From Weather Data to River Runoff: Leveraging Spatiotemporal Convolutional Networks for Comprehensive Discharge Forecasting

Abstract. The quality of the 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 performs similarly to traditional hydrological models, effectively capturing the intricate spatial and temporal patterns that influence individual river runoff across the Baltic Sea region. Our model offers the advantages of faster processing and easier integration into climate models.