification and upscaling efforts of permafrost soils. Third, we exemplify how high resolution data on some key soil properties can be analyzed to facilitate efforts at modeling SOC vulnerability and permafrost characteristics. To achieve these goals a comprehensive soil pedon dataset with high vertical resolution, including many key parameters, is analyzed statistically and upscaled to landscape scale using a LFC of dominant geomorphological units of the delta. The LFC is generated using data-fusion adding digital elevation model (DEM) data to the spectral bands of high spatial resolution satellite imagery (RapidEye©). 2. Study area The Lena River Delta is the largest river delta along the Arctic Ocean. It is located in North-Central Siberia where the Lena River discharges into the Laptev Sea (Fig. 1a and b). The Lena River Delta has a tundra climate. The mean annual air temperature on Samoylov Island is −12.5 °C with a range of mean monthly temperatures from −33.1° in February to 10.1 °C in August. Snow cover is highly variable due to redistribution by wind and the uneven surface of the ice-wedge polygon dominated tundra. It is an area of very cold (−8.6 °C at 10.7 m depth) continuous permafrost reaching to a depth of 400 to 600 m below the surface. The thickness of the active layer reaches its maximum in late August to mid-September with an average depth of 49 cm on Samoylov (Boike et al., 2013). The fan-shaped delta covers an area of around 32,000 km2 and has ~1500 islands. The delta is subdivided into three geomorphological terraces and a recent floodplain; the formation of these distinct geomorphic units is still under scientific debate (Grigoriev, 1993; Schwamborn et al., 2002; Bolshiyanov et al., 2015). The elevated third terrace represents remnants from an eroded Pleistocene Yedoma depositional environment. The remaining Ice Complex is elevated to 30–55 m a.p.s.l. in the central delta and characterized by several meter thick ice wedges (Suppl. 2; Schirrmeister et al., 2003, 2011; Morgenstern et al., 2011a). The second terrace is confined to the north-western part of the delta and is build up from sandy fluvial sediments of the Arga Islands, elevated to ca. 20 m a.p.s.l. This geomorphological unit is of Late Pleistocene to early Holocene age and is not present in our study area. The first terrace (Holocene terrace), a polygonal wet tundra landscape and the floodplain represent the subrecent and recent delta. These were formed during the Holocene. The

Fig. 1. a) Location of the Lena River Delta study area in northeastern Siberia, including main vegetation biomes (Stolbovoi et al., 1998). b) Overview of the Lena River Delta with black rectangle showing the extent of the LFC generated for this study. The red areas mark the occurrence of Ice Complex deposits (Grosse et al., 2013) such as the one investigated in this study. c) Study area in a natural color composite (RGB) of the RapidEye© optical satellite imagery displaying the sampling points of the soil pedons. d) Digital elevation data used in the data-fusion for the classification process. The Ice Complex is clearly elevated over the remaining area. Individual islands of the Holocene terrace can be recognized. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)


727

728


sediments are organic-rich sands with silty-sandy peat layers (Schwamborn et al., 2002; Bolshiyanov et al., 2015). The present day river floodplain rises to around 5 m above river level in the study area. The transition to the Holocene terrace, which reaches an elevation of 10–15 m, is marked by a sharp step in the landscape and has been deposited in the central delta around 3500 to 2500 years ago (Bolshiyanov et al., 2015). The present day floodplains are flooded every spring, while the Holocene terraces only get flooded during extreme events (Zubrzycki et al., 2013). Large parts of the floodplains are sparsely vegetated to unvegetated alluvial sediments with patches of wetlands and shrub lands occurring on more elevated areas. Small scale heterogeneity is particularly expressed on the Holocene terrace, which is dominated by ice-wedge polygons. The centers of the polygons are often water-logged and sedge covered, while the rims have a drier surface and are covered by mosses with grasses and dwarf shrubs. Similar variability occurs on the Ice Complex, but at larger spatial scale. This sub-pixel heterogeneity of the Holocene terrace and on the Ice Complex has been analyzed by Muster et al. (2012). A detailed description of different land covers in the delta is provided in Suppl. 2 and by Schneider et al. (2009). The study area is located at the apex of the Lena delta near Samoylov Island (Fig. 1c). Soils have been sampled on the southern tip of the Kurungnakh-Sise Ice Complex Island (Kurungnakh), including the degraded and non-degraded Ice Complex and the Holocene terrace, as well as on the Holocene terrace and floodplains of the island ArgaBilir-Aryta, located adjacent to Kurungnakh and on Yrbalakh-Aryta located North of Samoylov. 3. Methods 3.1. Field campaign During a field campaign within the framework of the RussianGerman Lena 2013 field expedition in late summer 2013 a total of 50 soil pedons have been described and sampled in the central Lena River Delta (Fig. 1c). A semi-random transect-based sampling scheme was combined with selective sampling of a few complementary sites. Five transects with a length of 4–10 sites and fixed sampling intervals of 100 m or 200 m were laid out to represent different environmental gradients in the landscape. Seven additional sites have been sampled to complement environmental gradient members otherwise not captured by the transects. A total of 541 samples were collected during the field campaign of which 396 are used in this study. On average 7.9 ± 2.7 samples were analyzed per pedon. 3.2. Soil description Soil sampling description and metadata collection followed the “Field Book for Describing and Sampling Soils” (Schoeneberger et al., 2012), and a complementary protocol for sampling permafrostaffected soils by Ping et al. (2013). At every field site, a soil pit was excavated down to the permafrost table, except for sites with standing water, where a fixed-volume Russian soil corer was used. At sites with patterned ground, e.g. earth hummocks, the soil pit was placed to cover the cryogenic feature from the center of the feature to the middle-point between two features. Each sampling site was sampled at the exact spot the hand-held GPS device indicated. For ice-wedge polygons the rim and the center form a complex of two different soil types; these have been considered as separate soil pedons in this study. In general a pedon width of 50–100 cm was described. For each pedon a careful site description including photos was made. Additional metadata was collected: topographical slope, slope aspect, soil drainage (categorized into dry, moist, wet and water-logged), geomorphic landform, occurrence of patterned ground (categorized into: hummocky

terrain, center of low-centered polygons, rim of low-centered polygons or no patterned ground occurrence), permafrost boundary topography (smooth or wavy) and the occurrence of cryoturbated material or buried organic-rich horizons. Each active layer soil horizon was described and named from the open pit. For cryoturbated soils a sketch of the soil horizons visible in the open pit was drawn in the field and later combined with dimensionally corrected and scaled photographs to digitally determine the surface and effective thickness of each horizon (Kimble et al., 1993; Ping et al., 1997). Each pedon was sampled according to soil horizons with at least one sample per horizon or, for thicker horizons, in 5 to 10 cm intervals. The surface organic layer was sampled completely. Sampling was performed by either cutting cubes of known dimensions from the soil or using a fixed volume cylinder. For every horizon separate metadata was collected. This includes the soil texture, horizon boundary topography, occurrence of redox features, as well as root occurrence, location and quantity. From the top of the permafrost table into the permafrost the soil was sampled by hammering a steel pipe into the frozen ground. Samples were then retrieved every 10 cm. Each sample was described noting roots, soil texture, signs of cryoturbation, visible ice content and ice structure (if applicable). The target sampling depth was 100 cm and the average sampling depth of all profiles was 86 ± 21 cm. 22 out of 50 profiles reached a sampling depth of 100 cm. Other profiles were interpolated to 100 cm. Depth zero was counted from the top of the organic layer and standing water above or in the profile (for floating peat) was excluded for depth calculations. We counted the sampling depth from the average soil surface at uneven ground in hummocky terrain. Sampling was stopped before reaching 100 cm depth if we cored into massive ground ice (e.g. an ice-wedge) or for very homogeneous soil properties. The gathered information was used to classify the soil pedons according to the U.S.D.A Soil Taxonomy (Soil Survey Staff, 2014), taking into account additions from (Ping et al., 2013). We identified, where possible, the PFTZ from the top of the permafrost table downwards. The criteria for the delineation was a marked disconformity in one or several of the variables ice content, water content, bulk density or carbon content, followed by more homogeneous properties (Shur et al., 2005; Bockheim and Hinkel, 2005). As the sampling only reached down to one meter, this might not cover the entire PFTZ and thus only represent the transient layer. However, often the outlined zone was followed by pure icewedge ice, indicating stable permafrost conditions at millennial timescales.

3.3. Soil chemical analysis All samples have been analyzed for dry bulk density and gravimetric water content after being oven dried at 75 °C for 5 days. Loss on ignition analyses (LOI, weight %) was performed for a subsample first dried at 105 °C overnight and then at 550 °C for 5 h to estimate soil organic matter (SOM) content and at 950 °C to estimate inorganic carbon content. Inorganic carbon contents were consistently below 0.5% which is considered negligible and this data is not presented or discussed further. A subset of 149 samples has been analyzed for C% by weight in an EA 1110 Elemental Analyzer (CE Instruments, Milan, Italy), at the Department for Chemical Ecology and Ecosystem Research, University of Vienna, Austria. These samples have been drawn from selected profiles to represent the landscape as a whole. To estimate the C% content of samples where only LOI was available, a polynomial regression model was derived and applied. The final model used 147 samples after removing one positive and one negative outlier from an initial model using the residuals. A second dataset of 124 samples with additional C% and N% data has later been merged into the first dataset. Total carbon and N% of this dataset were measured in solid samples by Dumas combustion (1020 °C) on an elemental analyzer (EA Flash 2000, Thermo Scientific, Bremen, Germany).


3.4. Variables, data processing and statistical analyses The soil organic carbon (kg C m−2; hereafter called SOC) storage was calculated per depth interval (cm, T) using the fraction of organic carbon (%; hereafter C%) and dry bulk density (g cm−3; excluding coarse fragments (b2 mm); hereafter BD; Formula 1). SOC ¼ ðC BD T Þ 10

