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Climate change affects occurrences of floods and droughts worldwide. However,predicting climate impacts over individual watersheds is difficult, primarilybecause accurate hydrological forecasts require models that are calibrated topast data. In this work we present a large-scale LSTM-based modeling approachthat -- by training on large data sets -- learns a diversity of hydrologicalbehaviors. Previous work shows that this model is more accurate than currentstate-of-the-art models, even when the LSTM-based approach operatesout-of-sample and the latter in-sample. In this work, we show how this modelcan assess the sensitivity of the underlying systems with regard to extreme(high and low) flows in individual watersheds over the continental US.

Using LSTMs for climate change assessment studies on droughts and floods