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