ð1Þ

Other variables used in the analysis are the nitrogen fraction (%; hereafter N%), visible ice (%; hereafter VI) from the field and the water content (%; hereafter WC). We distinguished soil depth intervals into the surface organic layer (dominated by organic soil materials and showing no to little mixing with mineral soil materials corresponding to O and OA horizons), the mineral active layer and the permafrost (for variables hereafter called OL, AL and PF respectively). The permafrost transition zone (PFTZ) is distinguished if indicated. Furthermore, soil horizons or pockets identified as cryoturbated material (hereafter called CT) or buried organic rich horizons (BO) were also separated. Material was identified as CT and BO in the field by plant remains and color in contrast to the remaining mineral subsoil. Material was marked as CT or BO if relative C enrichment over the mineral subsoil of the pedon was confirmed in the laboratory. We used the designator BO if field notes indicated a depositional origin rather than cryogenesis. The most relevant metric depth increments in this article are the interval 0–30 (also 30) and 0–100 (also 100). For the statistical analysis soil master horizons are distinguished following Schoeneberger et al. (2012). We refer to the O horizon analogous to the OL (O and OA). Ojj/Ajj designate cryoturbated soil horizons with clear organic C enrichment compared to surrounding mineral soil. Abbreviations will hereafter be used if we refer strictly to a variable. At times different variable designations are combined, e.g. SOC-OL identifies the SOC stored in the OL, while C%– 100 refers to the mean C% over the 0–100 cm depth interval. For depth intervals that were not sampled, soil properties were interpolated to horizon boundaries. This means that 68.2% on a depth basis of the pedon data on SOC, C%, BD, VI and WC is based on actual samples. Prior to the data analysis the interpolated soil sample data was subdivided into artificial depth increments of 1 mm down to 100 cm. These depth increments can then be averaged across several profiles or aggregated in specified vertical subdivisions. This strategy allows easy aggregation, comparison and visualization of the data (Wickham, 2011). A similar approach has been described by Beaudette et al. (2013). All advanced data calculations have been performed using the R statistical software package (R Core Team, 2015). 3.4.1. Statistical analyses of different spatial groupings An assumption of thematic upscaling is that the selected upscaling classes accurately depict the diverse natural environment of the studied landscape (Hugelius, 2012). To further explore this premise and to find a spatial grouping maximizing the amount of information derived from a detailed pedon dataset from periglacial terrain, we compare six different spatial groupings of soil pedons. We use the non-parametric Kruskal-Wallis rank sum test to identify any significant difference in soil property median values (H1). This test does not assume normality and is more robust than its parametric counter-part, the one-way analysis of variance (ANOVA) test. The compared variables include different intervals of SOC stocks, C% and N% concentration, C to N ratio, BD, VI and WC. The six investigated spatial groupings are: (1) geomorphological units (river terraces), (2) soil suborder, (3) soil great group, (4) patterned ground type and position, (5) drainage and (6) according to a vegetation based land cover map of the Lena River Delta by Schneider et al. (2009) (based on Landsat data with 30 m pixel resolution). To avoid groups of less than four pedons in grouping (4) and (6), we merged individual pedons into the class that seemed most associated following the field description. The original class and the merged class are documented in (Suppl. 1). The non-parametric Wilcoxon rank-

729

sum test was used as a post-hoc test to compare different SOC-stock intervals pairwise across different geomorphological units. For this, pedons from the same geomorphological unit were grouped and their SOC-storage, i.e. SOC in the OL, AL, etc., was compared pairwise to its counterpart on each of the other geomorphological units resulting in a cross-table. To compensate for multiple testing in Wilcoxon rank sum cross-tables, a false discovery rate correction was applied (Benjamini and Hochberg 1995). 3.4.2. Statistical analyses of different vertical soil subdivisions To investigate the vertical variability of soil properties and to support recommendations for soil sampling and data representation, we subdivided the aggregated soil pedon data values into seven different groupings. These include subdivisions into (1) OL, AL and PF; (2) OL, AL, PFTZ, PF, (3) OL, AL, CT, BO, PF; (4) according to master soil horizons: O, A, B, and C, as well as (5) O, A, B, Ojj/Ajj and C where the respective cryoturbated soil horizons have been treated separate; and finally according to metric intervals: (6) 0–30, 30–50, 50–100 and (7) 0–10, 10–30, 30–50, 50–75, 75–100. We excluded massive ice from icewedges, except for the metric increments. Each subdivision was analyzed using the non-parametric Wilcoxon rank sum test for significant difference in soil property median values (H1). To identify variable trends with depth and to investigate soil development, we aggregated the dataset per geomorphological unit and plotted key properties with depth at a vertical resolution of 1 cm. The percentage distribution of soil master horizons is also compared. Finally, we used density plots to visualize and analyze whether C concentrations for the OL, AL and PF have unimodal or multimodal distributions. 3.5. Land form classification and upscaling For upscaling purposes a LFC was derived from a 6.5 × 6.5 m resolution multi-spectral RapidEye© satellite image and the circum-Arctic 90 m × 90 m DUE-Permafrost DEM data, doi:10.1594/PANGAEA.779748 (Santoro and Strozzi, 2012; Fig. 1c and d). The LFC was created using GDAL/OGR, pktools and Orfeo Toolbox software (McInerney and Kempeneers, 2015). The satellite image has been pre-processed using geo-orthorectification with ground-reference points taken on previous expeditions and ATCOR-based atmospherical correction using PCI Geomatica Software (pers. com., F. Günther). The atmospherically corrected data was further processed using a mean-shift segmentation algorithm clustering the image into areas of similar spectral properties (Comaniciu and Meer, 2002). This approach is needed because the spectral characteristics of tundra land cover classes overlap due to high spectral similarity, which can be a problem for the classification of lowland tundra environments (Siewert et al., 2015). We applied a support vector machine (Chang and Lin, 2011) supervised classification with manually selected training areas in two combinations: i) using the RapidEye 5 spectral bands ii) adding DEM data to the 5 spectral bands in a datafusion process. The LFC geomorphology-related output classes are: the non-degraded Ice Complex and the degraded Ice Complex areas affected by thermokarst and thermal-erosion, the Holocene terrace, the (recent) floodplain and the partly vegetated alluvial sediments along the river. A more detailed description of the land forms and the vegetation cover is available in Suppl. 2. A water mask has been generated in a separate classification step. The final results are two LFC data sets: one LFC based on spectral surface properties, one LFC based on optical satellite and DEM data fusion. The LFC covers an area of 1127 km2 at the apex of the delta. For the accuracy assessment of the LFC, geo-botanical description and photo documentation from 50 soil sampling sites was available. This was combined with a dataset of 150 randomly generated points (including surface water) within the classified image area. All 200 ground control points were manually classified based on field information and upon visual inspection of the satellite image. As this has a resolution of 6.5 × 6.5 m, the different units are clearly distinguishable. The

730


Kappa value and Overall Accuracy were calculated from a confusion matrix (Congalton, 1991). The SOC storage of specific depth increments for all pedons from the same geomorphological unit was averaged to upscale the SOC storage per land form class and for the proportional coverage of each class to estimate the landscape mean SOC storage including the 95% confidence interval (Hugelius, 2012). 4. Results

These values are comparable or higher to other thematic maps in tundra environments (Schneider et al., 2009; Hugelius et al., 2011; Virtanen and Ek, 2014; Siewert et al., 2015; Obu et al., 2015). The confusion matrix (Table A.1) reveals remaining problems to distinguish classes that transition into each other. The data-fusion process including DEM information led to a clear improvement compared to the classification using only the 5 spectral bands of the RapidEye© image and no DEM with a Kappa value of 0.67 and an Overall Accuracy of 73%.

4.1. Land form classification 4.2. Soil classification The LFC datasets (Fig. 2a, b) show a marked improvement in the classification result targeted on the dominant geomorphological units if the DEM information is included. In Fig. 2a the classification has been produced using only spectral bands, while in Fig. 2b an additional elevation band from the DEM has been added as information in a datafusion process. In the final product all classified individual geomorphological units are well delineated with little noise in the classification. Through the inclusion of the DEM band, it was possible to delineate the elevated Ice Complex. This data-fusion approach was chosen based on the high correlation of SOC storage with elevation in this study, which is mainly an effect of the different delta terrace ages affecting SOC storage. In the surrounding lowland some patches are classified as degrading Ice Complex despite not being connected to the main plateau. This is in most cases a classification error. The total mapped area represents the central part of the Lena River Delta with floodplains, Holocene terrace and Ice Complex. The accuracy assessment revealed good results for the final classification with a Kappa value of 0.78 and an Overall Accuracy of 82%.

The sampled soils from the Lena River Delta show a great diversity (see dataset Suppl. 1). In total, 46 Gelisols and 4 Entisols have been classified. At the great group level, these are further divided into 23 Orthels, 19 Turbels, 4 Histels and 4 Aquents. The surface of the non-degraded Ice Complex is characterized by marked expressions of large Pleistocene ice-wedges (Fig. 3a and Suppl. 2). These are exposed along steep thaw-slopes (cliffs) at the rim of the Ice Complex. On top, Holocene to modern soils have formed. The area is mainly covered by complexes of Aquiturbels (n = 4) or Histoturbels (n = 1) on the polygon rims and Historthels (n = 2) or Aquorthels (n = 4) in polygon centers. A variety of subgroups were classified. If the pedon is associated with the rim of a polygon the Glacic subgroups, but also Ruptic-Histic, Typic and Fluvaquentic subgroups are present. The sandy-loamy Turbels show marked cryoturbation with fresh, modern (age 14C: 100.64 ± 0.36pMC, Poz-64164) and only moderately decomposed SOM accumulating at the bottom of the active layer and in the upper permafrost (Fig. 3b).

Fig. 2. Land form classification of the study area including the sampling points. a) Shows the land form classification result using 5 spectral bands optical satellite imagery b) Final classification result using 5 spectral bands and elevation (DEM) information. These results show reduced noise and reduced false classifications targeted to the main geomorphological units. The degraded and non-degraded Ice Complex are better differentiated and the misclassification of Ice Complex areas separate from the actual Ice Complex plateau is reduced.


731

Table 1 Results of Kruskal-Wallis rank sum test for SOC storage and soil properties for different landscape level groupings. Significant variables are bold (P b 0.05). Significance levels are P b 0.10 (near significant), *P b 0.05, **P b 0.01 and ***P b 0.001. P-values are adjusted for multiple comparisons using the false-discovery rate correction. Groupings Geomorphological unit Soil suborder Soil great group groups Patterned ground type position Drainage Landsat-based land cover classification Schneider et al. (2009)

Variablesa SOC–OL 0.432 0.005** 0.010* 0.758 0.204 0.171

SOC–AL 0.268 0.378 0.056. 0.197 0.34 0.011*

SOC–PF 0.000*** 0.008** 0.000*** 0.008** 0.675 0.001**

SOC–CT–BO 0.003** 0.080. 0.28 0.299 0.688 0.197

SOC–30 0.000*** 0.000*** 0.000*** 0.014* 0.003** 0.000***

SOC–100 0.000*** 0.009** 0.021* 0.007** 0.959 0.003**

C%–100 0.000*** 0.002** 0.000*** 0.001** 0.24 0.000***

N%–100 0.001** 0.042* 0.033* 0.109 0.346 0.052.

CN–100 0.006** 0.159 0.507 0.185 0.106 0.025*

BD–100 0.001** 0.005** 0.000*** 0.030* 0.173 0.002**

VI–100 0.001** 0.001** 0.001** 0.011* 0.193 0.000***

WC–100 0.001** 0.001** 0.000*** 0.031* 0.394 0.001**

a Variables are: SOC = soil organic carbon; C% = fraction carbon; N% = fraction nitrogen; CN = CN ratio; BD = bulk density; VI = visible ice content; WC = water content. Depth subdivisons are: OL = organic layer; AL = active layer; PF = permafrost; CT–BO = cryoturbated and buried organic material; 100 = 0–100 cm depth.

