Future climate-driven hydrological changes may strongly affect river exports of multiple pollutants to coastal waters. In large-scale water quality (WQ) models the effects are, however, associated with uncertainties that may differ in space and time but are hardly studied worldwide and for multiple pollutants simultaneously. Moreover, explicit ways to assess climate-driven uncertainties in large-scale multi-pollutant assessments are currently limited. Here, we aim to build trust in future river exports of nutrients (i.e. nitrogen and phosphorus), plastics (i.e. micro and macroplastics), and chemicals (i.e. diclofenac and triclosan) under climate-driven hydrological changes on the sub-basin scale worldwide. We used a soft-coupled global hydrological (VIC) and WQ (MARINA-Multi) model system, driven by five Global Climate Models (GCMs), to quantify river exports of selected pollutants to seas for 2010 and 2050 under an economy-driven and high global warming scenario. Subsequently, we developed and applied a new approach to build trust in projected future trends in coastal water pollution for the selected pollutants. Results reveal that in arid regions, such as the Middle East, East Asia, and Northern Africa, climate-driven uncertainties play a key role in future river exports of pollutants. For African sub-basins, high increases in river exports of pollutants are projected by 2050 under climate-driven hydrological uncertainty. Nevertheless, over 80% of the global sub-basin areas agree on the direction of change in future river exports of individual pollutants for at least three GCMs. Multi-pollutant agreements differ among seas: 53% of the area agrees on increasing river exports of six pollutants into the Indian Ocean by 2050, whereas 17% agrees on decreasing trends for the Mediterranean Sea. Our study indicated that even under climate-driven hydrological uncertainties, large-scale WQ models remain useful tools for future WQ assessments. Yet, awareness and transparency of modelling uncertainties are essential when utilising model outputs for well-informed actions.

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