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.
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.
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 depen- dence 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 km2 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 14m in homogeneous terrain. The effect of this high variability of an environmental variable on model validation and applications in alpine regions is discussed.