The Ice Complex is affected by thermal-degradation of different expressions: thermokarst lakes and depressions, thermo-erosional cliff formation, thaw subsidence along the rims and the formation of thermo-erosional valleys with gentle slopes. Towards the morphological edge of the Ice Complex the ice-wedge polygon complexes transition into hummocky terrain and develop into frost-stripes along the slopes. Here soils were classified as Aquiturbels (n = 3) or Psammoturbels (n = 4) depending on the drainage conditions, with either Typic or Glacic prefixes (Fig. 3c). In a thermokarst depression a Glacistel and a Glacic Aquiturbel were classified on weakly developed surface polygon structures. For the Holocene terrace, the floodplain and the river sediments, soil formation follows environmental gradients along the geomorphological unit. In some pedons the variability of the alluvial origin is more pronounced with buried layers of peat in the profile, whereas other pedons are characterized by homogeneous sandy textures throughout. On the Holocene terrace, some pedons show signs of cryoturbation and permafrost related pedogenesis in the upper soil profile, while deeper layers are formed and affected mainly by alluvial depositional processes. The conditions are normally water-logged in ice-wedge polygon centers to wet on polygon rims. The soils form a complex of Aquiturbels (n = 6; Fig. 3d and g) together with Historthels (n = 3) or Aquorthels (n = 3; Fig. 3e and g) depending on the thickness of the organic layer. The prefixes are mainly Psammentic and Fluvaquentic, but also the criteria for Glacic and Fluventic were fulfilled. In two pedons Histels have been classified, indicating considerable accumulation of SOC. The recent floodplain is typically flooded during the spring freshet. Here, the geomorphological development of ice-wedge polygons and soil development is weak. The drainage conditions are mostly waterlogged with vegetation dominated by sedges and willows. The soils are classified as Fluvaquentic Aquorthels (n = 5) or Fluvaquentic Historthels (n = 3). If no layers of silty/clayic sediment occur they are classified as Psammentic Aquorthels (n = 2). There are also limited signs of cryoturbation; in this case we classified one Ruptic-Histic Aquiturbel. The land form class ‘alluvial sediments’ represents floodplain areas where bare sediments dominate the ground cover. Here the depth of the active layer was in 4 pedons below 100 cm. These soils where classified as Entisols, e.g. Typic Cryaquents (Fig. 3f). With further distance from the river, permafrost can be found in the profile within 100 cm and soils are classified as Gelisols. We classified two Typic Psammorthels, and when a distinct silty or loamy layer with very little sand occurred, a Fluvaquentic Aquorthel (n = 1). 4.3. Spatial grouping of soil pedons This study addresses the grouping of soil pedons at landscape level to investigate meaningful upscaling tools of SOC and soil properties from point measurements to spatial coverage (Table 1). From the six investigated groupings, the geomorphological unit gives a significant difference for most variables, including SOC–PF, SOC–CT–BO, SOC–30,

SOC–100, C%, N%, C/N, BD, VI and WC, but no significant difference for SOC–OL and SOC–AL. The groupings according to soil suborder, soil great group and according to vegetation also perform well, with only one variable less that is significantly different. The subdivisions according to patterned ground and drainage showed statistically significant differences for some variables, but fewer variables compared to the other groupings. The SOC-OL is only differentiated when grouped according to soil taxonomy, while the SOC-AL is only significantly differentiated when grouped according to land cover. C/N ratio is only significantly differentiated by geomorphological units and land cover/ vegetation. The results of the post-hoc test compare details in SOC storage across different geomorphological units (Table 2). Values are mostly similar for the OL and the AL, while there are significant differences between SOC– PF, SOC–30 and SOC–100. For CT–BO the results are variable. The nondegraded Ice Complex SOC values most resemble the degraded Ice Complex SOC stocks. However, the degraded Ice Complex has similar values to the floodplain. SOC stocks of the Holocene terrace most resemble the degraded Ice Complex and the floodplain. The alluvial sediments are most similar to the floodplain. For the amount of CT-BO the degraded Ice Complex, the Holocene terrace and the floodplain could belong in the same group. In general, we can see that consecutive units by elevation have similar SOC stocks. 4.4. Vertical subdivision of soil profiles We compare the extent to which different vertical subdivisions of pedons reveal differences in SOC storage and soil properties. The analysis highlights similarities and differences between different vertical soil layers. This information supports the development of best practices to inventory SOC storage and sample soil pedons in permafrost environments (Table 3). The distinction into OL/AL/PF shows that only AL and PF share common properties for C% and N%. The OL has significantly different median values. Distinguishing the PFTZ with the subdivision OL/AL/PFTZ/PF reveals no significant difference in SOC between the AL and PFTZ and PFTZ to PF. However, PFTZ is significantly different to the AL when it comes to BD, VI and WC and to the PF for C%, N% and VI. A subdivision into OL/AL/ CT/BO/C/PF shows significant differentiation for C% across all layers and for N% except for AL and BO. If the soil pedons are vertically subdivided according to master horizons (O,A,B,C), the differentiation is good, with significant differences in C% for all subdivisions except for B and C and significant differences in SOC for all horizons except for O and A. These results include cryoturbated Ojj horizons, which are not necessarily positioned at the surface. If these horizons are further distinguished into a separate Ojj/Ajj category the differentiation of C% values remains significant, apart from Ojj/Ajj with A, B and C. Cryoturbated Ojj/Ajj horizons resemble A-horizons more than B-horizons. C-horizons show clear difference to other major horizons, except for Ojj/Ajj. A subdivision according to metric intervals (0/30/50/100) shows significant distinction between consecutive depths for SOC. However, particularly the

732


a)

d)

b)

c)

e)

f)

c) g)

Fig. 3. a) Tundra landscape in the center of Kurunjakh Island, Ice Complex. b) Detail from a Glacic Aquiturbel (KU-T4-01) at the same position. At the top of the profile the Oi and Oe horizons are visible, followed by a Bgjj horizon. At the bottom a cryoturbated Oijj horizon is visible (17–23 cm depth). The cryoturbated material is fresh and only moderately decomposed with a fabric texture. It has been dated to a modern age (100.64 ± 0.36pMC, Poz-64164). c) Typic Psammoturbel on the slope of the degraded Ice Complex (KU-T2-06). Signs of changing drainage conditions are visible. d) Example of a Psammentic Aquiturbel from the Holocene terrace with water-logged conditions (LF-T1-02). e) Picture of an icewedge polygon center on the Holocene terrace classified as a Fluvaquentic Hemistel (LF-T1-05). In the background the ice-wedge polygon rim can be seen, which would be classified as a different soil. f) Picture of a Typic Gelifluvent sampled in alluvial sediments (LF-T3-03). At a depth of 25–31 cm a clay/silt layer from fluvial deposition with a piece of wood can be seen. g) Complex of a Glacic Aquiturbel on a polygon rim (LF-T2-01) and Fluvaquentic Historthel in the polygon center (LF-T2-01b) on the Holocene terrace. Soil horizons are outlined from a field sketch on a perspective corrected photograph to determine their effective thickness.

distinction between 30 and 50 and 50–100 is insignificant for C%, N% C/ N, BD and WC. If the subdivision is more detailed according to 0/10/30/ 50/75/100, then the intervals have no longer significantly different

median SOC values except for the 0–10 interval. The consecutive depth intervals are not significantly differentiated for most other variables.

Variables are: SOC = soil organic carbon to the respective depth subdivision: OL = organic layer; AL = active layer; PF = permafrost; CT–BO = cryoturbated or buried organic material; 30 = 0–30 cm and 100 = 0–100 cm depth.

0.072. / 0.971 / 0.001** / 0.293 / 0.026* / 0.022*

733

4.5. High density datasets and vertical distribution of soil properties

a

0.731 / 0.533 / 0.005** / 0.135 / 0.647 / 0.022* 0.003** / 0.971 / 0*** / 0.041* / 0.008** / 0.001** 0.921 / 0.491 / 0.009** / 0.477 / 0.002** / 0.369 0.921 / 0.491 / 0.882 / 0.359 / 0.007** / 0.327 0.004** / 0.491 / 0.001** / 0.056. / 0.001** / 0.013*

Floodplain Holocene terrace Degraded Ice Complex Non-degraded Ice Complex

0.931 / 0.971 / 0.001** / 0.019* / 0.211 / 0.011* 0.921 / 0.387 / 0.026* / 0.026* / 0.001** / 0.001** 0.9 / 0.491 / 0.001** / 0.004** / 0.001** / 0*** 0.009** / 0.491 / 0.001** / 0.01* / 0.001** / 0.001**

Geomorphological Units

Degraded Ice Complex Holocene terrace Floodplain Alluvial sediment

Variables ordera: SOC–OL / SOC–AL / SOC–PF / SOC–CT–BO / SOC–30 / SOC–100

Table 2 Cross-table showing the results of the post-hoc Wilcoxon rank sum tests for different SOC storage intervals of pedons grouped by geomorphological units of the delta. The SOC storage increment of each pedon group is compared pairwise against the other geomorphological units. P-values indicate significant differences in medians of the populations the samples come from. Significant variables are bold. Stars designate significance levels. Significance levels are P b 0.10 (near significant), *P b 0.05, **P b 0.01 and ***P b 0.001(indicated as 0***). P-values are adjusted for multiple comparisons using false-discovery rate correction.


The analysis of different spatial groupings of pedons (section 4.2) showed that the geomorphological subdivision most effectively describes soil landscape variability. This subdivision is therefore used when presenting further results. Vertical soil property plots illustrate the variability of the soil pedon data averaged across these geomorphological units (Fig. 4a). Differences are well expressed for the SOC content. The Ice Complex shows the highest SOC content, almost throughout the profile, while the alluvial sediments show the lowest SOC content. There is an expressed increase of SOC at depth (~15–25 cm), mainly associated with an increase in BD. The C% declines rapidly until a depth of 25 cm for all classes. The Ice Complex has the highest C% closely followed by the Holocene terrace and by the degraded Ice Complex. The floodplain and the alluvial sediments have the lowest values and particularly lack elevated C% values in the upper profile. While N% resembles SOC, the C/N ratio is not uniform throughout the soil profile. It is highest in the upper soil profile and then decreases. The Holocene terrace has the highest C/N ratio at depth (ratios of 20–30), while other terraces have similar low ratios (ratios of 10–20). All classes have a similar BD, except the alluvial sediments. VI is most prevalent on the degraded Ice Complex, followed by the Ice Complex and the Holocene terrace, while the floodplain and the alluvial sediments have almost no VI. The high VI in the degraded Ice Complex is associated with sampling of ice-wedges, which may partly be intact Yedoma ice. At a depth of 65 cm, the average amount of VI is 45%, indicating that almost half the profiles have reached into ice-wedges. WC is similar in all soils for the upper 60 cm, except for the alluvial sediments. This likely reflects a low water-holding capacity because of low amounts of silt and clay in these freshly deposited fluvial fine sands. Below 60 cm the floodplain soils maintain low WC, while it is increasing for the other geomorphological units. Most deviations in SOC and C% can loosely be linked to layers of CT and BO that have their most common occurrence in depths of ~40–80 cm. To show the development of the different soils, we plotted the occurrence of classified soil genetic horizons per depth for the individual geomorphological units (Fig. 4b). We can see that the Ice Complex, degraded Ice complex and the Holocene terrace show the most developed soils horizons (O, A and B horizons). On the floodplain, soils are less developed and the alluvial sediments show little development of soil horizons. For the Ice complex and Holocene terrace more layers at depth are marked as O-horizons, indicating cryoturbation or burial of organic soil material. The degraded Ice Complex shows more classification of Bhorizons in the upper 50 cm. The floodplain and alluvial sediments show C-horizons even close to the surface. The effect of cryoturbation is visible from density distributions of C% for the subdivision of OL/AL/PF (Fig. 5). The density distribution across all terraces shows a unimodal distribution of C% for the OL stretching from 0% to 38%. For the AL and PF the density functions have secondary peaks. These are best explained by cryoturbated or buried soil layers and horizons that are C-enriched compared to the surrounding mineral subsoil. This is well expressed when looking at the different geomorphological units of the delta. For the AL and the PF on the Ice Complex secondary peaks exist for C% between 10% and 20% and the main curve has a distinct shoulder towards higher C% values. For the Holocene terrace this is very expressed for the C% density distribution that is trimodal for PF. These curves also show that PF has a general tendency towards higher C% values than the AL. For example, the mean C% for AL of the Ice Complex is 2.39C% and for PF it is 3.77C%. This effect is reduced on terrain affected by thermal erosion. 4.6. Soil organic carbon upscaling The total landscape mean SOC storage of the top 1 m for the area covered by the LFC, excluding water, is 19.2 ± 2 kg C m−2 (95% area

