Scale-dependent measurement and analysis of ground surface temperature variability in alpine terrain

Abstract

Measurements of environmental variables are often used to validate and calibrate physically-based models. Depending on their application, the models are used at different scales, ranging from few meters to tens of kilometers. Environmental variables can vary strongly within the grid cells of these models. Validating a model with a single measurement is therefore delicate and susceptible to induce bias in further model applications. <br><br> To address the question of uncertainty associated with scale in permafrost models, we present data of 390 spatially-distributed ground surface temperature measurements recorded in terrain of high topographic variability in the Swiss Alps. We illustrate a way to program, deploy and refind a large number of measurement devices efficiently, and present a strategy to reduce data loss reported in earlier studies. Data after the first year of deployment is presented. <br><br> The measurements represent the variability of ground surface temperatures at two different scales ranging from few meters to some kilometers. On the coarser scale, the dependence of mean annual ground surface temperature on elevation, slope, aspect and ground cover type is modelled with a multiple linear regression model. Sampled mean annual ground surface temperatures vary from −4 °C to 5 °C within an area of approximately 16 km<sup>2</sup> subject to elevational differences of approximately 1000 m. The measurements also indicate that mean annual ground surface temperatures vary up to 6 °C (i.e., from −2 °C to 4 °C) even within an elevational band of 300 m. Furthermore, fine-scale variations can be high (up to 2.5 °C) at distances of less than 14 m in homogeneous terrain. The effect of this high variability of an environmental variable on model validation and applications in alpine regions is discussed

Derivation and analysis of a high-resolution estimate of global permafrost zonation

“…This is mostly due to the effects of topography (Gruber and Haeberli, 2007), vegetation cover (Shur and Jorgenson, 2007), ground material (Gubler et al, 2011), water bodies and flow (Burn, 2005;Endrizzi et al, 2011) or snow distribution (Zhang, 2005;Sturm and Benson, 2004;Liston, 2004). Consequently, point (borehole) measurements in any but the most homogeneous conditions do not suffice to describe conditions over an area, making their use for model calibration and validation difficult.…”

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

“…Modelled results of both data sets are based on properties derived from a 10 m DEM. While this seems a reasonable comparison, Gubler et al (2011) …”

“…This data set covers a great diversity of locations but is biased towards permafrost monitoring sites and therefore clustered around MAGST of 0 • C. In the analysis, PERMOS data are split into two groups: (a) PERMOS1: predominantly coarse debris; (b) PERMOS2: bedrock (mainly steep rock walls). The second logger data set, iBUTTONS, originates from a single study (Gubler et al, 2011;Schmid et al, 2012). It covers years 2010-2011 in a single region in the Engadin.…”

“…SAFRAN-Crocus scheme; Durand et al, 1993Durand et al, , 1999 which classify topography according to fixed classes based on terrain parameters and enable application of numerical models over large areas in a semi-distributed fashion. Gubler et al (2011) have shown that fine-scale variability of surface processes can be high in complex terraine.g. variation in soil moisture, ground cover and local shading -can cause differences of as much as 3 • C mean annual ground surface temperature (MAGST) within a 10 m × 10 m grid.…”

Ground temperature variations in a talus slope influenced by permafrost: a comparison of field observations and model simulations

“…In addition to topo-climatic parameters like altitude, exposition, slope angle or albedo influencing the temperature and humidity at the surface, spatio-temporal dynamics of the snow cover are also of particular importance for seasonal and inter-annual variations of GST (usually measured 5-20 cm below the ground surface) and ground temperatures (GT, e.g. measured at greater depths in boreholes) (Delaloye, 2004;Gubler et al, 2011;Zhang, 2005). A snow layer of more than ∼ 80 cm thickness (depending on the roughness of the terrain and the inner structure of the snow cover) is able to thermally decouple the ground from the atmosphere (Goodrich, 1982;Ishikawa, 2003;Luetschg et al, 2008;Zhang, 2005).…”