Large-area land surface simulations in heterogeneous terrain driven by global data sets: application to mountain permafrost

Fiddes, Joel; Endrizzi, S.; Gruber, Stephan

doi:10.5194/tc-9-411-2015

Cited by 71 publications

(90 citation statements)

References 61 publications

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“…There exist a variety of approaches for how such small-scale variability of different factors can be included in modelling (e.g. Fiddes et al, 2015;Kurylyk et al, 2016;Westermann et al, 2015;Zhang et al, 2012). As exemplified by Gisnås et al (2014) for mountain permafrost environments in Norway, redistribution of snow due to wind drift could be a governing factor for the ground thermal regime also in palsa mires, especially since palsas and peat plateaus are elevated landscape elements which feature lower snow depths than the surrounding mire area (Seppälä, 1982).…”

Section: Implications For Permafrost Modelling and Mappingmentioning

confidence: 99%

Strong degradation of palsas and peat plateaus in northern Norway during the last 60 years

et al. 2017

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Abstract. Palsas and peat plateaus are permafrost landforms occurring in subarctic mires which constitute sensitive ecosystems with strong significance for vegetation, wildlife, hydrology and carbon cycle. Firstly, we have systematically mapped the occurrence of palsas and peat plateaus in the northernmost county of Norway (Finnmark, ∼ 50 000 km 2 ) by manual interpretation of aerial images from 2005 to 2014 at a spatial resolution of 250 m. At this resolution, mires and wetlands with palsas or peat plateaus occur in about 850 km 2 of Finnmark, with the actual palsas and peat plateaus underlain by permafrost covering a surface area of approximately 110 km 2 . Secondly, we have quantified the lateral changes of the extent of palsas and peat plateaus for four study areas located along a NW-SE transect through Finnmark by utilizing repeat aerial imagery from the 1950s to the 2010s. The results of the lateral changes reveal a total decrease of 33-71 % in the areal extent of palsas and peat plateaus during the study period, with the largest lateral change rates observed in the last decade. However, the results indicate that degradation of palsas and peat plateaus in northern Norway has been a consistent process during the second half of the 20th century and possibly even earlier. Significant rates of areal change are observed in all investigated time periods since the 1950s, and thermokarst landforms observed on aerial images from the 1950s suggest that lateral degradation was already an ongoing process at this time. The results of this study show that lateral erosion of palsas and peat plateaus is an important pathway for permafrost degradation in the sporadic permafrost zone in northern Scandinavia. While the environmental factors governing the rate of erosion are not yet fully understood, we note a moderate increase in air temperature, precipitation and snow depth during the last few decades in the region.

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Section: Implications For Permafrost Modelling and Mappingmentioning

confidence: 99%

Strong degradation of palsas and peat plateaus in northern Norway during the last 60 years

et al. 2017

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“…However, at present such application is limited by a number of shortcomings and complications: first, the model scale of 1 km 2 may be sufficient to represent the ground thermal regime in lowland tundra landscapes like the LRD but is significantly too coarse for heterogeneous terrain, e.g., in mountain areas (Fiddes et al, 2015). Since the grid cell size is determined by the spatial resolution of the remotely sensed land surface temperatures, it could only be improved with the deployment of higher-resolution remote sensors for surface temperature (which must also feature a high temporal resolution).…”

Section: Towards Remote Detection Of Ground Temperature and Thaw Deptmentioning

confidence: 99%

“…Spatially distributed permafrost modeling was, for example, demonstrated by Zhang et al (2013) and Westermann et al (2013), forced by interpolations of meteorological measurements, or by Jafarov et al (2012) and Fiddes et al (2015) by downscaled atmospheric model data. Remote-sensing data sets have been extensively used to indirectly infer the ground thermal state through surface observations, e.g., occurrence and evolution of thermokarst features (e.g., Jones et al, 2011), vegetation types characteristic for permafrost (Panda et al, 2014) or change detection of spectral indices (Nitze and Grosse, 2016).…”