734 Table 3 Cross-table comparing vertical subdivisions of aggregated soil profiles for all pedons using Wilcoxon rank sum tests. P-values indicate significant differences in medians of the populations the samples come from. Here significant variables are presented as bold and stars designate significance levels. Significance levels are P b 0.10 (near significant), *P b 0.05,**P b 0.01 and ***P b 0.001 (indicated as 0***). Layers of 100% ice-wedge ice have been removed, expect for the metric depth interval subdivisions. The subdivisions and variable names are explained in detail in Section 3.4. Sub-divisionsa

AL PF

AL PFTZ PF

A B C

OL

AL

0*** / 0*** / 0*** / 0*** / 0*** / − / 0*** 0*** / 0*** / 0*** / 0*** / 0*** / 0*** / 0***

0.02* / 0.346 / 0.706 / 0.014* / 0.001** / 0*** / 0.003**

OL

AL

PFTZ

0*** / 0*** / 0*** / 0*** / 0*** / − / 0*** 0*** / 0*** / 0*** / 0*** / 0*** / 0*** / 0*** 0.001** / 0*** / 0*** / 0*** / 0*** / 0*** / 0***

0.87 / 0.449 / 0.19 / 0.12 / 0*** / 0*** / 0.006** 0.103 / 0.111 / 0.099. / 0.169 / 0.065. / 0*** / 0.125

0.103 / 0.018* / 0.002** / 0.925 / 0.266 / 0.009** / 0.626

OL

AL

CT

BO

0*** / 0*** / 0*** / 0*** / 0*** / − / 0*** 0*** / 0*** / 0.015* / 0*** / 0*** / 0*** / 0*** 0*** / 0*** / 0*** / 0.028* / 0*** / 0*** / 0*** 0.241 / 0*** / 0*** / 0*** / 0*** / 0*** / 0.001**

0.07. / 0*** / 0.015* / 0.774 / 0.004** / 0*** / 0.004** 0.07. / 0.028* / 0.85 / 0.038* / 0.001** / 0*** / 0*** 0.008** / 0.001** / 0.021* / 0*** / 0.019* / 0*** / 0.105

0.48 / 0.028* / 0.006** / 0.072. / 0.85 / 0.055. / 0.545 0*** / 0*** / 0*** / 0.007** / 0.421 / 0.695 / 0.279

0*** / 0*** / 0.015* / 0*** / 0.327 / 0.076. / 0.136

O

A

B

0.646 / 0*** / 0.065. / 0*** / 0*** / 0.221 / 0*** 0*** / 0*** / 0*** / 0*** / 0*** / 0.167 / 0*** 0*** / 0*** / 0*** / 0*** / 0*** / 0*** / 0***

0.005** / 0*** / 0.005** / 0.778 / 0*** / 0.037* / 0.002** 0*** / 0*** / 0.005** / 0.386 / 0.038* / 0*** / 0.041*

0.001** / 0.654 / 0.678 / 0.595 / 0.029* / 0.01* / 0.211

O

A

B

Ojj/Ajj

A B Ojj/Ajj C

0.435 / 0*** / 0.112 / 0*** / 0*** / − / 0*** 0*** / 0*** / 0*** / 0*** / 0*** / 0.016* / 0*** 0.435 / 0.006** / 0.118 / 0.002** / 0*** / 0*** / 0.064. 0*** / 0*** / 0*** / 0*** / 0*** / 0*** / 0***

0.01* / 0*** / 0.007** / 0.88 / 0*** / 0.032* / 0.001** 0.648 / 1 / 1 / 0.946 / 0.611 / 0*** / 0.845 0*** / 0*** / 0.007** / 0.334 / 0.033* / 0*** / 0.049*

0.695 / 0.011* / 0.03* / 0.962 / 0.042* / 0.033* / 0.149 0.001** / 0.7 / 0.625 / 0.862 / 0.034* / 0.011* / 0.206

0.136 / 0.032* / 0.06. / 1 / 0.549 / 0.385 / 0.271

0−30

30−50

30−50 50−100

0*** / 0*** / 0.091. / 0.001** / 0.02* / 0.059. / 0.024* 0.099. / 0*** / 0.017* / 0.001** / 0.313 / 0*** / 0.759

0*** / 0.482 / 0.265 / 0.554 / 0.207 / 0.018* / 0.053.

0−10

10−30

30−50

50−75

0*** / 0*** / 0.022* / 0*** / 0*** / 0.003** / 0.008** 0*** / 0*** / 0.002** / 0*** / 0*** / 0*** / 0*** 0*** / 0*** / 0*** / 0*** / 0*** / 0*** / 0.018* 0*** / 0*** / 0*** / 0*** / 0.001** / 0*** / 0.258

0.235 / 0.031* / 0.563 / 0.075. / 0.51 / 0.087. / 0.267 0.579 / 0.023* / 0.268 / 0.062. / 0.904 / 0*** / 0.888 0.077. / 0.002** / 0.036* / 0.062. / 0.397 / 0.001** / 0.267

0.498 / 0.692 / 0.472 / 0.796 / 0.397 / 0.058. / 0.258 0.458 / 0.139 / 0.096. / 0.577 / 0.135 / 0.058. / 0.064.

0.196 / 0.29 / 0.268 / 0.823 / 0.401 / 0.817 / 0.34

10−30 30−50 50−75 75−100 a

Subdivisons are: OL = organic layer; AL = active layer; PFTZ = permafrost transition zone; PF = permafrost; CT = cryoturbated organic material; BO = buried organics; O, A, B and C stand for master soil horizons; Ojj/Ajj for cryoturbated master horizons; metric intervals indicate respective depth increments in cm, e.g. 0–30 is the depth increment 0–30 cm. b Variables are: SOC = soil organic carbon / C% = fraction carbon / N% = fraction nitrogen / CN = CN ratio / BD = bulk density / VI = visible ice content / WC = water content.


AL CT BO PF

Variable orderb: SOC / C% / N% / CN / BD / VI / WC


735

a) SOC kg C m-2 /100

C%

N%

CN ratio

Bulk density

Visible ice %

Water %

Samples

CN samp.

Cryoturbated %

Buried Organics %

0

Depth in cm

25

50

75

100 0.0

0.2

0.4 0.6 0

5

10 15

0.0 0.2 0.4 0.6 0 10 20 30 40 0

Geomorphological unit Non-degraded Ice Complex

b)

0.5 1.0 1.5 0

Non-degraded Ice Complex

20

40

0

Degraded Ice Complex


20 40 60

0

5

10

Holocene Terrace

Holocene terrace

0 2.5 5 7.5 10 0

25 50 75

0 20 40 60 80

Alluvial Sediment

Floodplain

Alluvial Sediment

Floodplain

−50

Depth in cm

0

50

100 0%

25%

50%

75%

100% 0%

25%

Master horizon

50%

75%

O

100% 0%

25%

A

50%

75%

B

100% 0%

C

25%

50%

W

75%

100% 0%

25%

50%

75%

100%

Wfm

Fig. 4. a) Vertical soil profiles provide an overview of soil properties per geomorphological unit. The representation interval is in cm. See the column heads for the respective variables and units. The last columns show the amount of samples that contributed at any single depth interval. b) Percentage of soil master horizons (plus Wfm) in the dataset with depth for the different geomorphological units. O = organic soil material; A = mineral horizon, organic enriched; B = subsurface accumulation horizon; C = little or no pedogenic alteration of parent material; W = water; Wfm = massive ice (f = permanently frozen, m = cementation). Standing water above the soil surface is illustrated as negative depth (see also Fig. 3e).

weighted confidence interval). A summary of the SOC stocks and the average depth of the organic layer and permafrost table and the average VI is shown in Table 4. The Ice Complex has the highest stocks of SOC in the top 1 m of soil with 36.9 ± 11.1 kg C m−2. This SOC is mainly stored below the organic layer and much in the permafrost with a storage of 24.0 ± 11.3 kg C m− 2. The Holocene terrace has the second highest SOC storage with 23.7 ± 5.4 kg C m−2 much of which is stored in the permafrost. While the floodplain has similar amounts of SOC in the organic layer and the 0–30 cm interval, it lacks the deeper SOC in the permafrost. The alluvial sediments have a very low SOC content evenly distributed throughout the profile. CT-BO contributes significantly to the high SOC stocks on the Ice Complex and Holocene terrace, with 24.7 ± 12.7 kg C m−2 and 12.7 ± 9.4 kg C m−2. The Holocene terrace has the thickest surface organic layer with 16 ± 15 cm while the other classes have a similar organic layer thickness of 9 ± 4 cm to 10 ± 12 cm, except for the alluvial sediments, which completely lack an organic layer. The permafrost table is shallowest on the stable Ice

Complex and Holocene terrace with 32 ± 13 cm and 38 ± 13 cm, while it reaches 45 ± 26 cm on the degraded Ice Complex, on the floodplain 51 ± 24 cm and close to 100 cm in the alluvial sediments. The nondegraded and degraded Ice Complex have the highest amount of VI in the profiles with 25.8 ± 21.3% and 34.6 ± 32.7%. The large variability is partly caused by ice-wedges, which have a VI content of 100%. 5. Discussion 5.1. Land form classification The final LFC generated for this study (Fig. 2d) clearly distinguishes the different geomorphological units found in the study area and has good agreement with previous mapping results that distinguish the third, second and first terrace of the Lena River Delta (Schwamborn et al., 2002; Morgenstern et al., 2008, 2011a,b; Zubrzycki et al., 2013; Haas et al., 2016). Morgenstern et al. (2011a) could show for

736


0.20 Non-degraded Ice Complex

0.15 0.10 0.05 0.00


0.2 0.1 0.0 0.3

Holocene terrace

0.2 0.1

0.3

Floodplain

Density

0.0 0.4

0.2 0.1 0.0

Alluvial Sediment

3 2 1 0 0.3

All units

0.2 0.1 0.0 0

10

C % 20 Organic Layer

Active Layer

30 Permafrost

Fig. 5. Density distribution of C% in soils subdivided per surface organic layer, active layer and permafrost for the different geomorphological units and for all soil pedons (density bandwidth is 1.7).

