Landscape controls and vertical variability of soil organic carbon storage in permafrost-affected soils of the Lena River Delta

Siewert, Matthias Benjamin; Hugelius, Gustaf; Heim, Birgit; Faucherre, Samuel

doi:10.1016/j.catena.2016.07.048

Cited by 60 publications

(81 citation statements)

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“…Adding the DEM allowed enhanced classification of yedoma uplands, since plant communities on yedoma uplands cannot be entirely distinguished from those in DTLBs based only on spectral signatures. This advantage was already demonstrated by Grosse et al (2006) and Siewert et al (2016), who showed that, by including a DEM, non-degraded yedoma uplands and partly degraded yedoma uplands could be better discriminated compared to image classification only.…”

Section: Landform Classification and Upscaling C And N Poolsmentioning

confidence: 76%

“…Walter Anthony et al (2014) estimated the total Holocene and Pleistocene soil C pools of the yedoma region with 429 ± 101 Pg C, while Hugelius et al (2014) calculated 181 ± 54 Pg C for all deposits in the yedoma region below 3 m depth and Strauss et al (2013) calculated 211 + 160/ − 153 Pg C for the entire yedoma deposits including the top 3 m. Despite the variation in these estimates they all suggest a very large C pool of several hundred Pg for this region and confirm that these ice-rich deep deposits are a globally important C pool in the northern circumpolar permafrost region. Detailed local studies for particular parts of the yedoma region are scarce so far but suggest significant landscape-scale and interregional variation in SOC stocks that warrant further local studies and regional syntheses (Schirrmeister et al, 2011b, c;Strauss et al, 2012;Siewert et al, 2015Siewert et al, , 2016Webb et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Carbon and nitrogen pools in thermokarst-affected permafrost landscapes in Arctic Siberia

et al. 2018

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Abstract. Ice-rich yedoma-dominated landscapes store considerable amounts of organic carbon (C) and nitrogen (N) and are vulnerable to degradation under climate warming. We investigate the C and N pools in two thermokarst-affected yedoma landscapes -on Sobo-Sise Island and on Bykovsky Peninsula in the north of eastern Siberia. Soil cores up to 3 m depth were collected along geomorphic gradients and analysed for organic C and N contents. A high vertical sampling density in the profiles allowed the calculation of C and N stocks for short soil column intervals and enhanced understanding of within-core parameter variability. Profile-level C and N stocks were scaled to the landscape level based on landform classifications from 5 m resolution, multispectral RapidEye satellite imagery. Mean landscape C and N storage in the first metre of soil for Sobo-Sise Island is estimated to be 20.2 kg C m −2 and 1.8 kg N m −2 and for Bykovsky Peninsula 25.9 kg C m −2 and 2.2 kg N m −2 . Radiocarbon dating demonstrates the Holocene age of thermokarst basin deposits but also suggests the presence of thick Holoceneage cover layers which can reach up to 2 m on top of intact yedoma landforms. Reconstructed sedimentation rates of 0.10-0.57 mm yr −1 suggest sustained mineral soil accumulation across all investigated landforms. Both yedoma and thermokarst landforms are characterized by limited accumulation of organic soil layers (peat).We further estimate that an active layer deepening of about 100 cm will increase organic C availability in a seasonally thawed state in the two study areas by ∼ 5.8 Tg (13.2 kg C m −2 ). Our study demonstrates the importance of increasing the number of C and N storage inventories in icerich yedoma and thermokarst environments in order to account for high variability of permafrost and thermokarst environments in pan-permafrost soil C and N pool estimates.

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Section: Landform Classification and Upscaling C And N Poolsmentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Carbon and nitrogen pools in thermokarst-affected permafrost landscapes in Arctic Siberia

et al. 2018

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“…Soil properties can also vary considerably within landscapes (Suvanto et al, 2014;Siewert et al, 2016). To extrapolate local carbon exchange into wider areas, a study area is typically categorized into land cover types (LCTs) using remote sensing methods supported by visual judgement of plant species 30 composition and coverage (e.g.…”

Section: Introductionmentioning

confidence: 99%

Spatial variation and linkages of soil and vegetation in the Siberian Arctic tundra – coupling field observations with remote sensing data

