Rapid warming in the Arctic leads to increased glacier melt and freshwater runoff, especially from tidewater glaciers. Here, runoff enters the fjord at depth; induces upwelling and enhances macronutrient delivery to the fjords. However, most studies have low temporal resolutions and so the effects of low-frequency, high-amplitude events on the marine environment remain poorly known. Here, we combine glacier observations with fjord and glacier lake sampling to describe the impact of the 2021 glacier lake outburst flood (GLOF) from lake Setevatnet into Kongsfjorden (Svalbard). We demonstrate the importance of changing subglacial conditions and examine their effects upon macronutrient availability in the inner fjord. Our observations reveal that direct nutrient subsidy from the glacier is most important in early summer, providing critical nitrate (NO3-) and silicate following the routing of meltwater through an inefficient drainage system. Increasing quantities of ice melt force the establishment of an efficient drainage system, creating a plume in the inner fjord, and resulting in upwelling of nutrient-rich bottom water. When the sudden drainage of a glacier lake with high NO3- concentrations occurred, it left little imprint on the NO3- content of the inner fjord, and instead induced seasonal maximum nitrite (NO2-) concentrations. This outcome implies that NO3- was removed by denitrification at the glacier bed and its product NO2- was discharged by the flood waters into the inner fjord. Our findings show that the delivery of key, productivity-limiting nutrients from tidewater glaciers not only depends on runoff, but also on characteristics of the glacier drainage system.

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. The detailed processes that control this intermittent behaviour remain elusive. We develop a machine learning framework to classify surge-type glaciers, based on their location, exposure, geometry, surface 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, surface 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. Our methodology could be extended to classify surge-type glaciers in other areas of the world.