Kurungnakh that only around 34% non-degraded Ice Complex is left (b2° slope) and the thermokarst-affected Ice Complex (N2° slope) accounted for 28% of the Ice Complex Island. This is also expressed in our LFCC that included the geomorphological information in form of data fusion of spectral bands with a DEM. Using only spectral band information, the LFC will show the spectrally visible surface characteristic such as vegetation and moisture. In this case, the spectral signature of the degraded Ice Complex did not separate well from the intact Ice Complex or the Holocene terrace. However, the analyses of the pedon data showed that upscaling is preferably based on a geomorphological land form scheme, followed by a land cover scheme. Therefore, we applied a data-fusion process, combining a high-spatial resolution optical RapidEye satellite image with 5 spectral bands with additional DEM data. The inclusion of the DEM improved the classification result towards geomorphological-related classes significantly. A similar approach including DEM information has been applied by Grosse et al. (2006) at Cape Mamontov Klyk, Russia. Obu et al. (2015) also needed to implement a DEM into their supervised classification on Herschel Island, Beaufort Sea to provide a suitable LCC for C and N upscaling. With evermore remote sensing data available, including synthetic aperture radar (SAR)-based products (Widhalm et al., 2015), the

fusion and combined use of different data for the generation of thematic maps should be pursued as needed to address specific questions (Kempeneers et al., 2011; Liao et al., 2015). A dominating feature of many lowland tundra environments are icewedge polygons. These can vary in size and diameter, but are typically 15–30 m across (Van Everdingen, 1998). Siewert et al. (2015) used optical imagery with a 2 × 2 m resolution to generate a LCC in a lowland tundra environment in Kytalyk, Russia, that clearly differentiates icewedge polygon centers and rims. In our case, visual examination indicated that the available 6.5 × 6.5 m pixel resolution may be effective enough to generate a LCC of LFC that could distinguish ice-wedge polygon centers and rims and the associated soil types. However, Muster et al. (2012) found in a study on Samoylov Island, that a minimum pixel resolution of 4 m would be necessary to map ice-wedge polygon centers. Our classification was designed to include small water bodies associated with ice-wedge polygon centers within the respective geomorphological unit. This was achieved by the segmentation pre-processing. We did not further correct for water bodies, as our semi-random sampling scheme did include pedons that where drawn from small water bodies. These were treated as regular soil pedons and the overlaying water was

0–88.7 9.2 ± 2 17–100

5.3. Vertical subdivision of soil pedons and implications for sampling This study investigates and statistically evaluates vertical soil sampling and subdivision strategies in permafrost environments. We can recommend a subdivision into OL/AL/CT/BO/PF for the quantification of SOC in permafrost environments, as has for example been applied by Hugelius et al. (2010, 2011), Palmtag et al. (2015) or Siewert et al. (2015). The subdivision according to soil master horizons also yields satisfactory results. The subdivision according to metric increments shows inferior results and should only be chosen if conditions do not permit otherwise. However, we acknowledge that these are important

Summary of all pedons Mean of Study area (weighted by area)

79.9

79.9

The classification of soil pedons after the US soil taxonomy system (Soil Survey Staff, 2014) shows a great soil diversity within a small area. We distinguished 10 different soil great groups and 21 different soil subgroups. Some of these great groups are more common, for example 14 Aquiturbels, 12 Aquorthels and 8 Historthels have been identified. Aquiturbels are the most common soil type on ice-wedge polygon rims and Aquorthels or Historthels in ice-wedge polygon centers. In the floodplain and alluvial sediments, we mainly classified weakly developed Aquorthels and Cryaquents. There is no clear trend for the occurrence of different soil great groups on specific terraces, other than above-mentioned complexes of Turbels and Orthels for areas with ice-wedge polygons. However, the soils on the nondegraded Ice Complex and Holocene terrace show more distinct soil horizons and development than the soils along the degraded Ice Complex and on the floodplain. The accumulation of SOM in alluvial sediments is clearly defined by repeated fluvial deposition rather than cryogenesis. Cryogenesis has little effect on these mineral soils developing on floodplains (Naumov 2004). In general we can confirm most soil classification findings by Sanders et al. (2010), Zubrzycki (2013) and Zubrzycki et al. (2013). In this study, we grouped soil pedons spatially according to different classification schemes. The best performing schemes to differentiate SOC stocks and soil properties were a geomorphological based scheme followed by a land-cover based scheme. The groupings according to soil taxonomy and according to patterned ground type perform well, but not for all variables. Notably, only the geomorphological and landcover based groupings revealed significant separation of soil C/N ratio. C/N has been suggested as an index for C decomposability in permafrost environments that could be used to upscale C release at landscape level (Schädel et al., 2014). It is therefore noteworthy, that in the Lena River Delta, soil C/N ratios vary with geomorphological units rather than different levels of soil taxonomy. Groupings according to soil taxonomy type perform well for SOC, BD, VI and WC and are the only groupings that significantly distinguish the SOC in the OL. Overall, this makes soil taxonomy based classifications in upscaling suitable, to assess geomorphological vulnerability of permafrost, but less effective for assessing post-thaw vulnerability to decomposition. Since soil taxonomy mainly reflects quantifiable parameters of soil development, Beaudette et al. (2013) suggest evaluating tailor-made soil classifications that include variables needed to accomplish the task at hand. In this particular case, our analysis suggests a classification to quantify SOC stocks and to assess the vulnerability of permafrost affected soils to climate change could effectively be based on geomorphology in combination with land cover.

901

0.9–17.8

5.2. Diversity of soils and landscape allocation

50

6.4 ± 0.5

19.2 ± 2.0

3.7–63.4

2 ± 0.4

0–14.6

8.9 ± 1.7

0–49.6

9.4 ± 1.2

0–52.9

9 ± 1.9

0–51

58.3 ± 9.8

0–88.7 13 ± 21.9 17–100

excluded from the profile (Fig. 4b). These profiles have a shallow or missing upper organic layer, but the deeper soil has SOC concentrations comparable to other profiles and many ponds may only be seasonally flooded, thus excluding these areas would underestimate the total terrestrial landscape carbon pool. Furthermore, Soil Taxonomy considers vegetated areas with water shallower than 2.5 m as soils (Soil Survey Staff, 1999), which applies to most of the ponds in the area.

901

0.9–17.8

737

50

7.6 ± 3.8

22.6 ± 11.2

3.7–63.4

2.3 ± 2.9

0–14.6

11.7 ± 9.8

0–49.6

11.8 ± 10.9

0–52.9

10.2 ± 11.0

0–51

49.5 ± 27.2

0–7.4 0–0 2.2 ± 2.5 0±0 18.3–100 90–100 2.5–11.7 0.9–6.5 172 257 11 7

15.3 22.8

6.3 ± 2.7 3.3 ± 2.0

17.7 ± 5.8 9.4 ± 6.0

9.7–28.8 3.7–20.7

2.8 ± 4.3 0.1 ± 0.2

0–14.6 0–0.4

7.9 ± 4.7 0.1 ± 0.2

0–14.5 0–0.4

6.6 ± 4.9 3.6 ± 5.6

0–13.1 0–15.6

10.0 ± 11.7 0.1 ± 0.4

0–34 0–1

51.3 ± 23.9 97.9 ± 3.9

0–43.3 6.1 ± 13.6 21–70 2.2–9.7 276 14

24.5

6.6 ± 2.3

23.7 ± 5.4

15.3–33.1

3.1 ± 3.3

0.3–12.2

15.0 ± 6.5

5.4–31.4

12.7 ± 9.4

0–31.4

16.5 ± 15.1

1–51

38.2 ± 13.5

0–60.3 0–88.7 25.8 ± 21.3 34.6 ± 32.7 17–57 18.3–88 7.6–17.8 7–11.3 60 136 9 9

Ice Complex Degraded Ice Complex Holocene terrace Floodplain Alluvial Sediment

5.3 12.0

12.4 ± 3.8 9.5 ± 1.2

36.9 ± 11.1 22.6 ± 10.7

24.9–63.4 9.8–46.8

2.5 ± 2.1 2 ± 1.3

0–6.8 0.6–4.9

24.0 ± 11.3 7.8 ± 4.6

8.9–49.6 2.6–17.0

24.7 ± 12.7 10.3 ± 8.6

10.3–52.9 1.9–29.4

9.8 ± 5 8.7 ± 4.1

0–18 5–17.4

32.0 ± 13.5 44.7 ± 26.3

Visible ice % min.– max. Visible ice % mean ± StD Permafrost table depth min.–max. Permafrost table depth mean ± StD Organic layer depth min.– max. Organic layer depth mean ± StD Cryoturbation/ buried organics SOC min.–max. Cryoturbation/ buried organics SOC mean ± StD Permafrost SOC min.–max. Permafrost SOC mean ± StD Organic layer SOC min.–max. Organic layer SOC Mean ± StD 0–100 cm SOC min.–max. 0–100 cm SOC mean ± StD 0–30 cm SOC min.–max. 0–30 cm SOC mean ± StD Area % Area in km2 Sites (n) Geomorphological unit

Table 4 Summary of SOC storage, organic layer and permafrost table depth and occurrence of visible ice in the permafrost for the different geomorphological units. Cryoturbated and buried organics SOC storage is combined from the active layer and the permafrost.


738


standard reference intervals and part of the scientific discourse. The soils for this study have been sampled according to soil horizon with several depth increments for thick horizons. This sampling strategy can be problematic when properties change gradually from the top to the bottom of the soil horizon and the horizon is sampled in the middle (Palmer et al., 2002). Grüneberg et al. (2010) found that fixed depth increments in sampling destroy important pedogenic information, but can be favorable for regional scale quantification of SOC stocks. Wiesmeier et al. (2012) recommend that soils should be analyzed by soil horizons instead of depth increments for more accurate SOC stock estimates, as they find a systematic bias particularly when SOC stocks are estimated from pedotransfer functions or not sampled all the way to the parent material. For permafrost affected soils, detailed and useful sampling instructions are available from Ping et al. (2013). These methods were used in this present study and we find that they generate the strongest results. The warping of entire horizons in the soil profile due to the cryoturbation process leads to high meter-scale horizontal variability typical for permafrost affected soils that can only be assessed when sampled by soil horizon (Fig. 3g) (Ping et al., 2015). We found that particularly the distinction between the OL and the remaining AL is significant for all analyzed variables. The further distinction between CT, BO and the remaining mineral subsoil (AL and PF) yields significant distinction of the vertical soil intervals. Furthermore, we were able to identify a PFTZ in 33 out of 50 soil profiles. The PFTZ was often associated with distinct cryostructures such as lenticular, reticular or ataxitic (suspended) structures. The mean thickness of the PZTZ is 33.9 ± 19.0 cm. As did Bockheim and Hinkel (2005), we find that the thickness of the PFTZ is inversely correlated to that of the AL (adjusted r2 = − 0.41, P b 0.001). We can also confirm an increase in SOC, C% and increased WC and VI in the PFTZ when compared to PF. In general, the PFTZ seems to share more properties with the AL than with the PF when it comes to SOC, C%, N% and C/N, but not for the WC. This indicates that the material has at some point been part of the active layer and should be separated from the deeper permafrost, particularly for geochemical analysis.

permafrost is bimodal or even multimodal on the Ice Complex and Holocene terrace. For the Holocene terrace this is partly related to the fluvial nature of the parent material, but on the Ice Complex this can clearly be attributed to cryoturbation in turbic soils (Fig. 3g). Further, one can see that the frozen mineral subsoil has higher C% values than the nonfrozen mineral subsoil. This can be attributed to either the high C% of the Yedoma parent material or to cryohomogenisation over long periods of time leading to continuous organic matter input and mixing of the soil (Gentsch et al., 2015). Beaudette et al. (2013) suggested aggregated soil characterization compared to single soil profiles as a more reliable classification method at the soil series level. Also in natural landscapes of non-permafrost environments, quantitative information on the horizontal and vertical variability of soil properties is sparse; for example Vanwalleghem et al. (2010) showed high natural vertical randomness in seemingly predictable loess landscapes. The importance to average out profiles in permafrost environments lies in the high local scale horizontal variability and super-imposed natural random effects associated with repetitive geomorphological landforms, e.g. ice wedge polygons may have variable SOC stocks from the center towards the rim. However, the aggregation of soil profiles also leads to a loss of information. In particular the contrast between the non C enriched mineral subsoil and C enriched cryoturbated pockets at different depths is averaged out. This will result in an apparent slight and homogenous enrichment of C. For studies that are dependent on quantitative differences of soil properties, this can result in false interpretation and prediction. For instance, microbial activity or geomorphological processes may depend on thresholds in soil properties, such as C to N ratio or VI. For example, Weiss et al. (2016) show a different potential SOM decomposability of the mineral subsoil and cryoturbated layers. In such situations it is important to preserve information from individual profiles. Although we see a high local variability of the soils, we also observe that the different geomorphological units dictate the overall SOC storage and background C% values of the soils. 5.5. SOC storage upscaling and relative differences in SOC stocks

