Abstract. Permafrost is a widespread phenomenon in mountainous regions of the world such as the European Alps. Many important topics such as the future evolution of permafrost related to climate change and the detection of permafrost related to potential natural hazards sites are of major concern to our society. Numerical permafrost models are the only tools which allow for the projection of the future evolution of permafrost. Due to the complexity of the processes involved and the heterogeneity of Alpine terrain, models must be carefully calibrated, and results should be compared with observations at the site (borehole) scale. However, for large-scale applications, a site-specific model calibration for a multitude of grid points would be very time-consuming. To tackle this issue, this study presents a semi-automated calibration method using the Generalized Likelihood Uncertainty Estimation (GLUE) as implemented in a 1-D soil model (CoupModel) and applies it to six permafrost sites in the Swiss Alps. We show that this semi-automated calibration method is able to accurately reproduce the main thermal condition characteristics with some limitations at sites with unique conditions such as 3-D air or water circulation, which have to be calibrated manually. The calibration obtained was used for global and regional climate model (GCM/RCM)-based long-term climate projections under the A1B climate scenario (EU-ENSEMBLES project) specifically downscaled at each borehole site. The projection shows general permafrost degradation with thawing at 10 m, even partially reaching 20 m depth by the end of the century, but with different timing among the sites and with partly considerable uncertainties due to the spread of the applied climatic forcing.

Abstract.Variations in surface and near-surface ground temperatures (GST) dominate the evolution of the ground thermal regime over time and represent the upper boundary condition for the subsurface. Focusing on the Lapires talus slope in the south-western part of the Swiss Alps, which partly contains massive ground ice, and using a joint observational and modelling approach, this study compares and combines observed and simulated GST in the proximity of a borehole. The aim was to determine the applicability of the physically based subsurface model COUP to accurately reproduce spatially heterogeneous GST data and to enhance its reliability for long-term simulations. The reconstruction of GST variations revealed very promising results, even though twodimensional processes like the convection within the coarse-blocky sediments close to the surface or ascending air circulation throughout the landform ("chimney effect") are not included in the model. For most simulations, the model bias revealed a distinct seasonal pattern mainly related to the simulation of the snow cover. The study shows that, by means of a detailed comparison of GST simulations with ground truth data, the calibration of the upper boundary conditions -which are crucial for modelling the subsurface -could be enhanced.

The timing and duration of snow cover in areas of mountain permafrost affect the ground thermal regime by thermally insulating the ground from the atmosphere and modifying the radiation balance at the surface. Snow depth records, however, are sparse in high-mountain terrains. Here, we present data processing techniques to approximate the thermal insulation effect of snow cover. We propose some simple 'snow thermal insulation indices' using daily and weekly variations in ground surface temperatures (GSTs), as well as a 'snow melt index' that approximates the snow melt rate using a degree-day approach with air temperature during the zero curtain period. The indices consider pointspecific characteristics and allow the reconstruction of past snow thermal conditions and snow melt rates using long GST time series. The application of these indices to GST monitoring data from the Swiss Alps revealed large spatial and temporal variability in the start and duration of the high-insulation period by snow and in the snow melt rate.

SummaryGeographic Information Systems (GIS) have been used in various fields and disciplines to summarize and analyse spatial patterns and distributions, for the purpose of understanding how geographic and non-geographic entities interact with each other over space and time.Although honey bees are directly related to and influenced by their local environment, few studies have incorporated honey bee data into GIS for the purposes of gauging these spatial relationships. This paper will briefly discuss some of the types of spatial analyses and GIS methods that have been used for bees, and also, how some methodologies developed in the non-Apis bee domain could be applied to honey bee research. With this paper, we aim to stimulate spatial thinking processes and thus the future use of GIS analyses to better understand the relationships between environmental characteristics and honey bee health and abundance. We will introduce the framework and some important basic concepts of GIS, as well as provide detailed instructions for becoming familiar and comfortable in using the GIS softwares ArcGIS and Quantum GIS (QGIS) (a commercial and free GIS package) for the basics of geospatial research. Uso estándar de las técnicas de Sistemas de InformaciónGeográfica (SIG) en la investigación de la abeja de la miel Resumen Los Sistemas de Información Geográfica (SIG) se han utilizado en diversos campos y disciplinas para resumir y analizar los patrones y distribuciones espaciales, con el fin de entender cómo las entidades geográficas y no geográficas interactúan entre sí en el espacio y en el tiempo. Aunque las abejas melíferas están directamente relacionadas e influidas por su ambiente local, pocos estudios han incluido estos datos en los SIG para analizar estas relaciones espaciales. Este artículo discutirá brevemente algunos de los tipos de análisis espaciales y métodos de SIG que se han utilizado para las abejas, así como también, algunas metodologías desarrolladas en dominios no-Apis que podrían ser aplicadas a la investigación de la abeja de la miel. El objetivo de este trabajo es fomentar los procesos de pensamiento espacial y por lo tanto el futuro uso de los análisis SIG para entender mejor las relaciones entre las características ambientales con la salud de las abejas melíferas y con la abundancia de estas. Vamos a introducir el marco y algunos conceptos básicos importantes de los SIG, así como proporcionar instrucciones detalladas para familiarizar y facilitar el uso de los programas SIG, ArcGIS y Quantum GIS (QGIS) (un paquete SIG comercial y libre) para los fundamentos de la investigación geoespacial.

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.