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confidence: 99%

Transient modeling of the ground thermal conditions using satellite data in the Lena River delta, Siberia

et al. 2017

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Abstract. Permafrost is a sensitive element of the cryosphere, but operational monitoring of the ground thermal conditions on large spatial scales is still lacking. Here, we demonstrate a remote-sensing-based scheme that is capable of estimating the transient evolution of ground temperatures and active layer thickness by means of the ground thermal model CryoGrid 2. The scheme is applied to an area of approximately 16 000 km 2 in the Lena River delta (LRD) in NE Siberia for a period of 14 years. The forcing data sets at 1 km spatial and weekly temporal resolution are synthesized from satellite products and fields of meteorological variables from the ERA-Interim reanalysis. To assign spatially distributed ground thermal properties, a stratigraphic classification based on geomorphological observations and mapping is constructed, which accounts for the large-scale patterns of sediment types, ground ice and surface properties in the Lena River delta.A comparison of the model forcing to in situ measurements on Samoylov Island in the southern part of the study area yields an acceptable agreement for the purpose of ground thermal modeling, for surface temperature, snow depth, and timing of the onset and termination of the winter snow cover. The model results are compared to observations of ground temperatures and thaw depths at nine sites in the Lena River delta, suggesting that thaw depths are in most cases reproduced to within 0.1 m or less and multi-year averages of ground temperatures within 1-2 • C. Comparison of monthly average temperatures at depths of 2-3 m in five boreholes yielded an RMSE of 1

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“…This is of great importance, since an accurately modelled snow cover evolution and its spatial patterns are crucial to correctly model the ground thermal regime (Fiddes et al, 2015;Hoelzle et al, 2001;Stocker-Mittaz et al, 2002) and assess contrasting influences of a heterogeneous snow cover on the ground thermal regime.…”

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confidence: 99%

“…The challenge of integrating representative precipitation input (e.g. Imhof et al, 2000;Fiddes et al, 2015;Stocker-Mittaz et al, 2002) in the rock walls and its redistribution by wind (Mott and Lehning, 2010), as well as gravitational transport (Bernhardt and Schulz, 2010;Gruber, 2007), was thus accounted for. Model performance for simulating snow depth distribution and consequently the influence on rock temperatures was tested against a dense network of validation measurements of snow depth and NSRTs at both the point and the spatial scales.…”

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confidence: 99%

Distributed snow and rock temperature modelling in steep rock walls using Alpine3D

et al. 2017

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Abstract. In this study we modelled the influence of the spatially and temporally heterogeneous snow cover on the surface energy balance and thus on rock temperatures in two rugged, steep rock walls on the Gemsstock ridge in the central Swiss Alps. The heterogeneous snow depth distribution in the rock walls was introduced to the distributed, processbased energy balance model Alpine3D with a precipitation scaling method based on snow depth data measured by terrestrial laser scanning. The influence of the snow cover on rock temperatures was investigated by comparing a snowcovered model scenario (precipitation input provided by precipitation scaling) with a snow-free (zero precipitation input) one. Model uncertainties are discussed and evaluated at both the point and spatial scales against 22 near-surface rock temperature measurements and high-resolution snow depth data from winter terrestrial laser scans.In the rough rock walls, the heterogeneously distributed snow cover was moderately well reproduced by Alpine3D with mean absolute errors ranging between 0.31 and 0.81 m. However, snow cover duration was reproduced well and, consequently, near-surface rock temperatures were modelled convincingly. Uncertainties in rock temperature modelling were found to be around 1.6 • C. Errors in snow cover modelling and hence in rock temperature simulations are explained by inadequate snow settlement due to linear precipitation scaling, missing lateral heat fluxes in the rock, and by errors caused by interpolation of shortwave radiation, wind and air temperature into the rock walls.Mean annual near-surface rock temperature increases were both measured and modelled in the steep rock walls as a consequence of a thick, long-lasting snow cover. Rock temperatures were 1.3-2.5 • C higher in the shaded and sunny rock walls, while comparing snow-covered to snow-free simulations. This helps to assess the potential error made in ground temperature modelling when neglecting snow in steep bedrock.

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Large-area land surface simulations in heterogeneous terrain driven by global data sets: application to mountain permafrost

Cited by 71 publications

References 61 publications

Strong degradation of palsas and peat plateaus in northern Norway during the last 60 years

Strong degradation of palsas and peat plateaus in northern Norway during the last 60 years

Transient modeling of the ground thermal conditions using satellite data in the Lena River delta, Siberia

Distributed snow and rock temperature modelling in steep rock walls using Alpine3D

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