5.4. High density datasets and vertical variability A large vertical variability of SOC storage and soil properties is shown for individual geomorphological units of the Lena River Delta using high resolution averaged plots (Fig. 4a). The individual units are distinct from each other and the variation can be traced across different depths. From this a lot of information can be read. For example, assumptions can be made on the effect of permafrost degradation with depth. It can be assumed that the profiles from the degraded Ice Complex have developed from soils that were similar to the soils of the intact Ice Complex surface. From the high resolution plot (Fig. 4a), it becomes apparent, that the system loses SOC in the active layer and upper permafrost (a section from ca. 10 to 80 cm depth), while the organic layer, characterized by more rapid C turnover, can retain similar amounts of SOC. Plotting the percentage of each master horizon with depth shows most soil horizon development on the Ice complex and Holocene terrace (Fig. 4b). These units show the occurrence of cryoturbated OMaster horizons even at depth. The floodplain and the alluvial sediments show little soil development. The surface of the Ice Complex has undergone long-term soil development, possibly longer than the Holocene. The soil is well differentiated and shows expressed signs of cryoturbation throughout the soil profile. The Holocene terrace also shows well developed soils. However, these formed on a Holocene floodplain subject to continuous deposition. Here the development of periglacial patterned ground in the form of ice-wedges overprints Holocene sedimentation and repeated alluvial deposition is the dominating genetic process. The high density plots of C% clearly illustrate the effect of cryoturbation on the depth distribution of organic matter in these soils (Fig. 5). The distribution of C% in the active layer and the

We calculated a landscape mean SOC storage of 19.1 ± 2.0 kg C m−2 (95% area weighted confidence interval) for the top meter of soil for the area extent covered by the LFC. This value is at the lower end of similar investigations for permafrost-affected soils and Arctic field sites. These have been summarized by Zubrzycki et al. (2014) with a range of 4.0 to 71.3 kg C m−2 reflecting a wide variety of environments. For example, Siewert et al. (2015) found a value of 27.9 ± 2.9 kg C m−2 for a lowland tundra environment featuring a similar Ice Complex affected by thermokarst and Alas formation located in Kytalyk, NE Siberia. Our value for the SOC 0–100 cm on the Holocene terrace is with 23.7 ± 5.4 kg C m−2 slightly lower than the 29.5 ± 10.5 kg C m−2 found by Zubrzycki et al. (2013) for the same location. This study distinguishes between the soil of the recent floodplain and the alluvial sediments, the latter showing almost no soil development. Zubrzycki et al. (2013) report a value of 13.6 ± 7.4 kg C m−2 for both classes combined, while we find 17.7 ± 5.8 kg C m−2 and 9.6 ± 5.9 kg C m−2 respectively. We can show in our detailed plotting, that the SOC is unevenly distributed throughout the soil profile, which highlights the need for detailed sampling of soil profiles. The occurrence of cryoturbation and buried organics makes this particularly important. We also find a high variability of VI for the different terraces, with high values of 25.8 ± 21.3% for the Ice Complex and 34.6 ± 32.7% for the degraded Ice Complex. For the Holocene terrace and the floodplain these values are 6.1 ± 13.6% and 2.2 ± 2.5%. This mainly reflects the development of massive icewedges. The thickness of the OL generally agrees with previous research on organic material in Siberian tundra soils presented for example in Naumov (2004) and Desyatkin (2008). The comparatively low C stocks found in this study likely reflect that the study area is located in a river delta with a particular landscape age


and depositional environment. The Ice Complex has several meter thick syngenetic ice-wedges formed under a cold Pleistocene climate (Schirrmeister et al., 2003, 2011), while the Holocene terrace only accumulated around 3500 to 2500 BP years ago (Bolshiyanov et al., 2015). The soils of the Ice Complex have a mean SOC storage of 35.1 ± 12.3 kg C m− 2, while the Holocene terrace has a mean value of 23.7 ± 5.4 kg C m−2. However, from the LFC we can derive that much of the study area in the central delta is covered by the recent active floodplain and alluvial sediments, which show little soil development. These surfaces have mean SOC values of 17.7 ± 5.8 kg C m−2 and 9.6 ± 5.9 kg C m−2. They cover large areas in our LFC, and even larger proportions of the entire delta (Schwamborn et al., 2002; Zubrzycki et al., 2013). The difference between the Ice Complex and the other geomorphological units likely reflects more developed soils that accumulated SOC over a longer time period. It is also possible that a more productive paleo-environment has contributed to a relatively high C enrichment in the parent material as suggested by Zimov et al. (2009). The association of SOC stocks with land form, geomorphology and disturbance as found here has been emphasized previously. For example, Obu et al. (2015) found significant SOC depletion and soil compaction due to disturbance on Herschel Island. Similar to our study, the inclusion of slope angle was necessary to map ecological units used for upscaling. Siewert et al. (2015) pointed out that the distribution of Yedoma sediments and associated thermokarst features seem to define the distribution of SOC storage in two Siberian study areas. Similarly, Palmtag et al. (2016) emphasize the importance of geomorphology for upscaling of SOC storage in two tundra environments on Taymyr Peninsula, Russia and find that aboveground phytomass has no significant relationship with SOC. One striking feature of this study area is the amount of driftwood in soils of the floodplain and the Holocene terrace (Fig. 3g and Suppl. 2). A thorough quantification and investigation on the potential contribution to the total carbon storage in the coarse fraction of soils in the Lena River Delta and other Arctic river deltas is still missing (Tarnocai et al., 2009; Zubrzycki et al., 2013). 6. Conclusions We present high resolution soil data for 50 pedons classified according to Soil Taxonomy from the Lena River Delta, NE Siberia. Data-fusion of a high-spatial resolution optical satellite image with a DEM was successfully applied to generate a land form classification of the centraldelta depicting dominating geomorphological terraces for SOC upscaling. The classified soil types are not confined to specific geomorphological units in the delta. The most common soil great groups are Aquiturbels, usually classified on ice-wedge polygon rims and Aquorthels and Historthels, usually classified in ice-wedge polygon centers. Key soil properties that have been analyzed here are soil organic carbon (SOC), C%, bulk density (BD), visible ice (VI) and water content (WC). Analysis of different spatial groupings of soil pedons shows the

739

best differentiation of soil properties is achieved by grouping according to geomorphological units. In fact, soil taxonomy classes explain less variability of soil properties than does the geomorphological setting or land cover. We subdivided and compared soil pedons vertically. This reveals that SOC storage and key properties in permafrost soils mainly differ below the active layer. We show that high vertical sampling density is necessary to reflect SOC variability with depth. Soil pedons are best subdivided into organic layer, active layer, permafrost and additionally by cryoturbated soil pockets and buried soil layers. A subdivision according to soil master horizons can also be recommended. Fixed metric sampling depth intervals yield less information and are not recommended. The landscape mean SOC content for a subregion of the delta was estimated to 19.2 ± 2.0 kg C m−2 (95% CI). The degree of pedogenic development is reflected in occurrences of specific soil genetic horizons which are related to SOC content. These properties are clearly related to geomorphological units with the strongest soil development and highest SOC density decreasing in the following order: Pleistocene age non-degraded Ice Complex N Holocene aged terrace N degraded Ice Complex N recent floodplain N alluvial sediments. The importance of landscape scale geomorphological features for the SOC storage overprinting land cover patterns highlights that sampling throughout the Arctic is important and that conclusions from one study area, soil suborder or sedimentological Suite may not necessarily be transferable to other areas. Our results suggest that landscape grouping of soils and thematic maps for upscaling of SOC and other soil properties need to be specifically designed to reflect local conditions and surface age. If the goal of a study is to extract the maximum amount of information relevant to the vulnerability of permafrost soils, it may be preferable to not simply follow existing land cover or soil order groupings. Acknowledgements This study was funded by the European Union Seventh Framework Programme—ENVIRONMENT project PAGE21 (grant agreement no. 282700). The fieldwork was supported by an EU FP7 INTERACT Transnational Access grant. The Lena 2013 expedition was supported and organized by AWI (Germany) and the Arctic and Antarctic Research Institute, AARI (Russia). We thank the Samoylov station manager and the station team and the AWI logistics for their support. We are in debt to Frank Günther, AWI, who orthorectified the RapidEye image with ground control points that he and colleagues took in the field on previous AWI-AARI expeditions. Frank Günther also processed the atmospherical correction. The RapidEye and DEM data are provided by the ESA DUE Permafrost project (2009–2012). We would like to thank Johannes Petrone and Christian Juncker Jørgensen for their participation in the fieldwork and their contribution to the laboratory analysis of the samples. We would like to thank two anonymous reviewers who provided useful and insightful comments.

Appendix A Table A.1 Confusion matrix of the land form classification based on 200 ground control points. Non-degraded Ice Complex Degraded Ice Complex Holocene terrace Floodplain Alluvial Sediment Water Unclassified Samples (N: 200) Non-degraded Ice Complex Degraded Ice Complex Holocene terrace Floodplain Alluvial Sediment Water Unclassified User' Accuracy (%) Producer Accuracy (%) Kappa: Overall Accuracy:

14 1 0 0 0 0 0 93 74 0.78 82%

4 19 0 1 0 0 0 79 63

0 6 39 2 0 0 0 83 83

1 4 6 21 4 0 0 58 88

0 0 1 0 46 0 0 98 84

0 0 1 0 2 25 0 89 100

0 0 0 0 3 0 0 0 0

19 30 47 24 55 25 0

740


Appendix B. Supplementary material The supplementary material is available via PANGAEA: https://doi.pangaea.de/10.1594/PANGAEA.862961. Suppl. 1: Overview of classified soils. A CSV file containing a summary of the 50 soil pedons used in this article including the different landscape groupings, SOC and soil property data for respective depth increments mentioned in the article. See supplement file: https://doi.pangaea.de/10. 1594/PANGAEA.862959. Suppl. 2: Landform documentation. See supplement file: http://epic.awi.de/41225/1/LenaDelta_landform_documentation.pdf. Suppl. 3: Soil organic carbon map for the study area. Shapefile with land form classification and SOC stocks for OL, AL, PF, 0–30 cm, 0–100 cm as attribute table. See supplement file: https://doi.pangaea.de/10.1594/PANGAEA.862960.

