In this paper, we present the results of the “Rate My Hydrograph” study, where we compare expert ratings of simulated hydrographs with quantitative metrics.

This paper performs a rigorous benchmark of traditional hydrologic models and an LSTM-based model for rainfall-runoff modeling.

This paper introduces the Caravan dataset, a global large-sample hydrology dataset that builds on cloud computing to be extensible by anyone.

This paper investigates the hypothesis that the lack of enforced mass conservation is the main reason that deep learning models outperform traditional hydrology models.

Technical note that compares autoregression to data assimilation for deep learning models and rainfall-runoff modeling.

In this paper we show the benefits of using multiple meteorological forcing products at the same time in a single LSTM-based rainfall-runoff model over just using a single product.

Deep learning based uncertainty estimation techniques and benchmarking procedure for rainfall-runoff modeling.

Opinion paper discussing the future of Hydrology, especially in the context of recent developments in Machine Learning

In this manuscript we test LSTM-based rainfall-runoff models on the task of prediction in ungauged basins and show, that a single LSTM-based model does better prediction in ungauged basins than a traditional hydrological model that was specifically calibrated for each basin individually.

In this manuscript we show for the first time how to train a single LSTM-based neural network as general hydrology model for hundreds of basins. Furthermore, we proposed the Entity-Aware LSTM (EA-LSTM) in which static features are used explicitly to subset the model for a specific entity (here a catchment).