The climate in Svalbard is undergoing amplified change compared to the global mean. This has major implications for runoff from glaciers and seasonal snow on land. We use a coupled energy balance–subsurface model, forced with downscaled regional climate model fields, and apply it to both glacier-covered and land areas in Svalbard. This generates a long-term (1957–2018) distributed dataset of climatic mass balance (CMB) for the glaciers, snow conditions, and runoff with a 1?km×1?km spatial and 3-hourly temporal resolution. Observational data including stake measurements, automatic weather station data, and subsurface data across Svalbard are used for model calibration and validation. We find a weakly positive mean net CMB (+0.09mw.e.a?1) over the simulation period, which only fractionally compensates for mass loss through calving. Pronounced warming and a small precipitation increase lead to a spatial-mean negative net CMB trend (?0.06mw.e.a?1 decade?1), and an increase in the equilibrium line altitude (ELA) by 17m decade?1, with the largest changes in southern and central Svalbard. The retreating ELA in turn causes firn air volume to decrease by 4% decade?1, which in combination with winter warming induces a substantial reduction of refreezing in both glacier-covered and land areas (average ?4% decade?1). A combination of increased melt and reduced refreezing causes glacier runoff (average 34.3Gta?1) to double over the simulation period, while discharge from land (average 10.6Gta?1) remains nearly unchanged. As a result, the relative contribution of land runoff to total runoff drops from 30% to 20% during 1957–2018. Seasonal snow on land and in glacier ablation zones is found to arrive later in autumn (+1.4d decade?1), while no significant changes occurred on the date of snow disappearance in spring–summer. Altogether, the output of the simulation provides an extensive dataset that may be of use in a wide range of applications ranging from runoff modelling to ecosystem studies.
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