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

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 predicts river runoff with an accuracy of ±5% when compared to the hydrological model. Compared to more complex process-based hydrological models, ConvLSTM offers fast processing times and easy integration into climate models, demonstrating its potential as a powerful tool for climate simulation and water resource management.