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RESEARCH PRODUCT

Contributions of statistical modelling for the understanding of the nivo-glaciological dynamics of a small arctic glacial basin (Austre Lovén glacier, Spitsberg)

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Since the middle of the 19th century, the Earth has experienced a climate shift marked by a high rise in temperature (+ 0.85 °C over the period 1880-2012). The Arctic is the region of the world that is warming the most rapidly, at a rate of 2 to 3 times faster than the global average. In this context, all components of the Arctic cryosphere are experiencing a change in their dynamics. Because of their direct links with the atmosphere, glaciers are among the best indicators of these climate variations. Like other glaciers on the globe, the glaciers of Svalbard, which cover 60% of the archipelago’s surface, have been retreating since the end of the Little Ice Age. This retreat, which is reflected in negative glacier mass balance measurements, accelerated from the 1980s onwards.In this thesis, we aim to improve the accuracy and reliability of past and future projections of Arctic ice basin flows by also understanding their origin. Based on data acquired for more than a decade on the Austre Lovén observatory glacier in Spitsbergen (78°N°), we first sought to identify the nivo-meteorological factors influencing the annual mass balance of this glacier (2008-2017). After applying a rigorous protocol for processing the data acquired in situ, we used explanatory statistical modeling to highlight not the role of a systematically preponderant factor but rather the influence of a combination of nivo-meteorological factors in explaining the mass balances of the Austre Lovénbreen. Although winter snow cover plays a major role, its influence is partly conditioned by temperature and precipitation factors. Moreover, considering the surface of the catchment area occupied by the hillsides (34%), this thesis work allows to evaluate their contribution to the hydro-glaciological balance measured at the outlet. Thanks to very high resolution measurements by terrestrial LiDAR, the depth of the snow cover was measured on a representative sample of hillsides (2013 and 2014). Statistical modeling has also allowed us to highlight the role of slope and orientation in the distribution of snowpack in these slopes. From these observations, we were able to evaluate the non-negligible water contribution of the hillsides to the annual hydro-glaciological balance.