References Beaudette, D.E., Roudier, P., O'Geen, A.T., 2013. Algorithms for quantitative pedology: a toolkit for soil scientists. Comput. Geosci. 52, 258–268. http://dx.doi.org/10.1016/j. cageo.2012.10.020. Benjamini, Y., Hochberg, Y., 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Methodol. 57, 289–300. Bockheim, J.G., Hinkel, K.M., 2007. The importance of “deep” organic carbon in permafrost-affected soils of arctic Alaska. Soil Sci. Soc. Am. J. 71, 1889–1892. Bockheim, J.G., Hinkel, K.M., 2005. Characteristics and significance of the transition zone in drained thaw-lake basins of the Arctic Coastal Plain, Alaska. Arctic 406–417. Boike, J., Kattenstroth, B., Abramova, K., Bornemann, N., Chetverova, A., Fedorova, I., Fröb, K., Grigoriev, M., Grüber, M., Kutzbach, L., Langer, M., Minke, M., Muster, S., Piel, K., Pfeiffer, E.-M., Stoof, G., Westermann, S., Wischnewski, K., Wille, C., Hubberten, H.W., 2013. Baseline characteristics of climate, permafrost and land cover from a new permafrost observatory in the Lena River Delta, Siberia (1998–2011). Biogeosciences 10, 2105–2128. http://dx.doi.org/10.5194/bg-10-2105-2013. Bolshiyanov, D., Makarov, A., Savelieva, L., 2015. Lena River delta formation during the Holocene. Biogeosciences 12, 579–593. http://dx.doi.org/10.5194/bg-12-579-2015. Chang, C.-C., Lin, C.-J., 2011. LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. TIST 2, 27. Comaniciu, D., Meer, P., 2002. Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24, 603–619. http://dx.doi.org/10.1109/34. 1000236. Congalton, R.G., 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 37, 35–46. http://dx.doi.org/10.1016/00344257(91)90048-B. Desyatkin, R.V., 2008. Soil Formation in Thermokarst Depressions - Alases of Cryolithozone (in Russian). Nauka, Novosibirsk. Desyatkin, R.V., Teterina, L.V., 1991. The soils of Lena River Delta (in Russian). Genesis and Melioration of Yakutian Soils, pp. 55–66. Gentsch, N., Mikutta, R., Alves, R.J.E., Barta, J., Čapek, P., Gittel, A., Hugelius, G., Kuhry, P., Lashchinskiy, N., Palmtag, J., Richter, A., Šantrůčková, H., Schnecker, J., Shibistova, O., Urich, T., Wild, B., Guggenberger, G., 2015. Storage and transformation of organic matter fractions in cryoturbated permafrost soils across the Siberian Arctic. Biogeosciences 12, 4525–4542. http://dx.doi.org/10.5194/bg-12-4525-2015. Grigoriev, M., 1993. Cryomorphogenesis in the Lena Delta (in Russian). Permafrost Institute Press, Yakutsk. Grosse, G., Robinson, J.E., Bryant, R., Taylor, M.D., Harper, W., DeMasi, A., Kyker-Snowman, E., Veremeeva, A., Schirrmeister, L., Harden, J., 2013. Distribution of late Pleistocene ice-rich syngenetic permafrost of the Yedoma Suite in east and central Siberia, Russia. US Geol. Surv. Open File Rep. 2013, 1–37. Grosse, G., Schirrmeister, L., Malthus, T.J., 2006. Application of Landsat-7 satellite data and a DEM for the quantification of thermokarst-affected terrain types in the periglacial Lena–Anabar coastal lowland. Polar Res. 25, 51–67. Grüneberg, E., Schöning, I., Kalko, E.K.V., Weisser, W.W., 2010. Regional organic carbon stock variability: a comparison between depth increments and soil horizons. Geoderma 155, 426–433. http://dx.doi.org/10.1016/j.geoderma.2010.01.002. Gundelwein, A., Müller-Lupp, T., Sommerkorn, M., Haupt, E.T.K., Pfeiffer, E.-M., Wiechmann, H., 2007. Carbon in tundra soils in the Lake Labaz region of arctic Siberia. Eur. J. Soil Sci. 58, 1164–1174. http://dx.doi.org/10.1111/j.1365-2389.2007.00908.x. Haas, A., Heim, B., Zubrzycki, S., Elger, K., Schäfer-Neth, C., Morgenstern, A., Fedorova, I., 2016. Assembly of the CarboPerm WebGIS for the Laptev Sea Region, Arctic Siberia – data visualisation as a WebGIS service. Polarforschung 85 (2). Hugelius, G., 2012. Spatial upscaling using thematic maps: an analysis of uncertainties in permafrost soil carbon estimates. Glob. Biogeochem. Cycles 26. http://dx.doi.org/10. 1029/2011GB004154. Hugelius, G., Kuhry, P., Tarnocai, C., Virtanen, T., 2010. Soil organic carbon pools in a periglacial landscape: a case study from the central Canadian Arctic. Permafr. Periglac. Process. 21, 16–29. http://dx.doi.org/10.1002/ppp.677. Hugelius, G., Strauss, J., Zubrzycki, S., Harden, J.W., Schuur, E.A.G., Ping, C.-L., Schirrmeister, L., Grosse, G., Michaelson, G.J., Koven, C.D., O'Donnell, J.A., Elberling, B., Mishra, U., Camill, P., Yu, Z., Palmtag, J., Kuhry, P., 2014. Estimated stocks of circumpolar permafrost carbon with quantified uncertainty ranges and identified data gaps. Biogeosciences 11, 6573–6593. http://dx.doi.org/10.5194/bg-11-6573-2014. Hugelius, G., Tarnocai, C., Bockheim, J.G., Camill, P., Elberling, B., Grosse, G., Harden, J.W., Johnson, K., Jorgenson, T., Koven, C.D., Kuhry, P., Michaelson, G., Mishra, U., Palmtag, J., Ping, C.-L., O'Donnell, J., Schirrmeister, L., Schuur, E.A.G., Sheng, Y., Smith, L.C., Strauss, J., Yu, Z., 2013a. Short communication: a new dataset for estimating organic carbon storage to 3 m depth in soils of the northern circumpolar permafrost region. Earth Syst. Sci. Data Discuss. 6, 73–93. http://dx.doi.org/10.5194/essdd-6-73-2013. Hugelius, G., Tarnocai, C., Broll, G., Canadell, J.G., Kuhry, P., Swanson, D.K., 2013b. The northern circumpolar soil carbon database: spatially distributed datasets of soil

coverage and soil carbon storage in the northern permafrost regions. Earth Syst. Sci. Data 5, 3–13. http://dx.doi.org/10.5194/essd-5-3-2013. Hugelius, G., Virtanen, T., Kaverin, D., Pastukhov, A., Rivkin, F., Marchenko, S., Romanovsky, V., Kuhry, P., 2011. High-resolution mapping of ecosystem carbon storage and potential effects of permafrost thaw in periglacial terrain, European Russian Arctic. J. Geophys. Res. 116. http://dx.doi.org/10.1029/2010JG001606. Karavaeva, N.A., 1969. Tundra Soils of Northern Yakutia. Nauka, Moscow. Kempeneers, P., Sedano, F., Seebach, L., Strobl, P., San-Miguel-Ayanz, J., 2011. Data fusion of different spatial resolution remote sensing images applied to forest-type mapping. Geosci. Remote Sens. IEEE Trans. On 49, 4977–4986. Kimble, J.M., Tarnocai, C., Ping, C.L., Ahrens, R., Smith, C.A.S., Moore, J.P., Lynn, W., 1993. Determination of the Amount of Carbon in Highly Cryoturbated Soils. Post-seminar Proceedings, Joint Russian-American Seminar on Cryopedology and Global Change, Pushchino, Russia, p. 16. Köchy, M., Hiederer, R., Freibauer, A., 2015. Global distribution of soil organic carbon – part 1: masses and frequency distributions of SOC stocks for the tropics, permafrost regions, wetlands, and the world. Soil 1, 351–365. http://dx.doi.org/10.5194/soil-1351-2015. Koven, C.D., Ringeval, B., Friedlingstein, P., Ciais, P., Cadule, P., Khvorostyanov, D., Krinner, G., Tarnocai, C., 2011. Permafrost carbon-climate feedbacks accelerate global warming. Proc. Natl. Acad. Sci. 108, 14769–14774. Liao, W., Huang, X., Van Coillie, F., Gautama, S., Pizurica, A., Philips, W., Liu, H., Zhu, T., Shimoni, M., Moser, G., Tuia, D., 2015. Processing of multiresolution thermal hyperspectral and digital color data: outcome of the 2014 IEEE GRSS data fusion contest. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 1–13 http://dx.doi.org/10.1109/ JSTARS.2015.2420582. McInerney, D., Kempeneers, P., 2015. Open Source Geospatial Tools. Springer International Publishing, Cham. Mishra, U., Jastrow, J.D., Matamala, R., Hugelius, G., Koven, C.D., Harden, J.W., Ping, C.L., Michaelson, G.J., Fan, Z., Miller, R.M., McGuire, A.D., Tarnocai, C., Kuhry, P., Riley, W.J., Schaefer, K., Schuur, E.A.G., Jorgenson, M.T., Hinzman, L.D., 2013. Empirical estimates to reduce modeling uncertainties of soil organic carbon in permafrost regions: a review of recent progress and remaining challenges. Environ. Res. Lett. 8, 035020. http://dx.doi.org/10.1088/1748-9326/8/3/035020. Morgenstern, A., Grosse, G., Guenther, F., Fedorova, I., Schirrmeister, L., 2011a. Spatial analyses of thermokarst lakes and basins in Yedoma landscapes of the Lena Delta. Cryosphere 5, 849–867. http://dx.doi.org/10.5194/tc-5-849-2011. Morgenstern, A., Röhr, C., Grosse, G., Grigoriev, M.N., 2011b. The Lena River Delta - Inventory of Lakes and Geomorphological Terraces. http://dx.doi.org/10.1594/PANGAEA. 758728. Morgenstern, A., Grosse, G., Schirrmeister, L., 2008. In: Kane, D.L., Hinkel, K.M. (Eds.), Genetic, Morphological, and Statistical Characterization of Lakes in the Permafrostdominated Lena Delta. Proceedings of the 9th International Conference on Permafrost, 29 June - 3 July 2008, Fairbanks, Alaska. University of Alaska Fairbanks. Muster, S., Langer, M., Heim, B., Westermann, S., Boike, J., 2012. Subpixel heterogeneity of ice-wedge polygonal tundra: a multi-scale analysis of land cover and evapotranspiration in the Lena River Delta, Siberia. Tellus B 64. http://dx.doi.org/10.3402/tellusb. v64i0.17301. Naumov, Y.M., 2004. Soils and Soil Cover of Northeastern Eurasia. In: Kimble, J.M. (Ed.), Cryosols. Springer, Berlin Heidelberg, pp. 161–183. Obu, J., Lantuit, H., Myers-Smith, I., Heim, B., Wolter, J., Fritz, M., 2015. Effect of terrain characteristics on soil organic carbon and total nitrogen stocks in soils of Herschel Island, Western Canadian Arctic. Permafr. Periglac. Process. http://dx.doi.org/10.1002/ ppp.1881/full. Palmer, C.J., Smith, W.D., Conkling, B.L., 2002. Development of a protocol for monitoring status and trends in forest soil carbon at a national level. Environ. Pollut. 116, S209–S219. Palmtag, J., Hugelius, G., Lashchinskiy, N., Tamtorf, M.P., Richter, A., Elberling, B., Kuhry, P., 2015. Storage, landscape distribution, and burial history of soil organic matter in contrasting areas of continuous permafrost. Arct. Antarct. Alp. Res. 47, 71–88. http://dx. doi.org/10.1657/AAAR0014-027. Palmtag, J., Ramage, J., Hugelius, G., Gentsch, N., Lashchinskiy, N., Richter, A., Kuhry, P., 2016. Controls on the storage of organic carbon in permafrost soil in northern Siberia. Eur. J. Soil Sci 67, 478–491. http://dx.doi.org/10.1111/ejss.12357. Pfeiffer, E.M., Wagner, D., Becker, H., Vlasenko, A., Kutzbach, L., Boike, J., Quass, W., Kloss, W., Schulz, B., Kurchatova, A., Pozdnyakov, V., Akhmadeeva, I., 2000. Modern processes in permafrost affected soils. Berichte Zur Polar- Meeresforsch, pp. 22–54. Pfeiffer, E.M., Wagner, D., Kobabe, S., Kutzbach, L., Kurchatova, A., Stoof, G., Wille, C., 2002. Modern processes in permafrost affected soils. Berichte Zur Polar- Meeresforsch, pp. 21–41. Ping, C.-L., Clark, M.H., Kimble, J.M., Michaelson, G.J., Shur, Y., Stiles, C.A., 2013. Sampling protocols for permafrost-affected soils. Soil Horiz. 54, 13. http://dx.doi.org/10.2136/ sh12-09-0027.

