Deep learning rainfall-runoff predictions of extreme events
Jonathan Frame, Frederik Kratzert, Daniel Klotz, Martin Gauch, Guy Shelev, Oren Gilon, Logan M. Qualls, Hoshin V. Gupta, Grey Nearing
Abstract
Abstract. The most accurate rainfall-runoff predictions are currently based on deep learning. There is a concern among hydrologists that data-driven models based on deep learning may not be reliable in extrapolation or for predicting extreme events. This study tests that hypothesis using Long Short-Term Memory networks (LSTMs) and an LSTM variant that is architecturally constrained to conserve mass. The LSTM (and the mass-conserving LSTM variant) remained relatively accurate in predicting extreme (high return-period) events compared to both a conceptual model (the Sacramento Model) and a process-based model (US National Water Model), even when extreme events were not included in the training period. Adding mass balance constraints to the data-driven model (LSTM) reduced model skill during extreme events.