Surge‐type glaciers are present in many cold environments in the world. These glaciers experience a dramatic increase in velocity over short time periods, the surge, followed by an extended period of slow movement, the quiescence. This study aims at understanding why only few glaciers exhibit a transient behavior. We develop a machine learning framework to classify surge‐type glaciers, based on their location, exposure, geometry, climatic mass balance and runoff. We apply this approach to the Svalbard archipelago, a region with a relatively homogeneous climate. We compare the performance of logistic regression, random forest, and extreme gradient boosting (XGBoost) machine learning models that we apply to a newly combined database of glaciers in Svalbard. Based on the most accurate model, XGBoost, we compute surge probabilities along glacier centerlines and quantify the relative importance of several controlling features. Results show that the surface and bed slopes, ice thickness, glacier width, climatic mass balance, and runoff along glacier centerlines are the most significant features explaining surge probability for glaciers in Svalbard. A thicker and wider glacier with a low surface slope has a higher probability to be classified as surge‐type, which is in good agreement with the existing theories of surging. Finally, we build a probability map of surge‐type glaciers in Svalbard. The framework shows robustness on classifying surge‐type glaciers that were not previously classified as such in existing inventories but have been observed surging. Our methodology could be extended to classify surge‐type glaciers in other areas of the world.
Abstract. Alpine glaciers are shrinking and rapidly loosing mass in a warming climate. Glacier modeling is required to assess the future consequences of these retreats on water resources, the hydropower industry and risk management. However, the performance of such ice flow modeling is generally difficult to evaluate because of the lack of long-term glaciological observations. Here, we assess the performance of the Elmer/Ice full Stokes ice flow model using the long dataset of mass balance, thickness change, ice flow velocity and snout fluctuation measurements obtained between 1979 and 2015 on the Mer de Glace glacier, France. Ice flow modeling results are compared in detail to comprehensive glaciological observations over 4 decades including both a period of glacier expansion preceding a long period of decay. To our knowledge, a comparison to data at this detail is unprecedented. We found that the model accurately reconstructs the velocity, elevation and length variations of this glacier despite some discrepancies that remain unexplained. The calibrated and validated model was then applied to simulate the future evolution of Mer de Glace from 2015 to 2050 using 26 different climate scenarios. Depending on the climate scenarios, the largest glacier in France, with a length of 20 km, could retreat by 2 to 6 km over the next 3 decades.
Abstract. Fast glacier flow and dynamic instabilities, such as surges, are primarily caused by changes at the ice-bed interface, where basal slip and sediment deformation drive basal glacier motion. Determining subglacial conditions and their responses to hydraulic forcing (e.g. rainfall, surface melt) remains challenging due to the difficulty of accessing the glacier bed. In this study, we monitor the interplay between surface runoff and hydro-mechanical conditions at the base of the Arctic surge-type glacier Kongsvegen, in Svalbard, over two contrasting melt seasons. Kongsvegen last surged in 1948, after which it entered a prolonged quiescent phase. Around 2014, flow speeds began to increase, sign of an imminent new fast-flow event. In 2021 we instrumented a borehole to assess subglacial conditions at the local scale and deployed seismometers to monitor the subglacial conditions at the kilometer scale. We measure both subglacial water pressure within the borehole with a water pressure sensor and till rheology with a ploughmeter inserted into the sediments at the bottom of the borehole. We use channel-flow-induced tremors recorded by a seismometer to characterize hydraulic conditions over a kilometre scale at the base of the glacier. The records cover the period from spring 2021 until summer 2022. To characterize the variations in the subglacial conditions caused by changes in surface runoff, we investigate the phase relationship (i.e. how two variables evolve in time) of the following hydro-mechanical condition proxies: water pressure, hydraulic gradient, hydraulic radius, and sediment ploughing forces. We analyse these proxies versus modelled runoff analyzed over seasonal, multi-day and diurnal time-scales. We compare our results with existing theories in terms of subglacial drainage system evolution and sediment shear strength to describe various aspects of subglacial conditions. We find apparent ambiguities in the interpretation of different variables recorded by individual sensors, thus demonstrating the importance of using multi-sensor records in a multi-scale analysis. This study highlights the different adaption of the subglacial drainage system during short, low melt intensity season in 2021, against long, high intensity melt season in 2022. In the short and low intensity melt season, we find that the subglacial drainage system evolves at equilibrium with runoff, increasing its capacity as the melt season progresses. In contrast, during the long and high intensity melt season 2022, we find that the subglacial drainage system evolves transiently to respond to the abrupt and high intensity input of precipitation and melt water conveyed to the bed. In this configuration, the subglacial channels evolution is not rapid enough to adapt immediately to the forcing conditions. The drainage capacity of the main active channels is exceeded, promoting the water to leak in poorly connected areas of the bed, increasing the water pressure, resulting in speed-up events. Another robust outcome of our analysis is, that, on a seasonal scale, till shear strength variations are mainly anti-correlated with water pressure variations (consistent with a Coulomb-plastic behavior), whereas on shorter time scales especially during speed-up events, the two variables correlate, describing a viscous rheology. To our knowledge, such contrasted behaviors of the sediment rheology and subglacial flow at the base of a glacier have not been reported before.
Abstract. All alpine glaciers are shrinking and retreating at an accelerating rate in a warming climate. Glacier modeling is required to assess the future consequences of this retreat on water resources, the hydropower industry and risk management. However, the performance of such ice flow modeling is generally difficult to evaluate because of the lack of long-term glaciological observations. Here, we assess the performance of the Elmer/Ice full-Stokes ice flow model using the long dataset of mass balance, thickness change, ice flow velocity and snout fluctuation measurements obtained between 1979 and 2015 on the Mer de Glace (Mont Blanc area). Ice flow modeling results are compared in detail to comprehensive glaciological observations over four decades including both a period of glacier expansion and a long period of decay. To our knowledge a comparison to data at this detail is unprecedented. We found that the model accurately reconstructs the velocity and elevation variations of this glacier despite some discrepancies that remain unexplained. The calibrated and validated model was then applied to simulate the future evolution of Mer de Glace from 2015 to 2050 using 26 different climate scenarios. Depending on the climate scenarios, this glacier, the largest in France, could retreat by 2 to 5 km over the next three decades.
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