Deep learning rainfall–runoff predictions of extreme events

Abstract. The most accurate rainfall–runoff predictions are currently based on deep learning. There is a concern among hydrologists that the predictive accuracy of data-driven models learning may not be reliable in extrapolation or for predicting extreme events. This study tests hypothesis using long short-term memory (LSTM) networks and an LSTM variant architecturally constrained to conserve mass. network (and mass-conserving variant) remained relatively (high-return-period) events compared with both conceptual model (the Sacramento Model) process-based US National Water Model), even when were included training period. Adding mass balance constraints reduced skill during