Gap-Filling Algorithm for Ground Surface Temperature Data Measured in Permafrost and Periglacial Environments

Abstract

Ground surface temperatures (GST) are widely measured in mountain permafrost areas, but their time series data can be interrupted by gaps. Gaps complicate the calculation of aggregates and indices required for analysing temporal and spatial variability between loggers and sites. We present an algorithm to estimate daily mean GST and the resulting uncertainty. The algorithm is designed to automatically fill data gaps in a database of several tens to hundreds of time series, for example, the Swiss Permafrost Monitoring Network (PERMOS). Using numerous randomly generated artificial gaps, we validated the performance of the gap‐filling routine in terms of (1) the bias resulting on annual means, (2) thawing and freezing degree‐days, and (3) the accuracy of the uncertainty estimation. Although quantile mapping provided the most reliable gap‐filling approach overall, linear interpolation between neighbouring values performed equally well for gap durations of up to 3–5 days. Finding the most similar regressors is crucial and also the main source of errors, particularly because of the large spatial and temporal variability of ground and snow properties in high‐mountain terrains. Applying the gap‐filling technique to the PERMOS GST data increased the total number of complete hydrological years available for analysis by 70 per cent (>450‐filled gaps), likely without exceeding a maximal uncertainty of ± 0.25 °C in calculated annual mean values. Copyright © 2016 John Wiley & Sons, Ltd.

The European mountain cryosphere: A review of past, current and future issues

“…Borehole temperatures are monitored in many European mountain ranges (GTN-P database, Biskaborn et al, 2015), several of the sites being accompanied by meteorological stations and ground surface temperature measurements (Gisnas et al, 2014;Staub et al, 2016). However, as mountain permafrost is often invisible from the surface (Merz et al, 2015a, b), various indirect methods need to be employed to detect, characterize and monitor permafrost occurrences.…”

Long‐term monitoring of sporadic permafrost at the eastern margin of the European Alps (Hochreichart, Seckauer Tauern range, Austria)

“…SCD was estimated by calculating the sum of days with a considerable snow cover damping effect. SCD values were considered as such when the weekly standard deviation of the mean daily ground surface temperature was ≤0.25°C . WEqT is the (rather) stable mean temperature in February and March and was calculated for sites with a SCD of at least 3 months indicating (widely) decoupled air and ground temperatures during winter.…”

“…SCD values were considered as such when the weekly standard deviation of the mean daily ground surface temperature was ≤0.25°C. 23 WEqT is the (rather) stable mean temperature in February and March and was calculated for sites with a SCD of at least 3 months indicating (widely) decoupled air and ground temperatures during winter. In more detail, the calculated WEqT value was the mean value of the two monthly values for February and…”

MANNGA: A Robust Method for Gap Filling Meteorological Data

“…In this case we have similar data to process, increasing the probability of a good estimation. Staub et al (2017) present other advantage using only a specific amount of data, which is a decrease in computations effort to build models. For this reasons it is a good approach to select only a small sample of meteorological measurements (1 to 3 months) to perform gap filling.…”