M.B. Siewert et al. / Catena 147 (2016) 725–741 Ping, C.L., Jastrow, J.D., Jorgenson, M.T., Michaelson, G.J., Shur, Y.L., 2015. Permafrost soils and carbon cycling. Soil 1, 147–171. http://dx.doi.org/10.5194/soil-1-147-2015. Ping, C.-L., Michaelson, G.J., Jorgenson, M.T., Kimble, J.M., Epstein, H., Romanovsky, V.E., Walker, D.A., 2008. High stocks of soil organic carbon in the North American Arctic region. Nat. Geosci. 1, 615–619. http://dx.doi.org/10.1038/ngeo284. Ping, C.L., Michaelson, G.J., Kimble, J.M., 1997. Carbon storage along a latitudinal transect in Alaska. Nutr. Cycl. Agroecosyst. 49, 235–242. Core Team, R., 2015. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Sanders, T., Fiencke, C., Pfeiffer, E.M., 2010. Small-scale variability of dissolved inorganic nitrogen (DIN), C/N rations and ammonia oxidizing capacities in various permafrost affected soils of Samoylov Island, Lena River Delta, Northeast Siberia (Kleinräumliche Variabilität des gelösten anorganischen Stickstoffs (DIN), des C/N-Verhältnisses und der Ammonium-Oxidationskapazität in verschiedenen Permafrostböden auf der Samoylow-Insel, Lena-Delta, Nordost-Sibirien). URL http://epic.awi.de/29715/ ([WWW Document]. Polarforschung (accessed 9.15.14)). Santoro, M., Strozzi, T., 2012. Circumpolar Digital Elevation Models N55°N With Links to geotiff Images. http://dx.doi.org/10.1594/PANGAEA.779748. Schädel, C., Schuur, E.A.G., Bracho, R., Elberling, B., Knoblauch, C., Lee, H., Luo, Y., Shaver, G.R., Turetsky, M.R., 2014. Circumpolar assessment of permafrost C quality and its vulnerability over time using long-term incubation data. Glob. Chang. Biol. 20, 641–652. http://dx.doi.org/10.1111/gcb.12417. Schirrmeister, L., Grosse, G., Schwamborn, G., Andreev, A.A., Meyer, H., Kunitsky, V.V., Kuznetsova, T.V., Dorozhkina, M.V., Pavlova, E.Y., Bobrov, A.A., 2003. Late Quaternary history of the accumulation plain north of the Chekanovsky Ridge (Lena Delta, Russia): a multidisciplinary approach. Polar Geogr. 27, 277–319. Schirrmeister, L., Kunitsky, V., Grosse, G., Wetterich, S., Meyer, H., Schwamborn, G., Babiy, O., Derevyagin, A., Siegert, C., 2011. Sedimentary characteristics and origin of the late Pleistocene ice complex on north-east Siberian Arctic coastal lowlands and islands – a review. Quat. Int. 241, 3–25 (Timing and Vegetation History of Past Interglacials in Northern Eurasia). (doi:10.1016/j.quaint.2010.04.004). Schneider, J., Grosse, G., Wagner, D., 2009. Land cover classification of tundra environments in the Arctic Lena Delta based on Landsat 7 ETM+ data and its application for upscaling of methane emissions. Remote Sens. Environ. 113, 380–391. http://dx. doi.org/10.1016/j.rse.2008.10.013. Schoeneberger, P.J., Wysocki, D.A., Benham, E.C., Soil Survey Staff, 2012. Field book for describing and sampling soils, version 3.0. Natural Resources Conservation Service. National Soil Survey Center, Lincoln, NE. Schuur, E.A.G., McGuire, A.D., Schädel, C., Grosse, G., Harden, J.W., Hayes, D.J., Hugelius, G., Koven, C.D., Kuhry, P., Lawrence, D.M., Natali, S.M., Olefeldt, D., Romanovsky, V.E., Schaefer, K., Turetsky, M.R., Treat, C.C., Vonk, J.E., 2015. Climate change and the permafrost carbon feedback. Nature 520, 171–179. http://dx.doi.org/10.1038/nature14338. Schwamborn, G., Rachold, V., Grigoriev, M.N., 2002. Late Quaternary sedimentation history of the Lena Delta. Quat. Int. 89, 119–134. Shur, Y., Hinkel, K.M., Nelson, F.E., 2005. The transient layer: implications for geocryology and climate-change science. Permafr. Periglac. Process. 16, 5–17. http://dx.doi.org/10. 1002/ppp.518. Siewert, M.B., Hanisch, J., Weiss, N., Kuhry, P., Maximov, T.C., Hugelius, G., 2015. Comparing carbon storage of Siberian tundra and taiga permafrost ecosystems at very high spatial resolution: ecosystem carbon in taiga and tundra. J. Geophys. Res. Biogeosci. 1973–1994 http://dx.doi.org/10.1002/2015JG002999. Soil Survey Staff, 2014. Keys to Soil Taxonomy. 12th ed. United States Department of Agriculture & Natural Resources Conservation Service, Washington, DC. Soil Survey Staff, 1999. Soil Taxonomy: A Basic System of Soil Classification for Making and Interpreting Soil Surveys. second ed. Agriculture HandbookUnited States Department of Agriculture & Natural Resources Conservation Service, Washington, DC.

741

Stolbovoi, V., Fischer, G., Ovechkin, S., van Minnen, J., Rojkova, S., 1998. The IIASA-LUC Project Georeferenced Database of the Former USSR, Vol. 4: Vegetation, Interim Report IR-98-114. International Institute for Applied Systems Analysis, Laxenburg, Austria. Tarnocai, C., Canadell, J.G., Schuur, E.A.G., Kuhry, P., Mazhitova, G., Zimov, S., 2009. Soil organic carbon pools in the northern circumpolar permafrost region. Glob. Biogeochem. Cycles 23, 11. http://dx.doi.org/10.1029/2008GB003327. Ulrich, M., Grosse, G., Chabrillat, S., Schirrmeister, L., 2009. Spectral characterization of periglacial surfaces and geomorphological units in the Arctic Lena Delta using field spectrometry and remote sensing. Remote Sens. Environ. 113, 1220–1235. http:// dx.doi.org/10.1016/j.rse.2009.02.009. Van Everdingen, R.O. (Ed.), 1998. Multi-Language Glossary of Permafrost and Related Ground-Ice Terms. National Snow and Ice Data Center, Boulder, CO. Vanwalleghem, T., Poesen, J., McBratney, A., Deckers, J., 2010. Spatial variability of soil horizon depth in natural loess-derived soils. Geoderma 157, 37–45. http://dx.doi.org/10. 1016/j.geoderma.2010.03.013. Virtanen, T., Ek, M., 2014. The fragmented nature of tundra landscape. Int. J. Appl. Earth Obs 27 (Part A), 4–12 (Geoinformation, Special Issue on Polar Remote Sensing 2013). (doi:10.1016/j.jag.2013.05.010). Weiss, N., Blok, D., Elberling, B., Hugelius, G., Jørgensen, C.J., Siewert, M.B., Kuhry, P., 2016. Thermokarst dynamics and soil organic matter characteristics controlling initial carbon release from permafrost soils in the Siberian Yedoma region. Sedimentary Geology, Limnological processes in permafrost environments 340, 38–48. http://dx.doi. org/10.1016/j.sedgeo.2015.12.004. Wickham, H., 2011. The split-apply-combine strategy for data analysis. J. Stat. Softw. 40, 1–29. Widhalm, B., Bartsch, A., Heim, B., 2015. A novel approach for the characterization of tundra wetland regions with C-band SAR satellite data. Int. J. Remote Sens. 36, 5537–5556. http://dx.doi.org/10.1080/01431161.2015.1101505. Wiesmeier, M., Spörlein, P., Geuß, U., Hangen, E., Haug, S., Reischl, A., Schilling, B., Lützow, M., Kögel-Knabner, I., 2012. Soil organic carbon stocks in southeast Germany (Bavaria) as affected by land use, soil type and sampling depth. Glob. Chang. Biol. 18, 2233–2245. http://dx.doi.org/10.1111/j.1365-2486.2012.02699.x. Wulder, M.A., Hall, R.J., Coops, N.C., Franklin, S.E., 2004. High spatial resolution remotely sensed data for ecosystem characterization. Bioscience 54, 511–521. http://dx.doi. org/10.1641/0006-3568(2004)054[0511:HSRRSD]2.0.CO;2. Zhang, T., Barry, R.G., Knowles, K., Heginbottom, J.A., Brown, J., 1999. Statistics and characteristics of permafrost and ground-ice distribution in the Northern Hemisphere. Polar Geogr. 23, 132–154. http://dx.doi.org/10.1080/10889379909377670. Zimov, N.S., Zimov, S.A., Zimova, A.E., Zimova, G.M., Chuprynin, V.I., Chapin, F.S., 2009. Carbon storage in permafrost and soils of the mammoth tundra-steppe biome: Role in the global carbon budget: carbon storage of the mammoth steppe. Geophys. Res. Lett. 36 (n/a–n/a. doi:10.1029/2008GL036332). Zubrzycki, S., 2013. Organic Carbon Pools in Permafrost-Affected Soils of Siberian Arctic Regions. University Hamburg, School of Integrated Climate System Sciences. Zubrzycki, S., Kutzbach, L., Grosse, G., Desyatkin, A., Pfeiffer, E.-M., 2013. Organic carbon and total nitrogen stocks in soils of the Lena River Delta. Biogeosciences 10, 3507–3524. http://dx.doi.org/10.5194/bg-10-3507-2013. Zubrzycki, S., Kutzbach, L., Pfeiffer, E.-M., 2014. Permafrost-affected soils and their carbon pools with a focus on the Russian Arctic. Solid Earth 5, 595–609. http://dx.doi.org/10. 5194/se-5-595-2014. Zubrzycki, S., Kutzbach, L., Pfeiffer, E.-M., Vakhrameeva, P., 2012. Variability of Soil Organic Carbon Stocks of Different Permafrost-Affected Soils: Initial Results from a NorthSouth Transect in Siberia. Tenth International Conference on Permafrost. Northern Publ., pp. 485–490.