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. Deep learning has emerged as a useful tool across geoscience disciplines; however, there remain outstanding questions regarding the suitability of unexplored model architectures and how to interpret model learning for regional-scale hydrological modelling. Here we use a convolutional long short-term memory network, a deep learning approach for learning both spatial and temporal patterns, to predict streamflow at 226 stream gauges across southwestern Canada. The model is forced by gridded climate reanalysis data and trained to predict observed daily streamflow between 1980 and 2015. To interpret the model's learning of both spatial and temporal patterns, we introduce a set of experiments with evaluation metrics to track the model's response to perturbations in the input data. The model performs well in simulating daily streamflow over the testing period, with a median Nash–Sutcliffe efficiency (NSE) of 0.68 and 35 % of stations having NSE&gt;0.8. When predicting streamflow, the model is most sensitive to perturbations in the input data prescribed near and within the basins being predicted, demonstrating that the model is automatically learning to focus on physically realistic areas. When uniformly perturbing input temperature time series to obtain relatively warmer and colder input data, the modelled indicator of freshet timing and peak flow changes in accordance with the transition timing from below- to above-freezing temperatures. We also demonstrate that modelled August streamflow in partially glacierized basins is sensitive to perturbations in August temperature, and that this sensitivity increases with glacier cover. The results demonstrate the suitability of a convolutional long short-termmemory network architecture for spatiotemporal hydrological modelling,making progress towards interpretable deep learning hydrological models.

Evaluation and interpretation of convolutional long short-term memory networks for regional hydrological modelling