From weather data to river runoff: using spatiotemporal convolutional networks for discharge forecasting

Abstract

The quality of river runoff determines the quality of regional climate projections for coastal oceans or other estuaries. This study presents a novel approach to river runoff forecasting using convolutional long short-term memory (ConvLSTM) networks. Our method accurately predicts daily runoff for 97 rivers within the Baltic Sea catchment by modeling runoff as a spatiotemporal sequence defined by atmospheric forcing. The ConvLSTM model predicts river runoff with an accuracy of ± 5 % when compared to the hydrological model. Compared to more complex process-based hydrological models, ConvLSTM networks offer fast processing times and easy integration into climate models, demonstrating their potential as a powerful tool for climate simulation and water resource management.