We use a neural network to predict the amount of water flowing into rivers. Our focus is on large river catchment areas in central Europe with pronounced human activity. Our model scales efficiently to large quantities of data and is thus able to process the input without prior aggregation, capturing fine spatial detail and improving prediction in large catchments. Our model's internal states can be adapted to capture human activity more explicitly in the future.