  Machine learning is playing an increasing role in hydrology, supplementing or
replacing physics-based models. One notable example is the use of recurrent
neural networks (RNNs) for forecasting streamflow given observed precipitation
and geographic characteristics. Training of such a model over the continental
United States has demonstrated that a single set of model parameters can be
used across independent catchments, and that RNNs can outperform physics-based
models. In this work, we take a next step and study the performance of RNNs for
river routing in land surface models (LSMs). Instead of observed precipitation,
the LSM-RNN uses instantaneous runoff calculated from physics-based models as
an input. We train the model with data from river basins spanning the globe and
test it in streamflow hindcasts. The model demonstrates skill at generalization
across basins (predicting streamflow in unseen catchments) and across time
(predicting streamflow during years not used in training). We compare the
predictions from the LSM-RNN to an existing physics-based model calibrated with
a similar dataset and find that the LSM-RNN outperforms the physics-based
model. Our results give further evidence that RNNs are effective for global
streamflow prediction from runoff inputs and motivate the development of
complete routing models that can capture nested sub-basis connections.
