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Regional rainfall-runoff modeling is an old but still mostly out-standingproblem in Hydrological Sciences. The problem currently is that traditionalhydrological models degrade significantly in performance when calibrated formultiple basins together instead of for a single basin alone. In this paper, wepropose a novel, data-driven approach using Long Short-Term Memory networks(LSTMs), and demonstrate that under a 'big data' paradigm, this is notnecessarily the case. By training a single LSTM model on 531 basins from theCAMELS data set using meteorological time series data and static catchmentattributes, we were able to significantly improve performance compared to a setof several different hydrological benchmark models. Our proposed approach notonly significantly outperforms hydrological models that were calibratedregionally but also achieves better performance than hydrological models thatwere calibrated for each basin individually. Furthermore, we propose anadaption to the standard LSTM architecture, which we call an Entity-Aware-LSTM(EA-LSTM), that allows for learning, and embedding as a feature layer in a deeplearning model, catchment similarities. We show that this learned catchmentsimilarity corresponds well with what we would expect from prior hydrologicalunderstanding.

Towards Learning Universal, Regional, and Local Hydrological Behaviors via Machine-Learning Applied to Large-Sample Datasets