Abstract. Soil and water acidification was first recognised as a severe environmental problem in the 1970s. The interest in establishing critical loads led to a peak in weathering research in the 1980s, since weathering is the long-term counterbalance to acidification pressure. Assessments of weathering rates and associated uncertainties have recently become an area of renewed research interest, this time due to demand for more harvest to provide renewable bioenergy. Increased demand for forest fuels increases the risk of depleting the soils of base cations produced in situ by weathering. This is the background to the research programme Quantifying Weathering Rates for Sustainable Forestry (QWARTS), which ran from 2012 to 2019. The programme involved research groups working at different scales, from lab experiments to extensive modelling. The aims of this paper are to summarise the state of knowledge about weathering rates in Swedish forest soils at different scales, with an emphasis on the knowledge added by the QWARTS programme, to discuss the uncertainties in relation to sustainable forestry, and to highlight knowledge gaps where further research is needed. The variation at single-site level was large, but most sites could be placed reliably in broader classes of weathering rates. At regional to national level, the results from the different approaches were in general agreement. Comparisons of base cation losses after stem-only and whole-tree harvesting showed sites with clear imbalances between weathering supply and harvest losses, and other sites where variation in weathering rates from different approaches obscured the overall balance. Clear imbalances appeared mainly after whole-tree harvesting in spruce forests in southern and central Sweden. Research findings in the QWARTS programme support the continued use of the PROFILE/ForSAFE family of models, but it is important to continue comparisons between these and other approaches. Uncertainties in the model approaches can be further reduced, mainly by finding ways to reduce uncertainties in input data on soil texture and associated hydrological parameters. Another way to reduce uncertainties is by developing the models to better represent the delivery of weathering products to runoff waters and biological feedbacks under the influence of climate change.