  Despite the huge success of Long Short-Term Memory networks, their
applications in environmental sciences are scarce. We argue that one reason is
the difficulty to interpret the internals of trained networks. In this study,
we look at the application of LSTMs for rainfall-runoff forecasting, one of the
central tasks in the field of hydrology, in which the river discharge has to be
predicted from meteorological observations. LSTMs are particularly well-suited
for this problem since memory cells can represent dynamic reservoirs and
storages, which are essential components in state-space modelling approaches of
the hydrological system. On basis of two different catchments, one with snow
influence and one without, we demonstrate how the trained model can be analyzed
and interpreted. In the process, we show that the network internally learns to
represent patterns that are consistent with our qualitative understanding of
the hydrological system.
