Earth Observation for the Assessment of Long-Term Snow Dynamics in European Mountains - Analysing 35-Year Snowline Dynamics in Europe Based on High Resolution Earth Observation Data between 1984 and 2018

Kurzfassung

Worldwide, cold regions are undergoing significant alterationsdue to climate change. Snow, the most widely distributed cold region component,is highly sensitive to climate change. At the same time, snowitselfprofoundly impacts the Earth’s energy budget, biodiversity, and natural hazards, as well as hydropower management, freshwater management, and winter tourism/sports. Large parts of the cold regions in Europe aremountain areas, whicharedensely populated because of the various ecosystem services and socioeconomic well-beingin mountains. At present, severe consequences caused byclimate change have been observed in European mountains and their surroundingareas. Yet, large knowledgegapshinder the development of effective regional and local adaptation strategies. Long-term and evidence-based regional studies are urgently needed to enhance the comprehension of regional responses to climate change.Earth Observation (EO) provides long-termconsistent records of the Earth’s surface. It is a great alternative and/or supplement to conventional in-situ measurements which are usually time-consuming, cost-intensive and logistically demanding, particularly for the poor accessibility of cold regions. With the assistance of EO, land surface dynamics in cold regions can be observed in an objective, repeated, synoptic and consistent way. Thanks to free and open data policies, long-term archivessuch as LandsatArchiveand SentinelArchivecan be accessed free-of-charge. The high-to medium-resolution remote sensing imagery from thesefreely accessible archivesgives EO-based time series datasets the capability to depict snow dynamics in European mountains from the 1980s to the present. In order to compile such a dataset, it is necessary to investigate the spatiotemporal availability of EO data, and develop a spatiotemporally transferable framework from which one can investigate snow dynamics.Among the available EO image archives, the Landsat Archive hasthe longest uninterrupted records of the Earth’s land surface. Furthermore, its 30 m spatial resolution fulfils the requirements for snow monitoring in complex terrains.Landsat data can yield a time series of snow dynamics in mountainous areas from 1984 to the present. However, severe Landsat data gaps have occurred across certain regions of Europe. Moreover, the Landsat Level 1 Precision and Terrain (L1TP)data is scarcer (up to 50% less) in high-latitude mountainous areas than in low-latitude mountainous areas. Given the abovementioned facts, the Regional Snowline Elevation (RSE) is selected to characterize thesnow dynamics in mountainous areas, as it can handle cloud obstructions in the optical images.In this thesis, I present a five-step framework toderive and densify RSE time series in European mountains, i.e. (1) pre-processing, (2) snow detection, (3) RSE retrieval, (4) time series densification, and (5) Regional Snowline Retreat Curve (RSRC) production. The results of the intra-annual RSE variations show a uniquely high variation in the beginning of the ablation seasons in the Alpine catchment Tagliamento, mainly toward higher elevation. As for inter-annual variations of RSE, median RSE increases in all selected catchments, with an average speed of around 4.66 m ∙ a−1(median) and 5.87 m ∙ a−1(at the beginning of the ablation season). The fastest significant retreat is observed in the catchment Drac (10.66 m ∙ a−1, at the beginning of the ablation season), and the slowest significant retreat is observed in the catchment Uzh (1.74 m ∙ a−1, at the beginning of the ablation season). The increase of RSEs at the beginning of the ablation season is faster than the median RSEs, whose average difference is nearly 1.21 m ∙ a−1, particularly in the catchmentDrac (3.72 m ∙ a−1). The results of the RSRCs show a significant rise in RSEs at the beginning of the ablation season, except for the Alpine catchment Alpenrhein and Var, and the Pyrenean catchment Ariege. It indicates that 11.8 and 3.97 degrees Celsius less per year are needed for the regional snowlines to reach the middle point of the RSRC in the Tagliamento and Tysa, respectively. The variation of air temperature is regarded as an example of a potential climate driver in this thesis. The retrieved monthly mean RSEs are highly correlated (meancorrelation coefficient R̅= 0.7) with the monthly temperature anomalies, which are more significant in months with extremely low/high temperature. Another case study that investigates the correlation between riverdischarges and RSEs is carried out to demonstrate the potential consequences of the derived snowline dynamics. The correlation analysis shows a goodcorrelation between river discharges and RSEs (correlation coefficient, R=0.52).In this thesis, the developed framework signifies a better understanding of the snow dynamics in mountainareas, as well as their potential triggers and consequences. Nonetheless, an urgent need persists for: (1) validation data to assess long-term snow-related observations based on high-resolution EO data; (2) further studies to reveal interactions between snow and its ambient environment; and (3) regional and local adaptation-strategies coping with climate change. Further studies exploring the above-mentioned research gaps are urgently needed in the future.