  Accurate streamflow prediction largely relies on historical meteorological
records and streamflow measurements. For many regions, however, such data are
only scarcely available. Facing this problem, many studies simply trained their
machine learning models on the region's available data, leaving possible
repercussions of this strategy unclear. In this study, we evaluate the
sensitivity of tree- and LSTM-based models to limited training data, both in
terms of geographic diversity and different time spans. We feed the models
meteorological observations disseminated with the CAMELS dataset, and
individually restrict the training period length, number of training basins,
and input sequence length. We quantify how additional training data improve
predictions and how many previous days of forcings we should feed the models to
obtain best predictions for each training set size. Further, our findings show
that tree- and LSTM-based models provide similarly accurate predictions on
small datasets, while LSTMs are superior given more training data.