Mikola¹,

Virtanen²,

Linkosalmi³

et al. 2018

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Abstract. Arctic tundra ecosystems will have a key role in future climate change due to intensifying permafrost thawing, plant growth and ecosystem carbon exchange, but monitoring these changes may be challenging due to the heterogeneity 20 of Arctic landscapes. We examined spatial variation and linkages of soil and plant attributes in a site of Siberian Arctic tundra in Tiksi, northeast Russia, and evaluated possibilities to capture this variation by remote sensing for the benefit of carbon exchange measurements and landscape extrapolation. We distinguished nine land cover types (LCTs) -bare soil, lichen tundra, shrub tundra, flood meadow, graminoid tundra, bog, dry fen, wet den and water -to classify the variation in our site. To characterize the LCTs, we sampled 92 study plots for plant (biomass and leaf area index, LAI) and soil 25 (organic matter OM%, bulk density, moisture, pH, litter layer depth, litter mass loss, temperature and active layer depth) attributes in 2014. Moreover, to test if variation in plant and soil attributes can be detected using remote sensing, we produced a normalized difference vegetation index (NDVI) and topographical parameters for each study plot using three very high spatial resolution multispectral satellite images (QuickBird and WorldView-2, portraying vegetation at 180, 220 and 750 growing degree days, DD with 0 °C threshold) and a digital elevation model (derived from a WV-2 stereo-30 pair image). We found that soils in our site ranged from mineral soils in bare soil and lichen tundra (on average 3.9 % OM) to soils of high OM% in graminoid tundra, bog, dry fen and wet fen (38 %), with shrub tundra and flood meadow Vascular shoot mass and LAI were also positively associated with soil OM content, and LAI with active layer depth, but the amount of variation explained was significantly lower (6-15 %). NDVI captured variation in peak season vascular LAI better than variation in moss biomass, but the difference depended on the phase of the growing season in the image:180-DD, 220-DD and 750-DD NDVI captured 23, 17 and 7 % of moss mass variation and 25, 34 and 50% of vascular 5 LAI variation, respectively. For this reason, soil attributes associated with moss mass were better captured by early season NDVI and those associated with LAI by late season NDVI. Topographic attributes were related to LAI and many soil attributes, but not to moss biomass and they could not increase the amount of spatial variation explained in plant and soil attributes above that achieved by NDVI. Our results illustrate a typical tundra ecosystem with great fine-scale spatial variation in both plant and soil attributes. Mosses dominate plant biomass and control many soil attributes, including 10 OM% and temperature, but variation in moss biomass is difficult to capture by remote sensing reflectance or topography.This suggests that using simple reflectance indices and DEM for spatial extrapolation of those vegetation and soil attributes that are relevant for regional ecosystem and global climate models warran...

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“…In most cases, soils were sampled to a depth of 1 m. The harmonised soil profiles were generated by averaging several soil pedons per landscape type at a 1 cm depth resolution. For more detailed descriptions of field sampling and laboratory procedures, see Palmtag et al (2015) and Siewert et al (2015Siewert et al ( , 2016. Top 1 m total soil carbon values were calculated from a weighted average of different typical profiles, based on the fractional coverage of landscape types in the footprint area of the flux towers.…”

Section: Soil Carbon Profilesmentioning

confidence: 99%

Carbon stocks and fluxes in the high latitudes: using site-level data to evaluate Earth system models

et al. 2017

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Abstract. It is important that climate models can accurately simulate the terrestrial carbon cycle in the Arctic due to the large and potentially labile carbon stocks found in permafrost-affected environments, which can lead to a positive climate feedback, along with the possibility of future carbon sinks from northward expansion of vegetation under climate warming. Here we evaluate the simulation of tundra carbon stocks and fluxes in three land surface schemes that each form part of major Earth system models (JSBACH, Germany; JULES, UK; ORCHIDEE, France). We use a site-level approach in which comprehensive, high-frequency datasets allow us to disentangle the importance of different processes. The models have improved physical permafrost processes and there is a reasonable correspondence between the simulated and measured physical variables, including soil temperature, soil moisture and snow.We show that if the models simulate the correct leaf area index (LAI), the standard C3 photosynthesis schemes produce the correct order of magnitude of carbon fluxes. Therefore, simulating the correct LAI is one of the first priorities. LAI depends quite strongly on climatic variables alone, as we see by the fact that the dynamic vegetation model can simulate most of the differences in LAI between sites, based almost entirely on climate inputs. However, we also identify an influence from nutrient limitation as the LAI becomes too large at some of the more nutrient-limited sites. We conclude that including moss as well as vascular plants is of primary importance to the carbon budget, as moss contributes a large fraction to the seasonal CO 2 flux in nutrient-limited conditions. Moss photosynthetic activity can be strongly influenced by the moisture content of moss, and the carbon uptake can be significantly different from vascular plants with a similar LAI.The soil carbon stocks depend strongly on the rate of input of carbon from the vegetation to the soil, and our analysis suggests that an improved simulation of photosynthesis would also lead to an improved simulation of soil carbon stocks. However, the stocks are also influenced by soil carbon burial (e.g. through cryoturbation) and the rate of heterotrophic respiration, which depends on the soil physical state. More detailed below-ground measurements are needed to fully evaluate biological and physical soil processes. Furthermore, even if these processes are well modelled, the soil carbon profiles cannot resemble peat layers as peat accumulation processes are not represented in the models.Thus, we identify three priority areas for model development: (1) dynamic vegetation including (a) climate and (b) nutrient limitation effects; (2) adding moss as a plant functional type; and an (3) improved vertical profile of soil carbon including peat processes.

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Landscape controls and vertical variability of soil organic carbon storage in permafrost-affected soils of the Lena River Delta

Cited by 60 publications

References 50 publications

Carbon and nitrogen pools in thermokarst-affected permafrost landscapes in Arctic Siberia

Carbon and nitrogen pools in thermokarst-affected permafrost landscapes in Arctic Siberia

Spatial variation and linkages of soil and vegetation in the Siberian Arctic tundra – coupling field observations with remote sensing data

Carbon stocks and fluxes in the high latitudes: using site-level data to evaluate Earth system models

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