  While long short-term memory (LSTM) models have demonstrated stellar
performance with streamflow predictions, there are major risks in applying
these models in contiguous regions with no gauges, or predictions in ungauged
regions (PUR) problems. However, softer data such as the flow duration curve
(FDC) may be already available from nearby stations, or may become available.
Here we demonstrate that sparse FDC data can be migrated and assimilated by an
LSTM-based network, via an encoder. A stringent region-based holdout test
showed a median Kling-Gupta efficiency (KGE) of 0.62 for a US dataset,
substantially higher than previous state-of-the-art global-scale ungauged basin
tests. The baseline model without FDC was already competitive (median KGE
0.56), but integrating FDCs had substantial value. Because of the inaccurate
representation of inputs, the baseline models might sometimes produce
catastrophic results. However, model generalizability was further meaningfully
improved by compiling an ensemble based on models with different input
selections.
