  Streamflow forecasting is key to effectively managing water resources and
preparing for the occurrence of natural calamities being exacerbated by climate
change. Here we use the concept of fast and slow flow components to create a
new mass-conserving Long Short-Term Memory (LSTM) neural network model. It uses
hydrometeorological time series and catchment attributes to predict daily river
discharges. Preliminary results evidence improvement in skills for different
scores compared to the recent literature.
