Can LSTM Neural Hydrology help us trade space for time in rainfall-induced megafloods?

The catastrophic July 2021 flood in the Ahr Valley (Rhineland-Palatinate, Germany) highlights the urgent need to improve our understanding and modelling of rainfall-induced megafloods. Conventional conceptual hydrological models often fail to accurately simulate flood peaks during such extreme events. Owing to their very rare occurrence, megafloods are typically absent from calibration periods, as available discharge observations are too short in time. In contrast, the spatial coverage of discharge observations is steadily increasing. Hydrologically and physiographically similar catchments may therefore provide valuable information on flood response behavior that has not yet been observed in the catchment of interest. In this study, we investigate whether spatial information can compensate for limited temporal observations by applying a long short-term memory (LSTM) neural network within the Neural Hydrology framework (Kratzert et al., 2021), which is capable of learning patterns from large datasets and transferring them to similar, yet distinct, hydrological settings.

For this purpose, in this study, we use a large dataset of catchments across Central Europe and Germany with observed discharge and meteorological data from 1970 onwards to model hourly discharge at Ahr Valley. A series of experiments is designed using different combinations of temporal coverage and sets of physiographically similar catchments to evaluate their ability to reproduce flood behavior at Ahr Valley. The methodological framework consists of two steps: (i) training the Neural Hydrology model on a set of similar catchments (excluding the Ahr catchment) using split-sample validation, and (ii) validating the trained models for extreme flood events, including the 2021 megaflood, at several Ahr sub-catchments.

By systematically comparing different configurations of spatial and temporal information, we address the following questions: Can time be successfully traded for space when simulating the 2021 Ahr megaflood? How can hydrologically similar catchments be identified most effectively? And can neural hydrology outperform the conventional conceptual models used operationally for the Ahr event (LARSIM and HBV-Light)